Letnik: Številka: 4 Volume: 67 Number: 2021 NAŠE GOSPODARSTVO Revija za aktualna ekonomska in poslovnavprašanja OUR ECONOMY Journal of Contemporary Issues in Economics and Business NAŠE GOSPODARSTVO Revija za aktualna ekonomska in poslovna vprašanja Letnik 67, št. 4, 2021 Izdajatelj: Ekonomsko poslovna fakulteta Maribor (EPF) Uredniški odbor: José Ernesto Amor (EGADE Business School, Tecnolico de Monterrey, Mehika), Jani Beko (EPF), Jernej Belak (EPF), Samo Bobek (EPF), Josef C. Brada (Arizona State University, AZ, ZDA), Mehmet Caner (North Carolina State University, NC, ZDA), Silvo Dajcman (EPF), Ernesto Damiani (The University of Milan, Italija), Paul Davidson (University of Tennessee, Knoxville, TN, ZDA), Mark M. Davis (Bentley University, Waltham, MA, ZDA), Jg Felfe (Helmut-Schmidt University, Hamburg, Nemcija), Lidija Hauptman (EPF), Timotej Jagric (EPF), Alenka Kavkler (EPF), Urška Kosi (University of Paderborn, Nemcija), Sonja Sibila Lebe (EPF), Monty Lynn (Abilene Christian University, Abilene, TX, ZDA), Borut Milfelner (EPF), Emre Ozsoz (Fordham University, Bronx, NY, ZDA), Peter Podgorelec (EPF), Peter N. Posch (Technical University Dortmund, Nemcija), Gregor Radonjic (EPF), Miroslav Rebernik (EPF), Kaija Saranto (University of Eastern Finland, Finska), Milica Uvalic (University of Perugia, Italija), Igor Vrecko (EPF), Martin Wagner (Technical University Dortmund, Nemcija) in Udo Wagner (University of Vienna, Avstrija) Glavna in odgovorna urednica: Darja Boršic Pomocnica glavne in odgovorne urednice: Romana Korez Vide Naslov uredništva: Maribor, Razlagova 14, Slovenija, telefon: +386 2 22 90 112 Elektronska pošta: nase.gospodarstvo@um.si Spletna stran: http://www.ng epf.si Revija je uvršcena v bibliografske baze podatkov EconLit, European Reference Index for the Humanities and the Social Sciences (ERIH PLUS), Directory of Open Access Journals (DOAJ), ProQuest, EBSCO, Ulrich's Periodicals Directory in številne druge. OUR ECONOMY Journal of Contemporary Issues in Economics and Business Vol. 67, No. 4, 2021 Published by: Faculty of Economics and Business, Maribor (FEB) Editorial Board: José Ernesto Amor (EGADE Business School Tecnolico de Monterrey, Mexico), Jani Beko (FEB), Jernej Belak (FEB), Samo Bobek (FEB), Josef C. Brada (Arizona State University, AZ, USA), Mehmet Caner (North Carolina State University, NC, USA), Silvo Dajcman (FEB), Ernesto Damiani (The University of Milan, Italy), Paul Davidson (University of Tennessee, Knoxville, TN, USA), Mark M. Davis (Bentley University, Waltham, MA, USA), Jg Felfe (Helmut-Schmidt University, Hamburg, Germany), Lidija Hauptman (FEB), Timotej Jagric (FEB), Alenka Kavkler (FEB), Urška Kosi (University of Paderborn, Germany), Sonja Sibila Lebe (FEB), Monty Lynn (Abilene Christian University, Abilene, TX, ZDA), Borut Milfelner (FEB), Emre Ozsoz (Fordham University, Bronx, NY, USA), Peter Podgorelec (FEB), Peter N. Posch (Technical University Dortmund, Germany), Gregor Radonjic (FEB), Miroslav Rebernik (FEB), Kaija Saranto (University of Eastern Finland, Finland), Milica Uvalic (University of Perugia, Italy), Igor Vrecko (FEB), Martin Wagner (Technical University Dortmund, Germany), Udo Wagner (University of Vienna, Austria) Editor-in-Chief: Darja Boršic Co-editor: Romana Korez Vide Editorial and administrative office address: Maribor, Razlagova 14, Slovenia, phone: +386 2 22 90 112 E-mail: our.economy@um.si WWW homepage: http://www.ng-epf.si The journal is indexed/abstracted in EconLit, European Reference Index for the Humanities and the Social Sciences (ERIH PLUS), Directory of Open Access Journals (DOAJ), ProQuest, EBSCO, Ulrich's Periodicals Directory and numerous of other bibliographic databases. Lektoriranje: Taia Translation Services Dtp: Univerzitetna založba Maribor Letno izidejo 4 (štiri) številke. Letna narocnina: za pravne in fizicne osebe 44,10 €, za tujino 54,60 €. ISSN 0547-3101 Revijo sofinancira Javna agencija za raziskovalno dejavnost Republike Slovenije. NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 67 No. 4 / December 2021 Vsebina / Contents Marko Senekovic Is There Asymmetry in the Relationship Between Government Consumption Dynamics and Economic Activity? Evidence from G7 Economies Jelko Plošinjak, Mejra Festic Empirical Testing of Purchasing Power Parity Validity in Selected European Union Countries 15 Romana Korez Vide, Irena Zavrl, Anica Hunjet Emerging Markets' Demographic and Macroeconomic Dynamics and the Middle Class Growth: The Case of China and India 33 Jia Yun Moerth-Teo, Vito Bobek, Tatjana Horvat The Effects of Consumers’ Buying Behavior on the E-commerce in Highly Developed Emerging Market and Developed Market: The Case of Singapore and Austria 56 Zoran Krupka, Andreja Dobra, Goran Vlašic Factors Influencing the Perception of Destination Brand Luxuriousness 74 Helena Nemec Rudež A Demand Side Analysis of Intellectual Capital in the Accommodation Industry: The Case of the Youth Market in Slovenia 87 Bilal Kimouche The Effect of Stock Market Listing on Real Earnings Management: Evidence From the Algerian Companies 4 Is There Asymmetry in the Relationship Between Government Consumption Dynamics and Economic Activity? Evidence From G7 Economies Marko Senekovic PhD Student at the University of Maribor, Faculty of Economics and Business, Slovenia marko.senekovic1@um.si Abstract In this paper, based on a quarterly dataset of G7 countries with the application of a nonlinear ARDL model we test for the presence of a short-run and long­term asymmetry in the relationship between government spending and economic activity. The main aim of this study is to analyze the relationship between government spending and economic activity in two separate scenarios, first, in periods when government spending increases and, second, in periods when government spending decreases. Our key findings are, first, the linear model that produces a positive relationship between government consumption and economic activity. Second, in the nonlinear model, more than half of the short- run and long-run coefficients are statistically significant. Third, short-run and long-run asymmetry are detected in four out of seven cases with recognized short-run asymmetry also in the remaining three cases based on graphical analysis. Finally, a negatively inclined short-run asymmetry is detected. The results thus imply a stronger output effect in periods of declining dynamics in government consumption. Future research should be focused on broadening the sample countries and model by adding additional variables. Keywords: fiscal policy, nonlinear ARDL model, G7 countries Introduction Over the last 15 years, the development of the economic conditions on a global scale led to many changes in the perception and understanding of the function­ing of economies at the macroeconomic level. From the great recession and the debt crisis-related deflationary pressures accompanied by the economics of depression to the later revival of economic activity mainly at the expense of extremely accommodative monetary policy measures, in the last two years, we found ourselves in the grip of covid economics. An attempt to cope with simul­taneous supply and demand shocks was necessary and, therefore, also addressed by central banks and governments across the world. However, as the global epidemic has not yet been resolved and economic policy stimulus still ongoing, the main concern is how to properly manage inflationary pressures without ORIGINAL SCIENTIFIC PAPER RECEIVED: NOVEMBER 2021 REVISED: NOVEMBER 2021 ACCEPTED: NOVEMBER 2021 DOI: 10.2478/ngoe-2021-0018 UDK: 336.145:330.34 JEL: C22, E60, E62 Citation: Senekovic, M. (2021). Is there Asymmetry in the Relationship between Government Consumption Dynamics and Economic Activity? Evidence from G7 Economies. Naše gospodarstvo/Our Economy, 67(4), 1-12. DOI: 10.2478/ngoe-2021-0018. NAŠE GOSPODARSTVO OUR ECONOMY Vol. 67 No. 4 2021 pp. 1–12 jeopardizing future economic growth and high levels of employment. We seem to have become accustomed to a state of persistent crisis which requires constant efforts by the authorities to maintain economic development on the right course and where certain measures are already in use for many years. Logically, questions are raised about the effectiveness of economic policy by managing the business cycle in changing and evolving macroeconomic conditions, espe­cially in the desire to overcome this turbulent epidemic period with as much as possible low direct and indirect costs. If over the last decade, a mild consensus has been established about reduced efficiency of monetary policy in periods of zero lower bound (Di Bucchianico, 2021; Bernanke, 2020), simultaneously, fiscal policy has gradual­ly gained in importance at both scientific (Buchner, 2020) and political (Gaspar et al., 2021; Schnabel, 2021) levels. Moreover, it was the epidemic period that demonstrated the true potency of fiscal transfer policy to stimulate aggre­gate demand in real-time. For this reason, in the last decade, the turn to a more com­ prehensive and in-depth study of the effectiveness of fiscal policy in managing the business cycle was understandable. A hot topic has become a comprehensive understanding of the functioning of the fiscal multiplier mechanism, which, however, remains unexplored with too many em­ pirical specificities, at least in some parts. The factors that influence the effectiveness of fiscal policy by promoting economic growth are particularly interesting. One of these peculiarities is the presence of nonlinear processes in the relationship between fiscal spending and economic activity (Horwath et al., 2020; Alqaralleh, 2020). The main aim of this study is, therefore, to analyze the rela­tionship between government consumption and economic activity in two separate scenarios, first, in periods when government spending increases and, second, in periods when government spending decreases. According to economic theory, a positive connection is assumed. There­fore, both variables should be moving in the same direc­tion. On the other hand, the question is whether this output response is symmetric or asymmetric in its magnitude. The nature of the relationship between those two variables is estimated based on the new dataset for the sample of G7 countries with applying an asymmetric ARDL methodo­logical framework. This paper is structured as follows. In chapter 2, we provide an empirical literature review from the field of modeling and examining the role of fiscal policy actions by stimulating aggregate economic activity. In chapter 3, the methodological framework used in this paper is presented. In chapter 4, we briefly describe our data sample. Results are documented and explained in chapter 5 while chapter 6 concludes. Empirical Literature Review According to recent development in the field of understand­ing the efficient functioning of fiscal policy measures, more comprehensive knowledge is yet to emerge gradually. In this chapter, we outline key findings of the relevant empir­ical literature concentrating on examining the relationship between fiscal spending and output dynamics with all its known specialties. Through empirical literature review, we provide some estimates of fiscal multipliers with focusing on papers that analyze specific characteristics which might affect the effectiveness of fiscal policy actions and address nonlinearities in the aforementioned fiscal mechanism. Perotti (2002) and Blanchard and Perotti (1999) set out foundations for the development of the study of fiscal policy and its impact on the gross domestic product and the other key macroeconomic variables based on the vector autoregression methodology. Blanchard and Perotti (1999) find positive government spending multipliers and negative tax multipliers in the United States while Perotti (2002) points out the reduced effect of fiscal stimuli in the years after 1980 in five developed countries. Studies that follow in that direction, e.g. Giordano et al. (2007) and Burriel et al. (2019), provide additional support of the significant output effect of discretionary fiscal policy in developed economies. Moreover, House et al. (2020) based on the analysis of 29 advanced economies found out that elimi­nating austerity measures in the period after great recession would have substantially reduced output losses. Further research showed that it is reasonable to distin­guish between different phases of the business cycle by estimating the output effect of fiscal stimuli. Auerbach and Gorodnichenko (2010, 2011, 2014) assessed fiscal multipliers in separate studies for the United States, OECD economies, and Japan. The common conclusion of these studies was that the estimated values of fiscal multipliers differed between recession and expansion with values in­creasing at the time of recession. Batini et al. (2012) also corroborated the premise of higher spending multipliers in recession periods. On the other hand, the results of Ramey and Zubairy (2014) for the United States did not indicate a statistically significant difference between the values of the multipliers relative to the phase of the business cycle. In addition to this, Qazizada and Stockhammer (2015) es­timated higher spending multipliers during contractions, while results do not indicate a difference in the impact of government spending during zero lower bound periods. Some researchers concentrate on the structural character­istics of countries and show that these factors contribute to the dynamic of output effect of government spending. Ilzetzki et al. (2013), for example, found that the output effect of fiscal stimuli is greater in more developed coun­tries, the size of fiscal multipliers is smaller in more open economies compared with the reference figures in closed economies, and that fiscal multipliers are smaller or even negative in countries with high public debt. On the sample of 48 emerging and advanced economies, Hory (2016) gauged a considerably lower size of spending multiplier in the case of emerging market economies than in the case of advanced economies. Moreover, the spending multipli­er is negatively correlated with imports, public debt, and savings. On the other hand, however, it is positively corre­ lated with unemployment level and financial development. In addition to this, Wierzbowska and Shibamoto (2018) figured out that capital flows, especially foreign direct in­vestment, play an important role in determining the sizes of fiscal multipliers. Koh (2017) confirms the findings of other studies and implies that fiscal multipliers are larger during periods of low public debt, in periods of the finan­cial crisis and economic downturn, and more developed countries. Contrary to the findings in Ilzetzki et al. (2013), Koh (2017) reports that fiscal multipliers are not necessar­ily smaller in the economies with high trade and financial openness. He also argues that the size of fiscal multipliers does not necessarily depend on the type of exchange-rate regime. Borsi (2018) estimated larger fiscal multipliers in times of a credit crunch. The great recession with a prolonged period of sluggish growth suggests that the scope for countercyclical monetary policy remains limited. Auerbach and Gorod­nichenko (2017) found that constraint on monetary policy coincides with a resurgence in activist fiscal policy which has moved from a focus on automatic stabilizers to strong reliance on discretionary measures reflecting not only a necessity but also growing evidence of the effectiveness of such policy to fight recessions. This development of recog­nizing fiscal policy as at least equally if not more efficient than monetary policy in times of long and deep recessions surfaced to mainstream thinking during a covid-19 slump when governments of major capitalistic countries urged, together with its independent central banks, for a massive fiscal expansion (Gaspar et al., 2021; Schnabel, 2021). Some studies scrutinize output response to fiscal policy actions also based on autoregressive distributed lag approach. For example, Alexiu and Nellis (2017) find for the Greek economy over the period 1960-2014 that the size of the fiscal multiplier does not differ substantially over the phases of the business cycle. In addition to this, their results indicate that irrespective of the scale of inflation, government spending positively affects output growth. At the same time, however, results do not clearly support the notion about the role of exceptionally low-interest rates in determining the relationship between government spending and economic activity. Furthermore, Sharma and Mittal (2019) provide some evidence about the presence of an asymmetric association between fiscal deficit and gross domestic product in the short and long run based on the nonlinear ARDL model for India where, according to their findings, fiscal deficit harms gross domestic product. Asan­dului et al. (2020) on a sample of twelve post-communist economies and asymmetric ARDL approach estimate that cumulative impact of fiscal policy generates inflationary output effect for countries in their sample. In recent years, more and more studies have emerged in the field of examining the role of fiscal policy actions with results implying gradual convergence in understanding the functioning of the transmission mechanism of fiscal policy. However, the empirical literature is still far from a unani­mous conclusion about the size dependence of fiscal mul­tipliers to country-specific structural and dynamic factors. Moreover, the nature of the transmission mechanism of fiscal measures is only partially explained, with new de­terminants still to be tested and thoroughly analyzed. In this paper, we apply the aforementioned nonlinear ARDL approach to a sample of G7 countries which are all charac­terized as large, open, and developed economies. Methodology The relationship between government consumption and economic activity is estimated via the autoregressive dis­tributed lag model following Pesaran et al. (2001) which is noted as a linear or symmetric ARDL model from here on. The model is presented in equation 1. nn ˜logY°ˆ..1ˆ˜..2ˆ˜logG ylogY. t0itt.2iti 11.i°1i°0(1) .ˆ˜logY.ˆ˜logGt... 3t.1 41t Where the coefficients a2i represent short-run effects of government consumption (measured in first differences) on economic activity and the coefficient a4 represents long-run effect estimated based on the lagged variable of government consumption. Notations are as follows. G stands for gov­ernment consumption and Y stands for aggregate output. Shin et al. (2014) upgrade the linear ARDL model by intro­ ducing some nonlinearities into it. To detect asymmetries in the relationship between independent and dependent variables Shin et al. (2014) decompose fluctuations in the independent variable, in our case in government consump­tion, into its positive and negative partial sums, which is represented in equation 2 and equation 3. tt logGt˜°..logG˜j°.(.j,0(2) maxlogG) j°0i°1 tt logGt˜°..logG˜j°.(.j,0(3) maxlogG) j°0i°1 Then Shin et al. (2014) construct a nonlinear model by re­placing independent variables, in our case government con­ sumption, with partial sum components defined in equations 1 and 2. The Nonlinear (asymmetric) ARDL approach is then defined in equation 4 as follows. nn ˜logY°ˆ..1ˆ˜..2ˆlogG. ylogY˜ t0itt.2iti 11.i°1i°0 n(4) ... ..3ˆ˜ logG.ˆlogY.ˆlogG.ˆlogG.. 3i.4t.5t.6t ti11.1ti°0 Where coefficients ß2i represent the short-run effect of gov­ernment spending on economic activity in case of positive dynamics of government consumption, and coefficients ß3i represent the effect of government spending on economic activity in case of negative dynamics in government con­ sumption. The long-run output effect of government spending for the case of positive and negative dynamics in government spending is represented by the coefficients ß5 and ß6, respec­tively. Aforementioned methodological approach was also used for example in Husein and Kara (2020). Data In our empirical study we use quarterly data for seven G7 economies. For consistency, we obtained data from the OECD database (2021a; 2021b) for all countries except for the United States. In the estimation process, we used the governmental final consumption component for govern­ment consumption, as defined in the expenditure structure of the gross domestic product. We used the gross domestic product as a proxy for economic activity. In the case of the United States, we use data from the BEA database (2021) for government consumption and gross domestic product. In this case, as in the other six countries, we use the gov­ ernment final consumption as defined in the expenditure structure of the gross domestic product. The variables of government consumption and gross domestic product were obtained for all seven countries in the form of absolute values expressed in domestic currencies. Variables were also seasonally adjusted by both databases. For model estimation, we designed time series that vary in length from country to country. For the United States, we used a data sample from 1947q1 to 2018q4, for Canada from 1981q1 to 2018q4, for Japan from 1994q1 to 2018q4, for the United Kingdom from 1955q1 to 2018q4, for France from 1960q1 to 2018q4, for Germany from 1991q1 to 2018q4, and for Italy from 1995q1 to 2018q4. For each of the seven countries, we converted nominal government consumption and nominal gross domestic product into real values using the consumer price index with a base in 2010. We then transform real government consumption and real gross domestic product into a logarithmic form. Results In this section, we present the estimates for each country separately, for both the symmetric and asymmetric models. In the case of the asymmetric model, the results are also shown in a graphical form where a short-dashed line represents the output response in the case of a positive change in real government spending while a long-dashed line indicates the response of real output in the case of negative change in real government consumption. The solid line represents the perceived asymmetry. The shaded area represents the 90% confidence interval in detecting output response asymmetry between the aforementioned two scenarios. Graphical representations of the results of the nonlinear model are plotted on a 20 quarters horizon. In the case of the United States (Table 1), the linear model shows a positive and statistically significant relationship between government consumption and output, both in the short and long run, with a greater long-run effect. Within the asymmetric model, a statistically significant relation­ship between the two aforementioned variables can be detected in the case of positive changes in government consumption while in the case of negative changes, both coefficients, short- and long-run, are statistically insignif­icant. The estimates also show that there is no statistically significant asymmetry in the response of output concerning the initial positive or negative change in government con­sumption (Figure 1). Table 2 shows that in the case of the linear model for the United Kingdom, the relationship between government consumption and output is positive in both the short and long run, but statistically significant only in the short term. Through the prism of magnitude, the long-term effect of Table 1. Results for the United States Coefficient Estimates t-statistic Prob. level Constant a0 0.045 2.53 0.012 Short-run a2 0.078 2.27 0.024 Long-run a4 0.800 12.8 0.000 Coefficient Estimates t-statistic Prob. level Constant ß0 0.163 2.40 0.017 Short-run positive ß2 0.017 2.07 0.040 Short-run negative ß3 0.009 0.55 0.585 Long-run positive ß5 0.798 86.5 0.000 Long-run negative ß6 -0.462 0.34 0.559 F-statistic Prob. level Short-run asymmetry 1.314 0.253 Long-run asymmetry 0.219 0.640 Table 2. Results for the United Kingdom Coefficient Estimates t-statistic Prob. level Constant a0 0.026 0.73 0.465 Short-run a2 0.311 7.17 0.000 Long-run a4 1.803 0.49 0.621 Coefficient Estimates t-statistic Prob. level Constant ß0 0.506 2.52 0.012 Short-run positive ß2 0.016 1.20 0.233 Short-run negative ß3 -0.021 -1.16 0.248 Long-run positive ß5 0.374 3.66 0.057 Long-run negative ß6 0.501 0.94 0.331 F-statistic Prob. level Short-run asymmetry 5.966 0.015 Long-run asymmetry 7.390 0.007 government consumption on output is stronger. In the case presence of asymmetry in the output response to the initial of the nonlinear model, only the long-term coefficient is positive or negative change in government consumption. statistically significant within a 10% probability level in the Asymmetry is perceived and significant in both the short case of positive dynamics in government consumption. On and long run. It is also depicted from Figure 2 that in the the other hand, the results show a statistically significant case of negative dynamics in government consumption, the output response is greater in absolute terms in the short The link between government consumption and economic run than in the case of positive dynamics in government activity is positive in the case of France in the short and consumption, but then gradually diminishes. long run and, at the same time, statistically significant Figure 1. United States – asymmetry testing Figure 2. United Kingdom – asymmetry testing (Table 3). The coefficients in the linear model are also comparable in magnitude. On the other hand, the coeffi­cients in the nonlinear model are statistically significant in the case of a positive change in government consumption both in the short and long run while in the case of negative dynamics in government consumption, the coefficients are statistically insignificant in both periods. No short-run asymmetry is observed for the French economy. Never­theless, Figure 3 shows a stronger output response in the Figure 3. France – asymmetry testing case of negative dynamics in government consumption. However, a statistically significant long-term asymmetry is detected where the negative response of economic activity gradually diminishes in the scenario of negative dynamics in government consumption. It then turns into a positive area around 13 quarter. Table 4 presents results for Italy where the linear model shows a statistically significant relationship between Figure 4. Italy – asymmetry testing completely symmetrical output response to shock in gov- initially decreases or increases. As a result, test statistics do ernment consumption is detected, regardless of whether it not support long-term asymmetry. Table 3. Results for France Constant a0 0.168 2.31 0.022 Short-run a2 0.734 8.51 0.000 Constant ß0 0.739 3.23 0.001 Short-run positive ß2 0.047 3.19 0.002 Short-run negative ß3 -0.042 -0.97 0.335 Long-run positive ß5 0.744 381 0.000 Short-run asymmetry 0.051 0.821 Long-run asymmetry 4.418 0.037 Table 4. Results for Italy Constant a0 0.693 2.61 0.011 Short-run a2 0.218 5.00 0.000 Long-run a4 0.534 5.61 0.000 Constant ß0 1.217 2.26 0.024 Short-run positive ß2 0.067 2.30 0.002 Short-run negative ß3 0.072 2.26 0.027 Long-run positive ß5 0.711 22.0 0.000 Short-run asymmetry 2.756 0.101 Long-run asymmetry 0.873 0.353 In the case of Germany (Table 5), the linear model assesses the positive and statistically significant effect of gov­ernment consumption on economic activity with a more pronounced long-term effect. In the asymmetric model, estimated coefficients are also statistically significant. It can be seen from Figure 5 that initially, in the case of both positive and negative dynamics in government consump­tion, the output response is negative, with the difference that in the case of the latter, the output response is deeper. After the first year, in the case of positive dynamics in government consumption, the output response skips to the positive area and then rises to a level that corresponds to Table 5. Results for Germany negative equivalents in the case of negative dynamics in government consumption more symmetrically in the long run. This is also confirmed by test statistics which show statistical significance according to the 5% probability level threshold only in the case of a short-run asymmetry. For Canada (Table 6), estimates of the coefficients are statistically significant only in the nonlinear model and even in this model, only in the case of positive dynamics in government consumption. In the short run, the response of economic activity is statistically insignificant. Neverthe­less, Figure 6 shows rather vague dynamics of the response Constant a0 0.097 0.65 0.518 Short-run a2 0.254 3.88 0.000 Constant ß0 2.039 3.65 0.000 Short-run positive ß2 0.107 3.59 0.001 Short-run negative ß3 0.085 2.39 0.004 Long-run positive ß5 0.701 54.2 0.000 Short-run asymmetry 6.143 0.015 Long-run asymmetry 3.875 0.052 Figure 5. Germany – asymmetry testing Figure 6. Canada – asymmetry testing of economic activity in the case of positive dynamics in the output response is relatively strongly negative at the government consumption. At the end of the first year, the beginning, but then markedly positive in the long run. Sta-output response is even slightly negative. On the other hand, tistically significant short-run and long-run asymmetries in the case of a negative change in government spending, were detected. Table 6. Results for Canada Constant a0 0.017 0.48 0.629 Short-run a2 0.094 1.24 0.216 Constant ß0 0.722 2.58 0.011 Short-run positive ß2 0.026 1.49 0.139 Short-run negative ß3 -0.080 -2.76 0.007 Long-run positive ß5 0.445 7.78 0.006 Short-run asymmetry 3.624 0.059 Long-run asymmetry 19.40 0.000 Table 7. Results for Japan Constant a0 0.937 1.66 0.100 Short-run a2 0.524 4.71 0.000 Constant ß0 0.753 1.21 0.228 Short-run positive ß2 0.012 0.48 0.634 Short-run negative ß3 -0.14 0.24 0.812 Long-run positive ß5 0.190 0.21 0.648 Short-run asymmetry 5.359 0.023 Long-run asymmetry 0.003 0.955 The coefficient estimates met the statistical standards worst in the case of Japan because only the short-run effect of government spending on economic activity proved to be statistically significant (Table 7). Nonetheless, Figure 7 offers us the perceived asymmetric short-run impact of government spending on economic activity. In the case of a negative shock or negative dynamics in government consumption, the response of economic activity is much stronger than in the case of a positive shock in government consumption. This also results in a statistically significant short-term asymmetry. In the long run, the output effect of the negative shock in government spending is waning gradually. There is also a weak positive output response noted to a positive shock in government consumption which stays low across the entire horizon. Figure 7. Japan – asymmetry testing To sum up, our empirical analysis via the linear ARDL model characterized six out of seven estimated short-run coefficients and four out of seven long-run coefficients as statistically significant. As expected, a positive relationship between government consumption and economic activity is confirmed based on results from the linear model. On the other hand, in the nonlinear ARDL model, in the short run, 50% of estimates satisfy the threshold for statistical significance while in the long run, nine out of fourteen co­efficients are statistically significant. Furthermore, short- run asymmetry is detected through test statistics in four out of seven cases. Based on the graphical analysis, however, we may conclude that even in the remaining three cases short-run asymmetry can be detected. Interestingly, in six out of seven cases, we estimate negatively inclined short-run asymmetry, which indicates that there is expected stronger output response in periods of negative dynamics in government consumption. In the long run, we detect a statistically significant asymmetric relationship between the dynamics of government consumption and the output response in four out of seven cases. The results thus show that there is more to a basic linear connection between government consumption and economic activity. Based on results, fiscal policy actions and their counter-cyclical function have an asymmet­ric effect on aggregate economic activity, which implies careful and tailored use of fiscal policy measures according to the different underlying macroeconomic conditions of the economy. This is especially important in times of re­cessions when a lot of countries apply austerity measures willingly or unwillingly and, therefore, gradually try to decrease government spending and consolidate public budgets. And if this is the case, the presence of negatively inclined asymmetry makes balancing the budget even more stressful and, ultimately, also inefficient because the inev­itable connection between the pace of economic activity and tax revenues still persists and, therefore, disables to achieve the aforementioned goal of a balanced budget. If we assume that government consumption frequently de­creases during the recession periods, despite the differenc­ es in methodological approaches, our findings are in line with Auerbach and Gorodnichenko (2010, 2011, 2014), Batini et al. (2014), and Koh (2017) who all assessed larger fiscal multipliers in times of recession. Conclusion The role and functioning of fiscal policy in stabilizing the business cycle have received considerable research attention due to the specific economic conditions in the post-recession period. Above all, the main focus is to understand specifici­ties in the multiplicative mechanism of fiscal actions on a macroeconomic scale, especially because the effectiveness of the stabilizing role of fiscal policy in different periods and conditions depends on many structural characteristics of individual economies as well as on built-in specific dynamic nonlinearities. In this paper, based on a quarterly dataset of G7 countries with an application of a nonlinear ARDL model we test for the presence of a short- and long-term asymme­try in the relationship between government spending and economic activity. The key findings of our study are the following. First, the linear model produces a positive relationship between gov­ernment consumption and economic activity. Second, in the nonlinear model, more than half of the short-run and long-run coefficients are statistically significant. Third, short-run and long-run asymmetry are detected in four out of seven cases. However, from the graphical analysis, the presence of a short-run asymmetry can be inferred in the remaining three cases. Finally, however, the main result of the present study is a detection of a negatively inclined short-run asymmetry which implies a stronger output effect in periods of declining dynamics in government consumption. In future research, it would be interesting to apply the existing methodological framework to countries that differ in fundamental economic characteristics from the countries included in this research, e.g. the use of a sample of smaller and less developed economies. In addition to this, expanding the model by including additional explanatory and control variables would help clarify the functioning of fiscal policy under different circumstances, especially to identify possible new asymmetric relationships between considered variables in the case of former or to increase the robustness of a model in the case of the latter. References Alexiou, C., & Nellis, J. G. (2017). Cyclical Multiplier and Zero Low Bound Effects of Government Expenditure on Economic Growth: Evidence for Greece. Australian Economic Papers, 56(2), 119–133. https://doi.org/10.1111/1467-8454.12084 Alqaralleh, H. (2020). The Fiscal Policy and the Dynamic of the Economic Cycle. 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Decision: Official Journal of the Indian Institute of Management Calcutta, 46(4), 353–363. https://doi.org/10.1007/s40622-019-00223-8 Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In R. Sickles, & W. Horrace (Eds.), Festschrift in Honor of Peter Schmidt. Springer. https://doi.org/10.1007/978-1-4899­8008-3_9 Wierzbowska,A., & Shibamoto, M. (2018). Cross-country Evidence on Determinants of Fiscal Policy Effectiveness: The Role of Trade and Capital Flows. Applied Economics, 50(32), 3493–3514. https://doi.org/10.1080/00036846.2018.1430332 Ali obstaja asimetrija v razmerju med dinamiko državne potrošnje in ekonomsko aktivnostjo? Analiza na vzorcu držav skupine G7 Izvlecek V tem clanku preverjamo prisotnost kratkorocne in dolgorocne asimetrije v razmerju med državno potrošnjo in ekonomsko aktivnostjo na osnovi cetrtletnega podatkovnega niza za države iz skupine G7 z uporabo nelinearnega ARDL modela. Glavni namen te raziskave je analizirati razmerje med državno potrošnjo in ekonomsko aktivnostjo v dveh locenih scenarijih, prvic, v obdobjih, ko se državna potrošnja povecuje, in drugic, v obdobjih, ko se državna potrošnja zmanjšuje. Naše kljucne ugotovitve so, prvic, linearni model prepoznava pozitivno razmerje med državno potrošnjo in ekonomsko aktivnostjo, drugic, v nelinearnem modelu je vec kot polovica kratkorocnih in dolgorocnih koeficientov statisticno znacilnih, tretjic, kratkorocna in dolgorocna asimetrija je bila ocenjena v štirih primerih od skupno sedmih, s tem da lahko na osnovi graficne analize sklepamo o prisotnosti kratkorocne asimetrije tudi v preostalih treh primerih. In nazadnje, ugotovljena je negativno usmerjena kratkorocna asimetrija. Rezultati tako kažejo na mocnejši odziv outputa v obdobjih, ko vladna potrošnja upada. Nadaljnje raziskovanje je smiselno usmeriti na širitev vzorca držav vkljucenih v analizo in na razširitev modela z dodatnimi spremenljivkami. Kljucne besede: fiskalna politika, nelinearni ARDL model, države G7 Empirical Testing of Purchasing Power Parity Validity in Selected European Union Countries Jelko Plošinjak Grajenšcak 27, 2250 Ptuj, Slovenia jelko.plosinjak@gmail.com Mejra Festic University of Maribor, Faculty of Economics and Business, Slovenia mejra.festic@um.si Abstract In this article, the authors carried out an empirical analysis of the validity of purchasing power parity (PPP) in Slovenia, Croatia, the Czech Republic, Slovakia and Austria. The results provide mixed support for PPP, which is typical for ex- transition economies. In the first phase of the empirical part of the research, the authors tested the stationarity of the real exchange rate in a logarithm, while in the second phase, the cointegration of nominal exchange rate, domestic and foreign price levels was tested.The Vector Error Correction Model (VECM) was used in the third phase to test if the signs of variables are in accordance with economics and econometrics theories, while in the final phase, restrictions were imposed for the symmetry and proportionality of coefficients. Slovenia is subject to limitations on the symmetry and proportionality of coefficients, which means the validity of both the absolute and relative versions of the PPP theory. Croatia is subject to a limitation on symmetry, but not to a limit on the proportionality of coefficients, which means the validity of the relative version of the PPP theory. In the case of the Czech Republic, Slovakia and Austria, restrictions on the symmetry and proportionality of the coefficients do not apply, which consequently constitutes an invalidity of both versions. However, to the authors' knowledge, and taking into account Liu (1992), who states that it is more important to check the presence of co-integration than to check the symmetry and proportionality of the coefficients, since there is a co- integratation between the nominal exchange rate, foreign prices and domestic prices, the theory of PPP is valid for all the selected countries. The empirical results suggest that all the real exchange rate time series are stationary, additionally, cointegration exists among all the variables for all countries, and the signs of coefficients are statistically significant for all variables in all countries, however, the coefficient restrictions are only statistically significant in Slovenia and Croatia. Keywords: PPP, stationarity, cointegration, ADF test, Johansen test ORIGINAL SCIENTIFIC PAPER RECEIVED: JUNE 2021 REVISED: OCTOBER 2021 ACCEPTED: NOVEMBER 2021 DOI: 10.2478/ngoe-2021-0019 UDK: 339.133.3:519.2 JEL: E32, E51, G21, G28 Citation: Plošinjak, J., & Festic, M. (2021). Empirical Testing of Purchasing Power Parity Validity in Selected European Union Countries. Naše gospodarstvo/Our Economy, 67(4), 13-32. DOI: 10.2478/ngoe-2021-0019. NAŠE GOSPODARSTVO OUR ECONOMY Vol. 67 No. 4 2021 pp. 13–32 Introduction The importance of international trade has increased since the Second World War. During this period, economies have in­creasingly acted outwardly and merged into different forms of integration, which represents the onset of accelerated globalisation. A shift from closed to open economies has in­creased the global trade volume, resulting in more and more countries integrating into the world trading system, which has led to increased trade between countries. As countries mainly have their own national currencies, apart from the euro area countries, the exchange rate represents an impor­tant factor in foreign trade for each individual country. Modern exchange rate theories include the monetary theory, the portfolio theory, and the balance of payment approach. The focus of this article is on the long-term exchange rate theory, i.e. purchasing power parity (PPP). The earliest records of the PPP theory date back to the 16th century and were written by scholars at Salamanca University. At that time, the development of the PPP theory was under the influence of three factors: the Salamanca University was then one of the leading educational institutions; the period called the Spanish Price Revolution, caused by the import of precious metals in Spain as the result of Spain expanding its territory to South America and leading to deterioration of the balance of payments; and prohibition by the Catholic church to charge interest. After this period, no significant progress in the field of the PPP theory was made until the First World War. In 1918, during the First World War, the Swedish economist Gustav Cassel further developed the PPP theory (Taylor, 2002, 2006). In his work, he wrote about his attempt to develop a theory during the time of war, presuming that the trade rate between two countries is defined by a quotient ratio of the purchasing power of money in one country and the purchasing power in another. He reported that the in­ flation rate during wartime decreased the purchasing power in all countries to a different level, therefore the conversion rates would be expected to deviate from the old parities pro­ portional to the inflation in an individual country. According to Cassel, the exchange rate could not deviate much from the PPP until a considerable trade between countries and free movement of goods existed. Even if a trade restriction had been applied, deviations from the PPP would not have occurred if they were the same for both parties. If trade re­strictions had been more drastic for one party, larger devia­tions from the PPP would occur (Cassel, 1918). After the Second World War, with the rise of Keynesian economics, the analysis of international economics was performed inside models with fixed prices, therefore no emphasis was placed on studying the PPP theory. The theory started gaining momentum among monetarists in the 1960s. Since then, the PPP theory – in different forms of strictness – has become one of the most important theories among econ­ omists dealing with international economics and finances (Taylor, 2006). Modern studies conducted from the year 2000 onwards have presented mixed results in terms of the validity of the PPP theory for certain countries only, but not all the analysed countries; it especially depends on the countries selected, price indices and time period, thus meaning that the PPP theory in general has still not been completely empirically confirmed. The objective of this article is to provide a detailed de­scription of the PPP theory, including its shortcomings and limitations, as well as to provide the foundation for its empirical evaluation by using the econometrics stationarity test and cointegration test. The validity of the PPP theory will be studied for five countries – Slovenia, Croatia, the Czech Republic, Slovakia and Austria – all of which are EU member states. Overview of Literature The long-term theory of PPP is one of the most contro­versial topics in international economics. The validity of the PPP theory carries important implications for policy makers, central banks, multinational enterprises and players in foreign exchange markets. PPP is important for policy makers for two reasons. First, it can be used in predicting whether a currency is over or undervalued, which is especial­ly important for less developed countries and those countries with significant differences in domestic and foreign inflation rates. Second, the PPP theory represents the foundation for many theories that aim at determining the exchange rate (Holmes 2002, 2001). Sideris (2006) divided the studies on the PPP theory into four groups. The first group are early correlation studies, which were not reliable, and their results demonstrated weak or no support for the validity of the PPP theory. The second group are studies testing the presence of a unit root or sta­ tionarity of the real exchange rate. The third group of studies verifies cointegration between prices and the exchange rate. The results of early cointegration tests in relation to the validity of PPP theories vary and indicate a weak presence of the exchange rate returning to PPP during a transitional period towards flexible exchange rates (Taylor & McMahon, 1988; Mark, 1990), whereas for the period between the first and second world wars, as well as for those countries with a high inflation rate, the studies confirmed the return of the exchange rate to the PPP level (Taylor & McMahon, 1988). The results of subsequent studies have demonstrated support for PPP for more countries, even during the transition towards flexible exchange rates (Kim, 1990; Kugler & Lenz, 1993), whereas for the transition countries, the studies using the cointegration tests do not demonstrate strong support for PPP theories (Sideris, 2006). Today, a combination of tests verifying the stationarity and cointegration is used, however, the time series are too short, and in order to deal with this issue, it is suggested that a longer time period should be used and the analysis should be performed on a larger number of countries by means of panel tests. Due to the weak statisti­cal power of the initial tests, which provided mixed results on the validity of the PPP theory, it is recommended that the latest studies should use advanced econometric tests, including a unit root test with improved power, which can also allow for structural breaks. The second option is to use unit root panel tests and cointegration tests with improved power, while the third option is tests with the autoregression parameter allowing for non-linear adjustment of the real exchange rate (Sideris, 2006). Generally, three important facts on the long-term PPP apply in terms of the results of cointegration studies: the support of the validity of PPP is greater in a fixed exchange rate than in a flexible exchange rate; the presence of cointegration is greater if the Wholesale Price Index (WPI) is used instead of the Consumer Price Index (CPI); and the evidence of PPP invalidity will, in principle, be greater if multivariate cointegration is employed instead of bivariate cointegration (Taylor, 2006; Froot & Rogoff, 1995). Table 1. Review of existing/available studies Bahmani-Oskoee and Chang (2015) pointed out that the latest studies use a unit root test to check whether the real exchange rate is returning to its average rate. They also determined that standard unit root tests assumed that the adjustment of the real exchange rate was a linear process, whereas the latest studies have shown that this process can also be non-linear. The studies assuming the non-linear adjustment of the real exchange rate largely confirm the validity of the PPP theory. Foreign exchange market interventions, transaction costs, the different adjustment pace of the commodity prices fol­lowing changes in the exchange rate, and structural breaks are among the main factors that cause non-linear exchange rate developments. The last option is the use of multivariate cointegration techniques, namely the Johansen test. Kasman, Kasman and Ayhan (2010) reported that in the 1990s, the transition countries experienced drastic insti­tutional and structural changes to create market-driven/ oriented economies. Through economic transformation, these countries liberalised their markets, introduced trade reforms, created competition and privatisation, adopted new currency exchange regimes, established financial institutions and led an even more open economy to attract direct foreign invest­ ments. There are two characteristic facts for these countries; the first is the transition process which led to real produc­tion shocks and caused a permanent deviation from the PPP theory, while the second is that these countries faced massive monetary shocks as the result of inflation expectations, which caused temporary deviations from the PPP theory. Sideris (2006) analysed the validity of the long-term PPP theory in 17 former transition countries and included four Table 1. Review of existing/available studies (cont.) Sideris (2006) 17 countries of Eastern and Central Europe, including Slovenia, Croatia, the Czech Republic, Slovakia Slovenia: 1992 (M1) – 2004 (M1), Others: 1993 (M1) – 2004 (M1) USD CPI Johansen and Larsson cointegration test Weak support of the PPP theory Cuestas and Regis (2009) Telatar and Husanov (2009) 8 countries of Eastern and Central Europe, including Croatia, the Czech Republic, Slovakia 12 countries of Eastern and Central Europe, including Slovenia, Croatia, the Czech Republic, Slovakia 1993 (M12) – 2006 E(M10) Slovenia: 1993 REER (currency (M7) – 2007 (M12) Croatia: 1992 (M1) trade partners) – 2007 (M11) The Czech Republic, Slovakia: 1990 (M1) – 2008 (M3) UR, USD, REER (currencybasket of main trade partners) basket of main CPI, HICP CPI Unit root test: Bierens test, Kapetanios test ADF, KPSS, KSS, ST-TAR PPP validity Following the gradual structural changes and asymmetric adjustments of PPP applicable for all the selected countries. Christidou and 15 EU member 1973 (M1) – 2009 USD CPI ADF, non- No validity of Panagiotidis states, including (M4) linear KSS, PPP theory, (2010) Austria. unit root except for panel test Great Britain. Giannellis and Papadopoulos (2010) 10 countries that joined the EU after 2004 and 12 countries that were already member states by the year 2004 1990 (M1) – 2006 (M7) Slovenija: 1992 (M1) – 2006 (M7), The Czech Republic, Slovakia 1993 (M1) – 2006 (M7) EUR CPI, TPI ADF, SETAR Confirmed convergence to a balanced real exchange rate. Kasman, Kasman, Ayhan (2010) 11 countries of Eastern and Central Europe and Cyprus, Malta and Turkey 1990 (M1) – 2006 (M9) Slovenia: 1991 (M12) – 2006 (M9) Croatia: 1992 (M12) – 2006 (M9) The Czech Republic: 1994 (M1) – 2006 (M9) USD 1993 (M6) – 2006 (M9) DEM Slovakia: 1993 (M1) – 2006 (M9) USD, DEM CPI Lagrange multiplier (LM) unit root test Considering the structural breaks, weak support of PPP in case of the USD, support of PPP in 8 countries in the case of DEM. Chang and Tzeng (2011) 9 countries of Eastern Europe. 1995 (M1) – 2008 (M12) USD CPI Univariate unit root test (ADF, PP) KPSS), panel test and SURKSS PPP validity in only two cases. Su and Chang (2011) 7 countries of Eastern Europe, including the Czech Republic, Slovakia. 1993 (M1) – 2008 (M12) USD CPI Breitung cointegration test PPP validity Boršic, Beko and Baharumshah (2012) 12 countries of Eastern and Central Europe, including Slovenia, Croatia, the Czech Republic, Slovakia 1994 (M1)–2008 (M12) USD, EUR CPI SURADF PPP applicable in the case of EUR for Croatia, Poland, and Bulgaria; in the case of USD applicable for Bulgaria, Poland and Romania. Bahmani – 14 countries 1994 (M1)–2012 REER (currency / Caner and PPP validity in Oskooee and of Eastern and (M6) basket of the Hansen TAR five countries, Chang (2015) Central Europe, main trade unit root test, including including partners) ADF, KPSS, PP Slovenia. Slovenia, Croatia, Adjustment the Czech to PPP is Republic, Slovakia non-linear. anf Austria. ADF: Augmented Dickey Fuller Test, KPSS: Kwiatkowski–Phillips–Schmidt–Shin Test, KSS: Kapetanios, Shin and Shell Test, ST-TAR: Smooth Transition Threshold AutoRegressive test, PP: Phillips Perron Test, SURADF: Seemingly Unrelated Regressions Augmented Dickey–Fuller Test, SURKSS: The seemingly unrelated regressions Kapetanios, Shin, Snell test, SETAR; Self-Exciting Threshold AutoRegressive model, TAR: Caner and Hansen TAR unit root test, MW: Mann-Whitney Test, IPS Test: Im-Pesaran-Shin Test. HICP: harmonised consumer price index, TPI: traded good price index, REER: real effective exchange rate, CPI: consumer price index, PPP: purchasing power parity. countries in the sample that are important from the perspec­tive of this analysis – Slovenia, Croatia, the Czech Republic and Slovakia. The time period ranged from January 1990 to January 2004, whereby this time period differed for certain countries in terms of the onset of transition reforms. Sideris chose the US dollar as the base currency and the CPI for the price index. The validity of the PPP theory was verified using the Johansen cointegration test and the Larsson panel cointegration test. The results of the Johansen test provide support for the long­ term PPP theory, however, the cointegration vectors violate the principle of symmetry and proportionality. For Slovenia and the Czech Republic, two of the countries analysed in this paper, the Johansen cointegration test rejected the null hypothesis at 5% risk and concluded that cointegration exists. In the case of Croatia and Slovakia, the test could not reject the null hypothesis and confirmed the existence of cointegration. In the Larsson test, the time period is uniform, specifically from January 1995 onwards. In six countries, which included Slovakia, the Larsson test could not reject the null hypothesis, showing there is no cointegration. In the study, the author concluded that the results are compliant with the studies relating to the validity of the PPP theory for transition countries. According to the author, the factors causing a deviation from long-term PPP are productivity shocks, managed exchange regimes requiring frequent in­terventions in foreign exchange markets, and the existence of non-exchangeable goods and services. Cuestas (2009) analysed the validity of PPP theory in eight Eastern Europe countries, including Croatia and Slovakia. The analysis covered the period from December 1993 to October 2006. He used the CPI for the US dollar as the base currency and the Harmonised Index of Consumer Prices (HICP) for the euro as the base currency. He used the real effective exchange rate (REER). In his analysis, he pointed out that many previous studies did not consider the existence of non-linearity, therefore he included it. He provided three arguments to support his decision. First, if limitations in international trade exist and there is absence of arbitrage, this leads to non-linear exchange rate movement. Second, foreign exchange intervention operations can lead to non-linear movement of the real exchange rate. The third argument refers to the existence of structural changes leading to a non-linear deterministic trend. Due to the exist­ ence of non-linearity, he used two unit root tests, specifically the Bierens and Kapetanios tests. Considering the non-linear deterministic trend, the results speaking in favour of PPP are more powerful because this trend encompasses structural changes during the transition period which strongly influenced the movement of exchange rates in these countries. For Slovakia, none of the tests confirmed the validity of PPP for any of the exchange rates. In the case of Croatia, the KSS test confirmed the validity of PPP in the REER and the real exchange rate with the euro as the base currency, whereas the Bierens test could not confirm the validity of the PPP theory for euro as the base currency. In the Czech Republic, the KSS test rejected the validity of the PPP theory regardless of the selected exchange rate and the Bierens test only confirmed the validity of PPP in the REER. Telatar and Husanov (2009) examined the validity of the PPP theory based on 12 countries in Eastern and Central Europe including Slovenia, Croatia, the Czech Republic and Slovakia. They used a commercially balanced REER. The time period differed for individual countries, depending on the available data. Their analysis shows that major structural changes in selected countries occurring during the transition period could result in non-linear adjustment of the exchange rate, which is why they used the stationarity tests enabling non-linearity and moderate structural changes in addition to classic tests such as ADF and KPSS. When the analysis did not include linearity and structural changes, the results of the ADF and KPSS tests only con­firmed the PPP theory in five countries, including Slovenia, Croatia and the Czech Republic. Considering the data non-linearity, the KSS test confirmed the validity of PPP in seven countries, including Slovenia, Croatia, the Czech Republic and Slovakia. When taking into account the structural changes and asymmetric adjustment by applying the ST-TAR test, the PPP theory was confirmed in all the countries. Giannellis and Papadopoulos (2010) analysed the validity of the long-term PPP theory for 10 countries that joined the EU in 2004, including Slovenia, the Czech Republic and Slovakia. In addition to these ten countries, they also analysed 12 member states that joined the EU prior to 2004, including Austria. They used the CPI and TPI, which repre­sent the weighted average of import and export prices. The ADF test confirmed stationarity for the CPI in all ten coun­tries that became an EU member state in 2004 and thereafter. Among the other 12 countries, the ADF test only confirmed stationarity for France at 10% risk and the Netherlands at 5% risk. The TPI was only used for those member states that joined the EU before 2004. The ADF test only confirmed stationarity for Germany, whereas for Austria, calculation of the TPI was not possible due to the lack of sufficient data. As only one case of stationarity was confirmed, the authors checked whether the TPI reflects a non-linear movement. They established that linearity was rejected for all 12 coun­tries, therefore they used the SETAR model which confirmed stationarity in six of the eight countries with enough data to calculate the TPI. They concluded that in most cases the real exchange rate converges to PPP. Kasman, Kasman and Ayhan (2010) wrote in their study that, unlike other authors, they used the Lagrange multiplier unit root test, thus enabling no more than two endogenous structural breaks for testing the stationarity of currencies against the US dollar and the DEM. The CPI was used, while the time period differed among the countries due to different data availability – it starts after 1990 and lasts until September 2006 for all countries. In an analysis not employing structural breaks, it is not possible to confirm the stationarity of the exchange rate in any of the countries except Romania and Slovakia. If the analysis considers one structural break, the stationarity of the exchange rate can be confirmed in Romania and Turkey for the US dollar as well as for the DEM. Exchange rate sta­ tionarity could only be confirmed for the DEM in Slovenia, Cyprus, Croatia, and Slovakia. In the analysis taking into account two structural breaks, exchange rate stationarity can only be confirmed in Romania and Turkey for both the USD and the DEM, whereas in Slovenia, Bulgaria, Croatia, Cyprus, Estonia and Slovakia, it can only be confirmed for the DEM. Christidou and Panagiotidis (2010) studied the validity of the PPP theory for 15 EU member states between January 1974 and April 2009; all data were monthly and seasonally adjusted. The authors chose two breakpoints (December 1991 and December 1998) to establish whether the Maas­ tricht Treaty had an impact on these relations. They used the USD as the base currency and the CPI. Their analysis included the ADF test, the KSS test and four panel tests, such as the IM test, Pesaran test, Hadri and Kurozumi test, and the Hadri test which, unlike the other three panel tests, does not apply the assumption on the heterogeneity of the parameters. The ADF test confirmed stationarity in Great Britain for the entire period and in Sweden only for the period following the Maastricht Treaty. The KSS test showed that PPP only applies for the entire period in Great Britain. In Sweden and Italy, the KSS test showed stationarity only for the period between 1973 and 1998 for the former state, and between 1992 and 2009 for the latter. As far as all the other countries are concerned, the PPP theory does not apply in any time period. However, the authors established that the half-life of the exchange rate following the introduction of the euro decreased only in Sweden. In all the countries except Austria, Greece and Ireland, the half-life decreased in the period following the Maastricht Treaty. Panel unit root tests provided results confirming the validity of PPP for the entire period. The results of the IM test, taking into account the Maastricht Treaty in 1992, indicate the rejection of the PPP validity. The Pesaran test only con­firmed the validity of the PPP theory after the introduction of the Maastricht Treaty when considering all fifteen coun­tries and not just the twelve countries with the euro as their currency. After the introduction of the euro, none of the tests provide results confirming the PPP theory. Su and Chang (2011) analysed the validity of the PPP theory for seven Central and Eastern Europe countries, including Slovakia and the Czech Republic. They used monthly data from 1993 to 2008, the CPI and the nominal exchange rate at the end of a monthly period, and the US dollar as the base currency. Using the Breitung nonparametric test, they first verified the existence of cointegration between variables and then determined the linearity of cointegration. Asymmetric price adjustment and non-proportional price and exchange rate movement were allowed in the long-term PPP. The result of the Breitung test showed the existence of cointegration between all countries included in the sample. The existence of bivariate linear cointegration was rejected in all cases except in Slovakia, and the existence of multi­variate cointegration was rejected in the case of Slovakia and Russia. The study provided robust results confirming the PPP theory in the selected countries and indicated the existence of nonlinear cointegration. Chang and Tzeng (2011) included nine East European countries in their analysis, including the Czech Republic. The period under observation was between January 1995 and December 2008. The year 2000 was chosen as the base year for the CPI. The US dollar was selected as the base currency. Prior to the analysis, they converted all data into natural algorithms. The choice of the US dollar as the base currency is justified by the fact that the internal exchange market is mainly dominated by the US dollar. Using the Jarque-Bera test, they determined that, with the exception of the Bulgarian Lev/USD exchange rate, the bilateral real exchanges rates are not normally distributed, and the graphic analysis demonstrated considerable upward and downward movements, showing non-linear adjustment of the real exchange rate. The authors first performed the ADF, PPand KPSS tests con­firming the stationarity of a time series only after using them on the first differentiation of the time series. The Im-Pesa­ran-Shin, Hadri, MW, Choi and Chang panel tests showed the same results as the unit root test, which is non-station­ arity of time series of the real exchange rate. The SURKSS panel test, which is a combination of the SURADF and KSS tests, can identify how many and which time series are sta­ tionary. The SURKSS test confirmed the validity of PPP in two countries, the Czech Republic not being one of them. They concluded that the support for PPP validity is low in transition countries, which is why PPP validity remains a questionable and inconclusive issue. Boršic, Baharumshah and Beko (2012) analysed the validity of the PPP theory in twelve Eastern and Central European countries. The real exchange rates used in the study were monthly-based, the base currency was the US dollar in the first case and the euro in the second. The time period extended from January 1994 to December 2008. When veri­fying the validity of PPP, they used the SURADF panel unit root test, which, unlike the Levin, IM, Fisher ADF and Fisher SURADF PP tests, enables the identification of how many and which countries from the panel include the unit root. The test is specific as it contains non-standard distributions, therefore critical values are to be calculated by means of the Monte Carlo simulation. In addition to this test, they also used the aforementioned tests. In the case of the US dollar as the base currency, only the Levin test rejected the null hy­pothesis of non-stationarity, by considering the constant and trend. When the euro is selected as the base currency, all the listed tests rejected the null hypothesis of non-stationarity. The results of the SURADF test showed that the validity of the PPP theory can be confirmed for certain countries in the panel, however, the validity depends on the base currency. The validity of the PPP theory with the US dollar as the base currency could be confirmed for seven countries, whereby Slovenia, Croatia, the Czech Republic and Slovakia were not among them. When the euro was used as the base currency, the validity of the PPP theory was confirmed for five coun­tries, including Croatia and Slovenia. The first study finding was that the validity of PPP cannot be generalised for a group of countries but can be applied only for individual countries. The second finding refers to the validity of PPP and its dependence on the selected base currency. Bahmani-Oskooee et al. (2014, 2015) analysed the PPP and real exchange rate in Central and Eastern European coun­ tries and verified the validity of PPP theory for fourteen transition countries, including all the five countries that are the subject of this analysis. They used monthly data for the REER for the period from 1994 to mid-2012. The validity was verified by using the Caner-Hansen TAR test examining non-stationarity and non-linearity. The authors wanted to demonstrate the difference between the Carner and Hansen test and the standard ADF, KPSS and PP unit root tests. The standard unit root tests showed weak support for the PPP theory. The KPSS test showed that no time series is station­ary, which is similar to the results of the ADF test estab­lishing a stationary time series for three countries, including Slovenia. The PP test displayed a stationary time series for four countries, including Slovenia. When the authors used the first time series differentiation, all the tests provided the same results, i.e. that the time series is stationary. The KPSS test was an exception showing a non-linear time series for three countries. They further performed the Caner and Hansen test consisting of two steps. The first step is verifying linearity by means of the Wald test WT and bootstrap p-value. Once they merged the results, the results rejected the null hypothesis verifying linearity and concluded that a simple linear model is inap­propriate. In the second step, they studied the characteristics of the threshold unit root of the real exchange rate. They rejected the unit root hypothesis at a 10% risk level in five countries, including Slovenia, but could not reject the null hypothesis for the remaining nine countries. Koukouritakis (2009) examined the validity of PPP between the twelve new EU countries vis-à-vis the Eurozone by using the Johansen cointegration methodology. The evidence also suggests that the PPP vector enters the cointe­gration space for Bulgaria, Cyprus, Romania and Slovenia, which means that the long-run PPP vis-à-vis the Eurozone was only verified for these countries. The study of Boršic et al. (2011) found support for the validity of PPP in some reforming European economies, and special attention was devoted to individual country-specific factors that cause PPP deviations. Yang-Cheng et al. (2012) applied the newly developed Autoregressive Distributed Lag (ADL) test for threshold cointegration to test the validity of long-run PPP for a sample of transition countries (i.e. Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania and Russia) over the period from January 1995 to December 2008. The empirical results indicate that PPP only holds true for five of these transition countries. The Cuestas and Regis (2013) study points to the fact that the PPP theory holds true in a greater number of countries. Together with Chang (2013), they proved that PPP holds true for most of the transition countries studied. Arize et al. (2015) explored the symmetry and proportion­ality conditions in the PPP and the results of a long-run cointegration analysis and short-run dynamics, all of which provide evidence for long-run PPP. Huang and Yang (2015) analysed the PPP by applying the Pesaran panel unit root test to real exchange rate data of eleven euro countries for the sample period of January 1957 to May 2013. They proved that the evidence for the mean-reverting in real exchange rates is much weaker in the post-1998 euro period than in the pre-euro period, and that for the four countries not using the euro – Norway, Sweden, Switzerland and the UK – the evidence for the mean-reverting in real exchange rates is strong in both the pre- and post-euro (post-1998) periods. Chun et al. (2016) applied in their a non-linear threshold unit-root test to test the validity of PPP to assess the non-sta­tionary properties of the convergence of RERs based on the Taylor rules for the ten Central Eastern European countries. In the Su et al. (2014) article, the Monte Carlo simulation was used with the wildbootstrapped KSS test for the period from 1994 to 2012, which included a number of crises, such as the Asian financial crisis, the Russian crisis, and global fi­nancial crises, therefore, the study provided strong evidence against PPP. Nazlioglu et al. (2021) proved that PPP is valid for Greece, Italy, Ireland, Portugal and Spain, considering that structural breaks in the non-linear framework play a crucial role. Beko and Kavkler (2019) analysed the PPP theory for a class of ten Central Eastern European economies covering the period from January 2001 to December 2016. The results of the unit root tests imply that the null hypothesis of non-sta­tionarity of real exchange rates cannot be rejected for the whole period. The weak evidence on PPP found in this study suggests that the process of real integration of Central and Eastern European economies, as well as the subsequent price convergence among European markets, remains incomplete. Bahmani-Oskooee (2017) analysed the PPP in 27 emerging markets by using the new Fourier non-linear quantile unit root test. Conventional unit root tests support PPP in half of the countries at most. The Fourier non-linear quantile unit root test reinforces PPP in 26 out of 27 countries in the case of emerging markets. Mladenovic and Bodor (2020) analysed the sustainability of PPP within the quantile au­toregression model in the economies of the Czech Republic, Hungary, Poland, Romania, Serbia and Turkey. To some extent, the empirical results support the PPP theory for the euro and US dollar-based real exchange rate in Romania, Serbia and Turkey. The euro-based real exchange rate in Hungary and Poland is also identified to confirm the PPP theory. The dynamics of the real exchange rate in the Czech Republic cannot be associated with validity of the PPP. The persistence of the euro-based real exchange rate is estimated to be more prominent after the depreciation shocks. Pažický (2020) proved the existence of the long-run rela­tionship by the vector error correction model (VECM); no cointegrating vector in the case of the Slovak Republic and the Czech Republic was proved, which rejects the existence of a persistent long-run equilibrium between the exchange rate, domestic prices (i.e. in the Slovak Republic) and foreign prices (i.e. in the Czech Republic). Yoon and Jei (2020) proved that, by applying a time-varying cointegra­tion model, the PPP varies over time for the United States, Canada and Mexico. The PPP elasticity based on the CPI is particiularly more volatile than the PPI. Seshaiah and Tripathy (2018) proved the co-integration of a positive relationship between GDP PPP per capita and the real exchange rate, real interest rate, and money supply, and a negative relationship between GDP, PPP and CPI in Brazil, Russia, India, China, and South Africa. Prabheesh and Garg (2020) used a structural break unit root test and structural break cointegration technique to test the presence of economic relationships between nominal exchange rates and each of the price and interest rate differentials in Brazil, Russia, India, China and South Africa. The study examines the validity of two cointegrating vectors representing PPP conditions. General conclusions of the literature on PPP Among the ten studies analysed in Table 1 in detail, the stationarity test is used, while the cointegration and panel tests were used additionally. The conclusions of the studies showed relatively mixed results. Only three studies showed the validity of the long-term PPP theory for all or most of the analysed countries, with one considering the structural breaks. In addition to these three studies, one demonstrated convergence with the real exchange rate. The other studies only showed support for the PPP theory for certain countries. The selected studies are among the more recent ones, there­fore six of them use tests for non-linear adjustment of the real exchange rate. The results of the studies taking into account the existence of non-linearity – Cuestas (2009), Telatar and Husanov (2009), Giannellis and Papadopoulos (2010), Christidou and Panagiotidis (2010), Su and Chang (2011), and Bahmani-Oskooee and Chang (2015) – show strong evidence of validity of the PPP theory, or, in other words, the validity of the PPP theory was proved for a larger number of the analysed countries than in studies that have used linear adjustment of the real exchange rate. The most frequently used currency in the analysis is the USD (in seven studies), while the EUR is used in three instances, the REER in two, and the DEM in one. From the perspective of international trade, the use of the USD and EUR is compliant with the theory relating to interna­tional trade, as those countries using the USD and EUR are the largest external trading partners among the analysed countries. Bahmani-Oskooee and Chang (2015), as well as Telatar and Husanov (2009), established in their studies that it is better to consider the REER rather than the bilat­eral exchange rates when analysing the validity of the PPP theory. The REER was used because the rate movement is the key for international trade flows. Bahmani-Oskooee and Chang (2015) argue that the PPP theory is not only valid for a bilateral trading partner but also for the majority of their trading partners if the REER is stationary. All the studies used the CPI as the price deflator. The HICP was only used in one study by Cuestas (2009). Giannellis and Papadopoulos (2010) point out that many studies state that the choice of the price index, in addition to the inadequate econometric test, are the reason for the rejection of the PPP theory. The analyses of the validity of the PPP theory have shown that the evidence speaking in favour of validity of the PPP theory is stronger if the PPI is used instead of the CPI, as it includes proportionally more exchange goods than the CPI, which is why the authors used the TPI in their analysis. They also highlighted the results of previous studies which concluded that the real exchange rate returns to its average level faster if the PPI is used instead of the CPI. Review of the Periods Important for the Development of Macroeconomic Conditions in Selected Countries During the last thirty years there has been intensive transfor­mation of the former transition economies from the 1990s, which have reintegrated into the global economy and, in most of these countries, the standard of living has improved considerably. The transformation towards a market economy was difficult and long-lasting. Liberalisation of trade and prices was implemented very rapidly, while the institutional changes in the field of governing, competition, the labour market, privatisation and restructuring often faced oppo­sition from different interests. In terms of macroeconomic policies, the period of the countries’ transformation process can be further categorised into four different subperiods (IMF, 2014). Four of the selected countries belong to this group, whereas Austria belongs to the developed economies and therefore the same characteristics do not apply. When the four coun­tries faced transformation of their economy, Austria had positive economic growth, low inflation, a low unemploy­ment rate and stable public debt. It was not until after 2009 that a similar movement of macroeconomic indicators was noticeable, as in the other analysed countries (IMF, 2019). Initial programs of stabilisation and reform This covers the period from 1990 to 1993 and the common features of this period are a drastic decline in GDP in all countries included in this study, interruption of trade links and high inflation rates. Those countries with better initial conditions and a more aggressive approach towards reforms stabilised their economies faster (IMF, 2014). The cumula­tive GDP decline during this period was 21% in Slovenia, 35% in Croatia, 18% in the Czech Republic, and 22% in Slovakia (Havrylyshyn, Izvorski, & van Rooden, 1998). The surveyed countries adopted two approaches during their reforms, one being the ‘shock therapy’ and the other the ‘gradual change’ approach. The former was adopted in Poland and later also in the then Czechoslovakia, whereas the latter was adopted in the then Yugoslavia, of which Slovenia and Croatia were a part. This process officially started in Yugoslavia in 1989 and included the elimination of social ownership of enterprises and liberalisation of exchange rate regimes and imports. The objective was to correct the economic, structural and institutional shortcomings in terms of fixed exchange rates. The initial results of the gradual approach brought a significant decline in inflation with a relatively low shortfall in BDP, however, it ended with the secession of individual Yugoslavian states (IMF, 2014). Market reforms From 1994 to 1996 the regional economic changes continued despite political uncertainties and different paces of macroe­conomic stabilisation. Globally, this was a period of integra­ tion, in 1994 the NAFTA agreement came into force, and in 1995 the World Trade Organization was established. Market reform in Central and Eastern European countries enabled in­tegration into the global economy and the increase of market and capital flows; during this period, the surveyed countries began to record economic growth and disinflation and achieve fiscal stability. There were five main objectives of the reforms in this period – macroeconomic stability, liberalisation of prices and convertibility of currencies, reforms relating to enterprises (privatisation), establishing and strengthening of social security, and the development of institutional and legal frameworks. It was also important to improve the labour market which had a common characteristic in all Central and Eastern European countries: a high employment rate, but with rather unproductive work (IMF, 2014). Economic shocks and recovery Major crises were globally present from 1997 and 2001 in the developing countries, for example, the crisis in Mexico from 1994 to 1995, the Asian crisis in 1997, and the crisis in Argentina in 2001. The developing countries in Europe were highly vulnerable under these conditions as they had not yet achieved complete macroeconomic stability, market institu­ tions were in the process of being developed and financial systems were still fragile. Many countries that had not es­ tablished a robust market-oriented framework were the first to succumb to internal crises and afterwards also the Russian crisis in 1998, which resulted from the Asian crisis and most affected the former Soviet Union States. At first, the Russian crisis spread to the developing countries in Latin America and then back to Asia, and even to Europe. Recovery took place in 1999 when regional GDP increased by 4% and in 2000 when it increased by 7%. By the year 2001, most coun­tries in the region had their current account balance, budget deficits and inflation under control (IMF, 2014). Economic boom The period between 2002 and 2007 was characterised by fast regional economic growth, encouraged by favourable global conditions and increasing trust in the process of converging towards the European Union. Economic growth in the region was on average 6% on an annual basis, meaning that GDP would double every twelve years. However, the main driver of economic growth was external borrowing for consump­tion and construction. With the highly increased domestic demand and credit boom generating increased consumption and investments in construction and real estate, external im­balances started to occur, and production capacities could not keep up with demand, which was not sustainable and led to overheating tendencies in the economy. The entire region faced increased deficits in their current account balance and increased credit-to-GDP ratio. There was also no proper feedback from the fiscal and monetary policies to decrease the overheating economies, partially due to the assessment of the output gap at the time, indicating an inflation gap of between 1% and 2%, however, subsequent revised data have shown that the output gaps for Slovenia, the Czech Republic and Slovakia were significantly higher, i.e. between 5% and 7% (IMF, 2014). Global crisis The onset of the global financial crisis in the developed econ­omies was recorded in the summer of 2007 and spread to former transition countries with a delay. During this period of delay, economic growth, credit growth and foreign capital inflow was still present in these countries, therefore it was argued that the developments in these countries are separate from those in the developed economies. The economic boom ended in 2008 and the former transition economies were very vulnerable due to imbalances stemming from this period. The combination of accumulated imbalances and external shocks, such as the collapse of Lehman Brothers in 2008 and the euro crisis between 2010 and 2012, led to the worst consequences for the former transition countries. The effects manifested themselves in economic growth, which was below the po­ tential level, high unemployment rates and fragile financial markets, and this also hindered accession to the EU. Due to decreased capital inflows, these countries faced a deep re­cession. It was not until 2010 that weak economic growth returned, however, during the years of the euro crisis, an economic downturn was recorded once again (IMF, 2014). The period after 2015 In Table 2, the observations stating that economic growth began after 2014 are confirmed. All the surveyed countries recorded strong economic growth; the lowest economic growth was in Austria, which may be attributed to the fact that the global crisis and the euro crisis did not have such a great impact in Austria as in the other four countries. This may be the reason why Austria recorded somewhat slower economic growth. In the other four countries, the global crisis and the euro crisis left their mark, hence these coun­tries recorded high economic growth rates when economic recovery took place. Differences were noted during the time when the selected countries reached the level of GDP they were at prior to the global crisis in 2008. Austria reached this level of GDP in 2011, Slovakia in 2010, the Czech Republic in 2014, Slovenia in 2017, and Croatia not until after 2019. The economies in some of the surveyed countries have somewhat transformed, and imports started taking on much greater importance. Slovenia has shifted towards an export-oriented economy and has been recording a positive current account balance since 2010. As seen in Table 2, the current account balance has been marked by a high percentage of GDP since 2015, which continued to rise until 2019. A similar situation can be seen also in Croatia, which started to record a positive current account balance in 2014. The Czech Republic also started to focus on exports, however, the current account recorded since 2015 shows negative and positive results, and the percentage of the current account balance in this period was between -0.4% and 0.4% of GDP. Since 2015, Slovakia has recorded a negative current account balance and con­sequently also a negative percentage of the current account balance ranging between -2% and -3% of GDP. Austria has recorded a positive current account balance since 2000. The Purchasing Power Theory and Empirical Analysis Law of one price The law of one price provides that in competitive markets, in the absence of transport costs and official barriers to trade, identical goods sold in different countries must be sold for the same price if their prices are expressed in the same currency (Krugman, Obstfeld, & Melitz, 2012). This is written using the equation: P˜°EP* ii(1) Table 2. Selected macroeconomic conditions after 2014 2015 GDP (growth in %) 2.2 2.4 5.3 4.8 1.0 CA (% of GDP) 3.8 3.3 0.3 -2.1 1.7 2016 GDP (growth in %) 3.1 3.5 2.5 2.1 2.1 CA (% of GDP) 4.8 2.1 -0.3 -2.7 2.7 2017 GDP (growth in %) 4.8 3.1 4.4 3.0 2.5 CA (% of GDP) 6.2 3.4 0.1 -1.9 1.5 2018 GDP (growth in %) 4.1 2.7 2.8 4.0 2.4 CA (% of GDP) 6.1 1.9 0.4 -2.6 2.3 2019 GDP (growth in %) 2.4 2.9 2.4 2.3 1.6 6.6 -0.4 -2.9 2.6 CA: current account, GDP: gross domestic product Sources: Eurostat, 2020 a, b; BS, 2020a, b; HNB, 2020, a, b; CNB, 2020, a, b; NBS, 2020 a, b, OeNB, 2020, a, b. where Pi is the price of an i good in the country of origin and Pi* is the price of an i good abroad, and E is the nominal exchange rate (Taylor & Sarno, 2002). The basic argument for why the law of one price is generally true is arbitration, which means buying something in countries where the price is low and selling in countries where the price is high, thus eliminating the price differences in the medium run (Lan, 2001). The assumptions on which the law of one price is based are (Krugman, Obstfeld, & Melitz, 2012): • absence of transport costs • economic operators have complete information on the prices of goods and services at home and abroad • there are no barriers to international trade and interna­ tional financial markets • the quality of the same goods is the same everywhere and the basket of goods is universal If the law of one price is not the case for a particular com­modity, some economists argue that prices and exchange rates should not deviate too much from the ratio envisaged by the PPP theory. When goods and services become tem­porarily more expensive in one country than in another, demand for the currency and its products declines in the country where products and services are more expensive, causing the exchange rate and home prices to return to PPP (Krugman, Obstfeld, & Melitz, 2012). Absolute purchasing power parity The absolute parity of purchasing power states that the exchange rate between two currencies is equal to the price ratio between the two countries (Krugman, Obstfeld, & Melitz, 2012, ). This is written using the equation: PtE= t* (2) Pt where Pt is the price of a good in the country of origin and Pt* is the price of a good abroad and E is the nominal exchange rate. Relative purchasing power parity The relative parity of purchasing power states that the per­centage change in the exchange rate between two currencies over any time period is equal to the difference in percentage changes between the price levels of two countries (Krugman, Obstfeld, & Melitz, 2012. This is written using the equation: E˜E tt˜1* °.˜. tt(3) E t˜1 where E is the nominal exchange rate, the p in the country of origin and p* is the price index in a foreign country. The relative version of PPP is also important because it may also apply when the absolute version of PPP does not apply. If the factors that lead to deviations from the absolute version of PPP are stable over time, the percentage changes in relative prices may roughly illustrate those in the exchange rates (Krugman, Obstfeld, & Melitz, 2012). As can be seen in equation 3, if the expected rate of domestic price inflation (p) is higher than foreign prices (p*), the foreign currency will appreciate against the domestic currency and the domestic currency will depreciate against the foreign currency. If the nominal foreign exchange rate of the in the country of origin depreciates exactly enough to compensate for the difference in inflation, the real exchange rate in the country of origin will remain unchanged. Purchasing Power Parity Limitations There are many problems with the validity of the theory of PPP, as there are many economic and specific factors in reality that cause deviations from PPP. These factors include the existence of transport costs, tariffs and non-tariff re­strictions, price discrimination, various consumables, gov­ ernment consumption, cumulative current account deficit, and Dutch disease. There are also specific restrictions in the case of transition countries – the initial undervaluation of exchange rates, trend appreciation, the Balassa-Samuelson hypothesis, and the Bhagwati-Kravis-Lipsey theory (more in Krugman, Obstfeld & Melitz, 2012). Definition of the real exchange rate The real exchange rate is the nominal rate between the cur­rencies of two countries, taking into account the price devel­ opments in the two countries. The equation by which the real exchange rate is calculated is: P* REt˜NE°(4) P where REt is the real exchange rate, NE is the nominal exchange rate, P* represents the price index in a foreign country, and P represents the price index in the country of origin. If the value of the real exchange rate increases, this refers to real depreciation and improving a country's international competitiveness, however, if the value of the real exchange rate decreases, this refers to real appreciation that exacer­bates a country's international competitiveness. Models for testing purchasing power parity The model for testing the absolute version of PPP can be recorded in the form (Cheung & Lai, 1993): e˜.°.p°.p*°.(5) t01t2tt All the variables are in the logarithmic form, with et the nominal exchange rate, pt the prices of domestic goods and pt* the prices of foreign goods, and it shows deviations from PPP. In the strictest form of the absolute version of PPP, a0 = 0, a1 = 1 and a2 = -1 is assumed. The proportionality assump­tion (a=a) and symmetry(a=-a) of the coefficients 1212 must be met (Froot & Rogoff, 1995). Since the studies that checked the validity of the PPP theory were rejected in the short term, econometric techniques began to be developed to verify whether the nominal exchange rate and relative prices stabilised at PPP levels. In the long term, the validity of PPP can be checked through the characteristics of the real exchange rate, because regard­less of the level at which the real exchange rate stabilises, it must return to its average (Taylor, 2006). The real exchange rate (ret) is recorded using the formula: re˜ne°p.p* (6) tttt where all the variables are in logarithms, net represents the nominal exchange rate, pt the domestic price level and pt* the foreign price level. Under the assumption of PPP, the real exchange rate should be 0, i.e. the real exchange rate movement is equal to the deviation from PPP (Taylor, 2006). In the first phase, the stationarity of the real exchange rate was checked. A stochastic process is stationary, when the average value and variance are constant over time, and the covariance between two time periods depends only on the distance between two time periods and not the actual time during which the covariance is calculated. Such a time series will tend to average, and the fluctuations around this average will mainly have a constant amplitude (Davidson & MacK­innon, 2003). To check the stationarity of the real exchange rate time series, the Augmented Dickey-Fuller test (ADF) derived from the Dickey-Fuller test, was used, which assumes the unreliability of the ut errors. In order to allow the correlation of the ut errors, the ADF test also includes deferred variables of the dependent variable .Yt. The ADF was carried out on the basis of an evaluation of the regression formula of the form (Gujarati & Porter, 2009): m˜Yt°ˆ1.ˆ2t..Yt.1..i.˜Yt.1.t(7) i°1 where et is error, .Yt-1 = (Yt-1 – Yt-2), .Yt-2 = (Yt-2 – Yt-3) and m is the number of deferrals determined in such a way that et is not correlated. The zero and alternative ADF test hypothesis are as follows: H0: the time series contains a unit root; (H0: d = 0) H1: the time series is stationary; (H1: d< 0) When validating or rejecting a zero hypothesis instead of classical t statistics, t statistics were used. The ADF test for rejecting the null hypothesis requires that t statistics must take up greater negative values than usual (Gujarati & Porter, 2009). In the second phase, the authors looked for a stationary linear combination of three variables. The problem arises if the time series of variables are integrated into different orders, but MacDonald (1993) argues that even if time series are integrated into different orders, the volatility of variables can lead to a stationary linear combination between them. To check co-synthesisation, the Johansen co-integration test was used, which is based on vector authored aggression (VAR) and is written in a shorter form (Johansen, 1991): YAY°. °AY°BX ˜°. t1 t.1 mt.mtt(8) where A1, Am and B are arrays, Yt is a vector to non-station­ary variables I(1), Xt is a vector of deterministic variables and .t vector of innovation. The equation (10) can also be recorded as: mˆ1 ˜Y.°Y..˜Y.BX.. ttˆ1 .itˆ1 tt(9) i.1 where: mm˜.ˆi.°ˆj(10) AI..A i.1 Ji..1 Matrix . contains information on long-term changes to the time series. Granger's representative theorem states that matrix . can be divided into two, to × r matrix . and a of order r (r = k-1) so that . = .-a, if matrix . also has the reduced order r < k. The a contains r linear co-integrative vectors, and the . array represents the adjustment parame­ ters in the 'vector error correction' (VEC) model (Boršic & Beko, 2007). The number of integrated vectors is checked using two statistics: 'Trace Statistics' (LRtr) and 'The Maximum Eigen Value Statistic' (LRmax). The null hypothesis and the alternative hypothesis in Trace Statistics are: H0: the number of co-integrated vectors is less than or equal to r H1: the number of co-integrated vectors is the same as k, where the number of endogenous variables for r = 0, 1, ... k-1 'Trace Statistics' are written in the form: k LRtr(|)rk˜°T.log(1..i)(11) ir1 ˜. where .i is the maximum own value of the Ai matrix in the equation (12). The null hypothesis and the alternative hypothesis in 'The Maximum Eigen Value Statistic' (LRmax) are: H0: the number of co-integrated vectors equals r H1: the number of co-integrated vectors is r+1 'The Maximum Eigen Value Statistic' (LRmax) can be calcu­lated by equation (12): LR(|rr˜°.Tlog(1 1)..) maxr˜1 LR(|)rk.LR(r˜1|) °k trtr(12) r°,,. ,k01.1 where the markings are the same as in the equation 10 and 11. The Johansen test also allows the limits to be checked, thus two limits were checked for the purposes of this study, namely the symmetry of the coefficients, ß = (1, -ß1, ß1), which confirms the validity of the relative version of the PPP theory and the second limit of the proportionality of the coef­ ficients, ß = (1, -1, 1), which confirms the absolute version of the PPP theory (Sideris, 2006). The first place in brackets is the nominal exchange rate, the second is the domestic price level and the third is the foreign price level. Liu (1992) states in his study that it is more important to check the presence of co-integration between the nominal exchange rate and the level of domestic and foreign prices than to check the symmetry and proportionality of the coefficients, therefore, if there is co-integration between these three variables, the theory of PPP holds true. Empirical Analysis Data on the bilateral nominal exchange rate and the CPI were obtained from the databases of the Institute for Economic Diagnosis and Prognosis of the Faculty of Economics and Business of the University of Maribor, while for Austria the CPI data were obtained from OECD databases and the exchange rate was obtained from the Eurostat database. The period considered is from January 1993 to December 2019. The stationarity verification of real exchange rates was carried out graphically as well as using the ADF test. Figure 1, which shows the dynamics of the logarithms of the real exchange rates of all the selected countries in the period from 1993 to the end of 2019, provides some characteristics that apply in four countries, with the exception of Austria. The real exchange rate of the Austrian shilling was in a trend of appreciation until April 1995, and a trend of depreciation until 1999. Austria adopted the euro in January 1999 and since then the real exchange rate movement with the base currency of the euro has only depended on a change in the price level in Austria and the European Union. It is evident that the real exchange rate has stabilised at a constant level, which may be an indicator of the stationarity stability of the real exchange rate, which is a precondition of the validity of PPP in the long term. While the other observed countries, which were still transi­tion countries at the beginning of the observed period, are subject to characteristics different from those applicable to Austria, overall, the dynamics of all the four real exchange rates of these countries showed significant depreciation. The real exchange rate of the Slovenian tolar depreciated from the beginning of the observed period up to its entry into the ERM II in 2004. The depreciation of the real exchange rate of the Croatian kuna was rapid, with the exchange rate depreciating from a ratio of one Croatian kuna to one euro to a ratio of around seven kunas to one euro within just 10 months. While the Czech koruna depreciated until 1997, the Czech Republic chose a managed flexible exchange rate regime as a result of the currency crisis that has led to specu­lative attacks on the Czech crown, and since 1997 the Czech koruna has been appreciating towards the euro, other than during the economic crisis after 2008 and the European debt crisis in 2013 when it depreciated against the euro. In terms of speculative attacks, the same applies to the Slovak crown, which depreciated until 2000, and then appreciated until the introduction of the euro in 2009. Figure 1 illustrates that the real exchange rates of the Slovenian tolar and the Croatian kuna moved similarly and stabilised at a constant level, which may indicate the stationarity of the real exchange rate, which is a prerequisite for the long-term validity of the PPP theory. The Czech koruna and Slovak crown demonstrated a similar real exchange rate trend until Slovakia adopted the euro, and the dynamics of the movements of these two currencies may indicate the non-stationarity of the real exchange rate time series, which in turn constitutes weak support for PPP or even its invalidity. Existing literature explains this pattern of real exchange rate developments as being the result of various factors, includ­ing the inherited macroeconomic instability of the transition countries, the mixed success of the performance of selected exchange rate regimes, monetary problems that originate from rising capital inflows, inflationary wage pressures and price adjustments, and real appreciation as a result of the catching-up process of developed countries (Halpern & Wyplosz, 1997; Brada, 1998). Figure 1. Evolution of the logarithms of real exchange rates of countries for the period 1993-2019 ln_slo ln_hrv 5.5 2.4 5.4 2.0 5.3 5.2 1.6 5.1 1.2 5.0 0.8 4.9 4.8 0.4 4.7 0.0 9395979901030507091113151719 9395979901030507091113151719 ln_cz ln_svk 3.7 3.9 3.6 3.8 3.5 3.7 3.4 3.6 3.3 3.5 3.2 3.4 3.1 3.3 9395979901030507091113151719 9395979901030507091113151719 ln_avt 2.64 2.63 2.62 2.61 2.60 2.59 2.58 2.57 2.56 9395 97 99010305 07 0911 13 15 1719 S bls: Slo Sl ni H Cr i Cz he C h R bli SkSl ki A A ia Symbols: Slo = Slovenia, Hrv = Croatia, Cz = the Czech Republic, Svk = Slovakia, Avt = Austria Source: 2020. The Institute for Economic Diagnosis and Prog­ nosis; calculation of the real exchange rate and the figures in Eviews 10. On the basis of the results presented in Table 3, it is evident that the ADF test rejected the zero hypothesis for Slovenia and Croatia at 1% risk, taking into account both the constant and the trend and constant. In the case of Austria, however, the zero hypothesis was rejected at 5% risk, taking into account both the constant and the constant and the trend. For these three countries, the ADF test provided results showing the stationarity of the real exchange rates of these three countries, which is a precondition for the validity of the PPP theory in the long term. While for the Czech Republic and Slovakia, the ADF could not reject the zero hypothesis either by taking into account the constant or the constant and trend at any level of risk. The ADF test result for these countries indicates that the real exchange rates of these two countries proved non-stationar­ity, which may indicate the invalidity of the PPP theory in the long term. Beko and Kavkler (2019) analysed the PPP theory for a class of ten Central Eastern European economies and the unit root tests imply that the null hypothesis of the non-stationarity of the real exchange rates cannot be rejected for the whole period. The weak evidence on PPP found in this study suggests that the process of the real integration of Central and Eastern European economies and the subsequent price convergence among European markets remains incomplete. Due to the difficulties of rejecting the zero hypothesis of the ADF test, and consequently non-stationarity of the real exchange rates of the Czech Republic and Slovakia, the authors of this study also decided to carry out the ADF test at the levels of the first differentiation of the real exchange rate of each country. When introducing the first real exchange rate differentials, the results of the ADF test in Table 4 reject the zero hypothesis in all countries, taking into account both the constant and the trend at a 1% risk. By introducing the first differentiation, all the time series of the real exchange rates become stationarity, which is a prerequisite for the validity of the PPP theory in the long term. The authors also found that by introducing the first differentials, the time series of the real exchange rate of the Czech Republic and Slovakia were integrated to the order of 1. Bahmani-Oskooee (2017) analysed the PPP in 27 emerging markets by using the new Fourier non-linear quantile unit root test. Conventional unit root tests support PPP in half of the countries at most. The Fourier non-linear quantile unit root test reinforces PPP in 26 out of 27 countries in the case of emerging markets. To check the co-integration between the nominal exchange rate and foreign and domestic price levels, the Johansen test was used to assess the long-term equilibrium ratio between the nominal exchange rate and the foreign and domestic price levels. A two-step verification of the presence of co-integra­tion was carried out, which included first checking whether co-integration exists and what is the rank of cointegration. Table 3. Results of the ADF test for logarithm of the real exchange rate -7,6023*** -4,8049*** Slovenia (1) (1) -*** (0,0000) (0,0005) -13,2948*** -13,5662*** Croatia (2) (2) -*** (0,0000) (0,0000) -1,1472 -2,0582 The Czech Republic (0) (0) + (0,6978) (0,5666) -0,4733 -2,4386 Slovakia (1) (1) + (0,8930) (0,3589) -2,9868** -3,6224** Austria (12) (14) -** (0,0372) (0,0295) Notes: *** shows the statistical significance of the test at a 1% significance level, ** shows the statistical significance of the test at a 5% significance level, and * shows the statistical significance of the test at a 10% significance level. The numbers in smaller brackets represent the number of deferrals (time-lags), while, the larger numbers in brackets represent the p-value of the test.The critical values for the constant are: -3.4505 (1%),-2.8703 (5%) and -2.5715 (10%).The critical values for the constant and the trend are: -3.9868 (1%),-3.4238 (5%) and -3.1349 (10%).The minus sign shows the H0 rejection, plus indicates its confirmation. Source: 2020. The Institute for Economic Diagnosis and Prognosis; calculation of the real exchange rate and analysis in Eviews 10. Table 4. Results of the ADF test for the first difference of the real exchange rate -10.5951*** -12.0007*** Slovenia (1) (1) -*** (0.0000) (0.0000) -5.9205*** -5.8436*** Croatia (1) (1) -*** (0.0000) (0.0000) -19.3963*** -19.3656*** The Czech Republic (0) (0) -*** (0.0000) (0.0000) -20.7351*** -20.7919*** Slovakia (0) (0) -*** (0.0000) (0.0000) -14.6668*** -14,64339** Austria (2) (2) -*** (0.0000) (0.0000) Notes: *** shows the statistical significance of the test at a 1% significance level, ** shows the statistical significance of the test at a 5% significance level, and * shows the statistical significance of the test at a 10% significance level. The numbers in smaller brackets represent the number of deferrals (time-lags), while the larger numbers in brackets represent the p-value of the test.The critical values for the constant are: -3.4505 (1%),-2.8703 (5%) and -2.5715 (10%).The critical values for the constant and the trend are: -3.9868 (1 %),-3.4238 (5 %) and -3.1349 (10%).The minus sign shows the H0 rejection, plus indicates its confirmation. Data source: 2020. The Institute for Economic Diagnosis and Prognosis; calculation of the real exchange rate and the analysis in Eviews 10. The Johansen test for co-integration rank is summarised in Table 5. The first step in the analysis is the assessment of the estimated five VARs of the third order in equation 9; the time period is the same for all the estimated VARs and all the systems of the deterministic variable Xt contain a constant; the number of deferrals (time-lags) was selected on the basis of the Akaike Information Criterion. The results of the 'Maximal Eigenvalue' and 'Trace Statis­tics' (Table 5) reject both zero hypotheses at 5% risk and indicate the existence of three co-integrated vectors in all the selected countries. In the second step, by using the vector error correction model (VEC), a check of whether the nominal exchange rate coefficients and foreign and domestic price levels have the appropriate signs, was carried out, and also whether they are statistically significant. From equation 6, it is evident that the sign of the nominal exchange rate coefficient and the sign of the foreign price coefficient must be positive and that of the domestic price coefficient must be negative. The results in Table 6 indicate that all the coefficients in all countries have signs according to the econometric and economic theory, as are t – statistics of all coefficients are statistically significant. When checking the hypotheses about the symmetry and proportionality of the coefficients, test statistics were used, which are asymptotically distributed by .2 – distribution by degrees of freedom equal to the number of limits. The results in Table 6 show that, in the case of Slovenia, they adhere to the limits on the symmetry and proportionality of the coefficients, which means that both the absolute and the relative version of the PPP theory are considered and proved. A limitation on the symmetry of coefficients applies only to Croatia, indicating the validity of the relative version of the PPP theory, while for the Czech Republic, Slovakia and Austria, both hypotheses were rejected, which means that, consequently, no version of the PPP theory is valid for these countries. However, as previously stated, Liu (1992) states that it is more important to check the presence of co-inte­gration than to check the symmetry and proportionality of coefficients, therefore if there is a correlation between the variables, and the PPP theory is valid, in so far as this con­clusion is taken into account, the PPP theory holds true for all the selected countries. Mladenovic and Bodor (2020) analysed the sustainability of PPP within the quantile autoregression model in the econ­omies of the Czech Republic, Hungary, Poland, Romania, Serbia and Turkey. The empirical results support to some extent the PPP theory for the euro and US dollar-based real exchange rate in Romania, Serbia and Turkey. The eu-ro-based real exchange rate in Hungary and Poland is also identified to confirm the PPP theory. The dynamics of the real exchange rate in the Czech Republic cannot be asso­ ciated with the validity of the PPP. The persistence of the Table 5. The Johansen test and the cointegration rank Slovenia (6) 62.28* 47.14* 15.97* 125.39* 63.11* 15.98* Croatia (7) 139.33* 59.96* 51.32* 250.63* 111.29* 51.32* The Czech Republic (8) 53.78* 40.41* 11.81* 106.01* 52.23* 11.82* Slovakia (4) 141.61* 57.68* 33.59* 232.89* 91.28* 33.59* Austria (4) 112.98* 75.99* 17.23* 206.41* 93.42* 17.43* Critical values at 95% significance level 21.13 14.26 3.84 29.79 15.49 3.84 Note: * indicates the rejection of the zero hypothesis at 5% risk Table 6. The Johansen test – testing of limitations -0.84 1.12 0.5112 4.0446 Slovenia 1 (-9.4638) (3.5267) (0.4746) (0.1323) -0.75 1.57 1.4377 25.9118*** Croatia 1 (-39.9072) (3.6512) (0.2305) (0.0000) -0.99 8.00 22.4360*** 24.9000*** The Czech Republic 1 (-2.7079) (7.8874) (0.0000) (0.0000) -3.69 25.03 86.4549*** 88.6886*** Slovakia 1 (-5.4754) (14.8063) (0.0000) (0.0000) -1.33 13.81 64.0013*** 132.3929*** Austria 1 (-3.2673) (12.4442) (0.0000) (0.0000) Notes: *** indicates the rejection of hypotheses at 1% risk, x2c at 1 degree of freedom and a = 0.05 equals 3.8415, at a = 0.01 equals 6.6349, x2c at 2 degrees of freedom and a = 0.05 equals 5.9975, at a = 0.01 equals 9.2703. euro-based real exchange rate is estimated to be more prom­inent following depreciation shocks. Conclusion The results of the empirical studies, which checked the validity of PPP in selected countries, indicated invalidity or weak support for the PPP theory. Only taking into account structural breaks and non-linear exchange rate adjustments improved the results in terms of the validity of PPP. It can therefore be concluded that adherence to a linear de­terministic trend generates results that indicate the existence of co-integration, which is a prerequisite for the validity of PPP. 'VECM' also provided statistically significant coeffi­cients with corresponding signs for all selected countries, while testing the hypotheses relating to limitations on symmetry and proportionality gives mixed results. Slovenia is subject to limitations on the symmetry and proportion­ ality of coefficients, which means the validity of both the absolute and relative versions of the PPP theory. Croatia is subject to a limitation on symmetry, but not to a limit on the proportionality of coefficients, which means the validity of the relative version of the PPP theory, while in the case of the Czech Republic, Slovakia and Austria, restrictions on the symmetry and proportionality of the coefficients do not apply, this consequently constitutes an invalidity of both versions. However, by taking into account Liu (1992), who states that it is more important to check the presence of co-in­tegration than to check the symmetry and proportionality of the coefficients – since there is co-integratation between the nominal exchange rate, foreign prices and domestic prices – the PPP theory is true for all the selected countries. In this analysis, the validity of PPP was checked in five countries – Slovenia, Croatia, the Czech Republic, Slovakia and Austria. Austria was already one of the developed econ­omies at that time, while the remaining four countries were going through the process of transition of their economies. Therefore, the macroeconomic picture among the four former transition countries is the same and differs from the macroeconomic picture in Austria; a similar macroeconomic picture was shared by these five countries after the onset of the global financial and economic crisis. By focusing on the selected countries, which have undergone many changes in a short period of time, five important periods can be highlighted, namely the period of initial stabilisation and reform during which exchange rate regimes were selected, which these countries also changed several times, and they recorded high falls in GDP and high inflation rates. In the following period, countries began implementing market reforms that underpinned convergence towards developed European countries. 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Nonlinear adjustment to purchasing power parity in transition countries: the ADL test for threshold cointegration. Applied Economics Letters, 19(7), 629–633. https://doi.org/10.1080/13504851.2011.591725 Empiricno preverjanje veljavnosti paritete kupne moci v izbranih državah Evropske unije Izvlecek V tem clanku empiricno analiziramo veljavnost paritete kupne moci v Sloveniji, na Hrvaškem, Ceškem, Slovaškem in v Avstriji. Rezultati kažejo mešano podporo paritete kupne moci, kar je znacilno za nekdanje tranzicijske države. V prvem koraku empiricnega dela smo preverili stacionarnost realnega deviznega tecaja vlogaritmu,vdrugem koraku smo preizkusili kointegracijo med nominalnim deviznim tecajem in domaco ter tujo ravnjo cen, v tretjem koraku pa smo uporabili model za vektorsko popravljanje napak (VECM), da bi preverili, ali so predznaki spremenljivk v skladu z ekonomsko in ekonometricno teorijo. V zadnjem koraku smo uvedli omejitve simetricnosti in sorazmernosti koeficientov. Za Slovenijo veljajo omejitve simetricnosti in sorazmernosti koeficientov, kar pomeni veljavnost tako absolutne kot relativne razlicice teorije paritete kupne moci. Za Hrvaško velja omejitev simetricnosti, ne pa tudi omejitev sorazmernosti koeficientov, kar pomeni veljavnost relativne razlicice teorije paritete kupne moci.V primeru Ceške,Slovaške in Avstrije pa omejitve simetricnosti in sorazmernosti koeficientov ne veljajo, kar posledicno pomeni, da sta obe razlicici neveljavni. Ce upoštevamo Liuja (1992), pa ta navaja, da je pomembneje preveriti prisotnost kointegracije kot simetricnost in sorazmernost koeficientov, saj obstaja kointegracija med nominalnim deviznim tecajem in tujimi ter domacimi cenami, torej teorija paritete kupne moci velja za vse izbrane države. Empiricni rezultati kažejo, da so vse casovne vrste realnih deviznih tecajev stacionarne, poleg tega obstaja kointegracija med vsemi spremenljivkami za vse države, znaki koeficientov so statisticno pomembni za vse spremenljivke v vseh državah, omejitve koeficientov pa so statisticno pomembne le v Sloveniji in na Hrvaškem. Kljucne besede: pariteta kupne moci, stacionarnost, kointegracija, test ADF, Johansenov test Exploring Emerging Markets' Demographic and Macroeconomic Dynamics and the Middle Class Growth: The Case of China and India Romana Korez Vide University of Maribor, Faculty of Economics and Business, Slovenia romana.korez@um.si Irena Zavrl University of Applied Sciences Burgenland, Austria irena.zavrl@fh-burgenland.at Anica Hunjet University North, Croatia anica.hunjet@unin.hr Abstract Emerging markets' political, economic, and social transition processes led to altered demographic trends and new macroeconomic dynamics in these economies. These changes triggered the growth of the middle class that became an essential factor of emerging markets' attractiveness for sales and production activities of foreign firms. The purpose of this article is to analyze the demographic and macroeconomic trends of China and India in the decades before the COVID-19 crisis, aimed at estimating their role in the growth of the middle class and the consequent attractiveness of these two important emerging markets for foreign firms. We formulate our research findings on the extensive theoretical foundations and empirical analysis of selected demographic and macroeconomic indicators related to the growth of the middle class. We established that India fell behind China considerably concerning demographic transition and macroeconomicdynamics in the observed periods.China enforced a radical demographic transition. The comparative analysis of macroeconomic dynamics showed a solid leadership of China in economic growth, international trade and investment openness, technological advancement, employment, the structure of the output, domestic investment, urbanization, and salaried workers. Consequently, we estimated the lower growth of the Indian middle class and its lower importance in foreign firms' decisions to enter the Indian market. The findings brought some implications for international managers at segmenting and selecting target foreign markets. Keywords: emerging market, middle class, demographic transition, macroeconomic dynamics, international business, China, India ORIGINAL SCIENTIFIC PAPER RECEIVED: OCTOBER 2021 REVISED: NOVEMBER 2021 ACCEPTED: NOVEMBER 2021 DOI: 10.2478/ngoe-2021-0020 UDK: 339.5:330.101.541(510)(540) JEL: F23, F62, J11, N35 Citation: Korez Vide, R., Zavrl, I., & Hunjet, A. (2021). Exploring Emerging Markets' Demographic and Macroeconomic Dynamics and the Middle Class Growth: The Case of China and India. Naše gospodarstvo/Our Economy, 67(4), 33-55. DOI: 10.2478/ngoe-2021-0020. NAŠE GOSPODARSTVO OUR ECONOMY Vol. 67 No. 4 2021 pp. 33–55 Introduction Firms that would like to internationalize their business must consider many factors before selecting foreign markets. By an in-depth analysis, they should systematically evaluate foreign markets' potentials. In the process of markets' eval­uation, among others, the human resources and customers' aspects should be explored. Identified characteristics of human resources/customers enable firms to better respond in international business. If they successfully analyze and select foreign markets' segments, they can react quickly and appropriately to potential markets' changes and adjust/redis­tribute resources effectively. Research on the middle class, economic growth, and de­velopment goes far back into history. Economic historians (e.g., Adelman & Morris, 1967; Landes, 1998) aligned the 19th-century industrial revolution with relatively large middle classes in European countries. Lately, empirical evidence exists on the middle class's impacts on economic growth (e.g., Brueckner et al., 2018; Easterly, 2001; Liu & Hu, 2013; Ozturk, 2016). It is assumed that the higher consumption potentials and higher education levels of the middle class and the high share of these people in the total population impact economies' growth. While the research findings show decreasing middle class in developed countries (e.g., Cavusgil et al., 2018; Daugherty, 2021; Schettino & Khan, 2020; Pew Research Center, 2016), for emerging markets, the topic of "new middle class" has opened the floor for discussions. Recent studies (e.g., Fengler & Kharas, 2017; Kharas, 2010, 2017; Roy, 2018) indicate that the "new middle class" has become a signifi­cant source of possible human resources and consumption and herewith an essential driver of emerging economies' growth and development. In emerging market economies, a new middle class has grown in the period of their political, economic, technological, and social transition that started several decades ago. Members of the new middle class can invest in education, health care, and consumption of various expensive goods and services. Research (see Boussie & Kersley, 2018, 13) on the shares of middle classes' final consumption in the GDP of G6 countries and the GDP of selected emerging markets shows that for the former, this share has fallen from 66% to 50% since 2000, while for the latter it has more than doubled over the same period. Therefore, the sizes and characteristics of middle classes at emerging markets represented a substantial market opportu­nity for foreign firms. The internationalization of their op­erations into emerging markets brought cost savings and a large pool of consumers. However, it offered new jobs, new sources of taxes, and investment to the target host emerging markets. These were the basis for economies' development, which increased the attractiveness of emerging markets for foreign firms. A certain level of dynamics is a constant in emerging markets and reflects various changes in the socio-economic environ­ment, including consumer thinking and habits (Kersley & Bhatti, 2018, 5). A new middle class, representing the driving force of consumption in emerging markets, is described as the young or middle-aged urban population with its own, steady middle-level income. They are educated, connected to the global media, and have the desire and the ability to choose various products, especially those originating from the Western world (Cavusgil et al., 2018, 103). They are also willing to exceed their budget to purchase non-essential goods and choose credit for the form of repayment. A sig­ nificant trend in investing time and money in this segment is also in leisure and modern lifestyle (Klerk & Irwin, 2018, 32). However, middle classes at various markets have het­erogeneous characteristics and contribute differently to the economy's dynamics (Brandi & Büge, 2014). Thus, the new middle class could be the starting point for a more advanced society if it could become a progressive driver of reforms that could benefit the majority. At the beginning of the 21st century, some Asian countries have taken advantage of their demographic transition, ex-port-oriented policies, and openness to foreign direct invest­ment (FDI). As Eastern and Southern Asian largest emerging markets, China and India have shown favorable socio-eco­nomic development paths, pulling numerous people out of poverty, improving their health conditions, educational and working opportunities, and increasing their income and consumption (see Bloom et al., 2010; Choundry & Elhorst, 2010; Colmenares et al., 2021; Curtis, Lugauer, & Mark, 2017; Goley & Tyers, 2013; Gradin & Wu, 2020; Schettino, Gabriele, & Khan, 2021; Woetzel et al., 2017). We did not come across comparative analyses of China's and India's long-term demographic and macroeconomic trends and their possible connections with the middle class phenomenon in both markets. Our research question aims to estimate how demographic transitions and macroeconomic dynamics of both economies in the decades before COVID-19 were related to the growth of their middle classes as factors of at­ tractiveness for foreign firms' sales and investment activities. The rest of this paper is structured as follows. In the theo­retical part of the research, we examine the existent findings on the importance of demographic trends for countries' de­velopment, critical factors of emerging markets' dynamics, market dynamics in China and India, middle class income levels differences and the emerging markets' new middle class as a factor of market attractiveness. In the third section, we present methodology and data and perform empirical analysis. Herein we first analyze the demographic trends and second, the macroeconomic trends of China and India in the decades before the COVID-19 crisis. In the fourth section, we discuss the results and test the hypothesis. The fifth section concludes with implications for foreign firms and proposals for further research. Theoretical Background Demographic trends and countries' development Demographic trends and countries' development enable each other. The population growth and characteristics influ­ence economic conditions, particularly labor availability. In addition to the availability of labour, long-term economic growth also requires growth in the economy's structural ef­ ficiency, namely how efficiently the economy can balance the sources of labor and capital to reap the highest and best possible returns. The aging of the population can harm the economy's structural efficiency. Demographic trends also affect consumption, labor, savings, investment, and tax policy. Along with the changed age structure, the financing of health care and pensions also changes (Mester, 2017, 4-7). Demographic indicators also influence decisions regarding infrastructure development and financing. A more critical factor in this is the number of population and households. The second factor is the age of the population, impacting the need for an education system, employment services, or services for the elderly, such as the health care system and homes for the elderly. Developed health and education in­frastructure impact lower birth rates and the higher age of the population. Critical factors of emerging markets' dynamics Liberalization of international trade and investment and reforms of the business environment With the liberalization of international trade and investment and the technological progress in the last decades, the im­portance and value of foreign direct investment (FDI) and the global value chains were growing (Baccini, 2018, 2-3; Casson & Wadeson, 2018, 1152; Chenaf Nicet & Rougier, 2016, 84–85). FDI brought an abundance of tangible and in­tangible resources to the target markets and thus contributed to the higher economic growth of the host economy (Ketteni & Kottaridi, 2019, 415). Reforms of the emerging markets' business environment had a stimulating effect on the growth of FDI in these markets. Particularly important for FDI flows to these markets was changed labor market regulation. The less rigid and inflexible the labor market was, the more at­tractive it was to the activities of foreign firms and their FDI (Gross & Ryan, 2008, 590). The growing flow of FDI from developed to emerging markets was also highly affected by the recession in developed markets after the financial crisis in 2008 (Chenaf Nicet & Rougier, 2016, 67–70). Transfer of knowledge and technology, low production costs, and modernization Knowledge and technology have spread to emerging markets through various contract forms of international business (e. g. licensing), strategic non-capital alliances, FDI, and location of a particular link in the supply chain to these markets. In emerging markets, local companies were engaged in export activities, most often part of a global supply chain by pro­ ducing semi-finished products and selling them to foreign companies in developed markets. In this way, local compa­nies gained new knowledge and experience from the global markets' customers and competitors and thus improved their productivity (Nuruzzaman, Singh, & Pattnaik, 2019, 3). Local companies in emerging markets also acquired knowledge from the foreign, stronger competition through imitative innovation, as they usually did not have enough capacity to develop their innovative solutions. In this way, they followed leading companies, used similar technologies, production processes, marketing activities, produced similar products, and thus ensured their existence in the market (ibid., 2019, 2). Emerging markets had to fulfill several other conditions to adopt new technologies successfully: a particular share of domestic financial resources, knowledge, and skills of employees, compliance with rules and regula­tions, basic infrastructure, working conditions, and culture (Casson & Wadeson, 2018, 1152). If these factors did not work synchronously in the market, the effects of the new technologies were also significantly smaller (Carillo Her-mosilla & Martinez Chafla, 2003, 2-3). An excellent combi­nation of factors, such as low production costs in the market and high customs restrictions on imports, attracted contrac­tors and foreign investors to emerging markets and gave these markets a considerable cost advantage over firms from developed markets (Casson & Wadeson, 2018, 1155). Based on this, the modernization of emerging markets intensified. Urbanization Emerging markets have industrialized quickly and become critical links in the global value chains. Urban areas became interesting for locating the activities of international firms due to the proximity of resources, access to capital, skilled labor, institutional support, shorter and cheaper distribu­tion routes, and connection to FDI resources. Increasing­ly expanding and advancing markets have accelerated urbanization (Estrin, Nielsen, & Nielsen, 2017, 328–329). People sought better education, employment, and living standards in cities. Urbanization with a combination of skills, talents, and resources in one place helped firms to achieve economies of scale, reduce operating costs, increase productivity and international competitiveness. To survive in the market, they must make the best use of their resources and competitive advantages. The struggle for survival and market share led to new ideas and innovations, new val-ue-added, and higher productivity. Urbanization is also one of the most critical levers of consumption. However, it is not necessarily the best indicator of the level of development in the country (see Shome, 2013). A large proportion of the urban population could be like or even poorer than some rural areas. Thus, even residents of rural areas can reach the standard of the middle social class without having to move to the city (Mishra, 2018). Favorable demographic environment In emerging markets, the share of the working-age popula­tion has been increasing over time due to still growing but lower natural population increase, progress in education, and improved health care. A larger labor force pool was the basis for poverty reduction and economic growth (Cruz & Ahmed, 2018, 95). Investments in human and physical capital increased labor productivity. Parallelly, the shares of middle-income social classes have increased, leading to higher consumption with its impacts on emerging markets' economic growth. Higher numbers of working-age popu­lation and consumers led to various international business opportunities for foreign firms at these markets. Ownership of natural resources Industries that have enabled the economic growth of emerging markets have required natural resources. Emerging markets' ownership of raw materials enabled rapid industri­ alization and attracted FDI inflows (Naglic, 2011, 39–40). Arable land in the regions of emerging markets has become increasingly valuable as they cultivated it to supply the whole world. Market dynamics in China and India China In the late 1970s, China introduced a one-child policy to balance the demographic situation and revitalize the economy, discouraging a higher proportion of the young population. After 1990, when the government embarked on economic reforms, several factors allowed China's economic expansion. One of them was a transition to state capitalism, where, apart from the state, the market regulates prices, and state-owned companies also compete. The second one was a labor market transition with the abolition of the life guaran­ teed job security with steady income and benefits (so-called Iron rice bowl (see Wang & Xi, 2015)). Agricultural reforms and investment in infrastructure were the subsequent reforms that also triggered higher urbanization. Investment in the population's education for more demanding and higher-paid jobs has also been crucial. The next factor was the openness for FDI and their promotion in the domestic economy (Silverstein et al., 2012, 5-6). Because of China's economic progress, a large proportion of the population moved into the middle class, and their standard of living and purchasing power rose. China became slowly uncompetitive for FDI in manufacturing as labor costs increased (Davies, 2013, 7). Therefore, the FDI in the Chinese service sector rose considerably. The strengthening of China and the whole East Asian region in the global economy can also be related to more than 21 concluded free trade agreements (FTA) of China with countries in the region (Ministry of Commerce, People's Republic of China, 2021). The last concluded FTA is Regional Comprehensive Economic Partnership (RCEP) between China and 14 Asian Pacific countries. It comprised about 30% of the world's population (2.2 billion people) and 30% of global GDP ($26.2 trillion) as of 2020 (Asian Development Bank, 2020). According to the forecasts, the RCEP will eliminate about 90% of the tariffs on imports between its signatories within 20 years of coming into force and establish standard rules for e-commerce, trade, and in­tellectual property, which will help facilitate international supply chains and reduce export costs throughout the bloc (European Parliament, 2021). So far, China's advantage was the network of regional free trade zones (FTZ) across the country, which eased and facilitated international trade and FDI with underdeveloped Chinese provinces. Eighteen FTZ (as per 2021 data, six new pilots FTZ were established in 2019 (see Wong, 2021)) offer preferential policies for the import, handling, manufacturing, and exporting of goods, as well as through tax incentives, free flow, and exchange of capital, and fast-tracked procedures for investment. The FTZ Network helped China face the challenge of too cen­tralized FDI. These focused mainly on major urban Chinese cities and the high-tech sector (manufacturing electronic and medical devices and communication equipment) (Yu, 2018, 6). The decision to establish more FTZ was also a response to the consequences of the trade war between the USA and China that started in 2018 and postponed, halted, or caused relocations of investment in China to other South­ east Asian countries (Bray, 2018). To mitigate the adverse effects of this trade dispute, China also released its financial and fiscal policies by lowering the level of required reserves and taxes and increasing the export tax refund. Government authorities further introduced lower tariffs on imports into China to promote the business environment. Domestic solid demand still contributed to positive economic figures despite the fall in economic growth due to reduced exports (The World Bank Group, 2019, 57 -58). Therefore, China kept the largest share in world GDP in 2017, the largest share in world exports of goods, and the second-largest share in world imports (Glawe & Wagner, 2020). India One of the significant reforms of the Indian economy was the abolition of "License Raj", a state-regulated system of licenses, regulations, and accompanying red tape that hindered the setup and running of businesses in India between 1947 and 1990. The following two reforms promoted international trade by reducing import costs and encouraging FDI. India has facilitated the economy's liber­alization by privatizing state-owned enterprises (Silverstein et al., 2012, 6-7). Thus, after 1991, India began to pursue an open market policy and economic liberalization to achieve economic growth and development. Stimulating macroe­ conomic environment - attempts to lower inflation, energy reforms, tax reforms, and a stable balance of payments - has fuelled India's economic growth. Private consumption and investment increased (The World Bank Group, 2018). Over the last 50 years, the share of value-added in industry and services in India's GDP has gradually increased. However, the primary income of more than half of the population is still from agriculture (Mitra & Kadam, 2018, 60). Overall factor productivity and human resource productivity improved. With the demonetization of some banknotes and a new tax on goods and services, India intended to lower the grey economy (Mitra, Chen, & Klerk, 2018, 49-50; The World Bank Group, 2018). Despite efforts to reduce unreg­istered production, employment, and income in India, the World Bank two years ago still recorded 90% of the grey economy in this market (The World Bank Group, 2019a, 19). India was recording GDP growth, but the link between GDP growth and employment was weak. The higher productivity and value achieved at work was reflected more quickly in GDP growth than in the employment indicator. A substantial share of unemployed was among young, highly educated staff, as the market did not create enough jobs for this pop­ulation (Centre for sustainable employment, 2018). Basole and co-authors (2018, 142-143) pointed out the challenge of the highly educated Indian population without practical skills and vice versa - the population with practical skills but without formal education. Previous studies (see DaVanzo, Dogo, & Grammich, 2011, 24-26) show that India invested less into the education system than China and achieved lower education participation and literacy levels. Middle class income levels differences The income levels of individuals to be classified as members of the middle class differ across the research community. Researchers define yearly/monthly/daily income boundaries (see Bhalla, 2007; Center for Strategic and International Studies, 2021; Fengler & Kharas, 2017; Ravallion, 2010; The World Bank, 2007) or divide middle class into income sub­ groups (see Banerjee & Duflo, 2007; Pew Research Center, 2009). According to Wheary (2010) and Kharas (2010), the middle class comprises all those who have enough income to be able to afford essential goods and a few secondary, with a lower income limit of 10 $ and upper of 100 $ per person daily, considering the PPP in the observed period. Due to income dynamics, defined middle-income class can be only short-term and valid for a particular country. The emerging markets' new middle class as a factor of market attractiveness While the middle class stagnates in developed markets, it grows in emerging markets. The growth of the population with middle income in emerging markets is critical for se­lecting these markets for international business activities. There is a strong link between the size of the new middle class in emerging markets and the presence of foreign firms in these markets in the form of FDI (Guercini & Runfola, 2016, 693). The more middle class members in the market, the more actively the company is present. Emerging markets are attractive for firms' international business due to the growing purchasing power of a large pool of potential new middle social consumers (Cavusgil et al., 2018, 95) and a new number of better educated and skilled working-age people. There are essential differences in the new middle classes across emerging countries, even at comparable levels of economic development (Brandi & Büge, 2014). It is possible to distinguish among countries with a larger middle class than predicted when looking at their GDP per capita and the countries with the opposite situation. The first has a more affluent middle social class. In their study, Brandi and Büge (2014) studied correlations among middle social class sizes (measured as a share of the total population with a certain level of daily consumption, following the definition of Kharas, 2010) and GDP per capita in PPP in 2012. Ac­cording to selected criteria for defining middle class, almost 160 million people in China were middle-class in 2010. In India, however, 60 million. The Kharas (2010) projection has shown that both nations will be the middle-class super­powers by 2020, with more than an estimated 590 million (China) and 280 million (India) middle class consumers. Brandi and Büge (2014) found that the level of economic de­velopment, measured only by GDP per capita, is not a good predictor of the middle class's share in the total population, measured by its consumption capacities. GDP per capita explained only 71% of the variability in the sizes of the mid-dle-classes across countries. Countries at the same levels of economic development can show very different patterns of middle class consumption capacities. According to the data from 2012, China had a 19% lower size of the consump­tion capacity of its middle class (16%) than predicted by its average GDP per capita (35%). These findings imply that countries at the same level of development, measured by GDP per capita, can show very different middle class levels, measured by their consumption capacities. According to this finding, the authors formulated a new typology, classifying China's middle class size as small with its consumption level as poor (below 90% of what is predicted by GDP per capita levels). In contrast, India's middle social class was classified as small; however, its consumption level was higher than China's and characterized not as poor but as an average. The Indian middle class was found as more affluent than the Chinese in 2012. The emerging markets' new middle class consumers There are differences in the consumer habits of the middle class of emerging markets and the middle class of developed countries. The former can afford the first car, the first mobile phone, or the first computer for the extra money they save, while the latter have owned these goods for a long time and then always buy the latest versions of cars, mobile phones, and computers. (Cavusgil et al., 2018, 103). When an in­dividual or the household crosses the income threshold of the middle social class, this person initially spends most of the money on better quality food, clothing, and household necessities. Later, however, such individual also begins to spend money on the first car, cosmetics, entertainment, travel, education (Silverstein et al., 2012, 37). Kardes (2016, 703-704) argues that in a large country, middle class con­sumers' habits and preferences for products differentiate across the country due to different ethnic groups, values, beliefs, language, shopping patterns, and infrastructure. Silverstein and co-authors (2012, 31-32) described the average member of the middle class in China as an individu­al with one or two children, living in a second- or third-order city in a medium-sized apartment. This individual devoted his time and money to education in looking for the best job opportunities, further increasing his income. The average middle class member in India was described as a person born into a poor or lower middle class, who managed to move into the middle class with hard work and two parallel jobs, and was the only employee in the family. This individual sought more opportunities to accomplish life goals and displayed entrepreneurial motivations (Javalgi & Grossman, 2016). Empirical Research Methodology and data This paper is a descriptive macroeconomic dynamic research based on the broad theoretical background and analysis of selected demographic and macroeconomic indicators' dynamics for China and India in the decades before COVID­ 19. We also compared China and India with the USA and the EU to depict the divide between the largest emerging markets and developed economies. The World Bank's and the United Nation's statistical databases were data sources for our analysis. The periods of indicators' observation vary according to all selected countries' data availability. Hypothesis development Apart from developed countries, emerging markets evi­denced the changes in their demographic environments in the decades of globalization. While the population in these markets was still increasing, mortality decreased, and the age structure changed. Part of the population moved from the poor to the middle-income social class. More working-age and middle-income individuals than the poor and rich social class represented emerging markets' prospective labor and consumers. Firms that searched for new international ex­ pansion opportunities could find Chinese and Indian middle classes as their target human resources and customers. These were younger, middle-aged, and middle-income individuals that could afford more than just the necessities needed to survive. The hypothesis is the following: H: Demographic and macroeconomic dynamics in China and India established conditions for substantial growth of middle classes in both countries in the decades before the COVID-19 crisis. The hypothesis was tested by comparing the relevant indica­tors' trends for both countries in the observed period and for­mulating our conclusions based on the synthesis of previous research and findings of our empirical analysis. Demographic trends in China and India Population growth affects many phenomena, such as the age structure of a country's population, the size of a country's workforce, international migration, and economic inequal­ ity. These factors both affect and are affected by overall economic growth. High population growth in low-income countries may slow their development, while low popula­tion growth in high-income countries may create social and economic problems (Peterson, 2017). Figure 1 shows that in the observed period, the natural population increase of China was significantly lower than in India, except for the period 1965-1970. We can ascribe such a trend to the effects of the Chinese one-child demographic policy. Figure 1. Rate of natural population increase in China and India between 1960 and 2020 (per 1000 population annually, estimates) Source: United Nations, n.d. Notes: Rate of natural population increase: Crude birth rate minus the crude death rate. The rate represents the portion of population growth (or decline) determined by births and deaths. It is expressed per 1000 population annually. Figure 2. Total population of China and India between 1960 and 2020 (in millions) Source: United Nations, n.d. The significantly faster cut of the Chinese rate of natural population increase compared to India could indicate China's higher short- and medium-term development opportunities. However, when a country is approaching high-income levels in the long term, such a low population increase could decrease its economic and social prosperity. The size of the country, measured by the population, can be an essential indicator of the possibility of achieving economy of scale and market potential and an indicator of the availability of production factors. However, both possi­ bilities are influenced by several other macroeconomic and microeconomic factors and a given country's geographical, historical, and political characteristics. Among others, we would have to be acquainted with the population's structure (e.g., the share of the working-age population, the share of the employed population, achieved levels of education, average levels of income, etc.) and its subsequent economic potentials. Figure 2 shows that the population of China was 2.2-times larger in 2020 than 60 years ago. For India, this number was 3-times. However, in 2020, the largest popula­tion still had China, but the size of the Indian population was much more like the size of the Chinese population in 2020 than 60 years ago. From Figure 3, we can see that 60 years ago, the potential support ratio was almost the same in China and India. In 2020 this ratio was 2.3-times lower for China and 1.7-times lower for India. These discrepancies indicate the differenc­es in the socio-economic development paths between the observed two countries and their future long-term economic perspective due to a more favourable potential support ratio in India than China. The large share of the younger, work-ing-age, educated, and employed population might support the country's economic development and growth with con­sequent tax inflows that lower the pressure of the older pop­ulation on public finance. Figure 3. Potential support ratio in China and India between 1960 and 2020 Source: United Nations, n.d. Notes: Potential support ratio: population 25-64 per population 65+. Figure 4. Age dependency ratio in China, India, USA, and the EU between 1960 and 2020 (% of working-age population) Source: The World Bank, 2021. Note: The age dependency ratio is the ratio of dependents – people younger than 15 or older than 64 – to the working-age popu­lation – those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population. Figure 4 shows the considerable decrease of the Chinese age dependency ratio compared to India, the USA, and the EU, indicating the increase of the Chinese middle-aged working population, particularly from the late 1990s. This demo­graphic characteristic gave China significant short- and medium-term socio-economic advantage and development potential over India, its largest emerging market competitor, and the USA and the EU, its largest developed markets com­petitors. However, we can see that the Indian age dependen­cy ratio was also falling in the last decades due to decreased share of the population under 15 age, larger shares of young and middle- age population, and still low share of 70+ population in the total population. On the other hand, the Chinese age dependency ratio increased in the last decade due to the rising share of the older population in the total working-age population. In contrast, the Indian age depend­ ency ratio decreased in the same period. These differences between China and India indicate that China started to lose the positive economic impact of the former one-child policy in the long term. Figure 5 shows that the size of the urban population, as an indirect indicator of society's transition to industrial and service economy, was almost the same in 1960 for China and India. However, in 2020 China recorded a 26.5 percent­ age points higher share than India. This data indicates that China has created employment opportunities in urban areas, bringing higher value-added. In India, on the other hand, these opportunities have grown very slowly, which could indicate a slower economy's restructuring and still existent reliance on the primary economic activities. However, to be sure about the benefits of rapid urbanization on Chinese de­velopment, it would be requisite to explore the income dif­ferences of the newly urbanized population. Based on these Figure 5. Urban population in China and India between 1960 and 2020 (% of the total population) Sources: United Nations, n.d.a. Note: Urban population: percentage of the population at mid-year residing in urban areas. findings, we could establish potential impacts of urbaniza­tion on the share of the middle class population. Similarly, we could explore the development of rural regions in both countries and its impacts on rural population income. We can see from Figure 6 that in China, approx. 57% of workers received salaries upon contracted employment in 2019. In India, this share was only approx. 24 % in the same period. In both countries, the share of salaried workers in total employment has increased in the observed period. However, the growth was much higher in China than in India. This data could indicate faster poverty reduction in China and a higher share of stable jobs, supporting the coun­try's socio-economic development leading towards a larger middle class. However, low shares of salaried workers in both countries could indicate high levels of grey economy and many casual jobs that do not bring a stable long-term standard of living and decrease development opportunities. From Figure 7, it is evident that in China, the rural popu­ lation has been significantly lowered, particularly from 1980 on, which could indicate the transition in the creation of GDP value-added. On the other hand, we can see that the Indian population was still predominantly rural in 2020 (approx. 65% of the total population). However, to establish the impact of urbanization on economies' development, it would be required to analyze the newly urbanized popula­tion's income levels. On the other hand, the investment into agricultural development could change the income levels of the rural population. The impacts on the middle class growth Figure 6. Salaried workers in China, India, USA, and the EU between 1991 and 2019 (% of total employment) Source: The World Bank, 2021. Figure 7. Rural population in China, India, USA, and the EU between 1960 and 2020 (% of the total population) could be established only after analyzing the income levels of both types of population. Figure 8 shows the changed age structure and life expectan­cy in China and India between 1960 and 2020. China drasti­cally lowered the share of children in the whole population and increased the share of the middle-aged population. The life expectancy also increased. The highest shares of the Chinese population in 2020 represented 30-34- and 45-54­ years old individuals. The share of the Chinese working-age population (25-65 years old) in 2020 was much higher than in 1960 and higher than in India. On the other hand, the share of the 65+ population in China was much higher than in India. India also lowered the share of children in the whole population, although much less than China. The shares of the Indian middle-aged and older populations did not significantly increase and were lower than in China. Indian life expectancy slightly increased but was lower than in China. The highest shares of the Indian population represented 10-40 years old individuals. Based on these findings, we can conclude that the demo­graphic transition of China was extensive. In the case of India, however, it was modest. From Figure 9, it is evident that in comparison to China and India, Europe and particularly the USA, have lower shares of the working-age population and at the same time a much lower number of the total population and higher shares of population 65+ with longer life expectancy. If we assume that the middle class could grow based on the share of the working-age population and if the age 65 is the Figure 8. Population pyramids of Chinese and Indian population in 1960 and 2020 Source: PopulationPyramid.net, 2019. Figure 9. Population pyramids in Europe and USA in 2020 Source: PopulationPyramid.net, 2019. Note: There is no EU population pyramid in the same data source. age of retirement, then the labor and consumption potentials Key macroeconomic trends of China and India of the Chinese middle class are much higher compared to Europe and the USA. Gross domestic product (GDP) per capita measures a coun­ try's economic output per person and analyzes prosperity In further research, we explore the possible congruities and national wealth. As shown in Figure 10, in the observed between the Chinese and Indian demographic trends and the period, there was a significant difference among GDPs per macroeconomic dynamics of their economies. capita of highly developed markets, like the USA and EU, and China, as one of the most thriving emerging markets so far. Since the 1990s, the Chinese and Indian GDP per capita gap has risen. China's GDP per capita boosted in the last fifteen years, indicating the increasing income levels of the population and growth of the middle class. Compared to India, these expansions could be much higher. Figure 11 shows that Chinese GDP per capita growth was much higher than the Indian in two periods: between the 1990s and 2000s and between 2000s and 2015 when it fell behind the Indian one. Chinese GDP per capita growth was much steadier than the Indian between the 1990s and 2015. Although the Chinese GDP per capita growth started to decrease from the 2008 economic crisis on, it was much higher than the Indian one in the period of fifteen years (2000-2015). Figure 12 shows that China was quickly transitioning towards a service economy, and on this path, it overtook India in 2013. In 2019 China created almost 55% of services value-added, while India approx. 50%. To establish the impacts of this rapid Chinese economic restructuring on the population's income and possible growth of the middle class, we should explore the types of newly created services and the share of the population employed in these industries. Figure 10. GDP per capita of China, India, USA, and the EU between 1990 and 2020 (PPP, current international $) Source: The World Bank, 2021. Figure 11. GDP per capita growth of China, India, USA, and the EU between 1971 and 2019 (annual %) Source: The World Bank, 2021. While China has decreased its value-added in agriculture to only approx. 7% of GDP in 2019, India, on the other hand, increased this value to approx. 17% of GDP in the same year (Figure 13). From Figure 13, we can also see the differenc­es in the shares of primary activities between China and India, as emerging markets, and USA and EU, as developed markets. The share of primary activities indirectly indicates the value-added created in the economy and the country's level of development. It shows the potentials for increasing the middle class share in the population. The volume and structure of international trade and the terms of trade substantially impact the growth of GDP and the balance of payments of individual countries. They indicate the economic structure of the country and its opportunities for growth and development. From Figure 14, we can see Figure 12. Services value added in China, India, USA, and the EU between 1997 and 2019 (% of GDP) Source: The World Bank, 2021. Note: Services value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal) services such as education, health care, and real estate services. Imputed bank service charges and import duties are included. Figure 13. Agriculture, forestry, and fishing value added in China, India, USA, and the EU between 1997 and 2019 (% of GDP) Source: The World Bank, 2021. Note: Services value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal) services such as education, health care, and real estate services. Imputed bank service charges and import duties are included. the rapidly growing share of Chinese exports of goods and services in GDP that surpassed the shares of India and the USA at the beginning of the 1980s. China overtook second place after the EU in this indicator; however, it also recorded the highest share drop in the 2008 economic crisis period. In recent years the values of this indicator in India and China were similar, with slight precedence of China. China's growing trade openness in the observed period indicates the country's new production and service capacities, new employment possibilities, and increased population income. Further analysis of exports structure would show its possible impacts on population income growth and the share of the middle-income population. From Figure 15, it is evident that China significantly led in the share of high-technology exports between 2008 Figure 14. Exports of goods and services in China, India, USA, and the EU between 1970 and 2020 (% of GDP) Source: The World Bank, 2021. Figure 15. High-technology exports of China, India, USA, and the EU between 2007 and 2019 (% of manufactured exports) Source: The World Bank, 2021. Note: High-technology exports are high R&D intensity, such as aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery. and 2019 compared to India, the USA, and the EU. High-imports and international contractual entry modes, it indi-tech exports can bring additional value-added and create cates the country's inward internationalization level. Due numerous positive spill-over effects in the economy. Among to many potential positive effects of inward FDI (e.g., others, it can raise the employees' wages in the value chain development of sectors and regions, jobs creation, devel-and increase the share of the middle-income population. opment of entrepreneurship and competitiveness, transfer of modern technology and managerial knowledge, market The share of inward FDI in GDP indicates the entry and expansion, favorable impact on the balance of payments), expansion of foreign capital in the domestic market. With high values of this indicator indicate the favorable business Figure 16. Foreign direct investment (FDI) in China, India, USA, and the EU between 1980 in 2018 (net inflows, % of GDP) Source: The World Bank, 2021. Figure 17. Foreign direct investment (FDI) of China, India, USA, and the EU between 1982 in 2020 (net outflows, % of GDP) Source: The World Bank, 2021. environment of the host country, its entrepreneurial specific the EU. The share of Chinese inward FDI in GDP was also knowledge, and capital, and thus opportunities for its de-higher than the Indian share until 2015. China's rapid inward velopment and growth. Figure 16 shows that Chinese GDP internationalization indicates the economy's expansion in growth and exports of goods and services were supported the observed period with various possible socio-economic by foreign capital. FDI started to flow into the economy at benefits, including rising income and increasing middle-in-the beginning of the 1990s. In the whole period until 2019, come population share. their inflows were much more stable than FDI inflows into Figure 18. Unemployment in China, India, USA, and the EU between 1991 and 2020 (% of the total labor force) Source: The World Bank, 2021. Note: modeled ILO estimate. Figure 19. Gross fixed capital formation in China, India, USA, and the EU between 1972 and 2018 (% of GDP) Source: The World Bank, 2021. Note: Gross fixed capital formation (formerly gross domestic fixed investment) includes land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchase; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. We can see from Figure 17 that with a small-time lag after the inflows of FDI into the economy, China started to invest abroad. Its level of FDI outflows was moderate between 1990 and 2005. However, after this period it started to increase. Almost in the entire observed period, the Chinese level of FDI outflows was higher than the Indian one, with the most considerable precedence recorded in the last years. The share of outward FDI in GDP indicates the growth and development of domestic firms' operations in international markets. Apart from exports and contractual entry modes, it represents a country's outward internationalization level. The share of outward FDI signifies an economy's capital ex­pansion capacities, with potential impacts on global growth of its firms and consequent socio-economic benefits for a country, including rising income of its population. It is evident from Figure 18 that China recorded lower un­employment than India and the EU between 1991 and 2020. Almost all that period its unemployment was also lower than the unemployment in the USA. China was able to align the needs of the employees with the labor market circumstances in the whole period. It is also evident that the 2008 crisis was less harmful to China and India since both economies could keep low unemployment levels. The share of unem­ployed people in China remained about the same (4 to 5%) between 2002 and 2019. Thus, China has shown resilience to economic shocks so far, indicating a more stable level of the population's income, including the middle-income population. Figure 19 shows a significantly higher level of China's domestic capital investment in the whole period between 1972 and 2018 compared to India. After the 2008 crisis, the leading position of China in this indicator against India started to strengthen. An increasing share of domestic in­vestment could indicate new economic activities, newly created jobs, and the rising population's income. However, sources and the structure of these investments are essential factors of their effects and sustainability. Discussion and Hypothesis Testing Demographic and macroeconomic market dynamics impact the middle class share in the entire country's population, bringing new opportunities for foreign companies to enter this market. Emerging markets' demography and economy are very dynamic. The size and growth of the middle class at a particular emerging market are essential factors of the market's attractiveness for foreign firms. It could represent a source of a large pool of labor and a broad base of target global consumers. Therefore, FDI decisions are, among others, based on demographic and macroeconomic trends at foreign markets (see Drabble et al., 2015, 11–12). The global middle class, structured from numerous different middle classes at individual markets, is considered the main driver of the global economy in the last century (see Roy, 2018, 32). The efficiency and success of firms in their in­ternational business expansion can be related to their choice of middle class members as their target consumer segments or as human resources. Compared to an impoverished and very wealthy social class, the middle class group represents the largest share of the world's population that will continue to grow in the future and thus become the most prominent potential consumer base (Fengler & Kharas, 2017). Cross-country estimations of middle-income class sizes are challenging because of different levels of the population's income. The middle class is also constantly changing due to countries' socio-economic dynamics. Concerning the existing data, despite the reference to the similar definition of the middle class, several studies estimate the size of the middle class differently. Compared to determining the size of the middle class in China (e.g., KPMG International, 2018; Barton, Chen, & Jin, 2013; Roy, 2018, 32), we even find more significant discrepancies between existent studies on the size of the middle class at the Indian market. Ac­cording to Shashidhar (2019), one of the more fundamental reasons for this ambiguity is the lack of statistical data on the income of the Indian population (see also Research Unit for Political Economy, 2015), which has proven also in our empirical analysis. Notwithstanding discrepancies in defining middle class and consequently measuring its size, we can argue that parallel to the integration of China and India into the global economy, the middle classes in both countries were growing in the past six decades. Conse­quently, the countries' economic potentials were increasing and vice versa. Our empirical analysis shows that China recorded ex­tensive demographic transition (lowering of natural pop­ulation increase and potential support ratio) and macro­economic dynamics in the decades until the COVID-19 crisis. The macroeconomic dynamics reflected in higher domestic investment (gross fixed capital formation), exports of goods and services, the share of high-tech­ nology exports, FDI flows, and the share of services in the output. Urbanization, employment, and the share of salaried workers also increased. These findings implicate the decrease of the country's poverty and consumption growth, fueled by the increased income of numerous previously low-income individuals. Data shows that, on average, 0.5% of people in China lived below the inter­national poverty line of 1.90% a day (in PPP) between 2009 and 2019 (World Bank, 2021a). On average, 22.5% of these people were in the total population in the same period in India. High numbers of working-age people with higher salaries spurred additional consumption, raised economic growth, and increased market attractiveness. China gained economic and social benefits from its sig­nificantly lower natural population increase and conse­quently lower potential support ratio within the observed short- and medium-term period. However, as we could see from our analysis, India's de­mographic transition was modest compared to China's. Indian natural rate of population increase and potential population support ratio was significantly higher than the Chinese ones. Also, Indian total exports and high-tech­ nology exports and FDI inflows, and domestic investment were substantially lower than the Chinese ones. The GDP growth remained moderate in comparison to China. The share of primary economic activities in GDP value-added was relatively high and the share of services low. The average share of salaried workers in the employed popu­lation in India was between 15 and 25% in the observed period. In China, however, this share was between 30 and 55% of all employees. The share of India's unemployed and rural populations was relatively high compared to China. The average rate of India's poverty was signifi­cantly higher compared to China. Therefore, our set hypothesis (H1: Demographic and macroeconomic dynamics in China and India established conditions for substantial growth of middle classes in both countries in the decades before the COVID-19 crisis) can be only partly confirmed. According to our research, it is valid for China but not for India. Forecasts, however, suggest that by 2027, India's middle class will become more extensive than the middle class of China, the USA, and Europe (Roy, 2018, 35). This prospect can be related to three the most important enablers of Chinese long-time rapid economic growth: high indebtedness of government, corporations, and households (Lee, 2021; Lee 2021a), exhaustion of natural resources, and the former one-child policy. With the emergence of the COVID-19 pandemic, the first two enablers emerged as significant obstacles for further similar economic expansion of China and herewith for the growth of its middle class. Together with the zero-COV­ID-19 government's strategy (Wong, 2021a), they have started to slow-down Chinese production, investment and demand. A particularly hard hit has been construction, which supports millions of Chinese jobs. The Chinese one-child policy's short- and medium-term benefits will not be extended into the long-term economic and social advantages. It is estimated that society's aging will cause additional pressures on the Chinese public finances and limit opportunities for high economic growth (see Beckley & Brands, 2021). Conclusion Firms' decisions on target markets for their international business activities depend on several factors of foreign markets' local business environments. Apart from political, economic, and technological factors, the demographic environment is crucial for foreign market attractiveness. With favorable demo­graphic trends, the middle-age and middle-income population increase, and large pools of potential human resources and consumers arise. In the last decades, some emerging markets of Asia became attractive not only for the relocation of produc­tion or certain links in the supply chain but also as potential markets to end consumers. International trade and investment liberalization, technology and knowledge transfer, industriali­zation, modernization, and demographic transition contributed to the economic growth of these countries. The latter stimulated new socio-demographic changes, with some countries falling behind the others in international competitiveness. Due to the liberalization and modernization of emerging economies and the technological progress in transport and information-tele­communication technology, the social conditions of emerging economies' populations have improved. Therefore, compared to developed countries, some of these economies recorded higher labor availability and structural efficiency due to larger pools of the younger, educated, working-age population and increased share of the people with higher income. The demo­graphic transition of emerging markets offers additional labor and consumers, essential factors for economic growth and market attractiveness for foreign firms. As the largest economy of Eastern Asia, China offered global firms a favorable demographic base of the middle class in the decades until the COVID-19 crisis. India lagged behind China in this regard. China used to be considered attractive for international firms because of the cheap labor. However, in the last years, it became attractive because of the vast pool of highly educated workforce and socially established consumers. The key enablers of higher labor availability and higher economy's structural efficiency were a better education system, higher education participation, higher gender equality regarding access to education, and a better health care system. To achieve economic growth, it was not enough to have a good labor supply. It was also necessary to have a labor demand. Since the demand of local employers was not high enough, the Chinese govern­ment stimulated foreign direct investment that created jobs. The improvement of demographic characteristics, including population's health conditions, educational attainment, and income, have made China more attractive for trade and FDI activities of foreign firms. While China's population doubled between 1960 and 2020, the population in India nearly tripled. Due to considerable differences in their reforms that led to demographic and macroeconomic changes, we estimated that the growth of new middle classes in both countries was different. Herewith, their roles in increasing markets' attractiveness were also not the same. The differences in education enroll­ment and investments in education in both countries indicate that people in both countries did not have similar local in­stitutional initiatives to raise their income. According to the data of previous studies and the findings of our analysis, India fell behind China significantly in the observed periods. The share of exports in GDP, high-tech exports, and FDI inflows in India was relatively low compared to China. The same is valid for the share of fixed capital formation or domestic investment in GDP. Indian investment abroad was modest in comparison to China's FDI outflows. The so-cio-economic progress of India was far behind the Chinese one, which resulted in its lower attractiveness for interna­ tional business activities of foreign firms and lowered the potentials for increasing the share of the middle class that would help raise the economy's dynamics. Due to the una­vailability of reliable data on the population's income in both countries, we did not establish and compare the shares of middle classes in both countries in our research. However, as a driving force for FDI and consumption, we estimated that the middle class substantially increased in the Chinese market, but not in Indian. Recently, China has started to face the aging population challenge, although it is still signifi­cantly lower than in developed countries. The COVID-19 crisis has revealed some negative consequences of the rapid Chinese economic growth, based on high indebtedness and excessive exploitation of natural resources. Additionally, with a zero-COVID-19 strategy, China is significantly limiting its trade and investment openness. Altogether, after decades of remarkable economic prosperity, China's per­spective and herewith its market attractiveness for foreign firms is becoming uncertain. To estimate the consumption market potentials of China and India concerning the affluence of their middle classes, it would be reasonable for foreign firms to continuously assess trends in the average consumption levels and income levels of the population. Consumers' behavior, which changes rapidly, is related to broader political, economic, social, technological, and natural environments. The effects of globalization allowed the possibility of achiev­ing a global consumer who has similar desires and needs across numerous countries. International managers could perform in-depth market analyses that would show the in­clination towards consumption and the typical consumption patterns of middle classes as the most significant popula­tion segments. Firms could assess the characteristics of consumption convergence or divergence in these segments. The first would allow standardization and would decrease firms' marketing costs. With glocalization marketing strate­gies, firms would balance divergent and convergent market characteristics. Besides consumption and income, firms should also follow the other demographic characteristics of the middle classes and their dynamics in both countries. Due to intense inter­ national migration flows and different levels of countries' progress, it would be reasonable to regularly explore the average age, education level, ethnic structure, religious be­ longing, values, attitudes of the most significant part of the population. Such research is essential when estimating the middle classes as potential employees. It would also show the dynamics of middle class contribution to the social devel­opment concerning democratic and other progressive values. Our research only estimates the middle classes' growth in China and India. The main reason for this was that the sta­tistical data on the population's income in China and India is incomplete. The valuable further research topic would be a predictive model of middle-class economic potentials according to econ­omies' demographic and macroeconomic dynamics. 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China's Free Trade Area Strategy: an alternative avenue to China's »peaceful« rise? The Social Science Journal, 57(2), 206-218. https://doi.org/10.1016/j.soscij.2018.10.009 Raziskovanje demografske in makroekonomske dinamike nastajajocih trgov in rasti srednjega družbenega razreda: primer Kitajske in Indije Izvlecek Politicni, gospodarski in družbeni tranzicijski procesi nastajajocih trgov so privedli do spremenjenih demografskih trendov in nove makroekonomske dinamike v teh gospodarstvih. Te spremembe so sprožile rast srednjega družbenega razreda, ki je postal bistven dejavnik privlacnosti nastajajocih trgov za prodajne in proizvodne aktivnosti tujih podjetij. Namen tega clanka je analizirati demografske in makroekonomske trende Kitajske in Indije v desetletjih pred krizo COVID-19, da bi ocenili njihovo vlogo pri rasti srednjega družbenega razreda in posledicni privlacnosti teh dveh pomembnih nastajajocih trgov za tuja podjetja. Izsledke naše raziskave oblikujemo na obsežnih teoreticnih osnovah in empiricni analizi izbranih demografskih in makroekonomskih kazalnikov, povezanih z rastjo srednjega družbenega razreda. Ugotovili smo, da je Indija v opazovanih obdobjih glede demografske tranzicije in makroekonomske dinamike znatno zaostajala za Kitajsko. Kitajska je uveljavila radikalen demografski prehod. Primerjalna analiza makroekonomske dinamike je pokazala trdno vodstvo Kitajske na podrocjih gospodarske rasti, mednarodne trgovinske in investicijske odprtosti, tehnološkega napredka, zaposlenosti, strukture outputa,domacih investicij,urbanizacije in rednih zaposlitev.Posledicno smo ocenili nižjo rast indijskega srednjega družbenega razreda in njegov manjši pomen pri odlocitvah tujih podjetij za vstop na indijski trg. Ugotovitve so prinesle nekaj implikacij za mednarodne menedžerje pri segmentiranju in izbiranju ciljnih tujih trgov. Kljucne besede: nastajajoci trg, srednji družbeni razred, demografska tranzicija, makroekonomska dinamika, mednarodno poslovanje, Kitajska, Indija ORIGINAL SCIENTIFIC PAPER RECEIVED: AUGUST 2020 REVISED: OCTOBER 2021 ACCEPTED: NOVEMBER 2021 DOI: 10.2478/ngoe-2021-0021 UDK: 658.89:004(592.3)(436) JEL: L81, D91 Citation: Moerth-Teo, J. Y., Bobek, V., & Horvat, T. (2021). The Effects of Consumers’ Buying Behavior on the E-commerce in Highly Developed Emerging Market and Developed Market: The Case of Singapore and Austria. Naše gospodarstvo/Our Economy, 67(4), 56-73. DOI: 10.2478/ngoe-2021-0021. NAŠE GOSPODARSTVO OUR ECONOMY Vol. 67 No. 4 2021 pp. 56–73 The Effects of Consumers’ Buying Behavior on the E-commerce in Highly Developed Emerging Market and Developed Market: The Case of Singapore and Austria Jia Yun Moerth-Teo University of Applied Sciences FH Joanneum, Austria jia.moerth-teo@edu.fh-joanneum.at Vito Bobek University of Maribor, Faculty of Economics and Business, Slovenia vito.bobek@um.si Tatjana Horvat University of Primorska, Faculty of Management, Slovenia tatjana.horvat@fm-kp.si Abstract The e-commerce is becoming increasingly essential and principally relevant in the modern world, especially in the current pandemic. Due to the increased use of the Internet, the growth of e-commerce has escalated.This research focuses on the e-commerce environments of Singaporean and Austrian markets. It contains an outline touching on the theoretical parts and the conduct of an empirical study where the survey results from 206 participants were studied, analysed, and compared. The statistical methods chosen for this research were the t-test analysis and the correlation analysis.The empirical study served as a quantitative discussion on both countries' e-commerce markets.The analysis of the completed questionnaires provided a better and clear understanding of the buying behaviours and their potential impact on the e-commerce markets in Singapore and Austria. Considering the results from the comparisons, the research has also highlighted some interesting findings and differences in the buying behaviours of Singaporean and Austrian shoppers. Keywords: e-commerce, pricing, buying behaviours, consumers, Singapore, Austria Introduction Fundamentally, e-commerce is a form of business with transactions of buying and selling goods or services over the worldwide net. From mobile shopping to online payment encryption and beyond, surrounding e-commerce is a wide variety of data, systems, and tools for online users, buyers, and sellers alike. Most businesses with an e-commerce presence use an e-commerce store and/or an e-commerce platform to conduct their online marketing and sales activi­ ties and oversee their logistics and fulfilment (Moore, n.d.). Since then, the growth of many e-commerce companies has ascended across the board. The introduction and role of e-commerce platforms have created an enormous impact and have taken the entire world by storm. In recent years, there has been a trend observed in the rise of retail e-commerce globally. The worldwide e-commerce trend has been seen as an ever-increasing climb upwards and is forecasted to grow continuously upwards from 2021 to 2023. E-commerce is proliferating, that it is already slowly eating away parts of the shares from tra­ ditional retail (Wonderflow, 2019). Based on the OECD, they believe that the growth of e-commerce can increase the competition within retail markets, enhance consumer choices significantly, and prompt and facilitate innovation in the product distribution section. Problem statement This research focuses on the Singaporean and Austrian e-commerce markets, consumers' buying behaviours, and their potential impact on the e-commerce environment. Therefore, it would be insightful to look into the statistics of both countries' e-commerce scenes. Due to its enduring popularity and the increasing importance and relevance of e-commerce globally and especially in Sin­gapore and Austria, it was compelling to delve deeper and take a closer look into this e-commerce topic. The research focused on the consumers' buying behaviours in a highly developed emerging market and a developed market, Sin­gapore and Austria, respectively, and how the buying be-haviours might potentially affect the e-commerce markets. A comparison between the buying behaviours was made to see if there are possibly any significant differences between a Singaporean consumer and an Austrian consumer. As time goes by, there are mutations and evolutions expected from the consumers' buying behaviours, no matter where one comes from initially. Many factors have to be considered before getting into the final stage of purchasing something through an e-commerce platform. Therein lies a massive number of dynamic factors that may affect each consumer's buying behaviours. Based on Kotler and Armstrong, there are four categorical groups to classify the factors affecting the behaviours of consum­ers, and they are psychological, personal, social, and cultural (The Open University, n.d.). The research was conducted from many of the existing researches and studies. The majority of the past research revolved around the effects of e-commerce on consumers' buying behaviours. This research will hopefully contribute to the bigger picture with studies of the consumers' buying behaviours and how it might affect the e-commerce envi­ronment, focusing on the Singaporean and Austrian e-com­merce markets. As online consumers ourselves, there are many factors that we will put into consideration before deciding to purchase something through an online platform. Therein lies a consid­erable number of factors that may affect every consumer’s buying behaviours. Based on Kotler and Armstrong, four categorical groups classify the factors affecting consumers' behaviors: psychological, personal, social, and cultural (The Open University, n.d). The research was conducted from many studies. The majority of them revolved around the impact and/or effects of e-com­ merce on consumers' buying behaviour. This research and its area of work will contribute to the big picture with studies of the consumers' buying behaviours and how they might impact the e-commerce market, focusing on the Singapore­an e-commerce markets. Research question and hypotheses The main research question formulated out of this study will be "How will the consumers' buying behaviours affect the e-commerce environment?". Based on the main research question, a sub-research question was then constructed: "Which type of factors may cause an impact on the consum­ers' buying behaviours?” Hypothesis was created with the research questions being kept in mind: Consumers' buying behaviours may affect the e-commerce environment. Literature Overview As shown in Figure 1, the research focus will be the in­tersection between the consumers' buying behaviours and e-commerce, which inter-joins both circles together. Factors regarding what may affect the consumers' buying behaviours and, subsequently, their consequences on the consumer side will be investigated in detail, and the factors that may affect the e-commerce business environment will also be studied. Based on the figure mentioned above, an overview of es­sential literature regarding the identified focus, which explains the previous work and relevant theories related to this context, will be found in the following paragraphs. The journal papers and articles chosen for this review can be classified into two categories; factors driving consumers' buying behaviours and the rise of e-commerce. Figure 1. The research focus Source: own research. Factors driving consumers’ buying behaviours Millions of consumers create and reinforce new online buying behaviours and habits (Columbus, 2020). Voinea and Filip (2011) have indicated that in recent years, there has been an emergence and rapid growth of new economic im­portance, of the new types of consumer – also known as the new consumer - whose attitudes, aspirations, and purchasing patterns are different as compared to those existing behav­iours in the past. Among the various reasons behind consum­ ers' buying behaviours, a few significant motivators will be discussed below. Said motivators are concerning competitive prices, customer service, customer reviews, and trust. Competitive prices Previous research depicts that prices come into consum­ers' minds before deciding factors (Guo et al., 2019). For many consumers, competitive pricing stays as the number one reason that attracts their attention and, as a result, pos­ itively affects their buying behaviours. The majority of the consumers are usually searching for product offers at very affordable or even discounted prices (Kerick, 2019). As per Urne (2020), competitive pricing remains one of the critical factors for success in e-commerce. Tanir (2018) states that the most crucial store features driving up to 80% of consumers' purchasing decisions is competitive pricing. He also mentioned that around 90% of the e-commerce shoppers are considered the 'masters' of deal hunting. Thanks to the advanced technology and comparison-shopping engines, consumers can now get alerts for multiple items from multiple e-commerce stores that further facilitate their comparison ability between the products offered. This comparison will allow the shoppers to get real-time information concerning the lowest best prices for their desired product or service. Customer service Customer service remains one of the critical touchpoints that affect the buying behaviour of consumers. According to Wertz (2017), attracting new customers costs approximately seven times more than retaining the existing customer base. So, the provision of excellent customer service can increase sales and profits and aid the companies, in the long run, to stand out in the virtual competitive marketplace. Customer reviews Based on eMarketer (n.d.), a complete 61% of respondents said they had checked online reviews, blogs, and other online customer feedback before moving to the next step, purchasing the new product or service. More than 80% said that such evaluations had held at least some influence on their purchases. This is further supported by research (Kaushik et al., 2018), which states that the reviews projected on the e-commerce platforms may help certain users during their decision-mak­ing process regarding the product itself. A large number of helpful reviews also conveys more information about the product to a customer. Source credibility, review popularity, and usefulness play a vital role in the sales of the product. Their study also confirms the positive effect of the balance of reviews on product sales. Customer reviews can help elim­inate any doubts that potential customers may have about their product or may even help when it comes to product selection (Charlton, 2012). Trust and loyalty Trust is expected to be even more critical in e-commerce than in traditional commerce because of the paucity of rules and customs in the regulation of e-commerce and that because online services and products typically are not immediately verifiable by the consumers (Gefen and Straub, 2004). The research conducted by Teo and Liu (2005) concludes that consumers' trust in e-commerce vendors and their risk per­ception can also be regarded as behavioural beliefs that may affect consumers' behavioural attitude – to purchase or not to purchase. Frequently, the lack of trust is a fundamental reason many users will not purchase goods or services from e-commerce websites. Trust is considered the critical factor for maintain­ing sustained relationships between the transacting consumer and the e-commerce seller (UK Essays, 2018). Coming hand-in-hand with trust comes the loyalty of the consumers. Without the glue of loyalty, even the best-designed e-busi­ness model will collapse. Besides purchasing more from the business, loyal customers would frequently refer new friends and family members to the e-business, providing yet another rich source of profits. Referrals are considered lucrative in traditional commerce, but the Internet further amplifies this effect since the word 'mouse and keyboard' spreads even faster with just some clicks than word of mouth (Reichheld and Schefter, 2000). Rise of e-commerce Technology The new generation can no longer remember a world without computers, emails, and cell phones (Taken Smith, 2009). Raja and Nagasubramani (2018) have reiterated the importance of technology, stating that today’s era of the 21st century is often regarded by many as an era of technology. Technology today plays a significant role in our life. It is frequently associated with growth, as it is seen as a basis of the growth of an economy. Information technology and the Internet have dramatically affected business operations and continue to affect business conduct dramatically. Markets, industries, and businesses are transformed by the ongoing technological wave (Dinu and Dinu, 2014). Business as a whole is changing, thanks to technology and its advance­ments (Pierson, 2018). A need for tech savviness As more technological advances enter the picture and an entirely digital world is foreseen to emerge shortly, many people are “pushed” or even “forced” to educate themselves on the new technology to not be left behind and remain up to date with society. The more technologically literate one is, the better-prepared one will be able to adapt to any form of change (Pierson, 2018). Nowadays, upskilling in technology is becoming a necessity rather than a choice (Rao, 2020). With our ever-changing environment and the shift from tra­ditional to virtual settings, people need to be equipped with technical skills. Technology will empower the human race and will open up opportunities that were otherwise impos­sible to reach before. Gupta (2006) illustrates the need to be computer literate. Employers nowadays prefer their workers who are computer literate to those who are not. Computer literacy contributes further to employee and productivity effi­ciency, therefore making them more valuable to the company. Explosive growth of e-commerce In our current digital age, e-commerce plays a vital role in our lives. With the unfortunate ongoing global COVID-19 pandemic that restricts people from heading out, many families will eventually replace their store and mall visits permanently with online grocery, apparel, and entertainment shopping (Columbus, 2020). This further contributes to the extensive growth of the e-commerce environment. Based on Kerick, the expected growth of e-commerce is to come from Asia and the US to Europe and throughout Africa to the Middle East. The e-commerce sector is expected to break the net, accounting for double-digit growth in all loca­ tions worldwide (Kerick, 2019). Taken Smith (2009) states that the annual growth rate of e-commerce is estimated to be up to 28% at the global level, while individual countries may even have much higher growth rates. Methodology A primary research process that is suitable for this thesis is presented by Karlsson (Karlsson, 2016). Based on this process, the actual methodology to achieve an efficient study has been derived and is illustrated in Figure 2. The first step starts with literature research. This is followed by the data collection stage, including preparing the questions suitable for the questionnaire and identifying potential par­ ticipants. The third step deals with the data analysis based on a quantitative review to identify possible correlations and derivations of factors. The evaluation and consolidation of the gathered results will represent the final step of this report. Figure 2. The research focus Research gap After conducting the first round of initial literature research, the studies have revealed that the majority of the literature reviews and focus were concentrated on the topic of e-com­merce and was covered mainly on the contents such as e-commerce in general, consumer’s buying behaviours, risk assessments in e-commerce, factors determining consum­ers’s e-satisfaction and consumer trust in e-commerce (Loo and Sze 2002; Lee et al. 2012; Singh and Sinha 2013; Nisa and Prabhakar 2017; Wagner et al. 2018; Dai et al. 2018). A void in the research on comparing buying behaviors in e-commerce between a highly developed and a developed market has been identified. Furthermore, a lack of research was observed on the linkage between the consumers' buying behaviors and its potential impact on the e-commerce envi­ ronment. The possibility of a linkage has received limited research attention. Certain factors, for example, the avail­ability of a product, have also received relatively limited attention in the research fields towards the e-commerce settings. Hence, this research study further enriches and con­tributes to the already available data and information with a study on the factors that may affect the consumers' buying behaviors in a highly developed market, Singapore and a developed market, Austria, and if these buying behaviors would potentially affect the e-commerce environment. Research design For the empirical part of this research, a questionnaire was structured and designed on QuestionPro, distributed to willing participants. A convenience sample of 80 partici­pants for each country was selected and expected, totaling 160 participants. Eighty participants are expected from Singapore, and the other 80 participants are expected from Austria. The statement with a total of 160 responses was assumed as the results would then be sufficient to arrive and contribute to a significant statistical statement. However, this research project aims to gather as many participants as possible within the research timeframe of two weeks. This was planned as there are drop-outs or invalid datasets to be anticipated during the questionnaire collection phase. In questionnaires, it is always better to have a more signif­icant number of participants than the intended target, as this may also reduce the accidental risk of having an extreme or biased group (Hydrocephalus Association, n.d.). When the questionnaire was completed, the participants were sent an online link through various communication platforms – mainly through personal contacts and social media to reach a bigger audience. The collation of results would come before the data analysis, as shown in Figure 3. The data will be analyzed in due course with the SPSS statistical software, version 26. Pre-testing the questionnaire Based on the works of Converse and Presser (1986), pre-test­ing of a survey is a crucial way to pinpoint problem areas, reduce respondent burden, determine whether or not the re­spondents are interpreting the questions correctly, and ensure that the order of questions does not influence the way the respondent might answer. They have also stated that pre-test­ing, in other words, is a critical examination of the survey in­strument that will help determine if the survey will function adequately as a valid and reliable social science research tool. Once the setup of the draft questionnaire was completed, a round of pre-testing was conducted. The draft questionnaire was sent out to two participants from Singapore and two participants from Austria. Pre-testing displayed the possible pitfalls of the questionnaire and, at the same time, tested if the questions made sense to the participants and if they would be able to understand what this research wishes to convey to them fully. Figure 3. The research design SM Two sets of the same questionnaire were designed and con­structed on Question Pro. The first questionnaire set was designed for the Singaporean participants and the second one for the Austrian participants. There was a complete set of 15 questions for the entire questionnaire. Some of the questions in the questionnaire have employed the usage of a 5-point Likert scale. The Likert (1932) scale remains one of the most commonly used instruments for measur­ing the participants' opinions, preferences, and/or attitudes (Leung, 2011, p. 412). Leung (2011, p. 412) has indicated that a typical Likert scale question consists of several items with around 4 to 7 points of categories each. Furthermore, Sachdev and Verma (2004, p. 104) have stated that ques-tion(s) with a 5-point Likert scale was most recommended by researchers, as it would reduce the frustration level of the respondents and increase the response rate its quality. In the following paragraphs, the questions from the questionnaire are examined and discussed. The beginning of the questionnaire started with the generic questions of gender and age. The age group depicts a variety of different options ranging from 23 and under to 56 and above. The age groups were specifically structured this way with references made to the generation chart as seen below in figure 14, which would enable the ability to construct a comparison and observe if there are any differences between the participants' buying behaviours based on their age group­ings – take, for example, the buying behaviours between the Generation Xs (1965 – 1980) as compared to the Generation Zs (1997 to 2012). Once the general questions are out of the way, the construc­tion and structure of the remaining questions were created to be the perfect match and the right fit for this fundamental research's objectives and aims. First of all, the question asked is how often the participants will purchase from brick-and- mortar stores: How often do you purchase from a retail store? Once the participants have completed this question, the fol­lowing question will be similar. However, the participants were questioned on how often they purchased from an e-commerce store for this time around. The reasoning behind this question was to check if e-commerce has increased the frequency of the participants' making purchases from the online store. After that, the participants will rate and choose from the Likert scale, ranging from strongly disagree to strongly agree to the previous statement that e-commerce has indeed increased the frequency of the participant making purchases: How often do you purchase from an e-commerce store? Has e-commerce increased the frequency of you making purchases? Upon completion, the participants were prompted to the next question, which continued the first two frequency questions. This time around, the question was adjusted to fit the subject of COVID-19. As the pandemic made its entry known in 2019, it has increased the importance and relevance of e-commerce to the entire world. As a result, the participant was also asked with a Likert scale agree/disagree question, whether COVID has increased the frequency of them making purchases from e-commerce. Following this question, the participants will be prompted further and questioned on how often they have made purchases due to COVID-19 from an e-commerce store: Has COVID increased the frequency of you making purchases from e-commerce? How frequent have you purchased due to COVID-19 from an e-commerce store? The participants were presented with three options: purchas­ing from retail stores, e-commerce stores, or both. For this questionnaire, the participants were not redirected or linked to another question to prevent the loss of useable datasets. No matter which option they choose, all participants will be prompted to the same question next: Do you prefer to purchase from: retail stores or e-commerce stores or both? The next question was designed to understand the reason(s) behind why participants prefer to purchase from retail stores. For this question, participants were able to choose the multiple answers where they deemed fit. Do they have the option to input other reasons under the 'Others' option if the answers provided do not fit into their consideration: Which of the factors below positively affect your buying behaviours from a retail store? Next, a similar question was set for the participants. However, this question was designed to find out and under­stand the factors and motivations behind the participants' buying behaviors from an e-commerce store. Likewise, par­ticipants could choose one or more factors, should the listed factors positively affect and support their buying behaviours from any e-commerce stores. When participants cannot locate a factor that supports their buying behaviours, they have the 'Others' option to pen down their factor(s): Which of the factors below positively affect your buying behaviours from an e-commerce store? The following question was designed to understand which factors have increased the frequency of the participants making purchases from the e-commerce store: Which of the factors below has increased your purchases from an e-com­merce store? The participants were asked for the reasons and/or moti­vation behind why they have stopped purchasing from an e-commerce store. For this question, participants were also able to choose multiple answers for the reasons behind why they have stopped purchasing from an e-commerce store. An additional option of 'Have not stopped purchas­ing online' was provided for the participants who have not stopped purchasing from e-commerce. In a similar fashion as the previous questions, participants were able to input other factors. Should they not be able to find a factor that has stopped them from purchasing online: Have you stopped purchasing from an e-commerce store because of …? The final two questions were added to the questionnaire and updated to the 5-point Likert scale measurement, which will enable a deeper analysis of the gathered datasets obtained from the participants. For the second last question, partici­pants were asked to rate the importance they placed on the six factors listed below, from a scale of 1 being not impor­tant at all to a 5, which is Essential. Participants were able to rate each factor with the range provided, from Not important to Essential. On a scale from 1 to 5, please rate the impor­tance of the factors below before you would purchase from an e-commerce store: availability of product, competitive pricing, convenience, customer service, customer reviews, loyalty/trust. The last question was aimed to tackle and understand the impact of the five factors on how each of the said factors will stop the participants from purchasing from an e-commerce store. Participants were able to choose from a scale of 1, being no impact at all, to a 5, a powerful impact. Participants were able to rate each factor with the range provided, from No impact to Powerful impact. On a scale from 1 to 5, please rate the impact of the factors below why you would stop purchasing from an e-commerce store: bad experience from previous buys, mistrust, negative customer reviews, not having the ability to choose and touch the product. Results A total response rate of 242 was collected from both groups of the target audiences. As predicted from the beginning, there would be a drop-out of the participants expected. From the Singaporean participants, 20 participants have dropped out before completing the entire questionnaire, and the drop-out rate sums up to 19.4%. For the Austrian participants, 16 of the participants have dropped out before completing the entire questionnaire, and the drop-out rate sums up to 15.5%. Once the drop-out datasets had been removed, there were 206 useable datasets. An equal number of participants have been obtained from both groups, 103 Singaporeans and 103 Austrians, ranging between 23 and over 56 years of age. Out of the 103 Singaporean participants, 75 of them were females, and 38 were males. The majority of the participants, 83 of them, were between 24 to 39 years old, followed by 24 under the age group of 23 and 6 in 40 to 55. The average time that each participant took to complete the question­ naire was approximately around 3 to 4 minutes. There were also 103 participants obtained, of which 35 of them were females, and 82 were males. The majority of the partici­pants, 98 of them falls under the age group between 24 to 39 years old, followed by 8 participants under the age group of 23 and under, followed by 10 participants who belong to the age group of 40 to 55 years and lastly 1 participant in the age group of 56 and above. The average time taken by the Table 1. Frequency of purchases from retail stores Austrian participants to complete the questionnaire took a little longer than the Singaporean participants, and the par­ticipants needed approximately 5 minutes to complete the questionnaire. Exciting findings Frequency of purchases for Singaporeans and Austrians Based on the questionnaires’ findings as observed in Table 1, 37 of the participants from Singapore purchased at least once a month from a retail store. This option contributed to the majority of choices chosen by the participants, followed by 29 participants who purchased at least twice a month, 22 participants who purchased more than five times a month, and 15 of them who purchased only once in a year or less. According to the responses from the Austrians, as seen in table 1, 36 of the participants purchased at least once a month from a retail store, and another 36 of them purchased at least twice a month. These two options contributed to the majority of the responses chosen by the participants, followed by 22 of them who purchased more than five times a month and 9 of them who purchased only once in a year or less. As seen in Table 2, most of the participants in Singapore, 33, answered with purchasing once a month. Followed by 28 of the participants who purchased at least twice a month, then 25 who purchased more than five times a month, and lastly, 17 purchased once in a year or less from the e-commerce stores. In Austria 51 participants have chosen that they purchased once in a month from e-commerce stores. This group constitutes the majority. They were followed by 31 who purchase at least twice a month, 13 who purchase once in a year or less, and lastly, eight who purchase more than five times in a month. Singapore 4 10 1 15 Once in a year or less Austria -7 2 9 Once in a month Singapore 5 31 1 37 Austria 4 30 2 36 At least two times a month Singapore Austria 5 2 22 30 2 4 29 36 More than five times a month Singapore Austria 4 - 16 20 2 2 22 22 Source: own research. Next, the participants were questioned if e-commerce has increased the frequency of them making purchases. Table 3 displays the overview of the participants' responses in Singa­pore and Austria. In Singapore 74% of the participants have either chosen the strongly agree or agree, with 43 of them choosing strongly agree and 34 of the participants choosing to agree. The remaining participants have either taken the neutral stand, disagreed, or strongly disagreed with this statement. Sixteen of the participants felt neutral to this state­ment, whereas eight disagreed, and lastly, 2 of them strongly disagreed with the statement. As in Austria, 52% of partic­ipants agreed with the statement of e-commerce increasing the frequency of them making purchases. Out of this, 47 of the participants have indicated that they agreed to this state­ ment, and seven have chosen the strongly agree option. The remaining participants have chosen that they felt neutral to the question, with 28 of them, followed by ten who disagreed and 11 who strongly disagreed with this statement. Table 2. Frequency of purchases from e-commerce stores Taking the worldwide pandemic and the stringent restric­tions into account, the participants were also questioned if the COVID-19 has increased the frequency of purchasing from an e-commerce store. Table 4 depicts the participants' responses. It can be observed that the responses obtained were similar to the previous statement. In Singapore 75% of the participants have agreed that COVID-19 has increased the frequency of them making purchases from an e-com­merce store, with 47 strongly agreeing and 31 of the partic­ipants who have agreed to this statement. Of the remaining participants, 13 felt neutral, 10 of them disagreed, and 2 of the participants strongly disagreed. In Austria 64% of the participants have agreed to this statement, with 34 strongly agreeing and 32 agreeing. The remaining participants indi­cated that they were neutral or disagreed with this statement, with 17 feeling neutral, 15 of them disagreeing, and 5 of the participants strongly disagreeing. Singapore 5 12 -17 Once in a year or less Austria 2 8 3 13 Once in a month Singapore Austria 9 3 21 43 3 5 33 51 At least two times a month Singapore Austria 3 1 22 28 3 2 28 31 Singapore 1 24 - 25 More than five times a month Austria - 8 - 8 Source: own research. Table 3. Frequency of purchases increased from online stores due to e-commerce How much do you Singapore 2 8 16 34 43 agree/disagree to this statement? Austria 11 10 28 47 7 Source: Own research. Table 4. Frequency of purchases increased from online stores due to COVID-19 How much do you Singapore 2 10 13 31 47 agree/disagree to this statement? Austria 5 15 17 32 34 Source: own research. This brings us to the next question, where the participants were questioned on how frequently have they purchased due to the pandemic from an e-commerce store. As observed in Table 5, most of the participants in Singapore, 41, have purchased at least two times a month. Twenty-five have in­dicated that they purchased more than five times a month, followed by 23 purchasing once a month and the last 14 participants purchasing once in a year or less. While most of the Austrian participants, 40 of them, have answered that they purchased once in a month, whereas 29 have purchased at least two times a month. Twenty-one of the participants has answered that they purchased once in a year or less, followed by 13 of them who have purchased more than five times a month from an e-commerce store. Preferences of Singaporean and Austrian shoppers Table 6 depicts an overview of the Singaporean partic­ipants buying preferences. As observed in the table, most of the participants, 79, have chosen to purchase from both retail and e-commerce stores. Out of these 79 participants, 58 belong in the age group of 24 to 39 years, 15 belong to the age group 23 and under, and six from 40 to 55 years of age. For the participants who have not chosen both options, 17 of them have chosen that they preferred to purchase from e-commerce stores. Out of these 17 participants, 15 belonged to the age group 24 to 39, and 2 belonged to 23 and under. The remaining 7 participants chose their preference as purchasing from retail stores. Six of them belonged to the age group 24 to 39, and 1 of them belonged to 23 and under. The primary purchasing preference of the Austrians also lies in both options, with a total of 57 participants. Out of these 57 participants, 51 belonged to the age group of 24 to 39 years, followed by three from age group 23 and under and three from age group 40 to 55. The second more popular option was to purchase from the retail stores, with 32 partic­ipants who have chosen this option. Of these 32 participants, 23 were in the age group of 24 to 39 years, followed by 6 of them who were 40 to 55 years of age, and lastly, three who belonged to the age group 23 and under. The 'least' popular option was to purchase from e-commerce stores, with 14 participants who have chosen it. Out of the 14 participants, 13 belonged to 24 to 39 and only 40 to 55. Table 5. Frequency of purchases due to COVD-19 from e-commerce stores Singapore 3 11 -14 Once in a year or less Austria 2 15 4 21 Once in a month Singapore 8 14 1 23 Austria 3 36 1 40 At least two times a month Singapore Austria 6 1 30 26 5 2 41 29 More than five times a month Singapore 1 24 - 25 Austria - 10 3 13 Source: own research. Table 6. Participants’ buying preferences Singapore 1 6 -7 To purchase from retail stores Austria 3 23 6 32 Singapore 2 15 -17 To purchase from e-commerce stores Austria -13 1 14 Both Singapore 15 58 6 79 Austria 3 51 3 56 Source: own research. Reasons for preferences on purchases Next, the participants were asked the reason(s) that positive­ly motivated their preferences to purchase from the retail stores. Participants were allowed to choose for more than one of the reasons where they deemed fit. Table 7 depicts an overview of the breakdown of the participants' motivation(s) that drives their preferences to shop in the retail stores. A total of 87 participants have chosen that they like to shop at the retail stores because of the ability to choose and touch the product they wish to purchase what comes next after their first preference was the availability of the product(s) in stores, where 62 of the participants have picked this option. Followed by 45 participants who think that competitive pricing drives their preferences, 36 of them have loyalty/trust with the retail stores. Two of the participants have provided other reasons, and the factors given were; the service factor and that the participant enjoys the human interaction with the service staff as well as the ambiance of the store, whereas the next participant has indicated that for the fashion buys, he/she would be able to try on the physical item as well as Table 7. Reasons behind participants’ retail store preferences being able to know the quality of the item that they wished to purchase as well as being able to examine the craft. Similarly in Austria, the bulk of the participants, 88 of them, have answered that they enjoyed choosing and touching their product before purchase. The following popular options were the product's availability, with 57 participants who opted for this choice, and the loyalty/trust the 56 par­ ticipants had and felt towards the retail stores. This was followed by 25 of the participants who felt motivated by the competitive pricing offered by the stores and lastly, one who had provided another factor, the ability to get the specific product that he/she was looking for instantly. Next, the participants were questioned about the positive motivation(s) driving their shopping preferences through e-commerce stores. Participants were also able to opt for more than one factor, should the factor(s) fit their preferences. Table 8 shows that in Singapore the most significant portion of the choices went to convenience, with 87 participants who opted for it. Following this factor, 79 of the participants felt Singapore 10 48 4 62 Availability of Product Austria 4 47 6 57 Singapore 8 33 4 45 Competitive pricing Austria 3 19 3 25 Singapore 16 67 4 87 Enjoy the ability of being able to choose and touch the product Austria 4 74 10 88 Singapore 11 23 2 36 Loyalty/trust Austria 5 47 4 56 Others Singapore - 1 1 2 Austria - 1 - 1 Source: own research. compelled by the competitive pricing offered by e-commerce stores. Ranking closely was customer reviews, where 77 participants have chosen as the reason that motivated them to purchase from e-commerce. Forty-eight participants have chosen the product's availability, with loyalty/trust trailing behind with 42 votes. Lastly, thirty-two participants have chosen customer service, and there was another option chosen, and the factor is written the ability to have more variety in the choices for the purchase that he/she wishes to make. Competitive pricing came out as the most chosen factor that drove the Austrian participants' preferences, with 83 votes (Table 8). Convenience was voted as the second favourite factor, with 73 participants who have chosen it. Following very closely behind comes the factor availability of the product, with 72 counts. Fifty-one participants have chosen customer reviews, 12 have chosen loyalty/trust with the e-commerce stores, followed by one who has inputted another factor; the factor provided was the possibility of a more extensive selection of choices for the item he/she wishes to purchase, and if the participant is looking for something unique, he/she would be able to find it faster in the e-commerce store. The participants were further prodded with the five factors. They were questioned about which of the factors would increase the frequency of their purchases from e-com-competitive pricing as the factor that increased their fre­merce. The collated results are displayed and seen in Table quency of e-commerce purchases. Customer reviews were 9. The top voted factor in Singapore, with 81 counts were next, with 52 votes. They were followed by the product's convenience. Next up, 68 of the participants have chosen availability with 46 votes, and 21 have chosen customer Table 8. Reasons behind participants’ e-commerce store preferences Singapore 6 41 1 48 Availability of Product Austria 6 59 7 72 Singapore 11 62 6 79 Competitive pricing Austria 5 71 7 83 Convenience Singapore 17 65 5 87 Austria 6 60 7 73 Customer Reviews Singapore 16 57 4 77 Austria 4 43 4 51 Customer Service Singapore Austria 5 1 26 10 1 - 32 11 Singapore 7 31 4 42 Loyalty/trust Austria -12 -12 Singapore - 1 - 1 Others Austria - 1 - 1 Source: own research. Table 9. Factors that increases the frequency of e-commerce purchases Singapore 4 39 3 46 Availability of Product Austria 5 56 5 66 Singapore 8 54 6 68 Competitive pricing Austria 5 56 7 68 Convenience Singapore Austria 13 4 64 45 4 6 81 55 Customer Reviews Singapore Austria 7 3 40 25 5 3 52 31 Customer Service Singapore Austria 2 - 18 7 1 - 21 7 Singapore 4 20 2 26 Loyalty/trust Austria -7 -7 Singapore - 2 - 2 Others Austria - 2 - 2 Source: own research. service. Two have chosen others and have provided the factors that have increased the frequency of their purchases. The first given factor was the ongoing promotion campaign, and an example of the 11.11 promotion was provided. The next factor was the delivery right to their doorstep, especial­ly if the participant purchases something heavy or bulky, he/ she will not have to carry the heavyweight back home. The most voted factor in Austria was competitive pricing, with 68 counts (Table 9). The next factor was the product's availability, with 66 of the participants who had opted for it. Convenience was voted next, with 55 votes. This was followed by customer reviews with 31 votes, seven chosen customer service, and seven opted for the loyalty/trust factor. Last but not least, 2 of the participants have chosen others but have failed to provide any reasoning behind their choices. Lastly, the participants were questioned about the factor(s) that have stopped them from purchasing from e-commerce. The overview of the data collected from the Singaporean participants can be observed in Table 10. Based on the data collected, the most off-putting factor to the Singaporeans were negative customer reviews, with 61 votes behind this option. The next factor chosen the most was the bad ex­perience obtained from their previous buys with 52 votes, followed by the factor of not having the ability to choose and touch the product they wished to purchase with 37 votes. Thirty-two participants have opted for uncompetitive pricing, followed by 29 who have chosen mistrust as the factor that has stopped them from purchasing from e-com­merce stores. Lastly, 25 of them have indicated that they have not stopped purchasing from e-commerce stores, and 1 participant has chosen the other option and has indicated a not applicable statement for him/her. A big part of the participants in Austria, 48 of them, have opted not to stop purchasing from e-commerce (Table 10). The next most voted factor, with 35 votes, was negative Table 10. Factors that have stopped participants from purchasing from e-commerce Singapore 9 41 2 52 Bad Experience from Previous Buys Austria 2 26 4 32 Mistrust Singapore 3 24 2 29 Austria - 19 1 20 Singapore 9 48 3 61 Negative Customer Reviews Austria 3 29 3 35 Singapore 6 30 1 37 Not having the ability of being able to choose and touch the product Austria 2 21 2 25 Singapore 7 25 -32 Uncompetitive Pricing Austria 1 10 1 12 Singapore 3 20 2 25 Have not stopped purchasing online Austria 3 42 3 48 Others Singapore - 1 - 1 Austria - 3 - 3 Source: own research. customer reviews, followed by 32 votes who have opted for terrible experiences from previous buys. Twenty-five of the participants have chosen that they have stopped purchas­ing from e-commerce because of not having the ability to choose and touch the product they wished to purchase, and 20 of them have chosen mistrust as the factor. Last but not least, the least voted factor was uncompetitive pricing with 12 votes, and 3 of the participants have opted for the others option. The participants provided the other factors that have stopped them from purchasing from e-commerce were; the potential CO2 impact on the environment, supporting the local economies, and having only bad delivery options. Analysis based on t-test In order to analyse the factors that may or may not affect the consumers' buying behaviours t-test analysis was chosen. A convenient significance value of 0.05% was assumed throughout this entire research. A significance value of 5% was assumed and utilized throughout this entire research. Table 11 shows an overview of the t-test analysis results for the Likert scale question 11, on how vital the six factors, namely availability of a product, competitive pricing, convenience, customer service, customer reviews, and loyalty/trust, are for the participants before they would purchase from e-commerce. The exhib­ited p-values for Singapore show that the only factor with p-value higher than 0.05 was availabity of product. Thus, the null hypothesis could not be rejected. Thus, the availability of product may not affect Singaporean consumers' buying behaviour in e-commerce settings. For all other factors the null hypothesis could be rejected since the p-value was lower than the assumed significance value of 0.05. As a result, competitive pricing, convenience, customer service, customer reviews and loyalty/trust might affect Singaporean consumers' buying behaviour (s) in e-commerce settings. Table 11. Results of t-test for question 11 Availability of Product 0.771 0.006 Competitive Pricing 0.000 0.415 Convenience 0.008 0.000 Customer Service 0.004 0.237 Customer Reviews 0.000 0.000 Loyalty/Trust 0.002 0.810 Source: own research. According to results of the t-test in the Austrian sample (Table 11), availability of product, convenience and customer reviews play an important role in the Austrian consumers' buying behavior(s) in e-commerce settings. In these three cases the p-value was lower than the 0.05 and the null hypothesis could be rejected. On the other hand, com­petitive pricing, customer services and loyalty/trust might not affect the Austrian consumers' buying behaviour(s) in the e-commerce settings since they exhibit p-values lower than 0.05 and the null hypothesis for these factors could not be rejected. Table 12 provides an overview of the t-test analysis results for the Likert scale question 12, on how the five factors, namely bad experience from previous buys, mistrust, negative customer reviews, inability to choose and touch the product, and lastly uncompetitive pricing, would stop the Singaporean and Austrian consumers from purchasing from the e-commerce. Table 12. Results of t-test for question 12 Bad Experience from Previous Buys 0.001 0.422 Mistrust 0.000 0.110 Negative Customer Reviews 0.246 0.000 Inability to Choose and Touch the 0.328 0.328 Product Uncompetitive Pricing 0.775 0.000 Source: own research. The t-test analysis conducted for Singapore on the factors bad experience from previous buys and mistrust have revealed p-values lower than 0.05. The null hypothesis could be rejected. Thus, these two factors might affect Singapore­an consumers' buying behaviour(s) in e-commerce settings. While negative customer reviews, inability to choose and touch the product and uncompetitive pricing might not affect Singaporean consumers' buying behaviour(s) in e-com­merce, since for these three factors the p-values are higher than 0.005 and the null hypothesis could not be rejected. While, the t-test results for Austria (Table 12) show that bad experience from previous buys, mistrust and inability to choose might not affect the Austrian consumers' buying behaviour(s) in the e-commerce settings since the p-values for these factors are higher than 0.05 and the null hypothesis could not be rejected. On the other hand, negative customer reviews and uncompetitive pricing revealed p-values lower than 0.05 and the null hypothesis could be rejected. Thus, negative customer reviews and uncompetitive pricing might affect the Austrian consumers' buying behaviour(s) in the e-commerce settings. Correlation analysis Subsequently, after conducting the t-test analyses, a cor­relation analysis between the six factors was also tested with Pearson's correlation test and the study of each of its significance values. Pearson’s r varies between +1 and -1, where +1 is a perfect positive correlation, and -1 is a perfect negative correlation. With a value of 0, it means that there is no linear correlation at all (Ezspss, n.d.). Table 13 depicts an overview of the datasets obtained from the Pearson correlation analysis on the six factors: availabil­ity of a product, competitive pricing, convenience, customer service, customer reviews, and loyalty/trust for the Singapo­rean participants. It can be observed that with the Pearson's correlation test for the availability of product shows a small, moderate correlation strength to competitive pricing, con­ venience, service and reviews with statistically significant Pearson Correlation r-values among of 0.248 and 0.366, and even lower r-value for loyalty/trust, which is not statistically significant. The correlation analysis results for the competitive pricing show weak correlation with convenience, service, reviews and loyalty/trust with statistically insignificant r-values among 0.016 and 0.190. Thus, there is no evidence of the Table 13. Pearson correlation analysis for Singapore existence of the correlation. As for convenience the r-values regarding other factors are statistically significant and exibit values among 0.269 and 0.404, providing evidence of weak correlation among convenience on one hand and service, review and loyalty/trust on the other. Correlation among customer service and reviews amounts to 0.323 and is statis­ tically significant, while correlation among customer service and loyalty/trust resulted in 0.361 and is also statistically significant. And the last pair of factors is correlation among customers reviews and loyalty/trust, where the statistically significant r-value is 0.280. Table 14 depicts an overview of the datasets obtained from the Pearson correlation analysis on the six factors: availabil­ity of a product, competitive pricing, convenience, customer service, customer reviews, and loyalty/trust for the Austrian Availability of Product 0.248 0.294 0.289 0.366 0.130 Sig. (2-tailed) 0.012 0.003 0.103 0.000 0.189 Competitive Pricing -0.118 0.019 0.190 0.016 Sig. (2-tailed) -0.233 0.847 0.055 0.870 Convenience --0.306 0.404 0.269 Sig. (2-tailed) --0.002 0.000 0.006 Customer Service ---0.323 0.361 Sig. (2-tailed) ---0.001 0.000 Customer Reviews ----0.280 Sig. (2-tailed) ----0.004 Source: own research. Table 14. Pearson correlation analysis for Austria Availability of Product 0.062 0.018 -0.046 -0.004 0.066 Sig. (2-tailed) 0.533 0.853 0.645 0.970 0.507 Competitive Pricing --0.044 0.049 0.160 0.154 Sig. (2-tailed) -0.657 0.623 0.107 0.121 Convenience --0.217 -0.162 0.072 Sig. (2-tailed) --0.028 0.101 0.470 Customer Service ---0.166 0.366 Sig. (2-tailed) ---0.094 0.000 Customer Reviews ----0.121 Sig. (2-tailed) ----0.224 Source: own research. participants. The availability of product shows a very weak statistically insignificant correlation to competitive pricing, convenience, service, reviews and loyalty/trust. Thus, there is no evidence that this correlation does exist within this sample population. Similarly, there is no proof of correlation among convenience and reviews, and among convenience and loyalty/trust, as there are relatively low statistically insignificant r-values. However, there is a weak statistical­ly significant correlation among convenience and service. Furthermore, there is no statistically significant correlation among customer service and customer reviews, and among customer reviews and loyalty/trust. But correlation among customer service and loyalty/trust is proven to be statistical­ ly significant with r-value of 0.366. Interpretation of Results The main hypothesis “Consumers' buying behaviours may affect the e-commerce environment.” can be divided into two parts considering a positive or negative effect of selected factors related to customer behaviours to the e-commerce environment. Regarding the positive factors in Singapore, the one-sample t-test analyses showed that the participants placed higher importance on certain factors before they purchased from e-commerce. It was found that the following factors: com­petitive pricing, convenience, customer service, customer reviews, and loyalty/trust, may play a role towards the contribution of a positive effect and impact on their buying behaviours in the e-commerce environment. Availability of product was ruled out with the t-test analysis, and thus it was implied that this factor might not affect their buying behaviour(s) for the e-commerce. As for Austria, the results of t-test analysis have shown that the following factors: product availability, convenience, and customer reviews, may positively affect customers’ buying behaviour(s) in the e-commerce environment. Competitive pricing, customer service, and loyalty/trust were ruled out and may not cause any positive impacts on their buying behaviour(s). Regarding the negative effect in Singapore and based on the t-test, the factors identified were bad experiences from previous buys and the sense of mistrust that would impact their buying behaviour(s) negatively. The following factors were ruled out from the one-sample t-test and thus would not negatively affect the Singaporeans' buying behaviour(s): negative customer reviews, inability to choose and touch the product, and lastly, uncompetitive pricing. In Austria, the results of the t-test have disclosed that negative customer reviews and uncompetitive pricing might negatively affect and impact their buying behaviour(s) in the e-commerce environment for the Austrians. The remaining factors, bad experience from previous buys, mistrust, the inabili­ty to choose and touch the product, were tested and ruled out as having any adverse effects on the Austrians' buying behaviour(s). Based on the outputs received from the Pearson's correla­tion tests, some weak signs of correlation, be it positive or negative, between the five factors existed. For this research's case, it was discovered from the sample of Singapore that when the factor availability of the product was chosen, it was also highly likely that the following factors competi­tive pricing, convenience, and customer reviews, would be selected well. Also, as most of the correlations were rela­tively weak, with r-values ranging from 0.2 to 0.4, it indi­cates weak relationships between the tested factors. With double-checking conducted on the results with the signifi­cance test, the outcomes have proven that specific correla­tions may have existed between the factors. There were no negative correlations detected from the Singapore sample. For the Austrians, it was discovered from the sample dataset that when the factor customer service was chosen, it was probable that the factor loyalty/trust would be selected. The majority of the correlations were relatively weak, with r-values ranging from 0.01 to 0.3, which indicated weak relationships between the tested factors. However, after conducting a check with the significance test results, some of the outcomes could not prove the correlations between the factors. On the other hand, several negative correla­tions were detected, for example, the factor availability of the product and its correlation with the factor of customer service. It could be interpreted that when the Austrians chose product availability, they are less likely to have also chosen customer service. However, a check with the significance test has shown otherwise and, as such, holds insufficient evidence to confirm that the negative correlations existed. Conclusion The data collected has revealed that most Singaporean con­sumers and a majority of the Austrian consumers mostly purchase once a month from the retail stores. Similarly, these two groups of sample consumers also purchase once a month from e-commerce. Based on the findings observed from the statement "E-commerce has increased the fre­quency of you making purchases," it can be observed that Singaporean participants feel that they are generally more impacted by e-commerce. A higher number of the Singapo­reans, 43 of them, feel a solid agreement to the prior state­ment than 7 of the Austrians' strong agreements. Lastly, the overall agreement rate (between agree and strongly agree) of 74% from the Singaporeans was also higher than the Austri­ans' agreement rate at 52%. A considerable portion of the Singaporean and Austrian con­sumers have indicated that they prefer to shop from retail and e-commerce stores. Seventy-nine of the Singaporeans and 57 of the Austrians have chosen both retail and e-com­merce as their preferences. For the remaining sample, which has not chosen both as a preference, it can be gathered that 32 of the Austrians prefer to shop from retail stores, and 17 of the Singaporeans prefer to shop from e-commerce. Overall, it can be concluded from this sample data that Sin­gaporeans may hold a higher preference towards purchasing from e-commerce in general, as compared to the Austrians. The primary motivator that may have positively impacted the buying behaviours of most Singaporeans and Austrians to purchase from retail stores was the ability to choose and touch the product(s) that they wish to buy. Following behind very closely was the factor availability of the product as the second most chosen motivator by both sample groups. Next, briefly will discuss the motivators that positively push and support the Singaporeans and Austrians to purchase from e-commerce stores. Based on the data, it was observed that convenience was the top motivator that most Singapo­reans have chosen, which may positively impact their online buying behaviours. The second most chosen factor was competitive pricing. Whereas for the Austrian participants, the bulk has chosen competitive pricing as the primary motivator that may positively impact their online shopping preferences and following very closely behind was the chosen factor, convenience. Statistical t-test analysis was conducted on the responses obtained from both of the sample groups. It was observed from the results that for Singaporeans, competitive pricing, convenience, customer service, customer reviews, and loyalty/trust are the five factors that may positively impact washing behaviours in e-commerce settings. As for the Austrian consumers, it was discovered that their results were different from that of the Singaporean consumers. 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Why is e-commerce winning against retail? Retrieved from: https://www.wonderflow.co/blog/why-is-e-commerce­ winning-against-retail Your Dictionary (n.d.). Positive Correlation Examples in Real Life. Retrieved from: https://examples.yourdictionary.com/positive-correla­tion-examples.html Ucinki nakupnega vedenja potrošnikov na e-trgovino na visoko razvitem nastajajocem trgu in razvitem trgu: primer Singapurja in Avstrije Izvlecek E-trgovina postaja v sodobnem svetu vse bolj pomembna in predvsem aktualna, zlasti v trenutni pandemiji. Zaradi povecane uporabe interneta je rast e-trgovine eskalirala. Ta raziskava se osredotoca na e-trgovinski okolji singapurskega in avstrijskega trga. Vsebuje oris, ki se dotika teoreticnih delov in izvedbe empiricne študije, v kateri smo proucili, analizirali in primerjali rezultate anketiranja 206 udeležencev. Za to raziskavo sta bili izbrani statisticni metodi analiza t-testa in korelacijska analiza. Empiricna študija je služila kot kvantitativna razprava o trgih e-trgovine obeh držav. Analiza izpolnjenih vprašalnikov je omogocila boljše in jasno razumevanje nakupnega vedenja in njegovega možnega vpliva na trge e-trgovine v Singapurju in Avstriji. Glede na rezultate primerjav je raziskava izpostavila tudi nekaj zanimivih ugotovitev in razlik v nakupnem vedenju singapurskih in avstrijskih kupcev. Kljucne besede: e-trgovina, oblikovanje cen, nakupno vedenje, potrošniki, Singapur, Avstrija ORIGINAL SCIENTIFIC PAPER RECEIVED: OCTOBER 2021 REVISED: NOVEMBER 2021 ACCEPTED: NOVEMBER 2021 DOI: 10.2478/ngoe-2021-0022 UDK: 338.48:339.138 JEL: M31, Z32 Citation: Krupka, Z., Dobra,A., & Vlašic, G. (2021). Factors Influencing the Perception of Destination Brand Luxuriousness. Naše gospodarstvo/Our Economy, 67(4), 74-86. DOI: 10.2478/ngoe-2021-0022 NAŠE GOSPODARSTVO OUR ECONOMY Vol. 67 No. 4 2021 pp. 74–86 Factors Influencing the Perception of Destination Brand Luxuriousness Zoran Krupka University of Zagreb, Faculty of Economics and Business, Croatia zkrupka@efzg.hr Andreja Dobra A1 d.d., Vrtni put 1, 10000 Zagreb, Croatia andreja.dobra@A1.hr Goran Vlašic University of Zagreb, Faculty of Economics and Business, Croatia gvlasic@efzg.hr Abstract The main purpose of this paper is to identify and investigate the factors that influence the building and managing of luxury destination brands. Based on a review of existing literature, the authors identified eight crucial factors: accommodation quality, quality of gastronomy offers, premium price, service quality, luxury shopping, unique experience, transportation infrastructure, and celebrity. The research was conducted on a sample of 619 respondents from 16 countries. Factor and regression analyses were used. The results indicate that the availabilityof celebrityand luxuryshopping has the strongest significant influence on luxury destination brand building, while the impact of accommodation quality and a premium price is non-significant, and, interestingly, transportation infrastructure has a negative and significant impact. A convenience sample and the possibility of omitting certain factors (e.g. safety) from the research represent the main research limitations. This research contributes to marketing and brand management literature by identifying and investigating the factors that help in building and managing a luxury destination brand, which to date has been neglected in literature. Keywords: brand management, luxury, destination, building factors, luxury branding Introduction Until 2020 and the COVID-19 pandemic, tourism was one of the fastest growing economic sectors in the world. According to the World Tourism Organization (UNWTO), international tourist arrivals grew by 7% in 2019 (the global economy grew by 3.8%) compared to the previous year to reach a total of 1.4 billion (two years ahead of forecasts). All the regions of the world – from Europe and Asia to the Americas and Africa – recorded growth, which implies that tourism has become an important industry for those countries that are not traditionally con­ sidered to be tourist-oriented. To maintain growth, continents, countries, cities, villages and man-made resorts, in other words destinations (Hanna, Rowley, & Keegan, 2021; Pike, 2004), need to differentiate themselves one from another. Therefore, destination brand management has become a strategic orientation for many countries, and the destination itself is one of the crucial parts of a holistic tourist experience (Kani, Aziz, Sambasivan, & Bojei, 2017; Baker & Cameron, 2008). Prayag (2010) defines a destination brand as a unique com­bination of functional and non-functional characteristics and the value-added products/services that consumers associate with the brand. From a marketing point of view, a destination brand is a combination of physical and emotional experienc­es communicated to consumers through various elements of a brand, with the aim of facilitating the choice of destina­ tion (Lim & Weaver 2014; Lichrou, O’Malley, & Patterson 2008). In contrast, from the consumer's point of view, a destination brand represents a set of perceptions that are the result of the associations they have about the destination and the experience they gain in the destination; delivered under a specific brand name (Balakrishnan, Nekhili & Lewis 2011). Today, consumers are offered various choices of destination and they choose one which has a positive image and whose image is in line with their perception of themselves (Rossidis et al., 2019). According to Dwivedi, Yadav and Patel (2009), destination image is a set of objective knowledge, impres­sions, prejudices and feelings a customer has about a certain destination. It represents tourists’ expectations of the desti­nation (Leisen 2001), which means that destination branding is critical for a destination to be identified and differentiated in the mind of a potential consumer (Qu, Kim & Im, 2011; Abodeeb, Wilson & Moyle, 2015). In the modern highly competitive global market, destina­tions that only rely on the ‘3S of tourism’ (sun, sea and sand) are easily substitutable. One possible way that a destination can stand out from the masses and differentiate itself is by creating a luxury destination brand. Although many papers have been written on topics related to destination branding (Reif, 2020; Iloranta, 2019; Kirant Yozcu, 2017; Kani et al. 2017; Tasci, Gartner & Cavusgil 2007; Mishra 2010; Hankinson 2009; Hudson & Ritchie 2009; Choi & Cho 2016), and whilst luxury as a concept has been present in marketing and branding literature for over two decades (Bev­ erland 2006; Dubois & Duquesne 1993; Mandel, Petrova & Cialdini 2006; Nueno & Quelch 1998), according to the authors’ knowledge, there is still a lack of research dealing with building and managing a luxury destination brand. From an economic perspective, luxury brands can be defined as those whose ratio of functional utility to price is low, while the ratio of intangible and situational utility to price is high (Nueno & Quelch 1998.) From a marketing perspec­tive, luxury brands are frequently defined in terms of their excellent quality, high transaction value, hedonic value and uniqueness (Lee, Hsu, Chen & Wu 2018; Tak & Pareek 2016; Fionda & Moore 2009). In addition to possible functional attributes, luxury brands are even more sought for their emo­tional and symbolic values arising from image, social status, exclusivity, high quality, superb performance and premium prices (Krupka, Ozretic-Dosen & Previsic 2014; Beverland 2006). Therefore, luxury brands can be understood as those that consumers buy because of the emotions they associate with them, and functional quality is what is implied. A luxury destination brand can be defined as a combination of various elements of a brand and offer in the destination, which consumers perceive as luxurious, with the aim of achieving recognition and distinction from other destina­tions by building a positive prestigious image. A luxury destination brand combines all luxury products, services and experiences, and meets all the needs and desires of the luxury tourist segment (Correia, Kozak & Del Chiappa, 2020; Seo & Buchanan-Oliver 2015). Park, Reisinger and Noh (2010) emphasise that experience is an essential part of a luxury offer, as well as one of the fundamental components of a destination brand. By consuming a luxury destination brand, consumers express their identity and personality to the public (Sung, Choi, Ahn & Song 2015). To address the deficit in luxury destination brand literature and research, this paper focus on identifying and investi­ gating the factors that influence building and managing a luxury destination brand. Factors That Influence Luxury Destination Brand Building and Management Accommodation Quality Accommodation is one of the key components when building a destination brand, since it determines which profile of visitors will choose a specific destination and what type of tourism will develop in the destination (Presbury, Fitzgerald, & Chapman, 2005). Luxury accommodation mostly refers to hotels, and visitors of such hotels expect delivery of superior value in terms of reputation and quality (Belyakova, 2018; Tkaczynski & Rundle-Thiele, 2011). Presbury et al. (2005) indicate that hotels are considered luxurious if they offer superior content and service, while Hwang and Heesup (2014), as well as Giglio, Pantano, Bilotta and Melewar (2020), emphasise quality at room level as an important element that has a positive effect on luxuriousness. Furthermore, high price is an indicator of high-quality accommodation (Maden, Göztas & Topsümer 2015; Park et al. 2010), but also visitors to luxury accom­modation expect delivery of high quality, good location, pleasant atmosphere, spacious rooms, comfort and internet access (Lu, Berchoux, Marek & Chen 2015). Based on the aforementioned, it can be concluded that customers will perceive a certain destination luxurious if that destination can offer high quality accommodation, by combining tangible and intangible elements of the overall accommodation offer. Accordingly, accommodation quality will have a positive impact on the luxuriousness of the at­mosphere along with the building of a prestigious brand image in the visitor's perception (Hwang & Heesup 2014). Therefore, the following hypothesis arises: H1: The perceived quality of accommodation has a positive effect on destination brand luxuriousness. Quality of gastronomy offer Gastronomy, in the context of the restaurant and food availa­ble in a destination, is an important component of the overall tourist experience and has a significant impact on tourist satisfaction and the differentiation of a destination (Correia, Moital, Ferreira da Costa & Peres 2013; Balakrishnan et al. 2011). In the past two decades, the concept of luxury tourism has moved from (traditionally) 5-star hotels and resorts to unique experiences – one of which is unique dining (Batat, 2021; Hanžek & Sušic, 2020; Park et al. 2010). Peng and Huiling Chen (2015) and Chen, Peng and Hung (2015) state that luxurious restaurants are full-service restaurants whose environment (interior design, atmosphere, staff service) and offer (food and beverage) are carefully prepared and presented, and are unique, noticeable and superior in terms of quality. Appetizers that are priced at higher than USD 20 (Chen et al. 2015) are an additional factor in the per­ception of a luxurious restaurant, as are other guests who give the restaurant a sense of exclusivity (Peng & Huiling Chen, 2015). The fact that gastronomy accounts for about one-quarter of a destination’s revenue stresses its importance for the destination (Daries, Cristobal-Fransi, Ferrer-Rosell & Marine-Roig, 2018). As a result of all the aforementioned, gastronomy is one of the attractive factors of a destination and has a great role in creating a destination’s brand image (Marine-Roig & Anton Clave, 2016). Given the above, it can be concluded that the quality of a destination’s gastronomy has an influence on customers’ perception and the overall experience of the destination as being more or less luxurious, consequently proposing the following hypothesis: H2: The perceived quality of a destination’s gastronomy has a positive impact on its brand luxuriousness. Premium price Brun and Castelli (2013) define luxury in the context of high price by saying that luxury goods are worth just because they are expensive, and that luxury applies to all goods/services with a high price, i.e. those with a price that is at least two to three times higher than the low-cost variants. For luxury consumers, premium prices are not an obstacle when buying products and services, as long as they are accompanied by high quality and exclusivity (Maden et al., 2015). The par­ticularity of luxury brands is that they communicate prestige and status and suggest the position of a luxury consumer in society, whereas premium price suggests a brand's ex­ clusivity. Therefore, premium price can be seen as the essence of luxury, highlighting the characteristics of rarity and exclusivity not only of the brand, but also its user (Seo & Buchanan-Oliver, 2015). Additionally, premium price is important for those who are concerned about their privacy during their stay in the destination, ensuring a certain level of privacy (Park et al., 2010). From the foregoing, the fol­ lowing hypothesis arises: H3: A perceived high price has a positive impact on the lux­uriousness of a destination brand. Service quality In today’s highly competitive environment, service quality helps to differentiate a company from the competition, in­ dicates its excellence (Hwang & Heesup, 2014) and is an important factor in building brand image (Lu et al., 2015). Service quality can be defined as the difference between the expected and delivered service (Hwang & Heesup, 2014), as it depends on the needs and expectations of customers and whether the level of provided service will meet their needs and expectations. It is one of the most important factors that influence customers’ satisfaction and perceived values (Cronin & Taylor, 1992; Petrick & Backman, 2002). In cases where a destination is striving to build a luxury brand image, the full range of services of the destination (restaurants, hotels, shops, public services, etc.) should be at a high level, since tourists who are willing to pay a premium price expect a premium service quality. Furthermore, in addition to the fact that it creates a destination brand image, service quality influences tourists’ decisions whether or not to return to a destination (Kim, Holland & Han, 2013). Delivered high service quality among customers triggers a sense of value and is an essential part of a luxury brand (Hwang & Heesup, 2014), therefore raising the following hypothesis: H4: Perceived service quality has a positive impact on the luxuriousness of a destination brand. Luxury shopping Following accommodation, tourists’ biggest expenditure is shopping, which accounts for 30% of all tourist activities (Kinley, Forney & Kim, 2012). Shopping is often cited as a significant, sometimes primary, reason for travelling and is associated with emotional motives such as relaxation, enjoyment and entertainment (Henderson, Chee, Mun & Lee, 2011). Balakrishnan (2009) stresses that shopping is becoming the number one leisure time activity, whereas Park et al. (2010) point out that many tourists travel abroad in order to purchase luxury products. Luxury stores are most commonly located in carefully selected tourist destinations, which benefit from the fact that they offer luxury shopping centres and luxury brand districts. Due to their convenience, recognised standard on a global scale, safety, cleanliness and service quality, they are popular among luxury tourists (Park et al. 2010). Given the effort involved when choosing a destination as a location for luxury stores, the possibility of luxury shopping suggests the existence of luxury elements, which is ultimate­ ly an indicator of a luxurious destination itself. Therefore, this raises the following hypothesis: H5: The possibility of luxury shopping has a positive impact on the luxuriousness of a destination brand. Unique experience The reasons for choosing a particular destination vary from individual to individual, but each of them is seeking some­ thing unique at the destination (Tkaczynski & Rundle-Thiele, 2011). Trends in the past ten years have shown that luxury goes beyond product and services – today it includes expe­rience and authenticity (Park et al. 2010). Factors by which a destination can build a unique experience are culture, heritage, history, art or unique natural beauties (Dwivedi et al., 2009; Konecnik & Go, 2008). Alongside the afore­mentioned factors, what influences a unique experience are factors such as entertainment and recreation, for instance, nightlife, golf courses, casinos or wellness centres, i.e. a form of product or service that creates an exotic experience among consumers (Park et al., 2010). According to Hwang and Heesup (2014), the uniqueness of a product or service – in this case the uniqueness of natural and cultural attractions, recreational and many other ac­tivities – is the key criterion according to which brand, in the context of this paper, a destination brand, is perceived prestigious. In accordance with all of the above stated, the following hypothesis is proposed: H6: A unique experience in a destination has a positive impact on the luxuriousness of a destination brand. Transportation infrastructure Transportation infrastructure is indispensable for tourism and a key determinant of destination development, therefore a developed infrastructure, such as airports and roads, in­creases the attractiveness of a destination (Dwivedi et al., 2009; Mostafavi Shirazi & Puad Mat Som, 2011). Conven­ient transportation not only means that a destination must be near an international airport, but is also observed in terms of the duration of the journey to and from the airport to the final destination, and the possibility of access to sites of interest, such as shopping centres and other tourist attractions (Huang & Chiu, 2006). Therefore, convenient transportation can be defined as effective transportation that ensures tourists have access to a reasonable level of transportation service. A well-connected destination is one that has easy access, is accessible from a tourist’s country of origin and has devel­oped local transportation infrastructure (Balakrishnan et al., 2011). Hence, the accessibility, in the context of a tourist destination, can be seen as a dimension of the quality of life (Tóth, Dávid & Vasa, 2014). According to Hwang and Heesup (2014), since quality of life is considered one of the key determinants of luxury, it can be concluded that trans­port connectivity is an important factor when building a luxury destination brand. In accordance with the above, the following hypothesis is proposed: H7: Developed transportation infrastructure has a positive impact on the luxuriousness of a destination brand. Celebrity A celebrity is a person who enjoys public recognition, as the general public attaches characteristics and distinctive attributes to them, such as attractiveness and reliability (Um 2013), and they therefore play an important role in marketing activities. Celebrities are an important source of information and have an influence on customers’perceptions, thus maga­zines such as In Style and Vogue, as well as numerous social media platforms, show famous and successful people when consuming luxury brands, thereby encouraging customers to imitate them (Seo & Buchanan-Oliver, 2015). Marketing experts expect that positive feelings toward celebrities will transfer to the brand that this celebrity uses and/or endorses (Ozretic-Dosen, Skare & Krupka, 2011). Diesbach (2012) points out that celebrities promote a destination and create an image of a luxury brand if they themselves are tourists in that destination. In that way, a spill-over effect of perceived luxury from celebrity to the destination brand occurs. Today, as well as in the past, those aspiring to luxury status want to stand out from the crowd and thereby often choose celebrities as their role model, who, because of their status, behaviour and consumption of luxury brands, attract public attention (Wong & Lai 2015). To conclude, if a destination is associated with a famous person who has important, at­tractive attributes and symbolic values to which luxury cus­tomers aspire, consequently it will have a positive impact on the luxury destination brand, thus the following hypothesis can be proposed: H8: Celebrities as brand destination users have a positive impact on the luxuriousness of a destination brand. Methodology and Results Conceptual framework According to brand management, destination branding and luxury branding literature, the conceptual framework (Figure 1) was defined to analyse the impact of the quality of accommodation and gastronomy, premium price, service quality, luxury shopping, unique experience, transportation infrastructure and celebrity on the perceived luxuriousness of a destination brand. Those factors were recognised as antecedents of luxury destination brand building both from academic and business points of view. A proposed concep­tual framework was developed to research the hypotheses it outlined. Figure 1. Conceptual framework and research hypotheses Sample and procedure To test the hypotheses, a survey was conducted on a sample of 619 respondents from 16 countries (Australia, Austria, Bosnia and Herzegovina, Croatia, Finland, France, Germany, Italy, Norway, Poland, Serbia, Slovenia, Spain, Sweden, the United Kingdom and the USA) who travelled at least 10 times in the last 30 months. The respondents were approached via the LinkedIn and Facebook platforms. A self-administered highly structured questionnaire was used to collect the data. The respondents were asked to think about the last destination they visited and to express their level of agreement with 38 items (see Table 1) on a 5-point Likert scale (1 – Strongly disagree; 5 – Strongly agree). Existing validated scales originally in English were used, which were then translated and tested using ‘back-to-back’ translation (English > OtherLanguage > English). Finally, the questionnaire was distributed in six languages – English, German, Italian, Spanish, French and Croatian. Additionally, respondents had to answer five demograph­ic questions about gender, age, nationality, education, and monthly household income. In terms of gender, 217 (35.06%) respondents were male, while 402 (64.94%) were female. Of all the respondents, 288 (46.53%) of them were aged 18 to 25, 216 (34.89%) were between the age of 26 and 40, 80 (12.92%) respondents were aged between 41-55, while 29 (4.69%) of them were aged between 56 and 65, and only 6 (0.97%) were 65 and over. In terms of their education level, most of them (255; 41.20%) have a Master’s degree, followed by a Bachelor’s degree (190; 30.69%), while others (150; 24.23%) had completed college courses, PhD degrees (17: 2.75%), primary school education (4; 0.65%), and a postgraduate degree (3; 0.48%). In terms of monthly house­hold income, the largest group of respondents (121; 19.55%) live in a household which has a monthly income of between EUR 901 and EUR 1,500, followed by households with a monthly income of between EUR 1,501 and EUR 2,000 EUR (111; 17.93%) and less than EUR 500 (91; 14.70%). Of the respondents, 86 (13.90%) live in a household with a monthly income of between EUR 501 and EUR 900, 80 (12.92%) between EUR 2,001 and EUR 3,000, 57 (9.21%) between EUR 3,001 and EUR 4,000, while 47 (7.59%) of respondents live in a household with a monthly income of more than EUR 5,500. The smallest group of respondents (26; 4.20%) live in a household with a monthly income of between EUR 4,001 and EUR 5,000. Results A confirmatory factor analysis (see Table 1) shows that for each variable all factors have loadings greater than 0.5 and a Cronbach’s alpha greater than 0.7, which are the levels recommended by Hair, Babin, Anderson and Black (2018). The model was tested using a regression analysis with factors explaining 56.20% of the dependent variable Desti­nation Brand Luxuriousness (R2 = 0.562; sig = 0.000). The model shows that the variables Accommodation Quality (sig = 0.743; VIF=2,195) and Premium Price (sig = 0.461; VIF=1,414) do not have a significant impact (p>0.05) on the extent to which a tourist perceives a destination’s luxurious­ ness. This implies that H1 and H3 are rejected. Such a result may be a reflection of the fact that each destination (regard­less of its luxuriousness) offers a diverse set of options in terms of accommodation quality and a diverse set of price levels to suit tourists’ budget and preferences. Therefore, it can be argued that these two do not depend on the destina­tion itself (and its luxuriousness), but rather on customers’ preference and choice within the destination, which they decided upon after having chosen the destination. Transportation Infrastructure has a negative and significant (p<0.05) impact on perceived Destination Brand Luxurious­ness (ß = -0.091; p = 0.010; VIF=1,652), thus rejecting H7. While better transportation infrastructure enables greater accessibility of the destination, greater accessibility implies more uniformly distributed tourists can visit the destination, thus creating a distribution of tourists and making the desti­nation perceived as ‘mass market’ destination. The other hypotheses are accepted. The most significant impact on perceived Destination Brand Luxuriousness is ex­hibited by the association of the destination with Celebrity (H8; ß = 0.389; p = 0.000; VIF=1,757) and the availability of Luxury Shopping (H5; ß = 0.237; p = 0.000; VIF=1,943). These two variables imply a transfer of brand perceptions between celebrities and luxury fashion brands on one side, and destination brand on the other. Quality of Gastronomy Offer (H2; ß = 0.142; sig = 0.003; VIF=3,080) and Service Quality (H4; ß = 0.139; sig = 0.003; VIF=2,947) positively and significantly (p<0.05) influence perceived Destination Brand Luxuriousness, thus confirm­ing H2 and H4. Service and gastronomy quality are expected to correlate with the type of tourists the destination is tar­ geting. These two do not have distribution across options within a destination but are rather a characteristic of a desti­nation with similar options across the whole destination. In other words, restaurants and service workers are expected to deliver great quality in a more luxurious destination. They are ‘destination-specific’, thus by selecting a destination, tourists select a certain set of options in terms of quality of gastronomy and service quality. Unique Experience (H6; ß = 0.085; sig = 0.018; VIF=1,672) significantly (p<0.05) and positively influences perceived Destination Brand Luxuriousness. Uniqueness of experienc­es, similar to quality of gastronomy and service quality, is ‘destination-specific’, and therefore exhibits the same type of influence where destinations that can provide more unique benefits are likely to be perceived as more luxurious. These three variables are ‘destination-specific’ and have a positive impact on perceived Destination Brand Luxuriousness. The model was tested using linear regression (R2 = 0.562; p = 0.000). The results are shown in Figure 2. Figure 2. Luxury Destination Brand Model Note: *p<0.05; **p<0.01; ***p<0.001; n.s. = non-significant Discussion and Implications This research contributes to marketing and brand manage­ment literature by identifying and investigating factors that help in building and managing a luxury destination brand. There have been lot of studies that focused on destination branding and luxury branding, however, few of them dealt with luxury destination brand building phenomena. Several theoretical and managerial implications arise from the findings of this research. Theoretical implications The first important implication is that celebrities and the availability of luxury shopping are two factors that most affect the luxuriousness of a perceived destination brand. Famous people, such as actors, singers, athletes, models and successful business people, have characteristics that are appealing to many people and lifestyles that a lot of people aspire to but cannot afford. The products and services they use are considered to be luxury brands and the destina­tions they visit also match their image and lifestyle (Seo Table 1. Measures and descriptive statistics The rooms are spacious in this destination. 3,836 1,125 0.801 The beds are comfortable in this destination. 3,847 1,064 0.837 Presbury et al. (2005); A range of toiletries are available in the bathroom. 3,636 1,255 0.842 0.890 Tkaczynski & Rundle-Thiele (2011); Hwang & Heesup (2014); Maden et al. (2015); The rooms are equipped with modern technology in this destination. 3,444 1,210 0.823 Park et al. (2010); Lu et al. (2015) The rooms are well designed in this destination. 3,419 1,235 0.867 This destination has excellent food. 3,803 1,127 0.832 This destination offers a wide selection of food 3,768 1,158 0.814 and beverages. There are many high-quality restaurants in this destination. 3,742 1,128 0.809 Correia et al. (2013); Restaurants in this destination are beautifully designed. 3,723 1,045 0.839 0.909 Balakrishnan et al. (2011); Park et al. (2010); Peng & The restaurant staff in this destination are professional. 3,845 0.980 0.837 Huiling Chen, (2015); Chen et al. (2015); Daries et al. (2018) The restaurant staff can provide necessary information about the food and drinks. 3,839 1,008 0.794 This destination has better restaurants than 3,172 1,058 0.711 other destinations. Products and services are expensive in this destination. 3,248 1,227 0.915 Brun & Castelli (2013); Prices are significantly above expectations in this destination. 3,163 1,192 0.863 0.878 Maden et al. (2015); Seo & Buchanan-Oliver (2015); Park Prices are higher than the prices in other destinations. 3,164 1,203 0.922 et al. (2010) Service quality is very high in this destination. 3,606 1,057 0.878 Hwang & Heesup (2014); People working with tourists are very professional and can answer my questions. 3,787 1,043 0.898 0.841 Lu et al. (2015); Hwang & Heesup (2014); Cronin & Taylor (1992); Petrick & I think that the service quality is better than the service I would receive in other destinations. 3,374 1,042 0.842 Backman (2002); Kim et al. (2013) This destination offers a great selection of famous luxury brands. 3,126 1,337 0.941 Kinley et al. (2012); There are various shops with an excellent range of products. 3,230 1,280 0.935 0.917 Henderson et al. (2011); Balakrishnan (2009); Park et The range of famous luxury brands is better than in other destinations. 2,966 1,315 0.907 al. (2010) Table 1. Measures and descriptive statistics (cont.) This destination is unique. 3,855 1,144 0.853 This destination offers something that no other 3,643 1,181 0.866 destination can offer. This destination offers ‘something different’. 3,686 1,160 0.877 Tkaczynski & Rundle-Thiele (2011); Park et al. (2010); This destination is unique due to its historical 0.859 Dwivedi et al. (2009); 3,557 1,252 0.691 monuments. Konecnik & Go (2008); Hwang & Heesup (2014) This destination is unique due to its natural 3,821 1,059 0.666 beauty. This destination offers a unique urban 3,506 1,181 0.686 experience (architecture, entertainment). This destination is easily accessible. 3,792 1,037 0.809 Dwivedi et al. (2009); This destination is well connected. Transport links within this destination are good. 3,804 3,688 1,072 1,094 0.877 0.898 0.877 Mostafavi Shirazi & Puad Mat Som (2011); Huang & Chiu (2006); Balakrishnan et Transport links to different attractions/locations in this destination are good. 3,651 1,034 0.830 al. (2011); Th et al. (2014); Hwang & Heesup (2014) Celebrities visit this destination. 3,243 1,270 0.903 Trendsetters are frequent guests of this destination. 2,995 1,187 0.934 Um (2013); Seo & Buchanan- It is known as being a trendy destination for the rich and famous. 2,881 1,399 0.923 0.917 Oliver (2015); Ozretic-Dosen et al. (2011); Diesbach (2012); Wong & Lai (2015) Celebrities make this destination more popular. 3,061 1,358 0.847 I perceive this destination as a luxury destination. 3,031 1,297 0.938 Beverland (2006); Dubois & When describing this destination, I would mention luxury as one of its characteristics. 2,901 1,338 0.954 0.941 Duquesne (1993); Mandel et al. (2006); Nueno & Quelch (1998); Lee et al. (2018); Tak Staying in this destination provides a sense of luxury. 2,913 1,333 0.947 & Pareek (2016) Note: In addition to CFA (confirmatory factor analysis) for previously developed and validated scales; EFA (exploratory factor analysis) was performed using items for all independent variables with results exhibiting no significant cross-loadings, KMO=0,941 and Barlett’s test of sphericity: Chi-square=16429,287; df=595; Sig=0,000. & Buchanan-Oliver, 2015). Therefore, there is a spill-over effect between celebrities’ brand image to a destination’s brand image (and vice versa). This has been particularly prominent in recent years with the growing impact of social network influencers. Luxury brand management literature stresses the importance of location (where luxury brands are sold) as one of the most important factors in building and maintaining a luxury image (Tak & Pareek, 2016). Therefore, the availability of luxury shopping at a destina­tion signals that it is a prestigious location and again there is a spill-over effect of luxury image from products/service brands to the destination brand. Celebrities and the avail­ability of luxurious shopping have the greatest impact on building and managing the luxuriousness of a destination brand because they can make an association and transfer the experience of luxury. This research also reveals that the perceived luxuriousness of a destination brand is not influenced by premium price and accommodation quality, which was one of the biggest surprises. Although premium price is one of the luxury market’s main characteristics (Brun & Castelli, 2013), it seems that it is not that important for luxury destination brand building in comparison to some other characteristics. A look at the results reveals that celebrities and the availa­ bility of luxury shopping have the strongest influence on the luxuriousness of a perceived destination brand; one could argue that customers get used to high prices and/or that they are willing to pay more than they have previously. When talking about destination marketing and brand management, accommodation quality is one of the key components that needs to be taken into consideration (Presbury et al., 2005). However, according to this research, it is not significant for the luxuriousness of a destination brand. The reason for this can be found in the fact that accommodation rep­resents a substitute short-term ‘home’ for customers, hence they expect high-quality. Another reason, perhaps an even stronger one, is the development of internet services/plat­ forms such as Booking.com, Kayak and, especially, Airbnb. They have enabled that in each destination, regardless of the luxury image it wants to project, a ‘large’ part of the accommodation available is not considered to be luxurious (the possibility of booking less spacious rooms at a low price which is opposed to the perceptions and description of quality accommodation), or that premium price and accom­modation quality are something that is considered condicio sine qua non for building a luxury destination brand and, as such, is expected to be as such. Finally, another surprising finding is that transportation infra­structure has a negative impact on the perceived luxurious­ness of a destination brand. According to Henderson (2009), transportation infrastructure is indispensable for tourism as one of the key factors for destination development, but at the same time developed transportation infrastructure, such as airports and roads, increases the attractiveness of a destination. In this case, increasing the attractiveness of a destination is equivalent to wider availability, which ul­timately leads to mass tourism. In addition, the growth in low-cost airlines and their development mean that many tourism destinations are easily and cheaply available (one of the reasons why premium price is not a significant factor for the perceived luxuriousness of a destination brand), thus encouraging mass tourism. The characteristics of luxury brands are rarity, elitism and uniqueness (Walley, Custance, Copley & Perry, 2013), which is opposite to the features of mass tourism. Managerial implications To build a luxury destination brand, all stakeholders (gov­ernment, companies, people, etc.) need to be coordinated and communicate the same associations and image. Without these factors, it is practically impossible to create a luxury destination brand. From a managerial perspective, perhaps the most important implication is that transportation connectivity does not lead to the creation of a luxury destination brand. Although, transportation connectivity means a better standard of life for local people, at the same time it means that the destina­ tion is easily accessible for a lot of people. This is extremely important for local government when deciding about the number of flights and, especially, attracting low-cost carriers to the destination. Another important implication, again for local government as well as for companies, is that service quality is an im­ portant factor in building a luxury destination brand. This means that local governments should be very attentive when employing and managing people who are in direct contact with tourists (public sectors such as public transportation, medical services, police, tourism-related offices, etc.). In contrast, service quality in restaurants, hotels, entertainment premises, department stores etc. should also be at a high level because the luxury tourist segment demands it. Finally, an implication that is again directed at both govern­ments and companies is that the unique experience tourists receive at a destination forms their perception about the luxuriousness of a destination brand. This means that com­panies, with government help, need to create an atmosphere in the destination (Michelin star restaurants, availability of luxury shopping, entertainment and sport activities, cultural and natural attractions) that will, in customers’ minds, create unique memories which will turn into the perception of a luxury destination brand. Conclusions To conclude, this research indicates that the underlying principle behind whether or not a certain factor is impor­tant for the perceived luxuriousness of a destination brand lies in the extent to which a customer can create/perceive a connection between brands. The strongest influence is by factors which enable direct brand luxury transfer (availabil­ity of luxury shopping - H5 and association with celebrities - H8). Further, the strong influence is by factors which are expected to have a limited distribution of choices once a destination is selected (i.e. factors which expect to have a co-determination with the selection of the destination – H2 and H4, followed by H6). No influence was exhibited by the factors which have a distribution of options, regardless of the choice of destination that a tourist makes (rejected H1 and H3). Finally, luxury destination brand management is an important area in which not enough research has been done to date, thus this paper helps to fill the gap. However, due to an increasing number of tourists and intensive global competition, as well as the ‘new normal’ caused by the COVID-19 pandemic, it is necessary to continue doing research in this area. This research has a couple of limitations that can be seen as opportunities for future research. Given that this is one of the few papers that deals with an investigation of the factors that influence building and managing a luxury destination brand, this study should be replicated and additional factors should be included, such as sense of a security in the sense of physical security as well as health considered security in the destination which could play a crucial role in the future. 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Marketing Intelli­gence & Planning, 31(7), 823-837. https://doi.org/10.1108/MIP-09-2012-0092 Wong, J-Y., & Lai, T-C. (2015). Celebrity attachment and behavioural intentions: The mediating role of place attachment. International Journal of Tourism Research, 17, 161-170. https://doi.org/10.1002/jtr.1974 World Tourism Organization (2019). 2018 International Tourism Results. Available at: www2.unwta.org (Last accessed: June 10th, 2019). Dejavniki, ki vplivajo na dojemanje luksuznosti destinacijske znamke Izvlecek Glavni namen tega clanka je identificirati in raziskati dejavnike, ki vplivajo na izgradnjo in upravljanje luksuznih destinacijskih znamk. Na podlagi pregleda literature smo identificirali osem kljucnih dejavnikov: kakovost namestitve, kakovost gastronomske ponudbe, višji cenovni razred, kakovost storitev, luksuzno nakupovanje, edinstvena izkušnja, prometna infrastruktura in prepoznavnost. Raziskava je bila izvedena na vzorcu 619 anketirancev iz 16 držav. V raziskavi smo uporabili faktorsko in regresijsko analizo. Rezultati so pokazali, da imata prepoznavnost in dostop do luksuznega nakupovanja najpomembnejši vpliv na izgradnjo luksuzne destinacijske znamke, medtem ko je vpliv kakovosti namestitve in višjega cenovnega razreda nepomemben, prometna infrastruktura pa ima, zanimivo, negativen ter pomemben vpliv. Prirocen vzorec in možnost izpušcanja nekaterih dejavnikov (npr. varnosti) iz raziskave predstavljata glavne omejitve raziskave. Naša raziskava prispeva k literaturi o trženju in upravljanju blagovnih znamk, saj identificira in raziskuje dejavnike, ki pomagajo pri izgradnji in upravljanju luksuzne destinacijske znamke, cemur se do sedaj v literaturi ni posvecalo pozornosti. Kljucne besede: upravljanje blagovnih znamk, luksuz, destinacija, dejavniki izgradnje, znamcenje luksuza ORIGINAL SCIENTIFIC PAPER RECEIVED: SEPTEMBER 2021 REVISED: OCTOBER 2021 ACCEPTED: NOVEMBER 2021 DOI: 10.2478/ngoe-2021-0023 UDK: 58.3:338.48-053.81(497.4) JEL: F23, F62, J11, N35 Citation: Nemec Rudež, H. (2021).A Demand-Side Analysis of Intellectual Capital in the Accommodation Industry: The Case of the Youth Market in Slovenia. Naše gospodarstvo/Our Economy, 67(4), 86-95. DOI: 10.2478/ngoe-2021-0023. NAŠE GOSPODARSTVO OUR ECONOMY Vol. 67 No.4 2021 pp. 86–95 A Demand-Side Analysis of Intellectual Capital in the Accommodation Industry: The Case of the Youth Market in Slovenia Helena Nemec Rudež University of Primorska, Faculty of Tourism Studies – Turistica, Slovenia helena.nemec@fts.upr.si Abstract To date, research on intellectual capital (IC) in tourism has dealt with the supply-side view. This study continues the research into IC and develops a new perspective dealing with the demand side of IC in the accommodation industry, focusing on young consumers. On the theoretical ground, the study adapts the supply-side IC indicators to the demand-side perspective. A convenience sampling approach is used, and data was collected from 150 students representing the youth market. Exploratory factor analysis (EFA) is used to identify the dimensions of human, customer and structural capital of the accommodation industry for the youth market. Five dimensions of IC perceived by the youth market are identified: ‘Employee attitudes towards work’ and ‘Employee qualification’ constitute human capital; ‘Connectedness with guests’ and ‘Accommodation reputation and image’ represent customer capital, and ‘Structural knowledge’ reflects structural capital for youth in the accommodation industry. Keywords: intellectual capital, accommodation industry, demand-side, youth market, Slovenia Introduction Corporate knowledge invested in people, products, information systems, customer relationships and reputation enhances the value of the customer experience and creates a competitive advantage in the marketplace. Thus, it forms a company’s intellectual capital (IC). IC has attracted much attention in recent decades, includ­ing in the tourism industry. There are many definitions of it (Marr and Moustagh-fir, 2005); however, Pedro et al. (2018, p.2,518) give the general definition of IC as ‘a combination of intangible resources represented by all types of knowledge, information, intellectual property, among others, deriving from human and tech­nological resources, which are sources for the generation of value added for a country, a region, an organisation or even for an individual, forming a renewable source of competitive advantage.’ To date, IC has been studied from the supply side, including the view of managers and/or employees, which provides a partial view of IC. This study examines the demand-side view of IC. In this way, it attempts to enrich the state of research in the tourism industry in two ways. First, by examining IC from the customer side, and second, by contributing to the body of research on the youth market. Young people are gradually replacing older generations of tourists, thus representing the tourists of the future (Cavagnaro et al., 2018). Nevertheless, this market is still under-researched. Over the last two decades, a wealth of literature has emerged examining different areas of IC in the tourism industry from the supply side, initially focusing on the hotel industry (Engstrom et al., 2003; Enz, 2006; Nemec Rudež and Mihalic, 2007; Saldamli, 2008; Pulic et al., 2009; Laing et al., 2010; Kim et al., 2011; Kim et al., 2012; Zeglat and Zigan, 2014), and only later on tourism businesses in general (Gomezelj Omerzel and Smolcic Jurdana, 2016), tourism bureaus (Sharabati et al., 2013) and tourism agencies (Aboushouk and Tamamm, 2021), with IC research of the accommoda­tion industry being left behind. Moreover, research on the consumer perspective, for which IC has essentially been de­veloped and represents the competitive advantage and added value, has consistently been neglected. The aim of this paper is to identify the elements of IC from the demand-view by focusing on consumer perceptions in the accommodation industry as an alternative view of IC. The demand-side view of the content of IC and how it is perceived by consumers has not yet been explored. Accord­ ingly, the paper contributes to the literature in two ways. The first contribution is to identify the elements of IC in tourism from a consumer perspective. The second contribution is to provide a foundation for marketing strategies and the devel­opment of competitive advantages based on IC the youth market. Since consumers in the accommodation industry are very heterogeneous, which makes it difficult to study demand-side perspectives in the accommodation industry in general, this study is limited to a more homogeneous group represented by the youth market, thus providing more valid results. The need to focus IC on specific consumer segments has already been identified by Roos et al. (2001). In other words, the knowledge behind IC and its development must be targeted to specific segments. Moreover, today's youth tourists are very specific travellers who are growing up in the midst of a rapidly changing world and are very different from other travellers (Cavagnaro et al., 2018; Robinson and Schaenzel, 2019). Therefore, a study that includes the general popula­tion may yield superficial results that are unlikely to repre­sent specific segments. This paper is organised as follows. First, an overview of the previous literature on IC is given followed by a description of the methodology and data used. The third section contains an explanation of the empirical results, while the fourth covers a discussion of the findings and a description of the main conclusions. The research provides a new view of IC that can also be applied to other industries. Literature Review IC improves business performance (Bontis, 1998; Bontis et al., 2000; Engstroem et al., 2004; Enz, 2006; Nemec Rudež and Mihalic, 2007; Khalique et al., 2020; Costa et al., 2020), creates market value of a business (Sveiby, 1997; Roos et al., 1998; Brooking, 1998; Steward, 1999; Laing et al., 2010; Kianto et al., 2014), supports innovativeness and growth of tourism companies (Gomezelj Omerzel and Smolcic Jurdana, 2016), and increases a firm's competitiveness in the marketplace (Klein, 1997; Sullivan, 2000; Edvinssson, 2002; Marr and 2004, Khalique et al., 2013). Marr et al. (2004, p.566) state that ‘a long-term competitive advantage can only be gained from the management of the knowledge assets underlying organisational capabilities.’ Knowledge that is integrated into the tourism product creates value for consumers. Thus, a market-oriented company needs a good understanding of what exactly creates value for consumers in the IC context. IC and its content have been analysed from the managers’ perspective reflecting the supply side. Marr et al. (2004) reviewed components and measures of IC. Later, Aiseneberg Ferenhof et al. (2015) reviewed 83 peer-reviewed articles on IC published between 2004 and 2014 covering various industries. Only two of them (Nemec Rudež and Mihalic, 2007; Laing et al., 2010) cover the field of hospitality and tourism, which shows the lack of IC clas­ sification in tourism research. Studies (Roos et al., 1998; Stewart, 1999; Sveiby 2001) mostly use three dimensions of IC – human, customer and structural (organisational) capital. Human capital is fundamental in creating new value for cus­tomers. It includes employees’ knowledge, their competen­cies and attitudes towards work, innovativeness and intellec­tual agility of employees (Roos et al., 1998; Stewart, 1999; Bontis, 1998; Sveiby, 1997). It is a key driver of innovation and should be continuously developed to gain or maintain a competitive advantage. Although developed by human capital, structural capital represents the knowledge embodied in a firm and the firm can thus easily control it. Bontis et al. (2000, p.88) defined it as the part of intellectual capital that ‘includes all the non-human storehouses of knowledge in organisations which include the databases, organisational charts, process manuals, strategies, routines and anything whose value to the company is higher than its material value.’ According to Stewart (1999), structural capital is meant to serve two purposes. One is to preserve procedures that might otherwise be lost, while the other is to connect people with information and expertise on a ‘just-in-time’ basis. Today, it has become clear that structural capital in the tourism industry is focused on IT and various new information channels, supported by the internet. Customer capital is developed in the interaction between supplier and customer. Neither human capital nor struc­tural capital has any value unless customer capital creates the supplier-customer relationship. Following Bontis et al. (2000), customer capital is the embedding of knowledge in the supplier-customer relationship. Knowledge invested in creating customer satisfaction and customer commitment to the supplier, supplier image and reputation form customer capital (Nemec Rudež and Mihalic, 2007; Khalique et al., 2018). Some studies, such as Nemec Rudež and Mihalic (2007) and Zeglat and Zigan (2014) refer to relationship capital, which is broader than customer capital and also include knowledge built into relationships with other groups (such as business partners, the local community and government). The components of human, structural and customer capital differ slightly among different authors. According to Marr and Moustaghfir (2005), going through the previous studies of IC, it can be categorised into seven dimensions: employee skills and expertise, organisational culture, relationships with stakeholders, organisational image and reputation, technological infrastructure, intellectual property and rights, and practices and routines. IC has proven to be the key element for success in hotels and represents the basis of service quality in the hotel sector (Sardo et al., 2018). Knowledge is dynamic and flows between IC dimensions. Using data collected from hotel employees in 13 hotels in Norway, Engstroem et al. (2003) identified a strong relationship between human capital and structural capital and found that ‘hotels with both high human and structural capital will yield a greater profit’ (Ibid, p.301). Kim et al. (2011) confirmed three dimensions of IC in the Korean hospitality industry following sub-di­mensions previously also identified by Engstroem (2003) and Nemec Rudež and Mihalic (2007). Using the IC dimen­sions and sub-dimensions of Kim et al. (2011), Kim et al. (2012) reported direct impacts between the IC dimensions and direct impacts of structural (organisational) capital and customer capital on firm performance in hotels in Korea. Sharabati et al. (2013) investigated IC in Jordanian tourism organisations. Zeglat and Zigan (2014) found that rela­tionship capital, human capital and structural capital have a positive and significant influence on firm performance in upscale Jordanian hotels. As shown in these studies, to date the relevance of IC has been studied from the supply-side perspective, usually from the perspective of managers or employees. Other areas of IC need to be addressed that cover different parts of knowledge and contribute to a competitive advan­tage of accommodation today. Caring for and communi­cating with people with special needs (Rodrigues Bailoa, 2015) as well as providing a safe environment for customers (Brooking, 1998, p.16; Guthrie et al., 2006) and sustainabil­ity (Dal Mas, 2019) need to be considered when examining the demand side of IC in the accommodation industry. In light of the COVID-19 pandemic, the knowledge built up in adapting safety measures is recognised as a valuable asset for customers. Identifying how to create value for customers requires de- mand-side IC research. To date, demand side perceptions of IC in the tourism industry (and elsewhere) have not been studied. Indeed, a consumer-driven business requires a good understanding of consumer needs and wants (Kotler and Armstrong, 2008) and co-creation where consum­ers are involved in creating product value (Prahalad and Ramaswamy, 2004). In view of this, a consumer-focused company needs to really understand the consumers’ perspec­tive of IC and manage it appropriately to meet consumers’ needs and wants and create value for them. The study of consumers’ views of IC requires a focus on homogeneous groups; otherwise, the results are too general to be used for targeted IC development. The following hypothesis is proposed: Human capital, structural capital and customer capital of the accommoda­tion industry are composed of different elements from the perspective of the young customer. Since customers are the ones who value an accommodation product, the IC and its elements should be understood from the consumers’ point of view. This research offers a new perspective on IC, which can be applied to other industries. Methodology and Data In developing the questionnaire, the same approach to as­sessing IC was used as in the supply-side studies. More spe­ cifically, human, customer and structural capital variables used in previous studies examining the human, customer and structural capital (Bontis, 1998; Nemec Rudež and Mihalic, 2007; Kim et al., 2011; Zeglat and Zigan, 2014; Rastrollo-Horrilloa and Rivero Diaz, 2019), including the new IC challenges of orientation to people with special needs (Rodrigues Bailoa, 2015), safety briefings (Brooking, 1998, p.16; Guthrie et al., 2006) and sustainability (Dal Mas, 2019) are transformed and adapted from the supply-side variables to the demand-side view of consumers. Thus, the questionnaire consists of two parts. The first part explores the importance of IC including 11 human capital items, 12 customer capital items and 12 structural capital items in the accommodation industry. Respondents were asked to rate their answers on a five-point Likert scale (from 1 - not at all important, to 5 - very important). The second part deals with the socio-demographic characteristics of the respondents. All the questions were coded. A convenience sampling was used for data collection. It is a non-probability sample which is useful in an exploratory study. The study focuses on university students as represent­atives of the youth population, specifically tourism students at the University of Primorska in Slovenia. There are two reasons for choosing this population. Firstly, tourists as a whole are too diverse in terms of their characteristics, needs, desires and lifestyles to be treated as one group. Further­more, according to Han et al. (2017), a sample consisting of college students provides a relatively homogeneous sample profile and helps to achieve high internal validity of the data. Secondly, at the time this research was conducted in spring 2021 travel was largely restricted, thus making access to tourists difficult. The data were collected from tourism students during May 2021. Using the web survey software 1KA (2021), 280 students were invited to complete the online questionnaire anonymously. After completing the questionnaire, the survey data were downloaded from 1KA (2021) into SPSS (version 26) and analysed. Following Khalique et al. (2015) and Dal Mas (2019), exploratory factor analysis (EFA) with varimax rotation was used to identify the underlying factors. The human capital, customer capital and structural capital factors for the accommodation industry were identified sep­arately. The Cronbach’s alpha coefficient was used to test internal consistency of items within each factor. A total of 150 usable questionnaires were completed and used for analysis; this equates to a response rate of 54%. The profile of the respondents is shown in Table 1. The respondents were predominantly women (79 %), while 19% of respondents were male and 9% did not provide any information on gender. In terms of the age distribution of respondents, 38% were aged 19-20, 37% were aged 21-22, 9% were aged 23-24, and 6% were 25 years or older. Of the respondents, 10% did not provide details of their age. Most respondents were undergraduates (87%), while only 4% were graduate students and 9% did not provide informa­tion on their education status. The travel intensity of the re­spondents shows that 2% of respondents do not travel, while 10% of the respondents travel, but not every year. Fifty-one percent of the respondents travel on average once or twice a year, while 28% of the respondents travel on average three times a year or more. A further 9% of the respondents gave a different answer or did not answer the question. Table 1. Descriptive statistics of the respondents’ profiles Male 108 79% Female 29 19% Non-specified 13 9% 19-20 57 38% 21-22 56 37% 23-24 14 9% 25+ 86% Not specified 15 10% Undergraduate study 130 87% Graduate study 6 4% Not specified 14 9% Never travel 3 2% Not every year 15 10% Travel once to twice a year on 77 51% average Travel 3 times a year on average 41 28% or more Other or not specified 14 9% Source: own research. Research Results To identify the underlying common factors, EFA was used with varimax rotation separately for each of the three IC components: on 11 items relating to human capital, on 12 items relating to customer capital, and on 12 items relating to structural capital. Thus, the sample is large enough to conduct EFA since 150 respondents meet the requirement stated by Nunnally (1978) that the ratio of respondents to items should be at least 10:1. The eigenvalue criterion and the scree plot are used to determine the number of common factors. All item communalities are adequate fol­ lowing Taherdoost et al. (2014) and the factor loadings are above 0.6. Thus, all the items and factors are retained for further analysis. Factor loadings, eigenvalues, percentage of variance explained and Cronbach's alpha are illustrated for the selected factors (Tables 2-4). The EFA revealed a two-factor structure for human capital. The communalities of all items are above 0.5, except the com­munality for the item ‘The staff speaks a (foreign) language that I understand’, which has a communality of 0.432, which is sufficient according to Taherdoost et al. (2014). The Cron­bach’s alpha values for the two factors were 0.893 and 0.676 (Table 2). Following Wim et al. (2008 in Hajjar, 2018), the Cronbach’s alpha above 0.6 is acceptable. The two factors explained 60.082% of the total variance across the 11 var­ iables included. The first identified factor is interpreted as ‘Employees’ attitudes towards work’ and consists of 8 items (eigenvalue = 4.434; variance explanation = 40.308%). This factor gives a very high grand mean value of 4.3, suggesting that people are a very important pillar of accommodation in the youth market. The second factor of human capital is titled ‘Employee qualification’ and includes three varia­bles related to employee qualification (eigenvalue = 2.175; explained variance = 19.774%). The grand mean score of 3.5 for these items indicate that young people do not rate employee qualification as very important. The mean values of the variables range from 2.9 to 4.1. The communalities for the items of customer capital are all above 0.5 with the exception of the communality for the item ‘The accommodation is suitable for the disabled’, whose value of 0.488 is still sufficient following Taherdoost et al. (2014). In terms of customer capital, two factors with eigenvalues greater than 1.00 emerged, explaining 69.1% of the total variance (Table 3). The first factor is named ‘Connectedness with guests’ (eigenvalue = 4.701; explained variance = 39.178%) with a Cronbach’s alpha of 0.911; this reflects the different aspects of interaction and connection between accommodation and guests and includes eight items. For young guests, it is important to build up a bond with the accommodation and its staff, as is also shown by the grand mean value of the items included in the factor (3.9). The second factor is called ‘Reputation and image of the accommodation’ and consists of four items related to the awareness and reputation of the accommodation (eigenvalue = 3.586; explained variance = 29.887) with a Cronbach’s alpha of 0.676. The grand mean value of this factor is very low (2.5), which shows that knowledge invested in the image and reputation of accommodation is not important for today’s youth market. All communalities of structural capital items are above 0.5. The EFA conducted on 12 items of structural capital yielded one factor solution, suggesting that all items fit a single the­oretical construct. All communalities are above 0.5. A single factor with quite high loadings summarises all the diverse aspects of structural knowledge embodied in a company. The factor is titled ‘Structural knowledge" (eigenvalue = 9.105; explained variance = 75.875%). The Cronbach’s alpha is 0.970. This factor results in a high grand mean value of 4.1, which shows the importance of this type of knowledge in the youth market. Since the lowest item means of structural capital are related to the latest market trends (mean = 3.3), innovativeness (mean = 3.4), sustainability (mean = 3.8) and guest satisfaction information for further improvement (mean = 3.3), it can be seen that the focus of structural knowledge is mainly on service performance and information access. Discussion and Conclusion This study provides insights into the customer view of IC and its structure. The empirical findings show the consum­er-based view of IC and confirm the existence of five IC dimensions that serve further research in academia and help accommodation and other tourism providers to better under­stand and develop IC and improve various aspects of the creation of IC. The first finding is that consumers perceive IC through five dimensions; ‘Employees’ attitudes towards work’ and ‘Employee qualification’ comprise human capital, ‘Con­nectedness with guests’ and ‘Reputation and image of the accommodation’ represent customer capital, and ‘Structural knowledge’ reflects structural capital. Comparing the com­ponents of IC with the study by Marr and Moustaghfi (2005), some parallels emerge with customer capital. They also divided customer capital into the dimensions of customer relationship and image and reputation. Human capital, on the other hand, seems to be divided into employees' com­petence and attitudes towards work from the perspective of young people, a division similar to Roos et al. (1998), which remains an important pillar of intellectual capital in tourism, as also highlighted by Abdullah and Othman (2019). Moreover, employee attitudes towards work show a stronger importance for the selected target group, as is illustrated by the higher mean scores of the respondents. Structural capital is considered more integral. Young people now in their early twenties, no longer distinguish between IT and other parts of embedded knowledge, as was the case in studies formulated two decades ago from the supply-side perspective. The second finding concerns human capital as perceived by customers. Employees’ attitudes towards work represent an important dimension for young people, who place less emphasis on employee qualifications. This does not diminish the importance of staff qualifications and experience and in line with Khalique et al. (2020) who state that human capital is ‘non-substitutable’, staff qualifications are essential in developing staff attitudes towards guests. The third finding is that the youth market is not concerned with the reputation and image of the accommodation, but rather the intensity of the various aspects of the bond between the accommo­dation provider and the guest is important. Accommodation providers targeting the youth market should invest in knowl­edge that fosters the relationship between accommodation and guests. The fourth finding shows that today's youths Table 2. Factor analysis results of human capital do not distinguish between the internet and IT aspects of structural capital and other, more traditional aspects of struc­tural capital (such as culture, coordinated work, following the latest trends, etc.). The reason for this is that they have grown up heavily networked with IT and social media, and therefore regard them as an integral part of structural capital. The proposed hypothesis is thus partially confirmed. Human capital and customer capital each consist of two different elements, while structural capital is perceived as one-dimen­sional in the youth market, contrary to expectations. The staff try to solve my problems when they arise. 0.791 4.6 The staff do their job conscientiously. 0.773 4.4 I feel comfortable with the staff in the 0.728 4.4 accommodation. The staff communicate well with guests. 0.794 4.5 The staff do their work on time. 0.795 4.3 The staff work together creatively to solve problems. 0.696 4.0 The staff speaks a (foreign) language that I 0.642 4.1 understand. I can see that the staff enjoy their work. 0.651 4.0 The staff know my needs and requirements well. 0.742 3.5 The staff are sufficiently qualified to do their job. 0.772 4.1 The staff are experienced. 0.740 2.9 KMO = 0.881, Bartlett’s test of sphericity = 743.85 at d.f. = 55 with a significance of p = 0.000 Source: own research. Compared with previous studies on the classification of IC (Bontis, 1998; Nemec Rudež and Mihalic, 2007; Kim et al., 2011; Zeglat and Zigan, 2014; Rastrollo-Horrilloa and Rivero Diaz, 2019), there are similarities between the sup-ply-side view and the demand-side view of IC. However, the results show that the demand-side view of IC should be investigated in addition to the supply-side management view. IC perceptions of targeted consumer segments would enhance the understanding of IC in the accommodation industry and give an integrated view of IC. Theoretical and practical implications are drawn from the study. From a theoretical point of view, the study highlights that IC should be analysed from both the supply and demand sides. It can expose the gap between the two views and per­ceptions of what constitutes a competitive advantage and helps to close the gap. From a managerial point of view, the findings are useful for tourism managers in increasing competitive advantage based on IC. There are some IC components that are rated as very important, while others are perceived as more average in importance. Therefore, development efforts for the youth market should be guided by the factors that have been highlighted as more impor­ tant: ‘Employees’ attitudes towards work’, ‘Connectedness with guests’ and ‘Structural knowledge’. Communication, relationships and digital marketing seem to be the most important aspects in which to invest and develop in order to address the youth market in the accommodation industry. In light of the present findings, young people are less con­cerned with staff qualifications and the reputation and image of the accommodation, as they are not the core IC for the youth market. In other words, knowledge invested in dif­ ferent aspects of human relations and broader IT use is a crucial component in creating and developing a competitive Table 3. Factor analysis results of customer capital Taking care of guests is the focus of the staff. 0.793 4.1 Information about the accommodation is available 0.875 4.4 online. The accommodation responds quickly to online 0.881 4.3 questions from guests. The accommodation is active on social networks. 0.748 3.7 Positive things are written on social networks. 0.784 4.1 The accommodation is suitable for the disabled. 0.601 3.7 Discounts are offered to regular guests. 0.696 3.5 Customers are thanked for positive feedback on 0.572 3.7 social networks. The accommodation is part of a well-known chain. 0.895 2.3 The accommodation is widely known. 0.864 2.4 The accommodation has a good reputation. 0.829 2.7 The accommodation is large. 0.817 2.4 KMO =0.896, Bartlett’s test of sphericity = 1260.21 at d.f. = 66 with a significance of p = 0.000 Source: own research. Table 4. Factor analysis results of structural capital The service is in line with the latest market trends. 0.777 3.3 Booking is easy. 0.898 4.4 Check-in and check-out are easy. 0.929 4.5 There is a pleasant atmosphere in the 0.917 4.5 accommodation. Operation of the accommodation is sustainable. 0.808 3.8 Reasonable guest requirements are solved quickly. 0.929 4.3 There is a quick response to complaints. 0.941 4.3 Guest satisfaction information is collected for further 0.743 3.3 improvement. The accommodation offers innovative products. 0.772 3.4 Safety measures are taken. 0.894 4.5 The work of the staff is coordinated. 0.893 4.2 Relevant information is given to guests in time. 0.917 4.5 KMO =0.942, Bartlett’s test of sphericity = 2355.15 at d.f. = 66 with a significance of p = 0.000 Source: own research. advantage when addressing the youth market. Knowledge management and IC that deepen relationships with guests require attention in academia and among accommodation providers. The findings cannot be generalised to the entire population because this exploratory study focused on young people. Since a more homogeneous group of people can provide relevant insights for a specific segment suitable for targeted marketing, the study is limited to the perception of IC in the youth market. Despite the study's limitation to the youth market, it reveals IC perceptions for the youth market and provides a good basis for extended research to other segments. Indeed, IC should address specific segments, their desires and lifestyles within the target marketing. Research replications with other segments would give an understand­ing of the importance of IC. Research can be extended to include other sectors of the tourism industry to identify customers’ perceptions for the purposes of targeted product design and IC. Although this study provides a relevant understanding of IC from the customer's perspective, there is still a gap in the un­derstanding of IC by other stakeholders (e.g. investors, regulators), as previously discussed by Krambia-Kapardis and Thomas (2006). References 1KA (Verzija 21.02.16). (2021). Software. Ljubljana: Fakulteta za družbene vede. Available at: https://www.1ka.arnes.si. Abdullah, N. N. & Othman, M. B. (2019) Examining the effects of intellectual capital on the performance of Malaysian food and beverage small and medium-sized enterprises. 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Value driven intellectual capital: how to convert intangible corporate assets into market value. New York: John Wiley & Sons. Sveiby, K. E. (1997). The New Organizational Wealth: Managing & Measuring Knowledge-Based Assets. San Francisco: Berrett-Koehler Publishers. Taherdoost, H., Sahibuddin, S. & Jalaliyoon, N. (2014). Exploratory Factor Analysis; Concepts and Theory. Jerzy Balicki. Advances in Applied and Pure Mathematics, 27, WSEAS, 375-382, Mathematics and Computers in Science and Engineering Series, 978-960-474-380-3 Zeglat, D. & Zigan, K. (2014). Intellectual capital and its impact on business performance: Evidence from the Jordanian hotel industry. Tourism and Hospitality Research, 13(2), 83-100. https://doi.org/10.1177/1467358413519468 Intelektualni kapital v nastanitveni dejavnosti s strani povpraševanja: primer trga mlajših potrošnikov Izvlecek Raziskave o intelektualnem kapitalu (IK) v turizmu so se do sedaj ukvarjale s pogledom s strani ponudbe. Clanek nadaljuje z raziskovanjem IK s strani povpraševanja v nastanitveni dejavnosti, s poudarkom na mladih potrošnikih. Pricujoca raziskava na osnovi teoreticnih izhodišc ponudbene strani IK prilagaja kazalnike IK strani povpraševanja. Uporabljen je pristop priložnostnega vzorcenja. Podatki za raziskavo so bili zbrani s strani 150 študentov, ki predstavljajo trg mlajših potrošnikov. Eksploratorna faktorska analiza (EFA) je uporabljena za identificiranje dimenzij cloveškega, odjemalskega in strukturnega kapitala v nastanitveni dejavnosti. Identificiranih je pet dimenzij IK, ki so zaznani s strani mlajših potrošnikov: "Odnos zaposlenih do dela" in "Kvalifikacija zaposlenih" predstavljata cloveški kapital; "Povezanost z gosti" in "Ugled in imidž nastanitve" predstavljata odjemalski kapital, "Strukturirano znanje" pa odraža strukturni kapital za mlajše potrošnike v nastanitveni dejavnosti. Kljucne besede: intelektualni kapital, nastanitvena dejavnost, stran povpraševanja, trg mlajših potrošnikov, Slovenija ORIGINAL SCIENTIFIC PAPER RECEIVED: JULY 2021 REVISED: OCTOBER 2021 ACCEPTED: NOVEMBER 2021 DOI: 10.2478/ngoe-2021-0024 UDK: 336.76:330.143(65) JEL: C13, C52, M41 Citation: Kimouche, B. (2021). The Effect of Stock Market Listing on Real Earnings Management: Evidence From Algerian Companies. Naše gospodarstvo/ Our Economy, 67(4), 96-107. DOI: 10.2478/ngoe-2021-0024. NAŠE GOSPODARSTVO OUR ECONOMY Vol. 67 No.4 2021 pp. 96–107 The Effect of Stock Market Listing on Real Earnings Management: Evidence From Algerian Companies Bilal Kimouche The Université 20 Août 1955-Skikda-Algérie, Faculty of Economics, Commerce Sciences and Management Sciences, Algeria b.kimouche@univ-skikda.dz Abstract This paper aims to explore the effect of the Algiers Stock Exchange listing on real earnings management. The study included 14 non-financial non-listed companies during the period 2015-2019 and six non-financial listed companies during the period 2010-2019. Due to the small number of companies listed on the Algiers Stock Exchange, the period of study was extended in the case of listed companies to provide enough observations. The measurement of real earnings management is based on the model of abnormal cash flows from operations (Roychowdhury, 2006), while the hypothesis testing is based on a model of multiple linear regression. The results indicate that the company size and the nature of financial statement (consolidation) do not have any effect on real earnings management in Algerian companies. The results are not consistent with the author’s hypothesis about the positive effect of stock market listing on real earnings management. The empirical evidence suggests that the Algiers Stock Exchange listing has had a negative effect on real earnings management in Algerian companies. This might be due to the scrutiny of auditors and regulators as the number of companies is easy to control, which decreases the opportunity for Algerian companies to rely on real earnings management in accordance with the opportunistic or informational view. Keywords: real earnings management, stock market listing, company size, consolidation, Algerian companies Introduction The main objective of accounting practice is to provide useful information for the users of financial statements to make decisions and allocate resources (IASB, 2018). To this end, conceptual framework and accounting policies and rules are developed carefully by standard-setting bodies to guide managers towards improv­ing the quality of accounting. However, that objective may not correspond with the objectives of managers and their interests as suggested by the agency theory (Jensen & Meckling, 1976). As a result, managers can engage in some practices to affect the financial statements in accordance with their specific objectives or to reassure users about their interests or provide signals to users about private information. Real earnings management is a widely used practice to ma­ nipulate financial statements, especially since implementation of the Sarbanes Oxley-Act in 2002 (Graham et al., 2005), and the reinforcement of internal control systems and transpar­ency by institutional bodies and companies. Real earnings management is usually used whether for opportunistic or informational objectives to change the form and content of financial statements, thus affecting users' perceptions of the company and their decisions. Previous studies have looked into numerous aspects of real earnings management, including the determinant factors, suggesting that earnings management is determined by many factors, such as the financial and economic characteristics of companies. Stock market listing is a characteristic that can be useful to classify companies in terms of real earnings man­agement or explain the differences between companies in this regard. Investigation of stock market listing as a potential de­ terminant of real earnings management can be justified by the many differences between listed and non-listed companies in terms of governance, financing resources, capital structure, ownership, consolidation of financial statements, institutional control and supervision, disclosure and transparency require­ ments, size, and users of financial statements. Since the 1990s Algeria has been undergoing a long transition period to shift from socialism to capitalism and integrate into the international economy. This shift has been accompanied by deep economic and institutional reforms to keep up with the new economic reality. These reforms imposed an account­ing reform to satisfy the needs of users who require different attributes of accounting quality within the new environment. As is the case in other countries, managers of Algerian com­panies have a wide range of incentives to manipulate earnings based on operational decisions, especially under the Financial Accounting System, which was adopted in 2010. Algeria has a young stock market that currently only includes six companies, because the majority of companies in Algeria are family and SMEs. The Algiers Stock Exchange is less active and less efficient compared to developed countries; however, it provides a complete database of market in­ formation and financial reports of companies. To date, no studies have been done to explore the effect of Algiers Stock Exchange listing on accounting practices in Algerian compa­ nies. Therefore, this study relates to the effect of stock market listing on real earnings management in Algerian companies through asking the following question: Has the Algiers Stock Exchange listing had an impact on real earnings management in Algerian companies? The purpose of this study is to explore whether the managers of Algerian companies employ real earnings management to manipulate financial statements and the extent to which real earnings management is employed. This study also aims to examine whether the Algiers Stock Exchange listing had an influence on the extent of real earnings management. The results indicate that the stock market listing has had a negative effect on real earnings management in Algerian companies, which reflects the reality of the Algerian institu­tional and economic environment, where the preparation of financial statements is primarily intended to meet the legal requirements rather than serving investors. The results also reflect the predominance of the scrutiny of auditors and reg­ulators as the number of companies is easy to control, which decreases the opportunity for Algerian companies to rely on real earnings management in accordance with an opportunis­tic or informational view. This paper contributes to the empirical research on real earnings management in several ways. On the one hand, it interested in the stock market listing as a determinant of real earnings management, to which not enough attention has been paid in previous studies, and also provides supple­mentary empirical evidence about real earnings management determinants. The study was conducted in Algeria, which is a developing country, providing additional evidence about earnings management practices in different environments. The remainder of this paper is structured as follows. Section 2 is a literature review, Section 3 clarifies the method and ma­terials used, Section 4 summarises the results and discussion, and Section 5 contains the conclusion. Literature Review and Hypotheses Previous studies Studies about earnings management determinants is a common research stream in accounting (Fakhfakh & Nasfi, 2012; Charfeddine, Riahi & Omri, 2013; Sousa Paiva & Costa Lourenço, 2016; Priharta & Rahayu, 2019; Abdullahi et al., 2020; Saleh, Abu Afifa & Haniah, 2020; Tran & Duong, 2020; Ghaleb, Kamardin & Tabash, 2020), which allows fluc­tuations in earnings management levels to be understood as well as managers’ behaviour in terms of earnings manage­ment. In this regard, many variables have been used as deter­minants, such as indebtedness, company size, performance, ownership structure, dividend policy, leverage, performance, audit quality, etc. Even though the literature uses many deter­ minants from the financial market (prices, returns, volume, etc), few studies were found that investigate stock market listing as a determinant of earnings management. As a result, through this literature the author tried to include studies that touch on any dimensions of this study. Burgstahler, Hail & Leuz (2006) examined whether capital market pressures and institutional factors motivate compa­ nies to report earnings that reflect economic performance, using the level of earnings management as a dimension of accounting quality. The study included 378,122 firm-year observations from private and public, non-financial compa­nies, across 13 countries of the European Union, during the period 1997-2003. The results revealed that private com­panies present higher levels of earnings management, and the strong legal systems are associated with lesser earnings management in private and public companies. They also sug­gested that private and public companies respond differently to institutional factors (book-tax alignment, outside investor protection, capital market structure). Lopes, Tukamoto & Galdi (2007) investigated the impact of cross-listing and adjustments towards the US GAAP on earnings management practices in Brazilian companies. Employing 1,026 firm-year observations during the period 1996-2003, and using five proxies of earnings management, they found evidence that neither cross-listing nor adjustments towards the US GAAP have impacted earnings management in Brazilian companies. Eng & Lin (2012) examined the quality of financial reporting of Chinese companies cross-listed in the United States and Hong Kong comparing the non-cross-listed Chinese compa­nies, based on earnings management, timely loss recognition, and price-earnings association as measures of financial re­porting quality. They indicated that both cross-listed and non-cross-listed companies showed significant earnings smooth­ing and earnings management, while they did not record any evidence about timely loss recognition practices. They concluded that the market considers the earnings and book value data of cross-listed companies to be more informative than those of non-cross-listed companies in the event of good news. Cornanic & Novak (2015) studied earnings management as a means of avoiding delisting from a stock market, using a sample of 47,599 firm-year observations for 2,532 compa­nies during the period 1998-2011. The results showed that companies in danger of being delisted from a stock market (NASDAQ) report higher performance-adjusted discretion­ ary accruals, and the inflated accruals are associated with an increased likelihood of maintained listing. Mellado-Cid, Jory & Ngo (2017) analysed real earnings man­agement by companies prior to their debt issuances and how they affect the bond yield spreads. The sample size employed consists of 5,608 firm-quarter observations, which is equivalent to 1,578 companies, during the period 1980-2012. According to the results, bond-issuing companies increased their real earnings management practices in the five quarters leading up to bond issuance. Additionally, an inverse associ­ation between the yield spread and pre-issue real activities manipulation was observed. Nuanpradit (2018) tried to explore the relationship between in­formation asymmetry and sales activities-based real earnings management, and the effect of CEO-Chairman duality on that relationship in Thailand. The study included 3,582 firm-year observations from the Stock Exchange of Thailand during the period 2001-2015. The findings revealed that informa­tion asymmetry increases real earnings management and that CEO-Chairman duality leads to real earnings management in companies without information asymmetry problems. Dang, Hoang & Tran (2018) investigated the factors that impact accrual-based earnings management (AEM) and real earnings management (REM) in Vietnam. The study included 260 companies listed on the Vietnam Stock Exchange during 2012-2016. The results showed that consolidation of financial statements, financial performance, financial leverage, and stock issuance have positive impacts on AEM, while audit has a negative impact on AEM. In contrast, financial perfor­mance and financial leverage have a positive impact on REM. Haga, Höglund & Sundvik (2018) analysed real earnings man­agement among privately-owned versus publicly listed com­ panies in the UK, using 90,594 private firm-year observations and 3,688 public firm-year observations for the years 2006 to 2014. The findings suggested that public companies engage in more earnings management through operating activities. According to the explanations, capital market pressure and ownership characteristics drive the results. Alawag (2021) explored the real earnings management behavior in the context of a parent-subsidiary relationship, by comparing between business groups and companies without any controlled subsidiaries. The study used the random-ef­fects generalised least squares (GLS) estimation, including 8,501 parent and nonparent companies from 73 countries, during the period 215-2019. The results showed that ultimate parent companies have lower abnormal production costs and abnormal discretionary expenses than nonparent companies. In contrast, parent companies have higher abnormal operating cash flow than non-parent companies. In Algeria, and within the limits of this literature review, there is no study directly in relation to the subject of the present study, other than a few studies that looked into some of the determinants of accounting and real earnings management. Kimouche & Cherroun (2020) were interested in the effect of financial leverage on accounting earnings management in Algerian companies. They employed the financial statements of 14 Algerian companies during the period 2006-2018, using a multiple linear regression model and panel data to test the hypotheses. According to the results, financial leverage does not have any effect on accounting earnings management in Algerian companies. However, the debt cost has a significant and positive impact on accounting earnings management. Kimouche & Charchafa (2020) examined whether financial performance and activity level have an effect on real earnings management in Algerian companies. The study included 82 firm-year observations for 17 Algerian companies during 2015-2019, using a multiple linear regression model that relates real earnings management, measured as the abnormal operating cash flows, with the financial performance and activity level. The results suggested that the effect of financial performance on real earnings management is not significant. However, they suggested a negative and significant effect of the activity level on real earnings management in Algerian companies. Kimouche & Cherroun (2020) analysed the impact of debts and their cost on real earnings management in Algerian com­ panies. The study used the model of Roychowdhury (2006) to measure real earnings management, and a multiple linear regression model to test the hypotheses, and included 119 firm-year observations for 17 Algerian companies during the period 2012-2018. According to the results, debts and the cost of debts do not have any impact on real earnings management in Algerian companies, which contradicts most studies, due to the specificities of the economic and institutional Algerian environment. Kimouche & Charchafa (2021) explored the effect of ac­counting accruals on real earnings management in Algerian companies, starting from the view that there is a negative re­lationship between accounting earnings management and real earnings management. The study included 100 observations relating to 20 companies from 2015 to 2019. The test of the hypothesis was based on a model under the form of linear regression that relates real earnings management proxy to accounting accruals. The results confirmed the assumption, suggesting a negative and statistically significant effect of accounting accruals on real earnings management in Algerian companies. It is foreseen that this study will be useful both for literature and in practice. On the one hand, it relates to the stock market listing as a determinant of real earnings management, which has not received enough attention in studies to date. The stock market listing is a prominent variable that can substantially affect the governance system in companies and their relation­ships with the external environment and also their obligations in terms of disclosure and transparency. On the other hand, this study was carried out in Algeria, which is a developing country, providing additional evidence about earnings man­agement practices in different environments. Real earnings management concepts Earnings management is an accounting practice that is widely used by managers, whether intentionally or not, in order to affect the form and content of financial statements, which can affect their quality, thus changing the views of users towards the company and their assessments for its future perspectives. Schipper (1989) defined earnings management as a “purpose­ful intervention in the external financial reporting process with the intent of obtaining some private gain”. Watts & Zimmerman (1990) used an opportunistic view to describe earnings management as the use by managers of different methods and interpretations in financial accounting to achieve certain objectives, instead of having a faithful presentation of the company's financial position. According to Abad et al. (2018), earnings management can be achieved through managerial discretion in the application of account­ing standards or by changing the timing or structuring of real transactions. Healy & Wahlen (1999) also focused on the opportunistic view considering that earnings management occurs when managers use judgment in financial reporting and in struc­turing transactions to alter financial reports to either mislead some stakeholders about the underlying economic perfor­ mance of the company or to influence contractual outcomes that depend on reported accounting numbers. Since the mid-1980s, studies have traced an explosive growth in accounting earnings management practices by managers (Beneish, 2001). These practices are accrual-based, so they are easy to execute since accruals are the main product of GAAP and the result of accounting decisions related to the accounting policies and estimations. Accounting earnings management includes some techniques that are conducted in accordance with the accounting rules and principles, through the selection of appropriate accounting policies, and the use of estimations and judgments in the application of those ac­counting policies, in order to control the level of earnings, which affects the performance of the company and its fi­nancial position in terms of form and content (Kimouche & Cherroun, 2020). Following the implementation of the Sarbanes Oxley-Act in 2002, which was introduced to increase transparency and support internal control, Cohen, Dey & Lys (2008) stated that the use of accounting earnings management significant­ly declined. Consequently, real earnings management has become an alternative, as it is difficult for auditors and regula­tors to discover because it can be identified as a legal response to the economic conditions (Cohen, Dey & Lys, 2008; Cohen & Zarowin, 2010; Kothari, Mizik & Roychowdhury, 2012; Abad et al., 2018). The increasing trend towards real earnings management is confirmed by Graham, Harvey, and Rajgopal (2005), who provide evidence that the financial officers of US public firms recognise that most earnings management actions are carried out via real actions. Roychowdhury (2006) described real earnings management as departures from the normal operational practices of a company, which are motivated by the desire of managers to mislead all or some stakeholders that the financial reporting objectives have been met. According to Cooper, Downes & Rao (2017), real earnings management refers to the action of managers to change a company’s real activities by their operating, investment, and financing decisions to affect the financial position of a company and its financial performance. Real earnings management is distinct from accounting earnings management because accrual-based earnings man­agement involves discretionary accounting choices allowed within accounting regulation with no direct cash flow effects. On the contrary, real earnings management comprises real business activities that have a direct influence on cash flows from operations. The impact of real earnings management on performance derives from cash flows, not from accruals as is the case for accounting earnings management (Janin, 2000). Real earnings management can be achieved through many managerial decisions related to the operation, such as sales manipulation (price discounts, flexible credit terms for cus­tomers), overproduction to report a lower cost of sales, and discretionary expenditure (general and administrative, selling, and advertising expenditures). Managerial decisions for real earnings management also contain the investment decisions such as the sales of fixed assets and a reduction in research and development expenditure, as well as financing decisions such as stock options, stock repurchases, hedges, debt-equity swaps, and securitisation. Existing literature has usually used two perspectives to explain earnings management motivations, the ‘Opportun­ istic view’ and the ‘Informational view’. The first assumes that managers employ earnings management to achieve self-benefits through misleading users or disclosing informa­tion consistent with their expectations about the performance and financial position of the company. The second assumes that earnings management is a means to reduce information asymmetry by providing relevant information for users and provide a signal for the financial market about the company's future perspectives, in order to improve its valuation. Based on the ‘Positive Accounting Theory’ of Watts & Zim­merman (1978) and the works about the ‘Value Relevance’ of accounting information (Lev, 1989; Amir, Harris & Venuti, 1993; Francis & Schipper, 1999; Barth, Beaver & Landsman, 2001), earnings management motivations can be classified into three groups: • Contractual motivations: where contracts between a company and stakeholders depend on financial state­ments, thus managers seek to provide accounting infor­mation consistent with contractual clauses to increase their incentives, improve their reputation or comply with debt covenants. • Financial market motivations: where managers tend to affect stock prices, especially in the lead-up to IPOs and stock offerings, and disclose earnings consistent with analysts' forecasts or management estimates, to prevent any significant volatility in stock prices in the short term. • Institutional motivations: that result from the expected relationship between accounting information and gov­ernmental decisions leading managers to manipulate earnings to reduce income tax payments or avoid any potential legislation (antitrust, environment protection, consumer and employee rights, etc.), thus reducing po­litical costs. Hypotheses development The explanations for the effect of stock market listing on real earnings management can be found in the financial market motivations of earnings management that correspond with the informational view. In this regard, earnings management can be useful, as it can potentially improve the informativeness of earnings by conveying private information on the future prospects of the company, which is beneficial to investors (Dechow & Skinner, 2000; Jiraporn et al., 2008; Siregar & Utama, 2008; Scott, 2011). Earnings management is also a means for managers to signal their expectations about the company's future performance and its cash flows. Compared to non-listed companies, listed companies have more motivations to employ earnings management. The main explanation for this is to reduce the conflict of interest between shareholders, creditors, and managers (Mitchell, Agle & Wood, 1997). According to Ambrose & Bian (2010), the in­formation contained in stock price volatility leads managers to avoid regulatory costs through earnings management. The adjustment of earnings figures through earnings management is also expected to reassure users of financial statements on the continuity and profitability of business operations, thus increasing the company's stock liquidity (Hieu et al., 2019). Earnings management may allow managers to maintain a good reputation with stakeholders, as it can affect positively stock prices and company value (Change & Warfield, 2005; Chen et al., 2006; Charfeddine, Riahi & Omri, 2013; Chen, Cheng & Wang, 2015). For these reasons, it can be expected that managers of listed companies have more tendencies to manage earnings; hence this study starts from the following hypothesis: Hypothesis: The Algiers Stock Exchange listing has had a positive effect on real earnings management in Algerian companies. Method and Materials Data collection This study covered 14 non-financial non-listed companies during the period 2015-2019 and six non-financial listed com­panies during the period 2010-2019. Due to the small number of companies listed on the Algiers Stock Exchange, the period of study in the case of listed companies was extended to provide enough observations for the model estimation. The only criterion used in the selection of the studied com­panies was the availability and accessibility of their financial statements, due to the secrecy and caution that characterise corporate governance in Algerian companies. The financial statements were collected through direct contact with the chief financial officers of companies. Model for hypotheses testing The model used in this study takes the form of a linear regres­sion that relates real earnings management (REMit) with the stock market listing (COTit), the nature of financial statements (NFSit), and company size (SIZEit) as shown in Equation (1). NFSit and SIZEit were added as control variables, because the consolidation procedures can affect the accounting practices of a company, whether in the separate or consolidated finan­cial statements, while accounting practices differ according to company size. REM˜.°.COT°.NFS°.SIZE°. it01it2it3itit(1) Where REMit is the extent of real earnings management; COTit is the listing on the Algiers Stock Exchange; it takes 1 if the company is listed on the Algiers Stock Exchange in the fiscal year, otherwise 0. NFSit is the nature of financial statements; it takes 1 if the company presents consolidated financial statements for the fiscal year, otherwise 0. SIZEitis the company size at the end of the period calculated as the logarithm of total assets divided by ten. a0, a1, a2 are the re­gressions coefficients; .it is the error term. Measurement of real earnings management The measure of real earnings management is based on the abnormal cash flows from operations (Roychowdhury, 2006) as shown in Equation (2). CFO1S itit °.....NFS 012itAit˜1Ait˜1Ait˜1 (2) ˆS ..itNFS.. 3itit A it˜1 Where CFOit is the net cash flows from operations for the period; A is the total assets at the end of the period; S is it-1itthe sales for the period; ß0, ß1, ß2, and ß3 are parameters; dit is the error term, which represents the abnormal cash flows and is used as a proxy for real earnings management (cash-based earnings management). Results and Discussion Descriptive statistics Table 1 presents the descriptive statistics for 124 firm-year observations of different variables. The table shows that the data includes 52 firm-year observations for listed companies versus 72 for non-listed companies, and 48 firm-year obser­vations for companies with consolidated financial statements versus 76 for companies with separate financial statements. For SIZE, a comparison between the mean and the median suggests that the data are normally distributed, unlike REM where the mean is more than the median, indicating that real earnings management practices measured by abnormal cash flows represent an average of 7.83% of the total assets. Ac­cording to the standard deviations, even though SIZE is more dispersed than REM, the dispersion of data is acceptable. Correlation analysis The results of correlation between different variables are summarised in Table 2, which indicates a negative medium and statistically significant relationship at 1% level between the Algiers Stock Exchange listing and real earnings manage­ment, which means that Algerian listed companies practice less real earnings management than non-listed companies. In contrast, the relationships of the nature of financial statements and company size with real earnings management are not statistically significant. The relationships between different independent variables are not statistically significant, except that between the nature of financial statements and the Algiers Stock Exchange listing, which is positive medium and statis­ tically significant at a level of 1%. Table 1. Descriptive statistics for the variables Observations 124 52 72 48 76 124 Mean 0.0783 ----0.7556 Median 0.0594 ----0.7441 Std. 0.0642 0.0964 Deviation Minimum 0.0015 0 0 0.6126 Maximum 0.2761 1 1 0.9959 Source: Depending on SPSS. Table 2. Correlation results Pearson Correlation 1 -0.376* -0.013 -0.071 REM Sig. (2-tailed) 0.002 0.884 0.431 Pearson Correlation 1 0.353* -0.019 COT Sig. (2-tailed) 0.000 0.830 Pearson Correlation 1 0.012 NFS Sig. (2-tailed) 0.896 Observations 124 124 124 124 *. The correlation is significant at 0.01 (2-tailed). Source: Depending on SPSS. Univariate analysis The univariate analysis was based on a test, in terms of real earnings management, of the differences between the listed and non-listed companies on the one hand, and the compa­ nies with consolidated financial statements and companies with separate financial statements on the other hand. The Mann-Whitney test was employed, which is used to make comparisons between independent groups when data distribu­ tion is not normal. The results shown in Table 3 indicate that the mean of real earnings management for listed companies (0.057) is less than those for non-listed companies (0.093) and that the difference is statistically significant at a level of 1% according to the Mann-Whitney test (Sig. = 0.000). The results also indicate that the mean of real earnings manage­ ment for companies with consolidated financial statements (0.077) is less than those for companies with separate finan­cial statements (0.079) and that the difference is not statisti­ cally significant according to the Mann-Whitney test (Sig. = 0.954). Table 3. Mann-Whitney test results Mean(0) 0.093 0.079 Mean(1) 0.057 0.077 U de Mann-Whitney 1154 1870 W de Wilcoxon 2429 3301 Z -3.545 -0.058 Sig. asymptotic (2-tailed) 0.000 0.954 a.Grouping variable: COT. b. Grouping variable: NFS. Source: Depending on SPSS. Regression model estimation results Table 4 summarises the results of the Model (1) estimation, which shows that the model of study is statistically significant at a level of 1% (Sig. = 0.010), as the calculated F statistic (3.965) is more than the F critical value in the table. As a result, the Adjusted R-squared is medium and reached 0.339, thus the independent variables in Model (1) explain 33.90% of the changes in real earnings management, and consequent­ly, the remainder of the changes in real earnings management (66.10%) can be explained by errors and other variables. In terms of the regression coefficients, it appears that the co­efficient related to the company size (SIZE) is not statistically significant (Sig. = 0.369), suggesting that company size does not affect real earnings management, or in other word, there is no difference between big and small companies in terms of real earnings management. The coefficient related to the nature of financial statements (NFS) is also not statistically significant (Sig. = 0.097), therefore, the consolidation process does not have any effect on real earnings management, thus, in terms of real earnings management, there are no differenc­ es between companies with consolidated financial statements and those with separate financial statements. The results indicate that the coefficient related to the Algiers Stock Exchange listing is statistically significant at a level of 1%, where the calculated T statistic (3.347) is more than the T critical value from the table. The coefficient reached 0.041 with a negative sign, suggesting that the Algiers Stock Exchange listing has had a negative effect on real earnings less real earnings management than non-listed companies, management. Thereby, Algerian listed companies practice which contradicts the hypothesis of the study. Table 4. Factor analysis results of structural capital (Constant) 0.129 2.889 0.005 ­ COT -0.041 -3.347 0.001 1.143 1 0.339 3.965 0.010 1.889 NFS 0.020 1.679 0.097 1.143 SIZE -0.052 -0.902 0.369 1.001 a.Dependent variable: REM. Source: Depending on SPSS. Finally, the author tested the validity of Model (1) through the Ordinary Least Squares (OLS) assumptions. As illustrated in Table 4, the Variance Inflation Factors (VIF) are very low for all variables, which is consistent with the results of correla­tion about the absence of any significant and strong correla­tion between the independent variables, thus the criterion of multicollinearity is met. Table 4 also shows that the calculated Durbin-Watson value reached 1.889 and is situated between Table 5. Normality test results for the residuals 2 and the upper critical value (dU = 1.774). This means that the criterion of no autocorrelation (no linear dependence) between residuals is met. Table 5 shows the results of the normality test for the residuals, indicating that the Kolmog­orov-Smirnov test and Shapiro-Wilk test are not statistically significant, neither at a level of 1% nor at 5%. Therefore, the residuals of Model (1) are normally distributed. Standardised Residual 0.103 124 0.200 0.773 124 0.063 a.Lilliefors Significance Correction. Source: Depending on SPSS. Table 6. Homoscedasticity test results for the residuals BP 8.024 0.056 Koenker 6.311 0.097 Null hypothesis: heteroscedasticity not present (homoskedasticity) if sig-value less than 0.05, reject the null hypothesis. Source: Depending on SPSS. In terms of Table 6, which shows the results of the homosce­dasticity test for the residuals, it appears that the Breusch-Pa­ gan test and Koenker test are not statistically significant, neither at a level of 1% nor at 5%. Therefore, the residuals of Model (1) are homoscedastic. According to these results, the OLS assumptions are met; hence the validity of Model (1) is confirmed. According to the results of this study, company size does not have any effect on real earnings management, which is not the case for most of the previous studies that suggest differences between companies in terms of real earnings management according to their size. This can be due to the structure of the Algerian economy, where the majority of companies are SMEs or family firms, and therefore their managers are subject to the same motivations of real earnings management. In terms of the nature of financial statements, it is supposed that accounting for consolidation provides managers with more flexibility to manipulate earnings. However, this is not the case in Algerian companies, where the results of study revealed no effect of consolidation on real earnings man­agement. This can be explained by the objectives of consoli­dated financial statements that tend to serve investors, while in Algeria the preparation of financial statements – whether consolidated or separated – is primarily aimed at meeting the legal requirements. Finally, this study indicates that stock market listing has a negative effect on real earnings management, This finding differs from the results of Lopes, Tukamoto, and Galdi (2007) and Eng and Lin (2012), who found that cross-list­ing did not have an effect on earnings management, and Cornanic and Novak (2015), Mellado-Cid, Jory, and Ngo (2017), Dang, Hoang, and Tran (2018), and Nuanpradit (2018), who found that stock market listing had a positive effect on real earnings management. The positive effect is due to the opportunistic motivation to maintain the listing or to achieve the expectations of managers or analysts, or to the informational motivation to reduce information asym­metry, providing relevant information for users, and signal the stock market about the future perspectives of a company. In Algeria, the explanations are different, where the negative effect of listing on real earnings management might be due to the scrutiny of auditors and regulators as the number of companies is easily controllable. Additionally, the listed companies are large and have clearly defined rules of gov­ernance including the board of directors, internal audit and audit committee, which limit the practices of real earnings management. Moreover, the users of financial statements of listed companies are conscious of, and have the ability to analyse information or consult financial analysts, and they can also obtain information from other sources. Conclusion The determinant(s) of earnings management is a common research field in accounting for the purpose of explaining fluctuations of earnings management and understanding managers’ behaviour in terms of earnings management. In this regard, this paper relates to stock market listing as a determinant of real earnings management due to the im­portance of stock market listing in the explanation of the behaviour of managers and their choices and explains the disparity between companies in terms of numerous char­acteristics. Stock market listing is a prominent attribute because listed companies are exposed to different conditions than non-listed companies, such as institutional control, su­pervision by market committees, reporting and transparency requirements, control of analysts and investors. Due to the importance of stock market listing, this paper explored the effect of the Algiers Stock Exchange listing on real earnings management. The study included 14 non-fi­nancial non-listed companies during the period 2015-2019 and six non-financial listed companies during the period 2010-2019. Due to the small number of companies listed on the Algiers Stock Exchange, the period of study in the case of listed companies was extended to provide enough observations for the model estimation. The study was based on the model of abnormal cash flows from operations (Roy­chowdhury 2006) to measure real earnings management, and used the nature of financial statements (consolidated or separate) and the company size as control variables. The data analysis employed descriptive statistics, correlation, the Mann-Whitney nonparametric test, and multiple linear regressions. The results indicate that company size and the nature of financial statements (consolidation) do not have an effect on real earnings management in Algerian companies, which contradicts the results of most previous studies. This can be explained by the particularities of the Algerian economy, which is mostly composed of SMEs and family firms. These results can also be assigned to the accounting culture in Algeria, where the preparation of financial statements – whether consolidated or separated – is primarily intended to meet the legal requirements rather than serving investors. The results are not consistent with the hypothesis of this study on the positive effect of the Algiers Stock Exchange listing on real earnings management. The empirical evidence of this study suggests that stock market listing has a negative effect on real earnings management in Algerian companies. This is due to the scrutiny of auditors and regulators as the number of companies is easy to control, which decreases the opportunity for Algerian companies to rely on real earnings management in accordance with the opportunistic or infor­mational view. Despite the importance of this study, it is limited in terms of its methodology due to the small sample size and the use of only one indicator of real earnings management (abnormal cash flows), where existing literature has distinguished between many practices of real earnings management in­cluding production costs and discretionary expenses that cannot be reflected by abnormal cash flows. Consequently, future studies must extend the sample size and use other in­dicators of real earnings management and other explanatory or control variables such as industry, ownership structure, governance, and debt. Auditors of Algerian companies must pay more attention to real earnings management practices through the valuation of the internal control system, es­pecially for non-listed companies, in order to enhance the quality of financial statements. Algerian regulators must put in place mechanisms of control for non-listed companies to reduce earnings management practices. References Abad, M. 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(2016). Determinants of Earnings Management in the Hotel Industry: An International Perspective. Corporate Ownership & Control, 14(1-3), 449-457. https://doi.org/10.22495/cocv14i1c3p5. Tran, K. H., & Duong, N. H. (2020). Earnings Management to Avoid Earnings decreases and Losses: Evidence from Vietnamese Listed Companies. Cogent Economics & Finance, 8(1), 1849980. https://doi.org/10.1080/23322039.2020.1849980. Watts, R. L., & Zimmerman, J. L. (1978). Towards a Positive Theory of the Determination of Accounting Standards, The Accounting Review, 53(1), 112-134. Ucinek kotacije na borzi na upravljanje realnih dohodkov: dokazi iz alžirskih podjetij Izvlecek Namen tega clanka je raziskati ucinek kotacije na borzi v Alžiru na upravljanje realnih dohodkov. V študijo je bilo vkljucenih 14 nefinancnih podjetij, ki niso kotirala na borzi v obdobju med letoma 2015 in 2019, in 6 nefinancnih podjetij, ki so kotirala na borzi v obdobju med letoma 2010 in 2019. Zaradi majhnega števila podjetij, ki kotirajo na borzi v Alžiru, smo za podjetja, ki so kotirala na borzi, podaljšali obdobje študije, da smo lahko prišli do zadostnega števila spoznanj. Merjenje upravljanja realnih dohodkov temelji na modelu nenormalnih denarnih tokov iz poslovanja (Roychowdhury, 2006), medtem ko preverjanje hipoteze temelji na modelu veckratne linearne regresije. Rezultati kažejo, da velikost podjetja in narava racunovodskega izkaza (konsolidacija) ne vplivata na upravljanje realnih dohodkov v alžirskih podjetjih. Rezultati niso skladni z našo hipotezo o pozitivnem ucinku kotacije na borzi na upravljanje realnih dohodkov. Empiricni dokazi kažejo, da kotacija na borzi v Alžiru negativno vpliva na upravljanje realnih dohodkov v alžirskih podjetjih. To je lahko posledica nadzora s strani revizorjev in regulatorjev, saj je število podjetij enostavno nadzorovati, kar zmanjšuje možnost za alžirska podjetja, da se zanesejo na upravljanje realnih dohodkov v skladu z oportunisticnim ali informacijskim pogledom. Kljucne besede: upravljanje realnih dohodkov, kotacija na borzi, velikost podjetja, konsolidacija, alžirska podjetja NAVODILA AVTORJEM INSTRUCTIONS FOR AUTHORS Revija Naše gospodarstvo / Our Economy objavlja znan­ stvene clanke iz vseh podrocij ekonomije in poslovnih ved. Avtorje vabimo, da v uredništvo revije pošljejo originalne prispevke, ki še niso bili objavljeni oziroma poslani v objavo drugi reviji. Avtorji podeljujejo lastniku revije ekskluziv- no pravico za komercialno uporabo clanka, ki stopi v veljavo na osnovi sprejetja clanka v objavo. Avtorji v celoti odgovar­jajo za vsebino prispevka. Objavljamo samo clanke, ki dobijo pozitivno oceno naših recenzentov. Revija avtorjem ne zaracu­ nava stroškov objave. Prispevki naj bodo napisani v angleškem jeziku. Na posebni strani navedite ime avtorja, njegov polni akademski ali strokovni naziv ter ustanovo, kjer je zaposlen. Prva stran naj vsebuje naslov, izvlecek (maksimalno 250 besed) in kljucne besede, vse troje v slovenskem in angleškem jeziku. Iz izvlecka naj bodo razvidni namen, metodologija/pristop, ugotovitve, omejitve, implikacije in izvirnost/vrednost. Dodajte tudi ustrezne kode JEL klasifikacije, ki jih najdete na https://www.aeaweb.org/econlit/jelCodes.php?view=jel. Prispevek naj bo v dolžini ene avtorske pole (30.000 znakov). Za poudarke v besedilu uporabljajte poševni tisk, ne krepkega ali podcrtanega tiska. Izpis naj bo enokolonski. Sprotne opombe naj bodo oštevilcene in navedene na dnu pripadajoce strani. Oštevilcite tudi enacbe. Morebitne tabele in slike naj bodo crno-bele in oštevilcene ter naslovljene nad, opombe in viri pa pod tabelo oziroma sliko. Vse tabele in slike pošljite tudi v izvornih datotekah (.xls, .ppt in podobno). Vire v tekstu in v seznamu virov je potrebno urediti skladno z APA standardom – navodila na http://www.apastyle.org/learn/ tutorials/basics-tutorial.aspx. Nekaj osnovnih napotkov: Navedbe virov v tekstu Primer 1a: Another graphic way of determining the stationari­ ty of time series is correlogram of autocorrelation function (Gujarati, 1995). Primer 1b: Another graphic way of determining the stationari­ ty of time series is correlogram of autocorrelation function (Gujarati, 1995, p. 36). Primer 2a: Engle and Granger (1987) present critical values also for other cointegration tests. Primer 2b: Engle and Granger (1987, p. 89) present critical values also for other cointegration tests. Navedbe virov v seznamu virov Primer 1 – Knjiga: Gujarati, D. N. (1995). Basic Econometrics. New York: McGraw-Hill. Primer 2 – Clanek v reviji: Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation and Testing. Econometrica, 55(2), 251-276. Primer 3 – Poglavje v knjigi, prispevek v zborniku: MacKinnon, J. (1991). Critical Values for Cointegration Tests. In R. F. Engle & C.W. J. Granger, (Eds.), Long-Run Economic Relationships: Readings in Cointegration (pp. 191-215). Oxford: University Press. Primer 4 – Elektronski vir: Esteves, J., Pastor, J. A., & Casanovas, J. (2002). Using the Partial Least Square (PLS): Method to Establish Critical Success Factors Interdependen­ce in ERP Implementation Projects. Retrieved from http://erp. ittoolbox.com/doc.asp?i=2321 Avtorji naj navedejo DOI številke virov, ce te obstajajo. Prispevek pošljite v MS Word datoteki na e-naslov nase.gospo­darstvo@um.si ali our.economy@um.si. Dodajte še celotni poštni naslov in elektronski naslov vseh avtorjev, za korespondenc­ nega avtorja pa še telefonsko številko, preko katere je dosegljiv uredništvu. The journal Naše gospodarstvo / Our Economy publishes scientific articles covering all areas of economics and business. Authors are invited to send original unpublished articles which have not been submitted for publication elsewhere. Authors are completely responsible for the contents of their articles. Only articles receiving a favorable review are published. The authors grant the Journal Owner the exclusive license for commercial use of the article throughout the world, in any form, in any language, for the full term of copyright, effective upon acceptance for publication. The journal does not have article processing charges (APC) nor article submission charges. Please write your text in English. The cover page should include the author's name, academic title or profession, and affiliation. The first page must contain the title, an abstract of no more than 250 words, and key words. The purpose, methodology/approach, findings, limitations, implications and originality/value should be evident from the abstract. Add also appropriate codes of JEL classification that can be found at https://www.aeaweb.org/ econlit/jelCodes.php?view=jel. The length of the manuscript should be composed of 30.000 characters. Emphasized parts of the text should be in italics, not bold or underlined. The text should be in single column layout. Footnotes should be numbered consecutively and placed at the bottom of the relevant page. Equations should be numbered. Tables and figures should be in black and white colour, numbered with a title above and notes and sources below. All tables and figures should be sent also in original files (.xls, .ppt and similar). References in the text and in the list of references should be arranged according to APA style – see http://www.apastyle.org/learn/ tutorials/basics-tutorial.aspx. Some elementary directions: References in the text Example 1a: Another graphic way of determining the station­ arity of time series is correlogram of autocorrela­tion function (Gujarati, 1995). Example 1b: Another graphic way of determining the station­ arity of time series is correlogram of autocorrela­tion function (Gujarati, 1995, p. 36). Example 2a: Engle and Granger (1987) present critical values also for other cointegration tests. Example 2b: Engle and Granger (1987, p. 89) present critical values also for other cointegration tests. References in the list of references Example 1 – Book: Gujarati, D. N. (1995). Basic Econometrics. New York: McGraw-Hill. Example 2 – Journal article: Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation and Testing. Econometrica, 55(2), 251-276. Example 3 – Book chapter or article from conference proceedings: MacKinnon, J. (1991). Critical Values for Cointegration Tests. In R. F. Engle & C.W. J. Granger, (Eds.), Long-Run Economic Relationships: Readings in Cointegration (pp. 191-215). Oxford: University Press. Example 4 – Web source: Esteves, J., Pastor, J. A., & Casanovas, J. (2002). Using the Partial Least Square (PLS): Method to Establish Critical Success Factors Interdependence in ERP Im­plementation Projects. Retrieved from http://erp.ittoolbox.com/ doc.asp?i=2321 Authors should state DOI numbers of references, if they exist. Send the manuscript in MS Word file to nase.gospodarstvo@ um.si or our.economy@um.si. Add also postal address and e-mail address of all authors, while for the corresponding author, please, add also a phone number. LETNIK67 VOLUME