Original Scientific Article Measuring Performance in the Hospitality Sector: Financial vs. Statistical Data Tanja Planinc University of Primorska, Faculty of Tourism Studies - Turistica, Slovenia tanja.planinc@turistica.si Štefan Bojnec University of Primorska, Faculty of Management, Slovenia stefan. bojnec@fm-kp.si Gordana Ivankovič University of Primorska, Faculty of Tourism Studies - Turistica, Slovenia gordana.ivankovic@turistica.si This paper analyses achievements of the hospitality sector in the Slovenian tourism industry by comparing statistical developments and certain financial data results. The analysis was performed for the 1995-2011 period. The statistical results were relatively favourable in terms of the number of tourist arrivals, overnight stays and accommodation facilities. However, the empirical analysis confirmed that less favourable financial results in the Slovenian hospitality sector were achieved even before the economic crisis in 2008. The analysis showed that there is a statistically significant correlation only between certain financial and statistical indicators. Keywords: hospitality sector, Slovenia, performance measurement, financial indicators, statistical data Introduction As an economic activity, tourism plays a prominent role in national economies (Bojnec, 2004). It provides many opportunities for employment, including self-employment, because in many cases this activity is not associated with high initial investment requirements and business risks (Vanhove, 2005). The majority of countries in the world recognize the importance of tourism and almost 130 members of World Trade Organization have made a commitment to open their tourism sector in the desire to attract foreign direct investment (oecd, 2008). The tourism industry is one of the world's largest industries and one of the fastest growing service industries. Due to its labour intensity, it is one of the main generators of employment, partic- ularly in remote and rural areas (wto, 1998). Because of all its positive impacts on economic growth and development, several governments support and promote tourism development (Ivanov & Webster, 2007). Service industries, and among them tourism and hospitality, are gaining importance in national economies; they are, therefore, a subject of research throughout the world (Chenhall, 2003). Hospitality is a significant sector of the tourism industry. According to the wttc (2011), in 2011 there were about 12.7 million hotel rooms worldwide. In Table 1, some basic facts on hospitality worldwide are presented. As evident from Table 1, hotels in Europe reached the highest occupancy rate in 2011. In Europe, the Académica Turística, Year 6, No. 1, June 2013 | 39 Tanja Planinc, Štefan Bojnec, and Gordana Ivankovič Measuring Performance Table 1 Global Hotel Index for 2011 Region (1) (2) (3) Europe 66.3 99.86 66.l7 Americas 60.2 80.58 48.53 Middle East/Africa 57-1 125.83 71.87 Notes Column headings are as follows: (1) occupancy (%), (2) average daily rate (€), (3) revenue per available room (€). Source 'str Global Releases World Hotel Performance Results 2011' (2012). highest jump in all three indicators was reported in Venice, Italy ('str Global Releases World Hotel Performance Results 2011,' 2012). As far as Slovenian hospitality sector is concerned, the average occupancy rate in 2011 was 43.1 percent (Slovenska turistična organizacija, 2012). Data for other two indicators are not available for Slovenia. In 2011, the total contribution of tourism to employment in Slovenia was 110,800 jobs or 13.1 percent of Slovenian employment, whilst the total contribution to gross domestic product (gdp) was €4.68 billion or 12.8 percent of Slovenian gdp (wTTC, 2013). The main focus of this study is to evaluate the viability and reliability of each performance measurement approach in the hospitality sector in the case of Slovenia. Moreover, with our analysis we aim to answer our research question: is there a statistically significant correlation between financial performance and statistical tourism development indicators of the Slovenian hospitality sector? The rest of the paper is organized as follows. We first explain the meaning and the importance of measuring the financial performance and then the importance of tourism statistics in the system of national accounts. Next, we briefly present the methodology of the analysis. Finally, the main results are presented and explained, focusing on the statistical and financial indicators that are calculated for the Slovenian hospitality sector. Recommendations for further work are presented in the final section of the paper. The Importance of Tourism Statistic Tourism statistics play a vital information role, because (among other things) they can also reflect the level of economic development of the country. Throughout the world as well as in Europe, there are many international organizations (e.g., World Tourism Organization, World Travel & Tourism Council, International Association of Scientific Experts in Tourism, Eurostat) that deal with the statistical monitoring of tourism. It is in the interest of each country to closely monitor the individual sectors of the economy. Among other things, this enables them to estimate how each sector will develop in the future. In Slovenia, there are several organizations involved in statistical data collection and monitoring of the tourism sector (e.g. the Statistical Office of the Republic of Slovenia (sors), the Agency of the Republic of Slovenia for Public Legal Records and Related Services (ajpes), the Bank of Slovenia and the Slovenian Tourist Board). These organizations also issue various publications on tourism topics. ajpes is an indispensable primary source of official public and other information on business entities in Slovenia. For a financial analysis, focusing on how Slovenian business entities have operated over a longer time period (from 1994 onwards), this agency offers access to a database of complete financial statements and the most influential financial indicators about companies, cooperatives, sole proprietors and associations. In a desire to measure the contribution of tourism to the national economy, a tourism satellite account (tsa) has been developed by United Nations World Tourism Organization and the Organisation for Economic Co-operation and Development (European Commission, 2011). In 2009, sors monitored the effects of tourism via the tsa methodology (Kalin, 2012). Based on the intensive development of tsa, we can assume that traditional tourism statistics (which are based on flows: number of tourists, overnight stays) became insufficient to analyse the tourism sector. Specifically, debate on the adequacy of statistical data when presenting performance of Slovenian tourism regularly appears in the media (Lipovsek, 2010, 2011a, 2011b, 2012; Pihlar, 2012). In our opinion, statistical data should serve as an introduction or as a basis to performance analysis. Both types of data (statistical and financial) should be used in a combination in order to obtain 40 | Académica Turística, Year 6, No. 1, June 2013 Tanja Planinc, Štefan Bojnec, and Gordana Ivankovič Measuring Performance the full picture of the performance of the hospitality sector. Measurement of Financial Performance A business success is defined as an income or a benefit that the company wants to achieve by carrying out commercial activities (Koletnik, 1997). The goal of measuring financial performance is to determine how efficiently the business has functioned, and to compare and evaluate that performance to specific benchmark measures (Hales, 2005). This is also true for measuring the financial performance of a specific sector of the national economy (Planinc, Bojnec, & Planinc, 2012). Keown, Martin, and Petty (2010) stated that the most appropriate way to measure financial performance is with the use of financial ratios. They give managers basic information and the ability to identify the strengths and weaknesses of a company's performance. According to Otley (2002), there are three main functions for financial performance measures: as a tool of financial management; as a major objective of a business organization; as mechanisms for motivation and control within the organization. Financial ratios can be calculated using the accounting statements, such as the balance sheet, income statement, cash flow statement (Wheelen & Hunger, 2005). According to Harris and Mangiello (2006), performance measurement is also an essential component of the decision-making processes. Meyer (2002) asserts that a perfect performance measurement should consist of relatively few measures to keep track of; that the same measures would apply everywhere; that the measurement system would be stable; and that the non-financial measures would be leading performance indicators. He then also explains the practical reasons such a perfect measurement does not exist: companies operate with too many measures; it is difficult to pinpoint non-financial measures that predict financial performance; non-financial measures are never static; compensating people for performance on multiple measures is extremely difficult. With the usage of a uniform accounting system, it became possible to determine industry norms on regional, national and international levels. Furthermore, annual industry statistics were produced on the ba- sis of uniform accounting systems. This means that annual performance indicators and analysis of industry trends became available (Harris & Brander Brown, 1998). There is a large volume of published studies describing the role of financial analysis of hotel performance. Baker and Riley (1994) suggest the use of ratios; Donaghy, McMahon and McDowell (1995) propose the use of yield management in order to analyse the efficiency of hotel management. Many studies have determined that budgeting as an indispensable tool for performance measurement in the hospitality industry (Atkinson & Brown, 2001; Cruz, 2007; Hak-tanir & Harris, 2005). Meyer (2003) examines seven purposes of performance measurement, which are divided into two groups; one group is more common for most organized firms (look ahead, lookback, motivate, compensate). The other group of purposes is more significant among larger and complex firms (the roll-up, the cascade-down, to compensate). In contrast, however, Phillips (1999) stated that the hotel industry relies too heavily on budgeting as a tool for performance measurement. Furthermore, budgeting is also criticised (Atkinson & Brown, 2001; Hansen, Otley, & Van der Stede, 2003). Ghalayini and Noble (1996) argue about problems when using only financial measures. One of their claims is that traditional performance measures tend to quantify performance, although there are many factors that are difficult to quantify; financial reports are expensive to prepare, closed monthly and, therefore, too old to be useful; measures are not related to corporate strategy. There are also studies of performance comparisons in the hotel industry (Pine & Phillips, 2005). Furthermore, Hua, Nusair, and Upneja (2012) in their study suggest using industry medians to benchmark financial performance in order to determine the financial outer performance of a lodging firm. Hotel performance was also viewed from the investor viewpoint. Gu (1994) studied the risks and returns in a 10-year comparative measurement. He concluded that hotels do not represent good investment opportunities. Along the way, a shift to the use of non-financial measures has been made. Kaplan and Norton (1992) propose their Balanced Scorecard, combining financial 40 | Académica Turística, Year 6, No. 1, June 2013 Tanja Planinc, Štefan Bojnec, and Gordana Ivankovič Measuring Performance measures with operational measures, consequently yielding a comprehensive view of the business. The hotel industry took a step forward in the implementation of Uniform System of Accounts for the Lodging Industry (usALi). usALi is now in its 10th edition, and its main purpose is the establishment of a uniform responsibility accounting system for the lodging industry ('Uniform System of Accounts for the Lodging Industry,' 2007). Numerous countries have implemented this standard, but Slovenia, unfortunately, is not one of them. According to Guilding (2012), this standard represents the first attempt to implement a uniform accounting system in a specific business, and it also allows comparison across hotels. Kavčič and Ivankovič, 2003 researched implementing us ali standards in Slovenia. Their main finding is that according to the existing accounting system in Slovenia, a number of accounting categories defined by usali are not possible to calculate or further convert. In 2009, a report on performance measurement of hospitality companies in Slovenia was issued. A model for performance measurement was proposed and authors provided internationally comparable indicators for measuring the performance of hotel companies (Mihalič, 2009). After the literature review, it was obvious that in order to obtain the full picture of performance of the hospitality sector, it is necessary to take into consideration not merely the financial indicators. This is in line with the finding of Meyer (2003, p. 30) that 'no single measure provides a complete picture of the performance of the organization.' In our opinion, this can also be true for a specific sector of a national economy. Methodology In order to obtain an answer to our research question, we analysed the financial indicators on the basis of data provided by ajpes and the statistical tourism development indicators obtained from sors. The financial analysis was performed for companies for the 1995-2011 period. The average number of companies in the hospitality sector was 203, while the average number of employees was 6,672 or 32.91 per company (ajpes, 2013). We included those financial indicators that are related to the business performance of enterprises in the hospitality sector: return-to-equity (roe), return-to-assets (roa), total revenue, total revenue per employee, and value-added per employee. We also analysed labour costs and average net monthly salary. In addition to the financial indicators, we analysed the main statistical tourism development indicators of the Slovenian tourism sector (e.g., number of tourist arrivals, overnight stays, and tourist beds). Statistical data for the years 2010 and 2011 are gathered according to the new methodology (owing to the break in time series), which was introduced by sors. The nominal financial data were deflated to the constant 1995 prices as the base year in order to obtain real values of financial indicators over the analysed years. The deflator for value of inflation was obtained from sors. In the next step, we calculated if there are statistically significant correlations between financial performance indicators and statistical tourism development indicators of the Slovenian hospitality sector. We used spss statistical software to conduct this analysis. The value of the correlation coefficient can be between -1.0 and +1.0. In the first case, we would have perfect negative correlation among variables, while in the second case we would have perfect positive correlation (Veal, 1997). The correlation coefficient does not imply a causal relationship between the variables, but only indicates that there is a correlation between variables (Buckingham & Saunders, 2007). Finally, to conclude our empirical analysis, we performed a partial regression analysis in order to predict the value of our dependent variable. Results and Discussion As far as tourism accommodation facilities are concerned, the results indicate that the number of rooms ranged from nearly 28,000 rooms in 1995 to almost 45,000 rooms in 2011. By far the most rooms are in hotels. In 1995, 49.51 percent of registered rooms were in hotels, while by 2011 this percentage had slightly declined and stood at almost 49.20 percent. Figure 1 shows the number of overnight stays and arrivals of all tourists to Slovenia in the 1995-2011 period. It can clearly be seen that the numbers are constantly increasing. The average annual rate of growth for overnight stays is at 3.05 percent, while for tourist arrivals is at 4.64 percent. As a consequence, there is a decline in the average length of stay of tourists. 40 | Académica Turística, Year 6, No. 1, June 2013 Tanja Planinc, Štefan Bojnec, and Gordana Ivankovič Measuring Performance Number of overnight stays 10,000,000 9,000,000 8,000,000 7,000,000 6,000,000 5,000,000 4,000,000 3,000,000 2,000,000 1,000,000 - 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Figure 1 Number of Overnight Stays and Arrivals for the 1995-2011 Period (sourca: sors, 2013) Number of arrivals Table 2 Number of Employees in Hotels and Similar Accommodation Facilities Item 1995 1996 1997 1998 1999 2OOO 2OOl 2OO2 2OO3 2OO4 2OO5 2OO6 2OO7 2OO8 2OO9 2OlO 2Oll n 5,536 5,633 5,468 5,3l4 5,947 6,46l 6,363 6,287 6,34O 6,578 6,959 6,966 7,889 8,O76 8,l37 7,962 8,Ol5 Tk 1-75 -2-93 -2.82 ll.9l 8.64 -1.52 -1.19 O.84 3-75 5-79 O.lO 13-25 2-37 O.76 -2.15 O.67 Notes n - number of hotels and similar accommodation facilities, Tk - annual rate of growth. Source: ajpes (2013). Table 2 shows the number of employees in hotels and similar accommodation facilities in the 1995-2011 period. As can be seen, the number of employees has increased. However, the annual rate of growth (Tk) has varied by individual years from declines in some years (e.g., 1997-1998, 2001-2002 and 2009-2010) and increases in other analysed years, particularly in 1999 and 2007. On average, the annual rate of growth was 2.47 percent. Table 3 shows financial indicators in hotels and similar accommodation facilities in real 1995 amounts in euros. A quick view onto the selected financial indicator in nominal amounts reveals that the values are on the rise. However, inflation is increasing faster than the values of some financial indicators. The consequence is that the real values are increasing much more slowly (total revenue and labour costs) or are declining (total revenue per employee, value added per employee and monthly salary). We also found that the average annual rate of growth of total revenue is lower than the average annual rate of growth of the number of employee (1.57 and 2.47, respectively). This does not seem to be a strong signal as far as the efficiency of employees is concerned. We can also see that the roe and roa indicators worsened in the year 2007, i.e. before the economic and financial crisis. Therefore, it cannot be argued that the financial results have worsened only due to the global economic and financial crisis. The values of both financial indicators are unfavourable for hotels and similar accommodation facilities. Therefore, managers in hotels and similar accommodation facilities should concentrate their efforts on improving the value of both financial indicators. An important step in this direction is to determine the causes of operating costs, and then managers should find ways to rationalize them (Ivankovič, Jerman, & Jankovič, 2009). The values of the value-added per employee are presented only for the 2002-2011 period, because ajpes started to calculate this financial indicator in 2002. This indicator is a fundamental measure of an economic activity success, as it shows how much value-added was created by each employee in the company. The higher is the indicator value (assuming that the company has a profit), the higher is the quality of the products and services (ajpes, 2010; Kavčič et al., 2005). In 1998, Slovenian tourism managers stated that Slovenian tourism was lagging behind in creating added value (Gomezelj Omerzel, 2006). In 2009, the Slovenian Chamber of Commerce and Industry 40 | Académica Turística, Year 6, No. 1, June 2013 Tanja Planinc, Štefan Bojnec, and Gordana Ivankovič Measuring Performance Table 3 Financial Indicators in Hotels and Similar Accommodation Facilities (in Real 1995 Amounts in Euros) (1) (2) (3) (4) (5) (4) (5) (6) l995 l85,286,l95 33,469 -2.2 -l.7 6l,6O8,O37 682 l996 l9l,5l3,87O 33,998 -2.8 -2.0 57,964,222 625 l997 l77,OO6,943 32,372 -5.5 -3.8 52,99l,496 6l4 l998 l54,lO4,825 29,OOO -3.9 -2.7 47,52l,895 544 l999 l59,796,959 26,87O -1.4 -l.O 53,l2O,582 54O 2OOO l7l,6l8,493 26,562 -2.O -l.4 54,223,767 5Ol 2OOl l65,52l,32l 26,Ol3 0.1 0.1 52,859,7O5 499 2OO2 l73,978,386 27,673 O.7 O.4 5l,265,29O 23,83O 488 2OO3 l68,29l,554 26,544 l.9 l.2 5O,338,259 22,532 473 2OO4 l76,Oll,389 26,758 -l.5 -0.1 53,524,O36 22,9O6 481 2OO5 l83,285,577 26,338 -O.5 -O.3 59,l92,O52 22,7l5 5O5 2OO6 l95,l83,658 28,Ol9 2.5 l.4 53,246,l38 24,7O9 509 2OO7 224,293,O68 28,431 2.O 1.1 56,776,766 24,389 5l5 2OO8 238,O94,983 29,482 -2.6 -l.3 66,449,37O 24,313 554 2OO9 227,992,2Ol 28,Ol9 -3.7 -l.7 73,6O7,58l 23,27l 553 2OlO 23l,925,lO3 29,l3O -6.4 -2.7 72,263,523 22,838 554 2Oll 229,744,889 28,666 -8.4 -3-6 70,966,937 22,922 538 Notes Column headings are as follows: (1) year, (2) total revenue, (3) total revenue per employee, (4) roe (%), (5) ROA (%), (6) labour costs, (7) value added per employee, (8) monthly salary. Source: ajpes (2013). conducted a survey on the state of breakthrough innovation in Slovenia. It was envisaged that in order to achieve breakthroughs in innovation, Slovenia should create new jobs that meet the added value-of €50,000, and restructure jobs that generate less than €30,000 of added value (Lozar, 2009). Labour costs have increased over the analysed period. The hospitality sector is a labour-intensive activity and the human factor is essential in performing tourist services. The labour costs in Slovenia have been over-burdened by taxes and social contributions (Vodopivec, Dolenc, Vodopivec, & Balde, 2007). Kosi and Bojnec (2010) found that in Slovenia the tax burden on labour is more than 40 percent, which ranks Slovenia among the countries with the highest tax burdens among Mediterranean countries. The results of high tax burdens are lower net salaries (Daneu, 2010). Such a high tax burden on labour has a negative impact on the competitiveness of Slovenia as a tourist destination (Kosi & Bojnec, 2010). In addition, we analysed whether there are statistically significant correlations between the financial indicators and statistical performance indicators of the Slovenian hospitality sector. Table 4 presents the results. It is evident that in some cases there is a statistically significant correlation among selected indicators. There is a strong positive correlation between the number of tourist arrivals and total revenues. This means that the higher number of tourist arrivals is reflected in the higher total revenues. Nevertheless, the coefficients are relatively far from being 1.0, which calls for additional analysis. A closer look at the numbers reveals that the average growth of revenues in the analysed period is 1.57 percent, while the average growth of arrivals is 4.64 percent. This leads us to think that either the prices of accommodations are falling or that tourists are spending less on accommodations. There is also a strong positive correlation between the number of tourist arrivals and the number of employees. Again, we can assume that a higher number 40 | Académica Turística, Year 6, No. 1, June 2013 Tanja Planinc, Štefan Bojnec, and Gordana Ivankovič Measuring Performance Table 4 Correlation Coefficients among Selected Indicators Indicators (1) (2) (3) (4) (5) (6) (7) Number of arrivals p 0.804** o.942** o.673** -0.234 o.ol8 -o.332 -o.372 Sig. o.ooo o.ooo o.oo3 o.365 o.947 0.l92 o.l42 N 17 17 17 17 17 17 17 Number of overnight stays p o.77o** o.929** o.637** -o.l97 o.o58 -o.37l -o.4l7 Sig. o.ooo o.ooo o.oo6 o.448 o.824 o.l43 o.96 N 17 17 17 17 17 17 17 Number of rooms p o.856** o.769** o.882** -o.656* -o.4l7 o.lll o.l46 Sig. o.o39 o.ool o.ooo o.oo4 o.o57 o.67l o.575 N 17 17 17 17 17 17 17 Notes Column headings are as follows: (1) total revenue, (2) number of employees, (3) labour costs, (4) roe, (5) roa, (6) revenue per employee, (7) salary. p - correlation coefficient, Sig. - significance: ** correlation is significant at the 0.01 level (2-tailed), * correlation is significant at the 0.05 level (2-tailed), N - number of observations. SS 9,000 - sy .2 8,000 CP s ¿j 7,000 o , ^ 6,000 5,000 1.500.000 1.700.000 1.900.000 2.100.000 2.300.000 2.500.000 2.700.000 2.900.000 Figure 2 Dependence of the Number of Employees on the Number of Tourist Arrivals (y = 2e-9x2 - 0.0053* + 14911; R2 = 0.9682) of tourist arrivals reflects the higher number of employees. The consequence of a higher number of employees is reflected in higher labour costs. The average growth of the number of employees in the analysed period is 2.45 percent. There are also positive correlations between labour costs and the three statistical indicators. There is a moderate negative correlation between the number of rooms and the return on equity. This suggests that companies have invested a share of their profit in expanding accommodation facilities. Consequently, an increase in the number of rooms means higher operating costs (Daneu, 2010). This might be the reason for a moderate negative correlation. Among other statistical and financial indicators, there are no statistically significant correlations. To complete our analysis, we also performed a partial regression analysis. In all three cases, our independent variable was the number of tourist arrivals. As can be seen from Figure 2, the dependent variable was the number of employees. We opted for the non-linear re- gression analysis, because the selected data better fit a quadratic equation rather than linear equation. With the value of the coefficient of determination, we can assess the quality of the regression model. In this case, we can see that the quality of the regression model is suitable, because of a high value of the coefficient of determination. With the use of a regression equation, we can predict the number of employees in the hospitality sector, if we know the number of tourist arrivals in Slovenia. In the second case, the dependent variable used was total revenues. Again, we opted for the non-linear regression analysis and the coefficient of determination is also quite high. With the use of a regression equation (shown in Figure 3), we can predict the total revenues in the hospitality sector, if we know the number of tourist arrivals in Slovenia. Finally, the dependent variable used was the average monthly salary. The regression model is presented in Figure 4. Again, we opted for the non-linear re- 40 | Académica Turística, Year 6, No. 1, June 2013 Tanja Planinc, Štefan Bojnec, and Gordana Ivankovič Measuring Performance g 300,000,000 ■ (3 iS 250,000,000 ■