NG OE NG N A Š E G O S P O D A R S T V O Revija za aktualna ekonomska in poslovna vprašanja OE L E T N I K O U R E C O N O M Y VOLUME 61 Journal of Contemporary Issues in Economics and Business NAŠE GOSPODARSTVO OUR ECONOMY NAVODILA AVTORJEM INSTRUCTIONS FOR AUTHORS Revija za aktualna ekonomska in poslovna vprašanja Journal of Contemporary Issues in Economics and Business Revija Naše gospodarstvo / Our Economy objavlja izvirne The journal Naše gospodarstvo / Our Economy publishes znanstvene članke iz vseh področij ekonomije in poslovnih original scientifi c articles covering all areas of economics and Letnik 61, št. 1, 2015 Vol. 61, No. 1, 2015 ved. Avtorje vabimo, da v uredništvo pošljejo originalne business. Authors are invited to send original unpublished articles prispevke, ki še niso bili objavljeni oz. poslani v objavo v drugi which have not been submitted for publication elsewhere. Authors reviji. Avtorji v celoti odgovarjajo za vsebino prispevka. Ob- are completely responsible for the contents of their articles. Only Izdajatelj: Published by: javljamo samo članke, ki dobijo pozitivno oceno recenzentov. articles receiving a favorable review are published. Ekonomsko-poslovna fakulteta Maribor (EPF) Faculty of Economics and Business, Maribor (FEB) Prispevki naj bodo napisani v angleškem jeziku. Na posebni Please write your text in English (American or British usage stani navedite ime avtorja, njegov polni habilitacijski in znan- is accepted, but not a mixture of these). The cover page should stveni naziv ter ustanovo, kjer je zaposlen. Prva stran naj include the author's name, academic title or profession, and af- Uredniški odbor: Editorial Board: vsebuje naslov, izvleček (maksimalno 650 znakov) in ključne fi liation. 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Posch Nekaj osnovnih napotkov za navajanje virov v tekstu: References in the text (Technical University Dortmund, Nemčija), Gregor Radonjič (Technical University Dortmund, Germany), Gregor Radonjič Primer 1a: Another graphic way of determining the stationari- Example 1a: Another graphic way of determining the station- ty of time series is correlogram of autocorrelation arity of time series is correlogram of autocorrela- (EPF), Miroslav Rebernik (EPF), Kaija Saranto (University of (FEB), Miroslav Rebernik (FEB), Kaija Saranto (University of function (Gujarati, 1995). tion function (Gujarati, 1995). Eastern Finland, Finska), Milica Uvalic (University of Perugia, Eastern Finland, Finland), Milica Uvalic (University of Perugia, Primer 1b: Another graphic way of determining the stationari- Example 1b: Another graphic way of determining the station- Italija), Igor Vrečko (EPF), Martin Wagner (Technical University Italy), Igor Vrečko (FEB), Martin Wagner (Technical University ty of time series is correlogram of autocorrelation arity of time series is correlogram of autocorrela- Dortmund, Nemčija) in Udo Wagner (University of Vienna, Dortmund, Germany), Udo Wagner (University of Vienna, function (Gujarati, 1995, p. 36). tion function (Gujarati, 1995, p. 36). Avstrija) Austria) Primer 2a: Engle & Granger (1987) present critical values Example 2a: Engle & Granger (1987) present critical values also for other cointegration tests. also for other cointegration tests. Glavni in odgovorni urednik: Editor-in-Chief: Primer 2b: Engle & Granger (1987, p. 89) present critical Example 2b: Engle & Granger (1987, p. 89) present critical Vesna Čančer Vesna Čančer values also for other cointegration tests. values also for other cointegration tests. Nekaj osnovnih napotkov za navajanje virov v seznamu virov: References in the list of references Naslov uredništva: Editorial and administrative office address: Primer 1 – Knjiga: Gujarati, D. N. (1995). Basic Econometrics. Example 1 – Book: Gujarati, D. N. (1995). Basic Econometrics. Maribor, Razlagova 14, Slovenija, Maribor, Razlagova 14, Slovenia, New York: McGraw-Hill. New York: McGraw-Hill. telefon: +386 2 22 90 112 phone: +386 2 22 90 112 Primer 2 – Članek v reviji: Engle, R. F., & Granger, C. W. J. Example 2 – Journal article: Engle, R. F., & Granger, C. W. (1987). Co-integration and Error Correction: Representation, J. (1987). Co-integration and Error Correction: Representation, Elektronska pošta: E-mail: Estimation and Testing. Econometrica, 55(2), 251-276. Estimation and Testing. Econometrica, 55(2), 251-276. nase.gospodarstvo@uni-mb.si nase.gospodarstvo@uni-mb.si Primer 3 – Poglavje v knjigi, prispevek v zborniku: MacKinnon, Example 3 – Book chapter or article from conference proceed- J. (1991). Critical Values for Cointegration Tests. In R. F. Engle ings: MacKinnon, J. (1991). Critical Values for Cointegration Spletna stran: WWW homepage: & C. W . J. Granger (Eds.), Long-Run Economic Relationships: Tests. In R. F. Engle & C. W . J. Granger (Eds.), Long-Run Readings in Cointegration (pp. 191-215). Oxford: University Economic Relationships: Readings in Cointegration (pp. ht p:/ www.ng-epf.si ht p:/ www.ng-epf.si Press. 191-215). Oxford: University Press. Primer 4 – Elektronski vir: Esteves, J., Pastor, J. A., & Example 4 – Web source: Esteves, J., Pastor, J. 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NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Vsebina / Contents Damijan Mumel, Sanja Jan, Sonja Treven, Domen Malc Mobbing in Slovenia: Prevalence, mobbing victim characteristics, and the connection with post-traumatic stress disorder 3 Anđelko Lojpur, Ana Aleksić, Sanja Vlahović, Mirjana Pejić Bach, Sanja Peković Examining Determinants of Leadership Style among Montenegrin Managers 13 Roma Mitra Debnath, Ashish Malhotra Measuring efficiency of nations in Multi Sport Events: A case of Commonwealth Games XIX 25 Vesna Trančar The Effect of the Combination of Different Methods of Stock Analysis on Portfolio Performance 37 1 Mobbing in Slovenia: Prevalence, ORIGINAL SCIENTIFIC PAPER mobbing victim characteristics, and the connection with RECEIVED: NOVEMBER 2014 post-traumatic stress disorder REVISED: JANUARY 2015 ACCEPTED: JANUARY 2015 Damijan Mumel Faculty of Economics and Business, University of Maribor, Slovenia damijan.mumel@uni-mb.si DOI: 10.1515/ngoe-2015-0001 Sanja Jan UDK: 331.47:343.4(497.4) Šumer d.o.o., Slovenia JEL: J28, J29, J71 sanja.jan@gmail.com Sonja Treven Faculty of Economics and Business, University of Maribor, Slovenia sonja.treven@uni-mb.si Domen Malc Faculty of Economics and Business, University of Maribor, Slovenia domen.malc@uni-mb.si Abstract An increasing number of organizations face the problem of mobbing, which represents a serious, widespread problem with numerous consequences for victims, organizations, and society. We also recognize the connection this phenomenon has with the emergence of post-traumatic stress disorder (PTSD). PTSD poses one of the most critical consequences for victims of mobbing, who mostly consist of employees at lower organizational levels. Our research focuses on the prevalence of mobbing in Slovenia, its correlation to PTSD, and some differences in the subjective and objective assessments of being exposed to mobbing. We found that the prevalence of mobbing in Slovenia can be compared to some previous assessments as well as data from other countries. Among the study’s participants, 24% could be classified as regular victims of mobbing. For the first time, we link mobbing with PTSD using a Slovenian sample. We also recorded some interesting differences between subjective and objective assessments of mobbing, thereby indicating the importance of subjective conceptualizations of mobbing acts, which should be investigated in greater detail in future research. Keywords: Mobbing, post-traumatic stress disorder, prevalence, subjective and objective assessment, workplace health. NAŠE GOSPODARSTVO 1 Introduction OUR ECONOMY The modern workplace is changing: The pace of work is accelerating while Vol. 61 No. 1 2015 work efficiency and performance depend on social interaction more than ever. New ways of doing business lead to increased competition and rivalry between pp. 3–12 coworkers. In addition to difficult interpersonal relationships and increasing 3 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 stress, the phenomenon of mobbing is becoming increas- (25%–50%) resembles the risks in survivors of aircraft acci- ingly noticeable. Mobbing is a sophisticated form of terror dents (25%), war veterans (25%–50%), and survivors of car in the workplace that can be used to disable a coworker accidents (20%). emotionally, mentally, socially, and economically (Bakovnik, 2006). The fourth edition of the Diagnostic and Statistical Manual of Mental Disorders ( DSM-IV-TR; American Psychiatric Several studies have confirmed that mobbing is an issue. Association, 2000) provides the most commonly used Researchers estimate that the prevalence of mobbing ranges definition of PTSD, categorizing it as an anxiety disorder. anywhere from 1% to 53% among various occupations It involves the following diagnostic criteria: (1) reliving and countries (Bentley et al., 2012; Cowie et al., 2000; symptoms (e.g., remembering the trauma), (2) demon- Leymann, 1996; Lutgen-Sandvik, Tracy, & Alberts, 2007; strating avoidance symptoms (e.g., avoiding thoughts and Mikkelsen & Einarsen, 2001; Vartia, 1996; Zapf, Einarsen, feelings associated with the traumatic event), and (3) ex- Hoel, & Vartia, 2003). Data for Slovenia also vary. The periencing symptoms of increased arousal (e.g., irritability, fourth European Working Condition Survey revealed a 7.4% lack of concentration). The diagnosis is justified when at prevalence (Parent-Thirion, Macias, Hurley, & Vermeylen, least one symptom of reliving, three avoidance symptoms, 2007) whereas the Slovenian Banking Union’ research and two arousal symptoms occur at least one month. The recorded a 15.1% prevalence (Robnik & Milanovič, 2008). symptoms typically also interfere with the individuals’ Mobbing most often affects subordinates in organizations ability to function in social, professional, or other fields of (Brinkmann, 1995; Zapf et al., 2003), which includes several human activity (American Psychiatric Association, 2000). other groups that are even more exposed, such as the elderly, people who are often absent, and women (Brečko, 2010). In addition, formal diagnosis requires an experience of a Kostelić-Martić (2007) pointed out that minorities—from death threat or threats of serious injury to the individual or religious and ideological minorities to homosexuals—are to others. Mobbing victims usually do not meet this crite- also victims of mobbing. rion (Rodriguez-Muñoz, Moreno-Jiménez, Sanz Vergel, & Garrosa Hernández, 2010). Many authors have discussed Yet we must emphasize that not every insolence or ordinary this dilemma (Arias & Pape, 1999; Gold, Marx, Soler-Bail- work requirement should be seen as an act of mobbing. Vie, lo, & Sloan, 2005; Ravin & Boal, 1989), noting that PTSD Glasø, and Einarsen (2010) suggested that the term mobbing can occur in the absence of a traumatic event. For example, should be treated within the individual’s experience of Long et al. (2008) showed that an even higher rate of PTSD a certain act that is caused by others. Some people might and severity of symptoms occurred when the criterion of interpret an action as a harmless joke, while others might traumatic experience was not present. see the same action as an act of mobbing. In any case, sub- jective assessments often differ from the results of objective Numerous researchers have repeatedly confirmed a positive measurements, although some coherency is also observed relationship between mobbing and PTSD. Leymann and (Notelaers, Einarsen, De Witte, & Vermunt, 2006; Zapf et Gustafson (1996) identified 59 participants, within a 64- al., 2003). person sample, who demonstrated PTSD symptoms. Mik- kelsen and Einarsen (2002) found a positive correlation The consequences of mobbing include a wide range of between mobbing and PTSD ( r = 0.34), and 76% of the problems that affect the victims as well as co-workers studied 118 mobbing victims displayed severe symptoms of (Brečko, 2010; Tkalec, 2001; Vartia, 2001), the organization PTSD. Moreover, Nielsen, Matthiesen, and Einarsen (2005) (Brečko, 2006; Di Martino, Vittorio, Hoel, & Cooper, 2003; determined that 84% of victims of mobbing had PTSD. The Tkalec, 2006), and society (Brečko, 2010; Di Martino et same authors made no observations about gender differenc- al., 2003). Nevertheless, mobbing affects victims the most es in the prevalence of PTSD among victims of mobbing. In because it impacts various aspects of their lives: mental general, however, PTSD is more prevalent among women and physical functions, interpersonal relationships and in- than men (Breslau, Davis, Andreski, Paterson, & Schultz, teractions, and economic stability. The most severe cases 1997; Christiansen & Elklit, 2012; Schüffel, Schade, & of mobbing lead to the emergence of post-traumatic stress Schunk, 2004). disorder (PTSD)—a complex, usually chronic, and tiring mental disorder caused by surviving an extremely severe In Slovenia, the limited amount of research that exists in event or trauma (Weathers, Keane, & Foa, 2009). Di Martino the field of mobbing often focuses on its prevalence and et al. (2003) reported that the rate of PTSD in victims of the characteristics of people involved. In the present study, mobbing exceeds those of people who experienced trau- we wanted to reexamine the prevalence of mobbing in matic accidents. Furthermore, Brečko (2006) noted that the Slovenia as well as gender differences, differences between level of risk for developing PTSD in victims of mobbing age groups, and organizational levels in terms of exposure 4 D. Mumel, S. Jan, S. Treven, D. Malc: Mobbing in Slovenia: Prevalence, mobbing victim characteristics, and the connection with post-traumatic stress disorder to mobbing. In the second part of the present study, we • Regular victim of mobbing: respondents who had explored the link between mobbing and PTSD, which has been victims of at least two negative acts weekly or not yet been studied in Slovenia. We also wanted to observe more often. potential differences between subjective and objectives Subjective assessments of exposure to mobbing were col- measures of mobbing exposure and differences in the inci- lected with the following question: “Are you a victim of dence of PTSD among male and female victims of mobbing. workplace mobbing?” Participants assessed their answer (taking into account the given definition of mobbing) on a scale ranging from 1 (not a victim of mobbing) to 5 (yes, almost daily, I am a victim of mobbing). 2 Method The post-traumatic symptom scale–10 items (PTSS-10; 2.1 Sample Raphael, Lundin, & Wisæth, 1989) was translated and adapted for Slovenian researchers by Jan (2011). It consists The research sample consisted of 150 participants (females = of 10 symptoms of PTSD (e.g., “I have trouble sleeping”; 81) who had been employed for at least six months. In terms “I’m having nightmares”). Respondents assess their fre- of the organizational structure, the sample includes 62% quency on a scale ranging from 1 (never) to 7 (always). The workers/contractors, 20% employees in lower management, respondent’s level of PTSD equals the overall score on the 12% in middle management, and 6% in upper management. scale. In the current study, participants whose total score was Table 1 shows participants’ age structure. 35 points or more were considered victims of PTSD; those whose scores fell between 27 and 35 points were considered potential victims of PTSD (Boer et al., 2007). 2.2 Instruments Both questionnaires used have been proven to be very We collected data using a structured questionnaire that con- reliable. The analysis of internal consistency of the NAQ sisted of three parts: revealed a Cronbach’s α of 0.94 whereas the analysis of PTSS-10 showed a Cronbach’s α of 0.93. (1) A set of demographic questions included questions about gender, age group, and the organizational level at which the participant was employed. 3 Results (2) The Negative Acts Questionnaire (NAQ; Einarsen, Raknes, Matthiesen, & Hellesøy, 1994) consists of 22 We analyzed the responses to the NAQ and found that 63% negative behaviors (e.g., “Someone withholding infor- of the participants fall into the category of occasional victims mation that affects your performance”) that are valued of mobbing, 24% of participants fall into the category of by respondents using a 5-point scale (1 = never; 5 = regular victims of mobbing, and only 13 % of respondents daily). According to the responses, respondents were reported no exposure to negative acts in their workplace. classified into three groups: (a) respondents who are not victims of mobbing; (b) respondents who are occasion- The subjective assessment provided a different picture: ally victims of mobbing; and (c) respondents who are 59% of the participants believed that they are not victims of regular victims of mobbing. For this classification, we mobbing, 36% saw themselves as occasional victims, and used the following key: 5% considered themselves as regular victims of mobbing. • Not a victim of mobbing: respondents who marked all items with 1 (never) and thus had not been We present the crosstabs of the NAQ results and the subjec- victims of negative acts in the preceding six months. tive assessment of mobbing exposure in Table 2. We can see • Occasional victim of mobbing: respondents who that the subjective measure rarely fits (in 37% of respond- had been victims of at least one negative act occa- ents) the results of the NAQ. Notably, the subjective rating sionally or monthly. was typically lower. Table 1 Participants’ Age Structure Age (years) ≤ 25 26 ≤ 30 31 ≤ 35 36 ≤ 40 41 ≤ 45 46 ≤ 50 51 ≤ 55 ≥ 56 Frequency 12 56 25 14 17 14 9 3 % 8.0 37.3 16.7 9.3 11.3 9.3 6.0 2.0 5 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 The highest scores on the NAQ were achieved by partic- Mann-Whitney’s test ( U = 2758.0; p < 0.05). Individuals ipants from the 36- to 40-year-old group ( M = 44.4; SD = from 26- to 30-year-old group (49%) and individuals who 16.75), participants who work in lower management ( M = work as workers/contractors (68%) represent the majority 36.8; SD = 13.80), and workers/contractors ( M = 36.5; of regular mobbing victims. We did not, however, record SD = 14.47). Men’s and women’s responses on the NAQ any gender differences in our sample of regular mobbing showed no statistically significant difference tested with victims. Table 2 Crosstabs Analysis of NAQ Results and Subjective Measures of Mobbing Exposure NAQ Not victim Occasional victim Regular victim Total Not victim 18 61 9 88 Occasional victim 1 32 22 55 Subjective measure Regular victim 0 1 6 7 Total 19 94 37 150 Table 3 Average Estimates for the Occurrence of Individual Symptoms in PTSS-10 M SD Me Irritability 2.81 1.51 2.5 Jumpiness 2.73 1.55 2 Sleep problems 2.72 1.63 2 Frequent mood swings 2.71 1.56 2 The need to withdraw from others 2.54 1.61 2 Depression (I feel dejected/down-trodden) 2.34 1.56 2 Muscular tension 2.15 1.56 2 A bad conscience, blame myself, have guilty feelings 2.13 1.35 2 Nightmares 1.97 1.31 1.5 Fear of places and situations that remind me of negative acts in the workplace 1.77 1.32 1 Note. Estimates were given on a 5-point scale for each item; M: mean; SD: standard deviation; Me: median. Figure 1: Comparison of subjective ratings of mobbing exposure and results of the NAQ based on participants’ gender 100 % 90 % 80 % 70 % 60 % 50 % 40 % 30 % 20 % 10 % 0 % Men: Men: Women: Women: Objective Subjective Objective Subjective Regular victim Occasional victim Not victim 6 D. Mumel, S. Jan, S. Treven, D. Malc: Mobbing in Slovenia: Prevalence, mobbing victim characteristics, and the connection with post-traumatic stress disorder The results of the NAQ do not suggest any gender differenc- 12.15), achieved typically higher scores ( U = 2181.0; p < es in exposure to mobbing. On the contrary, the subjective 0.05) compared to men ( M = 21.7; SD = 12.15). measures present a slightly different picture. A comparison of the objective and subjective exposure assessments of We also compared the scores on the PTSS-10 between men mobbing by participants’ gender are presented in Figure 1. and women on the level of mobbing exposure measured with The difference between the ratings was significantly higher the NAQ. The comparison of average scores is illustrated in in men ( U = 2139.00; p < 0.05). Figure 2. On the PTSS-10, respondents on average reached 23.9 Analysis using the Mann-Whitney test for two independent points ( SD = 11.63). Table 3 provides the average estimates samples showed that statistically significant differences of the frequency of occurrence for individual symptoms, as between the sexes was found only in the group of occasional assessed by the participants on a 5-point scale. victims of mobbing ( U = 734.00; p < 0.01). The groups most exposed to PTSD are workers/contractors We tested the correlation between the scores on the NAQ, ( M = 24.5; SD = 12.13), followed by middle management subjective measurement of mobbing exposure, and the test ( M = 24.1; SD = 11.14), lower management ( M = 23.2; results on the PTSS-10 using the Spearman’s rho correla- SD = 11.15), and lastly higher management ( M = 19.7; tion. We present our findings in Table 4, which shows that SD = 9.81). Based on age-group classifications, the results all correlations are statistically significant. show that 36- to 40-year-olds are the most exposed to PTSD ( M = 32.4; SD = 11.99) while 41- to 45-year-olds were the We were also interested in whether the level of PTSD least exposed ( M = 16.9; SD = 6.39). Focusing on gender differed for people who fall into the selected category of differences, we see that women, on average ( M = 25.7; SD = mobbing according to the subjective estimates and the test Table 4 Results of Spearman’s Correlation Test among Scores on the NAQ, the PTSS-10, and Subjective Measure of Mobbing Exposure NAQ PTSS-10 Subjective measure NAQ 1 - - Spearman’s rho PTSS-10 0.59** 1 - Subjective measure 0.67** 0.48** 1 ** p < 0,01 (one-tailed tests) Figure 2: Comparison of average PTSS-10 scores between men and women according to the NAQ results of exposure to mobbing 40 35.84 35 30 31.56 ores 24.38 25 20 PTSS-10 sc 16.11 18.64 15 15.3 10 Not Occasional Regular victim victim victim Women Men 7 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Figure 3: Comparison of assessments on the PTSS-10 based on objective and subjective measures of exposure to mobbing 45 41.9 40 35 ores 28.77 30 33.2 25 19.4 PTSS-10 sc 20 21.8 15 15.7 10 Not Occasional Regular victim victim victim Subjective NAQ results of the NAQ. Figure 3 illustrates the test results of the not find any gender differences in exposure to mobbing, PTSS-10 with respect to these conditions. which is contrary to some previous claims (Brečko, 2010), although such results are not unique (Einarsen et al., 1994; Figure 3 clarifies that the participants, who were arranged in Hoel et al., 2001; Rayner, Hoel, & Cooper, 2002). their respective groups according to the subjective measure, scored higher on the PTSS-10, compared to those classified Subjective estimates of mobbing exposure were considera- based on the NAQ scores. bly lower. According to the data, 36% of the present study’s participants categorize as occasional victims and 5% fall in the category of regular mobbing victims. Other researchers have reported such differences between subjective and ob- Discussion jective assessments of mobbing exposure (Notelaers et al., 2006; Zapf et al., 2003). Differences of this type occurred Our findings reaffirm the troublesome prevalence of more often in men, which raises questions about the impor- mobbing among Slovenian employees. The findings also tance of the subjective conceptualization of acts of mobbing. reveal significant differences between the subjective as- Escartín, Salin, and Rodríguez-Carballeira (2011) provided sessments of exposure to mobbing and estimates obtained some answers that drew attention to higher sensitivity in by an objective method. Furthermore, we confirmed with evaluating mobbing in women. Nevertheless, this remains a Slovenian sample that exposure to mobbing significantly an under-researched area with considerable potential. correlates with the emergence of PTSD. One of the main goals of the present research was to explore Our findings deviate from previous research in the percent- the link between exposure to mobbing and PTSD. Our age of mobbed individuals (Parent-Thirion et al., 2007; findings show a statistically significant positive correlation Robnik & Milanović, 2008). The NAQ test results indicate between PTSD and the results of the NAQ (ρ = 0.59) as well that 24% of participants are regular victims of mobbing, as the results of the subjective assessment (ρ = 0.48). Using whereas 68% of participants reported being occasional a Slovenian sample, this connection was confirmed for the victims of mobbing. These results fit the data for other coun- first time, although it had already been detected in previous tries, which as noted, range from a 1% to a 53% prevalence research on foreign samples (Mikkelsen & Einarsen, 2002; (Bentley et al., 2012; Cowie et al., 2000; Leymann, 1996; Nielsen et al., 2005). The descriptive analysis of our results Lutgen-Sandvik et al., 2007; Mikkelsen & Einarsen, 2001; also suggests such a connection. We found that workers/ Vartia, 1996; Zapf et al., 2003). Our results show that victims contractors are the most at-risk of PTSD and are the most of mobbing are usually 26 to 30 years old (49%) and at the exposed to mobbing (Brinkmann, 1995; Zapf et al., 2003). organizational level of workers/contractors (68%). These findings are consistent with the findings of other studies Our analysis also shows that women scored significantly (Brinkmann, 1995; Zapf, 2000). On the other hand, we did higher on the PTSS-10 scale than men in the entire sample. 8 D. Mumel, S. Jan, S. Treven, D. Malc: Mobbing in Slovenia: Prevalence, mobbing victim characteristics, and the connection with post-traumatic stress disorder Such data are consistent with general estimates of the prev- that such differences stem from women’s higher sensitivity alence of PTSD (Breslau et al., 1997; Christiansen & Elklit, to acts of mobbing, although we assume that other variables 2012; Schuffel et al., 2004). However, among the regular could be important as well. Vie et al. (2010), for example, victims of mobbing, no significant gender differences were highlighted the importance of personal characteristics; found in the PTSS-10 scores. meanwhile, Ireland (2006) studied the effect of organiza- tional context, Lewis (2001) the role of media, and Escartín, The findings of the present study should be viewed in light Zapf, Arrietta, and Rodríguez-Carballeira (2011) the moder- of its limitations, which are derived primarily from the ating effect of the national context. characteristics of our sample. The number of study partic- ipants deviated across age groups and organizational levels The current situation clarifies that society fails to view and was relatively low. In addition, we chose to divide mobbing as a wider social phenomenon and treats it with a the methods into subjective and objective assessments of lack of urgency. Slovenian legislation of this field remains mobbing exposure. We used a questionnaire, the NAQ, as highly problematic. No specific law prevents mobbing, an objective measure; despite its reasonably good psycho- which makes it difficult to prove mobbing legally. However, metric characteristics, it is still based on self-report. Inter- we have several regulations that indirectly govern proce- estingly, according to this limitation, we would expect more dures in cases of mobbing and sanctioning employers where consistent results when comparing the subjective and ob- mobbing actions occur. For example, the Employment Re- jective measures, which was not the case. Finally, although lationship Act (2009), the Civil Servants Act (2012), and we chose the PTSS-10 scale for its promising psychometric the Occupational Health and Safety Act (2011) all address characteristics, it is still one of the many instruments used to mobbing. evaluate PTSD. Finally, we must also note that the PTSS-10 is more of a research tool than it is diagnostic. However, there are some solutions for addressing workplace mobbing, which can be adopted by managers, employees, and even the victims. Niedl (1996) suggested that detec- tion of negative acts is possible in an early stage, thereby Conclusions enhancing the possibility for their prevention. Generally, these solutions focus on eliminating tolerance for bullying Our research deepens the understanding of mobbing in our and mobbing through surveillance, policy development, country. We have confirmed its prevalence and relevance training, coaching, mediation, and different reward systems among Slovenian employees and, for the first time, have that motivate collaborative behavior at work (Ferris, 2009). also confirmed its connection with PTSD in a Slovenian The victims are usually encouraged to seek help that in- sample. Our findings unravel the seriousness of the problem tegrates the individual, organization, and psychotherapy of mobbing in Slovenia. The problem has been explored to (Duffy & Sperry, 2012). some extent by several prior studies, although we are still waiting for a larger research project in this area. Further- Yet mobbing is still not recognized as a social problem, and more, future research should focus on detecting specific it is high time for some organized preventive-oriented efforts features of mobbing conceptualizations by individuals. The to fight against it. On the one hand, we must educate and present findings show that men identify mobbing to a lesser inform; on the other hand, we must introduce more precise extent compared to women. Escartín et al. (2011) suggested legal regulations in this area. References 1. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author. 2. Arias, I., & Pape, K. T. (1999). Psychological abuse: Implications for adjustment and commitment to leave violent partners. Violence and Victims, 14(1), 55–67 . 3. Bakovnik, R. (2006). Vloga sveta delavcev pri odkrivanju in preprečevanju mobbinga. Industrijska demokracija, 12(10), 3-5. 4. Bentley, T. A., Cately, B., Cooper-Thomas, H., Gardner, D., O’Driscoll, M. P., Dale, A., & Trenberth, L. (2012). Perceptions of workplace bullying in the New Zealand travel industry: Prevalence and management strategies. Tourism Management, 33(2), 351-360. http://dx.doi.org/10.1016/j.tourman.2011.04.004 5. Boer, K. R., Van Ruler, O., Van Emmerik, A. A. P., Sprangerrs, M. A., De Rooij, S. E., Vroom, M. B., . . ., The Dutch Peritonitis Study Group (2008). Factors associated with posttraumatic stress symptoms in a prospective cohort of patients after abdominal sepsis: A nomogram. Intensive Care Medicine, 34(4), 664-674. http://dx.doi.org/10.1007/s00134-007-0941-3 9 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 6. Brečko, D. (2006). Mobbing—Psihoteror tekmovalne družbe. Industrijska demokracija, 12(10), 12–18. 7. Brečko, D. (2010). Recite mobbingu ne: Obvladovanje psihičnega in čustvenega nasilja. Ljubljana, Slovenia: Planet GV. 8. Breslau, N., Davis, G. C., Andreski, P., Paterson, E. L., & Schultz, L. R. (1997). Sex differences in posttraumatic stress disorder. Archives of General Psychiatry, 54, 1044–1048. http://dx.doi.org/10.1001/archpsyc.1997.01830230082012 9. Brinkmann, R. (1995). Mobbing, bullying, bossing. Treibjagd am Arbeitsplatz. Heidelberg, Germany: Sauer Verlag. 10. Christiansen, D., & Elklit, A. (2012). Sex differences in PTSD. In Emilio Ovuga (Ed.), Post traumatic stress disorders in a global context (pp. 113–142). Rijeka, Croatia: InTech. http://dx.doi.org/10.5772/28363 11. Civil Servants Act. (2012). Official Gazette RS, No. 40/2012. Retrieved November 28, 2012, from http://zakonodaja.gov.si/rpsi/r07/ predpis_ZAKO3177.html 12. Cowie, H., Jennifer, D., Neto, C., Angulo, J. C., Pereira, B., del Barrio, C., & Ananiadou, K. (2000). Comparing the nature of workplace bullying in two European countries: Portugal and the UK. In M. Sheehan, S. Ramsey, & J. Patricks (Eds.), Transcending the boundaries: Integrating people, processes and systems. Proceedings of the 2000 Conference (pp. 128–133). Brisbane, Australia: Griffith University. 13. Di Martino, V., Hoel, H., & Cooper, C. L. (2003). Preventing violence and harassment in the workplace. Luxembourg: Office for Official Publications of the European Communities. 14. Duffy, M., & Sperry, L. (2012). Mobbing: Causes, consequences, and solutions. Oxford, UK: Oxford University Press. http://dx.doi. org/10.1093/acprof:oso/9780195380019.001.0001 15. Einarsen, S., Raknes, B. I., Matthiesen, S. B., & Hellesøy, O. H. (1994). Mobbing og harde personkonflikter. Helsafarlig samspill på arbeid-splassen (Bullying and severe interpersonal conflicts. Unhealthy interactions at work). Soreidgrend, Norway: Sigma Forlag. 16. Employment Relationship Act. (2009). Official Gazette RS, No. 83/2009. Retrieved November 28, 2012, from http://zakonodaja.gov. si/rpsi/r00/predpis_ZAKO1420.html 17. Escartín, J., Salin, D., & Rodríguez-Carballeira, Á. (2011). Conceptualizations of Workplace Bullying: Gendered Rather than Gender Neutral?. Journal of Personnel Psychology, 10(4), 157-165. http://dx.doi.org/10.1027/1866-5888/a000048 18. Escartín, J., Zapf, D., Arrieta, C., & Rodríguez-Carballeira, Á. (2001). Workers’ perception of workplace bullying: A cross-cultural study. European Journal of Work and Organizational Psychology, 20(2), 178–205. http://dx.doi.org/10.1080/13594320903395652 19. Ferris, P. A. (2009). The role of the consulting psychologist in the prevention, detection, and correction of bullying and mobbing in the workplace. Consulting Psychology Journal: Practice and Research, 61(3), 169–189. http://dx.doi.org/10.1037/a0016783 20. Gold, S. D., Marx, B. P., Soler-Baillo, J. M., & Sloan, D. M. (2005). Is life stress more traumatic than traumatic stress? Journal of Anxiety Disorders, 19(6), 687–698. http://dx.doi.org/10.1016/j.janxdis.2004.06.002 21. Hoel, H., Cooper, C. L., & Faragher, B. (2001). The experience of bullying in Great Britain: The impact of organizational status. European Journal of Work and Organizational Psychology, 10(4), 443–465. http://dx.doi.org/10.1080/13594320143000780 22. Ireland, J. (2006). Bullying among mentally-ill patients detained in a high-secure hospital: An exploratory study of the perceptions of staff and patients into how bullying is defined. Aggressive Behavior, 32(5), 451–463. http://dx.doi.org/10.1002/ab.20145 23. Jan, S. (2011). Mobbing na delovnem mestu in njegova povezava s posttravmatsko stresno motnjo (Master’s thesis). Retrieved October 10, 2012, from http://www.epf.uni-mb.si/ediplome/pdfs/jan-sanja-mag.pdf 24. Kostelić-Martić, A. (2007). Psihično nasilje na delovnem mestu. HRM, 15, 26–32.Leymann, H. (1996). The content and development of mobbing at work. European Journal of Work and Organizational Psychology, 5(2), 165–184. 25. Leymann, H., & Gustafsson, A. (1996). Mobbing at work and the development of post-traumatic stress disorders. European Journal of Work and Organizational Psychology, 5(2), 251–275. http://dx.doi.org/10.1080/13594329608414858 26. Lewis, D. (2001). Perceptions of bullying in organizations. International Journal of Management and Decision Making, 2(1), 48–63. http://dx.doi.org/10.1504/IJMDM.2001.001221 27. Long, M. E., Elhai, J. D., Schweinle, A., Gray, M. J., Grubaugh, A., & Frueh, B. (2008). Differences in posttraumatic stress disorder diagnostic razes and symptom severity between criterion A1 and non-criterion A1 stressors. Journal of Anxiety Disorders, 22, 1255–1263. http://dx.doi.org/10.1016/j.janxdis.2008.01.006 28. Lutgen-Sandvik, P., Tracy, S. J., & Alberts, J. K. (2007). Burned by bullying in the American workplace: Prevalence, perception, degree, and impact. Journal of Management Studies, 44(6), 837–862. http://dx.doi.org/10.1111/j.1467-6486.2007.00715.x 29. Mikkelsen, G. E., & Einarsen, S. (2001). Bullying in Danish work-life: Prevalence and health correlates. European Journal of Work and Organizational Psychology, 10(4), 393–413. http://dx.doi.org/10.1080/13594320143000816 30. Mikkelsen, G. E., & Einarsen, S. (2002). Basic assumptions and post-traumatic stress among victims of workplace bullying. European Journal of Work and Organizational Psychology, 11(1), 87–111. http://dx.doi.org/10.1080/13594320143000861 31. Niedl, K. (1996). Mobbing and well-being: Economic and personnel development implications. European Journal of Work and Organizational Psychology, 5(2), 239–249. http://dx.doi.org/10.1080/13594329608414857 32. Nielsen, M. B., Matthiesen, S. B., & Einarsen, S. (2005). Ledelse og personkonflikter: Symptomer på postraumatisk stress blant ofre for mobbing fra ledere (Leadership and bullying. Posttraumatic symptoms among victims after bullying by their leaders). Nordisk Psykologi, 57(4), 391–415. http://dx.doi.org/10.1080/00291463.2005.10637381 33. Notelaers, G., Einarsen, S., De Witte, H., & Vermunt, J. K. (2006). Measuring exposure to bullying at work: The validity and advantages of the latent class cluster approach. Work and Stress, 19(4), 289–302. http://dx.doi.org/10.1080/02678370601071594 34. Occupational Health and Safety Act. (2011). OG, Republic Slovenia, No. 43/2011. Retreived November 28, 2012, from http://zakonodaja.gov.si/rpsi/r03/predpis_ZAKO1643.html 10 D. Mumel, S. Jan, S. Treven, D. Malc: Mobbing in Slovenia: Prevalence, mobbing victim characteristics, and the connection with post-traumatic stress disorder 35. Parent-Thirion, A., Maciass, E. F., Hurley, J., & Vermeylen, G. (2007). Fourth European working conditions survey. Luxembourg: Office for Official Publications of the European Communities. 36. Raphael, B., Lundin, T., & Weisæth, L. (1989). A research method for the study of psychological and psychiatric aspects of disaster. Acta Psychiatrica Scandinavica, 80 (Supp. 353), 1–16. http://dx.doi.org/10.1111/j.1600-0447.1989.tb03041.x 37. Ravin, J. M., & Boal, C. K. (1989). Post-traumatic stress disorder in the work setting: Psychic injury, medical diagnosis, treatment, and litigation. American Journal of Forensic Psychiatry, 10(2), 5–23. 38. Rayner, C., Hoel, H., & Cooper, C. L. (2002). Workplace bullying: What we know, who is to blame and what can we do? London: Taylor & Francis. 39. Robnik, S., & Milanovič, I. (2008). Trpinčenje na delovnem mestu: rezultati raziskave Sindikata bančništva Slovenije in priporočila za delodajalce. Ljubljana: Sindikat bančništva Slovenije. 40. Rodriguez-Muñoz, A., Moreno-Jiménez, B., Sanz Vergel, A., & Garrosa Hernández, E. (2010). Post-traumatic symptoms among victims of workplace bullying: exploring gender differences and shattered assumptions. Journal of Applied Social Psychology, 20(10), 2616–2635. http://dx.doi.org/10.1111/j.1559-1816.2010.00673.x 41. Schüffel, W., Schade, B., & Schunk, T. (2004). A brief inventory to investigate stress reactions. Post-traumatic symptom scale, 10 items (PTSS-10). Marburg, Germany: Philipps University Clinic. 42. Tkalec, L. (2001). Šikaniranje. Teorija in praksa, 38(5), 908–926. 43. Tkalec, L. (2006). Mobbing—Psihoteror na delovnem mestu. Industrijska demokracija, 12(10), 6–12. 44. Vartia, M. (1996). The sources of bullying—Psychological work environment and organizational climate. European Journal of Work and Organizational Psychology, 5(2), 203–214. http://dx.doi.org/10.1080/13594329608414855 45. Vartia, M. (2001). Consequences of workplace bullying with respect to the well-being of its targets and the observers of bullying. Scandinavian Journal of Work, Environment & Health, 27(1), 63–69. http://dx.doi.org/10.5271/sjweh.588 46. Vie, T. L., Glasø, L., & Einarsen, S. (2010). Does trait anger, trait anxiety, or organizational position moderate the relationship between exposure to negative acts and self-labeling as a victim of workplace bullying? Nordic Psychology, 62(3), 67–79. http:// dx.doi.org/10.1027/1901-2276/a000017 47. Weathers, W. F., Keane, T. M., & Foa, E. B. (2009). Assessment and Diagnosis of Adults. In E. B. Foa (Ed.), Effective treatments for PTSD: Practice guidelines from international society for traumatic stress studies (pp. 23–61). New York: The Guilford Press. 48. Zapf, D. (2000). Mobbing—Eine extreme Form sozialer Belastungen in Organisationen. In H. P. Mushal, & T. Einsenhauer (Eds.), Psychologie der Arbeits-sicherheit. Beiträge zur Föderung von Sicherheit und Gesundheit in Arbeitssystemen (pp. 142–149). Heidelberg, Germany: Asanger. 49. Zapf, D., Einarsen, S., Hoel, H., & Vartia, M. (2003). Empirical findings on bullying in the workplace. In S. Einarsen, H. Hoel, D. Zapf, & Cooper, C. L. (Eds.), Bullying and emotional abuse in the workplace. International perspectives in research and practice (pp. 103–126). London: Taylor & Francis. Damijan Mumel works as a professor of marketing at the University of Maribor, Faculty of Economics and Business. He is the head of the Marketing Institute and vice dean for research. His main areas of interest and lecturing are consumer behavior, research methodology, qualitative research, and communication. He has presented his work at international scientific conferences and published original scientific papers in domestic and foreign scientific journals. He is a member of the Slovenian Marketing Association and the Slovenian Psychologists’ Association as well as the European Marketing Academy (EMAC). Sanja Jan, M.A., is a marketing graduate of the Faculty of Economics and Business and earned a graduate degree in economics and business science—management and organization. She is currently employed in the business sector. 11 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Sonja Treven, Ph.D., is a professor employed at the Faculty of Economics and Business at the University of Maribor in Slovenia, focusing in the field of human resource management and organizational behavior. She is the head of the Department of Management and Organization as well as the head of the Institute of Organization and Information Systems. She is the author/co-author of 13 books as well as more than 80 scientific articles. She has participated in more than 100 domestic and international conferences an author or co-author of various papers. Domen Malc is a Ph.D. student at the University of Maribor’s Faculty of Economics and Business in the Department of Marketing. He acquired his bachelor’s and master’s degrees in marketing from the Faculty of Arts at the University of Maribor. He is employed by the Faculty of Economics and Business as an assistant in the field of marketing. Mobing v Sloveniji: razširjenost, značilnosti žrtev mobinga in povezava s posttravmatsko stresno motnjo Izvleček Organizacije se vse pogosteje soočajo z mobingom. To je resen in predvsem razširjen problem. Že dolgo poznamo tudi njegovo povezavo s posttravmatsko stresno motnjo. Za žrtve mobinga, med katerimi so najpogosteje zaposleni na nižji organizacijski ravni, je ta motnja je ena najresnejših težav. Z raziskavo smo želeli ugotoviti razširjenost mobinga v Sloveniji, raziskati njegovo povezanost s posttravmatsko stresno motnjo ter proučiti razlike med subjektivnimi in objektivnimi ocenami izpostavljenosti mobingu. Naše ugotovitve kažejo, da je prevalenca mobinga v Sloveniji primerljiva s podatki iz prejšnjih merjenj pa tudi s podatki za druge države. Med udeleženci raziskave je kar 24 % takih, ki se uvrščajo v skupino rednih žrtev mobinga. Prvič potrjujemo njegovo povezavo s posttravmatsko stresno motnjo na slovenskem vzorcu. Zabeležili smo tudi zanimive razlike med subjektivnimi in objektivnimi ocenami, ki kažejo na pomen subjektivne konceptualizacije dejanj mobinga. Ključne besede: mobing, posttravmatska stresna motnja, prevalenca, subjektivno in objektivno ocenjevanje, zdravje na delovnem mestu 12 Examining Determinants ORIGINAL SCIENTIFIC PAPER of Leadership Style among Montenegrin Managers Received: May 2014 Revised: October 2014 Anđelko Lojpur Accepted: November 2014 Faculty of Economics, University of Montenegro, Montenegro andjelko@ac.me Ana Aleksić DOI: 10.1515/ngoe-2015-0002 Faculty of Economics and Business Zagreb, University of Zagreb, Croatia UDK: 005.3(497.16) aaleksic@efzg.hr JEL: M5, M54, M12, P2 Sanja Vlahović Ministry of Science, Montenegro sanjavlahovic@t-com.me Mirjana Pejić Bach Faculty of Economics and Business Zagreb, University of Zagreb, Croatia mpejic@efzg.hr Sanja Peković Université Paris-Dauphine, Paris, France sanja.pekovic@dauphine.fr Abstract As a leader’s behavior can have a strong impact on different employee work- related outcomes, various approaches have been put forth in an effort to determine the most effective form of leadership and determinants of individuals’ choice of leadership style. This paper analyzed whether one’s choice of leadership style is due more to personal or organizational characteristics. We used survey data to investigate the determinants of leadership style among Montenegrin managers. Our analysis showed that, although demographic characteristics such as gender, age, and education do not influence the choice of leadership style, internal organizational characteristics such as hierarchical level, managerial orientation to tasks/people, and decision-making characteristics such as decision- making style and decision-making environment are positively associated with the choice of democratic leadership style. This contributes to recent research in leadership that shows how some personal characteristics are considered to be less important in developing certain styles and that the choice of style is more dependent and contingent on external influences and situations. Keywords: decision-making characteristics, demographic characteristics, internal organizational characteristics, leadership style, Montenegro NAŠE GOSPODARSTVO OUR ECONOMY 1 Introduction Vol. 61 No. 1 2015 Research on leadership and leadership style has been present in scientific research for decades, yet despite its strongly recognized importance it remains an elusive pp. 13–24 13 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 concept (Singh, Nadim, & Ezzedeen, 2012) and an object of Therefore, this study collects and analyzes different data on interest for many researchers. According to Bhatti, Maitlo, a number of demographic as well as organizational and deci- Shaikh, Hashmi, and Shaikh (2012, p. 192) leadership can sion-making characteristics that can be considered important be defined as in explaining leadership styles. The characteristics analyzed age, gender, and educational level (demographic); hierar- a social influence process in which the leader seeks the chical level and managerial characteristics (organizational); voluntary participation of subordinates in an effort to and decision-making style and decision-making environment reach organization goals. It is a process whereby one (decision making). The objective of the study is to examine person exerts social influence over other members of the whether these characteristics can be seen as determinants of group and a process of influencing the activities of an leadership style. As Oshagbemi (2008) stated, although a individual or a group of individuals in an effort towards significant amount of existing research on leadership styles goal achievement in given situations. has focused on only one personal dimension or one organiza- tional aspect and its impact on leadership, it is believed that Because of its strong influence not only on the employ- a better approach would be to examine both various personal ee’s motivation, job satisfaction, and other work-related and organizational dimensions as determinants of leadership outcomes, but also on the overall organizational perfor- style. Thus, this paper attempts to give a broader picture of mance, various approaches have emerged in attempts to the influence of not only demographic, but also organization- give an answer to the most effective form of leadership al and decision-making characteristics on leadership style. and leadership style. Different theories and assumptions, Thus, this paper conducts an empirical study of the sample of based on personality, behaviorist, and contingency theories, 105 managers from 96 organizations in Montenegro. have been used to establish the traits and behaviors that determine effective leadership and leadership style (Jonsen, The remainder of this paper is structured as follows. In Maznevski, & Schneider, 2010). Leadership style can be the next section, we provide a theoretical and conceptual defined as a set of behaviors, beliefs, and focus of power that framework of the influence of different demographic, or- a manager adopts toward its subordinate staff (i.e., the way ganizational, and decision-making characteristics on lead- in which the manager typically behaves toward members ership style. The third section presents data and methods of the group; Mullins, 2005). Looking at the continuum or employed. Section four analyzes the results. Concluding range of possible leadership behavior based on manager and remarks as well as limitations of the study are presented in non-manager power, influence, and freedom (Tannenbauem the final section. & Schmidt, 1973), one of the most accepted distinctions is between autocratic and democratic leadership styles. The notions of autocracy and democracy have been used to distinguish these two styles (Choi, 2007). Democratic 2 Related Literature and Hypotheses leadership is defined as the performance of three functions: distributing responsibility among the membership, empow- From an economic and management viewpoint, the managers’ ering group members, and aiding the group’s decision-mak- decision to adopt a certain leadership style can be explored ing process (Gastil, 1994). On the other side, an autocratic in the context of a discrete choice model, where the rational leader maintains a high level of individual control over all manager chooses the alternative (one of the leadership styles) decisions, defines all the activities, and seeks no participa- that maximizes the net expected benefits. Different variables tion from group members. are considered to determine the choice of one’s leadership style. Previous research has explored several of these factors The style that a leader adopts is based on a combination of and those usually include personal characteristics (i.e., demo- their beliefs, ideas, norms, and values (Iqbal, Inayat, Ijaz, graphic factors such as gender, age, educational level, ethical & Zahid, 2012). It is a permutation of various personal background, nationality, work experience) (e.g., Eagly traits and characteristics, attributes, and qualities that influ- & Carli, 2003; Eagly & Johnson, 1990; Kabacoff, 2002; ence group members for the accomplishment of the targets Merchant, 2012; Posner, 1992; Toren et al., 1997; van Engen, (Ansari & Naeem, 2010). In that sense, various demograph- van der Leeden, & Willemsen, 2001), organizational position ic characteristics were investigated to determine their rele- (e.g. Manning, 2002; Yukl, 2002) managerial orientation vance to a leadership style. Demographic characteristics of (e.g., van Engen & Willemsen, 2000), and decision-making the workforce in the management of an organization have characteristics (e.g., Puffer, 1990; Snowden & Boone, 2007). received increased attention among researchers in recent years because of its importance in predicting workers’ In accordance with these studies, it is possible to see that behavioral outcomes, such as efficiency and effectiveness individuals’ choice of leadership style can depend on their (Shadare, 2011). demographic, organizational, and managerial characteristics 14 A. Lojpur, A. Aleksić, S. Vlahović, M. Pejić, S. Peković: Examining Determinants of Leadership Style among Montenegrin Managers Figure 1: Research framework Demographic Characteristics Gender Age Education level H1 Decision-Making Characteristics H2 H3 Decision-making style H4 Leadership Style Decision-making environment H5 H6 H7 Internal Organizational Characteristics Hierarchical level Managerial characteristics as well as their decision-making situation based on different (Gutek, 1985). Moreover, as indicated by Barbuto, Fritz, decision-making variables. Following these approaches of Matkin, and Marx (2007), women are expected to behave previous research and the literature review, we formulate like leaders (authoritative, confident) while simultaneously several hypotheses regarding the determinants of leadership being feminine (friendly, kind, etc.). The literature presents style structured along the following lines: (1) demograph- conflicting arguments concerning the impact of gender on ic characteristics (gender, age, and education); (2) internal leadership style. Although one group of researchers finds organizational characteristics (hierarchical level and mana- that differences in leadership behaviors are based on gender gerial orientation to tasks/people and change); and (3) deci- (e.g., Collard, 2001; Druskat, 1994; Eagly & Johnson, 1990; sion-making characteristics (decision-making style and de- Taylor, 1998; Vikenburg et al., 2011), another group has cision-making environment). With this approach, we try to found no effect (e.g., Komives, 1991; Oshagbemi, 2008; integrate these three groups of characteristics to determine van Engen et al., 2001). For instance, using a meta-analysis which leadership style should be used. Figure 1 illustrates based on 162 reports, Eagly and Johnson (1990) found sig- the research framework. nificant gender differences in the reported use of democratic or participatory styles of leadership. More precisely, the authors indicated that men were more likely than women 2.1 Demographic Characteristics to use autocratic, or direct, controlling styles. Gender was also found to have an effect on the process of the creation Gender of leadership and interface between the leader and his or her followers (Bartkus, Kaminskas, & Grunda, 2012). In Traditionally, studies relating to gender and leadership contrast to these findings, several authors have rejected the have used masculine norms as the standards for behaviors, relationship between gender and leadership style. In this leading to conclusions that men are often viewed as better sense, Komives (1991), Oshagbemi (2008), and Yamma- leaders while women often adopt masculine behaviors to rino Dubinsky, Comer, and Jolson (1997) found no effect fit into male-dominated hierarchical structures and systems of gender on leadership style. In order to further examine 15 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 the relationship between leadership style and gender, we information, and the extent to which they coordinate differ- propose the following hypothesis: ent resources of input (Driver & Brousseau, 1990, as cited in Rehman & Waheed, 2012). Puffer’s (1990) research showed H1: The leadership style is associated that decision style, decision outcome, and organizational role with the manager’s gender. of the observer have a significant impact on attributions of the charismatic leadership style. Similar results were iden- tified by Kedia and Nordvedt (2002, as cited in Rehman & Age and educational level Waheed, 2012), whose study showed a relationship between leadership styles and decision-making styles. These research- Studies of age and educational level as predictors of lead- ers argued that transformational leaders use a more compre- ership style are nearly absent from the research literature. hensive style (a high number of alternatives used, a large As Barbuto et al. (2007) emphasized, the very few studies amount of information, and a high coordination of different that have examined age and leadership have been limited to resources of input) of decision making. Therefore, we argue: retirement or adolescence factors. Even fewer of them have studied the relationship between leadership and educational H4: Leadership style is associated with level. However, age serves as an important factor that in- the decision-making style. fluences leadership style as there are important differences in attitudes and behavior between different generations. In this sense, it is argued that younger workers are more ad- Decision-making environment optable in fast-changing environments, take risks, consider new approaches, etc. (Kabacoff, 2002; Kabacoff & Stoffey, Different aspects of the decision-making environment also 2001; Oshagbemi, 2008). Moreover, using a sample of Ohio determine leadership style. In situations of an unstable and AmeriCorps members, Kazan (2000) found that age influ- turbulent environment, leadership style would be character- ences the self-leadership style. Similar results were obtained ized by a very direct top-down communication (Snowden by Payden (1997), Taylor (1998), and Thomas (1996). & Boone, 2007), which is seen as a characteristic of an au- tocratic leadership style. Decisions are made in a political The leader’s level of education can produce a significant manner based on the relative power of those involved and effect on followers’ perceptions of leadership behaviors. without any particular pattern characterizing the criteria Barbuto et al. (2007) found significant differences among used (Smart et al., 1997, as cited in Hassan, Shah, Zaman, educational level groups, and additional research done by Ikramullah, & Ali Shah, 2011). In a more stable environ- Ali and Ali (2011), Kao (2006), and Nayak (2011) confirmed ment, more information is available, decisions can be easily the significant positive relationships between leadership delegated, and a more democratic leadership style can be style and educational level. Consistent with these findings, applied. In this sense, we propose the following hypothesis: Shadare (2011) found that a manager with higher education tends to be more efficient on the job than one with a lower H5: Leadership style is associated with the educational achievement. In light of these arguments, the decision-making environment. following hypotheses can be tested: H2: The leadership style is associated 2.3 Internal Organizational Characteristics: with the manager’s age. Hierarchical level and managerial characteristics H3: The leadership style is associated with the manager’s educational level. Hierarchical level According to Hunt (1971), the research has increasingly 2.2 Decision-Making Characteristics: emphasized the possible differences in leadership require- Decision-making style ments at different managerial levels, yet very few empirical and decision-making environment studies have been conducted. Yukl (2002) suggested that differences in job requirements and discretion exist across Decision-making style levels in organizations and that hierarchy is one of the de- terminants of leadership style. Although Eagly and Johnson Each leadership style is characterized by a specific deci- (1990) found that organizational level had little impact on sion-making style. These decision-making styles can differ the effect sizes of autocratic versus democratic leadership with respect to the number of alternatives used, amount of styles, strong evidence does suggest that there are distinct 16 A. Lojpur, A. Aleksić, S. Vlahović, M. Pejić, S. Peković: Examining Determinants of Leadership Style among Montenegrin Managers patterns of behavior across different hierarchical levels in 3 Data and Model Specification organizations (e.g., Edwards & Gill, 2012). Kabacoff (1999) found differences in the leadership styles and practices of in- The data presented in this study were collected as part of a dividuals in terms of both organizational level and function. larger study conducted among all business organizations in Specifically analyzing different organizational levels indi- Montenegro, with the aim of making a comparative analysis cates that middle-level leadership styles differs significantly of management functions in Montenegro. To this end, a ques- from either senior or lower-level leaders. Ansari and Naeem tionnaire survey was conducted; it consisted of 11 sections, (2010) showed that lower management applied a signifi- where nine sections covered some different aspects of man- cantly higher degree of autocratic style than middle man- agement behavior and two sections covered basic questions agement. However, Oshagbemi and Gill (2004) found that a regarding organizational and personal characteristics. significant difference exists between the senior and first-lev- el managers’ leadership styles, but not between senior and In this paper, we present and analyze data concerned with middle-level managers or middle- and first-level managers. various characteristics of Montenegrin managers as leaders Thus, we formulated the following hypothesis: as well as their preferred leadership and decision-making styles. The survey was conducted by a professional agency H6: The leadership style is associated with from June to September 2007. We obtained a sample of 105 the manager’s hierarchical level. managers from 96 organizations in Montenegro. Although they are usually used as control variables, the variables of age, gender, and educational and hierarchical levels are Managerial characteristics used as independent variables in this study; only size and the sector in which organizations act are used as control As previously mentioned, various classifications of leader- variables. The democratic leadership style was used as a ship styles have been used in research practice. A widely dependent variable. accepted classification is the dimensions of autocratic and democratic leadership styles, emphasizing a strong dis- tinction between managers oriented toward directive and Explanatory variables participative or job-centered versus employee-centered leadership (van Engen & Willemsen, 2000). A democratic To operationalize hypothesis H1 (gender), we used a dummy leader encourages employee participation by creating a variable ( GENDER) that has a value of 1 if the employee is sense of ownership among the employees an environment a man. The second hypothesis, H2 (age), is tested using two in which all employees feel at ease working. Employees dummy variables: AGE 1 has value of 1 if the employee is and team members feel in control of their own destiny between 18 and 40 years old and AGE 2 has a value of 1 if the and are motivated to work hard by more than just a finan- employee is more than 41 years old. AGE 2 was also a ref- cial rewards (Bhatti et al., 2012). In addition, democratic erence category. The effect of education on leadership style leaders communicate regularly with employees about the in hypothesis H3 was measured using two dummy variables: organization’s purpose, goals, and mission. They treat each EDUCATION1 has a value of 1 if the employee has a doc- worker as an individual, transmitting their values and ethical torate, master’s degree, or university degree; EDUCATION2 principles, providing challenging goals and communicating has a value of 1 if the employee has two years of higher edu- a vision of the future while encouraging strategy making, cation, a high school degree, or a primary school degree. This group synergy, innovation, change, and creativity (Ansari & was also a reference category. Hypothesis H4 (decision-mak- Naeem, 2010). On the other hand, the autocratic leadership ing style) was tested using three dummy variables (i.e., style is characterized by one’s strong orientation to tasks, rarely, often, always): DECISION-MAKING STYLE equals results, procedures, and rules, along with a strong emphasis 1 if the employee rarely, often, or always makes decisions on high standards for performance and making leaders’ and with his colleagues. To test how the decision-making envi- subordinates’ roles explicit (Eagly & Johnson, 1990). With ronment affects the leadership style (H5), we used a continu- respect to the identified factors, we plan to test to determine ous variable representing the percentage of people who make if a manager who chooses the democratic leadership style decisions in a stable or unstable environment. Hypothesis also has managerial characteristics that include a people ori- H6 (hierarchical level) was tested using three dummy vari- entation and affinity toward the implementation of changes. ables: HIERARCHICAL LEVEL equals 1 if the employee’s Thus, the following hypothesis is set: position is at the top level, middle level, or low level. To test hypothesis H7 (managerial characteristics), we used three H7: The democratic leadership style is dummy variables: CHARACTERISTIC1 has a value of 1 if associated with a people orientation the employee is more oriented to reaching the objective than and the implementation of changes. to following the leader, CHARACTERISTIC2 has value of 1 17 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 if the employee is more oriented to procedures and results variables. We used three sectors of activity: manufacturing, than to interaction between employees, and CHARACTERIS- service, and trade. In terms of size, we used three groups TIC3 that has a value of 1 if the employee is more oriented to of organizational size: Size 1 (1 to 10 employees), Size 2 results than to the implementation of changes. (11 to 50 employees), and Size 3 (more than 50 employees). The definition of variables and sample statistics are given in The dependent variable, denoted as DEMOCRATIC LEAD- Table 1. By using adequate statistical methods, no problem ERSHIP STYLE, is a binary variable equal to 1 if the of multicollinearity was detected. employee chooses the democratic leadership style rather than a mixed (between democratic and autocratic leadership styles) or autocratic style (looking at the continuum of the The Empirical Model democratic–autocratic leadership style, from pure democrat- ic to pure autocratic style). We used a linear model for the underlying latent variable driving certification: Dummy variables were used to compile data into mutually exclusive categories (Gujrati, 2003). In this way, we could (1) clearly analyze differences among the analyzed catego- ries. As mentioned, we used sector and size as the control Table 1 Definition of Variables and Sample Statistics Variable Description Mean SD Min Max Dependent variable Democratic Leadership The employee uses democratic rather than a mixed (between Style democratic and autocratic or pure autocratic) leadership 0.40 0.49 0.00 1.00 style Dummy variable (= 1 if yes) Independent variables Manufacturing 0.25 0.43 0.00 1.00 SECTOR Service 0.31 0.47 0.00 1.00 Trade (ref) 0.44 0.50 0.00 1.00 Size1 (1 to 10 employees) (ref) 0.29 0.45 0.00 1.00 SIZE Size2 (11 to 50 employees) 0.34 0.48 0.00 1.00 Size3 (more than 51 employees) 0.37 0.48 0.00 1.00 GENDER The employee is a man Dummy variable (= 1 if yes) 0.75 0.44 0.00 1.00 AGE AGE1 (between 18 and 40 years old) 0.52 0.50 0.00 1.00 AGE2 (more than 40 years old) (ref) 0.48 0.50 0.00 1.00 EDUCATION1 (Ph.D., master, or university degree) 0.44 0.50 0.00 1.00 EDUCATION EDUCATION2 (two years of superior education, high school degree, primary school degree) (ref) 0.56 0.50 0.00 1.00 Top level 0.55 0.50 0.00 1.00 HIERARCHICAL LEVEL Middle level 0.32 0.47 0.00 1.00 Low level (ref) 0.13 0.34 0.00 1.00 The employee is more oriented to reaching the objective CHARACTERISTIC1 than to following the leader 0.84 0.37 0.00 1.00 Dummy variable (= 1 if yes) The employee is more oriented to procedures and results CHARACTERISTIC2 than to interactions between employees 0.53 0.50 0.00 1.00 Dummy variable (= 1 if yes) The employee is more oriented to results than to the CHARACTERISTIC3 implementation of changes 0.61 0.49 0.00 1.00 Dummy variable (= 1 if yes) DECISION-MAKING Stable environment 52.32 25.06 0.00 100.00 ENVIRONMENT Unstable environment 43.39 24.39 0.00 100.00 DECISION-MAKING Rarely (ref) 0.19 0.39 0.00 1.00 STYLE (participation) Often 0.50 0.50 0.00 1.00 Always 0.26 0.44 0.00 1.00 18 A. Lojpur, A. Aleksić, S. Vlahović, M. Pejić, S. Peković: Examining Determinants of Leadership Style among Montenegrin Managers where X represents the vector of variables for leadership 4 Results and discussion i style ( SECTOR, SIZE, GENDER, AGE, EDUCATION, HIERARCHICAL LEVEL, MANAGERIAL CHARACTER- The results of the multinomial regression regarding determi- ISTICS, DECISION-MAKING ENVIRONMENT, and DECI- nants of leadership style are presented in Table 2. Accord- SION-MAKING STYLE); β –β are the slope coefficients to ing to the results, R2 is 0.30, indicating that the model fits 1 10 be estimated; and α and μ are the intercept and the distur- the data adequately. The following paragraphs present the bance term, respectively. validity of each hypothesis based on the statistical signifi- cance of associated parameters. The model of the employee’s leadership style was a dis- crete-choice model, with the dummy variables indicating Our first hypothesis (H1)—the leadership style is associ- DEMOCRATIC LEADERSHIP STYLE as the dependent ated with the manager’s gender—was not supported in the variable Y : model. The results show that no effect of gender exists when i using the democratic leadership style, indicating that men and women equally use this style. This is, as mentioned (2) in the literature review, in line with Komives’ (1991), Os- hagbemi’s (2008), and Yammarino et al.’s (1997) results, We specified logistic distributions for μ and maximized the which indicated that gender had no effect on leadership log-likelihood of the logit models (Greene, 2000) to estimate style. In addition, overviews of studies of sex differences in the model’s parameters up to a positive constant. cognition demonstrate that these differences have become Table 2 Determinants of Leadership Style Democratic Leadership Style Variables Estimate Standard Error Intercept -2.48* 1.34 MANUFACTURING 0.17 0.52 SECTOR SERVICE 0.17 0.44 SIZE1 1.04*** 0.49 SIZE SIZE2 1.27*** 0.58 GENDER 0.33 0.43 AGE1 0.26 0.39 EDUCATION1 -0.16 0.42 TOP 1.51*** 0.57 HIERARCHICAL LEVEL MIDDLE 1.72*** 0.59 CHARACTERISTIC1 -0.82 0.55 MANAGERIAL CHARACTERISTICS CHARACTERISTIC2 0.66* 0.39 CHARACTERISTIC3 -0.06 0.38 DECISION-MAKING STABLE 0.00 0.01 ENVIRONMENT UNSTABLE 0.02*** 0.01 OFTEN -0.02 0.48 DECISION-MAKING STYLE ALWAYS -1.09** 0.52 Max Rescaled R2 0.30 -2 log L 194.293 -2 log L (Intercept only) 238.407 Likelihood ratio 44.11 Percent concordant 76.9 Number of observations 177 Note: (*), (**), and (***) stand for parameter significance at the 10%, 5%, and 1% levels, respectively. 19 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 considerably smaller or have even vanished within the last to create such an environment in which all employees feel at 30 or 40 years (van Engen & Willemsen, 2000). One po- ease working and are asked to participate in decision making tential reason for these results might be the changed roles in the organization. This creates a sense of ownership among and characteristics of women as business leaders. As Jonsen the employees, and they work more enthusiastically (Bhatti et al. (2010) stated, there is an evident trend in increasing et al., 2012). Montenegrin managers encourage their col- similarities in the styles of men and women as a result of leagues’ participation, which can be seen as being associat- the changed roles and self-perceptions of women in indus- ed with the democratic leadership style. Moving toward this trialized countries and the appearance of women in formerly highly participative decision-making style, as Vroom (2000) all-male occupations. Thus, according to our results, we can emphasized, can contribute to the organization by (1) de- conclude that male and female Montenegrin managers use veloping individual members’ knowledge and competencies similar leadership styles and gender cannot be seen as a de- by providing them with the opportunities to work through terminant of leadership style used. More and more research problems and decisions usually happening at higher organ- has shown that the differences in leadership styles between izational levels; (2) increasing teamwork and collaboration men and women are slowly vanishing; our research contrib- by providing individuals with the opportunities to solve the utes to this line of thinking. problems as part of the team; and (3) helping in increasing individual identification with organizational goals. Hypotheses H2 (the leadership style is associated with the manager’s age) and H3 (the leadership style is associated Hypothesis H% tested whether the leadership style is asso- with the manager’s educational level) were also note sup- ciated with the decision-making environment. The results ported in our model. Previous research does indicate that the confirmed that the democratic leadership style is associated older the manager, ceteris paribus, the more likely it is for with the unstable decision-making environment. This is in- consultative and participative leadership styles to be used. In teresting to see because it was expected that the democratic other words, older leaders prefer more collective decisions leadership style would be more of a characteristic of a stable compared to younger managers, who prefer making decisions environment, where the leader has enough time to take into that might not necessarily get the approval of the majority of consideration all of the alternatives and resources while en- workers (Oshagbemi, 2008). However, our results show no couraging employees’ participation and exchanges of infor- significant difference between young workers (between 18 mation. In a more unstable environment, decisions have to be and 40 years old) and older workers (more than 40 years old) made quickly and with a minimum of costs, so it is surprising when using the democratic leadership style. This suggests and rather unusual to see that Montenegrin managers use the that, for the managers in Montenegrin organizations, age democratic style in an unstable environment. This result does not represent a significant determinant or predictor of might suggest that they are more oriented to getting the right the use of the democratic leadership style. This is in line and appropriate decision instead of making a risky but not with research of Ekaterini (2010), who explained such a necessarily right and, in the long-term, satisfactory decision. result as the intention of older workers to not necessarily make decisions with their colleagues as they can draw on The hypothesis that leadership style is associated with the their years of experience to make decisions with a greater manager’s hierarchical level (H6) was strongly supported degree of confidence, which younger workers usually do by our research for top and middle management levels. Our not have. In addition, for Montenegrin managers, education results indicated that strong differences in leadership style cannot be seen as associated with the democratic leadership exist between top and middle management compared to style as our results show no difference among employees lower management, but no difference exists between top with a doctorate, master’s degree, or university degree and and middle management. This is a somewhat expected result employees having two years of higher education, a high as the work itself and job responsibilities and requirements school degree, or a primary degree. Although, as mentioned, at different organizational levels call for a higher or lower the level of education influences people’s values, wants, and level of democratic or autocratic leadership style. First-lev- needs, our results suggest that this factor is not connected el management is more oriented to tasks, procedures and with the leadership style used, but presumably more with results, and day-to-day activities, where a more autocratic different expectations and motivation in the workplace. or mixed leadership style is suitable, while higher organi- zational levels tend to use more democratic ones as higher Hypothesis H4 tested whether leadership style is associated organizational levels are more strategic and change oriented, with the decision-making process. The results suggest a sig- motivating and encouraging people to do more than they ini- nificant and positive relationship exists between the demo- tially thought possible (Oshagbemi, 2008). cratic leadership style and managers’ decision making when the manager always makes decisions with his colleagues. The results of our final hypothesis, H7 (democratic lead- The results suggest that the democratic leader always tries ership style is associated with people orientation and 20 A. Lojpur, A. Aleksić, S. Vlahović, M. Pejić, S. Peković: Examining Determinants of Leadership Style among Montenegrin Managers implementation of changes), were somewhat expected. The according to age and education level. This leads to the con- results strongly confirmed that orientation to people is as- clusion that leadership style could be under a greater influ- sociated with one’s choice of democratic leadership style, ence of different situational characteristics and contingencies but they did not confirm that the democratic leadership that are not a direct characteristic of a manager, but condi- style is associated with following the leader and the imple- tions in which the manager works (e.g., hierarchical level or mentation of changes. These findings suggest that Monte- organizational characteristics) that surround the leader. negrin managers—although oriented to people—still have to develop their abilities for inspiring employees to follow The results also confirm that the democratic leadership style them, create, innovate, and change. is more people oriented and transformational and encour- ages participation more. As such, it is not surprising that Finally, regarding the sector activity and size that we used as our results confirm that the democratic leadership style is control variables, the results showed no support for the dif- associated with interactions with people and an orientation ferences in democratic leadership style in organizations re- to people. garding their sector activity, but they did support that certain organizations—according to their size—are more sensitive There are three limitations of our study that future work to the democratic leadership style than others. For instance, might seek to address. First, we used a rather small sample the results show that the democratic leadership style is of Montenegrin managers. Our analysis is restricted by the associated with smaller organizations—concretely, with choice of this sample. Thus, future research should include organizations with 1 to 10 employees or with 11 to 50 em- a larger sample in order to gain a deeper understanding of ployees. Larger organizations usually require more control the issues examined. It would be interesting to examine mechanisms, rules, and procedures; thus, it is possible that these issues in other countries to determine whether national the autocratic or mixed leadership style is more present in culture also plays a significant determinant of leadership larger organizations. style. Second, further research should analyze the combined effects of various characteristics of leadership style. It would also be useful and interesting to consider and analyze how and if these differences can produce differences in the effec- 5 Conclusion tiveness of leaders. This is a complex question that future research should address by considering different measures In this study, our aim was to analyze and examine various of organizational outcomes in line with different measures variables that seem to contribute to and determine one’s of leadership style and its determinants. choice of its leadership style. As most of the previous studies counted for only one or several closely interlinked variables, The findings of this study could be useful for theory and our goal was to examine several demographic, organization- practice in understanding different influences on one’s al, and decision-making characteristics that could be seen as choice of leadership style. The research is also a way of en- determinants of leadership style. According to the literature hancing existing research as previous management literature review, we proposed several hypotheses that were tested on has not provided a similar approach to researching at the a sample of managers in Montenegrin organizations. same time various personal as well as organizational factors. This is especially interesting in the context of Montene- Our results yielded several interesting outcomes that can help gro as, together with the broader results of the study, this us better understand the effect of demographic, organization- research helped develop a deeper understanding of manage- al, and decision-making characteristics on leadership style. ment practice in Montenegro and, more specifically, leader- The results have shown that there is no significant differ- ship characteristics and determinants of leadership styles in ence regarding leadership style among men and women or Montenegrin organizations. References 1. Ali, H., & Ali, H. (2011). Demographics and spiritual leadership: Empirical evidence from Pakistan. Business and Management Review, 1(10), 36–42. 2. Ansari, A. H., & Naeem, F. (2010). Different leadership styles across hierarchical levels: A case study on Indian automobile industry. Asia Pacific Business Review, VI(3), 115–123. 3. Barbuto Jr., J. E, Fritz, S. M., Matkin, G. S., & Marx, D. B. (2007). Effects of gender, education, and age upon leaders’ use of influence tactics and full range leadership behaviors. Sex Roles, 56(1/2), 71–83. http://dx.doi.org/10.1007/s11199-006-9152-6 21 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 4. Bartkus, E. V., Kaminskas, G., & Grunda, R. (2012). Importance of age and gender in the interface between a leader and followers. Transformations in Business & Economics. 11 (1), 184–198. 5. Bhatti, N., Maitlo, G. M., Shaikh, N., Hashmi, M. A., & Shaikh, F. M. (2012). The impact of autocratic and democratic leadership style on job satisfaction. International Business Research, 5(2), 192–201. http://dx.doi.org/10.5539/ibr.v5n2p192 6. Choi, S. (2007) Democratic leadership: The lessons of exemplary models for democratic governance. International Journal of Leadership Studies, 2(3), 243–262. 7. Collard, J. L. (2001). Leadership and gender: An Australian perspective. Educational Management and Administration, 29(3), 343–353. http://dx.doi.org/10.1177/0263211X010293008 8. Druskat, V. (1994). Gender and leadership style: Transformational and transactional leadership in the Roman Catholic Church. Leadership Quarterly, 5(2), 99–119. http://dx.doi.org/10.1016/1048-9843(94)90023-X 9. Eagly, A. H., & Carli, L. L. (2003). The female leadership advantage: An evaluation of the evidence. The Leadership Quarterly, 14(6), 807-834. http://dx.doi.org/10.1016/j.leaqua.2003.09.004 10. Eagly, A. H., & Johnson, B. T. (1990). Gender and leadership style: A meta-analysis. Psychological Bulletin, 108(2), 233–256. http://dx.doi.org/10.1037/0033-2909.108.2.233 11. Edwards, G., & Gill, R. (2012). Transformational leadership across hierarchical levels in UK manufacturing organizations. Leadership & Organization Development Journal, 33(1), 25–50. http://dx.doi.org/10.1108/01437731211193106 12. Ekaterini, G. (2010). The impact of leadership styles on four variables of executives' workforce. International Journal of Business and Management, 5(6), 3–16. 13. Gastil, J. (1994). A definition and illustration of democratic leadership. Human Relations, 47(8), 953–975. http://dx.doi.org/10.1177/001872679404700805 14. Greene, W. H. (2000). Econometric analysis. Upper Saddle River, NJ: Prentice Hall. 15. Gujrati, D. N. (2003). Basic econometrics. Boston: McGraw Hill 16. Gutek, B. A. (1985). Sex and the workplace. San Francisco: Jossey-Bass. 17. Hassan, F. S. U., Shah, B., Zaman, T., Ikramullah, M., & Ali Shah, I. (2011). Effect of leaders’ styles of decision making on perceived organizational effectiveness: An example from Pakistan. International Journal of Business and Social Science, 2(22), 297–307. 18. Hunt, J. G. (1971). Leadership-style effects at two managerial levels in a simulated organization. Administrative Science Quarterly, 16(4), 476–485. http://dx.doi.org/10.2307/2391767 19. Iqbal, J., Inayat, S., Ijaz, M., & Zahid, A. (2012). Leadership styles: Identifying approaches and dimensions of leaders. Interdisciplinary Journal of Contemporary Research in Business, 4(3), 641–659. 20. Jonsen, K., Maznevski, M. L., & Schneider, S. C. (2010). Gender differences in leadership—Believing is seeing: Implications for managing diversity. Equality, Diversity and Inclusion: An International Journal, 29(6), 549–572. http://dx.doi.org/10.1108/02610151011067504 21. Kabacoff, R. I. (1999). Management level, job function and leadership style: A large sample study. Paper presented at the 107th Annual Convention of the American Psychological Association. Boston, MA, USA. 22. Kabacoff, R. I. (2002). Leadership: What has age got to do with it? Research release. New York: Management Research Group. 23. Kabacoff, R. I., & Stoffey, R. W. (2001). Age differences in organisational leadership. Paper presented at 16th Annual Conference of the Society for Industrial and Organisational Psychology. San Diego, CA, USA. 24. Kao, H. (2006). The relationship between leadership style & demographic characteristics of Taiwanese executives. International Business & Economics Research Journal, 5(2), 35–48. 25. Kazan, A. L. (2000). Exploring the concept of self-leadership: Factors impacting self-leadership of Ohio AmeriCorps members. Dissertation Abstracts International: Section A: Humanities & Social Sciences, 60(11-A), 3870. 26. Komives, S. R. (1991). Gender differences in the relationship of hall directors’ transformational and transactional leadership and achieving styles. Journal of College Student Development, 32(2), 155–165. 27. Manning, T. T. (2002). Gender, managerial level, transformational leadership and work satisfaction. Women In Management Review, 17(5), 207–216. http://dx.doi.org/10.1108/09649420210433166 28. Merchant, K. (2012). How men and women differ: Gender differences in communication styles, influence tactics, and leadership styles. CMC Senior Theses (Paper 513). Retreived from http://scholarship.claremont.edu/cmc_theses/513 29. Mullins, L. J. (2005). Management and organisational behaviour. Essex: Prentice Hall. 30. Nayak, B. (2011). Leadership style of managers and supervisors: A case study of Rourkela Steel Plant in India. European Journal of Economics, Finance and Administrative Sciences, 42, 29–41. 31. Oshagbemi, T. (2008). The impact of personal and organisational variables on the leadership styles of managers. The International Journal of Human Resource Management, 19(10), 1896–1910. http://dx.doi.org/10.1080/09585190802324130 32. Oshagbemi, T., & Gill, R. (2004). Differences in leadership styles and behaviour across hierarchical levels in UK organizations. The Leadership & Organization Development Journal, 25(1), 93–106. http://dx.doi.org/10.1108/01437730410512796 33. Payden, B. L. (1997). The relationship between perceived leadership behaviors and job satisfaction based on age, gender, and education level variables. Dissertation Abstracts International: Section A: Humanities & Social Sciences, 57(7-A), 3127. 34. Posner, B. Z. (1992) Person–organization values congruence: No support for individual differences as a moderating influence. Human Relations, 45(4), 351–361. http://dx.doi.org/10.1177/001872679204500403 22 A. Lojpur, A. Aleksić, S. Vlahović, M. Pejić, S. Peković: Examining Determinants of Leadership Style among Montenegrin Managers 35. Puffer, S. M. (1990). Attributions of charismatic leadership: The impact of decision style, outcome, and observer characteristics. Leadership Quarterly, 1(3), 177–192. http://dx.doi.org/10.1016/1048-9843(90)90019-E 36. Rehman, R. R., & Waheed, A. (2012). Transformational leadership style as a predictor of decision making styles: Moderating role of emotional intelligence. Pakistan Journal of Commerce & Social Sciences, 6(2), 257–268. 37. Shadare, O. A. (2011). Management style and demographic factors as predictors of managerial efficiency in work organizations in Nigeria. International Business and Economics Research Journal, 10(7), 85–93. 38. Singh, P., Nadim, A., & Ezzedeen, S. R. (2012). Leadership styles and gender: An extension. Journal of Leadership Studies, 5(4), 6–19. http://dx.doi.org/10.1002/jls.20239 39. Snowden, D. J., & Boone, M. E. (2007). A leader’s framework for decision making. Harvard Business Review, November, 1–9. 40. Tannenbauem, R., & Schmidt, W. H. (1973). How to choose a leadership pattern. Harvard Business Review, 51(3), 162–180. 41. Taylor, T. R. (1998). Factors influencing the effectiveness of cross-functional work teams in a research and development organization. Dissertation Abstracts International: Section B: The Sciences & Engineering, 58 (10-B), 5685. 42. Thomas, B. B. (1996). The relationship of leadership style to teacher leadership preferences. Dissertation Abstracts International: Section A: Humanities & Social Sciences, 57 (1-A), 0064. 43. Toren, N., Konrad, A. M., Yoshioka, I., & Kashlak, R. (1997). A cross-national cross-gender study of managerial task preferences and evaluation of work characteristics. Women in Management Review, 12(6), 234-243. http://dx.doi.org/10.1108/09649429710182459 44. van Engen, M. L., van der Leeden, R., & Willemsen, T. M. (2001). Gender, context, and leadership styles: A field study. Journal of Occupational and Organizational Psychology, 74(5), 581–598. http://dx.doi.org/10.1348/096317901167532 45. van Engen, M. L., & Willemsen, T. M. (2000). Gender and leadership styles: A review of the past decade. WORC Paper 00.10.09, 1–33. Retrieved from http://arno.uvt.nl/show.cgi?fid=4218. 46. Vinkenburg, C. J., van Engen, M. L., Eagly, A. H., & Johannesen-Schmidt, M. C. (2011). An exploration of stereotypical beliefs about leadership styles: Is transformational leadership a route to women's promotion? The Leadership Quarterly, 22(1), 10–21. http://dx.doi.org/10.1016/j.leaqua.2010.12.003 47. Vroom, V. H. (2000). Leadership and the decision-making process. Organizational Dynamics, 28(4), 82–94. http://dx.doi.org/10.1016/ S0090-2616(00)00003-6 48. Yammarino, F. J., Dubinsky, A. J., Comer, L. B., & Jolson, M. A. (1997). Women and transformational and contingent reward leadership: A multiple-levels-of-analysis perspective. Academy of Management Journal, 40(1), 205–222. http://dx.doi.org/10.2307/257027 49. Yukl, G. (2002). Leadership in organisations. Englewood Cliffs, NJ: Prentice Hall International. Anđelko Lojpur, Ph.D., is a full professor at the Faculty of Economics, University of Montenegro, and also a prorector of the University of Montenegro, Montenegro. He has published more than 80 scientific papers in the field of organization, organization development, privatization, and economic development. He is a member of several journal editorial boards and coordinates two Tempus projects. He also provides consulting services to several organizations in Montenegro. Ana Aleksić, Ph.D., is a senior teaching and research assistant at the Department of Organization and Management, Faculty of Economics and Business, University of Zagreb. Her research interests include various aspects of organizational behavior, with a special emphasis on the role of organizational design practices in shaping behavior within different organizational levels. She is the author and co-author of several book chapters and journal publications and has participated as a consultant on several scientific and commercial projects. Sanja Vlahović, Ph.D., currently works as the Minister of Science in Montenegro. She has built her career at academic institutions in Montenegro and abroad. She teaches strategic management, leadership and theory of management, entrepreneurial leadership, and a doctoral course in tourism business systems management. She has also been a visiting lecturer at universities in the Netherlands, Hungary, Poland, Italy, and the United States. Throughout her career, she has participated in several scientific research projects in Montenegro. 23 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Mirjana Pejić Bach, Ph.D., is a full professor at the Department of Informatics, Faculty of Economics and Business, University of Zagreb, Croatia. Her research interests include decision support systems, electronic commerce, and data mining. She has published more than 140 publications in scientific journals, conference proceedings, and books She has also participated in a number of EU FP7 projects and served as a consultant for several scientific and commercial projects. Sanja Pekovic, Ph.D., is an associate professor in economics at the University of Montenegro, Montenegro and a researcher at the University of Paris–Dauphine, France. She has participated in several international scientific conferences and has published papers in recognized journals, with a special interest in industrial organization and relations, international management practices, and environmental economics. Proučevanje dejavnikov stila vodenja med črnogorskimi menedžerji Izvleček Obnašanje vodje ima lahko močan vpliv na različne izide, povezane z delom zaposlenega, zato so bili razviti različni pristopi za najučinkovitejši stil vodenja in dejavnikov za izbiro stila vodenja. Namen prispevka je analizirati, ali na izbiro stila vodenja vplivajo bolj osebni ali bolj organizacijski dejavniki. Pri proučevanju dejavnikov stila vodenja med črnogorskimi menedžerji je analiza podatkov iz raziskave pokazala naslednje: demografski podatki, kot so spol, starost in izobrazba, ne vplivajo na izbiro stila vodenja, interne organizacijske značilnosti, kot sta hierarhična raven in menedžerska usmeritev k nalogam/ ljudem, in značilnosti odločanja, kot sta stil odločanja in odločevalsko okolje, pa so pozitivno povezane z izbiro demokratičnega stila vodenja. Ta prispevek k novejšim raziskavam o vodenju kaže, da so nekatere osebne značilnosti upoštevane kot manj pomembne pri razvoju določenega stila in da je izbira stila bolj odvisna od zunanjih vplivov in razmer. Ključne besede: značilnosti odločanja, demografske značilnosti, interne organizacijske značilnosti, stil vodenja, Črna gora 24 Measuring efficiency of nations ORIGINAL SCIENTIFIC PAPER in Multi Sport Events: A case of Commonwealth Games XIX Received: September 2014 Revised: December 2014 Roma Mitra Debnath Accepted: January 2015 Indian Institute of Public Administration, New Delhi, India roma.mitra@gmail.com DOI: 10.1515/ngoe-2015-0003 Ashish Malhotra Indian Institute of Management Lucknow, Noida, India UDK: 005.523:796:330.43 ashishmalhotra99@gmail.com JEL: L83, C14 Abstract This paper used a data envelopment analysis (DEA) to measure the performance of the nations participating in the Commonwealth Games. To increase the consistency of the research, multiple models were employed to validate the result, but the nature of the input and output remained same throughout the paper. The objective of this study was to establish some realistic targets in terms of number of players for all participant countries and evaluations of their performance as well as benchmarks against the most efficient country. This study would help the nations optimize the size of their players to maximize the outcome in terms of the number of medals won in sporting events. Key words: Performance measurement, data envelopment analysis, efficiency, Commonwealth Games. 1 Introduction The XIX Commonwealth Games 2010 (CWG 2010), held in Delhi on October 2 through 14, were a major success. The games attracted the participation of 71 nations who are part of the Commonwealth Games Associations (CGAs), rep- resenting one-third of the world’s population. Approximately 6,500 athletes and team officials competed in 17 sports and four para-sports in 290 sessions. In the end, two new world records (power lifting and athletics) and 108 new Common- wealth records were established. In general, athletes compete on behalf of the nation, and ranking is based on the total number of gold medals won by each country. Usually the gold medals are worth more than silver ones, which are worth more than bronze ones. At the end of the games, the sum of the medals is computed and used for ranking the partic- ipating countries. NAŠE GOSPODARSTVO OUR ECONOMY 2 The Commonwealth Games Vol. 61 No. 1 2015 The Commonwealth is an alliance of 53 nations across the globe. Although pp. 25–36 there are 53 Commonwealth nations, presently 71 CGAs can enter a team in the 25 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Commonwealth Games, as one nation can have multiple Although various multi-sport events are held globally, the CGAs. For example, the United Kingdom is a single Com- Commonwealth Games are a unique, world-class, mul- monwealth nation that consists of seven CGAs: Scotland, ti-sport events held once every four years. Table 1 summa- Wales, England, Guernsey, Isle of Man, Jersey, and Northern rizes a few of the most popular multi-sport events along with Ireland. The Commonwealth Games are also known as the their descriptions. Friendly Games as they are held between a family of nations that share a common history. Her Majesty Queen Elizabeth There is still great diversity in the relative performance of II is the head of the Commonwealth and patron of the Com- athletes, as indicated by nations’ rankings, which makes monwealth Games Federation (CGF). Prince Edward, HRH it difficult to understand how and where to improve. This the Earl of Wessex KCVO, is the vice patron. article employs a data envelopment analysis (DEA) to compare the relative efficiency of the utilization of resources The first edition of these prestigious games took place in (i.e., players) by nations who have won medals in the XIX Hamilton, Canada, in 1930, with 11 countries and 400 Commonwealth Games 2010 in Delhi. In an attempt to find athletes competing six sports. Since then, 19 games have new ways to establish alternative performance rankings, been held, being scheduled every four years (except for this paper uses the DEA model with an output orientation. 1942 and 1946 due to World War II). From 1930 to 1950, The total number of players from each country is used as an the games were known as the British Empire Games, from input, whereas the outputs are the total number of medals 1954 to 1966 they were the British Empire and Common- (gold, silver, and bronze). The unit of analysis is all coun- wealth Games, and in 1970 and 1974 they were known tries that won at least one medal. as the British Commonwealth Games. Finally, in 1978, in Edmonton (Canada), the name of the games were changed The objective of the paper is twofold. In the first stage, the to the Commonwealth Games, a name that remains to today paper ranks the nations in terms of all medals won and gold (http://www.thecgf.com/games/story.asp) medals won by calculating their relative efficiency. In the second stage, the paper decides the optimal number of players The number of teams competing in the Commonwealth to be sent to win medals in the CWG. To achieve the second Games depends on the number of nations in the Common- objective, a DEA model with input orientation is used. wealth itself as, from year to year, nations are admitted and suspended for various reasons. Since 2002, there has been The paper is organized as follows: Section 2 discusses the an increase in attendance as all Commonwealth nations have DEA models used for evaluating the performance of the been represented in all editions of these prestigious games. participating countries. Section 3 presents an empirical As the number of nations taking part has increased, so too study using different DEA models. Section 4 presents the have the number of athletes participating, sports included, methodology, while Section presents the findings. Section 6 and events held. contains conclusions and discussions. Table 1 Major International Multi-sporting Events and Descriptions Event Description Summer Olympics The world’s premier multi-sport and multi-country sporting competition, held every four years. Winter Olympics The winter sports version of the Olympic Games, held every four years, two years after the Summer Olympics. Paralympic Games A major event for athletes with disabilities, now run in conjunction with the Summer Olympic Games, every four years. Commonwealth Games Held every four years, most recently held in Glasgow in 2014. Asian Games The Asian Games, officially known as Asiad, is a multi-sport event along the lines of the Olympics, though only for Asian countries. They were first held in 1951. Gay Games The Gay Games, held every 4 years, is open to all who wish to participate, without regard to sexual orientation. Military World Games For military athletes from more than 100 countries. European Games A multi-sport event along the lines of the Summer Olympic Games, though limited to athletes from European nations. Youth Olympics The Youth Olympic Games is an international multi-sport event, held every four years for athletes aged 14 to 18. Source: Wood 2010 26 R. M. Debnath, A. Malhotra: Measuring efficiency of nations in Multi Sport Events: A case of Commonwealth Games XIX 3 Data Envelopment Analysis Efficiency measurement is a commonly used tool to measure the performance of any DMU and estimate the relative effi- DEA is a non-parametric approach developed by Farrel ciency of the DMUs. Generally speaking, simple efficiency (1957). Charnes, Cooper and Rhodes (1978) subsequently can be calculated using a ratio of outputs to inputs, as given made a major breakthrough in the same field. Since then, in Equation 1. DEA has been widely accepted, particularly in its appli- cation to public sector operations, such as education and Efficiency = Outputs / Inputs (1) healthcare, where the policy objectives are vaguely defined as a functional form of input–output relationships. DEA is a non-parametric technique for assessing the relative per- However, in DEA, multiple inputs and outputs are linearly formance of a set of similar units. Each decision making aggregated using weights. Therefore, the efficiency is unit (DMU) has a certain number of inputs and produces measured as a ratio of: a certain number of outputs. In this case, the countries that Weighted Sum of Outputs won at least one medal are considered DMUs. The aim is to Efficiency = Weighted Sum of Inputs (2) identify which country is operating efficiently in converting the inputs into outputs in an optimum way, indicating that it belongs to the efficiency frontier, and which DMUs do Efficiency = (3) not operate efficiently (i.e., not able to convert the inputs to outputs) and therefore should make appropriate adjustments in their input and/or output in order to attain efficiency. where u is the weight assigned to input x and v the weight i i j assigned to output y as given in Equation 3. j DEA has been applied in a number of different areas, such as hospitality, healthcare (hospitals, doctors), education DEA models assume CRTS and VRTS. In a CRTS, the (schools, universities), banks, manufacturing, benchmark- change in the output is proportionate to the change in the ing, management evaluation, energy efficiency, fast food input. However, in a VRTS, the change in output is not pro- restaurants, and retail stores (Cooper, Seiford, & Tone, portional to the change in the input. Figure 1 shows various 2004; Cooper, Thompson, & Thrall, 1996; Debnath & types of RTS. Shankar, 2009; Debnath & Shankar, 2013; Färe, Grosskopf, & Lovell, 1994; Rhode & Southwick, 1993; Sinauny-Stern, Mehrez, & Barboy, 1994; Thenassoulis & Dunstan, 1994; Tomkins & Green, 1988). Anderson (1995) compiled more Figure 1: Various returns to scale in DEA than 360 papers on the application of DEA, and there has been a constant increase in the number of DEA applications reported on Portland State University’s website. DEA is used to compute a score that defines the relative efficiency of a particular DMU versus all other DMUs observed in the sample. The various inputs and outputs are assigned optimal weights by which the output can be maximized. Decreasing Output returns The two most frequently applied models used in DEA are 19,4 to scale (C) the CCR model, named after Charnes et al. (1978), and Constant returns the BCC model, named after Banker, Charnes, and Cooper to scale (B) (1984). The basic difference between these two models is the Increasing returns returns to scale (RTS). Whereas the latter takes into account to scale (A) the effect of variable RTS (VRTS), the former restricts Inputs DMUs to operate with constant RTS (CRTS). Charnes et al. (1978) developed DEA to evaluate the efficiency of public sector non-profit organizations. DEA aims to measure how Point A represents the units present in the region of increas- efficiently a DMU uses the resources available to generate ing RTS. If we assume that an increase in inputs will increase a set of outputs. DMUs can include manufacturing units, outputs above the dashed line that would result in a greater departments of big organizations (e.g., universities, schools, than proportionate increase in outputs. If the units increase bank branches, hospitals, power plants, police stations, tax their inputs, the ratio of inputs to outputs will change so that offices, defense bases), a set of firms, and even practicing the unit moves along the efficiency horizon and the unit will individuals like medical practitioners. move into the region of CRTS. Point B falls into a CRTS. 27 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Point C falls in the region of decreasing RTS or non-increas- Carmichael, Thomas, and Ward (2001) to investigate the ing RTS. This implies that increases in inputs will result in production function in English association football. Haas a ratio of inputs to outputs that continue to fall along the (2003a, 2003b) used the DEA model to analyze the effi- frontier. If that assumption holds, increases in inputs will ciency of the English Premier League and applied the DEA result in proportionately smaller increases in output. The model to Major League Soccer. Barros and Garcia-del-Bar- only point not identified by any region is an inefficient unit. rio (2008) estimated a stochastic frontier latent class model to analyze cost efficiency. Anderson and Sharp (1997) used deterministic non-parametric frontier to create a new measurement to evaluate the batsmen in baseball games. 4 DEA and Sports Carmichael, Thomas, and Ward (2000) applied DEA to formulate the production function in rugby games. Fezel The existing literature shows that researchers have used and D’Itri (1997) also used deterministic non-paramet- diverse mathematical models to study the results of mul- ric frontier to measure coaches’ efficiency in basketball. ti-sport games. Lozano, Villa, Guerrero, and Cortes (2002) The authors concluded that the results would help replace and Estellita Lins, Gomes, Soares de Mello, and Soares de coaches and enhance teams’ performance. Hadley, Poitras, Mello (2003) analyzed the relative efficiency of the partic- Ruggiero, and Knowles (2000) used DEA in American ipating countries that won at least one medal in Olympic football to evaluate the team’s performance with respect to Games in relation to their available resources, where inputs its potential. Scully (1994) applied stochastic frontier and were the country’s population and gross domestic product deterministic frontier in American football and baseball, (GDP) and outputs were the numbers of gold, silver, and respectively, to study the relationship between coaches’ bronze medals. performance in terms of the team and efficiency of its management. Benicio, Bergiante, and Soares (2013) applied the free disposal hull (FDH) model to measure the efficiency of the Winter Olympic Games held in 2010. The authors used the BCC input-oriented model, where the number of athletes was 5 Research Methodology considered as an input and the number of gold, silver and bronze medals was considered as output parameters. Mean- while, Lozano et al. (2002) measured the performance of the 5.1 Model Selection nations at the Summer Olympics Games using DEA, where the gross national product (GNP) and population of the par- As the objective of the study is to optimize the number of ticipating countries were input variables while the output players participating in the international games to maximize variables were the number of gold, silver, and bronze medals. the efficiency of the team in terms of winning medals, an Zhang, Li, Meng, and Liu (2009) used DEA to measure the output-oriented model was selected for the same purpose. performance of nations of the Olympic Games. However, the The BCC model was chosen as the change in the input does authors used lexicographic preference in the DEA. Churilov not guarantee a proportionate increase in the output. As pre- and Flitman (2006) used several social economics varia- viously discussed, the BCC model would have VRTS. The bles—not only GDP and population, but also the DEL index change in the number of medals (output) cannot be propor- and IECS index—to evaluate the performance and rank the tional to the number of players (input) in our case. participating nations in the Sydney Olympics held in 2000. Cesaroni (2011) used the FDH model to analyze the efficien- cy of Italian drivers and vehicle agencies. Other important 5.2 Data Collection European leagues have been investigated using the DEA model as well, such as the Spanish league (Gonzalez-Gomez The data were collected from the CWG office in New & Picazo-Tadeo, 2010), the Italian Serie A (Bosca, Liern, Delhi, India. In total, 71 countries participated in the Martínez, & Sala, 2009), the German Bundesliga (Haas, games, representing various region of the world, includ- Kocher, & Sutter, 2004), and the French Ligue 1 (Jardin, ing the Caribbean, Asia, Oceania, Africa, Europe, and 2009). Sexton and Lewis (2003) applied the two-stage DEA America. Approximately 4400 players participated, in- model to baseball and an apportion of the duties of a typical cluding 1700 females. The participating countries won 762 baseball club among the two operating units. medals, which were nearly equally distributed among gold, silver, and bronze medals. Table 2 shows that, of the 71 Dawson, Dobson, and Gerrard (2000a, 2000b) applied participating countries, only 34 (approximately 47%) won stochastic frontier analysis to investigate managerial effi- medals in international sports events; of these, 23 countries ciency in English soccer. A similar approach was used by (67%) won gold medals. 28 R. M. Debnath, A. Malhotra: Measuring efficiency of nations in Multi Sport Events: A case of Commonwealth Games XIX 6 Findings are considered to correspond to the participating nations that won at least one medal. Two models were used to analyze This paper performed an independent DEA of the 2010 the performance of the medal-winning nations. In the first Commonwealth Games held in India. In the DEA, the DMUs model, three output variables were considered: he number Table 2 Highlights of Participating Nations and Medals Won VER ONZE AL VER ONZE AL CODE COUNTRY REGION GOLD SIL BR TOT CODE COUNTRY REGION GOLD SIL BR TOT AIA Anguilla Caribbean 0 MDV Maldives Asia 0 ANT Antigua and Barbuda Caribbean 0 MLT Malta Europe 0 AUS Australia Oceania 74 55 48 177 MOZ Mozambique Africa 0 BAH Bahamas Caribbean 1 1 4 6 MRI Mauritius Africa 2 2 BAN Bangladesh Asia 1 1 MSR Montserrat Caribbean 0 BAR Barbados Caribbean 0 NAM Namibia Africa 1 2 3 BER Bermuda Americas 0 NFK Norfolk Island Oceania 0 BIZ Belize Americas 0 NGR Nigeria Africa 11 8 14 33 BOT Botswana Africa 1 3 4 NIR Northern Ireland Europe 3 3 4 10 BRU Brunei Darussalam Asia 0 NIU Niue Oceania 0 CAN Canada Americas 26 17 33 17 NRU Nauru Oceania 1 1 2 CAY Cayman Islands Caribbean 1 1 NZL New Zealand Oceania 6 22 8 36 CMR Cameroon Africa 2 4 6 PAK Pakistan Asia 2 1 2 5 COK Cook Islands Oceania 0 PNG Papua New Guinea Oceania 1 1 CYP Cyprus Europe 4 3 5 12 RSA South Africa Africa 12 11 10 33 DMA Dominica Caribbean 0 RWA Rwanda Africa 0 ENG England Europe 37 60 45 142 SAM Samoa Oceania 3 1 4 FLK Falkland Islands Americas 0 SCO Scotland Europe 9 10 7 26 GAM Gambia Africa 0 SEY Seychelles Africa 1 0 1 GGY Guernsey Europe 0 SHN St. Helena Americas 0 GHA Ghana Africa 1 3 4 SIN Singapore Asia 11 11 9 31 GIB Gibraltar Europe 0 SKN St. Kitts and Nevis Caribbean 0 GRN Grenada Caribbean 0 SLE Sierra Leone Africa 0 GUY Guyana Americas 1 1 SOL Solomon Islands Oceania 0 IND India Asia 38 27 36 101 SRI Sri Lanka Asia 1 1 1 3 IOM Isle of Man Europe 2 2 SVG St. Vincent and The GrenadinesCaribbean 0 IVB British Virgin Islands Caribbean 0 SWZ Swaziland Africa 0 JAM Jamaica Caribbean 2 4 1 7 TAN Tanzania Africa 0 JEY Jersey Europe 0 TCA Turks and Caicos Islands Caribbean 0 KEN Kenya Africa 12 11 9 32 TON Tonga Oceania 2 2 KIR Kiribati Oceania 0 TRI Trinidad and Tobago Caribbean 0 LCA St. Lucia Caribbean 1 1 TUV Tuvalu Oceania 0 LES Lesotho Africa 0 UGA Uganda Africa 2 2 MAS Malaysia Asia 12 10 13 35 VAN Vanuatu Oceania 0 MAW Malawi Africa 0 WAL Wales Europe 2 7 10 19 ZAM Zambia Africa 0 29 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 of gold, silver, and bronze medals won by a country in the the data set represents both poor and rich countries. India CWG 2010. The DEA output is summarized in Table 3. is the most populous country that participated in the game, The input variable was the total number of players repre- while the Cayman Islands and Nauru are the least populated senting a country across chosen sports events. In the second countries; their GDP is also small compared to India. The model, only one output variable was considered in terms of analysis primarily computes the relative efficiency of the the number of gold medals won by the nations. The analysis nations participating in the game, irrespective of their size of this model is presented in Table 4. The data set is heter- and economy. Both models are BCC output oriented, where ogeneous in terms of the size of the nations and the GDP as the output (number of medals) is maximized under at most Table 3 DEA Results Considering Three Outputs No. DMU Score Rank Reference set (lambda) 1 Australia 1 1 Australia 1 2 Bahamas 1 1 Bahamas 1 3 Bangladesh 0.175799 31 Bahamas 0.831169 Nigeria 0.168831 4 Botswana 0.611111 14 Bahamas 0.909091 Nigeria 9.09E-02 5 Canada 1 1 Canada 1 6 Cayman Islands 0.430323 19 Australia 1.81E-02 Nauru 0.981865 7 Cameroon 1 1 Cameroon 1 8 Cyprus 0.618182 13 Bahamas 0.452941 Nigeria 0.270588 Singapore 0.276471 9 England 1 1 England 1 10 Ghana 0.356481 23 Bahamas 0.558442 Nigeria 0.441558 11 Guyana 0.208633 30 Nauru 0.62069 Singapore 0.37931 12 India 0.75 10 Australia 1 13 Isle of Man 0.386935 21 Bahamas 0.883117 Nigeria 0.116883 14 Jamaica 0.30529 26 England 4.29E-02 Singapore 0.957096 15 Kenya 0.466896 17 Australia 0.202689 England 7.43E-02 Singapore 0.722994 16 St. Lucia 1 1 St. Lucia 1 17 Malaysia 0.501869 16 Australia 9.33E-02 Canada 0.310652 England 9.12E-02 Nigeria 0.504805 18 Mauritius 0.249191 28 Bahamas 0.597403 Nigeria 0.402597 19 Namibia 0.426952 20 Bahamas 0.865169 Nigeria 2.04E-03 Singapore 0.132789 20 Nigeria 1 1 Nigeria 1 21 Northern Ireland 0.358951 22 Bahamas 0.104322 Nigeria 0.533035 Singapore 0.362643 22 Nauru 1 1 Nauru 1 23 New Zealand 0.7048 11 England 0.412541 Singapore 0.587459 24 Pakistan 0.288394 27 Bahamas 0.398374 Nauru 8.13E-03 Singapore 0.593496 25 Papua New Guinea 7.93E-02 34 England 3.30E-02 Singapore 0.966997 26 South Africa 0.533079 15 Australia 0.152079 England 7.42E-02 Nigeria 0.231174 Singapore 0.542525 27 Samoa 0.335458 25 Australia 0.108808 Nauru 0.891192 28 Scotland 0.336404 24 Australia 0.146703 England 0.250434 Singapore 0.602863 29 Seychelles 0.243697 29 Nauru 0.689655 Singapore 0.310345 30 Singapore 1 1 Singapore 1 31 Sri Lanka 8.20E-02 33 Australia 1.31E-03 England 4.29E-02 Nigeria 0.320506 Singapore 0.6353 32 Tonga 0.628571 12 Bahamas 0.727273 St. Lucia 0.272727 33 Uganda 0.169782 32 Australia 0.147668 Nauru 0.852332 34 Wales 0.461319 18 Canada 0.249382 England 9.48E-02 Nigeria 0.655821 30 R. M. Debnath, A. Malhotra: Measuring efficiency of nations in Multi Sport Events: A case of Commonwealth Games XIX the present input (number of players) consumption. Table output variable is the number of gold medals won by the 3 shows that countries like Australia, Bahamas, Canada, participated nations in CWG 2010. Compared to the earlier Cameroon, England, St. Lucia, Nigeria, Nauru, and Sin- analysis, a drastic change can be seen as the number of fully gapore were fully efficient countries in terms of winning efficient countries dropped to three—namely, Australia, medals, even though these countries differ in terms of their India, and Nauru. size and economic conditions. Table 5 shows the benchmark of the inefficient countries VRTS is assumed to hold. The inputs represent the number under the BCC (O)-oriented models. This table summarizes of players representing their respective countries, which can the information from Tables 3 and 4, where Table 3 rep- be controlled by the countries. As this paper also measures resents the model (called model 1) with three outputs and the efficiency of countries in winning medals, the BCC Table 4 represents one output (called model 2). In model output (O)-oriented model was considered for the analysis. 1, the numbers of output variables are the number of gold, silver, and bronze medals; model 2 has one output variable— As Table 3 suggests, very few countries were fully efficient namely, the number of gold medals won in the CWG 2010. in terms of winning at least one medal in the game. For The inferences have been drawn heavily from peer group instance, Australia, Bahamas, Canada, Cameroon, England, analysis that plays a significant role in DEA modeling. The Nigeria, Nauru, and Singapore are fully efficient countries result is particularly significant for inefficient countries (efficiency = 100%). to improve their efficiency by referring to the peer group located on an efficient frontier. For instance, countries like Table 4 presents the BCC (O)-oriented model, where the Canada and Singapore are fully efficient countries in model number of players are used as the input variable and the only 1 (efficiency = 100%; Table 3), which means these countries Table 4 DEA Results Considering One Output (Gold Medal) No. DMU Score Rank Reference set (lambda) 1 Australia 1 1 Australia 1 2 Bahamas 0.274148 14 Australia 3.63E-02 Nauru 0.963731 3 Botswana 0.201146 18 Australia 5.44E-02 Nauru 0.945596 4 Canada 0.549286 5 Australia 0.634715 Nauru 0.365285 5 Cayman Islands 0.430323 9 Australia 1.81E-02 Nauru 0.981865 6 Cyprus 0.404506 11 Australia 0.121762 Nauru 0.878238 7 England 0.534126 6 Australia 0.935233 Nauru 6.48E-02 8 India 0.513514 7 Australia 1 9 Jamaica 0.138625 21 Australia 0.183938 Nauru 0.816062 10 Kenya 0.416659 10 Australia 0.380829 Nauru 0.619171 11 Malaysia 0.323261 13 Australia 0.494819 Nauru 0.505181 12 Nigeria 0.604069 4 Australia 0.235751 Nauru 0.764249 13 Northern Ireland 0.207937 17 Australia 0.183938 Nauru 0.816062 14 Nauru 1 1 Nauru 1 15 New Zealand 0.168498 20 Australia 0.474093 Nauru 0.525907 16 Pakistan 0.233515 16 Australia 0.103627 Nauru 0.896373 17 South Africa 0.44283 8 Australia 0.357513 Nauru 0.642487 18 Samoa 0.335458 12 Australia 0.108808 Nauru 0.891192 19 Scotland 0.254096 15 Australia 0.471503 Nauru 0.528497 20 Singapore 0.919048 3 Australia 0.150259 Nauru 0.849741 21 Sri Lanka 6.06E-02 23 Australia 0.212435 Nauru 0.787565 22 Uganda 0.169782 19 Australia 0.147668 Nauru 0.852332 23 Wales 6.60E-02 22 Australia 0.401554 Nauru 0.598446 31 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 are able to use their resources (capability of the players) to their peer group. Although the peers for Canada are Aus- to win at least one medal. However, they are inefficient tralia and Nauru (5th and 7th column of Table 4), Australia has in model 2, as shown in Table 4, in terms of winning gold more weightage (0.634715) than Nauru (0.365285). Hence, medals. Their efficiency scores are 0.549268 and 0.919048, the most appropriate peer for Canada would be Australia in respectively (see Table 4). Furthermore, if these two coun- terms of improving the efficiency in winning gold medals. tries want to improve their performance, they need to refer Similarly, for Singapore, the peers are Australia (0.150259 Table 5 Benchmarks According to Both Models Sr. Model 1 (Gold+ Model 2 Sr. Model 1 (Gold+ Model 2 No Country Silver+Bronze) (Gold Medal) No Country Silver+Bronze) (Gold Medal) 1. Anguilla N/A N/A 36. Maldives N/A N/A 2. Antigua and Barbuda N/A N/A 37. Malta N/A N/A 3. Australia Australia Australia 38. Mozambique N/A N/A 4. Bahamas Bahamas Nauru 39. Mauritius Bahamas N/A 5. Bangladesh Bahamas N/A 40. Montserrat N/A N/A 6. Barbados N/A N/A 41. Namibia Bahamas N/A 7. Bermuda N/A N/A 42. Norfolk Island N/A N/A 8. Belize N/A N/A 43. Nigeria Nigeria Nauru 9. Botswana Bahamas Nauru 44. Northern Ireland Nigeria Nauru 10. Brunei Darussalam N/A N/A 45. Niue N/A N/A 11. Canada Canada Nauru 46. Nauru Nauru Nauru 12. Cayman Islands Nauru Nauru 47. New Zealand Singapore Nauru 13. Cameroon Cameroon N/A 48. Pakistan Singapore Nauru 14. Cook Islands N/A N/A 49. Papua New Guinea Singapore N/A 15. Cyprus Bahamas Nauru 50. South Africa Singapore Nauru 16. Dominica 51. Rwanda N/A 17. England England Nauru 52. Samoa Nauru Nauru 18. Falkland Islands N/A N/A 53. Scotland Singapore Nauru 19. Gambia N/A N/A 54. Seychelles Nauru N/A 20. Guernsey N/A N/A 55. St. Helena N/A N/A 21. Ghana Bahamas N/A 56. Singapore Singapore Nauru 22. Gibraltar N/A N/A 57. St. Kitts and Nevis N/A N/A 23. Grenada N/A N/A 58. Sierra Leone N/A N/A 24. Guyana Nauru N/A 59. Solomon Islands N/A N/A 25. India Australia Australia 60. Sri Lanka Singapore Nauru 26. Isle of Man Bahamas N/A 61. St. Vincent and The Grenadines N/A N/A 27. British Virgin Islands N/A N/A 62. Swaziland N/A N/A 28. Jamaica Singapore Nauru 63. Tanzania N/A N/A 29. Jersey N/A N/A 64. Turks and Caicos Islands N/A N/A 30. Kenya Singapore Nauru 65. Tonga Bahamas N/A 31. Kiribati N/A N/A 66. Trinidad and Tobago N/A N/A 32. St Lucia St Lucia N/A 67. Tuvalu N/A N/A 33. Lesotho N/A N/A 68. Uganda Nauru Nauru 34. Malaysia Nigeria Nauru 69. Vanuatu N/A N/A 35. Malawi N/A N/A 70. Wales Nigeria Nauru 71. Zambia N/A N/A 32 R. M. Debnath, A. Malhotra: Measuring efficiency of nations in Multi Sport Events: A case of Commonwealth Games XIX Table 6 Ideal Number of Players in Two Situations (BCC input-oriented model) e) e) ers ers under ers under ers ers under ers under er+Bronz er+Bronz f play f play f play f play f play f play Sr. Sr. No. Country Actual number o Ideal number o Model 1 (Gold+Silv Ideal number o Model 2 (Gold Medal) No. Country Actual number o Ideal number o Model 1 (Gold+Silv Ideal number o Model 2 (Gold Medal) 1. Anguilla N/A N/A 36. Maldives N/A N/A 2. Antigua and Barbuda N/A N/A 37. Malta N/A N/A 3. Australia 396 396* 396* 38. Mozambique N/A N/A 4. Bahamas 24 24* 10 39. Mauritius 55 17 N/A 5. Bangladesh 37 13 40. Montserrat N/A N/A 6. Barbados 41. Namibia 30 17 7. Bermuda N/A N/A 42. Norfolk Island N/A N/A 8. Belize N/A N/A 43. Nigeria 101 101* 63 9. Botswana 31 21 10 44. Northern Ireland 81 30 21 10. Brunei Darussalam N/A N/A 45. Niue N/A N/A 11. Canada 255 255* 143 46. Nauru 10 10* 10* 12. Cayman Islands 17 10 10 47. New Zealand 193 137 37 13. Cameroon 25 25* N/A 48. Pakistan 50 20 16 14. Cook Islands N/A N/A 49. Papua New Guinea 78 10 N/A 15. Cyprus 57 36 25 50. South Africa 148 76 69 16. Dominica 51. Rwanda 17. England 371 371* 201 52. Samoa 52 21 21 18. Falkland Islands N/A N/A 53. Scotland 192 63 53 19. Gambia N/A N/A 54. Seychelles 28 10 N/A 20. Guernsey N/A N/A 55. St. Helena N/A N/A 21. Ghana 58 21 N/A 56. Singapore 68 68* 63 22. Gibraltar N/A N/A 57. St. Kitts and Nevis N/A N/A 23. Grenada N/A N/A 58. Sierra Leone N/A N/A 24. Guyana 32 10 N/A 59. Solomon Islands N/A N/A 25. India 407 285 206 60. Sri Lanka 92 14 10 26. Isle of Man 33 17 N/A 61. St. Vincent and The Grenadines N/A N/A 27. British Virgin Islands N/A N/A 62. Swaziland N/A N/A 28. Jamaica 81 28 16 63. Tanzania N/A N/A 29. Jersey N/A N/A 64. Turks and Caicos Islands N/A N/A 30. Kenya 157 72 69 65. Tonga 21 17 N/A 31. Kiribati N/A N/A 66. Trinidad and Tobago N/A N/A 32. St. Lucia 13 13* N/A 67. Tuvalu N/A N/A 33. Lesotho N/A N/A 68. Uganda 67 16 16 34. Malaysia 201 96 69 69. Vanuatu N/A N/A 35. Malawi N/A N/A 70. Wales 165 72 16 71. Zambia N/A N/A 33 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 weightage) and Nauru (0.849741 weightage), as depicted 7 Conclusion and Discussion in Table 4. Nauru has greater weightage (0.849741 > 0.150259), making it a role model for Singapore to improve The present study discussed DEA models with a various its efficiency. Nauru only participated in weight lifting and combination of input and output variables for the evaluation won medals in that. Similarly, India—being an inefficient of the relative efficiency of nations that won medals at CWG DMU in model 1 (Table 3) with only 75% efficiency and held in India in 2010. The findings are interesting and in- 51% efficiency in model 2 (Table 4)—has to follow Austral- sightful too. In an international sports event, the primary ob- ia if it wants to win at least a medal in the game or a gold jective of any nation is to show its superiority over other par- medal. On a similar note, according to Tables 3 and 4, coun- ticipating nations by winning a maximum number of medals, tries like Sri Lanka, Scotland, and Pakistan should follow especially gold medals, in multi-sports events. However, it Singapore’s example to win at least a medal. Surprisingly, is also usual practice among many nations to represent the when comparing Tables 3 and 4, Nigeria is a fully efficient country in multiple sports without any expertise. This leads country in terms of winning at least one medal; however, it to a huge participation in terms of the number of players, needs to follow the strategy adopted by Nauru in order to delegations, and officials without winning any laurels for win a gold medal. the country. This is obviously not a desirable situation as it causes embarrassment for the participating nation. Given As one of our objectives is to estimate the ideal number of that one of the contributions of this study is to optimize the athletes that a country should select to represent in mul- number of players to maximize efficiency in terms of medals ti-sports events like the CWG in order to win at least a medal won in international sports events, DEA modeling has been or only gold medal, an input-oriented model was also run to used. The different DEA models show different results as the analyze the performance of the medal-winning nations. For number of DMUs (countries in our case) changed when the this purpose, the BCC input-oriented model was selected numbers of output parameters were used as a variable. The for both situations—the first for the three output variables result is essentially useful for the policymakers of the inter- (model 1) and the second for one output variable (model 2). national sports events who decide the number of players to In this input-oriented model, which aims to reduce the input represent their country in the international sports arena. The amounts by as much as possible while keeping at least the result of the present study would help them strategize the present output levels, the number of athletes was considered number of players to maximize the probability of winning as an input variable. Table 6 represents the ideal number of medals in various sports. athletes under two different situations: when three outputs (model 1) compared to only one output is considered (model An interesting aspect of this paper was the effort to identify 2). In model 1, the output is the number of gold, silver, and the trend among participating nations in CWG 2010 in terms bronze medals won by the nations; in model 2, the number of of representing the ideal number of players. The countries gold medals won is considered an output variable. Numbers with fewer players were found to be more efficient in terms with an asterisk (*)represent an optimum number of players of their performance than countries with more players in to win at least a medal or a gold medal. CWG 2010. Indeed, Nauru, with the fewest participating players (only 10), was able to win a gold and silver medal, The optimum number of players for a country to win at whereas Sri Lanka won one medal in each category with 92 least a medal is given in the fourth and fifth columns of participating players. Although there is pride in taking part Table 6. As an explanation, Kenya should be represented in international sports events and national pride has its own by only 72 instead of 157 to win at least a medal, and it significance, in terms of performance efficiency, the number requires only 69 to win a gold medal. Similarly, India of medals matter to a great extent. should represent only 285 and 206 players to win at least a medal and a gold medal, respectively. Scotland was rep- As per the results, only a handful of nations have been resented by 192 players but it needs only 53 to win a gold identified as being completely efficient, whether in terms of medal or 63 players to win at least a medal. South Africa sending an appropriate number of players to represent the needs only 69 to win a gold medal and 76 to win at least a country or to win a medal. Most countries exhibit more of a medal. This analysis is useful for those countries represent- disappointment and only modest success. ed by a huge number of players, but not able to compete with the participants of other countries. Therefore, a good Future research should consider analyzing the same coun- strategy could be to select players in such a combination tries for the CWG recently concluded in Glasgow in 2014. that the players are able to win in the international games. As the number of countries remains the same, the efficiency The reputation of a country also depends on success in in- of these countries can be observed with a different number ternational games like CWG. of players in different sports activities. 34 R. M. Debnath, A. Malhotra: Measuring efficiency of nations in Multi Sport Events: A case of Commonwealth Games XIX References 1. Anderson, T. R., & Sharp, G. P. (1997). A new measure of baseball batters using DEA. Operations Research, 73, 141–155. http://dx.doi. org/10.1023/A:1018921026476 2. Anderson, T. (1995). A Data Envelopment Analysis (DEA) Home Page . Retrieved December 20, 2014, from http://www.emp.pdx.edu/ dea/homedea.html 3. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092. http://dx.doi.org/10.1287/mnsc.30.9.1078 4. Barros, C. P., & Garcia-del-Barrio, P. (2008). Efficiency measurement of the English Football Premier League with a random frontier model. Economic Modelling, 25(5), 994–1002. http://dx.doi.org/10.1016/j.econmod.2008.01.004 5. Benicio, J. D. C.T., Bergiante, N. C. R., & Soares, D. M. J. C. C. B. (2013). A FDH study of the Vancouver 2010 Winter Olympic Games. WSEAS Transaction on Systems, 12(3), 179–188. 6. Bosca, J. E., Liern, V., Martínez, A., & Sala, R. (2009). Increasing offensive or defensive efficiency? An analysis of Italian and Spanish football. Omega, 37(1), 63–78. http://dx.doi.org/10.1016/j.omega.2006.08.002 7. Carmichael, F., Thomas, D., & Ward, R. (2000). Team performance: The case of English premiership soccer. Managerial and Decision Economics, 21(1), 31–45. http://dx.doi.org/10.1002/1099-1468(200001/02)21:1<31::AID-MDE963>3.0.CO;2-Q 8. Carmichael, F., Thomas, D., & Ward, R. (2001). Production and efficiency in association football. Journal of Sports Economics, 2(3), 228–243. http://dx.doi.org/10.1177/152700250100200303 9. Cesaroni, G. (2011). A complete FDH efficiency analysis of a diffused production network. Journal of Productivity Analysis, 36(1), 1–20 10. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(2), 429–444. http://dx.doi.org/10.1016/0377-2217(78)90138-8 11. Churilov, L., & Flitman, A. (2006). Towards fair ranking of Olympics achievements: The case of Sydney 2000. Computers and Operations Research, 33, 2057–2082. http://dx.doi.org/10.1016/j.cor.2004.09.027 12. Cooper, W. W., Seiford, L. M., & Tone, K. (2004). Data envelopment analysis. New York: Kluwer Academic Publishers. 13. Cooper, W. W., Thompson, R. G., & Thrall, R. M. (1996). Extensions and new developments in DEA. Annals of Operations Research, 66(3), 3-45. 14. Dawson, P., Dobson, S., & Gerrard, B. (2000a). Estimating coaching efficiency in professional team sports: Evidence from English Association Soccer. Scottish Journal of Political Economy, 47(4), 399–421. 15. http://dx.doi.org/10.1111/1467-9485.00170 16. Dawson, P., Dobson, S., & Gerrard, B. (2000b). Stochastic frontiers and the temporal structure of managerial efficiency in English soccer. Journal of Sports Economics, 1(4), 341–362. http://dx.doi.org/10.1177/152700250000100402 17. Debnath, M. R., & Shankar, R. (2009). Assessing performance of management institutions: An application of data envelopment analysis. The TQM Journal, 21(1), 20–33. http://dx.doi.org/10.1108/17542730910924727 18. Debnath, R., & Shankar, R. (2014). Does good governance enhance happiness: A cross-nation study. Social Indicators Research, 116(1), 235–253. http://dx.doi.org/10.1007/s11205-013-0275-1 19. Estellita Lins, M. P., Gomes, E. G., Soares de Mello, J. C. C. B., & Soares de Mello, A. J. R. (2003). Olympic ranking based on a zero sum gains DEA model. European Journal of Operation Research, 148, 312–322. http://dx.doi.org/10.1016/S0377-2217(02)00687-2 20. Färe, R., Grosskopf, S., & Lovell, C. A. K. (1994). Production frontiers. Cambridge: Cambridge University Press. 21. Farrell, M .J. (1957). The measurement of production efficiency. Journal of the Royal Statistical Society, 120(3), 253–290. http://dx.doi. org/10.2307/2343100 22. Fizel, J. L., & D’Itri, M. P. (1997). Managerial efficiency, managerial succession and organizational performance. Managerial and Decision Economics, 18(4), 295–308. 23. http://dx.doi.org/10.1002/(SICI)1099-1468(199706)18:4<295::AID-MDE828>3.0.CO;2-W 24. Gonzalez-Gomez, F., & Picazo-Tadeo, A. J. (2010). Can we be satisfied with our football team? Evidence from Spanish professional football. Journal of Sports Economics, 11(4), 418–442. http://dx.doi.org/10.1177/1527002509341020 25. Haas, D., Kocher, M. G., & Sutter, M. (2004). Measuring efficiency of German football teams by data envelopment analysis. Central European Journal of Operations Research, 12(3), 251–268. 26. Haas, D. J. (2003a). Technical efficiency in the major league soccer. Journal of Sports Economics, 4(3), 203–215. http://dx.doi. org/10.1177/1527002503252144 27. Haas, D. J. (2003b). Productive efficiency of English football teams—A data envelopment analysis approach. Managerial and Decision Economics, 24(5), 403–410. http://dx.doi.org/10.1002/mde.1105 28. Hadley, L., Poitras, M., Ruggiero, J., & Knowles, S. (2000). Performance evaluation of national football league teams. Managerial and Decision Economics, 21, 63–70. 29. http://dx.doi.org/10.1002/1099-1468(200003)21:2<63::AID-MDE964>3.0.CO;2-O 30. Jardin, M. (2009). Efficiency of French football clubs and its dynamics (Paper No. 19828). Munich Personal RePEcArchive, MPRA, Munich. 31. Lozano, S., Villa, G., Guerrero, F., & Cortes, P. (2002). Measuring the performance of nations at the Summer Olympics using data envelopment analysis. Journal of Operation Research Society, 53, 501–511. http://dx.doi.org/10.1057/palgrave.jors.2601327 32. Rhode, E., & Southwick, L., Jr. (1993). Variations in public and private university performance. Applications of Management Science, 7, 145–170. 35 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 33. Scully, G. W. (1994). Managerial efficiency and survivability in professional team sports. Managerial and Decision Economics, 15(5), 403–411. http://dx.doi.org/10.1002/mde.4090150503 34. Sexton, T. R., & Lewis, H. F. (2003). Two-stage DEA: An application to major league baseball. Journal of Productivity Analysis, 19(2–3), 227–249. http://dx.doi.org/10.1023/A:1022861618317 35. Sinauny-Stern, S., Mehrez, A., & Barboy, A. (1994). Academic departments’ efficiency via DEA. Computers and Operations Research, 21(5), 543–556. http://dx.doi.org/10.1016/0305-0548(94)90103-1 36. Thenassoulis, E., & Dunstan, P. (1994). Guiding schools to improved performance using data envelopment analysis: An illustration with data from a local education authority. Journal of the Operational Research Society, 45(11), 1247–1262. http://dx.doi.org/10.1057/ jors.1994.198 37. Tomkins, C., & Green, R. (1988). An experiment in the use of data envelopment analysis for evaluating the efficiency of UK university departments of accounting. Financial Accountability & Management, 4(2), 147–164. 38. Wood, R. (2010). Major International Sports Events. Retrieved October 28, 2013, from http://www.topendsports.com/events/sport-events.htm 39. Zhang, D., Li, X., Meng, W., & Liu, W. (2009). Measuring the performance of nations at the Olympic Games using DEA models with different preferences. The Journal of the Operational Research Society, 60, 983. http://dx.doi.org/10.1057/palgrave.jors.2602638 Roma Mitra Debnath is currently a faculty member in applied statistics at the Indian Institute of Public Administration (IIPA). She has more than 10 years of experience in management teaching in reputed management schools. She has published a large number of national and international research papers in various fields, like the service sector, hospitality, and public policy. In addition to teaching, she is a trainer and trains government officials and corporate employees in business statistics, quality management, six sigma, project management, business analytics, etc. She is also involved in policy research related to Government of India (GoI) policies. She conducts training programs for various ministries. She has been a visiting faculty member at many reputable institutes, like the Indian Institute of Technology (IIT), Indian Institute of Management (IIM), and Faculty of Management Studies (FMS), among others. Ashish Malhotra has been practicing strategic marketing (B2B) and business development for the sports/ events industry for more than a decade. He played 1st class cricket (Ranji trophy) for Delhi and the Premier Country Club league in the United Kingdom. He is an alumnus of the Indian Institute of Management, Lucknow and the National Institute of Fashion Technology, Delhi. He presently works at the National Skill Development Corporation and is leading the initiative for India’s participation in WorldSkills international competitions. He has developed a sponsorship strategy for WorldSkills India/CII/Commonwealth Games in marketing and allied areas. Merjenje učinkovitosti nacij pri večšportnih dogodkih: primer XIX. iger Commonwealtha Izvleček V prispevku je bila za merjenje uspešnosti nacij, sodelujočih na igrah Commonwealtha, uporabljena analiza podatkovne ovojnice. Da bi lahko preiskali veljavnost rezultatov, smo za povečanje doslednosti raziskave uporabili več modelov, vendar je narava vložkov in izložkov ostala nespremenjena. Namen raziskave je ugotoviti najbolj smiselno število udeleženih športnikov iz vseh sodelujočih držav ter oceniti njihovo uspešnost glede na najučinkovitejšo državo. Raziskava je lahko v pomoč nacijam pri optimizaciji števila udeleženih igralcev, da bi maksimizirali izide, tj. število medalj, dobljenih na športnih dogodkih. Ključne besede: merjenje uspešnosti, analiza podatkovne ovojnice, učinkovitost, igre Commonwealtha 36 The Effect of the Combination ORIGINAL SCIENTIFIC PAPER of Different Methods of Stock Analysis on Portfolio Performance Received: January 2014 Revised: August 2014 Vesna Trančar Accepted: August 2014 Šolski center Ptuj, Ptuj, Slovenia vesna.trancar@guest.arnes.si DOI: 10.1515/ngoe-2015-0004 Abstract UDK: 336.76:330.133.2:330.43 The literature that examines the stock analysis is often faced with the same questions: Which stock analyses should be chosen and which indicators of JEL: G30, G31, G32 individual stock analyses give the best information on whether a particular stock should be included in the portfolio? How many indicators and which combination of indicators should you choose to forecast future stock prices as accurately as possible? Can investors use stock analyses to create such a portfolio to meet the investment expectations? The main purpose of this article is to use theoretical methodology to determine whether the use of a combination of indicators from different stock analyses has a positive impact on the profitability of the portfolio. Keywords: Portfolio, stock analysis, portfolio manager, indicators, investment decisions, stock prices 1 Introduction Future movements in stock prices can be assessed using a variety of methods and indicators. In the literature, the most commonly represented methods are the fundamental and the technical analyses of stocks. The job of stock market analysts and portfolio managers is to try to find the best method or the best model to forecast future stock prices in a certain period of time as the aforementioned methods and models are constantly updated and supplemented. Of course, it would be irrational if stock analysts continuously used only the current “safest” methods. Unfortunately, the use of graphic examples of stock price movements or the indicators of the fundamental analysis of stocks only does not suffice for a sound method, which could be used to forecast the future stock price movements. However, the results of both simulations shown in this contribution demonstrate that it is the best for using the combination of both types of stock analyses. 2 Overview of the Theoretical Basis of Fundamental and Technical Analysis of Stocks NAŠE GOSPODARSTVO OUR ECONOMY A fundamental analysis argues that the stock price is influenced by many factors, such as the company’s profit, the company’s reputation, risk degree, the impact of monetary policies, fiscal policies, the impact of macroeconomic aggregates, and Vol. 61 No. 1 2015 the economic cycle phase of the global economy. All of these factors represent basic information for a fundamental analysis of stocks, which does not relate only pp. 37–50 to the company, but also to the industry and the overall economy. With the data 37 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 collected, we can estimate what will happen with regard to exchange rate “oscillations.” Several tools can be used for the prices of stocks in the future. A fundamental analysis these purposes: the trend line and the formations signaling requires a lot of time, as it includes a number of indicators recommendations about buying or selling stocks. The next for the analysis of a relatively small number of stocks (Baker, most commonly used indicator is the moment at which we 2010; Braun, 2007; Goldberg & von Nitzsche, 2000). measure the degree of variability in stocks’ trends (Knight, 2007). Another important technical indicator is the relative The price-to-earnings ratio (P/E ratio) indicator represents strength index, an oscillator that compares the output rate the ratio between the market price per stock and net income at a given moment with the output rate in the past. MACD (earnings) per stock. It is the most frequently used indicator is an oscillator that measures the relationship between two among investors making investment decisions (Kleindienst, moving averages of the rate. It shows the difference between 2001; Madura, 2011). By comparing the earnings per stock 26-day and 12-day exponential moving average rates and dividend per stock, we can also calculate the stock of (Knight, 2007; Steiner & Bruns, 2008). payments to companies or the payout ratio, which represents the ratio of dividends paid in comparison to the entire net Equity analysts and portfolio managers are important in- income of the company. Another indicator is the price-to- formation intermediaries (Ellmann, 2006) on the capital book ratio (P/B ratio), representing the ratio between the market. Their primary role is that, by actively managing stock price and its book value. Analysts can also use the investments, they achieve additional value for an investor price-to-sales ratio (P/S ratio), which represents the ratio (Budelmann, 2013). This is why the experts favoring the between the stock price and turnover of the company, to fundamental analysis underscore the view that, before assess the current stock value per stock (Matschke & Brösel, deciding to purchase stocks, the investor’s priority task is 2013; Pernsteiner, 2008). The expected success of a company to take into consideration the psychological aspects of stock and the expected growth of a company’s stock value in the market participants in addition to indicators of fundamental future can also be determined by using indicators such as analysis, as this consideration, in their opinion, makes it EBIT and EBITDA1 (Born, 2009; Mattern, 2005). possible to gather a large amount of relevant data. In addition to the fundamental analysis of stocks, financial In contrast, technical analysts do not deal with the funda- markets analyses use a slightly newer stocks analysis— mental data of each stock. For each stock, they can accu- namely, the technical analysis of stocks—which, with respect rately describe its position and possible future trends of a to their observations and forecasts, can sometimes surpass corporation, but they do not consider that the movement even the fundamental analysis of stocks. The technical of the stock price largely depends on the subjective assess- analysis uses past prices and other past data to predict future ments of the investor and stock market participants (Bazdan, market movements. In practice, all major portfolio managers 2010; Steiner & Bruns, 2008). Heese (2011) argued that the publish technical commentaries on the market, and many of comprehensive analysis of stocks necessarily involves the the advisory services are based on technical analyses (Han, use of indicators from both analyses. Yang & Zhou, 2013). Their approach assumes that stock prices move cyclically and that all the facts and the relevant stock price data reflect fluctuations in stock prices. By es- timating the movements in stock prices in the past, we can 3 Hypothesis predict future stock market trends. We can presumably use certain information to predict the changes in trends and their Based on the previous discussion, the portfolio manager’s continuity. To analyze the formations and trends, the most decisions about which stocks will be included in the portfo- commonly used techniques are the line, bar, and point and lio depend upon a wide range of factors, which are dealt with figure charts as well as the Japanese candlestick approach in detail by the stocks analysis methods already described. (Nison, 2001; Welcker, 1994), which is why this analysis is When studying the stock analyses, we can argue that both also called a chart analysis as it assumes that the purchase analyses have their strengths and weaknesses; thus, none signals of individual stocks in the graph can be significantly can be described as “better.,. From our starting point, and faster read than through the information available via the use given the fact that selecting the right portfolio is a delicate of the fundamental analysis. act that plays a decisive role in determining whether we will achieve the desired return or not, this study focuses on The challenge or the art of forecasting price movements of verifying the following hypothesis: When we combine the stocks in the future stems from the reliability of chart-read- indicators from various stock analyses to include stocks in ing systems in the close monitoring and assessing of a portfolio, there is a greater likelihood that the portfolio will be more profitable. The verification of the hypothesis is 1 Earning Before Interest, Taxes, Depreciation and Amortization connected with the risk of obtaining different results when 38 V. Trančar: The Effect of the Combination of Different Methods of Stock Analysis on Portfolio Performance other types of stocks are selected—namely, the final result 5 Limitations and Calculations is dependent on the selection of indicators, the chosen time period, stock selection for joint portfolio for the purpose of Due to its usefulness and transparency, the New York stock selection, and some additional factors. Stock Exchange (NYSE, 2013) database was used for data collection. Randomly selected stocks corresponding to the set filters were used; the movements of their prices were observed and then categorized into portfolios using the an- 4 Research Methodology alytical methods. To test the hypothesis, we used three simulations compris- Portfolio A comprises stocks selected based on the funda- ing 20 randomly selected stocks that were monitored for 10 mental analysis indicators. The filters or selection criteria years; next, another three simulations comprising 26 stocks are the indexes of the fundamental analysis: P/E < 26.26, were monitored for a year. The stocks were selected based on P/B < 3.40, P/S < 1.71. The values represent the average of various criteria. A statistical analysis was carried out using the fundamental indicators of the S&P 500 index on January 25, SPSS 21.0 program. Two versions of the analyses were used: 2002. The numbers correspond to the current average of the a one-way ANOVA and a t-test for independent samples. The S&P index according to the Bloomberg filter. stocks categorized into the three simulations were identi- fied, and portfolios A, B, and C were designed based on the Information from the database was considered from January selected stocks’ analyses. 25, 2002, to December 31, 2012, as a long-term average. Table 1 Selection of Stocks according to Fundamental Indicators Stock code (NYSE) P/E P/B P/S Price on Price on 25/1/2002 31/12/2012 AEP-American Electric Power Company Inc. 12.62 1.62 0.55 41.55 42.68 ALL-The Allstate Corp. 16.11 1.34 0.80 32.40 40.17 CR-Crane Co. 14.20 2.18 0.89 23.86 46.28 D-Dominion Resources, Inc. 13.97 1.84 1.38 29.20 51.80 DUK-Duke Energy Corp. 8.03 1.28 0.88 108.12 63.80 DVN-Devon Energy Corp. 7.73 1.51 1.66 19.29 52.04 ETR-Entergy Corp. 12.52 1.19 0.92 40.45 63.75 FMC-FMC Corp. 11.25 2.94 0.55 8.64 58.52 HAL-Halliburton Company 11.20 1.30 0.47 7.17 34.69 JCI-Johnson Controls, Inc. 15.05 2.28 0.36 13.24 30.67 M-Macy's, Inc. 11.20 1.34 0.49 20.20 39.02 NEE-NextEra Energy, Inc. 11.34 1.63 1.12 26.81 69.19 NOC-Northrop Grumman Corp. 15.89 1.39 0.61 52.40 67.58 PNW-Pinnacle West Capital Corp. 10.62 1.43 0.91 42.50 50.98 PPL-PPL Corp. 8.09 2.65 0.88 16.80 28.63 R-Ryder System, Inc. 15.52 1.18 0.28 23.91 49.93 SVU-Supervalu, Inc. 13.85 1.63 0.14 23.55 2.47 TAP-Molson Coors Brewing Company 16.03 1.98 0.79 26.29 42.79 TE-TECO Energy, Inc. 11.41 1.72 1.29 24.43 16.76 WMB-Williams Companies, Inc. 9.81 1.72 0.43 24.77 32.74 Source: S&P 500, Bloomberg, NKBM 39 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Table 2 Selection of Stocks according to Technical Indicators Stock code (NYSE) MACD on 25/1/2002 Price on 25/1/2002 Price on 31/12/2012 AP-Ampco-Pittsburgh Corp. 0.05 11.11 19.98 BAX-Baxter International, Inc. 0.96 52.52 66.66 BHLB-Berkshire Hills Bancorp, Inc. 0.71 21.59 23.86 CEC-CEC Entertainment, Inc. 1.03 28.40 33.19 CI-CIGNA Corp. 0.08 31.10 53.46 CRT-Cross Timbers Royalty Trust 0.45 18.70 26.92 EBF-Ennis, Inc. 0.31 9.67 15.47 ETR-Entergy Corp. 0.54 40.45 63.75 FNFG-First Niagara Fin. Group, Inc. 0.25 6.90 7.93 GAS-AGL Resources, Inc. 1.42 45.55 39.97 GSH-Guangshen Railway Co., Ltd. 0.22 8.90 19.74 IDA-IDACORP, Inc. 0.28 38.43 43.35 LMT-Lockheed Martin Corporation 2.22 50.00 92.29 MRF-American Income Fund, Inc. 0.04 8.71 8.37 MSB-Mesabi Trust 0.03 3.14 25.45 PRE-Partnerre, Ltd. 0.19 51.52 80.49 RCI-Rogers Communications, Inc. 0.18 7.98 45.40 SCG-SCANA Corporation 0.16 27.59 45.64 SQM-Sociedad Quimica y Minera de Chile 0.08 2.18 57.64 WMT-Wal-Mart Stores, Inc. 1.67 58.40 68.23 Source: S&P 500, Bloomberg, NKBM The same source of information was used to select stocks Portfolio C consists of the best stocks of portfolios A and for portfolio B, which consists of stocks analyzed using B. The primary criteria used were the MACD in the tech- the technical analysis indicators of which only the follow- nical analysis and the P/E in the fundamental analysis. ing will be applied: MACD > 0, RSI < 50, Stochastic Buy thus, portfolio C represented the selection of stocks based Signal < 30 days. These values are set theoretically accord- on both fundamental and technical analyses. So as not to ing to Bloomberg. The relative strength index (RSI) is one neglect any of them, we chose exactly one half of portfolio of the most well-known technical indicators, which is why A and one half of portfolio B stocks. The first and the second it was included in the criteria for selecting stocks for port- simulations comprise portfolios of 10 stocks, while the third folio B. RSI focuses on the movement of the stock price includes portfolios of 20 stocks. and measures the ratio between the average surge and drop in the price of a stock. The stochastic oscillator compares After setting filters and selecting stocks for all three simu- the final price of the stock in relation to the interval of the lations, monthly stock prices were monitored from January stock’s movement within a specified period of time. The 25, 2002, to December 31, 2012, for the first part and from MACD indicator, which proved to be the best indicator of January 27,2012, to December 12, 2012, for the second part. the purchase or sale of stocks (Trančar, 2000), was chosen as the primary criterion. Whenever a signal line intersects Based on the hypotheses, we expected portfolio C to be more the value 0 from the bottom to the top, it is time to buy the profitable in all simulations than portfolios A and B as portfo- stock as it is expected that its value will go up in the future lio C comprises portfolio A’s stocks with the lowest P/E index and vice versa. Based on this information, stocks whose and portfolio B’s stocks where the MACD index is positive MACD indicator value was either positive or close to zero and close to zero. Table 1 summarizes the selection of the and showed a rising value were included in portfolio B. stocks; the values in all tables are expressed in U.S. dollars. The value of the MACD indicator for individual stocks was chosen on January 25, 2002, and the prices of all stocks were The results of monitoring all three simulations representing subsequently monitored. the three portfolios were based on different criteria. The first 40 V. Trančar: The Effect of the Combination of Different Methods of Stock Analysis on Portfolio Performance simulation represents the movement of the individual port- Supposing that we ennoble our capital for 10 years and do folios’ values: portfolio A (10 stocks), selected using fun- not change portfolios A, B, or C, the variable will be t-1 = damental indicators; portfolio B (10 stocks), selected using 25/1/2002, t = 31/12/2012. The same methodology is used technical indicators; and portfolio C (20 stocks), selected to calculate the value and profitability of portfolios B and C. according to the combination of fundamental and techni- Because we wanted to acquire realistic results of our sim- cal analyses. The second simulation also comprises three ulations, we monitored the movement of stock prices for a portfolios consisting the second half of the chosen stocks. period of 10 years, at the end of which the profitability of Using the same principle, the third simulation was designed, each portfolio was determined. except the portfolios comprise the whole group of stocks. However, the portfolio always covers only one stock from an individual joint-stock company. 6 Analysis and Results For portfolio A: Table 3 shows the values (in U.S. dollars) and profitability of individual portfolios in each simulation. (1) Using the same methodology, 26 stocks were chosen for the where, three simulations from January 27, 2012, to December 27, A = value of portfolio A at time t 2012. If we ennoble our capital for only one year and do not t i = {1, 2, 3, …, n} change portfolios A, B, and C, the variable is t-1 = 27/1/2012, n = number of stocks included in portfolio A t = 27/12/2012. The same methodology was used to calculate = value of stock i at time t the value and profitability of portfolios A, B, and C. Portfolio t = time A comprises stocks selected using the fundamental analysis indicators: P/E < 13.35, P/B < 2.15, P/S < 1.29. The values What follows is the calculation of the profitability rate of represent the average of fundamental indicators of the S&P portfolio A. 500 index on January 27, 2012. The same methodology was used to calculate the value and profitability of portfolios A, B, and C. The values in Table 4 are expressed in U.S. dollars. in % (2) In addition, the statistical analysis carried out with the where, SPSS 21.0 program proved our hypothesis. Two versions % A = profitability rate of portfolio A (in %) of analyses were presented, with the results being the same A = value of portfolio A at time t in both of them. In the first version the one-way ANOVA t A = value of portfolio A at time t-1 (one-way analysis of version) was used; a t-test for inde- t-1 pendent samples was used in the second version. 41 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Table 3 Value and Profitability of Portfolios of Individual Simulations (10 years) SIMULATION 1 / PORTFOLIO A SIMULATION 1 / PORTFOLIO B SIMULATION 1 / PORTFOLIO C Stock code 25/1/2002 31/12/2012 Stock code 25/1/2002 31/12/2012 Stock code 25/1/2002 31/12/12 AEP 41.55 42.68 AP 11.11 19.98 DVN 19.29 52.04 ALL 32.40 40.17 BAX 52.52 66.66 DUK 108.12 63.8 CR 23.86 46.28 BHLB 21.59 23.86 HAL 7.17 34.69 D 29.20 51.80 CEC 28.40 33.19 FMC 8.64 58.52 DUK 108.12 63.80 CI 31.10 53.46 ETR 40.45 63.75 DVN 19.29 52.04 CRT 18.70 26.92 AP 11.11 19.98 ETR 40.45 63.75 EBF 9.67 15.47 CI 31.1 53.46 FMC 8.64 58.52 ETR 40.45 63.75 FNFG 6.9 7.93 HAL 7.17 34.69 FNFG 6.90 7.93 EBF 9.67 15.47 JCI 13.24 30.67 GAS 45.55 39.97 CRT 18.7 26.92 Value of portfolio 323.92 484.40 Value of portfolio 265.99 351.19 Value of portfolio 261.15 396.56 Profitability 49.50% Profitability 32.00% Profitability 51.85% SIMULATION 2 / PORTFOLIO A SIMULATION 2 / PORTFOLIO B SIMULATION 2 / PORTFOLIO C M 20.20 39.02 GSH 8.90 19.74 PPL 16.80 28.63 NEE 26.81 69.19 IDA 38.43 43.35 WMB 41.55 42.68 NOC 52.40 67.58 LMT 50.00 92.29 PNW 42.50 50.98 PNW 42.50 50.98 MRF 8.71 8.37 M 20.20 39.02 PPL 16.80 28.63 MSB 3.14 25.45 NEE 26.81 69.19 R 23.91 49.93 PRE 51.52 80.49 MSB 3.14 25.45 SVU 23.55 2.47 RCI 7.98 45.40 MRF 8.71 8.37 TAP 26.29 42.79 SCG 27.59 45.64 SQM 2.18 57.64 TE 24.43 16.76 SQM 2.18 57.64 SCG 27.59 45.64 WMB 24.77 32.74 WMT 58.40 68.23 RCI 7.98 45.40 Value of portfolio 281.66 400.09 Value of portfolio 256.85 486.60 Value of portfolio 197.46 413.00 Profitability 42.00% Profitability 89.40% Profitability 109.16% SIMULATION 3 / PORTFOLIO A SIMULATION 3 / PORTFOLIO B SIMULATION 3 / PORTFOLIO C AEP 41.55 42.68 AP 11.11 19.98 DVN 19.29 52.04 ALL 32.40 40.17 BAX 52.52 66.66 DUK 108.12 63.8 CR 23.86 46.28 BHLB 21.59 23.86 PPL 16.8 28.63 D 29.20 51.80 CEC 28.40 33.19 WMB 41.55 42.68 DUK 108.12 63.80 CI 31.10 53.46 PNW 42.5 50.98 DVN 19.29 52.04 CRT 18.70 26.92 HAL 7.17 34.69 ETR 40.45 63.75 EBF 9.67 15.47 M 20.2 39.02 FMC 8.64 58.52 ETR 40.45 63.75 FM 8.64 58.52 HAL 7.17 34.69 FNFG 6.90 7.93 TE 24.43 16.76 JCI 13.24 30.67 GAS 45.55 39.97 NEE 26.81 69.19 M 20.20 39.02 GSH 8.90 19.74 MSB 3.14 25.45 NEE 26.81 69.19 IDA 38.43 43.35 MRF 8.71 8.37 NOC 52.40 67.58 LMT 50.00 92.29 AP 11.11 19.98 PNW 42.50 50.98 MRF 8.71 8.37 CI 31.1 53.46 PPL 16.80 28.63 MSB 3.14 25.45 SQM 2.18 57.64 R 23.91 49.93 PRE 51.52 80.49 SCG 27.59 45.64 42 V. Trančar: The Effect of the Combination of Different Methods of Stock Analysis on Portfolio Performance SIMULATION 3 / PORTFOLIO A SIMULATION 3 / PORTFOLIO B SIMULATION 3 / PORTFOLIO C Stock code 25/1/2002 31/12/2012 Stock code 25/1/2002 31/12/2012 Stock code 25/1/2002 31/12/12 SVU 23.55 2.47 RCI 7.98 45.40 PRE 51.52 80.49 TAP 26.29 42.79 SCG 27.59 45.64 RCI 7.98 45.4 TE 24.43 16.76 SQM 2.18 57.64 GSH 8.9 19.74 WMB 24.77 32.74 WMT 58.40 68.23 FNFG 6.9 7.93 Value of portfolio 605.58 884.49 Value of portfolio 522.84 837.79 Value of portfolio 474.64 820.41 Profitability 46.00% Profitability 60.20% Profitability 72.85% Source: NYSE, NKBM, author’s calculations Table 4 Value and Profitability of Portfolios of Individual Simulations (one year) SIMULATION 1 / PORTFOLIO A SIMULATION 1 / PORTFOLIO B SIMULATION 1 / PORTFOLIO C Stock code 27/1/2012 27/12/2012 Stock code 27/1/2012 27/12/2012 Stock code 27/1/2012 27/12/2012 ADM 29.82 27.49 AA 10.43 8.62 BAC 7.29 11.47 AET 43.43 46.24 ACE 69.46 79.62 ADM 29.82 27.49 AFL 49.04 53.01 AET 43.43 46.24 AFL 49.04 53.01 BAC 7.29 11.47 AMGN 68.34 86.15 CVX 103.96 108.52 C 30.87 39.25 AMT 63.01 76.29 COP 52.9 57.9 CHK 22.05 16.86 APD 88.19 84.80 C 30.87 39.25 CI 45.18 53.66 BDX 79.09 78.28 AET 43.43 46.24 COP 52.90 57.90 BMY 32.29 32.14 AMT 63.01 76.29 CVX 103.96 108.52 BXP 104.24 105.67 AA 10.43 8.62 ETN 49.57 53.63 CAT 111.28 87.66 AMGN 68.34 86.15 FE 42.26 41.65 EMR 51.67 52.67 BDX 79.09 78.28 GD 70.35 69.02 FDX 92.95 91.50 HNZ 51.73 57.86 HES 55.26 52.45 FLR 57.23 58.20 BMY 32.29 32.14 HUM 88.26 68.00 HNZ 51.73 57.86 EMR 51.67 52.67 Value of portfolio 690.25 699.15 Value of portfolio 923.34 945.70 Value of portfolio 673.87 735.89 Profitability 1.29% Profitability 2.42% Profitability 9.20% SIMULATION 2 / PORTFOLIO A SIMULATION 2 / PORTFOLIO B SIMULATION 2 / PORTFOLIO C MET 35.52 32.88 INTU 57.35 60.34 VLO 24.12 33.83 MRO 31.24 30.32 KSS 46.69 42.54 MET 35.52 32.88 MUR 61.42 59.47 MDLZ 25.17 25.36 NOC 58.71 67.74 NOC 58.71 67.74 MON 80.53 93.99 RTN 48.64 57.8 PCG 40.83 40.02 NOV 77.40 66.97 XRX 7.88 6.79 PRU 57.22 53.05 NUE 44.50 43.15 WLP 65.42 60.48 RTN 48.64 57.80 PXD 97.42 104.95 NUE 44.5 43.15 TEL 34.30 36.93 ROK 76.90 82.74 MDLZ 25.17 25.36 UNH 51.02 54.44 SNDK 46.70 43.22 SWN 32.04 33.13 VLO 24.12 33.83 SWN 32.04 33.13 KSS 46.69 42.54 WLP 65.42 60.48 TEL 34.30 36.93 MON 80.53 93.99 XRX 7.88 6.79 XOM 85.83 86.86 PXD 97.42 104.95 Value of portfolio 516.32 533.75 Value of portfolio 704.83 720.18 Value of portfolio 566.64 602.64 Profitability 3.37% Profitability 2.17%Profitability 6.30% 43 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 SIMULATION 3 / PORTFOLIO A SIMULATION 3 / PORTFOLIO B SIMULATION 3 / PORTFOLIO C ADM 29.82 27.49 AA 10.43 8.62 VLO 24.12 33.83 AET 43.43 46.24 ACE 69.46 79.62 BAC 7.29 11.47 AFL 49.04 53.01 AET 43.43 46.24 ADM 29.82 27.49 BAC 7.29 11.47 AMGN 68.34 86.15 AFL 49.04 53.01 C 30.87 39.25 AMT 63.01 76.29 CVX 103.96 108.52 CHK 22.05 16.86 APD 88.19 84.80 COP 52.9 57.9 CI 45.18 53.66 BDX 79.09 78.28 MET 35.52 32.88 COP 52.90 57.90 BMY 32.29 32.14 C 30.87 39.25 CVX 103.96 108.52 BXP 104.24 105.67 NOC 58.71 67.74 ETN 49.57 53.63 CAT 111.28 87.66 AET 43.43 46.24 FE 42.26 41.65 EMR 51.67 52.67 CI 45.18 53.66 GD 70.35 69.02 FDX 92.95 91.50 RTN 48.64 57.8 HES 55.26 52.45 FLR 57.23 58.20 XRX 7.88 6.79 HUM 88.26 68.00 HNZ 51.73 57.86 NUE 44.5 43.15 MET 35.52 32.88 INTU 57.35 60.34 AMT 63.01 76.29 MRO 31.24 30.32 KSS 46.69 42.54 AA 10.43 8.62 MUR 61.42 59.47 MDLZ 25.17 25.36 MDLZ 25.17 25.36 NOC 58.71 67.74 MON 80.53 93.99 SWN 32.04 33.13 PCG 40.83 40.02 NOV 77.40 66.97 KSS 46.69 42.54 PRU 57.22 53.05 NUE 44.50 43.15 AMGN 68.34 86.15 RTN 48.64 57.80 PXD 97.42 104.95 MON 80.53 93.99 TEL 34.30 36.93 ROK 76.90 82.74 BDX 79.09 78.28 UNH 51.02 54.44 SNDK 46.70 43.22 PXD 97.42 104.95 VLO 24.12 33.83 SWN 32.04 33.13 HNZ 51.73 57.86 WLP 65.42 60.48 TEL 34.30 36.93 BMY 32.29 32.14 XRX 7.88 6.79 XOM 85.83 86.86 XOM 85.83 86.86 Value of portfolio 1206.57 1232.90 Value of portfolio 1628.17 1665.88 Value of portfolio 1254.43 1365.9 Profitability 2.12% Profitability 2.31%Profitability 8.88% Source: NYSE, NKBM, author’s calculations Table 5 ANOVAa Descriptivesa in % 95% Confidence Interval for Mean a-10 years N Mean Std. Deviation Std. Error Minimum Maximum Lower Bound Upper Bound A 3 45.8333 3.75278 2.16667 36.5109 55.1557 42.00 49.50 B 3 60.5333 28.70145 16.57079 -10.7650 131.8317 32.00 89.40 C 3 77.9533 28.99383 16.73959 5.9287 149.9780 51.85 109.16 Total 9 61.4400 24.76953 8.25651 42.4005 80.4795 32.00 109.16 ANOVAa in % Sum of Squares df Mean Square F Sig. Between Groups 1551.241 2 775.620 1.386 0.320 Within Groups 3356.997 6 559.500 Total 4908.238 8 44 V. Trančar: The Effect of the Combination of Different Methods of Stock Analysis on Portfolio Performance Table 6 Post Hoc Tests a-10 years Multiple Comparisonsa Dependent Variable: in % Bonferroni 95% Confidence Interval (I) portfolio (J) portfolio Mean Difference (I-J) Std. Error Sig. Lower Bound Upper Bound B -14.70000 19.31320 1.000 -78.1913 48.7913 A C -32.12000 19.31320 0.442 -95.6113 31.3713 A 14.70000 19.31320 1.000 -48.7913 78.1913 B C -17.42000 19.31320 1.000 -80.9113 46.0713 A 32.12000 19.31320 0.442 -31.3713 95.6113 C B 17.42000 19.31320 1.000 -46.0713 80.9113 *. The mean difference is significant at the 0.05 level. Table 7 Independent Samples Testa A–C Independent Samples Testa in % Levene's Test for Equality of Variances t-test for Equality of Means a-10 years 95% Confidence Interval F Sig. t df Sig. Mean Std. Error of the Difference (2-tailed) Difference Difference Lower Upper Equal variances assumed 5.105 0.087 -1.903 4 0.130 -32.12000 16.87923 -78.98426 14.74426 Equal variances not assumed -1.903 2.067 0.193 -32.12000 16.87923 -102.53577 38.29577 Table 8 Independent Samples Testa B–C Independent Samples Testa Levene's Test for Equality of Variances t-test for Equality of Means a-10 years 95% Confidence Interval F Sig. t df Sig. Mean Std. Error of the Difference (2-tailed) Difference Difference Lower Upper Equal variances assumed 0.016 0.906 -0.740 4 0.501 -17.42000 23.55430 -82.81723 47.97723 Equal variances not assumed -0.740 4.000 0.501 -17.42000 23.55430 -82.81987 47.97987 As Tables 5 through 8 demonstrate, among the three port- folios, no statistically significant differences occur at the 0.05 level of significance, which means that the differences are not statistically important. Nevertheless, the profitabil- ity of portfolio C is, on average, 32.12% greater than the profitability of portfolio A over 10 years (i.e., 3.21% a year) 17.42% greater than the profitability of portfolio B (i.e., 1.74% a year). 45 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Tables 9 through 12 summarize the results of the statistical analyses of stocks’ profitability when held for only one year. Table 9 ANOVAa Descriptivesa in % 95% Confidence Interval for Mean a-One year N Mean Std. Deviation Std. Error Minimum Maximum Lower Bound Upper Bound A 3 2.2600 1.04704 0.60451 -0.3410 4.8610 1.29 3.37 B 3 2.3000 0.12530 0.07234 1.9887 2.6113 2.17 2.42 C 3 8.1267 1.59001 0.91799 4.1769 12.0765 6.30 9.20 Total 9 4.2289 3.07510 1.02503 1.8652 6.5926 1.29 9.20 ANOVAa in % Sum of Squares df Mean Square F Sig. Between Groups 68.369 2 34.185 28.173 0.001 Within Groups 7.280 6 1.213 Total 75.650 8 Table 10 P ost Hoc Testsa a-One year Multiple Comparisonsa Dependent Variable: in % Bonferroni 95% Confidence Interval (I) portfolio (J) portfolio Mean Difference (I-J) Std. Error Sig. Lower Bound Upper Bound B -0.04000 0.89940 1.000 -2.9967 2.9167 A C -5.86667* 0.89940 0.002 -8.8234 -2.9099 A 0.04000 0.89940 1.000 -2.9167 2.9967 B C -5.82667* 0.89940 0.002 -8.7834 -2.8699 A 5.86667* 0.89940 0.002 2.9099 8.8234 C B 5.82667* 0.89940 0.002 2.8699 8.7834 * The mean difference is significant at the 0.05 level. Table 11 Independent Samples Testa A–C Independent Samples Testa in % Levene's Test for Equality of Variances t-test for Equality of Means a-One year 95% Confidence Interval of F Sig. t df Sig. (2-tailed) Mean Std. Error the Difference Difference Difference Lower Upper Equal variances assumed 1.184 0.338 -5.337 4 0.006 -5.86667 1.09916 -8.91841 -2.81492 Equal variances not assumed -5.337 3.460 0.009 -5.86667 1.09916 -9.11574 -2.61759 46 V. Trančar: The Effect of the Combination of Different Methods of Stock Analysis on Portfolio Performance Table 12 Independent Samples Testa B–C Levene's Test for Equality of Variances t-test for Equality of Means a-One year 95% Confidence Interval F Sig. t df Sig. Mean Std. Error of the Difference (2-tailed) Difference Difference Lower Upper Equal variances assumed 12.458 0.024 -6.328 4 0.003 -5.82667 0.92084 -8.38333 -3.27001 Equal variances not assumed -6.328 2.025 0.023 -5.82667 0.92084 -9.74250 -1.91083 According to Tables 9 through 12, among the three portfoli- determine which stock to choose and the latter to determine os, there are statistically significant differences at the 0.001 when it is the right time to buy. level of significance. The profitability of portfolio C is on average statistically significantly bigger than the profita- However, we still have not answered the questions of how bility of portfolio A (i.e., by 5.87%) and the profitability of many and which indicators to choose for individual stock portfolio B (i.e., by 5.83%). In addition, the statistical results analyses. If many false indicators are used, the model for show that, in terms of the stock analysis, it is reasonable to predicting the stock’s movement will also be false. Even a use and choose the right combination of indicators of both very skilled investor is not capable of indefinitely studying a types of stock analysis—fundamental and technical—as great number of indicators. doing so enables the reduction of risks in asset management. The point of designing a model is to define not only the right Nevertheless, we are aware of the fact that the verification number of chosen indicators, but also the right ones. If we of the hypothesis is connected with the risk of obtaining dif- choose too many indicators, the model will not bring the ferent results as the result is dependent on the selection of wanted synergetic effects as individual indicators can inter- indicators, the chosen time period, stock selection for joint fere with each other. This results in the phenomenon where portfolios, and other factors. each individual indicator shows a better prognosis of stock price movement than all the indicators together. Therefore, it is incorrect to monitor individual indicators in isolation as it leads to one indicator’s weakness equaling the potential 7 Conclusions power of predicting the stock price movement of another one (Heese, 2011). For this reason, portfolio managers’ analysts Regarding the design of investment portfolios, the most should find an appropriate set of complementary indicators frequently used methods of stock analysis are fundamental that can then be used to design a valid model. The accuracy and technical analyses, which analyze economic, struc- of predicting the stock price movement will increase; con- tural, and political factors influencing the development of sequently, the probability of portfolio outcomes coming true market capital. The main focus of this view is analyzing the will also increase. numerous data of a conjunctive nature as well as the indica- tors of economic trends, structural factors, the effects of labor In conclusion, let us briefly review the empirical conclu- market flexibility, political decisions, and the like. Although sions from the research, which was based on three simu- the latter focuses mainly on the history of the monitored lations, comprising portfolios A, B, and C. The average of stocks’ value, it favors methodology that indicates that it is fundamental indicators for S&P 500 index on a specific possible to predict the stock movement in the future with the day’s filters was the basis for selecting stock according to help of its past graphical forms. The flexibility of the stock the primary criteria. Among the known criteria (i.e., P/E, analyses, indicators, and principles used is mainly depend- P/S, and P/B), P/E was selected as the primary filter for the ent on the model chosen by the analyst or portfolio manager. selection of stocks for portfolios A and C. In principle, each analytical method is only as flexible as its analyst or portfolio manager, who is definitely aware of Regarding technical indicators, our choice was limited to the fact that both analyses have positive and negative sides MACD, RSI, and stochastic buy signal. The MACD index and therefore should be combined by using the former to was chosen for the categorization of portfolios B and C. 47 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Due to its usefulness and transparency, the NYSE database of trading profitability in assessing market efficiency. He was used for the data collection. Randomly selected stocks further highlighted the importance of transactions costs and corresponding to the set filters were used; movements of the other market microstructure issues for defining market effi- prices were observed and then categorized into portfolios ciency (Schwert, 2002). with the help of analytical methods. All of these considerations lead to the assumption that the The results of the portfolios’ analysis led to the following American capital market is more effective, which is not conclusions. The profitability of portfolio A (comprised true according to Fama (1970; 1991), whose hypothesis stocks selected based on fundamental indicators) and B of an effective market is based on the presumption that the (designed with the help of technical analysis criteria) was prices of securities contain all the available and relevant in all simulations lower than the profitability of portfolio C. information and that all participants on the market—buyers Due to the combinations of both stocks’ analyses, a better as well as sellers—act rationally. Thus, all market informa- filter was chosen for the selection of stocks for portfolio tion is at any time reflected in the rates. According to the C than for portfolios A and B. It is possible that the com- theory of market efficiency, no market participant can, in bination of different indicators from the fundamental and the long-term, outrun the market (Fama, 1970). Our model technical analyses had an influence on the profitability of of portfolio formation rejects all three major components the portfolio. The combined use of indicators from both of Fama’s hypothesis of weak efficiency, which states that technical and fundamental analyses of stocks had a positive we cannot draw conclusions about the future of stock rates impact on the profitability of the portfolio. from past rate movements (Steiner & Bruns, 2000). The technical analysis of stocks is mostly based on the past The answer to the question as to whether such results can movements of stock rates, which—according to defenders be expected on other stock markets as well as would be of this approach—gives good results in terms of moderate affirmative as the profitability of the portfolio is more de- efficiency, which claims that all markets’ important and pendent on a positive economic situation, macro and micro public information is incorporated into the actual rate itself. economic factors, investment period, investment dispersion, Fama (1970) concluded that the basic analysis is useless, as and similar location as the market in which we invested. all public information is already included into the actual rate itself (Scheufele & Haas, 2008). Meanwhile, high efficien- Yet compared to the European market, the American cy refers to the claim that all markets’ relevant public and market has by its nature a larger number of business op- internal information is reflected in the rate; thus, the use of erations, greater liquidity, and a broader choice of invest- insider information on the financial market—especially in ment products (Mai, 2004). A better allocation of capital is stock exchange business—is useless. The fact that doing ensured by unified business conditions, which enable the business on the stock exchange using insider information investors to invest their capital into the investments that is lucrative in the short term also rejects this hypothesis they expect to yield the best results (Baele et al., 2004). A (Scheufele & Haas, 2008; Steiner & Bruns, 2000), just as it concentration of capital markets ends up in de facto unified is rejected by our model of portfolio selection. national regulations and supervision. In addition, the market capitalization of American stock markets is twice as large as Ultimately, the chosen stock selection model can be used all European stock markets combined. Thus, dealing with for the American or any other capital market. It is essential stocks on various national stock exchanges on the European that the basic data reflect the actual state of the company market causes bigger transaction costs, which leads in- and the economy as a whole, especially as there are as many vestors to concentrate primarily on home securities (Mai, portfolio formation models as there are portfolio managers 2004). In addition, Jensen (1978) stressed the importance and investors creating their own models. 48 V. Trančar: The Effect of the Combination of Different Methods of Stock Analysis on Portfolio Performance References 1. Baele, L., Baele, L., Ferrando, A., Hördahl, P., Krylova, E., & Monnet, C. (2004). Measuring European financial integration. Oxford Review of Economic Policy, 20(4), 509-530. http://dx.doi.org/10.1093/oxrep/grh030 2. Baker, H. K. (2010). Behavioural finance, investors, corporations, and markets. Hoboken, NJ: John Wiley & Sons. http://dx.doi. org/10.1002/9781118258415 3. Bazdan, Z. (2010). Sell when the violins are playing—Buy when the cannons rumble. Case Study: Technical analysis and chartists. Naše gospodarstvo, 3–4, 11-18. 4. Born, K. (2009). Intelligente Kapitalanlage. Durch Aktienanalyse zum langfristigen Börsenerfolg. Wien: Linde Verlag. 5. Braun, J. (2007). Fundamentalanalyse, technische Analyse und Behavioral Finance. Saarbrücken: VDM, Müller Verlag. 6. Budelmann, T., C. (2013). Portfolio—Gastbeitrag: Mehrwert durch systematische Aktienselektion. Börsen-Zeitung, 38, 2. 7. Ellmann, F. (2006). Finanzierungsform von Analysen muss klar erkennbar sein. Börsen-Zeitung, 96, 8. 8. Fama, E. F. (1970). Efficient capital markets. A review of theory and empirical work. Journal of Finance, 25(2), 383. http://dx.doi. org/10.1111/j.1540-6261.1970.tb00518.x 9. Fama, E. F. (1991). Efficient capital market II. Journal of Finance, 46(5), 1575–1617. 10. http://dx.doi.org/10.1111/j.1540-6261.1991.tb04636.x 11. Goldberg J., & von Nitzsch, R. (2004). Behavioural finance: Gewinnen mit Kompetenz. München: Finanz Buch-Verlag. 12. Graham, B. (2009). Modri investitor z dodanimi komentarji Jasona Zweiga. Ljubljana: Soleco. 13. Han, Y., Yang, K., & Zhou, G. (2013). A new anomaly: The cross-sectional profitability of technical analysis. Journal of Financial & Quantitative Analysis, 48(5), 33–35. http://dx.doi.org/10.1017/S0022109013000586 14. Heese, V. (2011). Aktienbewertung mit Kennzahlen, Kurschancen und risiken fundiert beurteilen. Wiesbaden: Gabler Verlag. http://dx.doi. org/10.1007/978-3-8349-6446-5 15. Jensen, M. C. (1978). Some anomalous evidence regarding market efficiency. Journal of Financial Economics, 95–102. http://dx.doi. org/10.1016/0304-405X(78)90025-9 16. Kleindienst, R. (2001). Varčevanje v domačih in tujih delnicah: najboljša pot za doseganje dolgoročnih finančnih ciljev. Ljubljana: Založba GV. 17. Knight, T. (2007). Chart your way to profits. The online trader’s guide to technical analysis. New York: John Wiley and Sons, Inc. 18. Madura, J. (2011). Financial markets and institutions. Mason Ohio: South-Western Cengage Learning. 19. Mai, H. (2004). Effizienterer US-Finanzmarkt. Deutsche Bank Research. Retreived from http://www.dbresearch.de 20. Matschke, M. J., & Brösel, G. (2013). Unternehmensbewertung: Funktionen-Methoden-Grundsätze. Wiesbaden: Springer Fachmedien. http://dx.doi.org/10.1007/978-3-8349-4053-7 21. Mattern, C. (2005). Fundamentalanalyse im Portfoliomanagement. Konjunkturindikatoren verstehen und analysieren. Stuttgart: Schäffer-Poeschel Verlag. 22. Nison, S. (2001). Japanese candlestick charting techniques: A contemporary guide to the ancient investment techniques of Far East. New York: New York Institute of Finance. 23. NYSE. (2013). Database: Bloomberg, KBM Infond, d.o.o. & NYSE. Retrieved from https://nyse.nyx.com & http://www.bloomberg.com/ markets/stocks/ 24. Pernsteiner, H. (2008). Finanzmanagement Aktuell: Unternehmensfinanzierung, Wertpapier-management, Kapitalmarkt, Bank-Ver-sicherung. Wien: Linde Verlag. 25. Scheufele B., & Haas, A. (2008). Medien und Aktien, Springer VS. Verlag für Sozialwissenschaften, 16–27. 26. Schwert, G. W. (2002). Anomalies and market efficiency. Chapter 15 in Handbook of the Economics of Finance. (NBER Working Paper No. W9277, 4-5) 27. Steiner, M., & Bruns, C. (2008). Wertpapiermanagement: Professionelle Wertpapieranalyse und Portfoliostrukturierung. Stuttgart: Schäffer-Poeschel Verlag. 28. Trančar, V. (2000). Tehnična analiza delnic in njena uporaba v praksi. Magistrsko delo. Maribor: Ekonomsko-poslovna fakulteta. 29. Welcker, J., & Audörsch, J. (1994). Technische Aktienanalyse. Zürich: MI Verlag. Vesna Trančar graduated from the Faculty of Economics and Business in Maribor in 1995 and was employed in Comtron, Ltd., a computer company, in Maribor from 1995 to 1996. Since then, she has worked at the School Centre in Ptuj. She completed her teaching and adult education exams (1997), professional examinations (1999), and MBA in corporate finance and banking from the Faculty of Economics and Business in Maribor (2000). She participated in the stock market seminar at the Faculty of Economics and Business in Maribor in 2006 and completed her professional examinations on the Administrative Procedure Act in 2007. Since 2008, she has been the head of the school development team and a member of the expert group for the school’s development projects. 49 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 61 No. 1 / February 2015 Vpliv kombinacije različnih metod analiz delnic na donosnost portfelja Izvleček V literaturi, v kateri so bile proučevane analize delnic, se pogosto srečujemo z istimi vprašanji: Katero od naštetih analiz delnic izbrati, kateri kazalniki posamezne analize delnic dajejo najboljše informacije o tem, ali določeno delnico vključiti v portfelj ali ne? Koliko kazalnikov in katero kombinacijo med njimi izbrati, da bodo napovedi prihodnjega gibanja cen delnic čim bolj natančne? Ali lahko investitorji z analizami delnic oblikujejo takšen portfelj, da bo izpolnil njihova naložbena pričakovanja? Glavni namen članka je, da z metodologijo, ki smo jo predstavili v teoretičnem delu, preverimo, ali uporaba kombinacije kazalnikov različnih analiz delnic pozitivno vpliva na donosnost portfelja ali ne. Ključne besede: portfelj, analiza portfelja delnic, upravljavec portfelja, kazalniki analiz delnic, investicijsko odločanje, cena delnice 50 NAŠE GOSPODARSTVO OUR ECONOMY NAVODILA AVTORJEM INSTRUCTIONS FOR AUTHORS Revija za aktualna ekonomska in poslovna vprašanja Journal of Contemporary Issues in Economics and Business Revija Naše gospodarstvo / Our Economy objavlja izvirne The journal Naše gospodarstvo / Our Economy publishes znanstvene članke iz vseh področij ekonomije in poslovnih original scientifi c articles covering all areas of economics and Letnik 61, št. 1, 2015 Vol. 61, No. 1, 2015 ved. Avtorje vabimo, da v uredništvo pošljejo originalne business. Authors are invited to send original unpublished articles prispevke, ki še niso bili objavljeni oz. poslani v objavo v drugi which have not been submitted for publication elsewhere. Authors reviji. Avtorji v celoti odgovarjajo za vsebino prispevka. Ob- are completely responsible for the contents of their articles. 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Posch Nekaj osnovnih napotkov za navajanje virov v tekstu: References in the text (Technical University Dortmund, Nemčija), Gregor Radonjič (Technical University Dortmund, Germany), Gregor Radonjič Primer 1a: Another graphic way of determining the stationari- Example 1a: Another graphic way of determining the station- ty of time series is correlogram of autocorrelation arity of time series is correlogram of autocorrela- (EPF), Miroslav Rebernik (EPF), Kaija Saranto (University of (FEB), Miroslav Rebernik (FEB), Kaija Saranto (University of function (Gujarati, 1995). tion function (Gujarati, 1995). Eastern Finland, Finska), Milica Uvalic (University of Perugia, Eastern Finland, Finland), Milica Uvalic (University of Perugia, Primer 1b: Another graphic way of determining the stationari- Example 1b: Another graphic way of determining the station- Italija), Igor Vrečko (EPF), Martin Wagner (Technical University Italy), Igor Vrečko (FEB), Martin Wagner (Technical University ty of time series is correlogram of autocorrelation arity of time series is correlogram of autocorrela- Dortmund, Nemčija) in Udo Wagner (University of Vienna, Dortmund, Germany), Udo Wagner (University of Vienna, function (Gujarati, 1995, p. 36). tion function (Gujarati, 1995, p. 36). Avstrija) Austria) Primer 2a: Engle & Granger (1987) present critical values Example 2a: Engle & Granger (1987) present critical values also for other cointegration tests. also for other cointegration tests. Glavni in odgovorni urednik: Editor-in-Chief: Primer 2b: Engle & Granger (1987, p. 89) present critical Example 2b: Engle & Granger (1987, p. 89) present critical Vesna Čančer Vesna Čančer values also for other cointegration tests. values also for other cointegration tests. Nekaj osnovnih napotkov za navajanje virov v seznamu virov: References in the list of references Naslov uredništva: Editorial and administrative office address: Primer 1 – Knjiga: Gujarati, D. N. (1995). Basic Econometrics. Example 1 – Book: Gujarati, D. N. (1995). Basic Econometrics. Maribor, Razlagova 14, Slovenija, Maribor, Razlagova 14, Slovenia, New York: McGraw-Hill. New York: McGraw-Hill. telefon: +386 2 22 90 112 phone: +386 2 22 90 112 Primer 2 – Članek v reviji: Engle, R. F., & Granger, C. W. J. Example 2 – Journal article: Engle, R. F., & Granger, C. W. (1987). Co-integration and Error Correction: Representation, J. (1987). Co-integration and Error Correction: Representation, Elektronska pošta: E-mail: Estimation and Testing. Econometrica, 55(2), 251-276. Estimation and Testing. Econometrica, 55(2), 251-276. nase.gospodarstvo@uni-mb.si nase.gospodarstvo@uni-mb.si Primer 3 – Poglavje v knjigi, prispevek v zborniku: MacKinnon, Example 3 – Book chapter or article from conference proceed- J. (1991). Critical Values for Cointegration Tests. In R. F. Engle ings: MacKinnon, J. (1991). Critical Values for Cointegration Spletna stran: WWW homepage: & C. W . J. Granger (Eds.), Long-Run Economic Relationships: Tests. In R. F. Engle & C. W . J. Granger (Eds.), Long-Run Readings in Cointegration (pp. 191-215). Oxford: University Economic Relationships: Readings in Cointegration (pp. ht p:/ www.ng-epf.si ht p:/ www.ng-epf.si Press. 191-215). Oxford: University Press. Primer 4 – Elektronski vir: Esteves, J., Pastor, J. A., & Example 4 – Web source: Esteves, J., Pastor, J. A., & Casanovas, Revija je indeksirana v ABI/INFORM Global, EconLit in The review is indexed in ABI/INFORM Global, EconLit and Casanovas, J. (2002). Using the Partial Least Square (PLS): J. (2002). Using the Partial Least Square (PLS): Method to ProQuest ter vključena v EBSCO in Ulrich's Periodicals bazo. ProQuest. It is included in EBSCO and Ulrich's Periodicals Method to Establish Critical Success Factors Interdependence Establish Critical Success Factors Interdependence in ERP Im- Directories. in ERP Implementation Projects. Retrieved May 5, 2010, from plementation Projects. Retrieved May 5, 2010, from http:/ erp. http:/ erp.ittoolbox.com/doc.asp?i=2321 ittoolbox.com/doc.asp?i=2321 Prispevek naj ne bo daljši od ene avtorske pole (30.000 znakov). The size of the article should not exceed 30,000 characters Stran naj bo velikosti A4, s tricentimetrskimi robovi in oštevilčeni- and should be prepared on A4 paper with 3 cm margins and mi stranmi. 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(obsega 500 do 550 znakov, upoštevajoč presledke) ter fotogra- range from 500 to 550 characters including spaces) in one fi jo v jpg ali podobni obliki. paragraph, and photos in jpg or other comparable form. ISSN 0547-3101 Revijo sofi nancira Javna agencija za raziskovalno dejavnost Republike Slovenije. NG OE NG N A Š E G O S P O D A R S T V O Revija za aktualna ekonomska in poslovna vprašanja OE L E T N I K O U R E C O N O M Y VOLUME 61 Journal of Contemporary Issues in Economics and Business Document Outline Editing _GoBack Mobbing in Slovenia: Prevalence, mobbing victim characteristics, and the connection with post-traumatic stress disorder Examining Determinants of Leadership Style among Montenegrin Managers Measuring efficiency of nations in Multi Sport Events: A case of Commonwealth Games XIX The Effect of the Combination of Different Methods of Stock Analysis on Portfolio Performance