Organizacija, Volume 51
Reviewers in 2018
Issue 4, November 2018
DOI: 10.2478/orga-2018-0021
The Level of Disclosure in Annual Reports of Banks: The Case of Slovenia
Iztok KOLAR, Nina FALEZ
University of Maribor, Faculty of Economics and Business, Razlagova 14, 2000 Maribor, Slovenia
iztok.kolar@um.si, nina.falez@gmail.com
Background and Purpose: Many studies have explored the disclosures in annual reports of companies. Annual reports of banks differ significantly from annual reports of other business entities, particularly in terms of disclosed items. The aim of this article is to investigate the level of disclosures and which factors influence the level of disclosure in the annual reports of banks in Slovenia.
Design/Methodology/Approach: We have observed disclosures of all banks in Slovenia for year 2012 and 2015. The factors as used in the study are age, size, the government share, profitability and complexity of a bank. Our disclosure checklist consists of 144 voluntary and mandatory items. Statistical analysis is performed using linear regression analysis.
Results: The average score for banks in Slovenia is near 94 points or 63% of all possible disclosures. The results of analyses indicated positive associations and statistical correlations between the level of disclosure in annual reports and the size of a bank, the share of government ownership and negative statistical influence of the age of bank on the level of disclosure. Our results do not show statistically significant correlation between the level of disclosure and a bank's profitability and complexity, which is against theory and findings from other similar research. Conclusion: In our opinion, results well reflect the Slovenian banking system and how banks reveal their information. Our finding is that banks in Slovenia provide less information to the public compared to the average companies in other branches or banks in similarly developed countries. The paper's main contribution is to deepen our knowledge about disclosures in the bank's annual reports and the answers what are the influential factors of disclosures for banks.
Keywords: government ownership; information disclosures; ages; Slovenia
1 Introduction
Banks in Slovenia have become the subject of intense public scrutiny. In December 2013, Slovenia recapitalized its ailing state-owned banks with 3.2 billion Euros (Bank of Slovenia, 2014) in order to escape the looming EU bailout. Today, details about their past activities are leaking into the public sphere, and banks are faced with a number of accusations and speculations regarding their use of non-transparent practices. Transparency has never been so important. Increased transparency of fair value reduces crash risk among U.S. banking firms (Wen-hsin Hsu, Pourjalali and Songa, 2018). The Basel Committee on Banking Supervision (1998) issues Guidance on Bank Transparency, with
Received: June 20, 2018; revised: September 15, 2018; accepted
strongly recommends that banks address important disclosures in their financial reports and other disclosures to the public. With that banks will follow up a key to transparency as a key element of an effectively supervised, safe and sound banking system. Such information facilitates market participants' for assessment of banks and more efficient allocation of capital between banks since it helps the market to accurately assess and compare the risk and return prospects of individual banks (Hossain, 2008). Disclosure of accurate, comprehensive and timely information is critical for the functioning of an efficient capital market (Pivac, Vuko and Cular, 2017).
The problem we address is how banks in Slovenia reach recommendations of disclosures. The aim of the paper is to explore how banks in Slovenia disclose informa: November 4, 2018
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tion, and compare the level of disclosure with companies in other branches and banks in countries, by examining the factors that affect the level of disclosure in their annual reports. This paper examines the relation between company characteristics and the extent of disclosure, so the hypotheses are that size, age, and profitability of a company, its board structure, the share of government, ownership and the number of subsidiaries impact on the level of disclosures in annual reports of the Slovenians banks.
Our methodology for the assessment of disclosure scores is based on Hossain's formula (Hossain, 2008). In this research, we analysed nearly one and a half million words from published annual reports to examine possible 144 items of disclosures of all Slovenians banks for years 2012 and 2015. The limitation of this study is that it only discusses data for two non-consecutive years which is due to the enormous amount of words (text) to examine..
The remainder of the paper is organized as follows. Section 2 describes the regulatory environment for disclosure in Slovenia. Section 3 discusses the theoretical background for development a hypothesis and aims the importance of disclosure. The research design is outlined in Section 4. Section 5 presents the results and analysis. Finally, Section 6 presents the conclusions, limitations and directions for future research.
2 The regulatory environment for disclosures of banks in Slovenia
Banks are required to prepare annual and consolidated annual reports for the previous fiscal year in compliance with relevant legal and professional provisions. The framework for financial reporting in Slovenia is provided by the Companies Act, the International Financial Reporting Standards (IFRS) and other applicable regulations (Bank of Slovenia, 2013a, Article 2). A bank's business and financial reports are essentially similar to reports prepared by other companies; however, they are adapted to the specificities of the banking business and, therefore, differ from financial statements prepared by other companies. An important distinction is the disclosure of mandatory items, which banks are legally required to provide in their annual reports.
Disclosure in annual reports of banks in Slovenia is governed by the following legal acts, implementing provisions, and professional standards:
•	the International Financial Reporting Standards,
•	the Banking Act (Slo. Zakon o bančništvu),
•	the Decision on the Books of Account and Annual Reports of Banks and Savings Banks (Bank of Slovenia, 2013a),
•	the Regulation on Disclosures by Banks and Savings Banks (Bank of Slovenia, 2013b).
The International Financial Reporting Standards and the Banking Act provide a list of relevant disclosures, while the Decision and the Regulation determine and define their content in more detail.
The Decision on the books of account and annual reports of banks and savings banks is issued by the Bank of Slovenia, i.e. the Slovenian Central Bank (2013a). The Regulation on Disclosures by Banks and Savings Banks specifies (Bank of Slovenia, 2013b, Article 1):
•	which banks and savings banks are subject to disclosure provisions;
•	the scope, manner and frequency of disclosure;
•	type of disclosure.
3 Theoretical background and hypotheses
Disclosures are an important source of information for shareholders and the interested public (Shehata, 2014). Shehata (2014) defines disclosure as a way of informing the public by means of annual reports. Banks are companies with special business model, so what is valid about disclosure in companies it can be applied to banks. Ow-usu-Ansah (1998) considers disclosure to be a means of communication of financial and non-financial information about a company's financial position and performance.
Disclosures are divided into two major categories: mandatory and voluntary. Mandatory items are those which, based on the current legislation, must be disclosed by a company in its annual report. Voluntary disclosure, i.e. items disclosed by a company on a voluntary basis, is the providing of additional information when mandatory disclosure does not provide an accurate picture of a company. Meek et al. (1995, in Shehata, 2014) define voluntary disclosure as additional financial and other relevant information e. g. corporate social responsibility Obafemi et al. (2018), complementing the management's disclosures in order to assist readers of annual reports and enable them to make the best possible decisions.
What are the influencing factors of the level of disclosure in banks? Francis, Huang, Khurana, and Pereira (2009) find that industry growth rates across 37 observed countries pairs are higher when there is a greater level of corporate transparency. Baumann and Nier (2004) also observe an association between share price and the level of disclosure in banks: share prices are less volatile in banks disclosing more information and more volatile in banks disclosing less information. Lower share price volatility, in turn, means lower capital cost. Thus, more disclosure benefits both investors and banks. Neifara and Jarbouib (2018) research reveal the significant impact of independent directors on the voluntary disclosure of Islamic bank. It is also of advantage to the supervisors: the more items get disclosed, and hence the lesser the stock price volatil-
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ity, the lower the likelihood that the stock price will give wrong signals about a company's performance and risk. Tadesse (2006) argues for a positive association between the level of disclosure and transparency, which contributes to greater stability of the entire banking system. He notes that a banking crisis is less likely to occur in countries that have introduced stricter disclosure regulations in annual reporting because in such an environment it is less likely that banks will take excessive risk.
Do Slovenian banks meet the average score of disclosures? The Center for International Financial Analysis and Research (CIFAR) has calculated the index of transparency. The CIFAR index is based on the average number of 90 different items disclosed by a sample of firms in each country. This measure widely used to measure cross-country differences in accounting standards and disclosure intensity. (CIFAR, 1993). La Porta et al. (1998) found out, having investigated a large data set, that companies make up 70% of all possible points of disclosures. Similar Brown and Martinsson (2014) got a result of disclosure intensity by mean 71.95% of 20 annual reports in 20 countries across a World. So, if banks in Slovenia cover a 70% of total list of disclosures (see Appendix A) the Slovenian banks are on average score of disclosures and we can say that banks in Slovenia care about transparency.
Authors (e.g. Soliman, 2013, Owusu-Ansah, 1998, Shehata, 2014, Hossain, 2008, Barako, Hancock and Izan, 2006, Wen-hsin Hsu, Pourjalali and Songa, 2018) have examined factors influencing the level of disclosure in annual reports and the manner in which they impact different stakeholders. Among the most commonly stated factors are the following: size, age, and profitability of a company, its board structure, the share of government, ownership and the number of subsidiaries, i.e. its complexity.
Owusu-Ansah (1998) argues that older companies disclose more relevant items, and relates this fact to lower cost of acquisition, processing, and communication of information to the public. He adds that younger companies that have yet to strengthen their competitive position on the market may suffer greater harm by disclosing certain information, as these might be used to the advantage of their competitors. Another argument he puts forward to support his claim is that older companies maintain fairly well-organized databases and have thus lower cost--both in terms of invested money and effort—when obtaining relevant information for disclosure. Akhtaruddin (2005 in Feytimi, 2014) notes that older companies disclose more relevant information because they wish to strengthen their position on the market and improve their reputation.
Hossaini (2008) finally concludes that the age of a bank does not have a statistically significant impact on the scope of disclosures in annual reports of banks in India. Based on this conflicting evidence, we set out to investigate our first hypothesis: H1: The level of disclosure is positively associated with the age of the bank.
Kahl and Belkaoui (1981) were investigating the over-
all extent of disclosure by 70 banks located in 18 countries, they found out that the extent of disclosure was different among the countries examined, and that there was a positive relationship between the size of the bank and the level of disclosure indicated. Xudong et al. (2018) find out that larger banks better collect and share information. The size of a company, as measured by its average volume of assets, is a frequently used variable when assessing the level of disclosure in its annual reports (Zdolsek and Kolar, 2013). Hossain (2008) highlights three aspects that influence this association in banks annual report: first, the cost of information gathering, which is lower in larger companies than in smaller; second, the intrinsic need of larger companies to disclose more information because they are more frequently listed on regulated or alternative markets; third, he argues that smaller companies feel more vulnerable and exposed if they disclose more information. Based on this, we stated the second hypothesis as follows: H2: The level of disclosure is positively associated with the size of a bank.
Most researchers also report a positive association between profitability and the level of disclosure in annual reports of banks, e. g. Baumann and Nier (2004), Hossain and Hammami, 2009, Hossain (2008). Inchausti (1997 in Hossain and Hammami, 2009) offers a tentative explanation of this relationship in terms of the agency theory, according to which managers of companies with higher profits want to disclose more information due to three reasons: first, by disclosing more items the managers can prove to shareholders and owners that they can be trusted to run the company well; second, by presenting their work in a good light they consolidate their position within the company; finally by revealing the data depicting their company as safe and stable, they hope to solicit potential investors. Feyitimi also observes that companies with low profit or no profit at all, want to disclose as little information as possible in order to cover up losses and declining profits (Feyitimi, 2014). This leads to our third hypothesis: H3: The level of disclosure is positively associated with the profitability of a bank.
Hossain and Hammami (2009) note a positive association between the level of disclosure of a company and its complexity measured in the number of its subsidiaries. They maintain that companies with a more complex and diversified structure have implemented a more effective system of information management and gathering, which allows them to access the gathered data in an easier and more cost-efficient way. Thus, they reason, in general, companies with more subsidiaries disclose more information. Haniffa and Cook (2002), on the other hand, do not report a statistically significant relationship between the two variables. This is why we wanted to test our fourth hypothesis: H4: The level of disclosure is positively associated with the complexity of a bank.
Eng and Mak (2003 in Juhmain, 2013) examined the relationship between ownership structure and voluntary
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disclosure. They noted that mostly government-owned companies carry higher agency costs, which they attributed to their conflicting objectives. On the one hand, they seek to maximize their profits, while on the other they want to act in the government's best interest. Disclosing more information helps decrease their agency cost. Government-owned companies also want to communicate more information to their shareholders and the general public. They are under much stricter control by their respective governments, and consequently, face greater demands as to transparency. As a result, they disclose more voluntary items in their annual reports that companies with lesser government ownership. Ghazi and Weetman (2006 in Ju-hmain, 2013) do not agree. In their opinion, government ownership alone does not amount to more disclosures in annual reports, quite the contrary. In government-owned companies, they found strong political ties, and argue that less disclosure should help to cover up such links. As a result of the above conflicting evidence we formulated our last hypothesis as: H5: The level of disclosure is positively associated with the share of government ownership.
4 Methods and data
The list of banks included in our study is based on the list of banks published on the website of Bank of Slovenia.1 Our dataset includes all banks operating in Slovenia in the year 2012 it was total of 17 banks and in the year 2015 it was 14 banks. The second year for observing the data was the year 2015, because in 2016 three banks merged into one, and 2 more banks were closed due to controlled liquidation. In year 2017, only 12 banks were left in business in Slovenia. Hence, the actual sample represents the population of operating banking companies in Slovenia, what is exactly the same case as in Hossain (2008) research. So, we followed the same methods as Hossain (2008) and Soliman (2013).
The decision to observe annual reports for two years only has already been mentioned as a limitation of this research; for more, see the introductory section of this paper. We analysed the comprehensive set of annual reports for two years (2012 and 2015), and this means that we had to count and analyze nearly a one and half million words. 2
The data for this survey are drawn from disclosures and annual reports of Slovenian banks. The banks' annual reports in PDF format were accessed via the Agency of the Republic of Slovenia for Public Legal Records and Related Services AJPES3 information portal, and the gvin.
com4, a referencing website offering relevant business data on Slovenian public and private companies. Not all banks' disclosures were published separately, i.e. in a separate document; if this was the case, we relied on the data published in their annual reports.
Disclosed items in annual reports of banks were analyzed by compiling a list of all possible disclosures, and then by checking an individual bank's disclosures against it. Researchers such as Wallace et al. (1994), Cooke (1992 and 1993), and Hossain (2000, 2001 and 2008), adopted a dichotomous procedure in which an item scores one if disclosed and zero if not disclosed. The suppliance of a particular disclosure was awarded 1 point and the non-sup-pliance 0 points, with the assumption that all disclosures were equally important. The total disclosure score (£ discl) was calculated based on Hossain's formula (2008):
n
"Ldiscl = ^ di (1)
!=1
Whereas:
d = 1 if a disclosure is supplied d = 0 if a disclosure is not supplied n = the total number disclosures
When compiling the list of relevant disclosures, we considered only those items featured in the Decision on the books of account and annual reports of banks and savings banks (Bank of Slovenia, 2013a), and the Regulation on disclosures by banks and savings banks, (Bank of Slovenia, 2013b), which were used by banks in the preparation of their annual reports. Since then, both legal documents have been amended. In order to facilitate comparability, only those disclosures were considered which related to all banks. The obtained disclosures, totaling 144 items or points, were divided into 4 major sections, as shown in Table 1. The content of individual sections is presented in more detail in Appendix 1.
For the independent variables we use the variables as we predicted the relation with the extent of disclosure, for each hypothesis we set one factor influencing the level of disclosure, this factors we named independent variables. Table 2 shows independent variables and the type of data acquired.
It is common that the observed variable assets and number of business unit are transformed from original value to log value due to meet normal distribution of these items (Baumann and Nier, 2004). In our research we use
1	Source: https://www.bsi.si/publikacije/mesecna-informacija-o-poslovanju-bank
2	Typical annual report consists of more than 50.000 words on average: 200 pages and 250 words per page.
3	Source: http://www.ajpes.si/jolp/
4	Source: http://www.gvin.com/index.php/storitve/gvin-baze/
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Table 1: Disclosures by section. Sources: compiled by the authors, Bank of Slovenia (2013a and 2013b)
Disclosure	Total S	Source
Disclosures related to the Statement of financial position (short: BS items)	33	Decision on the books of account and annual reports of banks and savings banks (Bank of Slovenia, 2013a)
Disclosures related to the Income Statement (short: IPI items)	22	Decision on the books of account and annual reports of banks and savings banks (Bank of Slovenia, 2013a))
Mandatory Business Report disclosures (short: PP items)	10	Decision on the books of account and annual reports of banks and savings banks (Bank of Slovenia, 2013a)
Disclosures pursuant to Regulation on disclosures by banks and savings banks (short: SR items)	79	Decision on the books of account and annual reports of banks and savings banks (Bank of Slovenia, 2013a)
S	144	
Table 2: Independent variables. Source: compiled by the authors.
Independent variable	Type of data
Age	years of business since date of the establishment on Dec. 31, 2012 and 2015
Size	Log of total asset value as of Dec. 31, 2012 and 2015
Profitability	ROA (Return on Assets) in year 2012 and 2015
Complexity	Log of No. of subsidiaries in Slovenia on Dec. 31, 2012 and 2015
Government ownership	The share of the Government of the Republic of Slovenia in a bank's ownership on Dec. 31, 2012 and 2015
the logarithmic form of variable assets and number of subsidiaries to reach the normal distribution of variables, as we can find in Soliman (2013), Baumann and Nier (2004) and Hossain (2008).
The independent variable was set as we set the hypothesis of this research, thus based on previous theoretic background research. In regression analysis, we follow the Hossain (2008) model and statistic test development. The following Ordinary Least Square (OLS) regression model is to be fitted to the data in order to assess the effect of each variable on the disclosure level:
Y = P0 + P1X1 + P2X2 + P3X3 + P4X4 + P5X5 + e (2) Whereas:
Y = total disclosure score received for each bank
P0 =the intercept;
P1 - P5 = independent variables
e = the error term
5 Data analysis 5.1 Level of disclosures
The acquired data were analyzed using the SPSS 24 statistical software program. Based on statistical tests and calculations we were able to observe that, on average, banks in Slovenia publish 63.15% of a total of 144 items of disclosures in their annual reports. Table 3 shows descriptive statistics for average disclosure scores of banks, derived from the analysis of 31 annual reports (n=31). The scores for BS items ranged from 17 to 28, with the average value
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Table 3: Descriptive statistics. Source: compiled by the authors. Notes: * for the meaning of abbreviation see Table 1 (above)
Independent variable	Type of data
Age	years of business since date of the establishment on Dec. 31, 2012 and 2015
Size	Log of total asset value as of Dec. 31, 2012 and 2015
Profitability	ROA (Return on Assets) in year 2012 and 2015
Complexity	Log of No. of subsidiaries in Slovenia on Dec. 31, 2012 and 2015
Government ownership	The share of the Government of the Republic of Slovenia in a bank's ownership on Dec. 31, 2012 and 2015
Table 4: Correlation coefficients between independent variables. Source: compiled by the authors. * Statistically significant correlation at the 0.05 level (one-tailed)
	Range	Min	Max	Mean	Standard deviation
Disclosures related to the Statement of financial position (BS items)*	11	17	28	23.76	2.461
Disclosures related to the Income Statement (IPI items)*	5	15	20	17.35	1.228
Mandatory Business Report disclosures (PP items)*	2	8	10	9.79	0.485
Disclosures pursuant to Regulation on disclosures by banks and savings banks (SR items)*	28	26	56	40.04	10.215
Total	39	73	113	90.94	12.383
at 23.76 points or 70% of all possible points for BS. Disclosure scores for IPI items ranged from 15 to 20 averaging at 17.35 points or 80% of IPI. The average score value for disclosures in business reports amounted to 9.79 with the total value of 10 items. The most varied scores were obtained in relation to disclosures under Regulation on disclosures by banks and savings banks, ranging from 26 to 56, and averaging at 40.04 points or 50% of all possible points for this disclosure. The total number of disclosures amounted to 144 items with banks achieving between 73 and 113 points, with their mean value at 90.94 points.
5.2 Correlation Matrix and Multicollinearity Analysis
In order to make valid inferences from the regression analysis, the residuals of the regression should follow a normal distribution (Statistics Solutions, 2018). We test multicollinearity in explanatory variables. Multicollinearity refers to when your predictor variables are highly correlated with each other. Multicollinearity has been diagnosed through analyses of correlation factors and Variable Inflation Factors (VIF), consistent with Hossain (2008) and Hair et al. (2006). Independent variables should not be too strongly correlated, i.e. two or more variables should not be highly linearly related (multicollinearity). Multicollinearity can be detected by calculating correlation coefficients and the
5 Variance inflation factor (VIF) quantifies how much the variance is inflated and measures the variance of an estimator compared to what the variance would have been if the independent variable was not collinear. More on: https://onlinecourses.science.psu. edu/stat501/node/347/
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Table 5: Model Summary and ANOVA. Source: compiled by the authors.
(a)	Predictors: (Constants), Complexity-Log of Number of subsidiaries in Slovenia, Log of total asset value, profitability-ROA, age, government ownership
(b)	Dependent variables: Disclosed items
	R	R Square		Adjusted R Square	Std. Error of the Estimate
Model Summaryb	.833	.694		.632	7.509
ANOVA	Sum of Squares	df	Mean of Squares	F	Sig.
Regression1	3190.310	5	638.062	11.317	.000(a)
Table 6: Multiple regression coefficients (a). Source: compiled by the authors. (a) Dependent variable: Disclosed items
Sample	Unstandardized Coefficients B	Sig.	Collinearity statistics	
			Tolerance	VIF
Constant	33.740	.201		
Age	-.100	.001	.714	1.400
Log of Asset value	10.552	.021	.781	1.280
Gvt. ownership	11.931	.012	.588	1.702
ROA	-1.222	.245	.834	1.200
Log of subsidiaries in SLO	-3.174	.166	.854	1.170
variance inflation factor5 (VIF) and is present when the simple correlation coefficient exceeds 0.8 or when the VIF surpasses 10, with an associated tolerance value below 0.1 (Hossain, 2008).
The VIF values are presented in the last column of Table 6. With the maximum value of 1.702 calculated for variable government ownership, none of the values exceed 10, which would have been considered an indication of multicollinearity. Tolerance levels ranged between 0.588 and 0.854 and did not fall below 0.1, suggesting that there were no problems with multicollinearity.
Based on correlation and VIF values it can thus be safely assumed that correlations between independent variables were not so strong as to constitute a problem in the interpretation of data obtained by multiple regression.
Table 4 shows correlation coefficients for surveyed independent variables. The strongest relationship, calculated at -0.480, existed between independent variables age and government ownership and this correlation is significant at the 0.01 level (2-tailed). As none of the coefficients does not exceed an absolute value of 0.8, no strong multicollin-earity was established that would have negatively impacted the results of multiple regression analysis.
5.3 Multiple regression and hypotheses
Multiple correlation coefficient (r) indicates the strength of the relationship between the dependent and independent variables. Our calculated value r=0.833 suggests a strong correlation. The multiple coefficient of determination (R2) was established at R2=0.694, which means that nearly 70% of the total variance in the dependent variable (disclosure items in annual reports of banks) can be explained by the variability in the independent variables (age, size, profitability and complexity of a bank, and the government share). The value of the corrected coefficient of determination was 0.632.
Table 5 shows data on the reliability of the regression function. It provides information as to whether the correlation between the dependent and independent variables indeed exists, and whether changes in the independent variables cause changes in the dependent variables, or are these changes merely coincidental. A low p-value (p<0.05) means that the variable significantly contributes to the prediction and, therefore, the correlation may be confirmed. The p-value we calculated was very low (p= 0.000), in-
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Figure 1: Predicted Probability for residuals of dependent predicted variable and observed value Source: compiled by the authors.
dicating that the connection between the dependent and independent variables was strong, and confirming the statistical significance of the regression function.
Table 6 presents data on statistical significance of selected independent variables. P-values of less than 0.05 (p<0.05) suggest that a particular variable has a statistically significant impact on disclosures in annual reports of banks. If p>0.05, the effect is statistically insignificant. Statistically significant coefficients were calculated for variables age (p = 0.001), size (p=0.021) and government ownership (p=0.012). No statistical significance could be established for complexity which represents the variable log of number of subsidiaries in Slovenia (p>0.166) and profitability measured with Return on assets (p=0.245).
Our regression model is:
Level of disclosures = p0 + pi age of years old + p2 Log of Asset value + p3 Gvt.ownership + p4 ROA +p5 Log of subsidiaries in Slovenia +e
In order to make valid inferences from your regression, the residuals of the regression should follow a normal distribution. We have examined a normal Predicted Probability (P-P) with plotting (See Figure 1) residuals of the regression model. In figure 1 we can see that the residuals are normally distributed and we can assume normality of the
residuals of our regression. We conform to the diagonal normality line indicated in the plot, and we can say that our findings are valid.
The next step is to reveal our findings and make conclusions, this follows in section 6.
6 Findings and conclusions
The correlation between the dependent and independents variable we were able to determine was between the level of disclosure in annual reports of a bank and its age. However, the degree of correlation was the strongest for banks that fall within the "middle age" category (mean age of 76 years), and not for banks that exist on the longest. The established correlations between levels of disclosure and age for young and old banks were significantly lower; hypothesis 1 is thus not supported by our findings, and we have to reject it. Authors who have studied this association in the past have come to differing conclusions. Hossain (2008) and Soliman (2013), for example, did not find a statistically significant effect of a bank's age on the level of disclosure, whereas Hossain and Hammami (2009) report a positive and significant variable of age, which suggests that a more advanced age of a company directly influences the level of disclosure.
The obtained values for the variable of size, which is
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measured in the value of a bank's assets (variable is expressed logarithmic form), were significant, and suggest a positive correlation between company size and the level of disclosure. This suggests that larger banks disclose more information in their annual reports that smaller banks, which supports our Hypothesis 2. Our results are also consistent with findings of other studies, e.g. Hossain (2008), Hossain and Hammami (2009), Soliman (2013), Juhmain (2013) and Hancock, et al. (2009). The results of multiple regression do not indicate a correlation between the level of disclosure and a bank's profitability, therefore our Hypothesis 3 is not supported. Hossain and Hammami (2009) and Juhmani (2013) also found no association between profitability and the level of disclosure, while Hossain (2008) and Soliman (2013) report a positive correlation between both variables.
No statistically significant relationship could be found between the complexity of a bank and the level of disclosure in its annual reports; therefore, we reject Hypothesis 4. Hossain (2008) also concludes that the number of subsidiaries as the measure of the complexity does not affect the level of disclosure. Contrastingly, Hossain and Hammami (2009) report a positive correlation between the two variables.
The multiple regression results finally revealed a positive relationship between the share of government ownership and the level of disclosure. Our findings, therefore, lend support to our Hypothesis 5, that banks with a higher share owned by the Republic of Slovenia disclose more items. The research results from other countries, however, show a different picture. Juhmani (2013) and Jalil and Devi (2012), for example, report that state-owned companies reveal less than those in private ownership.
This paper reports on the level of disclosure in annual reports of banks in Slovenia over the period 2012 - 2015. The first findings are that banks in Slovenia have below average all of disclosures with banks achieving between 73 (min.) and 113 (max.), their mean at 90.94 points or 63% of all possible points for disclosures in their annual reports, against previous comparable research of disclosures, e. g. La Porta et al. (1998) where companies make up 70%, and Brown and Martinsson (2014) got a result of disclosure intensity by mean 71.95% of 20 annual reports in 20 countries from across the World.. Banks in Slovenia do not cover 70% of the total list of disclosures (see appendix A), so, we can say that banks in Slovenia have not above average care about their transparency. And on the other hand, banks in Slovenia on average, publish 63% of the total disclosure, and this is above the score of analyzed Indian banks (Hossain, 2008) which scored 60%. The population was not the same, but it can be said that Slovenian banks disclosure less information than is average in other companies in other countries, and more than banks in India.
Why the banks in Slovenia disclosure less than banks in other countries? Maybe in Slovenia banks think they
have a strong position, and they act arrogate because of weak institutional controls and low competition on banks and capital markets in Slovenia. Since banks in Slovenia unveil a sub-average amount of information in their annual reports compared to other surveys, we propose more public awareness by the Bank of Slovenia and audit companies that audit the annual reporting of banks and more control activities from Bank of Slovenia on this focus.
Our results largely coincide with the findings of other studies, e.g. Hossain (2008) or Soliman (2013). In our opinion, the observed differences can be explained, at least in part, by the specificities of the Slovenian banking system, due to its past development and organization.
Our first interesting finding is a establish a correlation between independent variable government ownership and age, a calculated correlation coefficient at -0.480 shows a negative relationship, correlation is significant at the 0.01 level (2-tailed) and it can understand that the older that the bank is, the less ownership belongs to the state of Slovenia. This is some kind of truth because Slovenia has to move out of state banks due to Slovenia had recapitalized its ailing state-owned banks with 3.2 billion Euros in 2013 (Bank of Slovenia, 2014). Slovenia has had one of the recapitalized banks already sold (NKBM bank, d.d.) and the second one (NLB bank, d.d.) is in process of selling them. Results of regression analysis provide us a basis, that we have rejected the first hypothesis, that the level of disclosure is positively associated with the age of the bank. Our model equips us with findings, that the age of a bank does have a significant negative statistical impact (p=0.001) with -0.100 points of total disclosures for each year of age on the scope of disclosures in annual reports of banks in Slovenia. Based on this evidence, we can say that the bank older than 76 years (mean is 76 years old), the bank disclosure a little less information for every additional year of age.
Our findings suggest that the most important underlying factor that affected the level in disclosure in Slovenian banks was the share of government ownership and size, which is measured in the value of a bank's assets. Thus, the largest number of items was disclosed by banks which were partially or wholly owned by the Republic of Slovenia and these banks are the largest in Slovenia. We found out that the smaller Slovenian banks revealed more than larger banks, but we can say that any increase in assets of bank also means more disclosures in their annual report.
One of our most interesting findings is that the results of multiple regression don't indicate a correlation between the level of disclosure and a bank's profitability, this is completely opposite of most observed researches which report a positive association between profitability and the level of disclosure in annual reports of banks, e. g. Baumann and Nier (2004), Hossain and Hammami, 2009, Hossain (2008). We have calculated negative impact (which is insignificant p=0.245) with -1.22 point of total disclosures for each rising percentage of return on assets on scope of
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disclosures in annual reports of banks in Slovenia. This means (not statistically significant) the higher profits want to disclose less information, maybe due to reasons: first, by disclosing fewer items the bank can hide what's really happening in bank to shareholders; second, by avoidance of disclosures they do not want to encourage suspicions about the poor performance of the bank and neither remain a good reputation of bank.
We believe that the obtained results largely reflect the legal framework of the Slovenian banking system, which is more rigorous for Slovenian banks and more lenient to foreign banks, which are consequently able to disclose certain information only at the level of their parent company. The annual reports of a majority of Slovenian banks are supplemented by disclosures under the Decision regulating disclosures by banks and savings banks as a separate document. Banks disclose this information in their annual reports, but frequently in less depth and detail. The practical implications suggested by results of our research are, that the regulation and control institution in Slovenia (in this case this is The bank of Slovenia) should increase a control over the older and state-owned banks in Slovenia and their disclosures in annual reports.
A higher level of disclosure is also required from larger banks, which is understandable since their business operations are, as a rule, more complex and cover more areas. Smaller banks, however, do not disclose certain information because it is irrelevant or immaterial to their business. Additionally, smaller banks have to consider if the value of disclosing the information may not be higher than the cost of its gathering.
The limitation of the research is that it covers a two year and a single specific country, and in order to understand the nature of variations of overall disclosure in the annual report of Slovenian banks, it is necessary to undertake a study taking more data in the future, perhaps in next five and 10 years data. It will be more realistic when the consolidation of the banking system in Slovenia will be done, for this will maybe take some more than 10 years. We think that annual reports with so many disclosures as possible can contribute significantly to a bank's success and public trust in their business.
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Zdolšek, D., & Kolar, I. (2013). Management disclosure practices for disaggregated (financial) information in Slovenian unlisted companies, Journal for East European Management Studies, 18(2), 264-289, https:// ssrn.com/abstract=2668121 ZGD-1 - Zakon o gospodarskih družbah (Companies Act) (2014). The Official Journal of Republic Slovenia (RS) 42/2006, 2014.
Iztok Kolar Assistant professor at the Department of Accounting and Auditing of University of Maribor, Faculty of Economics and Business. He is the Head of the Department for Accounting and Auditing. His research fields include financial reporting, management accounting issues, auditing and forensic accounting. Personal website: http://www.epf.um.si/o-fakulteti/ organiziranost/pedagoski-sodelavci/oseba/Staff/ details/57/
Nina Falez received her Bachelor degree from Economics and Business in 2015 from Faculty of Economics and Business, University of Maribor. Her research interests are accounting and auditing.
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Appendix 1 - Disclosures by items. Sources: Bank of Slovenia (2013a) and Bank of Slovenia (2013b)
Sources: Bank of Slovenia (2013a) and Bank of Slovenia (2013b)
	Disclosures related to the Statement of financial position (BS items)
1	Cash and balances with central bank
2	Financial assets held for trading
3	Financial assets designated at fair value through profit or loss
4	Financial assets available for sale
5	Credits
6	Financial assets held to maturity
7	Derivatives held for hedging purposes
8	Changes in fair value of common items hedged against interest rate risk
9	Tangible fixed assets
10	Investment property
11	Intangible assets
12	Investments in the equity of subsidiaries, associates and joint ventures
13	Tax assets
14	Other assets
15	Non-current assets held for sale and discontinued operations
16	Financial liabilities to central bank
17	Financial liabilities held for trading
18	Financial liabilities designated at fair value through profit or loss
19	Financial liabilities measured at amortized cost
20	Financial liabilities associated with transferred financial assets that do not qualify for derecognition
21	Derivatives held for hedging purposes
22	Changes in fair value of the items hedged against interest rate risk
23	Provisions
24	Tax liabilities
25	Other liabilities
26	Liabilities related to non-current assets held for sale and discontinued operations
27	Share capital
28	Capital reserves
29	Equity component of compound financial instruments
30	Revaluation surplus
31	Reserves from profit
32	Own shares
33	Net profit/loss of the financial year (retained profit/loss)
	Disclosures related to the Income statement (IPI items)
34	Interest income
35	Interest expenses
36	Dividend income
37	Fee and commission income
38	Fee and commission expenses
39	Realized gains (losses) on financial assets and liabilities not measured at fair value through profit or loss
40	Net gains (losses) on financial assets and liabilities held for trading
41	Net gains (losses) on financial assets and liabilities designated at fair value through profit of loss
42	Fair value adjustments from hedge accounting
43	Net gains (losses) from exchange rate differences
44	Net gains (losses) from derecognition of assets other than non-current assets held for sale
45	Other net operating gains (losses)
46	Administrative expenses
47	Amortization
48	Provisions
49	Impairments
50	Negative goodwill
51	Share of profits (losses) from associates and joint ventures accounted for using the equity method
52	Total profit (loss) from non-current assets classified as held-for-sale and the thereto related liabilities
53	Corporate income tax from continuing operations
54	Basic earnings per share
55	Diluted earnings per share
	MANDATORY BUSINESS REPORT DISCLOSURES (PP items)
	Business performance
56	Macroeconomic environment
57	Operating policies
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58	Key performance data and indicators
59	Share capital and shareholders
60	Strategic directions
	Management
61	Management structure
62	Senior management
63	Branch network
64	Organizational structure
65	Organizational structure of the group of associated companies
	DISCLOSURES PURSUANT TO REGULATION ON DISCLOSURES BY BANKS AND SAVINGS BANKS
	Risk management policies and objectives
66	Risk management strategies and processes
67	Structure and organization of the relevant risk management functions or other appropriate arrangements
68	Scope and nature of internal risk reporting and risk measurement systems
69	Policies for hedging and mitigating risk, and the strategies and processes for monitoring the continuing effectiveness of hedges and mitigants
	Information on entities included in disclosures
70	Name of the bank required to provide disclosure
71	Outline of the differences between consolidation for financial reporting and consolidation for the purposes of supervision on a consolidated basis, with a brief description of entities
72	Aggregate amount by which the capital (own funds) is lower than the required minimum in all subsidiaries not included in the consolidation, and the name(s) of these subsidiaries
	Capital (Own funds)
73	Key information on the main features of all capital items and components
74	Basic own funds (Tier I)
75	Total amount of Tier II and Tier III capital as defined by the Regulation on the calculation of own funds of banks and savings banks
76	Deductions from Tier I and Tier II capital
77	Amount of capital as specified in Article 3 of the Regulation on disclosures by banks and savings banks, net of deductions specified in Article 22 of the mentioned regulation and under consideration of the ratios and limits between individual capital items as specified in the second paragraph of Article 5 of the mentioned regulation
	Minimum capital requirements and process of internal capital adequacy assessment
78	Summative statement on the approach to assessing the adequacy of a bank's internal capital to support its current and planned activities
79	Amount of capital requirements for all categories of exposure
80	Capital requirement for market risks
81	Capital requirement for operational risks
	Counterparty credit risk
82	Description of the methodology used to assign internal capital and credit limits for counterparty credit exposures
83	Description of policies for securing collaterals
84	Description of policies with respect to wrong-way risk exposures
85	Description of effects of a downgrade in the bank's credit rating on the increase in collateral to be provided by the bank
86	Gross positive fair value of contracts, netting benefits, netted current credit exposures, collateral at the bank's disposal, and net credit exposure to derivatives
87	Description of the method used for calculating exposure to derivatives, swaps, securities or commodities lending or borrowing transactions, margin lending transactions, and long settlement transactions
88	Nominal value of credit derivatives used for hedging, and the distribution of current credit exposure by types of credit exposure
89	Nominal value of credit derivatives transactions, the value of these instruments for the bank's own portfolio and the values for clients being illustrated separately, and an indication of the types of credit derivatives further broken down as bought and sold
	Credit risk and dilution risk
90	Definition of past due and impaired items for accounting purposes
91	Description of the methodology for making value adjustments to items and provisions
92	Total amount of exposure, less impairments and provisions, without taking the effects of credit protection into consideration, and the average exposure amount in the reporting period, broken down by category of exposure
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93	Geographic distribution of exposure, broken down by material category of exposure, and further detailed if appropriate
94	Distribution of exposures by institutional sector or counterparty type, broken down by category of exposure, and further detailed if appropriate
95	Breakdown of all categories of exposure into residual maturities of up to one year and more than one year, and further detailed if appropriate
96	For important institutional sectors or counterparty types: the amount of past due exposures, including the amount of impaired exposures, the amount of value adjustments due to impairments and provisions and the amount of eliminated/formed value adjustments due to impairments and provisions
97	The amount of past due exposures and the amount of impaired exposures by important geographic areas, including the amounts of impairments and of provisions for individual geographical areas
98	For impaired exposures, an illustration of the changes in value adjustments and of the changes in provisions
	Operational risk
99	Approach used to calculate operational risk capital requirement
	Investments in equity securities not held in the trading book
100	Purpose of the investment, including the treatment of capital gains and strategic purposes, accounting techniques and valuation methods used and any changes in accounting practices
101	Balance sheet value and the fair value of investments, and for exchange-traded securities a comparison with the market price if the latter materially differs from the fair value
102	Types, nature and amounts of exposures to exchange-traded securities, exposures to private equity if sufficiently diversified, and other exposures
103	Cumulative realized gains and losses from the sale of investments in equity securities in the reporting period
104	total amount of unrealized gains and losses, and any of these amounts, that the bank includes in the core capital (basic own capital) or tier I capital (additional own funds)
	Interest-rate risk from items not held in trading book
105	Nature of the interest-rate risk and the key assumptions (including assumptions about the early repayment of loans and the movement of sight deposits), and the frequency of interest-rate risk measurement
	Securitization
106	Bank's objectives in relation to securitization activity
107	Nature of other risks associated with securitized exposures, including liquidity risk
108	Types of risks in terms of seniority of the underlying securitization positions and in terms of assets underlying exposures, which form the final link in the securitization chain of title, obtained and retained through re-securiti-zation
109	Different roles of the bank in the securitization process
110	The extent of the bank's involvement in each of these roles
111	Description of the procedures for monitoring changes in the credit and market risks of securitization exposures including how the behavior of the underlying assets impacts securitization exposures and a description of how those processes differ for re-securitization exposures
112	Description of the bank's policy governing the use of hedging and unfunded protection to mitigate the risks of retained securitization and re-securitization exposures, including identification of material hedge counterparties by relevant type of risk exposure
113	Approaches to calculating risk-weighted exposure amounts that the institution follows for its securitization activities including the types of securitization exposures to which each approach applies
114	Types of securitization special purpose entities (SSPE) that the bank, as sponsor, uses to securitise third-party exposures including whether and in what form and to what extent the institution has exposures to those SSPEs, separately for on- and off-balance sheet exposures, as well as a list of the entities that the institution manages or advises and that invest in either the securitisation positions that the institution has securitized or in SSPEs that the institution sponsors
115	Summary of the bank's accounting policies for securitiza-tion activities
116	Names of the ECAIs used for securitizations and the types of exposure for which each agency is used
117	Explanation of significant changes to any of the quantitative disclosures in points (n) to (q) since the last reporting period
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118	Separately for the trading and the non-trading book, the following information broken down by exposure type: the total amount of outstanding exposures securitised by the institution, separately for traditional and synthetic securitisations and securitisations for which the institution acts only as sponsor; the aggregate amount of on-balance sheet securitisation positions retained or purchased and off-balance sheet securitisation exposures; the aggregate amount of assets awaiting securitisation; for securitised facilities subject to the early amortisation treatment, the aggregate drawn exposures attributed to the originator's and investors' interests respectively, the aggregate capital requirements incurred by the institution against the originator's interest and the aggregate capital requirements incurred by the institution against the investor's shares of drawn balances and undrawn lines; the amount of securitisation positions that are deducted from own funds or risk-weighted at 1 250 %: a summary of the securitisation activity of the current period, including the amount of exposures securitised and recognised gain or loss on sale
119	Separately for the trading and the non-trading book, the following information:: the aggregate amount of securiti-sation positions retained or purchased and the associated capital requirements, broken down between securitisation and resecuritisation exposures and further broken down into a meaningful number of risk-weight or capital requirement bands, for each capital requirements approach used; the aggregate amount of re-securitisation exposures retained or purchased broken down according to the exposure before and after hedging/insurance and the exposure to financial guarantors, broken down according to guarantor credit worthiness categories or guarantor name
120	For the non-trading book and regarding exposures securitized by the institution, the amount of impaired/past due assets securitized and the losses recognized by the institution during the current period, both broken down by exposure type
121	For the trading book, the total outstanding exposures securitized by the institution and subject to a capital requirement for market risk, broken down into traditional/ synthetic and by exposure type
	Liquidity risk
122	Methodologies for managing liquidity risk
123	Methodologies to reduce liquidity risk
124	Measures to prevent and eliminate the causes of liquidity shortage
	Remuneration system
125	Description of the decision-making process used to determine the bank's remuneration policy
126	Explanation of the impact of the performance of an employee, an employee's organisational unit and the general operating results of the bank, i.e. performance, on an employee's remuneration
127	Most important contextual characteristics of the remuneration policy
128	Performance criteria, based on which an employee is entitled to shares, options and other forms of variable remuneration, and the main parameters and rationale for using any form of variable remuneration and other noncash benefits for employees
129	Information regarding the aggregate amount of remuneration paid in the previous financial year, broken down by business area
130	Information regarding the aggregate amount of remuneration paid for the previous financial year, broken down by employee category
	Significant business contact
131	Number of agreements concluded with an individual person
132	Name of the person and his or her function
133	Date an individual agreement was concluded
134	Subject of an individual agreement
135	Value of an individual agreement and the total value of all agreements
136	Payment terms
	Compliance with regulations
137	List of conflicts of interest identified in the previous year involving the members of management and supervisory bodies of subsidiaries with a registered office outside the Republic of Slovenia
138	Measures adopted by the supervisory board to prevent and limit the conflicts of interest specified
	Credit protection
139	Policies and processes for using balance-sheet netting, and the extent of use of this type of protection
140	Policies and processes for collateral valuation and management
141	Description of the main types of collateral taken by the credit institution
142	Major types of personal guarantor and counterparties in credit derivatives transactions, and their creditworthiness
143	Information about market or credit risk concentrations within the credit protection taken
144	Total exposure value (after balance sheet netting, if used) that is covered by collateral, after the application of volatility adjustments, for each category of exposure
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Obseg razkritij bank v letnih poročilih: primer bank v Sloveniji
Ozadje in cilji: Mnoge študije raziskujejo razkritja informacij v letnih poročilih podjetij. Letna poročila bank se pomembno razlikujejo od letnih poročil drugih poslovnih subjektov, zlasti v pogledu razkritij. Cilj tega članka je raziskati raven razkritij in ugotoviti katere dejavnike vplivajo na raven razkritij v letnih poročilih bank v Sloveniji. Zasnova / metodologija / pristop: Opazovali smo razkritja vseh bank v Sloveniji za leti 2012 in 2015. Proučevani vplivni dejavniki v študiji so: starost, velikost, delež države, donosnost in kompleksnost banke. Kontrolni seznam za merjenje obsega razkritij je sestavljen iz 144 prostovoljnih in obveznih postavk. Statistična obdelava zbranih podatkov je izvedena z linearno regresijsko analizo.
Rezultati: Povprečni rezultat razkritij v letnem poročilu banke v Sloveniji je blizu 94 točk ali 63% vseh možnih točk za razkritja. Rezultati analiz kažejo statistično značilne povezave in vpliv velikosti banke in deležem državnega lastništva na stopnjo razkritij, ter negativni statistično značilen vpliv starosti banke na raven razkritij. Dobljeni rezultati, presenetljivo, ne kažejo vpliv donosnosti banke na stopnjo razkrivanja, kar je proti teoriji in ugotovitvam iz drugih podobnih raziskav.
Zaključek: Glavni prispevek raziskave je poglobitev znanja o razkritjih v letnih poročilih bank. Po našem mnenju rezultati dobro odražajo slovenski bančni sistem in kako banke razkrivajo svoje informacije. Članek prispeva k akademski literaturi odgovore na vprašanje, kaj so vplivni dejavniki razkritij, in da banke v Sloveniji zagotavljajo manj informacij javnosti, v primerjavi s povprečjem razkritij v drugih dejavnostih ali primerljivo razvitih državah. Lahko rečemo, da banke v Sloveniji s svojim razkrivanjem informacij ne gradijo večjega zaupanja vlagateljev in javnosti v njihovo poslovanje.
Ključne besede: lastništvo države; razkrivanje informacij; starost; Slovenija
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Reviewers in 20181
Ana Arzenšek, University of Primorska, Faculty of Management, Koper, Slovenia Mustafa Batuhan Ayhan, Marmara University, Department
of Industrial Engineering, Istanbul, Turkey Nasoor Bagheri, Shahid Rajaee Teacher Training University, Department of Electrical and Computer Engineering, Tehran, Iran Manuel Benazic, Juraj Dobrila University of Pula, Faculty of Economics and Tourism "Dr. Mijo Mirkovic", Pula Croatia
Štefan Bojnec, University of Primorska, Faculty of Management, Koper, Slovenia Ljiljana Božic, The Institute of Economics, Zagreb, Croatia
Pawel Bryla, University of Lodz, Department of International Marketing and Retailing, Lodz, Poland Petr Cermak, Moravian University College, Olomouc,
Czech Republic Dragan Cockalo, University of Novi Sad, Technical Faculty "Mihajlo Pupin" Zrenjanin, Serbia Dorota Dobija, Kozminski University, Department of Accountancy, Warsaw, Poland Marina Dobrota, University of Belgrade, Faculty of Organizational Sciences, Belgrade, Serbia Marko Ferjan, University of Maribor, Faculty of Organizational Science, Kranj, Slovenia Petra Grošelj, University of Ljubljana, Biotechnical Faculty, Ljubljana, Slovenia Anton Hauc, University of Maribor, Faculty of Economics
and Commerce, Maribor, Slovenia Miloš Hitka, Technical University in Zvolen, Faculty of
Wood Sciences and Technology, Zvolen, Slovakia Anton Ivaschenko, Samara State Technical University, Samara, Russia
Milena Jakšic, University of Kragujevac, Faculty of Economics, Kragujevac, Serbia Eva Jereb, University of Maribor, Faculty of Organizational Science, Kranj, Slovenia Laura Južnik Rotar, University of Novo mesto, Faculty of
Economics and Informatics, Novo mesto, Slovenia T. Bartosz Kalinowski, University of Lodz, Faculty of
Management, Lodz, Poland Sajjad Nawaz Khan, University Malaysia Sarawak, Sarawak, Kuching, Malaysia Davorin Kofjač, University of Maribor, Faculty of Organizational Science, Kranj, Slovenia Anita Kolnhofer Derecskei, Obuda University, Faculty of
Business and Management, Budapest, Hungary Jure Kovač, University of Maribor, Faculty of Organizational Science, Kranj, Slovenia
1 Until November 25, 2018
Miklos, Kozma, Corvinus University of Budapest, Budapest, Hungary
Gregor Lenart, University of Maribor, Faculty of Organizational Science, Kranj, Slovenia Yuxin Lin, Columbia University, Community College Research Center, Teachers College, New York, USA Jerzy Mqczynski, University of Social Sciences, Lodz, Poland
Ensar Mekic, International Burch University, Faculty of Economics and Social Sciences, Sarajevo, Bosnia and Herzegovina
Josip Mikulic, University of Zagreb, Faculty of Economics & Business, Zagreb, Croatia Marian Niedzwiedzinski, University of Lodz, Faculty of
Economics and Sociology, Lodz, Poland Vesna Novak, University of Maribor, Faculty of Organizational Science, Kranj, Slovenia Oyeniyi Samuel Olaniyan, University of Bergen, Faculty
of Psychology, Bergen, Norvay Mislav Ante Omazic, University of Zagreb, Faculty of
Economics & Business, Zagreb, Croatia Marija Ovsenik, Institute of Management, Ljubljana, Slovenia,
Radoslaw Pastusiak, University of Lodz, Faculty of Economics and Sociology, Lodz, Poland, Katarina Pažur Aničic, University of Zagreb, Faculty of
Organization and Informatics, Varaždin, Croatia, Ivica Pervan, University of Split, Faculty of Economics, Split, Croatia,
Iztok Podbregar, University of Maribor, Faculty of Organizational Science, Kranj, Slovenia, Danijela Rabar, Juraj Dobrila University of Pula, Faculty of Economics and Tourism "Mijo Mirkovic", Pula, Croatia,
Mladen Radujkovic, Alma Mater Europaea ECM, Maribor, Slovenia
Mervi Rajahonka, South-Eastern Finland University of Applied Sciences, Small Business Center, Mikkeli, Finland
Blaž Rodič, Faculty of Information Studies, Novo mesto, Slovenia
Vesna Rodic Lukic, University of Novi Sad, Faculty of Education, Novi Sad, Serbia Andrijana Rogošic, University of Split, Faculty of Economics, Split, Croatia Maciej Rostanski , University of Dqbrowa Gornicza ,
Dqbrowa Gornicza , Poland Malgorzata Rozkwitalska, Gdansk School of Banking,
Management Department, Gdansk, Poland Wafaa Salah Mohamed, The British University in Egypt,
327
Organizacija, Volume 51
Reviewers in 2018
Issue 4, November 2018
Faculty of Business Administration, Economics & Political Science, Cairo, Egypt Justyna Sarnowska, University of Social Sciences and Humanities, Faculty of Arts and Social Sciences, Warsaw, Poland
Monika Sipa, Czestochowa University of Technology,
Faculty of Management, Czestochowa, Poland Mario Spremic, University of Zagreb, Faculty of Economics & Business, Zagreb, Croatia Richárd Szántó, Corvinius University of Budapest, Budapest, Hungary
Maja Seric, University of Valencia, Department of Marketing, Valencia, Spain Iveta Simberová, Brno University of Technology, Faculty
of Business and Management, Brno, Czech Republic Evrem Üstünlüoglu, Izmir University of Economics,
School of Foreign Languages, Izmir, Turkey Lucie Vnoucková, University of Economics and Management, Prague, Czech Republic Katrin Winkler, University of Applied Sciences, Faculty of
Business Administration, Kempten, Germany Lidija Zadnik Stirn, University of Ljubljana, Biotechnical
Faculty, Ljubljana, Slovenia Artürs Zeps, Riga Technical University, Riga, Latvia Hana Zídková, University of Economics, Department of
Public Finance, Prague, Czech Republic Hans-Dieter Zimmermann, University of Applied Sciences, FHS St. Gallen, Switzerland
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