Is Trust in Banks in Slovenia Put to the Test?
Sabina Taskar Beloglavec
University of Maribor, Faculty of Economics and Business Maribor, Slovenia sabina.beloglavec@um.si
Urban Sebjan
University of Maribor, Faculty of Economics and Business Maribor, Slovenia urban.sebjan@um.si
Abstract
The question of the banking system's stability in connection to trust since the 2008 crisis has been the subject of many debates seeking to find permanent solutions to banking system problems, as the current situation affects bank customers' behavior. This article examined trust in banks during the financial crisis and offers, via demographic variables, explanations as tow whether or not customers tend to withdraw their deposits during a crisis. The results contribute to banks' decision-making regarding deposits management and understanding customers' behavior, especially during a crisis. The results show a negative relationship between trust and deposit withdrawal intention, where gender and education level play an important role.
Keywords: trust, financial institution, bank, Slovenia, logistic regression
1 Introduction
This article deals with trust. In economics, trust is the main factor allowing the fractional reserve bank system to exist and is the most important characteristic in the relationship between financial institutions, including banks, and their customers. Bank customers have a tendency to buy banking services from a bank, which they consider to be trustworthy and sound. This already fragile connection is under a lot of pressure in normal circumstances, let alone during times of crisis, when banks are often held responsible (Hurlburt, Miller & Voas, 2009; Schelkle, 2011). However, the complexity of the phenomenon itself is shown by the fact that trust has not only been examined in economics, but also by psychologists, sociologists, anthropologists, and others (for example, see Mayer, 2004; Whitney, 1994). One angle is common to all: the fragility of trust. Trust is gradually built, but can be destroyed in a moment (more for example Rempel, Holmes & Zanna, 1985; Weber, Malhotra & Murnighan, 2004). Authors also talk about trust's antipode—namely, mistrust (Tyler & Stanley, 2007)—and the perceived fairness of bank services (Szykman, Rahtz, & Plater, 2005). It is important to research and, in practice, implement trustworthy relationships because trust is a central concept on which other concepts, like loyalty and satisfaction (see Anderson & Narus, 1990) are based. These are a sustainable source of banks' competitive advantage (see Trif, 2013). Taking all these facts into consideration, it is necessary to research trust in banking systems. Therefore, our research deals with the relationship of trust, deposit safety scheme, and deposit withdrawals during times of financial crisis
ORIGINAL SCIENTIFIC PAPER
RECEIVED: MARCH 2015 REVISED: MAY 2015 ACCEPTED: MAY 2015
DOI: 10.1515/ngoe-2015-0012 UDK: 336.71:330.4(497.4) JEL: G01, G02, G21
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. 61 No. 3 2015
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in the case of the Slovenian banking system. We connected these variables with selected socioeconomic factors.
The article continues as follows. We start with a theoretical background. AS this is an empirically oriented study, we test the conceptual model and three main hypotheses regarding (1) the impact of gender, age, and level of education of bank customers on their perceived bank deposit safety; (2) the impact of those factors on customers' perceived trust in the bank; and (3) the withdrawal of bank deposits connected to gender, age, and education. The third section of the article gives the research framework and delivers results. In the discussion and conclusion section, we assess the findings and give some suggestions for further research.
2 Literature Review
This literature review deals with trust, connecting it to other variables that we considered in our research. In the following paragraphs, we summarize the limited literature on trust as a concept in the financial markets context, with financial institutions being the foundation of that part of the economy. We then discuss trust in connection with gender, age, and education level. The financial crisis and its consequences for all levels of economy and society offer a sound reason to also define trust through the optics of its resilience during a financial crisis, when it is tested over and over again in the relationship between banks and customers. In addition, banks are burdened with the process of restructuring and are even subject to failures. Customers seem to have different attitudes toward these processes, ranging from fear to understanding.
The following section of the article is structured based on the research model depicted in Figure 1. This model incorporated concepts dealt with in the literature review; the hypotheses are presented and tested in the empirical part of the article.
Figure 1: Conceptual research model
2.1 Concept of trust in financial institutions
The body of literature in this field is mainly dedicated to banks rather than other financial institutions, which is probably due to the specifics of a bank as a company and its influence on the individual (Suchting, 1998). Trust is the most important category in the process of reaching a personal financial decision. As with all other services, banking services are intangible products and defined only by contract, which remove trust and make taking risks meaningless (Calderon, Chong, & Galindo, 2009; Sapienza & Zingales, 2009).
Moorman, Deshpande, and Zaltman (1992) defined trust as preparedness to relate to a trustworthy partner in the broadest meaning of the word, while Coulter and Coulter (2002) added that a higher level of trust leads to better cooperation. Authors in general tend to define trust as a dynamic category, resulting from a process, and talk about different dimensions of trust. Therefore, it can be defined through different viewpoints. As McKnight & Chervany (2000) pointed out, trust can be seen as a notion, as systemic trust, as belief, and an intention to trust (Sztompka, 1999), where it develops from a quality relationship (Whitney, 1994) over individual characteristics to a cultural standard. Therefore, bank customers and banks have built systemic trust, which is theoretically defined as trust between an institution and an individual. Customers tend to perceive the banking institution as a relationship to a system as a whole (Bennet & Kottasz, 2012); here, trust built between the customer and an individual banking clerk is in the foreground and an important basis for bank customer loyalty (Gulati, 1995; Parkhe, 1993; Zineldin, 1995).
Trust is accompanied by other similar concepts, like loyalty and satisfaction that are interconnected and decisive in preserving the role of an individual bank as a customer's main bank. Despite the fact that individuals' subjective impression plays a leading role in defining what loyalty or satisfaction might be, trust and reliability are of great
H
Gender, age, level of education	
H2b	
1	1
Bank deposit safety in times of crisis
H
Trust in banks in times of crisis
H
Withdrawal of funds in times of crisis
Source: Authors
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Sabina Taskar Beloglavec, Urban Sebjan:
Is Trust in Banks in Slovenia Put to the Test?
importance in building and preserving customer loyalty (Bloemer, de Ruyter, & Peeters, 1998) Morgan and Hunt (1994) incorporated the concept of commitment into their model and defined it as a key element of banks' customer relations strategy. A long-term relationship has a positive relationship to trust while a bad image has a negative relationship to trust.
However, trust needs a wider context than the bank-bank clerk-bank customer communication triangle to exist. It needs an operating, stable institutional framework (Aghion, Algan, Cahuc & Shleifer, 2010); Carlin, Dorobantu, & Viswanathan, 2009) and, consequently, a stable banking system given the regulatory and legislative demands. Stevenson and Wolfers (2011) argue that the level of trust in this connection depends on the country's development stage. In an international comparison (Coupé, 2011) of transition countries in Slovenia, 55% of respondents declared having trust in their banks. In the highly developed Netherlands (90%) or Austria (70%), the percentage is of course higher (Knell & Stix, 2010; Mosch & Prast, 2010), while for example in Bulgaria that level is significantly lower (Mudd & Valev, 2009).
2.2 Trust and financial crisis
Trust, being a dynamic factor, changes over time. Stevenson and Wolfers (2011) demonstrated that changes in trust are connected with unemployment rate changes, which is quite a good predictor for future crises connected to bank deposit withdrawal (Guiso, Sapienza, & Zingales, 2008; Ramirez, 2009; Sapienza & Zingales, 2009). Deb and Chavali (2010) argued that the intention to deposit money is positively related to trust both pre- and post-crisis.
During our literature review, we did not come upon any research dealing with trust in connection to the views and demographic characteristics of bank customers in Slovenia, as addressed to in this article. Various public polls have been conducted in this regard in Slovenia, and the results were published in mass media (see Delo, 2013; Slovenske novice, 2012). Interestingly, regardless of events in Spain and Greece, 71% of Slovenians have not thought about withdrawing their deposits (Slovenske novice, 2012). Bank runs did not occur in our banking system, although the financial crisis affected the amount of savings deposited in banks as 60.9% of respondents had lower savings than before the crisis (Slovenske novice, 2012). Vox populi research in 2012 found contradicting results: Half of respondents worried about their savings in banks, while 27% thought that their deposits were no longer safe (Delo, 2013).
2.3 The impact of gender, age, and education on trust
Existing research on trust in the economic or business sense of the word has found that women have more troubles with trust in general (Alesina & La Ferrara, 2002). As consumers, women give more second thoughts to trust than men (Sheehan Bartel, 1999). Buchan, Croson, and Solnick (2008), in their research of behavioral differences in the investment game, discovered that men trust more than women, but women tend to be more trustworthy.
Regarding the influence of educational level on trust, the literature is relatively scarce. When it comes to dealing with trust within the economic context and in the field of financial institutions and/or banks, it is even scarcer. However, based on the research in Mexico, which examined numerous factors affecting the relationship, it has been argued that people with less education feel uncomfortable with banking issues (Djankov, Miranda, Seira, & Sharma, 2008). Although the study focuses on a less developed banking system, this example is nevertheless a good example of the complexity involved in researching the bank-customer relationship. Many possible angles have to be considered, such as present customers' needs, future needs, retaining customers, and differences in the banking system's development level. Thus, the current paper provides insights into the relationship of trust based on selected demographic variables and between various trust viewpoints and circumstances, as defined in the conceptual research model.
3 Research
3.1 Research methodology
3.1.1 Sample and data collection
A questionnaire was used to collect data from December 10, 2013, to January 27, 2014. The target population represented random users, over the age of 18, who were legally able to buy bank services in Slovenia independently. All the returned questionnaires were correctly completed. For the hypothesis testing, the data was collected based on a convenience non-random sample of 150 customers of bank services from Slovenia. In the total sample, 57% were male (n = 64) and 43% female (n = 86) respondents. A more detailed sample description is given in Table 1.
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Table 1 Sample Survey Results
Control variables	fi	fi %
Education:		
Less than secondary education	13	8.7 %
Secondary education	S3	35.3%
More than secondary education	84	56.0%
Gender:		
Male	64	57.3 %
Female	86	42.7%
Age:		
18-28 years (young population)	S9	39.3 %
29-39 years (middle population)	34	22.7 %
More than 40 years (older population)	57	38.0 %
Source: Authors' calculations
The time of period was chosen deliberately due to the fact that some significant changes occurred in the Slovenian banking system at that time. Two main events in the banking sector took place: The ownership structure changed due to the Slovenian government's recapitalization of five banks, and the banking system's restructuring process began with the supervised liquidation of two private banks, Factor Banka and Probanka. Non-performing assets were then transferred to the Bank Assets Management Company, established in March 2013 (BAMC, 2015; BS, 2015). Such events were so powerful and present in day-to-day media that they were expected to make the public think about the banking crises even more and put their trust in banks to the test.
3.1.2 Instrument of research
The theoretical framework and conceptual research model served to develop questions to study three variables: bank deposit safety, trust in banks during a crisis, and withdrawal of savings during a crisis. Demographic characteristics were also considered. The bank deposit safety variable was measured with yes (1) or no (0) answers to a question about whether individuals considered their deposits to be safe in banks. Trust in the bank was measured with the same di-chotomous answers to a question about trust in one's bank in times of crisis. Finally, the withdrawal of savings in times of crisis also used the same dichotomous answers to a question about whether individuals would in times of financial crisis withdraw their bank deposits.
Three control variables were included in the questionnaire to check if hypothesized predictor variables affected bank deposit safety, trust in banks in times of crisis, and withdrawal of savings in times of crisis beyond the impact of these variables. The control variables were age (categorical variable: young population from 18 to 28, middle-aged population from 29 to 39, and older population over 40 years of
age), gender (dichotomous variable: females were assigned value 0 and males value 1), and education (categorical variable: less than secondary education, secondary education, and post-secondary education).
3.1.3 Data analysis
We formally tested three hypotheses:
•	H^ Bank customers' gender, age, and level of education affect their perceived bank deposit safety in times of crisis.
•	H2: Bank customers' perceived bank deposit safety in times of crisis, gender, age, and education have a significant effect on their perceived trust in the bank in times of crisis.
•	H3: Customers' perceived trust in the bank in times of crisis, gender, age, and level of education play a significant role in the decision to withdraw funds from their savings account in times of crisis.
We used binomial logistic regression (Hosmer & Lemeshow, 2000; Kedmenec, Tominc, & Rebernik, 2014), which estimates the probability of an event—in our case, the recognition of opportunities or not. We ran five binomial logistic regressions. While Model I includes only control variables, Models II, III, IV, and V include both the predictor variables and control variables. The parameters of the logistic response functions were estimated using the maximum likelihood method, which denotes changes in the log odds of the independent variable. In logistic regression, the observed and predicted values can be used to assess the fit of the model. The measure we use is the log-likelihood based on summing the probabilities associated with the predicted and actual outcomes (Field, 2009, p. 267):
Log-likelihood = Sili[YiIn(P(Yi)) + (1 - Yj)In(l - P(Yi))] (1)
To test whether the relationship between dependent and independent variables is direct or indirect, binary logistic regression was used to develop a model as follows:
P(Y) =
e-(bo+tJixii+t,2x2i+ •••+ibnxni)
(2)
where:
P(Y) is the probability of the dependent variable (Model I: bank deposit safety in times of crisis; Models II and III: trust in banks during a crisis; Models IV and V: withdrawal of savings in times of crisis)
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Sabina Taskar Beloglavec, Urban Sebjan:
Is Trust in Banks in Slovenia Put to the Test?
b0 = a constant
b. = the estimated coefficients X. = the independent variables e = the base of the natural logarithm
In order to test whether the inclusion of predictor variables led to statistically significant improvements of the model, we used the Blok x2-test. We computed the improvement of the model as follows:
The goodness of fit of the model was assessed using the Model x2-test, the rate of correct classifications, Nagelkerke's (1991) RN, Cox and Snell's (1989) R2CS, and Hosmer and Le-meshow's (2000) RH:
rn
Rcs
[2(LL(baseline)l
(3)
X2 = 2[LL(new) - LL(baseline)], (df = k^ - ^J (8)
where x2 is the chi-square distribution, df is degrees of freedom, and k is number of parameters. The 0.05 (two-tailed) significance level was used. To test the hypotheses, it was appropriate to use SPSS 21 software.
_ j _ e[~(LL(new))-(LL(baseline))]
Rs
-2LL (model) -2LL (original)
(4)
(5)
where LL is log-likelihood and n is sample size.
Hair, Anderson, Tatham, and Black (1998) argued that Cox and Snell's RfS is reported less frequently because it cannot reach the maximum value of 1. In order to test the significance of the regression coefficient, we used the Wald statistic, which is "usually used to ascertain whether a variable is a significant predictor of the outcome" (Field, 2009, p. 270):
Wald =
seb
(6)
where b is the regression coefficient and SEb the standard error.
.CS , and Nagelkerke's RN
The Wald statistic, Cox and Snell's Rf are statistical tools used to test the effectiveness of a model by looking at whether a model fits the data (Seo, Ranganathan, & Babad, 2008). We also measured the value of the odds ratio (Exp(P), which is an indicator of the change in odds resulting from a unit change in the predictor. "The odds of an event occurring are defined as the probability of an event occurring divided by the probability of that event not occurring" (Field, 2009, p. 271). We can calculate the odds as:
0ddS = P (ncTevent)' ^^ = i+.-CbUiXO»
P(no event Y) = 1 - P(event Y)
P(Y) = the probability of Y occurring e = base of natural logarithms b0 = constant
bn = coefficient (or weight) attached to predictor Xn = predictor variable
3.2 Results
In Model I in Table 2, bank deposit safety in times of crisis is the dependent variable and demographic factors are the control variables. It can be seen that only gender and age are significant at the 0.05 level (Model x2 = 30.267, p < 0.001).
Table 2 Results of Logistic Regressions-Model I (Bank deposit safety in times of crisis (PBAD); 0-no, 1-yes)
Variables	Variable	Model I		
	categories	Coeff. ß	Wald	Exp(ß)
		S.E.		
Gender	0-female	1.193"	8.943	3.296
	1-male	(0.399)		
Age	Young	-1.377'	4.638	0.252
	population	(0.640)		
	Middle	-0.174ns-	0.084	0.841
	population	(0.598)		
	Older bc-			
	population			
Education	Less than	-1.438™	3.474	0.237
	secondary	(0.772)		
	Secondary	0.370ns-	0.681	1.448
	degree	(0.449)		
	More than			
	Secondary bc-			
Constant		0.652	1.419	1.919
		(0.547)		
Model x2 (df)
30.267'' (5)
Block x2 (df)
-2LL (final model)
169.903
(7) Nagelkerke RN
0.248
Cox & Snell RN
0.183
X2 Hosmer and Lemeshow's R^_L
28.064"
% of correct predictions
72.0
Notes: *** significant at p < 0.001; ** significant at p < 0.01; * significant at p < 0.05; n s' not significant; b,c base category Source: Authors' calculations.
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The relationship between gender and bank deposit safety in times of crisis is significant (P = 1.193, p < 0.01), indicating that male customers are more likely to perceive bank deposit safety in times of crisis compared to female ones. We also found that the younger population is less likely than the older population to perceive bank deposit safety in times of crisis (P = -1.377, p < 0.05), indicating that Hypothesis 1 can only be partially accepted.
Nagelkerke's RN is a further modification of the Cox and Snell coefficient R^S to ensure that it can vary from 0 to 1 (Nagelkerke, 1991). For the model estimates, Cox and Snell's R2S and Nagelkerke's RN measures are 0.183 and 0.248, respectively, which confirm the statistical robustness of the estimated results. Hosmer and Lemeshaw's R^_L test (X2 = 28.064; df = 7; p < 0.001) indicated that the predicted model fits well with the data.
From Table 3, it can be seen that Model III, which includes both predictor and control variables, is significant at the 0.001 level (Model x2 = 99.285, p < 0.001). As Block x2 is also significant (Block x2 = 27.646, p < 0.001), the inclusion of predictor variables into the model leads to significant improvement of the model compared to Model II. Bank deposit safety significantly predicts (Wald = 34.799, p < 0.001) the odds of trust in banks in times of crisis. In Model III, the relationship between bank deposit safety and trust in banks in times of crisis is significant (P = 4.551, p < 0.001), indicating that those customers who perceive bank deposit safety in times of crisis are more likely to have trust in banks in times of crisis.
Gender significantly predicts (Wald = 5.279, p < 0.05) the odds of trust in banks in times of crisis. Male customers are less likely to perceive trust in banks in times of crisis (P = -1.417, p < 0.001) than female customers.
Table 3 Results of Logistic Regressions-Models II and III (Trust in banks in times of crisis (TB); 0-no, 1-yes)
Variables	Variable	Model II			Model III		
	categories	Coeff. ß S.E.	Wald	Exp(ß)	Coeff. ß S.E.	Wald	Exp(ß)
Bank deposit safety in the crisis time	0-no 1-yes	3.317''' (0.466)	50.662	27.574	4.551''' (0.772)	34.799	94.770
Gender Age	0-female 1-male Young population Middle population Older bc-population				-1.417' (0.617) -0.151-. (0.809) 1.937' (0.699)	5.279 0.035 7.687	0.242 0.860 6.937
Education	Less than secondary Secondary degree More than Secondary b	c.			0.632 ns. (1.683) 2.340'' (0.683)	0.141 11.739	1.881 10.379
Constant		-1.833 (0.381)			-3.848 (0.874)		
Model x2 (df)		71.640''' (1)			99.285''' (6)		
Block x2 (df)					27.646''' (5)		
-2LL (initial model) -2LL (final model)		134.595			106.949		
R2					79.5		
Nagelkerke RN		0.508			0.648		
Cox & Snell R2CS					0.484		
X2 Hosmer and Lemeshow's RHL					3.346 ns.		
% of correct predictions		83.3			83.3		
Notes: *** significant at p < 0.001; ** significant at p < 0.01; * significant at p < 0.05; n,s- not significant; b,c base category; = 1 - [2LL (final model) / - 2LL (initial model)] Source: Authors' calculations.
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Sabina Taskar Beloglavec, Urban Sebjan:
Is Trust in Banks in Slovenia Put to the Test?
The relationship between age and trust in banks in times of crisis is significant (P = 1.937, p < 0.05), indicating that middle-aged adults are more likely to perceive trust in banks in times of crisis than the older population. This means that Hypothesis 2 was partly proven.
Educational attainment is also significant, having a positive effect (P = 2.340, p < 0.01) on trust in banks in times of crisis. We also found that customers with a secondary degree are more likely to perceive trust in banks in times of crisis than those with a post-secondary degree. The R^ model equaled 79.5%, which means that 79.5% of the variation in the dependent variable is explained by the model. The current model's Nagelkerke's RN is 0.648, which is fairly high, suggesting a good fit for the model. The predictive power of the model is very good, with an overall accuracy of 83.3%.
Table 4 summarizes the results of the binary logistic regression for Models IV and V. One predictor variable and three control variables were included in Model V. Trust in banks in times of crisis was included in Model IV; gender, age, and education were included in Model V. As the Block x2 is also significant (Block x2 = 48.609, p < 0.001), the inclusion of control variables in the model leads to a significant improvement of the model compared to Model V. It accounted for 27.7% (Cox and Snell's R^) to 37.2% (Nagelkerke's RN) of the variance in withdrawals of savings in times of crisis. The result of the Hosmer and Lemeshow test R^_L was significant (x2 = 23.0; df = 8; p < 0.01), indicating that the model was good and the data fit the model well. This model correctly classified 76.7% of rates. The R£ model equaled 84.6%, which means that 84.6% of the variation in the dependent variable is explained by the model.
Table 4 Results of Logistic Regressions-Models IV and V (Withdrawal of savings in times of crisis (SW); 0-no, 1-yes)
Variables	Variable	Model IV		Model V		
	categories	Coeff. p S.E.	Wald Exp(P)	Coeff. p S.E.	Wald	Exp(P)
Trust in banks in crisis time	0-no 1-yes	-1.539''' (0.354)	18.840 0.215	-2.416''' (0.501)	23.254	0.089
Gender Age	0-female 1-male Young population Middle population Older bc population			-1.272'' (0.416) 0.503ns. (0.670) 1.248 ns. (0.668)	9.337 0.564 3.494	0.280 1.654 3.482
Education	Less than secondary Secondary degree More than Secondary b	c.		0.279 ns. (0.709) 1.941''' (0.539)	0.154 12.970	1.321 6.966
Constant		0.519 (0.253)	4.218 1.680	-0.006 (0.602)	0.000	0.994
Model x2 (df)		20.190''' (1)		48.609''' (6)		
Block x2 (df)				28.419''' (5)		
-2LL (initial model) -2LL (final model)		184.516		156.097		
Ri				84.6		
Nagelkerke RN		0.169		0.372		
Cox & Snell R2CS				0.277		
X2 Hosmer and Lemeshow's Ri , ^ H-L				23.0''		
% of correct predictions		68.7		76.7		
Notes: *** significant at p < 0.001; ** significant at p < 0.01; * significant at p < 0.05; ns' not significant; b,c base category; = 1 - [2LL (final model) / - 2LL(initial model)] Source: Authors' calculations.
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The results of the main Model V (Table 4) show that trust in banks in times of crisis is associated with a higher likelihood of withdrawal of funds from bank accounts in times of crisis (P = -2.416, p < 0.001). This result explains that those customers who trust in their bank during a crisis are perceived to be less likely to withdraw their savings in times of crisis. Gender is also significant, having a negative effect (P = -1.272, p < 0.01) on withdrawal of savings in times of crisis. Males are less likely to perceive the need to withdraw money in times of crisis than females. The results also indicate that no significant relationship exists between age and withdrawal of funds in times of crisis. Finally, the level of education presents a positive, significant sign (P = 1.941, p < 0.001), indicating that those with a secondary degree perceive are more likely to withdraw money in times of crisis than those with a post-secondary degree. These results partially support Hypothesis 3.
4 Discussion and Conclusions
In this study, we used a conceptual research model to study the behavior of bank customers in times of crisis. We found a link between bank customers' gender and age and bank deposit safety in times of crisis. Our research results show that male customers are on average 3.3 times more likely to perceive bank deposit safety in times of crisis than female customers (Exp (P) = 3.296). In addition, the younger population is on average only 0.3 times less likely to perceive bank deposit safety in times of crisis than the older population (Exp (P) = 0252). From these results, it can be concluded that female customers are more cautious than males about perceived bank deposit safety in times of crisis. The younger
population also perceives less bank deposit safety in times of crisis than the older population. The reasons for such results could be the lack of both experience and insight into banks' operating models in younger customers. The results showed no significant correlation between customers' level of education and bank deposit safety in times of crisis.
In the next phase, we examined the relationship between bank deposit safety in times of crisis, demographic factors, and trust in banks in times of crisis. We found that individuals who perceive bank deposit safety in times of crisis are on average only 94.7 times as likely to trust in banks in times of crisis as those who do not perceive bank deposit safety in times of crisis (Exp (P) = 94.770).
We also focused on the influence of confidence in banks during a crisis and the impact of demographic factors on the withdrawal of money from a savings account in times of crisis. Customers who trust banks in times of crisis are on average only 0.1 times less likely to withdraw their savings during a crisis than those who do not trust banks in times of crisis ((Exp (P) = 0.089). Thus, banks have to continually invest resources in maintaining their customers' trust to be able to maintain an adequate level of savings in their customers' bank accounts.
This study could be expanded to include other factors, such as the reliability of banks and trust in deposit schemes in times of crisis, as well as additional demographic factors (income, status). The study could also be extended to other comparable geographic markets. It is important to note that the bank customers were asked only about the possible decisions in times of crisis, but this might not necessarily reflect their concrete decisions and actions in times of crisis.
References
1.	Aghion, P., Algan, Y., Cahuc, P., & ShLeifer, A. (2010). Regulation and Distress. The Quarterly Journal of Economics, MIT Press, vol. 125(3), pages 1015-1049. http://dx.doi.Org/10.1162/qjec.2010.125.3.1015
2.	Alesina, A., & La Ferrara, E. (2002). Who trusts others? Journal of Public Economics, 85(2), 207-234. http://dx.doi.org/10.1016/ S0047-2727(01)00084-6
3.	Anderson, J. C., & Narus, J. A. (1990). A model of distributor firm and manufacturer firm working partnerships. The Journal of Marketing, 54(1), 42-58.
4.	BAMC. (2015). BAMC started the third asset transfer phase, transferring the assets of 92 companies. Retrieved from http://www.dutb.eu/ en/for-the-media/press-releases/61-dutb-je-zacela-tretjo-fazo-prenosa-terjatev-v-kateri-so-terjatve-do-92-tih-podjetij
5.	Banka Slovenije (BS).(2014). Financial stability review, March 2014. Retrieved from https://www.bsi.si/en/publications.asp?MapaId=784
6.	Bennett, R., & Kottasz, R. (2012). Public attitudes towards the UK banking industry following the global financial crisis. The International Journal of Bank Marketing, 30(2), 128-147. http://dx.doi.org/10.1108/02652321211210877
7.	Bloemer J., de Ruyter, K., & Peeters, P. (1998). Investigating drivers of bank loyalty: the complex relationship between image, service quality and satisfaction. International Journal of Bank Marketing, 16(7), 276-286. http://dx.doi.org/10.1108/02652329810245984
8.	Buchan, N. R., Croson, R. T., & Solnick, S. (2008). Trust and gender: An examination of behavior and beliefs in the Investment Game. Journal of Economic Behavior & Organization, 68(3), 466-476. http://dx.doi.org/10.1016/jjebo.2007.10.006
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Sabina Taškar Beloglavec, Urban Šebjan:
Is Trust in Banks in Slovenia Put to the Test?
9.	Calderon, C., Chong, A., & GaLindo, A. (2002). Development and efficiency of the financial sector and links with trust: Cross-country evidence. Economic Development and Cultural Change. 51(1), 189-204. http://dx.doi.org/10.1086/344547
10.	Carlin, B. I., Dorobantu, F., & Viswanathan, S. (2009). Public trust, the law, and financial investment. Journal of Financial Economics, 92(3), 321-341. http://dx.doi.org/10.1016/jjfineco.2008.07.001
11.	Coulter, K. S., & Coulter, R. A. (2002). Determinants of trust in a service provider: the moderating role of length of relationship. Journal of Service Marketing 16(1), 35-50. http://dx.doi.org/10.1108/08876040210419406
12.	Coupé, T. (2011). Mattresses versus banks—The effect of trust on portfolio composition. Kyiv School of Economics and Kyiv Economics Institute Discussion Paper No. 40.
13.	Cox, D. R., & Snell, E. J. (1989). The analysis of binary data (2nd ed.). London: Chapman & Hall.
14.	Deb, M., & Chavali, K. (2010). Significance of trust and loyalty during financial crisis: A study on customer behavior of Indian banks. South Asian Journal of Management, 17(1), 43-60.
15.	Delo. (2013). Anketa dela: Še zadostno zaupanje v banke. Retrieved from http://www.delo.si/novice/politika/anketa-dela-se-zadost-no-zaupanje-v-banke.html
16.	Djankov, S., Miranda, P., Seira, E., & Sharma, S. (2008). Who are the unbanked?World Bank Policy Research Working Paper No. WPS4647.
17.	Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: Sage.
18.	Guiso, L., Sapienza, P., & Zingales, L. (2008). Trusting the stock market.Journal of Finance, 63(6), 2557-2600. http://dx.doi.org/10.1111/ j.1540-6261.2008.01408.x
19.	Gulati, R. (1995). Does familiarity breed trust? The implications of repeated ties for contractual choices in alliances. Academy of Management Journal, 38(1), 85-112. http://dx.doi.org/10.2307/256729
20.	Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis. Englewood Cliffs, NJ: Prentice-Hall International.
21.	Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: Wiley. http://dx.doi.org/10.1002/0471722146
22.	Hurlburt, G. F., Miller, K. W., & Voas, J. M. (2009). An ethical analysis of automation, risk, and the financial crises of 2008. IT Professional Magazine, 11(1), 14-19. http://dx.doi.org/10.1109/MITP.2009.2
23.	Kedmenec, I., Tominc, P., & Rebernik, M. (2014). Gender differences in the usage of resources in the entrepreneurial opportunity identification process in Slovenia and Croatia. Economic Research-Ekonomska Istraživanja, 27(1), 366-377. http://dx.doi.org/10.10 80/1331677X.2014.966973
24.	Knell, M., & Stix, H. (2010). Trust in nanks—Evidence from normal times and from times of crises. Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie-Session: Trusting Banks in a Financial Crisis, No. A1-V1.
25.	Mayer, J. (2004). Zaupanje kot pogoj za ustvarjalno sodelovanje. In: Rozman R. & Kovač, J. (Eds.): Zaupanje v in med organizacijami (združbami). Kranj: Fakulteta za organizacijske vede, Ekonomska fakulteta, Zveza organizatorjev Slovenije.
26.	McKnight, D. H., & Chervany, N. L. (2001). Trust and distrust definitions: One bite at a time. In: Falcone, R., Singh, M. & Tan, Y.-H. (Eds.): Trust in Cyber-societies. LNAI 2246, 27-54, Springer-Verlag Berlin Heidelberg.
27.	Moorman, C., Deshpandé, R., & Zaltman, G. (1992). Relationships between providers and users of market research: the dynamics of trust within and between organizations. Journal of Marketing Research, 29(3), 314-329. http://dx.doi.org/10.2307/3172742
28.	Morgan, R. M., & Hunt, S. D. (1994). The commitment trust theory of relationship marketing. Journal of Marketing, 58(3), 20-38. http:// dx.doi.org/10.2307/1252308
29.	Mosch, R., & Prast, H. (2010). Confidence and trust: empirical investigations for the Netherlands and the financial sector. Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie-Session: Trusting Banks in a Financial Crisis, No. A1-V2
30.	Mudd, S. B., & Valev, N. T. (2009). Once bitten, twice shy: experiences of a banking crisis and expectations of future crises. Wiliam Davidson Institute Working paper. No.969. Retrieved from http://deepblue.lib.umich.edu/bitstream/handle/2027.42/64382/wp969. pdf?sequence=1 http://dx.doi.org/10.2139/ssrn.1490072
31.	Nagelkerke, N. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691-692. http://dx.doi. org/10.1093/biomet/78.3.691
32.	Parkhe, A. (1993). Strategic alliances structuring: a game theoretic and transaction cost examination of inter-firm cooperation. Academy of Management Journal, 36(4), 739-794. http://dx.doi.org/10.2307/256759
33.	Ramirez, C. (2009). Bank fragility, "money under the mattress," and long-run growth: U.S. evidence from the "perfect" panic of 1893. Journal of Banking and Finance, 33(12), 2185-2198. http://dx.doi.org/10.1016/jjbankfin.2009.05.020
34.	Rempel, J. K., Holmes, J. G., & Zanna, M. P. (1985). Trust in close relationships. Journal of personality and social psychology, 49(1), 95. http://dx.doi.org/10.1037/0022-3514.49.1.95
35.	Sapienza, P., & Zingales, L. (2009). A trust crisis. Retrieved from http://faculty.chicagobooth.edu/brian.barry/igm/atrustcrisis.pdf
36.	Schelkle, W. (2011). A tale of two crises: The euro area in 2008/09 and in 2010. European Political Science:EPS, 10(3), 375-383. http:// dx.doi.org/10.1057/eps.2011.23
37.	Seo, D. B., Ranganathan, C., & Babad, Y. (2008). Two-level model of customer retention in the US mobile telecommunications service market. Telecommunications Policy, 32, 182-196. http://dx.doi.org/10.1016/j~.telpol.2007.09.004
38.	Sheehan Bartel, K. (1999). An investigation of gender differences in on-line privacy concerns and resultant behaviors. Journal of Interactive Marketing, 13(4), 24-38. http://dx.doi.org/10.1002/(SICI)1520-6653(199923)13:4<24::AID-DIR3>3.0.C0;2-0
39.	Slovenske novice. (2012). Vse težje je kaj privarčevati, a bankam (še) zaupamo. Retrieved from http://www.slovenskenovice.si/novice/ slovenija/vse-tezje-je-kaj-privarcevati-bankam-se-zaupamo
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40.	Stevenson, B., & Wolfers, J. (2011). Trust in public institutions over the business cycle. IZZA Discussion Paper No. 5570. http://dx.doi. org/10.2139/ssrn.1787737
41.	Süchting, J. (1998). Die Theorie der Bankloyalität-(immer noch) eine Basis zum Verständnis der Absatzbeziehungen von Kreditinstituten?. In Handbuch des Bankmarketing (pp. 1-25). Wiesbaden: Gabler Verlag. http://dx.doi.org/10.1007/978-3-322-93179-5_1
42.	Sztompka, P. (1999). Trust: A sociological theory. Cambridge: Cambridge University Press.
43.	Szykman, L., Rahtz, D. R., & Plater, M. (2005). Living on the edge: financial services for the lower socio-economic strata. Discourse, Diversity and Distinction: Macromarketing at 30 Years, 59.
44.	Trif, S. M. (2013). The influence of overall satisfaction and trust on customer loyalty. Management & Marketing, S(1), 109.
45.	Tyler, K., & Stanley, E. (2007). The role of trust in financial services business relationships. Journal of Services Marketing, 21(5), 334-344. http://dx.doi.org/10.1108/08876040710773642
46.	Weber, J. M., Malhotra, D., & Murnighan, J. K. (2004). Normal acts of irrational trust: Motivated attributions and the trust development process. Research in organizational behavior, 26, 75-101. http://dx.doi.org/10.1016/S0191-3085(04)26003-8
47.	Whitney, J. O. (1994). The trust factor: Liberating profits and restoring corporate vitality. New York: McGraw-Hall, Inc.
48.	Zineldin, M. (1995). Bank-company interactions and relationships: some empirical evidence. International Journal of Bank Marketing, 13(2), 30-43. http://dx.doi.org/10.1108/02652329510078677
Authors
Sabina Taskar Beloglavec is a senior lecturer at the Faculty of Economics and Business of the University of Maribor. She completed her graduate and postgraduate studies at the University of Maribor, Faculty of Business and Economics. She is active in other fields of interest, outside her major occupation, including economic subject curriculum development and supervision for secondary schools and high school at The National Education Institute of the Republic of Slovenia. She has published several national and international articles and other publications (see IZUM - SICRIS, 20429).
Urban Sebjan is currently a Ph.D. student in the Department of Quantitative Economic Analysis and Organization and Informatics at the University of Maribor-Faculty of Economics and Business. He has long worked with Triglav Insurance Company. His research focuses primarily on quantitative methods, statistical analysis software, analytical CRM, and modern information technology. He is employed by the Faculty of Economics and Business as an assistant in the field of quantitative economic analysis.
ALi je zaupanje v banke v Sloveniji na preizkušnji?
Izvleček
Vprašanje stabilnosti bančnega sistema in zaupanje v banke sta, zlasti od leta 2008 naprej, v središču iskanja trajnih rešitev za težave bančnega sistema, ki temelji na zaupanju, trenutno stanje pa vpliva na vedenje uporabnikov bančnih storitev. V prispevku proučujemo zaupanje uporabnikov v banke v času finančne krize z izbranimi demografskimi spremenljivkami in s tem povezano možnost dviga prihrankov z bančnih računov. Rezultati raziskave so za banke koristni pri učinkovitem upravljanju prihrankov uporabnikov in razumevanju vedenja uporabnikov v času finančne krize. Med drugim smo namreč ugotovili, da obstaja negativna povezava med zaupanjem uporabnikov v banke in dvigom prihrankov uporabnikov bančnih storitev v času finančne krize, rezultati pa so odvisni od spola in ravni izobrazbe.
Ključne besede: zaupanje, finančna institucija, banka, Slovenija, logistična regresija
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