New Dynamics in Banking and Finance: 5th International Conference on Banking and Finance Perspectives, Famagusta, Cyprus (Springer Proceedings in Business and Economics) 3030937240, 9783030937249

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Springer Proceedings in Business and Economics

Nesrin Özataç Korhan K. Gökmenoğlu Bezhan Rustamov   Editors

New Dynamics in Banking and Finance 5th International Conference on Banking and Finance Perspectives, Famagusta, Cyprus

Springer Proceedings in Business and Economics

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Nesrin Özataç  •  Korhan K. Gökmenoğlu Bezhan Rustamov Editors

New Dynamics in Banking and Finance 5th International Conference on Banking and Finance Perspectives, Famagusta, Cyprus

Editors Nesrin Özataç Department of Banking and Finance Eastern Mediterranean University Via Mersin 10, Turkey

Korhan K. Gökmenoğlu Department of Banking and Finance Eastern Mediterranean University Via Mersin 10, Turkey

Bezhan Rustamov Department of Banking and Finance Rauf Denktas University Via Mersin 10, Turkey

ISSN 2198-7246     ISSN 2198-7254 (electronic) Springer Proceedings in Business and Economics ISBN 978-3-030-93724-9    ISBN 978-3-030-93725-6 (eBook) https://doi.org/10.1007/978-3-030-93725-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

 Impact of Ownership Concentration on the Profitability of the Banking Sector: The Case of Turkey ��������������������������������������������������    1 Tuba Özkan, Sevgi Cengız, and Mehmet Emin Karabayır  Leading Indicators of Turkey’s Financial Crises������������������������������������������   15 Mohamad Kaakeh and Korhan K. Gökmenoğlu  The Effects of Social Media Influencers on Consumers’ Buying Intentions with the Mediating Role of Consumer Attitude��������������������������   45 Sadaf Damirchi, Emrah Öney, and Seyed Arash Sahranavard  Impact of Political Uncertainties on the Dividend Policies of Nonfinancial Firms in Turkey��������������������������������������������������������������������   73 Foday Joof and Asil Azimli  Job Satisfaction and Turnover in Educational Institutions: Reasons and Variables Affecting Job Satisfaction and the Turnover Decision ��������   85 Tarek Adhami and Tarik Timur  Performance Analysis of the Northern and Southern Banking Sectors on Cyprus Island Under the Covid-19 Era����������������������������������������������������  101 Veclal Gündüz  The Amalgamation of Social Media and Tourism in Ghana������������������������  121 Selira Kotoua and Felicity Asiedu-Appiah  Co-movement of the Shanghai Stock Exchange and COVID-19 in China: Evidence from Wavelet Coherence������������������������������������������������  143 Hasan Güngör and Derviş Kirikkaleli  Analysis of Factors Affecting the Capital Adequacy Ratio in the Turkish Banking Sector������������������������������������������������������������������������  157 Ayşegül Berrak Köten

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 Credit Rating Agency’s Response to Covid-­19 by Logical Analysis of Data ��������������������������������������������������������������������������������������������������������������  181 Elnaz Gholipour and Béla Vizvári  The Relationship Between Interest Rates and Inflation: Time Series Evidence from Canada ��������������������������������������������������������������  191 Negar Fazlollahi and Saeed Ebrahimijam

Impact of Ownership Concentration on the Profitability of the Banking Sector: The Case of Turkey Tuba Özkan, Sevgi Cengız, and Mehmet Emin Karabayır

1  Introduction The concept of ownership or ownership concentration relates to the distribution of working capital among shareholders. If the majority of the shares are held by one person or a limited number of people, this indicates ownership concentration (Grob, 2006: 10). If a high percentage of the shares in the enterprise is owned by a small number of shareholders, then there is highly concentrated ownership (Çıtak, 2007: 231). In other words, while the increase in the number of shareholders decreases the concentration, the decrease in the number of shareholders increases the concentration (Tanrıöven & Aksoy, 2010: 218). Different rates have been used in the literature as a criterion for ownership concentration. Specifically, while La Porta et al. (1999) considered a concentration percentage of 10% or more shares to be the criterion in their study, Claessens et al. (2000) took this rate as 5% above, and Cronqvist and Nilsson (2003) accepted this rate as 25% and above. In cases below the thresholds used as the degree of ownership concentration in these studies, there is a common ownership structure. This structure means that many investors have small investments in more than one company. While the common ownership structure is more prevalent in the United States of America and the United Kingdom, dense ownership structures are more common in the Far East and Continental Europe (Claessens et al., 2000). T. Özkan (*) Faculty of Humanity and Social Sciences, Atatürk University, Erzurum, Turkey e-mail: [email protected] S. Cengız Social Sciences Vocational School, Kafkas University, Kars, Turkey M. E. Karabayır Faculty of Economics and Administrative Sciences, Kafkas University, Kars, Turkey © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 N. Özataç et al. (eds.), New Dynamics in Banking and Finance, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-93725-6_1

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In this study, the effect of ownership concentration of 17 commercial banks operating in the Turkish banking sector between 2009 and 2019 on bank profitability is investigated. Accordingly, the study includes a literature review after the introduction part, and then the analyses performed and the findings obtained were explained. Finally, the study is completed by conveying the results obtained and the recommendations made. The findings and evaluations of the study are of great importance in terms of providing useful information and contributing to bank managers, shareholders, decision-makers, and investors. In addition, the study has originality and contributes to the literature both in terms of the variables used and the period and scope examined.

2  Literature Review Numerous studies have entered the literature that examines the relationship between ownership structure or concentration and profitability or firm value. The important ones of these studies are summarized below in chronological order. Morck et al. (2000) investigated the relationship between bank ownership structure and firm value with the 1986–2000 data of banks, which are mostly partners of 373 manufacturing companies operating in Japan. As a result of the research, a statistically significant and negative relationship was found between parent bank ownership and firm value. A statistically significant and positive relationship was found between management ownership and corporate block ownership and Tobin’s Q ratio. Fodelberg and Griffith (2000) examined the relationship between ownership structure and firm performance by using the 1996 year-end data of 100 banks traded in the US banking sector. The researchers found a nonlinear relationship between ownership structure and bank performance in their studies. Micco et al. (2004) examined the relationship between the ownership structure of banks and their performance with the data of 111 banks operating in developed and developing countries for the period 1995–2002. The results revealed no statistically significant relationship between bank ownership structure and bank performance in developed countries, but a statistically significant relationship was found in developing countries. The study also revealed that public banks in developing countries operate with lower profitability than private banks due to higher costs and nonperforming loans. Another result of the research showed that foreign-owned banks in developing countries operate with less cost and higher profitability than other banks. Adams and Mehran (2005) examined the relationship between the size and structure of the board of directors and the performance of the banks in the US banking sector by using 1959–1999 data. Even though, statistically, significant results could not be obtained between bank performance and board structure, positive and significant results were found between bank performance and board size.

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Tanrıöven et al. (2006) examined the relationship between the ownership structure and control of the banks listed on the İstanbul Stock Exchange (ISE) during 1997–2001 and the rates used in bank performance measurements based on quarterly balance sheet data and tried to determine whether there was a difference in the variables of family-owned banks, holding banks, and banks with distributed capital. They found that while the difference between family owned and holding banks is less, financial performance indicators used in banks with distributed capital differ from the others. Zulkafli and Samad (2007) investigated the relationship between ownership structure and bank performance on 107 banks operating in Asian markets. As a result of their analysis, they could not detect a statistically significant relationship between ownership structure and bank performance. Staikouras et  al. (2007) examined the relationship between bank performance and board size and structure in their study using data from 58 major European banks for the period 2002–2008. They ultimately obtained a statistically significant negative relationship between the size of the board of directors and the profitability of the bank. Košak and Čok (2008) investigated the effects of ownership structure on profitability in the banking sector with data from 6 Eastern European countries (Croatia, Bulgaria, Romania, Serbia, Macedonia, and Albania) for the period 1995–2004. According to the results of the research, no statistically significant relationship was found between foreign and domestic ownership and bank profitability. Belkhir (2009) analyzed the relationship between board structure, ownership structure, and firm performance with the data of 260 savings and investment banks for the year 2002 and found a statistically significant and negative relationship between block share ownership and internal ownership and performance. According to the results, ownership structure and board structure are interrelated. He also stated that in structures where executive ownership and internal ownership are more intense, foreign manager ownership tends to decrease. Furthermore, he predicted that foreign manager ownership and internal ownership were designed as a management mechanism in reducing conflicts of interest between managers and capitalists. Antoniadis et al. (2010) investigated the relationship between ownership structure and bank performance in the banking sector with 2000–2004 data of 15 banks operating in the Greek Stock Exchange. As a result of the research, a statistically significant and nonlinear relationship was found between ROA and ROE and ownership structure. It was determined that ROA and ROE first declined with the concentration of ownership structure and then increased with the further increase in ownership concentration. In their studies, Arouri et al. (2011) investigated the effect of ownership structure and board characteristics on bank performance in Gulf Cooperation Council countries. They found that there is a positive and significant relationship between foreign ownership level and bank performance. However, they determined that while high ownership concentration has a significant negative effect on performance, corporate

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ownership has no effect. They also found that other management variables such as CEO duality and board size do not have a significant effect on performance. Kiruri (2013) analyzed the relationship between ownership structure and bank profitability with the data of 43 banks operating in Kenya for the period 2007–2011. According to the analysis results, ownership concentration and public ownership have negative relationships with bank profitability, while foreign ownership and local ownership and bank profitability have a positive correlation. Srairi (2013) investigated the relationship of ownership concentration and ownership identity of 131 banks in ten countries in the Middle East and North Africa with their risk behaviors between 2005 and 2009. He found a negative relationship between ownership concentration and risk. In other words, while banks with family-­ concentrated ownership tend to take less risk, those with state-concentrated ownership structure tend to take higher risk, and the bad loans of these banks are higher. Another result indicates that risky loans of Islamic banks are lower than traditional banks. Zouari and Taktak (2014) examined the ownership structure and financial performance of 53 Islamic banks in 15 countries between 2005 and 2009. The study in which ROE and ROA were used as performance measures revealed that there is no relationship between ownership concentration and performance of Islamic banks. Another result is that a combination of family and government investors would be beneficial for bank performance. Son et al. (2015) analyzed the effect of ownership structure on bank performance with the 2010–2012 data of 44 banks operating in Vietnam. The research findings revealed that there is a statistically significant and positive relationship between ownership concentration and ROA, and ownership concentration strongly affects ROA. Tükenmez et al. (2016) investigated the relationship between ownership concentration and financial performance in the banking sector with the 2008–2014 data of 11 banks. According to the results of the research, an increase in ownership concentration causes the large shareholders to act without considering the interests of the small shareholders, and this situation has a negative effect on financial performance indicators. Mari et al. (2017) examined the effects of ownership concentration on the quality of earnings in 35 countries for the 2001–2016 period on the banking sector. The results of the analysis performed using 6323 observations revealed that ownership concentration significantly increases earnings quality. Migliardo and Forgione (2018) investigated the effects of ownership structure on bank performance on 1459 banks operating in EU-15 countries between 2011 and 2015. In particular, they examined the extent to which shareholder type and to what degree of shareholder concentration affect banks’ profitability, risk, and efficiency. The analysis conducted showed that bank performance is affected by the type of shareholder. The researchers determined that banks with large block shareholders are more profitable, less risky, and more efficient.

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Huang (2020) examined the relationship between ownership concentration and bank profitability over 16 publicly traded Chinese banks in the 2007–2018 period. The study revealed that ownership concentration has a positive effect on the profitability of the banks, but this effect decreases as the size of the bank increases. In addition, the researcher suggested that banks could create a concentrated ownership structure to increase their profitability.

3  Empirical Study 3.1  Purpose, Methodology, and Limitations The purpose of this study is to determine the effects of the ownership concentration of banks on profitability using 187 firm-year observations obtained from 17 commercial banks that uninterruptedly operated from 2009 to 2019 in Turkey, without missing any data. The ordinary least squares (OLS) method was applied within the scope of the simple regression analysis using EViews 9.0. The limitation of the study is that the analysis periods are not long enough and the number of observations is below the target due to the small sample size.

3.2  Data Collection Method, Hypotheses, and Models The data for the study were gathered from the Banks Association of Turkey (TBB) – Statistical Reports and the Public Disclosure Platform (KAP). The hypotheses are as follows: H1: Ownership concentration has an effect on return on assets (ROA). H2: Ownership concentration has an effect on return on equity (ROE). The models of the study are as follows:

ROA   0  1OC   2SIZE   3 AGE   4 FD   (1)



ROE   0  1OC   2SIZE   3 AGE   4 FD   (2)

In the equations, ROA is return on assets, ROE is return on equity, OC is ownership concentration, SIZE is size of the bank, AGE is age of the bank, FD is whether the bank is foreign or domestic, β0 is the constant term, and ε is the error term (Table 1).

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Table 1  Calculation method and the type of variables in the models Name of the variable Return on assets Return on equity Ownership concentration Size Age Foreign or domestic

Symbol ROA ROE OC

Calculation of the variable Net income (before tax)/total assets Net income/paid capital Share of the largest shareholder

SIZE AGE FD

Natural logarithm of total assets Current year – foundation year 0 if the capital is domestic, 1 if the capital is foreign

Type of the variable Dependent Independent Control Control Dummy

3.3  Estimation of Models and Findings The ordinary least squares (OLS) method is used in the estimation of the models. This method aims to make the error sum of squares the smallest. It ensures reliable estimates in cases where the observed data provide assumptions such as normality, constant variance (homogeneous), and deviance and aims to achieve optimum results if the variances of error terms are homogeneous and show normal distribution (Neter et al., 1996). 3.3.1  Tests Used in Data Analysis • Normality test of the dependent variable is carried out with the Jarque-Bera test (a fit test used to measure departure from a normal distribution). • Three separate unit root tests (Levin, Lin, and Chu-LLC; Im, Pesaran, and Shin-­ IPS; and Augmented Dickey Fuller-ADF) are used to determine the stationarity of the series. • Variance inflation factor (VIF) is used to solve multicollinearity problems. • White test is employed to control heteroskedasticity. • Durbin-Watson test statistics are used to test autocorrelation. 3.3.2  Descriptive Statistics The normal distribution can be mentioned when the skewness value is 0 and the kurtosis value is 3. In Table 2, it is seen that the series, except ROA and ownership concentration, is right skewed. Ownership concentration, foreign-domestic, and size variables are more kurtosis than normal distribution. Mean and variance values should also be considered to determine kurtosis. The high difference between the maximum and minimum values of the variables indicates the height of the variation, that is, a wide distribution. It is seen that the highest variation and standard

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Table 2  Descriptive statistics of the variables Mean Median Maximum Minimum Std. dev. Skewness Kurtosis Jarque-Bera Probability Sum Sum sq. dev. Observations

ROA 1.535353 1.546523 3.947859 −2.200000 0.970289 −0.667197 5.092652 47.99511 0.095000 287.1111 175.1118 187

ROE 58.19082 42.72980 298.0370 −66.32124 56.52765 1.107838 4.459367 54.84532 0.100000 10881.68 594339.8 187

OC 72.90963 75.02000 100.0000 24.22000 25.52875 −0.290385 1.551508 18.97601 0.070076 13634.10 121219.4 187

AGE 54.48128 58.00000 156.0000 1.000000 35.82009 0.897949 3.744181 29.44514 0.082000 10188.00 238652.7 187

FD 0.358289 0.000000 1.000000 0.000000 0.480785 0.591082 1.349378 32.11776 0.084000 67.00000 42.99465 187

SIZE 5.663451 3.796627 16.23719 0.096916 5.034005 0.491413 1.720480 20.28264 0.075039 1059.065 4713.465 187

deviation is in ROE.  Looking at the Jarque-Bera normal distribution test for the model, it can be stated that the Jarque-Bera probability values of all variables are greater than 5% significance level, and therefore the model has a normal distribution; that is, error terms show a normal distribution. When the descriptive statistics of the variables are evaluated, the 1.53 ROA average, which represents the efficiency of the banks’ investments in a certain period of time, means that the bank uses its assets effectively, while the 58.19 ROE average, which represents the profit rate corresponding to the shareholders’ capital, indicates that the resources are used efficiently and the shareholders receive high returns against their shares in the equity. The average OC variable representing the share of the largest shareholder is 72%, indicating that more than half of the banks’ capital belongs to a single shareholder. The average AGE variable representing the experience and survival time of the banks is 54 years, indicating that the banks are large/mature and their experience is high. The average of the FD variable that shows what percentage of the bank capital is foreign-domestic is 35%, which indicates that an average of 35% of the banks’ capital is foreign owned. The average of the SIZE variable representing the total assets of the banks is 5.66, which indicates that the banks’ assets are relatively high. 3.3.3  Correlation Analysis In the correlation matrix, it is seen that there is a medium (0.50–0.69) and weak (0.26–0.49) correlation between the variables, and there is no high (0.70–0.89) correlation. The lack of high correlation between variables makes it possible to interpret the analyses performed more accurately and objectively (Table 3).

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Table 3  Correlation matrix of the variables ROA ROE OC AGE FD SIZE

ROA 1.000000 0.485539 −0.213980 0.277105 −0.253471 0.475510

ROE

OC

AGE

FD

SIZE

1.000000 −0.281938 0.571065 −0.297180 0.303474

1.000000 −0.299977 0.509709 −0.378502

1.000000 −0.472098 0.362676

1.000000 −0.420820

1.000000

Table 4  Variance inflation factor test Variance inflation factors Sample: 2009–2019 Included observations: 187 Coefficient Variable Variance OC 0.021200 AGE 0.017347 FD 67.31516 SIZE 0.995911 C 151.6118

Uncentered VIF 15.41346 8.975582 2.940145 6.954230 18.48229

Centered VIF 1.675288 2.698784 1.886724 3.060144 1.795245

3.3.4  Variance Inflation Factor (VIF) Test Since the VIF value greater than 10 indicates that the model is problematic or that the independent variables have multicollinearity (Gujarati & Porter, 2003: 18), the VIF values of the current models (1.67, 2.69, 1.88, 3.06, and 1.79) indicate that there is no multicollinearity problem (Table 4). 3.3.5  Heteroscedasticity Control Test One of the basic assumptions of the regression regarding the error term is constant variance (homoscedasticity). If homoscedasticity is not valid, it is called heteroscedasticity. Heteroscedasticity is the case where the variance of the error term is not the same for all observations. Since the chi-square value is greater than 0.05, it can be stated that there is no heteroscedasticity problem (Baltagi, 2008: 151) and the variance is constant. To test this situation, the heteroscedasticity LR test was employed, and the fact that the chi-square probability values for the model are greater than 0.05 means that the homoscedasticity assumption is valid (Table 5).

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Table 5  Heteroscedasticity test Panel cross-section heteroscedasticity LR test Null hypothesis: residuals are homoscedastic Equation: untitled Specification: ROA, ROE, OC, SIZE, AGE, FD, C Value Likelihood ratio 56.02160 LR test summary: Value Restricted LogL −225.3085 Unrestricted LogL −197.2977

df 17

Probability 0.1310

df 187 187

Table 6  Unit root tests Group unit root test: summary Series: ROA ROE OC SIZE AGE FD Sample: 2009–2019 Exogenous variables: individual effects Automatic selection of maximum lags Newey-West automatic bandwidth selection and Bartlett kernel Balanced observations for each test Method Statistic Prob.** Null: unit root (assumes common unit root process) Levin, Lin, & Chu t* −1.19522 0.0160 Null: unit root (assumes individual unit root process) Im, Pesaran, & Shin W-stat −5.73530 0.0000 ADF – Fisher chi-square 71.2465 0.0000 PP – Fisher chi-square 73.1578 0.0000

Cross-sections

Obs

6

187

6 6 6

187 187 187

** Probabilities for Fisher tests are computed using an asymptotic chi-square distribution. All other tests assume asymptotic normality

3.3.6  Unit Root Tests Unit root tests are used to check the stationarity of series. The series for each variable must be stationary. In stationary (without unit root) series, spurious regression is prevented and values such as variance, covariance, and the mean of the series remain constant and not changing over time (Gujarati & Porter, 2003: 670) (Table 6). According to the results of the LLC, IPS, and ADF unit root tests, the level (raw) values of the variables and the first difference values are significant at 1%, 5%, and 10% levels for all variables, and therefore the series used in the model is stationary.

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3.3.7  Estimation of the Models 3.3.7.1  ROA and OC Relationship Model Estimation Results When the general estimation status of the model is examined, it is seen that OC does not affect ROA. The F statistic (Prob F-statistic) is generally significant at the 1% significance level (0.00 < 0.05), and approximately 52% of the change in the dependent variable is explained by the independent variable. In addition, the Durbin-­ Watson (DW) test statistic, which tests autocorrelation in the model, is 1.90 (between 1.5 and 2.5, i.e., within acceptable limits (Hutcheson & Sofroniou, 1999: 49)), which means that there is no autocorrelation. The results of the analyses and evaluations show that the “H1: Ownership concentration has an effect on Return on Assets” hypothesis is rejected (Table 7). 3.3.7.2  ROE and OC Relationship Model Estimation Results Considering the general estimation status of the model, it can be seen that OC has a negative significant effect on ROE. F statistic (Prob F-statistic) is generally significant at a 1% significance level (0.00 < 0.05), and approximately 85% of the change Table 7  Test results of model 1 Dependent variable: ROALOG Method: panel EGLS (cross-section weights) Date: 04/07/21 Time: 12:19 Sample: 2009–2019 Periods included: 11 Cross-sections included: 17 Total panel (balanced) observations: 187 Linear estimation after one-step weighting matrix Variable Coefficient Std. error t-statistic OC 0.030449 0.086088 0.353702 SIZE 0.330911 0.040427 8.185436 AGE −0.248332 0.059890 −4.146474 FD −0.096399 0.038894 −2.478509 C 0.373059 0.177986 2.096005 Weighted statistics R-squared 0.525663 Mean dependent var Adjusted R-squared 0.510842 S.D. dependent var S.E. of regression 0.262564 Sum squared resid F statistic 21.97366 Durbin-Watson stat Prob(F-statistic) 0.000000 Unweighted statistics R-squared 0.248423 Mean dependent var Sum squared resid 13.25616 Durbin-Watson stat

Prob. 0.7240 0.0000 0.0001 0.0141 0.0375 0.283496 0.404060 12.54709 1.900172

0.138729 1.251826

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Table 8  Test results of model 2 Dependent variable: ROELOG Method: panel EGLS (cross-section weights) Date: 04/07/21 Time: 12:22 Sample: 2009–2019 Periods included: 11 Cross-sections included: 17 Total panel (balanced) observations: 187 Linear estimation after one-step weighting matrix Variable Coefficient Std. error t-statistic OC −0.735153 0.298152 −2.465697 SIZE −0.857382 0.215052 −3.986865 AGE 1.206115 0.218175 5.528207 FD 0.293426 0.114665 2.558978 C 1.261201 0.650938 1.937514 Effects specification Cross-section fixed (dummy variables) Weighted statistics R-squared 0.855536 Mean dependent var Adjusted R-squared 0.838131 S.D. dependent var S.E. of regression 0.243589 Sum squared resid F statistic 49.15378 Durbin-Watson stat Prob(F-statistic) 0.000000 Unweighted statistics R-squared 0.787535 Mean dependent var Sum squared resid 10.40028 Durbin-Watson stat

Prob. 0.0147 0.0001 0.0000 0.0114 0.0544

2.589997 1.785486 9.849682 1.367065

1.561028 1.657653

in the dependent variable is explained by the independent variable. In addition, the Durbin-Watson (DW) test statistic is 1.65 and within acceptable limits (Hutcheson & Sofroniou, 1999: 49), which indicates that there is no autocorrelation. The results of the analyses and evaluations show that the “H2: Ownership concentration has (negative) effect on Return on Equity” hypothesis is accepted (Table 8).

3.4  Evaluation of Findings In studies examining the relationship between ownership concentration and profitability, different results (positive/negative effects/ineffectiveness) are obtained. This study also reveals different results in line with the literature. While there is no relationship between ownership concentration and ROA, it is observed that this result supports the findings obtained in similar studies (Adams & Mehran, 2005; Zulkafli & Samad, 2007; Zouari & Taktak, 2014).

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A negative significant effect was found between ownership concentration and ROE, and this result also supports the results obtained in similar studies (Staikouras et al., 2007; Antoniadis et al., 2010; Kiruri, 2013).

4  Conclusion and Suggestions Different results were obtained in studies examining the relationship between ownership concentration and ROA/ROE, which are used as indicators of profitability. These opposite results are estimated to be caused by the intention of the largest shareholders of the companies. When the shareholders who have large shares in the companies exercise their management and supervision authority in favor of the company and all the shareholders, this situation increases the profitability of the company. However, when this situation is abused by those who have a large concentration in the shares and when these shareholders use their power in their interests, the profitability of the company decreases. In this context, this study investigated the relationship between ownership concentration and profitability of 17 commercial banks (seven domestic, seven foreign, and three state owned) operating uninterruptedly during the period of 2009–2019 in Turkey (please see Table 9). To determine the direction and magnitude of the effect between ownership concentration and profitability, ROA and ROE are used as dependent variables and ownership concentration as the independent variable. In addition, bank age and bank size, which are thought to have effects on dependent Table 9  Banks used in the study Name of the bank T.C Ziraat Bankası Türkiye Halk Bankası Türkiye Vakıflar Bankası T.A.O. Akbank T.A.Ş. Anadolubank A.Ş. Fibabanka A.Ş. Şekerbank T.A.Ş. T. Ekonomi Bankası A.Ş. Türkiye İş Bankası A.Ş. Yapı ve Kredi Bankası A.Ş. Alternatifbank A.Ş. Burganbank A.Ş. Denizbank A.Ş. ING Bank A.Ş. QNB Finansbank A.Ş. Türkiye Garanti Bankası A.Ş. HSBC

Domestically owned

Foreign owned

✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

State owned ✔ ✔ ✔

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variables, are included in the model as control variables, and foreign-domestic variable, which takes the value 1 when the bank belongs to foreigners and 0 when it belongs to locals, is included as a dummy variable. The study revealed no relationship between ownership concentration and ROA, while a significant negative effect of ownership concentration on ROE was found. Increasing the number of observations by extending the time period, adding different sectors (i.e., all financial sector firms), or employing comparative analyses between countries may lead to more accurate results.

References Adams, R. B., & Mehran, H. (2005). Corporate performance, board structure and its determinants in the banking industry (Working paper). Federal Reserve Bank of New York. Antoniadis, I., Lazarides, T., & Sarrianides, N. (2010). Ownership and performance in the Greek banking sector. International Conference on Applied Economics, 3(4), 11–21. Arouri, H., Hossain, M., & Muttakin, M. B. (2011). Ownership structure, corporate governance and bank performance: Evidence from GCC countries. Corporate Ownership and Control, 8(4), 365–372. Baltagi, B. H. (2008). Forecasting with panel data. Journal of Forecasting, 27(2), 153–173. Belkhir, M. (2009). Board structure, ownership structure and firm performance: Evidence from banking. Applied Financial Economics, 19(19), 1581–1593. Çıtak, L. (2007). The impact of ownership structure on company performance: A panel data analysis on Istanbul Stock Exchange Listed (ISE–100) companies. International Research Journal of Finance and Economics, 9, 231–245. Claessens, S., Djankov, S., & Lang, L. H. P. (2000). The separation of ownership and control in East Asian corporations. Journal of Financial Economics, 58(1–2), 81–112. Cronqvist, H., & Nilsson, M. (2003). Agency costs of controlling minority shareholders. Journal of Financial and Quantitative Analysis, 38(4), 695–719. Fodelberg, L., & Griffith, J. M. (2000). Control and bank performance. Journal of Financial and Strategic Decisions, 13(3), 63–69. Grob, K. (2006). Equity ownership and performance: An empirical study of German traded companies. Physica Verlag a Springer Company. Gujarati, D. N., & Porter, D. C. (2003). Basic econometrics. McGraw-Hill. Huang, Q. (2020). Ownership concentration and bank profitability in China. Economics Letters, 196, 109525. Hutcheson, G. D., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. Sage. Kiruri, R. M. (2013). The effects of ownership structure on bank profitability in Kenya. European Journal of Management Sciences and Economics, 1(2), 116–127. Košak, M., & Čok, M. (2008). Ownership structure and profitability of the banking sector: The evidence from the SEE region. Zbornik Radova Ekonomskog Fakulteta u Rijeci: Časopis za Ekonomsku Teoriju i Praksu, 26(1), 93–122. La Porta, R., Silanes, F.  L., & Shleifer, A. (1999). Corporate ownership around the world. The Journal of Finance, 54(2), 471–517. Mari, L.  M., Soscia, M., & Terzani, S. (2017). Ownership concentration and earnings quality of banks: Results from a cross-country analysis. Corporate Ownership and Control, 15(1), 288–297. Micco A., Panizza, U., & Yañez, M. (2004). Bank ownership and performance (IDB working paper No. 429). Available at SSRN: https://ssrn.com/abstract=1818718

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Migliardo, C., & Forgione, A.  F. (2018). Ownership structure and bank performance in EU-15 countries. Corporate Governance: The International Journal of Business in Society, 18(3), 509–530. Morck, R., Nakamura, M., & Shivdasani, A. (2000). Banks, ownership structure, and firm value in Japan. The Journal of Business, 73(4), 539–567. Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models. Irwin. Son, N. H., Tu, T. T. T., Cuong, D. X., Ngoc, L. A., & Khanh, P. B. (2015). Impact of ownership structure and bank performance – An empirical test in Vietnamese banks. International Journal of Financial Research, 6(4), 123–133. Srairi, S. (2013). Ownership structure and risk-taking behaviour in conventional and Islamic banks: Evidence for MENA countries. Borsa Istanbul Review, 13(115), 127. Staikouras, P. K., Staikouras, C. K., & Agoraki, M. E. K. (2007). The effect of board size and composition on european bank performance. European Journal of Law and Economics, 23, 1–27. Tanrıöven, C., & Aksoy, E. (2010). İMKB’de İşlem Gören Şirketlerde Ortaklık Yoğunlaşmasının Firma Performansına Etkileri. Muhasebe ve Finansman Dergisi, 46, 216–231. Tanrıöven, C., Küçükkaplan, İ., & Başçı, E. S. (2006). Kurumsal Yönetim Açısından Sahiplik ve Kontrol Yapısı ile Üst Düzey Yönetici Durumunun İMKB’de Faaliyet Gösteren Bankalarda İncelenmesi. İktisat, İşletme ve Finans Dergisi, 21(241), 87–104. Tükenmez, N.  M., Gençyürek, A.  G., & Kabakcı, C. Ç. (2016). Türk Bankacılık Sektöründe Sahiplik Yoğunlaşması ile Finansal Performans İlişkisinin İncelenmesine Yönelik Ampirik Bir Çalışma. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 30(3), 625–644. Zouari, S. B. S., & Taktak, N. B. (2014). Ownership structure and financial performance in Islamic banks: Does bank ownership matter? International Journal of Islamic and Middle Eastern Finance and Management, 7(2), 146–160. Zulkafli, A.  H., & Samad, F.  A. (2007). Corporate governance and performance of banking firms: Evidence from Asian emerging markets. Issues in Corporate Governance and Finance, 12, 49–74.

Leading Indicators of Turkey’s Financial Crises Mohamad Kaakeh and Korhan K. Gökmenoğlu

1  Introduction Financial crises are very costly and principally unpredictable events. These properties of financial crises have made the issue of their predictability a vivid topic among academicians and policy makers. Early warning indicator (EWI) literature on financial crises began to emerge following a series of Latin American crises during the 1970s. Then in each new wave of crisis, there was a renewed interest in such efforts. These efforts led to a large body of empirical research on EWI, which offers many valuable lessons and various sets of indicators for crisis prediction (Berg & Pattillo, 1999; Bussiere & Fratzscher, 2006; Kaminsky et  al., 1998; Nguyen & Nguyen, 2017; Pedro et al., 2018). Following the 2008 global financial crisis, the G20 countries requested new early warning systems to be applied by the International Monetary Fund (IMF). The April 2009 London summit also revoiced this request. Despite all these efforts, researchers have not achieved a satisfactory record in crisis prediction, and one vital question remains: Is it possible to find an EWI set to predict a crisis? Our research aims to contribute to this discussion by using Turkey as a case study. Establishing an early warning system to predict a crisis in advance faces many challenges, and researchers have essential conflicts. Challenges begin with defining a financial crisis (Kaminsky et  al., 1998; Kindleberger & Aliber, 1978; Minsky, 1972; Sevim et al., 2014). For an empirical study, “how to measure the crisis” is an important issue. There are considerable differences in the definition and determination of the endpoint of crises across published research. The fact that empirical studies define the concept of crisis in different ways and use different dependent and M. Kaakeh (*) · K. K. Gökmenoğlu Department of Banking and Finance, Eastern Mediterranean University, Famagusta, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 N. Özataç et al. (eds.), New Dynamics in Banking and Finance, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-93725-6_2

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independent variables in their models makes it difficult to compare the findings obtained from these studies. Many researchers aim to provide an EWS that is appropriate for all countries (Candelon et al., 2014). However, each economy and financial crisis have unique characteristics. Considering the differences between the economic structures of countries and the changing structure of the economy over time, the idea of finding an early warning indicator set that is suitable for every country and valid in every period seems too ambitious. Mostly, cross-country studies do not take into account these factors and ignore many vital elements (Berg & Pattillo, 1999); hence, the success of this method is arguable. Other studies analyze a single country or financial crisis and generalize their findings as leading principles (Duan & Bajona, 2008; Knedlik & Scheufele, 2008; Nguyen & Nguyen, 2017; Roy & Kemme, 2011). The second approach is the one that the present research is following. We perform an in-depth analysis of a single country with multiple crisis episodes to understand the changing nature of crises, form an early warning system, and reach general conclusions. Turkey is suitable for testing the usefulness of an early warning system due to two reasons. The first of these reasons is that the country has experienced many crises of different structures in the last three decades. The 1994 crisis resembles the first-generation crises proposed by Krugman (1979), in contrast to the 2000/2001 twin crises, which can be explained by both the first- and second-generation models. The 2009 financial turmoil in Turkey was triggered by the 2008 global financial crisis and can be partially elucidated by the third-generation model (Dapontas, 2011). The fact that crises have different structures constitutes a rich source for our research. The second reason is that the Turkish economy has undergone a significant structural transformation in the last few decades, from a closed import-substituting country to an export-oriented country with liberalized money and capital markets. This transformation has changed the economy’s fundamentals considerably, which might change the crisis’ underlying factors. Some new variables came into the agenda in this period, and some others lost their importance. Mentioned properties make the country a good laboratory for testing the role of various variables as the potential predictors of financial crises. Another issue that has been widely criticized in the EWI literature is that the early warning system that is successful for one crisis series does not work for subsequent crises. In this research, we determine early warning signs for each crisis episode and see whether they have explanatory power for the next one. In this way, we would like to discuss the effect of the changing nature of the economy on the determinants of financial crises. By doing so, we reach some broader conclusions about the central question of early warning studies, that is, whether it is possible to establish a general early warning system for crisis prediction. Another criticism is that many papers fall into the hindsight bias trap when selecting variables to be included in their models. We pay close attention to the variable selection to alleviate this problem. We set our variables based on a comprehensive review of more than 30 empirical papers and survey articles. Rather than focusing on variables that can be selected for a particular financial turmoil, we placed more emphasis on leading indicators that the literature has agreed to be useful.

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Another discussion in the literature is related to the econometric methodology employed to investigate EWI. Although there is a vast range of methods employed in EWI literature, we applied the most widely used and generally accepted econometric methods. To refrain from the problem of overfitting, using stepwise regression, we determined the most relevant variables to be used in the probit and logit models. The rest of the article is organized as follows: literature review, description of the methodology used, discussion of the empirical results, and presentation of the conclusion.

2  Literature Review Although the history of the financial crisis is dated back to the early third century, the Latin American crisis of the 1970s can be taken as the starting point of the contemporary literature on this subject. The cost of these crises led academicians to investigate and explain crisis episodes theoretically and empirically. The canonical model developed by Krugman (1979) successfully explains the Latin American crises, also called first-generation crises, theoretically. The 1990s had a new wave of financial crisis episodes that could not be explained by the canonical model, which motivated researchers to search for further explanations. This effort led to an increase in financial crisis literature, and thus crisis research had its golden age in the 1990s. Researchers suggested that two additional theoretical models explain the financial crisis of the 1990s. The 1992/1993 Western Europe exchange rate mechanism (ERM) crisis was theorized by the second-generation models, which are mainly based on expectations and multiple equilibria (Krugman, 1998; Obstfeld, 2002). The Asian crisis was explained by the third-generation models that stress several important concepts, such as contagion, moral hazard, adverse selection, asymmetric information, and herding behavior (Irwin & Vines, 1999; Krugman, 2001; Masson, 1999; Zhang, 2001). A surge in empirical studies accompanied the development in theoretical studies and, in a short time, formed immense literature. The present paper focuses on the early warning indicator segment of the mentioned empirical literature. The first early warning system dates back to the article of Bilson (1980), where he tried to find leading indicators of the currency crisis in 15 selected countries. The partial success of Bilson in identifying two indicators with forecasting capacity (consumer price index and gross domestic products) led to increased interest in the investigation of the leading indicators of the crisis. Researchers investigated leading indicators of currency and financial crises in developed and developing countries by using many methods and models. One of the most exciting aspects of the literature is the wide-ranging methodological debate (Frankel & Rose, 1996; Kaminsky & Reinhart, 1999). The various methodological approaches that were used in the early warning indicator literature can be classified into three categories.

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The first approach investigates early warning indicators employing the linear regression and/or binary dependent variable (probit/logit) techniques. The probit and logit models gained their popularity due to multiple reasons. Eichengreen et al. (1996) and Frankel and Rose (1996) are the early examples of researchers who applied the probit model in the examination of the determinants of currency crises. Many others (Berg & Pattillo, 1999; Bussiere & Frazscher, 2006; Komulainen & Lukkarila, 2003; Kumar et  al., 2003; Woo et  al., 2000) followed these studies. Following the 2008 global financial crisis, probit-logit methods were widely used to investigate the financial crises of many countries, including the United States (Giovanis, 2010), the Commonwealth of Independent States (CIS) (Fedorova & Lukasevich, 2012), and the Western Balkan countries (Karasavvoglou & Polychronidou, 2013). Esaka (2010) applied the probit model to examine whether de facto exchange rate regimes cause a currency crisis. Zhao et al. (2014) used probit regression to see the effect of different exchange rate systems on the leading indicators of currency crises for 88 countries. Dawood et al. (2017) investigated the sovereign debt crises in 38 advanced and emerging economies using binary and multinomial logit regression. Chiaramonte and Casu (2017) utilized the logit model to investigate banking crises in 28 EU countries. The second group of research uses the signal approach. This method, also known as the KLR (Kaminsky, Lizondo, Reinhart) approach, dates back to the pioneering papers of Kaminsky et al. (1998) and Kaminsky and Reinhart (1999). This method uses a binary crisis variable and transforms the independent variables into binary signals. Kaminsky and Reinhart (1999) utilized the signal approach to examine the case of the twin crises. Recently, KLR was used in many applications, including the prediction of currency, banking, and debt crises (Alessi & Detken, 2011; Borio & Drehmann, 2009; Christensen & Li, 2014; Duan & Bajona, 2008; El-Shagi et al., 2013; Megersa & Cassimon, 2015; Shi & Gao, 2010). Being traceable and straightforward are the main advantages of this approach. However, the method is nonparametric, which does not allow researchers to derive crisis probabilities directly. Moreover, it cannot judge the contribution of a single indicator in a composite indicator (El-Shagi et al., 2013). Dreger and Kholodilin (2013) employed the probit and logit models alongside the signal approach to predict the housing bubbles of 12 Organisation for Economic Co-operation and Development (OECD) countries. Their results indicate that the probit and logit models offer a more precise prediction of the bubbles than the signal approach does. The third category encompasses the use of many different methodologies, such as operation research models and the Markov-switching approach to predict financial failures and banking crises (Alam et al., 2000; Boyacioglu et al., 2009; Celik & Karatepe, 2007; Fioramanti, 2008; Knedlik & Scheufele, 2008; Ravi & Pramodh, 2008; Yu et al., 2010). Turkey is an emerging economy that faced three major economic crises in the last 30 years. Researchers have investigated these crises using different approaches, such as the logit model (Bucevska, 2015; Feridun, 2007; Yurdakul, 2014), the signal approach (Karacor & Gokmenoglu, 2012), and artificial neural networks (Celik & Karatepe, 2007). Most of the existing research is devoted to analyzing a specific

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crisis episode (Bucevska, 2015; Mariano et  al., 2004; Özatay et  al., 2002; Yılmazkuday, 2008; Yurdakul, 2014). Several researchers examined both the 1994 crisis and the twin crises (Celik & Karatepe, 2007; Feridun, 2007, 2008; Öztunç et al., 2013; Sevim et al., 2014; Tamgac, 2011). Recently, Ari and Cergibozan (2018) examined the Turkish case by applying a multivariate logit model on a data span from 1990 to 2014 to build an EWS.  Danielsson et  al. (2018) investigated stock market volatility’s ability to forecast financial crisis in a panel of countires, including Turkey, using panel logit regression. Geršl and Jašová (2018) utilized the signal approach to explore the prediction ability of credit-based variables on the banking crisis in emerging markets, including Turkey. The studies examining the case of Turkey haven’t considered all three crises together. Also, the usage of a broader data set and variable selection approach distinguishes our research from the rest of the literature on the financial crises of Turkey.

3  Data and Model Specification 3.1  Identification of “Crisis” and “Crisis Incidence” The definition of a financial crisis in a study is critical as it dramatically affects the results of empirical models. Researchers have used many criteria to identify a financial crisis. Frequently, a financial crisis is identified through substantial fluctuations in key macroeconomic variables, such as exchange rates (Frankel & Rose, 1996; Kaminsky & Reinhart, 1999). Also, it is possible to recognize a financial crisis by measuring its cost (Caprio & Klingebiel, 2002; Valencia & Laeven, 2008), such as rapid decreases in asset prices (Alessi & Detken, 2011) and stock market crashes (Grammatikos & Vermeulen, 2012). Some researchers use these two criteria together to identify a financial crisis (Frankel & Saravelos, 2012; Rose & Spiegel, 2011). In this research, we use the National Bureau of Economic Research (NBER) standards to define crisis episodes, which is the most widely agreed measure in the literature. According to NBER, two or more consecutive quarterly declines in a country’s real gross domestic product are indicators of a crisis. Using this standard, we identified the following crisis periods in Turkey: 1993Q3 to 1995Q1, 2000Q4 to 2002Q1, and 2008Q2 to 2009Q3. We created binary dependent variables to represent the identified crisis periods, taking the values 1 when a crisis occurs and 0 otherwise.

3.2  Independent Variables A large number of variables have been investigated as potential leading indicators of a crisis in the literature. Determining the variables to be used in the empirical model is another challenge for research examining the leading indicators of crises.

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For this purpose, a researcher may rely solely on theoretical papers (Kaminsky & Reinhart, 1999) or previously published empirical studies (Frankel & Saravelos, 2012; Rose & Spiegel, 2011). However, each of these methods runs the risk of ignoring some critical potential variables. Therefore, we created a comprehensive data set using a mix of both approaches in our study. We selected our independent variables based on the previous surveys and more than 30 theoretical or empirical papers published between 2009 and 2017. Table 1 presents the frequency with which a particular variable was found to be statistically significant in the reviewed literature. The variables used in our model can be classified as financial, international, bank-specific, and macroeconomic ones and have a quarterly frequency. In total, 42 independent variables were employed. Table 2 shows which variables were utilized to analyze the leading indicators of each crisis. The data set was divided into three subperiods to analyze the leading indicators of each financial crisis separately. The 1990Q1–1999Q4, 1996Q3–2005Q2, 2005Q3–2015Q3 periods are used to investigate the leading indicators of the 1994, 2000/2001, and 2009 financial crises, respectively. All data were acquired through Datastream.

Table 1  Frequency of leading indicators in the existing literature Variables Reserves Real exchange rate GDP Credit Current account Money supply Exports or imports Inflation Equity returns Real interest rate Debt composition Budget balance Terms of trade Contagion Political/legal Capital flows External debt Frankel and Saravelos (2012)

a

Pre-2008a 50 48 25 22 22 19 17 15 13 13 10 9 9 6 6 3 3

Post-2008 21 22 21 17 12 17 15 13 9 19 7 4 7 4 – 3 5

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Table 2  Independent variables used in the analysis Variable Bank lending Bank lending to the private sector Bank lending to the private sector – credit cards Bist100 Budget balance Capital adequacy 1* Capital adequacy 2* CPI Current account balance Deposit rate Domestic credit Domestic debt: bills Exports External debt Total foreign assets Foreign assets, deposit money banks Foreign direct investment Foreign liabilities, deposit money banks Gold price Government expenditure Imports Industrial production International reserves Long-term interest rates M1 M2 M3 Oil price Past due loans Portfolio investment Real effective exchange rates Total credit cards Total deposits Total loans Trade balance Turkish lira to euro Turkish lira to US dollars Unemployment US Fed fund rate US interbank rate US overnight rate US Treasury bill rate

1994 crisis ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

2000/2001 crises

2009 crisis

● ● ● ●

● ● ● ●

● ● ● ● ● ● ●

● ● ● ● ● ● ●

● ● ● ● ● ● ● ●

● ●

● ● ● ● ● ● ●

*

● ● ● ●

●

● ●

●

● ● ● ● ● ●

● ● ● ● ● ●

Capital adequacy 1 refers to nonperforming loans net of provisions Capital adequacy 2 refers to nonperforming loans to total loans

*

● ● ● ● ●

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4  Methodology Although a number of econometric methods have been used to acquire the leading indicators of financial crises, binary and regression models are the most widely used ones. This study utilized both stepwise regression and binary models in a complementary way. Overfitting the model is a crucial issue for binary choice models (Windmeijer, 1995). To eliminate this issue and determine the best candidates for the leading indicators of financial crises, we employed stepwise regression as a filtering process. Then by utilizing the selected variables, the probit and logit models were estimated.

4.1  Stepwise Regression Efroymson (1960) developed the stepwise regression model, a selective choice of predictive variables done by an automated technique, usually using t-tests or f-tests. Stepwise regression uses several approaches to filter variables: forward selection, backward selection, and bidirectional selection. Stepwise regression enables the researcher to handle a large amount of potential explanatory variables and gives an easily interpreted result. In addition, the t-values or f-values of independent variables can offer valuable information about the quality of the predictor. Stepwise regression is extensively used in many disciplines, such as medicine (Adebayo & Gayawan, 2013), engineering (Zhou et al., 2012), and chemistry (Nazarpour et al., 2016). The stepwise regression formula is indicated below: i 1



y     i Xi   n

(1)

4.2  Probit and Logit Models Probit and logit models are statistical tests that examine the association between a dichotomous dependent variable and continuous independent variables. They transform the dependent variable, where it is a discrete variable that can take two probabilities, either 0 or 1, into a probability. The logit model is based on the maximum likelihood method to form a conditional logistic regression. Cumulative logistic distribution is defined as follows:



Prob Y  1  F   Xi  

e  X 1  e  X (2)

Leading Indicators of Turkey’s Financial Crises

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where Y represents the dependent binary variable, β is the vector of the coefficients to be estimated, Xi is the vector of independent variables, and F(βXi) is the cumulative logistic function. The logarithm of the likelihood function to estimate parameters is given by Eq. (3).



l  g1

l g2

n

n

log  L    log F   Xl    log 1  F   X l  

(3)

The estimators can be obtained by solving max (log (L)). Logit regression examines binomially distributed data: Yi~B(ni, pi) i = 1, 2, 3……n, where ni is the binominal trial and pi is the probability of success. The logit equation can be written in a general format, as shown in Eq. (4): i 1



y     i Xi   n

(4)

Unlike the logit model, the probit model calculates the cumulative standard normal distribution function by using the following formula: 

F   Xi   

  Xi 

1

2

 2 

1/ 2

e  z / 2 dz (5)

5  Empirical Analysis and Discussion of the Results This section discusses the findings from the empirical analysis. First, the data set was divided into three periods to examine the three crisis episodes. To investigate the leading indicators of the 1994, 2000/2001, and 2009 crises, 30, 34, and 42 variables were selected, respectively. Afterward, stepwise regression was applied to choose the variables that have the highest potential as leading indicators. We included different numbers of lags of independent variables (from 0 to 4 lags) into the model and used both forward and backward selection processes in this process. Lastly, probit and logit models were applied on the variables picked by stepwise regression. The summary of results and detailed results are presented in the Appendix.

5.1  1994 Financial Crisis The findings using stepwise regression reveal that 11 variables, including domestic credit, domestic debt, external debt, real effective exchange rate, and unemployment, have a positive relationship with the probability of the crisis. In contrast, eight

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variables, including current account balance, federal fund overnight rate, foreign direct investment (FDI), government consumption, imports, and inflation, negatively affect the binary dependent variable. Obtained results are in accordance with the economic theory and the findings of the previous empirical literature. For example, an increase in lending to the private sector, credit cards, and domestic debt translates to an increasing probability of defaulted loans, which leads to the fragility of the banking sector and an increased likelihood of the crisis (Kibritçioğlu, 2003). Afterward, probit regression and logit regression were applied to the variables that were nominated by stepwise regression. The results from probit and logit are consistent with stepwise regression results. Although both the probit model and stepwise regression offered the same direction for the relationships examined, the probit model coefficients cannot be interpreted as a marginal effect because of the way they are calculated. These findings are consistent with early studies examining the case of Turkey (Feridun, 2007; Kibritçioğlu et  al., 1999; Yentürk, 1999). For example, the current account balance was found to be negatively related to the probability of the crisis. When the current account balance is in deficit, it indicates the inability to finance the balance of payment, increasing the likelihood of the crisis.

5.2  2000/2001 Twin Crises Stepwise regression results demonstrate that nine variables, including bank lending to the private sector, deposit rate, domestic credit, and real effective exchange rate, have positive signs across all lags. However, commercial banks’ foreign assets, commercial banks’ foreign liabilities, credit cards to the private sector, imports, M2, past-due loans, unemployment, US T-bill rate, and trade balance negatively relate to the crises. The results of Probit and Logit models are similar to Stepwise regression results in both significances and signs of coefficients and are theoretically reasonable. For example, appreciation of the currency makes the exports relatively more expensive and the country less competitive. These developments lead to a decrease in export, lower the ability of the country to obtain foreign currency, and harm the economy. Furthermore, when deposit rates rise, it heightens the likelihood of loan defaults and signals that banks need liquidity, elevating the crisis’s probability. The M2 expansion could offer the liquidity required by the banks, hence lower the likelihood of a crisis. These results are consistent with the findings of Yurdakul (2014).

5.3  2009 Financial Crisis According to the stepwise regression results, commercial banks’ foreign assets, total consumer loans, credit cards to the private sector, current account balance, exports, London Interbank Offered Rate (LIBOR), long-term interest rates, real effective exchange rate, and US T-bill rate contribute to the likelihood of the crisis.

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In contrast, M1, M3, domestic credit, stock market index, industrial production, and gold were found to be reducing the probability of a crisis. Obtained findings indicate that external debt is an important potential early warning indicator. The oil price hike also increases the possibility of a crisis, which is expected as Turkey is an oil importer country. Results obtained by means of the probit and logit models are consistent with stepwise regression results. The surge in bank lending to the private sector and total credit cards issued will enlarge the rate of bad loans and increase default risks in banks, leading to the weakening of the banking system. An increase in M2 would strengthen the inflationary pressure, forming devaluation expectations of the currency (Brüggemann & Linne, 2002). Industrial production is also inversely related to the probability of a crisis, in line with expectations. In summary, weak economic fundamentals are the primary underlying reason for the 1994 crisis. Hence, the 1994 crisis can be explained by the first-generation model of Krugman (1979). In the case of the twin crises, empirical findings reveal the importance of bank-related variables as potential early warning indicators. The variables found to be the leading indicators of the 2009 crisis signify the structural change experienced by the Turkish economy. In addition, the contagion effect is evident in the formation of the 2009 crisis.

6  Conclusion Turkey was hit by several financial crises for the last three decades, making it an ideal sample for examining the early warning indicators of crises. Although financial crises have similar consequences, their underlying reasons and nature differ due to the changing nature of the economy. We investigated the leading indicators of Turkish financial crises, taking into account the changing nature of the economy, by using stepwise regression, and probit and logit models. Our main aim is to illuminate the changing nature of the crises and come up with a new set of early warning indicators that are compatible with the current structure of the economy. Our empirical results show that early warning indicators can be divided into two categories: common and specific factors. Common indicators are the ones observed in all three crises analyzed in this study, while specific factors are unique to a particular episode of crisis. The 1994 crisis can be mainly explained by the canonical model (Krugman, 1979), which points to weak economic fundamentals as the underlying factors of crises. Analyzing the twin crises of Turkey, it is noticeable that besides poor macroeconomic performance, the fragility of the banking sector played a significant role in increasing the probability of the crises. The significant predictive power of oil prices, US Treasury bill rates, and federal fund rates indicate the importance of the global economy’s state on the twin crises. The results regarding the 2009 crisis revealed the increasing importance of the global economy to economic developments in Turkey. Moreover, the introduction of new indicators, such as capital adequacy and long-term interest rates, signifies the Turkish economy’s changing nature.

26

M. Kaakeh and K. K. Gökmenoğlu

Our findings imply several policy recommendations. Primarily, regulators should pay close attention to common leading indicators such as current account balance, domestic debt, exports, external debt, and real effective exchange rate and correct any misalignment to alleviate the probability of a new financial crisis. Moreover, policy makers should be aware of the new indicators of economic weaknesses that may lead to a financial crisis. Specifically, ensuring the stability of the banking system is of vital importance. In this regard, carefully monitoring the liquidity levels in the markets and imposing higher reserve requirements and tighter regulations when necessary will serve this purpose. Furthermore, accelerating globalization strengthens the contagion effect and makes the careful monitoring of the global economy essential. To answer the question of “whether the proposed new set of variables can be used to predict the next crisis,” we need to refer to our findings. First, early warning indicators might change in time but not completely. There are a limited number of indicators that are significant across all crises. Hence, despite the changing nature of the economies and crises, we expect that major macroeconomic fundamentals will always matter. Second, the integration of the economies and the strengthening of countries’ interdependence dictate the need for the policy makers to follow the global indicators more closely. Third, the changing nature of the economies should be taken into account. For example, the increasing importance of the derivative market makes it a potential determinant of a future crisis. By considering all these points, we conclude that our indicator set will be helpful as a guideline to predict or prevent a financial crisis. However, the struggle for a more effective early warning system will undoubtedly continue.

3rd

Bank lending

3rd

SW

0

1st, 2nd, 1st/2nd & 3rd

1st, 2nd, 3rd & 3rd

1st, 2nd, 2nd & 3rd

1st, 2nd, 1st/3rd & 3rd

Credit cards to private sector

Current account balance

Deposit rate

Domestic credit

1st/3rd

2nd

3rd

1st/3rd

2nd

3rd

1st/2nd

3rd

Consumer total loans

1st/2nd

2nd/3rd 2nd/3rd

2nd & 3rd

Commercial banks’ foreign liabilities

2nd/3rd

3rd

3rd

2nd

L

1st, 2nd, 3rd & 3rd

2nd

P

Commercial banks’ foreign assets

3rd

1st, 2nd, 1st & 3rd

Capital adequacy 3rd

Budget balance

Bank lending to 1st, 2nd, 2nd the private sector & 3rd

Used in

Variable

Significant

Appendix Results Summary Table

1st/3rd

1st/2nd

3rd

1st/2nd

3rd

2nd

3rd

1st/2nd

3rd

SW

1

1st

2nd

3rd

1st/2nd

2nd

3rd

P

1st

2nd

3rd

1st/2nd

2nd

3rd

L

1st/3rd

2nd

1st

1st/2nd/3rd

3rd

3rd

3rd

2nd

1st/3rd

3rd

SW

2

3rd

3rd

1st

3rd

L

3rd

1st

3rd

1st/2nd/3rd 1st/2nd

3rd

3rd

2nd

1st/3rd

P

1st

1st

2nd

1st/2nd

3rd

1st

1st/3rd

3rd

SW

3

1st

1st

3rd

P

1st

1st

3rd

L

1st/2nd

2nd

2nd

2nd/3rd

1st/3rd

1st

3rd

SW

4

1st/2nd

2nd

L

(continued)

1st/2nd

2nd

2nd

P

1st, 2nd, 1st/3rd & 3rd

1st, 2nd, 1st & 3rd

1st, 2nd, 1st/3rd & 3rd

1st, 2nd, & 3rd

1st, 2nd, 3rd & 3rd

1st, 2nd, 2nd & 3rd

1st, 2nd, 1st /2nd & 3rd

1st, 2nd, 1st/2nd/3rd 2nd/3rd 2nd/3rd & 3rd

1st, 2nd, 1st & 3rd

1st, 2nd, 3rd & 3rd

3rd

Federal fund middle rate

Federal fund overnight rate

Foreign direct investments

Gold price

Government consumption

Imports

Industrial production

Inflation

LIBOR rate

Long-term interest rates (6 months)

1st/2nd

2nd

3rd

1st/3rd

1st

3rd

1st/2nd

2nd

3rd

1st/3rd

1st

3rd

2nd

External debt

2nd

1st, 2nd, 2nd & 3rd

Exports

L

1st, 2nd, & 3rd

P

Domestic debt (bills)

SW

Used in

Variable

0

Significant P

3rd

1st/2nd

1st/3rd

1st/2nd

1st

2nd/3rd

1st

2nd

1st/2nd

2nd

1st

1st/2nd/3rd 1st/2nd

SW

1

1st/2nd

2nd

1st

1st/2nd

L

2

3rd

1st

3rd

1st

1st

3rd

3rd

2nd/3rd

2nd

1st/2nd/3rd

2nd

SW

1st

3rd

1st

2nd

2nd

1st/2nd

2nd

P

1st

1st

2nd

2nd

1st/2nd

2nd

L

3

3rd

1st/3rd

1st

1st

3rd

2nd/3rd

1st/3rd

2nd/3rd

2nd/3rd

1st

SW

3rd

1st/3rd

1st

1st

3rd

3rd

1st/3rd

2nd/3rd

2nd/3rd

P

4

3rd

1st/3rd

1st

1st

3rd

1st/2nd

1st/2nd

1st

1st

2nd

2nd

2nd/3rd 1st/3rd

3rd

P

L

1st/2nd

1st/2nd

1st

2nd

3rd

1st/2nd

1st

1st

2nd

3rd

2nd/3rd 2nd/3rd 2nd

SW

2nd/3rd 3rd

L

1st, 2nd, 2nd & 3rd

3rd

M3

Net foreign assets

3rd

1st, 2nd, 2nd & 3rd

1st, 2nd & 3rd

1st, 2nd, 3rd & 3rd

3rd

2nd & 3rd

3rd

Portfolio investment

Real effective exchange rate

Stock price index

TL to dollars

TL to euro

Total credit cards 3rd

3rd

2nd & 3rd

Past-due loans

1st/3rd

1st/3rd

2nd

3rd

1st, 2nd, 1st/2nd/3rd 1st/2nd & 3rd

Oil price

3rd

3rd

1st/3rd

3rd

P

Nonperforming loans/total loans

3rd

1st, 2nd, 1st/3rd & 3rd

M2

Net international 1st, 2nd, reserves & 3rd

1st, 2nd, 3rd & 3rd

M1

SW

Used in

Variable

0

Significant

3rd

3rd

3rd

2nd

3rd

1st/2nd

1st/3rd

3rd

L

3rd

1st/3rd

2nd/3rd

P

2nd/3rd

3rd

1st/2nd/3rd 1st/2nd

3rd

1st

1st/3rd

2nd/3rd

SW

1

1st/2nd

3rd

1st/3rd

2nd/3rd

L

2

3rd

3rd

3rd

1st/3rd

1st/2nd/3rd

1st

3rd

2nd

1st/3rd

2nd/3rd

SW

3rd

3rd

3rd

1st/3rd

1st

1st/3rd

/3rd

P

3rd

1st/3rd

1st

1st/3rd

/3rd

L

3

1st

2nd

3rd

3rd

1st/3rd

P

3rd

1st/2nd/3rd 1st/3rd

1st/2nd

1st/3rd

2nd/3rd

3rd

3rd

1st/2nd

3rd

1st/3rd

SW

1st/3rd

1st

2nd

3rd

3rd

1st

L

4

3rd

1st/2nd

3rd

3rd

3rd

3rd

3rd

2nd/3rd

1st/2nd

SW

3rd

2nd

3rd

3rd

3rd

1st/2nd

L

(continued)

1st/2nd

P

1st, 2nd, 2nd & 3rd

1st, 2nd, 1st & 3rd

1st, 2nd, 3rd & 3rd

Trade balance

Unemployment

US T-bill rate

3rd

2nd

P

3rd

2nd

L

1st/2nd

2nd

1st/2nd

SW

1

1st/2nd

2nd

1st/2nd

P

2nd

2nd

1st/2nd

L

2

3rd

2nd

1st/2nd

SW

2nd

1st

P

2nd

1st

L

3

3rd

1st

2nd

3rd

SW

1st

2nd

P

1st

2nd

L

4

1st/2nd

3rd

SW

1st/2nd

P

1st/2nd

L

Note: SW stands for stepwise regression, P stands for probit models, L stands for logit models, 1st stands for the 1994 crisis, 2nd stands for the twin crises of 2000/2001, 3rd stands for the 2009 crisis, and 0, 1, 2, 3, and 4 indicates the number of lags of independent variables

2nd & 3rd

Total deposits

SW

Used in

Variable

0

Significant

Leading Indicators of Turkey’s Financial Crises

31

Detailed Results Tables (Tables 3, 4, 5, 6, 7, 8, 9, 10, and 11) Table 3  Stepwise regression results for the 1994 crisis

Variable Bank lending to the private sector Budget balance Commercial bank’s foreign assets Credit cards to the private sector Current account balance Deposit rate Domestic credit Domestic debt (bills) External debt Federal fund middle rate Federal fund overnight rate Foreign direct investments Gold price Government consumption Imports Industrial production Inflation LIBOR rate M1 M2 M3 Net international reserves Oil price

Significant 0 Coefficient

1 Coefficient 0.664∗10−6*

2 3 Coefficient Coefficient 0.724∗10−6* 0.357∗10−6*

0.0846∗10−6**

6.92∗10−6*

4 Coefficient

0.138∗10−6** 0.187∗10−6** −6 −0.415∗10 ** −0.211∗10−6** 6.78∗10−6*

8.38∗10−6*

−0.00015** −0.00018* 0.0133*

0.0143* 0.21∗10−6**

0.486∗10−6* 0.22∗10−6**

0.34∗10−6***

0. 288∗10−6** 4.73∗10−5* 1.480*

5.48∗10−5* 0.473**

−1.51∗10−5***

4.42∗10−5* 0.471***

−1.524* −0.00168*** −0.0119*

0.00958** −1.79∗10−6* −8.18∗10−5* −0.0248***

−9.14∗10−5** −0.0255***

−0.00019*

−0.25626*

−0.742*

−0.616*

9.63∗10−5*

−0.000281*** −0.0009* −0. 608∗10−6*** 6.95∗10−5*

−0.0208**

−0.0234*

0.0367* −0.264* −0.563** −0.00211* −0. 787∗10−6***

0.056*

−0.00073**

−0.0219*

(continued)

Table 3 (continued) Significant 0 Variable Coefficient Real effective exchange rate Stock price −0.00015** index TL to dollars Trade balance Unemployment US T-bill rate

1 Coefficient

2 Coefficient 0.0147***

3 Coefficient 0.0241**

4 Coefficient

0.0004*

0.000222***

5.220** −0.000219*

−0.00015** 0.312*

−0.00015** 0.284*

−0.633**

Note: 0, 1, 2, 3, and 4 indicate the number of lags of independent variables. *, **, and *** denote significance at 1%, 5%, and 10%, respectively Table 4  Probit regression results for the 1994 crisis

Variable Bank lending to the private sector Credit cards to the private sector Current account balance Domestic credit External debt Federal fund middle rate Federal fund overnight rate Gold price Imports Industrial production Inflation LIBOR rate M1 M2 Net international reserves Oil price Real effective exchange rate Stock price index TL to dollars Trade balance Unemployment US T-bill rate

Significant 0 Coefficient

1 Coefficient

6.5∗10−6***

−1.61∗10−5*** 4.79∗10−5**

2 3 Coefficient Coefficient 4.26∗10−6***

4 Coefficient

−0.00065*** −0.00175*

11.042**

1.28∗10−6*** 0.000559** −3.5761**

0.000179**

1.32∗10−6*** 1.51∗10−6***

−0.00072**

−0.00067***

−0.371***

−11.150** −0.00041*

−0.0859 0.1933**

−2.951**

0.000113**

−0.00214***

−0.00313** 0.000221**

−0.203*

−0.515**

−0.412*

−4.966** −0.629*** −0.0088***

0.285**

−0.00297***

0.547*** −0.00112*** 243.78** −0.00156**

−0.00079*** 2.159**

−0.00087** 2.150***

−0.756***

Note: 0, 1, 2, 3, and 4 indicate the number of lags of independent variables. *, **, and *** denote significance at 1%, 5%, and 10%, respectively

−0.00546** 0.000372*** −0.72622**

−0.00139***

−0.00358*** −0.91927**

−0.00274**

−0.40133**

−5.23271***

0.000206**

−0.00113***

0.000309**

−0.001285**

0.000992** −6.23775**

2 Coefficient 0.00000756*** 0.0000836**

−0.000753**

19.63572*** −19.8061**

0.177748**

−0.0000288***

0.0000131*** 0.00000215***

1 Coefficient

3.581886**

0.990869*** 426.923**

0.323204*** −8.68466** −1.08774** −0.01567***

−0.00319** 0.00000234***

3 Coefficient

−0.0015** 3.748847***

−0.00498***

0.49086**

−0.14679**

0.00000254***

4 Coefficient

Note: 0, 1, 2, 3, and 4 indicate the number of lags of independent variables. *, **, and *** denote significance at 1%, 5%, and 10%, respectively

Variable Bank lending to the private sector Credit cards to the private sector Current account balance Domestic credit Deposit rate External debt Federal fund middle rate Federal fund overnight rate Gold price Imports Industrial production Inflation LIBOR rate M1 M2 Net international reserves Oil price Real effective exchange rate TL to dollars Trade balance Unemployment

Significant 0 Coefficient

Table 5  Logit regression results for the 1994 crisis

Leading Indicators of Turkey’s Financial Crises 33

34

M. Kaakeh and K. K. Gökmenoğlu

Table 6  Stepwise regression results for the twin crises

Variable Bank lending to the private sector Budget balance Commercial banks’ foreign assets Commercial banks’ foreign liabilities Credit cards to the private sector Deposit rate Domestic credit Domestic debt (bills) Exports External debt Federal fund middle rate Federal fund overnight rate Imports Industrial production Inflation M1 M2 M3 Oil price Past-due loans Real effective exchange rate Stock price index TL to dollars Trade balance Unemployment US T-bill rate

Significant 0 Coefficient 0.628∗10−7*

1 Coefficient 0.486∗10−7*

2 Coefficient

3 Coefficient

4 Coefficient

0.595∗10−8** −0.728∗10−7** −0.106∗10−6* −0.556∗10−7*

0.445∗10−7***

−0.317∗10−6* −0.264∗10−6* −0.123∗10−6* −0.181∗10−6*

−0.28∗10−6*

0.0223*

0.0147** 0.206∗10−7*

0.0236*

0.0167**

−0.412∗10−7* 0.000449* 5.27∗10−5*

0.594* −0.0004* −0.0384*

−0.00013* 3.78∗10−5*

2.369*

0.437*

2.0025* −1.60**

−0.00023* 0.0537* −0.0271* 0.000123*

−0.0378* −0.0322*

−0.00029* 2.86∗10−5* −1.893*

−0.0165**

0.022141** 8.55∗10−5** 0.0901* −0.0227*

0.0599*

−6.63∗10−5* 0.0737*

−0.135∗10−6*

0.0172*** −6.65∗10−5* −0.00027**

−0.00038* −0.101*** −0.545**

−0.00014** −0.182*

3.39∗10−5**

0.000109*

0.891** −0.00013*

−0.00023*

Note: 0, 1, 2, 3, and 4 indicate the number of lags of independent variables. *, **, and *** denote significance at 1%, 5%, and 10%, respectively

Leading Indicators of Turkey’s Financial Crises

35

Table 7  Probit regression results for the twin crises

Variable Bank lending to the private sector Budget balance Commercial banks’ foreign liabilities Credit cards to the private sector Deposit rate Domestic credit Domestic debt (bills) Exports External debt Federal fund middle rate Federal fund overnight rate Government consumption Imports Industrial production Inflation M1 M2 Oil price Past-due loans Real effective exchange rate Stock price index Trade balance Unemployment US T-bill rate

Significant 0 Coefficient 0.214∗10−6**

1 Coefficient

2 Coefficient

3 Coefficient

4 Coefficient

0.475∗10−6*** −0.231∗10 *** −1.36∗10 ** −6

−6

0.468∗10−6***

−3.23∗10−6** 0. 327∗10−6***

−2.9∗10−6***

0.103**

0.128**

−0.0763** −0.286∗10−7** −0.602∗10−6**

−0.00128*** 0.000265*

4.622**

−0.00063* 0.000349*** −6.456***

−0.00034** 0.000149* 15.686***

7.571**

−0.439∗10−6** −0.0007** −0.232**

−0.00157** 0.201*** −0.574*** 0.000814**

−0.065***

0.077*** −3.22∗10−5**

−0.180***

−0.616∗10−6***

0.088*** 0.00068*** 0.000474**

−0.00243** −1.725*** −5.596**

−4.02**

−0.00061**

−0.00093**

Note: 0, 1, 2, 3, and 4 indicate the number of lags of independent variables. *, **, and *** denote significance at 1%, 5%, and 10%, respectively

36

M. Kaakeh and K. K. Gökmenoğlu

Table 8  Logit regression results for the twin crises

Variable Bank lending to the private sector Commercial banks’ foreign liabilities Credit cards to the private sector Deposit rate Domestic credit Domestic debt (bills) Exports External debt Federal fund middle rate Federal fund overnight rate Government consumption Imports Industrial production Inflation M1 M2 Oil price Past-due loans Real effective exchange rate Trade balance Unemployment US T-bill rate

Significant 0 Coefficient 0.36∗10−6**

1 Coefficient

2 Coefficient

3 Coefficient

4 Coefficient

−2.3∗10−7*** −2.4∗10−6*** 1.01∗10−6*** −5.41∗10−6** 6.5∗10−7*** 0.207436**

−0.12639*** −4.77∗10−8** −0.00000115**

0.232407**

−0.00216*** 0.000444**

7.958655**

−0.00109** −0.00059*** 0.000592*** 0.000249** −11.96801***

27.10046***

14.25548***

−4.39∗10−7** −0.00119** −0.39542**

−0.00284**

0.151924*** 0.00147** −0.0000539**

−0.11508*** −0.32725***

−1.1∗10−6***

0.147348*** 0.001101**

−0.00438** −2.94127*** −9.58513**

−6.75734**

−0.00107**

−0.00156**

Note: 0, 1, 2, 3, and 4 indicate the number of lags of independent variables. *, **, and *** denote significance at 1%, 5%, and 10%, respectively

37

Leading Indicators of Turkey’s Financial Crises Table 9  Stepwise regression results for the 2009 crisis

Variable Bank lending Bank lending to the private sector Capital adequacy Commercial banks’ foreign assets Commercial banks’ foreign liabilities Consumer total loans Credit cards to the private sector Current account balance Domestic credit Domestic debt (bills) Exports External debt Federal fund middle rate Federal fund overnight rate Foreign direct investments Gold price Industrial production LIBOR rate Long-term interest rates (6 months) M1 M2

Significant 0 Coefficient 0.729∗10−8*

1 Coefficient 0.615∗10−8*

2 Coefficient 0.799∗10−8* −0.499∗10−8**

3 4 Coefficient Coefficient 0.259∗10−8*** −0.445∗10−8* −0.491∗10−8*

0.000148**

0.000124*

9.24∗10−5***

0.000131**

0.12∗10−7*

0.773∗10−8**

0.175∗10−7*

−0.615∗10−8*

0.207∗10−8*

0.297∗10−8** 0.774∗10−8** 0.123∗10−6***

2.97∗10−5*

2.59∗10−5*

−0.854∗10−8*

−0.694∗10−8* −0.8∗10−8* −0.336∗10−7*

−5

1.01∗10 *

0. 233∗10−5***

−5

1.16∗10 *

4.61∗10−5* 1.04∗10−5*

5.98∗10−5* 1.46∗10−5*

−1.80* −0.653*

−0.152*

−0.913*

0.910*

6.13∗10−5*** −0.00083* −0.0206*

−0.0276*

−0.00051* −0.0296*

−0.00055*

0.279*

0.132*

0.504*

0.394* 0.0811*

−2.9∗10−5* 1.1∗10−5*

−2.45∗10−5* 8.99∗10−6*

−2.61∗10−5* 8.43∗10−6*

−2.36∗10−5** 5.1∗10−6** (continued)

38

M. Kaakeh and K. K. Gökmenoğlu

Table 9 (continued)

Variable M3 Net foreign assets Net international reserves Nonperforming loans/total loans Oil price Past-due loans Real effective exchange rate Stock price index TL to dollars TL to euro Total credit cards Total deposits US T-bill rate

Significant 0 Coefficient

1 Coefficient

2 Coefficient

3 Coefficient

0. 267∗10−5*

−1.04∗10−5**

−0.429∗10−5

4 Coefficient −0.0222* −0.613∗10−5** 0.58∗10−5***

0.228**

0.00419* −0.00287* −0.529∗10−7** 0.0186**

0.0147***

−1.48∗10−5*

−1.93∗10−5*

0.00431*

−1.81∗10−5*

−0.644*** 0.886**

−0.915**

0.00514* −0.471∗10−7* 0.0104**

0.00707* −0.415∗10−7* 0.0324*

−1.293* 0.8**

−0.940*

−0.126∗10−6**

0.293***

0.446**

−0.587∗10−8** −0.394∗10−8* 0.509*

Note: 0, 1, 2, 3, and 4 indicate the number of lags of independent variables. *, **, and *** denote significance at 1%, 5%, and 10%, respectively Table 10  Probit regression results for the 2009 crisis Significant 0 Coefficient

1 Variable Coefficient Bank lending to the private sector Capital 0.00186*** adequacy Commercial 0.151∗10−6*** banks’ foreign assets Commercial 0.927∗10−7*** banks’ foreign liabilities Credit cards to the private sector

2 3 Coefficient Coefficient −0.416∗10−7***

0.00152**

4 Coefficient

−0.00099**

0.278∗10−6***

0.42∗10−6***

(continued)

Leading Indicators of Turkey’s Financial Crises

39

Table 10 (continued)

Variable Current account balance Domestic credit Domestic debt (bills) Exports External debt Federal fund middle rate Federal fund overnight rate Gold price Industrial production LIBOR rate Long-term interest rates (6 months) M1 M2 Net foreign assets Net international reserves Oil price Past-due loans Stock price index TL to dollars TL to euro Total credit cards US T-bill rate

Significant 0 Coefficient 0.00048**

0.725∗10−8***

1 Coefficient 0.000203***

2 Coefficient

3 Coefficient

4 Coefficient

−0.143∗10−7** −0.43∗10−6*** 0.000268* 3.28∗10−5***

6.59∗10−5**

−4.45∗10−5***

2.074*** −1.275** −0.0104* −0.118***

−3.525***

−0.815***

−0.00323**

3.469*** 1.415**

−0.00041*** 7.81∗10−5**

−0.00035** −0.00023** 7.35∗10−5** 4.71∗10−5** 8.31∗10−5***

−0.00013*** −0.974∗10−5*

0.000141***

0.0520**

0.0238**

−0.541∗10−6** −0.00025***

−0.00019***

−10.943**

−47.953***

−13.794***

−19.259

6.864*** 0.733∗10−6 −1.836**

Note: 0, 1, 2, 3, and 4 indicate the number of lags of independent variables. *, **, and *** denote significance at 1%, 5%, and 10%, respectively

40

M. Kaakeh and K. K. Gökmenoğlu

Table 11  Logit regression results for the 2009 crisis

Variable Capital adequacy Commercial banks’ foreign assets Commercial banks’ foreign liabilities Current account balance Domestic credit Exports External debt Federal fund overnight rate Gold price Industrial production LIBOR rate Long-term interest rates (6 months) M1 M2 International reserves Oil price Past-due loans Stock index TL to dollars TL to euro US T-bill rate

Significant 0 Coefficient

1 Coefficient

2.62∗10−7***

2 3 Coefficient Coefficient 0.002629** −0.00201** 4.78∗10−7***

4 Coefficient

1.62∗10−7***

0.000826**

0.000346***

1.27∗10−8***

−0.25∗10−7** 0.000506** 0.0000657*** −0.0000767*** −6.20059***

0.000113*** −2.23141** −0.01956** −0.20976***

4.78∗10−7*** −0.00664**

6.068605*** 2.405501**

−0.00069*** −0.00063** 0.00013** 0.000134**

−0.0004** 0.0000802**

−0.0000182** 0.000245***

0.09012*** −9.3∗10−7*** −0.00044*** −18.7917** 11.73484*** −3.53636**

−0.00033***

0.05338**

−25.6449***

−28.1994**

Note: 0, 1, 2, 3, and 4 indicate the number of lags of independent variables. *, **, and *** denote significance at 1%, 5%, and 10%, respectively

Leading Indicators of Turkey’s Financial Crises

41

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The Effects of Social Media Influencers on Consumers’ Buying Intentions with the Mediating Role of Consumer Attitude Sadaf Damirchi, Emrah Öney, and Seyed Arash Sahranavard

1  Introduction Technology and especially the Internet are growing at a fast pace today. The Internet and the applications that are born out of it provide organizations and individuals with the latest opportunities (Berthon et  al., 2012). Instagram has successfully established itself as the most popular social network among other social platforms. For individuals, Instagram is mostly considered a fun application by which they can share their stories and posts with their friends and have constant interactions with them. But from a business perspective, Instagram is considered the most cost-­ effective way of boosting brand awareness and reaching potential customers. Businesses mostly take advantage of influencers and endorsers who are ordinary people who gained fame by having online activities. Since social media influencers have lots of followers and interact with them regularly, it is easy for them to influence people (Uzunoglu & Kip, 2014) The power of influencers to impact people or the ones who follow them attracts companies whose target customers are the same as a particular influencer’s followers or whose products and services match an influencer’s characteristics or personalities (Hilker, 2017; Schroder, 2017). Since people tend to trust recommendations more from people they know, like their family, friends, coworkers, etc., influencers are now trying to create a special bond with their followers and make a relationship in which followers may involve an influencer’s everyday life by sharing stories and posts, hence boosting their trust as they may be involved in their private lives. By increasing trust in followers, social media influencers have the power to affect consumers’ attitudes to a large extent and even persuade them to buy the product S. Damirchi (*) · E. Öney · S. A. Sahranavard Department of Business Administration, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin, Turkey © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 N. Özataç et al. (eds.), New Dynamics in Banking and Finance, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-93725-6_3

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they are endorsing for a particular brand or even sway them not to buy a special product from a competing brand. Companies are aware of this power and make the most of it by collaborating with social media influencers in their advertisements as the ambassador of the company to reach potential customers that may not be accessible using traditional marketing techniques, as a result increasing company sales and revenue. However, not all social media influencers are successful in persuading and convincing consumers. Various elements can affect the level of an influencer’s effectiveness in changing or shifting consumers’ attitudes toward the desired behavior. The factors that will be investigated in this study are source credibility, source attractiveness, influencer-product fit, and meaning transfer.

2  Literature Review 2.1  Influencer Marketing The act of concentrating on a special person rather than the whole target market is considered influencer marketing (Forbes.com, 2016). Marketers and advertisers attempt to make use of influencer marketing by communicating their advertisements and messages through social media endorsers. Social media influencers can considerably enhance the effectiveness of commercial messages. Marketers want to get the attention of consumers toward their products and services to purchase their products by transmitting the positive attributes of influencers onto the product or service (Atkin & Block, 1983). Various tools can be employed for executing influencer marketing. Bloggers have become important endorsers since they are authentic in the eyes of consumers and have loyal followers. When an endorser or blogger promotes a product, it seems more reliable than traditional advertising. It’s not necessary to have a blog to be a successful influencer anymore; you just need to have a substantial Instagram presence. From a marketing point of view, having a smart, well-executed Instagram, instead of a blog, is essential (Brannigan, 2016). Influencers of social media platforms have successfully become well known since they established themselves as experts in a particular domain (Khamis et al., 2017). Moreover, influencers are an effective tool for promoting brands, because they have lots of followers gained by sharing user-generated content almost every day with their followers like beauty bloggers, fashion bloggers, etc.

2.2  Source Credibility In the 1950s, Hovland and Weiss improvised a model called the source credibility model. Ohanian (1990) stated that the term “source credibility” can be used to point to the positive characteristics of an influencer who can alter the consumer’s decision

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whether to accept the message or not. In the past, endorser credibility was an important factor only in advertisements and commercials (Aronson et al., 1963). But now, it is identified as an element that can have a considerable influence on consumers’ decision to buy a product, and what makes up their marketing technique is nothing but source credibility (Lutz et al., 1983). The three major elements of source credibility are trustworthiness, expertise, and attractiveness.

2.3  Trustworthiness Ohanian (1990) described trustworthiness as the extent to which the recipients of a message place confidence in an influencer’s purpose of providing them with the most valid assertions. Many pieces of literature support the fact that trustworthiness can enhance the effectiveness of a message (Chao et al., 2005). That is why advertisers mostly attempt to select influencers who have high levels of trustworthiness, affinity, and honesty (Shimp, 2003). When the source is believed to be trustworthy by the audience, they will also perceive the communicated message as highly believable (Hovland & Weiss, 1951). Having trust in influencers will result in more influential endorsers who have the power to change consumers’ minds toward behaviors desired by an organization in the hope of achieving what they view as suitable for their brand (Miller & Baseheart, 1969).

2.4  Expertise The information and skills that a particular influencer possesses are considered as his/her expertise which can be perceived as a source of cogent claims by the audience  (Hovland et  al., 1953). According to Ohanian (1990), it is not important whether the influencer has expertise in the field, but it is important that in the eyes of the consumers, the influencer is perceived as an expert. Pointing out what Van der Waldt (2009) has indicated, an influencer who possesses sufficient information, proficiency, and experience to endorse a brand is regarded as an expert in the advocated domain. Influencers and endorsers who have a high level of expertise are more effective than those who have low (Maddux & Rogers, 1980). Generally, receivers of the message will more likely trust a person who has related knowledge or expertise in a specific field (Belch & Belch, 1994).

2.5  Attractiveness As proposed by Erdogan (1999), attractiveness not only entails physical appearance but also includes the personality of an endorser (Erdogan, 1999), as well as his/her intellectual skills and way of living. An important element in a person’s initial

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judgment of another person in an advertising and communication context is physical attractiveness (Baker & Churchill, 1977; Joseph, 1982; Chaiken, Communicators Physical Attractiveness and Persuasion, 1979; Kahle & Homer, 1985). Attractive endorsers and influencers are generally mostly believed, liked, and preferred by consumers, and in addition, they have a positive effect on products than unattractive ones (Joseph, 1982).

2.6  Source Attractiveness Concepts that are most considered in the source attractiveness model are likability, familiarity, and similarity, as well as the physical characteristics of the influencer and his/her personality traits and even position in society (McCroskey & McCain, 1974). However, consumers are more inclined to form positive perceptions about influencers who are attractive, so most advertisements use such people (Erdogan, 1999).

2.7  Similarity The concept of similarity emerges when there is a resemblance between the consumers and the endorser and can be measured if the influencer and the target audience share similar goals, needs, and lifestyles (Ohanian, 1990). More identical individuals tend to communicate with each other more often (De Bruyn & Lilien, 2008). That is why influencers are chosen based on their characteristics that will best match consumer traits to instill empathy and create a bond between the influencer and his/her target audience (Belch & Belch, 2004).

2.8  Familiarity Familiarity is the knowledge that an influencer has through exposure (Erdogan, 1999; Belch & Belch, 2004). The degree of comfort between the influencer and the audience can also be considered familiarity (Kiecker & Cowles, 2001). The exposure effect mentioned by Zajonc (1968) states that when a person is familiar with an endorser and is more exposed to him/her, they will spontaneously like that influencer more. When there are brief exposures of the endorser and longer delays between those exposures, the effect of familiarity will be enhanced. In contrast, when there are long exposures of influencers and the delays between the exposures are shorter, the effect of familiarity will lessen (Bornstein, 1989).

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2.9  Likability Liking an influencer because of his/her physical appeal or personality traits is identified as likability (Belch & Belch, 2001). The effectiveness and influence of a message provided by a source with high likability will be enhanced since these sources can create significant awareness and message remembrance (Jain & Posavac, 2001). When the audience likes the influencer, they will also like the brand he/she endorses, and that is why advertisers use influencers in their advertisements (McGuire, 1985).

2.10  Influencer-Product Fit The similarity between an influencer’s characteristics and the features of a product is referred to as the product matchup model (Jamil & Rameez ul Hassan, 2014). The remembrance and impact of the brand and product will be improved if there is a similarity between the source and the brand he/she is endorsing. The use of endorsers in advertisements transfers more information than a verbal message does, as suggested by the matchup hypothesis (McCormick, 2016). Consumers tend to relate products to their own personality traits, colleagues, friends, or family members, meaning consumers are inclined to consume those products they can find an association with (Fortini-Campbell, 1992).

2.11  Meaning Transfer Numerous studies suggest that the fact that consumers tend to symbolize rather than just consume a product may be the basis for purchasing certain products (Levy, 1959). McCracken (1989), the person who developed the meaning transfer model, stated that influencers can convey a wide range of meanings to their audiences (Schlecht, 2003). What is happening within the endorsement process is that influencers personify certain images to themselves that these images will be conveyed in consumers’ mind by interacting with them on social media platform and when this particular influencer advertise a product the images he/she personified in eralier stages will convey to the product he/she in promoting as well (McCracken, 1989). McCracken (1989) believed that consumers patronize the meaning attributed to the brand rather than the product itself.

2.12  Consumer Attitude Consumer attitude can be described as an individual's personal and permanent evaluation of a person or product (Mitchell & Olson, 1981). Attitude takes its origin from social psychology (Eagly & Chaiken 1993). It is permanent as it tends to be

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enduring (Solomon et  al., 2006). Consumer attitudes can be either favorable or unfavorable toward a brand after seeing its advertisement (Phelps & Hoy, 1996). Attitude has been used in various studies as an efficient tool for measuring the effectiveness of an advertisement (Tripp et  al., 1994; Bright & Cunningham, 2012; Subhadip, 2012; Bhatt et al., 2013). It plays a vital role as such since consumers tend to be more attracted to the advertisements they like (Mehta, 2000).

2.13  Buying Intentions Purchase intention is defined as the buying intention and possible transaction behavior displayed by a consumer after assessing a product (Schiffman & Kanuk, 2000). Some reasons may cause a consumer to think about buying a certain product. These can be a need for that product or service; opinions about the firm, brand, and product; or the need to learn about the product (Bradmore, 2004). Consumers often look for their past experiences with a certain good to assess if they want to buy it (Bradmore, 2004).

2.14  Social Learning Theory In this research, to form our conceptual framework and hypotheses, the social learning theory, proposed by Bandura, has been used. It is a theoretical framework that can predict consumers’ behaviors (King & Multon, 1996; Martin & Bush, 2000). According to Bandura, “In the social learning system, a new pattern of behavior can be acquired through direct experience or by observing the behaviors of others” (Bandura, 1971).

3  Statements of Hypotheses 3.1  T  he Relationship Between Source Credibility and Consumer Attitude “Source credibility” is a term often employed to indicate an influencer’s positive features that have an effect on a recipient’s acceptance of the message (Ohanian, 1990). An endorser who has a high level of credibility can affect a consumer’s perspective (MacKenzie et  al., 1986; Goldberg & Hartwick, 1990; Goldsmith & Lafferty, 1999; Goldsmith et al. 2002; Mitchell & Olson, 1981). As mentioned in the literature review (Hovland et al., 1953), two major elements of source credibility are trustworthiness and expertise, but recently, researchers also consider

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attractiveness as another important aspect of source credibility (Goldsmith et al., 2000; Ohanian, 1990, 1991). Trustworthy influencers generally have more persuasive power over consumers. In this case, where the endorser is trustworthy, consumers may become unconcerned about what the advertisement is trying to convey and just tend to accept the message delivered by the influencer (Metzger et al., 2003). Influencer expertise can affect consumers’ attitudes, behavioral purpose, and real behavior (McGinnies & Ward, 1980). For the information to be convincing, the influencer’s expertise on the brand or product can be considered the most important aspect (Dholakia & Strenthal, 1977). The attractiveness of the source is also an important factor for creating an effective message (Schlecht, 2003). Influencers who are seen as attractive have more influence on the consumers’ minds (Joseph, 1982; Kahle & Homer, 1985). H1: Source credibility has a significant and positive effect on consumer attitude.

3.2  T  he Relationship Between Source Attractiveness and Consumer Attitude We consider a source as being attractive when the endorser is seen as fascinating and appealing by the audience (Kiecker and Cowles, 2001; Teng et al., 2014). The dimensions of source attractiveness, as identified by McGuire (1985), are similarity, familiarity, and likability. The source attractiveness model clarifies that the recipients of the message can better identify with influencers who are similar to themselves (Kelman, Process of Opinion Change, 1961). Generally, similar influencers are more effective and influential than dissimilar ones (Feick & Higie, 1992). When consumers know and have a good relationship with an endorser, they will more probably believe the endorser; hence, they have less perceived risk when they want to make purchasing decisions (Lee & Yurchisin, 2011). As Zhang and Ghorbani (2004) asserted, familiarity has a positive impact on online trust. The likability of the influencer can positively affect the consumer’s attitude (DeBono & Harnish, 1988; Chaiken, 1980), and a high degree of likability results in higher convincingness (O’Hara et al., 1991; Chaiken, 1980). H2: Source attractiveness has a positive and significant effect on consumer attitude.

3.3  T  he Relationship Between Influencer-Product Fit and Consumer Attitude The matchup hypothesis states that the effectiveness of an influencer is in part associated with his/her congruency with a brand or product (Kamins, 1990). Research studies indicate that an endorser is considered more influential when he/she fits the

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product. The perfect match results in a higher positive attitude toward the product endorsed by an influencer, whereas incongruence results in negative brand assessment (Kamins & Gupta, 1994) and higher brand recall (Misra & Beatty, 1990). H3: Influencer-product fit has a positive and significant impact on consumer attitude.

3.4  T  he Relationship Between Meaning Transfer and Consumer Attitude McCracken’s meaning transfer model (1989) asserts that consumers are also using the meanings of the products while they consume the actual products. McCracken (1989) explained a viable transmitting of endorser meaning to the product advertised by the influencer and eventually to the audience when they buy or use a product. So it is suggested that meaning transfer could influence consumers’ opinions of an endorsed product. Meaning transfer can convince consumers and affect their beliefs about the image of a product and also its perceived advantages. When consumers are convinced about a certain product and its advantages by the influencer during the meaning transfer process, then these beliefs of the consumers about the product can also be transferred to the brand. Upon this notion, this perceived image that consumers hold about a certain influencer is determinant in the endorsement process (Atkin & Block, 1983). H4: Meaning transfer has a positive and significant impact on consumer attitude.

3.5  T  he Relationship Between Source Credibility and Buying Intention The positive features of influencers can have an impact on the recipients’ admission of the delivered message (Ohanian, 1990). As Ananda and Wandebori (2016) exhibited, consumers’ buying decision and their opinion about a brand or product can be predicted through an endorser’s credibility. Yoon et  al. (1998) stated that source credibility is a normally important aspect when it comes to consumers’ buying decisions. The extent to which an influencer can provide its audience with honest and fair assertions, known as trustworthiness, can affect consumers’ perception and their purchasing decision (Ohanian, 1991). A source can be qualified in terms of its expertise, which directly impacts consumers’ perception to acquire a product. Influencers who have a high level of expertise have been shown to be more influential and persuasive compared to those whose expertise is low (Aaker & Myers, 1987), and they have the ability to create more purchase intentions (Ohanian 1991). Since attractiveness is able to create interactions between people and between

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people and firms, it can exert a positive effect on consumers’ perception about an endorser and also their decision to purchase a certain product (Lee & Yurchisin, 2011; Haley, 1996; Kelman & Eagly, 1965; Kiecker & Cowles, 2001). H5: Source credibility has a positive and significant impact on consumers’ buying intentions toward endorsed products.

3.6  T  he Relationship Between Source Attractiveness and Buying Intention The effectiveness of the message delivered by an endorser mainly depends on its attractiveness, which has three dimensions: likability, familiarity, and similarity (McCracken, Who is the celebrity endorser? Cultural foundations of the endorsement process, 1989; Mehulkumar, 2005). Consumers are more inclined to pay attention and be attracted toward influencers with whom they share common interests, goals, etc. Attractive influencers may form a relationship with persons or also firms which can help them to be identified with a certain brand or firm and impact the consumers' buying intentions. (Lee & Yurchisin, 2011). The chance of making a purchase can be increased when the influencer is perceived to be familiar with the product or brand he/she is endorsing (Zhang & Ghorbani, 2004). Moreover, consumers who are familiar with the source are more likely to purchase an endorsed product (Bianchi & Andrews, 2012). Influencers who are seen as attractive in terms of likability, which refers especially to the physical attributes of the endorsers, familiarity, and similarity, tend to arouse a positive attitude toward the product or brand. In conclusion, attractive endorsers are more successful in shifting consumers’ beliefs (Chaiken, 1979; Baker & Churchill, 1977) and perceptions (Chaiken, 1979; Caballero & Solomon, 1984; Baker & Churchill, 1977) and influencing their buying decisions (Petty & Cacioppo, 1980; Till & Busler, 2000). H6: Source attractiveness has a significant and positive effect on buying intentions toward endorsed products.

3.7  T  he Relationship Between Influencer-Product Fit and Buying Intention To generate an effective advertising campaign, there should be congruence between the features of a product and the influencer, as the matchup hypothesis asserts. Studies have also shown that attributes that create influencer/brand congruency continuously have been associated with positive opinion changes and enhanced buying decisions (Simons et al., 1970; Kahle & Homer, 1985; Braunstein & Zhang, 2005; Ohanian, 1991).

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H7: Influencer-product fit has a positive and significant influence on buying intentions toward endorsed products.

3.8  T  he Relationship Between Meaning Transfer and Buying Intention Meaning transfer refers to the effectiveness of the endorser by evaluating the meanings consumers link to the endorser and finally convey to the brand (Schlecht, 2003). The suitable match between product features and an influencer’s personality traits enhances the probability of consumer observation and buying intention (McCracken, 1986). A number of studies investigated the impact of meaning transfer on consumers and identified that it can affect their buying decisions (Peetz et al., 2004) or real usage (Byrne et al., 2003). H8: Meaning transfer has a significant and positive impact on buying intentions toward endorsed products.

3.9  T  he Relationship Between Consumer Attitude and Buying Intention Attitude is a person’s assessment of an object and has been a crucial notion in marketing studies. Hoyer and MacInnis (1997) described attitude as a “quite permanent assessment of an issue, person or object.” Future buying decisions and consumers’ interactions with a brand can be predicted by the most authentic variable, which is attitude (Lloyd & Luk, 2010; Kim et al., 2010). Amos et al. (2008) stated that consumers’ positive attitudes toward endorsers who promote the product increase the intention to purchase. Attitude and buying decisions display a collateral connection in consumer research (Tarkiainen & Sundqvist, 2005; Ting & de Run, 2015). H9: consumer attitude has a positive impact on buying intentions toward endorsed products.

3.10  T  he Mediating Role of Consumer Attitude on the Relationship Between Source Credibility and Buying Intention Tagg, Baker, and Erdogan (2001) stated that efficient advertising with the help of endorsers has a positive influence on real buying intentions and sales. There is an indirect relationship between influencer marketing and possible purchase intention

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behavior within the market through desirable advertising impact. Endorser credibility has indirect influence on consumers’ buying intentions through consumers’ attitude. Endorsement is an efficient advertising tool that can lead to buying intentions (Wang et al., 2012). All the three elements of Source Credibility which are trustworthiness, attractiveness, and expertise have indirect influence on consumers’ buying intentions through consumer attitude as proposed by Sallam (2011). There is a significant and direct relationship between Source Credibility and Consumer Attitude and also between Source Credibility and Purchase Intention Wu et al. (2012). H10: Consumer attitude mediates the relationship between source credibility and buying intention toward endorsed products.

3.11  T  he Mediating Role of Consumer Attitude on the Relationship Between Source Attractiveness and Buying Intention The effectiveness of a message is associated with the physical attributes of an influencer, which makes it appealing, as stated by the source attractiveness model (Till & Busler, 2000; Chao et al., 2005). Consumers are more likely to live the life that endorsers are living or also behave in the same way as influencers do when they realize that they have the same interests, goals, and values as a particular endorser (Kelman, 2006; Cialdini, 1993). Individuals more probably accept recommendations from influencers who are similar to them in certain ways (Basil, 1996). Knowledge of the source, which is acquired through exposure, is considered familiarity (Roy, 2006). Advertising display rates can alter consumer opinions and perceptions and boost buying decisions (Anand et al., 1988; Laroche et al., 1996). The influencer’s physical appearance is expected to have an impact on the audience’s acceptance of the advertisement. An attractive influencer can enhance consumers’ reactions toward the source (Bardia et al., 2011). Kahle and Homer (1985) stated that advertisements that are promoted by an attractive influencer can alter consumers’ attitudes and purchase intention. H11: Consumer attitude has a mediating effect on the relationship between source attractiveness and buying intention toward endorsed products.

3.12  T  he Mediating Role of Consumer Attitude on the Relationship Between Influencer-Product Fit and Buying Intention Consumers’ buying decisions and their preferences can typically be boosted as the resemblance between the influencer and the product features increases (Wright, 2006). If consumers perceive that there is a strong relationship between product

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features and the influencer’s characteristics, their reactions toward such a brand or product will be positive. In contrast, if they see no resemblance between the influencer’s characteristics and product features, the advertisement may become confusing and will lead to weak buying intent (Fleck et al., 2012). H12: Consumer attitude mediates the relationship between the influencer-product fit and buying intention toward the endorsed product.

3.13  T  he Mediating Role of Consumer Attitude on the Relationship Between Meaning Transfer and Buying Intention Generally, consumers tend to show higher buying intentions when they have a positive sense of the meanings that are transformed by the influencers (Thwaites et al., 2012). Goldsmith et al. (2000) declared that influencers are important in advertising a certain good as they convey their image to that good by transmitting an unknown to well-known good by developing consumers’ positive emotions and their buying intention. H13: Consumer attitude mediates the relationship between meaning transfer and buying intention toward endorsed products.

4  Conceptual Framework of the Study With the help of Social Learning Theory, we came up with variables affecting consumers’ purchase intentions as described earlier and named Source Credibility, Source Attractiveness, Influencer-Product Fit, and Meaning Transfer. Also, in this study, we try to investigate the mediating role of Consumer Attitude on the relationships between these variables and consumers’ purchase intention. Based on this information, for proposing the hypotheses of the study, we developed a conceptual framework which can be seen in Fig. 1, consisting of variables of the study as well as their direct and indirect relationships with purchase intention.

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Fig. 1  Conceptual framework

5  Method 5.1  Procedure and Sample Demographics In this research, for collecting the needed data, a questionnaire has been developed that asked close-ended questions about the variables of the study, namely, source credibility, source attractiveness, influencer-product fit, meaning transfer, consumer attitude, and buying intentions, and also some demographic questions. The survey has been conducted online, and since there was no direct interaction between the researcher and the respondents, all the needed instructions, as well as the purpose of the study, have been explained at the beginning of the survey. Taking part in the survey was 100% voluntary, and all information of the respondents was kept anonymous. A pretest with 20 respondents has been conducted on the same target population as that for the main questionnaire to ensure there were no errors or mistakes in the survey. As the respondents did not report any errors or did not ask for more clarification, the main survey was conducted with more participants. The needed data were collected from the target population, which consisted of Iranian people who use social media networks. As there is no strict rule for determining sample size, the sample for this study consisted of 300 participants, which were sufficient.

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For gathering data, the snowball sampling technique has been used, wherein information will be gathered by the researcher from direct referrals provided by other informants. In this research, for analyzing the demographic information of the respondents, descriptive analysis has been conducted. Also, a T-test has been used to investigate whether there is a statistical difference between men and women regarding their purchase decisions toward endorsed products. Partial least square structural equation modeling (PLS-SEM) was used in this study.

5.2  Measures For measuring the variables of this study, we used scales proposed by previous literatures. For source credibility, Ohanian (1990) scale has been used for seven questions, and for source attractiveness, we have used Ohanian (1990) and Eyal and Rubin (2003) scales for six questions. For measuring influencer-product fit and meaning transfer, Scholar.waset.org (2014) has been used for seven questions for each. Consumer attitude has been measured using Calder (1981) and Churchill (1971) scales for five questions. Also, buying intention has been measured using Sia et al. (2009) and La Ferle and Choi (2005) for five questions. For answering these questions, respondents are asked to show their opinions by a Likert scale provided which ranges from 1 to 7, with 1 means “Strongly Disagree” and 7 means “Strongly Agree”.

6  Results 6.1  T-Test for Gender Comparison The T-test is often conducted to examine whether there is a statistical difference between two different groups (for example, men and women) when it comes to their mean scores. In this research, the T-test has been conducted to investigate whether two groups of men and women are statistically different in their intentions toward buying endorsed products. As Table 1 illustrated, Levene’s test for the equality of variances is not significant since it is more than .05 (P > 0.05). So equal variances are assumed. Then it can be concluded that there is no significant difference between men and women when it comes to their buying intentions, which is illustrated in Table 1.

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Table 1  Independent sample test

Equal variances assumed

Levene’s test for equality of variances

T-test for equality of means

F .027

t −1.608

df 294

−1.618

218.228

Sig .870

BI_mean Equal variances not assumed

Equal variances assumed

T-test for equality of means Sig. (two Mean tailed) difference .109 −.229

Std. error difference .142

BI_ mean Equal variances not assumed

.107

−.229

.141

Equal variances assumed

T-test for equality of means 95% confidence interval of the difference Lower Upper −.509 .051

Equal variances not assumed

−.508

BI_mean .050

6.2  Measurement Model For evaluating internal consistency reliability, composite reliability (CR) and Cronbach’s alpha (CA) are evaluated. Composite reliability (CR) should normally range from 0 to 1, where 1 shows perfect reliability. According to Henseler et al. (2012), for confirmatory purposes, composite reliability needs to be equal to or above 0.7. Moreover, Cronbach’s alpha (CA) has to be equal to or above 0.7 (Garson, 2016) (Table 2). For ensuring convergent validity, all the outer loadings should have a value above 0.708 (Hair et al., 2017), and also the average variance extracted (AVE) needs to indicate a value more than 0.5. In Table 3, convergent validity has been confirmed since all the outer loading values are above 0.708. Furthermore, all the values regarding AVE are greater than 0.5. Internal consistency is confirmed because Table 6 shows that the values of CA and CR for all the constructs are above the recommended limit of 0.7. The Fornell-Larcker criterion and the heterotrait-monotrait ratio of correlation (HTMT) criterion were assessed to evaluate discriminant validity. When each latent variable has a square root of AVE, which is greater than other correlation values

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Table 2  Measurement model Constructs BI

CA

IPF

MT

SC

Items BI1 BI2 BI3 BI4 BI5 BI6 CA2 CA4 CA5 IPF1 IPF2 IPF3 IPF5 IPF6 MT1 MT2 MT3 MT4 MT5 SC3 SC5 SC6 SC7

Outer loadings 0.816 0.777 0.81 0.799 0.78 0.749 0.797 00.858 0.843 0.813 0.797 0.711 0.778 0.722 0.8 0.747 0.766 0.806 0.802 0.833 0.722 0.778 0.766

Cronbach’s alpha 0.879

Composite reliability 0.908

AVE 0.622

0.779

0.872

0.694

0.823

0.876

0.586

0.844

0.889

0.615

0.784

0.858

0.602

Table 3  Discriminant validity (Fornell-Larcker criterion) BI CA IPF MT SC

BI 0.789 0.748 0.592 0.642 0.394

CA

IPF

MT

SC

0.833 0.558 0.653 0.38

0.765 0.565 0.284

0.784 0.289

0.776

among the other constructs, discriminant validity is identified (Fornelll & Larcker, 1981). Based on what Fornell-Larcker stated, discriminant validity can be confirmed since the square root of AVE for each variable is greater than the correlation values with other variables, as illustrated in Table 4. The HTMT value should be less than 0.9 to confirm discriminant validity (Hensler et al., 2015). Discriminant validity has been confirmed for this research, as is shown in Tables 4 and 5, in which all the values are below 0.9.

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Table 4  Heterotrait-monotrait ratio (HTMT) BI CA IPF MT SC

BI

CA

IPF

MT

0.894 0.686 0.742 0.457

0.688 0.8 0.464

0.673 0.338

0.336

SC

Table 5  Collinearity statistics BI CA IPF MT SC

BI

CA

2.028 1.627 1.941 1.18

1.503 1.508 1.117

IPF

MT

SC

6.2.1  Common Method Bias What is known as a common method bias is caused by the measurement model used in the SEM study. For instance, respondents may be influenced by the instruction at the beginning of the questionnaire when they want to answer them, or also social desirability can make respondents answer questions in a particular way, which in both cases will cause indicators to share a certain amount of common variation (Kock, 2015). In this study, we have analyzed variance inflation factor (VIF) values to check for common method bias, and from 20 items, only two of them had values above the limited threshold of 3.3. And because they were a minor problem in the analysis, it did not necessitate changing our data.

6.3  Structural Model After developing the measurement model, the structural model was also assessed. Values of variance inflation factor (VIF) have been checked for identifying the probability of multicollinearity problems between latent variables. The suggested limit for VIF is above 0.2 but less than 5 (Ringle et al., 2015). Table 6 indicates that the multicollinearity issue does not exist since there are no VIF values greater than 5. 6.3.1  Results of the Proposed Relationships A bootstrapping technique was conducted to evaluate the significance of the relationships between variables. It is indicated in Table 7 that all the proposed relationships in the model are significant. The variable named source attractiveness has

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Table 6  Results of the proposed relationships Path SC -> CA IPF -> CA MT -> CA SC -> BI IPF -> BI MT -> BI CA -> BI

Path coefficient 0.380 0.561 0.654 0.189 0.303 0.416 0.478

P value 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Table 7  Mediating effect of CA SC -> BI R2 ΔR2

Total effect 0.187** 0.521 0.112

Indirect effect SC -> BI 0.084** 0.633

Direct effect 0.103**

Mediation Partial

Note: *p < .10, **p < .05, and ***p < .001

been removed from the model because it had low factor loadings. After removing the items that had low outer loadings, the number of items that had adequate factor loading was low, so the construct was deleted from the model. 6.3.2  The Explanatory Power of the Model The R2 value, which estimates how much of the variance in the dependent variable can be explained by the model or the independent variables, is also examined in this study. R2 value can also be considered as the explanatory power of the proposed model (Shmueli & Koppius, 2011). As Ridgon (2012) indicated, the R2 value can be introduced by its in-sample predictive power. The range of R2 values varies from 0 to 1. The better explanatory power of the model is demonstrated by higher R2 values. It is suggested that the value of 0.25 is regarded as weak, 0.50 is considered moderate, and 0.75 is strong (Hensler & Ringle, 2009; Hair et al., 2011). However, in some cases, the low value of R2, for example, 0.10, is accepted (Raithel et al., 2012). The proposed model in this study can explain 63.3% of the variance in BI, as indicated by the R2 value.

6.4  The Mediating Role of Consumer Attitude To identify the indirect effects of consumer attitude, the bootstrapping technique was conducted (Preacher & Hayes, 2008). The outcomes of the bootstrapping technique with a 95% confidence interval illustrate that source credibility can indirectly

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affect consumers’ buying intentions for purchasing endorsed products through consumer attitude. Also, influencer-product fit can indirectly affect consumer buying intention through consumer attitude; moreover, meaning transfer indirectly impacts buying intention through consumer attitude, as demonstrated in Tables 8, 9, and 10. Considering the results, the mentioned indirect effects are significant and positive. Also, the direct effects of source credibility, influencer-product fit, and meaning transfer on buying intention are positive and significant. We can then conclude that consumer attitude partially mediates the relationships between source credibility and buying intention, influencer-product fit and buying intention, and meaning transfer and buying intention, so hypotheses 10, 12, and 13 are supported.

Table 8  Mediating effect of CA IPF -> BI R2 ΔR2

Total effect 0.304** 0.521 0.112

Indirect effect IPF -> BI 0.118** 0.633

Direct effect 0.185**

Mediation Partial

Direct effect 0.195**

Mediation Partial

Note: *p < .10, **p < .05, and ***p < .001

Table 9  Mediating effect of CA MT -> BI R2 ΔR2

Total effect 0.416** 0.521 0.112

Indirect effect MT -> BI 0.221** 0.633

Note: *p < .10, **p < .05, and ***p < .001

Table 10  Hypotheses testing H1 H3

Source credibility has a significant and positive effect on consumer attitude. Influencer-product fit has a significant and positive effect on consumer attitude. H4 Meaning transfer has a significant and positive effect on consumer attitude. H5 Source credibility has a significant and positive effect on buying intentions. H7 Influencer-product fit has a significant and positive effect on buying intentions. H8 Meaning transfer has a significant and positive effect on buying intentions. H9 Consumer attitude has a significant and positive effect on buying intentions. H10 Consumer attitude has a mediating effect on the relationship between source credibility and buying intention. H12 Consumer attitude has a mediating effect on the relationship between influencer-product fit and buying intention. H13 Consumer attitude has a mediating effect on the relationship between meaning transfer and buying intention.

Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported

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6.5  Hypotheses Testing The table below shows which of the proposed relationships has been supported in this study, and as the table demonstrated, all the relationships have been supported.

7  Discussion The effects of source credibility, source attractiveness, influencer-product fit, and meaning transfer on consumer attitudes and also their buying intentions have been investigated. First of all, we have found that the source credibility of social media endorsers can positively impact consumers’ attitudes and their buying intentions (H1 and H5 were supported). The answers of the participants revealed that the credibility of the endorser can significantly impact their purchase intentions and also their reactions to certain brands, while in many previous studies, like in Evans (2013), it was stated that credibility can sometimes negatively affect consumers’ attitudes in cases where the influencer does not have related or even sufficient knowledge about what he/she is endorsing. Also, it asserted that it is not easy for consumers to exert positive reactions toward endorsers’ credibility, especially on occasions where influencers do not have enough expertise about the product or brand. We also concluded that source attractiveness does not have any effect on consumers’ reactions and their buying decisions, which is also confirmed in Ohanian’s (1990) studies, which explain that source attractiveness cannot affect consumers’ attitudes and purchase intentions (H2, H6, and H11 were rejected). Moreover, we have found that an ideal match between the influencer and the product, like what Kamins and Gupta (1994) stated, can generate more effective advertising messages and also result in more favorable reactions to products and buying decisions (H3, H7 were supported). It was found that meaning transfer can create more positive consumer reactions to a particular brand, which will result in buying intentions (H4 and H8 were supported). The meanings and images that are transferred into consumers’ mind by influencers can affect their attitudes and purchase intentions toward a certain product in a positive way  Escalas and Bettman (2005). Consumer attitude has been revealed to exert a positive impact on consumers’ intentions to buy particular products, and previous studies, like Ha and Janda (2012), affirmed that consumers’ favorable reactions to brands can generate more buying intentions toward promoted products (H9 was accepted). Finally, it has been concluded that consumer attitude has a mediating role in the relationships between source credibility, influencer-­ product fit, meaning transfer, and buying intention, and in many previous studies, the mediating role of consumer attitude toward creating purchase intention was confirmed (H10, H12, and H13 were accepted).

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8  Implications This study is beneficial for those businesses that are considering taking advantage of social media influencers or widening their businesses through social media influencers on online platforms. In today’s world, influencer marketing, which is considered a new form of celebrity endorsement, has become popular due to the fact that it is able to reach online consumers who may be hard to reach with traditional forms of marketing (Phua et al., 2017). Social media influencers are often used by businesses to deliver their marketing messages since they have lots of followers. Influencers create a relationship with their followers as they share posts and stories about their everyday life and engage them in whatever they are doing during the day. In fact, this interaction increases trust in the followers. Nowadays, social media influencers are becoming role models for their followers. Generally, people learn to behave in a special way or do certain things from a model according to the social learning theory. So it is the most cost-effective way for businesses to deliver their marketing messages or enhance their sales through the use of social media endorsers by promoting their brands. When a particular influencer who has lots of followers and who is successful in creating a trusted relationship starts to consume a certain good, his/her followers would tend to consume that good since they like to behave in the same way as their favorite influencer or to use products and services that his/her favorite influencer consumes. Considering the analysis results of this research, there are some elements that are able to affect the effectiveness of a particular social media influencer. First of all, the concept of source attractiveness has nothing to do with the buying intentions of customers toward endorsed products based on the analysis. So when looking for influencers to promote their brands, businesses should consider attractiveness as the least important factor. In today’s digital world, businesses should be aware that as face-to-face interaction has decreased, trusting a brand or an influencer has become a big deal. According to the analysis, the experience and trustworthiness of the influencer are important elements that can influence consumers’ buying decisions. Hence, in choosing influencers to become ambassadors of their brands, businesses should pick experts or those with experience in related domains. Also, it is important to always choose an influencer who is considered trustworthy from the consumers’ perspective. In order to choose an influencer to advertise or promote a particular brand, attention should be paid to whether there is a match between the influencer and what he/ she is endorsing. This actually helps businesses enhance their brand awareness as when customers see the influencer, they will unintentionally remember the brand and, in most cases, will not likely forget the brand easily. It should also be taken into consideration that consumer attitude and preferences have a mediating effect on the relationship between the model constructs and the intentions of the consumers to purchase certain products. It means that each of the constructs—source credibility, influencer-product fit, and meaning transfer—can positively affect consumers’ attitudes and preferences, and when attitudes toward a brand or product are successfully changed, consumers will be persuaded to purchase the product or brand.

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9  Limitations This research has a number of limitations that will be discussed. The first limitation of this study is that we only analyzed data from participants who use social networks and, most importantly, who follow at least one or more influencers on social media, so respondents who do not follow any social media influencers are not included in the study. As we stated in the methodology chapter, a cross-sectional method was used to gather information from prospective participants, and this is regarded as the second limitation of this study. As a result, there is a probability that respondents may change their responses to the questions and may have different answers to the questions later on. The questionnaire by which we collected data from respondents was prepared in English, which is not the mother tongue of Iranian people, who were our target population. The difficulty in comprehending the questions provided in the survey could be regarded as the third limitation of the research. The fourth and last limitation of the study is that since this thesis is considered quantitative research, to gain better insights or a deep understanding of the topic, it would be better to perform qualitative research as well.

10  Future Research This study is based on results from various income levels, gender, educational levels, and marital status. Other demographics and characteristics of the respondents that can affect consumers’ intentions to purchase an endorsed or promoted brand or product can be regarded in future studies. This research analyzes data regarding the impact of social media influencers on consumers’ buying intentions from only Iranian people who use social media platforms and follow influencers on social networks. Future studies can extend the research to include all people, without paying attention to their nationality, who use social networks and follow at least one influencer. In the managerial implications part, we talked about the benefits that businesses could gain by using social media influencers in their advertisements. Future studies can be concentrated on why people tend to follow influencers on social media and what advantages or benefits they gain by following various types of influencers on social media.

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Impact of Political Uncertainties on the Dividend Policies of Nonfinancial Firms in Turkey Foday Joof and Asil Azimli

1  Introduction Governments can alter the environment in which corporations operate via the channel of uncertainty about future political and economic decisions. Empirical evidence showed that policy uncertainty has important effects on corporate decisions, such as capital investments (e.g., Akron et al., 2020; Gulen & Ion, 2016; Ilyas et al., 2021; Jens, 2017; Julio & Yook, 2012; Wang et al., 2014) and distributions (e.g., Farooq & Ahmed, 2019). Studies examining the relationship between policy uncertainty and capital investments concluded that policy-induced uncertainty reduces capital investments in both developed and developing economies. Farooq and Ahmed (2019) examined the impact of policy uncertainty on the distribution policy of corporations and found that dividend payouts were reduced around national elections in the United States. Tran examined the impact of the economic policy uncertainty (EPU) index of Baker et  al. (2016) on the distribution policy of banks’ operation in the United States and revealed lower distributions during the episodes of higher policy uncertainty. Unprecedented changes in government policies were found to influence inflation and saving decisions (Baker et  al., 2016; Gulen & Ion, 2016). These factors are relevant to consumption, and investment patterns are thus also expected to influence the valuation of firms. Accordingly, Arouri et al. (2016), Bali et al. (2017), Ko and Lee (2015), and Pastor and Veronesi (2012) documented a significant negative impact of policy uncertainty on stock market valuations. Against this backdrop, this F. Joof (*) Risk Management Department, Central Bank of The Gambia, Banjul, The Gambia e-mail: [email protected] A. Azimli Department of Accounting and Finance, Cyprus International University, Haspolat, Turkey © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 N. Özataç et al. (eds.), New Dynamics in Banking and Finance, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-93725-6_4

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paper investigates how corporations cope with political uncertainty associated with policy uncertainty in an emerging market setting with relatively lower investor protection. Specifically, it tests whether companies in Borsa Istanbul substitute political uncertainty with dividends for the period from 1998 to 2019. The Turkish financial market sets a nice ground to test the impact of political uncertainty on corporate dividend policies for several reasons. First, Turkey is a civil law country, and according to La Porta et al., civil law countries have the weakest protections for the minority of shareholders. Thus, during times of political uncertainty, corporations could substitute uncertainty with higher dividends to protect their reputation (e.g., La Porta et al., 2000). Second, Turkey has been experiencing a lively policy environment, and thus policy uncertainty could be a major factor in determining corporate-level decisions in Turkey. Finally, policy chronic budget deficit and the relative performance of the Turkish lira put additional pressure on government and institutions to make continuous policy changes. This further stimulates policy-­related uncertainty. Overall, the findings reveal that uncertainty related to economic and political policies negatively impacts cash dividends. Accordingly, during the episodes of higher policy uncertainty, managers distribute lower dividends. A possible explanation is that the uncertainty increases the cost of capital, which might cause unwillingness for firms to pay a dividend during these periods. Likewise, higher debt-to-equity (D/E) ratio and capital expenditure (CAPEX) were found to influence dividend payouts. These results are robust against the use of local and global measures of economic and political policy uncertainties. Therefore, managers do not substitute policy uncertainty by distributing higher dividends in Turkey.

2  Literature Review 2.1  R  elationship Between Presidential Elections and Information Asymmetry Recently, scholars have tried to associate information asymmetries with political uncertainties. For instance, according to Durnev (2013), in election years, stock prices are more likely to become unpredictable due to possible policy changes that may affect taxation and fundamental economic outcomes. These variables greatly influence the environment in which corporations operate, and thus during elections, uncertainties related to corporate valuation are higher. Mei and Guo (2004) highlighted the fact that eight financial crises out of nine emerged either during elections or a political transition. Accordingly, we argue that the uncertainty surrounding elections influences common stock valuation. Secondly, differences in ideologies (capitalist, socialist etc) and economic policies (tax policies, interest rate policies etc) held by various political parties, contribute to creating high uncertainties during elections  coupled with the unpredictable  nature of electoral  outcomes

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(Farooq & Ahmed, 2019). Finally, Jens (2017) stated that corporations react to electoral uncertainties by decreasing or delaying investments and paying lower taxes, respectively. Furthermore, according to Durnev (2013), because managers are better informed of the probable consequences of a regime change on the corporation’s assets than investors, information asymmetry among/between managers and investors is anticipated to be higher during elections than otherwise.

2.2  R  elationship Between Uncertainties, Presidential Elections, and Dividend Policy Elections influence financial markets via the channel of uncertainty (Julio & Yook, 2012; Jens, 2017). In theory, the value of common stocks is determined by discounting future cash flows to their present value by using an appropriate risk-adjusted discount factor. During election periods, uncertainties coupled with potential regime changes and macroenvironmental reforms may considerably influence common stock valuation as a result of the increased risk averseness of investors in the capital markets (Bekaert et al., 2014). This risk averseness compounded by elections may influence a firm’s dividend policies since an increase in the risk averseness of external investors increases the discount factor used in valuation and thus decreases the current value. Leaning toward La Porta et al. (2000), during elections, where the possibility of a policy change is high, rational managers may substitute policy change uncertainty with dividends. La Porta et al. (2000) argue that corporations operating in environments with high information asymmetry tend to pay a dividend to create a reputation and prevent the management from being lavish. For instance, Farooq and Ahmed (2019) used data from six presidential elections in the United States, from 1996 to 2016, and they found that firms pay a higher percentage of their earnings as dividends during the election years compared to nonelection years. This implies a positive correlation between elections and dividend policies. They further propounded that the higher are the economic uncertainties related to fiscal, monetary, and national security policies in the years of election, the higher are the dividend payout ratios. The first hypothesis of the study is formulated as follows. H1: Elections increase uncertainties and managers alleviate this issue by paying high dividends. In contrast, Myers and Majluf (1984) suggested a positive association between information asymmetry and high financing cost. Thus, uncertainties during national elections increase the cost of capital, which might cause unwillingness for firms to pay a dividend during elections. Huang et al. (2015) examined a panel of 35 countries from 1918 to 2008 and found that global political crises increase superficial uncertainties in the market and the external cost of capital. Moreover, they also found that firms are likely to stop paying dividends, and corporations not paying dividends are also unlikely to start paying dividends during political crises when

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there are uncertainties related to the future of the business environment. This implies that political risks have an inverse association with the dividend payout ratio. Accordingly, we formulate our second hypothesis as follows. H2: Policy uncertainty increases the cost of capital via the channel of uncertainty, and managers decrease dividends during these times.

2.3  Determinants of Dividend Policy Dividend policy and its determinants have been a focal point of debate for more than five decades. Based on the classical theory, Miller and Modigliani (1961) speculated that dividend payment has no consequences on a corporation’s value if there is no information heterogeneity and no agency cost. According to Bhattacharya (1979) and Easterbrook (1984), these costs do exist. With regard to these costs, they proposed the signaling hypothesis and agency cost theory, respectively. Based on the agency theory, the wide-ranging ownership composition in big firms limits shareholders’ ability to supervise financing activities, which aggravates asymmetric information and the cost of monitoring managerial behavior. Thus, Al-Malkawi (2007) regarded firm size as a major indicator of firms’ dividend policy. In a study conducted by Harada and Nguyen (2011) in Japan, the duo found a negative correlation between firm size and dividend policy. On the contrary, Patra et al. (2012) and Yusof and Ismail (2016) suggested the positive impact of size on dividend policy. These conflicting results can be attributed to the difference in sample size and markets. However, in emerging markets, firms are smaller and ownership is concentrated (Al-Najjar & Kilincarslan, 2016); in other words, small firms can heavily rely on retained earnings to finance their projects, thereby reducing their ability to pay a high dividend. So we expect a negative relationship between size and dividend payout. Accordingly, total book assets are also included in the model to account for the effect of size. Due to the negative signaling effect of issuing stocks, corporations favor the use of their internally generated funds to invest, and when the need for peripheral funding arises, debt is preferred to equity financing; hence, it helps in minimizing agency cost and information asymmetry (Myers & Majluf, 1984). This relationship between profitability and corporate dividend can be better understood using the pecking order and signaling hypothesis; hence, corporations normally finance their investment primarily with retained earnings, then with safe debt, and finally with equity in order to mitigate information asymmetry and other costly financing options (Fama & French, 2001). Hence, the cost of borrowing are costly to obtain, by firms with relatively lesser profitable assets, they tend to pay lower dividend. Yarram and Dollery (2015) establish an inverse correlation between profitability and dividend policy, while Bokpin (2011) and Botoc and Pirtea (2014) found a positive effect of profitability on dividend payment. Furthermore, Fama and French (2001) propounded that nonprofitable corporations tend to pay lesser or no dividends as

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compared to profitable corporations. Therefore, net income dividends to preferred shareholders/average outstanding shares are included in the model as a proxy for earnings per share (EPS), and we expect a positive relationship between dividend and EPS. Based on the agency theory, leveraged corporations are more favorable for stockholders; hence, the debt can be used to control managerial spending. Therefore, high financial leverage is anticipated to lessen agency costs. Furthermore, Agrawal and Jayaraman (1994) and Kashif (2011) stated that highly leveraged corporations are more inclined to pay low dividends, and this indicates a negative correlation between debt and dividends. Furthermore, found a negative association between leverage and dividend in Turkey. Therefore, we used D/E in our model as a proxy for debt, and we expect a negative relationship because Turkish corporations are highly leveraged. Thus, they pay low dividends to reduce transaction costs. “High dividend payment is connected to firms with slow or low growth prospect while corporations with high growth opportunities are considered to paying low dividend hence high cash flow limits the reliance on external funding which in turn sends a positive signaling effect to investors” (Milgrom and Roberts (1992), p. 507). Thus, Alli et  al. (1993) and Mitton (2004) established an inverse relationship between growth opportunities and dividend policies. Therefore, we include the book-to-market (B/M) ratio in our model as a proxy for growth to examine its impact on dividend payout. In Turkey, we expect a negative association between growth and dividend payout. Grullon et  al. (2002) propounded that corporations evolving from growth to maturity stage tend to pay high dividends; hence, old-age organizations are faced with a decrease in investment opportunities, which retards their growth and reduces their capital expenditure requirement, which eventually leads to high payout ratio. Therefore, age is also included in the model for robustness check. According to Al-Najjar (2011), investment opportunity has a positive consequence on a firm’s decision to pay dividends. Correspondingly, Patra et al. (2012) also classify investment opportunity to be a significant indicator of dividend policy. “Contrarily, corporations with high growth opportunities are considered to pay low dividends hence high cash flow limits the reliance on external funding which in turn sends a positive signaling effect to investors” (Milgrom & Roberts, 1992; Alli et al., 1993). Therefore, we expect a positive or negative relationship between investment opportunity and dividends. Farooq and Ahmed (2019) argue that corporations with high capital expenditures tend to have less free cash flows (FCFs) to disburse to shareholders as dividends. Therefore, the ratio of capital expenditures to total assets is included in the model as a proxy for CAPEX, and we expect a negative relationship between dividends and CAPEX. Andres et al. (2009) employed a “dynamic panel data model” on firms in Germany to examine the impact of free cash flow on dividend policy; their finding was in line with the partial adjustment model of cash flows. Likewise, Kadioglu and Yilmaz examined the FCF hypothesis of Jensen in the Turkish market. Their finding

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suggested a positive association between FCF and dividend payout. Therefore, free cash flow per share is included in our model, and a positive relationship is hypothesized.

3  Methodology 3.1  Data A panel data of listed nonfinancial firms in Turkey are used to analyze the impact of political uncertainties on the dividend policies of firms during the period 1998 to 2019. The period selected is based on the availability of data. The data were obtained from Eikon database provided by Thomson Reuters. The rationale behind the chosen nonfinancial firms is the fact that financial firms have unique choices of financing, and evaluating them together with financial firms may reveal a biased outcome. The basis of this paper is to use the policy uncertainty index of Ahir et al. (2018) as a proxy for the uncertainties associated with probable economic and political policy changes.

3.2  Measurement of Variables To investigate the impact of political uncertainties on the dividend policies of nonfinancial firms from Turkey, a panel generalized method of moment (GMM) model is employed. Based on the data sample, the dependent and explanatory variables were measured, as shown in Table 1. The dependent variable is the dividend yield, while the control variables are presidential elections, firm size, financial leverage, earning per share, capital expenditure, market capitalization, growth opportunity, dividend per share, net sales, cash flow, and free cash flow. The model can be expressed in the following manner: Table 1  Explanation of the variables Symbols Variables Definition DY Dividend yieldDividend per share/share price CIU Country uncertainty Definition given in Ahir et al. (2018) index SIZE Size Natural log of a firm’s total assets Leverage Total-debt-to-total-equity ratio DE EPS Earnings per share Net income dividend to preferred shareholders/average outstanding shares CAPEX Capital expenditure The ratio of capital expenditures to total assets Growth Growth opportunity Market value per share/book value per share Source: Created by the authors from the literature review

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InDYt   0  1CUI  2 SIZE  3 DE  4 EPS  5 CAPEX  6 MVB   it; (1) Dividend yield is determined to be the dividend per share/share price. We used it as a dependent variable, so it is believed to have the ability to ease any trepidations about the capabilities of corporations to cope with undesirable results that might arise due to elections. Macroeconomic Uncertainty Indicators Country uncertainty index is found to have a significant influence on variables that may influence consumption and investment patterns (Ahir et al., 2018). Control Variables This paper has included various firm-specific determinants and macro-level control variables of dividend policies: • SIZE: firm size is a measure of a corporation’s total asset. The literature provided a mixed result when it comes to the association between size and dividend policy. Patra et al. (2012) and Yusof and Ismail (2016) establish a positive relationship, while a negative association between size and dividend payout is posited by Al-Najjar and Kilincarslan (2016). However, we expect a negative association between firm size and dividend due to the small firm size in emerging markets; as a result, they highly depend on undistributed profits to finance their investment, and this will limit their ability to disgorge cash as a dividend. • LEVERAGE: it is the total debt or total equity of a corporation. We proposed that high financial leverage is anticipated to lessen agency costs and dividends. This confirms the findings of Agrawal and Jayaraman (1994) and Kashif (2011), who posit a negative association between leverage and payout ratio. • EPS: this is short for earnings per share. Fama and French (2001) propounded that nonprofitable corporations tend to pay lesser or no dividends as compared to profitable corporations. Therefore, net income dividends to preferred shareholders/average outstanding shares are included in the model as a control variable to act as a proxy for earnings per share, and we expect either a positive or a negative relationship. • CAPEX: this is the ratio of capital expenditures to total assets. According to Farooq and Ahmed (2019), corporations with high capital expenditures tend to have less free cash flows to disburse to shareholders as dividends. Therefore, we use CAPEX as a control variable, and a negative relationship between dividends and CAPEX is expected. • GROWTH: growth refers to the market value per share/book value per share. Based on the literature, “corporations with high growth opportunities are considered to paying low dividend hence high cash flow limits the reliance on external funding which in turn sends a positive signaling effect to investors” (Milgrom & Roberts, 1992; Alli et al., 1993). However, Grullon et al. (2002) propounded that corporations evolving from growth to maturity stage tend to pay high dividends; hence, old-age organizations are faced with a decrease in investment ­opportunities; this retards their growth and reduces their capital expenditure requirement, which

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eventually leads to high payout ratio. Therefore, growth opportunity is employed as a control determinant for dividends since a negative or positive relationship is expected.

4  Data Presentation and Analysis 4.1  Descriptive Statistics (Table 2)

4.2  Correlation Matrix The correlation table below suggests that the model is free from multicollinearity problems; hence, the coefficient between the independent variables is less than the 0.8 thumbs-up rule (Table 3).

4.3  Generalized Method of Moments In this paper the authors used the country uncertainty index of Ahir et al. (2018) to capture the effect of political uncertainty on dividend policy. To alleviate the endogeneity problem, the world trade uncertainty index of Ahir et  al. (2018) and the economic policy uncertainty index of Baker et al. (2016) are used as instruments to country uncertainty index in estimations. The estimations control for time effects. It also clusters the standard errors and covariances according to the White period (Table 4). Table 2  Descriptive statistics Mean Median Maximum Minimum Std. dev. Skewness Kurtosis Jarque-Bera Probability Sum Sum sq. dev. Observations

CUI 0.104284 0.087284 0.223412 0.008913 0.049175 1.055186 3.475716 706.8713 0.000000 378.0283 8.763345 3625

SIZE 4631802. 264432.0 4.98E+08 225.0000 27305060 10.59992 132.1138 2585803. 0.000000 1.68E+10 2.70E+18 3625

DE 87.84673 33.80000 14132.99 −10221.52 509.7078 3.551290 354.9757 18719700 0.000000 318444.4 9.42E+08 3625

MVB 172.4290 1.230000 84074.81 −210.0600 3001.499 20.71159 469.4734 33125445 0.000000 625055.1 3.26E+10 3625

CAPEX 77128.42 5669.000 4665964. −86801.00 262246.0 8.024121 95.70599 1337013. 0.000000 2.80E+08 2.49E+14 3625

EPS 0.389490 0.070000 24.25000 0.000000 1.171372 8.396377 102.3921 1534701. 0.000000 1411.900 4972.538 3625

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Table 3 Correlations CUI SIZE DE MVB CAPEX EPS

CUI 1.000000 0.048723 0.050724 −0.013895 0.057504 0.030744

SIZE

DE

MVB

CAPEX

EPS

1.000000 0.045613 −0.008936 0.350418 0.085624

1.000000 −0.006286 0.029780 −0.014844

1.000000 −0.014705 0.015038

1.000000 0.130086

1.000000

Table 4  Panel generalized method of moments (dependent variable = DY) Variable LCUI(-1) LSIZE(-1) DE(-1) MVB(-1) CAPEX(-1) EPS(-1) C

Coefficient Std. error −2.550500 0.738730 0.351789 0.241664 −0.000104 7.93E-05 7.98E-05 4.34E-05 −3.89E-07 6.82E-07 0.289386 0.067652 −8.388025 4.522296 Effect specification Cross-section fixed (dummy variables) R-squared 0.322179 Adjusted R-squared 0.251942 S.E. of regression 4.015999 Durbin-Watson stat 1.664237 Instrument rank 334

T-statistic −3.452546 1.455690 −1.315044 1.838846 −0.570031 4.277563 −1.854816

Prob. 0.0006 0.1456 0.1886 0.0660 0.5687 0.0000 0.0637

Note: LCUI log of country uncertainty index, LSIZE log of total assets, DE debt-to-equity, MVB market value of equity to book value of equity ratio, CAPEX capital expenditures and EPS Earnings per share.***, **, * represents 1%, 5% 10% significance while (-1) is first lags of the variables”

4.4  Robustness Checks (Table 5)

4.5  Analysis and Discussions The GMM regression divulges that the country uncertainty index (CUI) has a negative impact on dividend policy; this implies that Turkish firms tend to pay low or no dividends during high political uncertainties. The analysis confirms our second hypothesis, which states that elections increase the cost of capital via the channel of uncertainty and managers decrease dividends during these times. This is the case because uncertainties during national elections increase the cost of capital, which might cause unwillingness for firms to pay dividends during elections. Moreover, global political crises increase the market’s superficial uncertainty and the external

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Table 5  Method: panel generalized method of moments Dependent variable: CASHDIV/TA Variable Coefficient Std. error LCUI(-1) −0.017023 0.007178 LSIZE(-1) 0.005659 0.002472 DE(-1) −1.03E-06 6.97E-07 MVB(-1) −3.56E-07 2.35E-07 CAPEX(-1) −1.45E-08 6.65E-09 EPS(-1) 0.004855 0.001235 OILVOL(-1) −0.001208 0.000748 C −0.084608 0.041681 Effect specification Cross-section fixed (dummy variables) R-squared 0.499947 Adjusted R-squared 0.445497 S.E. of regression 0.028847 Durbin-Watson stat 1.672583 Instrument rank 322

T-statistic −2.371401 2.289215 −1.483602 −1.516468 −2.182759 3.930201 −1.616394 −2.029867

Prob. 0.0178 0.0221 0.1380 0.1295 0.0291 0.0001 0.1061 0.0425

cost of capital, which might cause dividend-paying firms to stop paying dividends, and corporations not paying dividends are also unlikely to start paying dividends (Huang et al., 2015). Likewise, debt-to-equity ratio and capital expenditure have an inverse relationship with dividend policies, meaning high leverage and capital expenditure reduce the dividend payout policy of these firms, and high debt and capital expenditure reduce the free cash flow available for disbursement to shareholders in the form of dividends. This is in line with our initial expectation and is also supported by Farooq and Ahmed (2019). However, firm size has a positive significant impact on the payout policies of nonfinancial firms in Turkey. Similarly, growth opportunity (MVB) has a positive association with dividend payout policies. This can be attributed to the fact that corporations evolving from growth to maturity stage tend to pay high dividends; hence, old-age organizations are faced with a decrease in investment opportunities, which retards their growth and reduces their capital expenditure requirement, which eventually leads to a high payout ratio. Earnings per share are positively associated with dividend policies; thus, profitable firms are more likely to pay higher dividends than nonprofitable firms.

4.6  Conclusion This paper aims to understand the impact of political uncertainty on the dividend policy of nonfinancial firms in Turkey. The study used data from 350 firms from 1998 to 2019 and employed a panel generalized method of moment technique. The

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country uncertainty index of Ahir et al. (2018) is used as a proxy for political uncertainty in order to alleviate the endogeneity problem. We hypothesized that political uncertainties increase the cost of capital via the channel of uncertainty and managers decrease dividends during these times. Our outcome indicates that political uncertainty has a negative impact on dividend policy, implying that Turkish firms tend to pay lower or no dividends during high political uncertainties. This is the case because uncertainties increase the cost of capital, which might cause unwillingness for firms to pay dividends during these periods.

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Job Satisfaction and Turnover in Educational Institutions: Reasons and Variables Affecting Job Satisfaction and the Turnover Decision Tarek Adhami and Tarik Timur

1  Introduction Employee turnover is important for any organization and is used to measure the health of an organization. Intention to leave is a critical concern in educational systems, which should be taken into consideration in order to secure and maintain a qualified workforce (Lishchinsky & Rosenblatt, 2009). The literature on employee turnover, in general, consists of examinations of turnover at various individual-level predictors, including employee demographics, job satisfaction, and organizational commitment (Griffeth et al., 2000; Hancock et al., 2011). Employee turnover can have many potential consequences on organizational performance indicators, such as effects on profits, revenues, customer service, scrap rates, and other firm performance outcomes (Detert et al., 2007; Hancock et al., 2011). As avoidable and voluntary separations are more subject to an organization’s control, these are of more interest to researchers (Price, 1977; Neal, 1989). Voluntary employee turnover is a significant problem for many organizations (Proudfoot et al., 2009; Preenen et al., 2011) because the cost of turnover to organizations can be high (Chen, 2006), considering that hiring new employees and educating and training them will be costly. In many cases, the rate of turnover is considered high, and these high rates observed in different industries have prompted efforts to identify predictors of turnover and turnover intention (Matz et al., 2012). The retention of talent has, therefore, become more critical (Takawira et al., 2014). Thus, it is important to investigate employee turnover in organizations, which is the aim of this paper, which relies on “exit survey” data collected by the Department of Training, Education and Employment T. Adhami (*) · T. Timur Department of Business Administration, Eastern Mediterranean University, Famagusta, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 N. Özataç et al. (eds.), New Dynamics in Banking and Finance, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-93725-6_5

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(DETE) of the Queensland government in Australia. The survey was developed to identify and examine the opinions and attitudes of employees leaving educational institutions. Staff feedback provides the Department with valuable information on the reasons why employees resign or retire and enables policy makers to identify organizational and personal variables that are influential on the decision to leave. The information is used to know about attraction and retention initiatives and improve work practices across the Department to ensure it is considered an employer of choice.

2  Literature Review 2.1  Job Satisfaction Job satisfaction is a positive emotional state resulting from a favorable work experience (Locke, 1976). It has been defined as an affective attachment to the job or as an emotional state of employees caused by the evaluation of their jobs’ experiences (Kooij et  al., 2009; Roxana, 2013). Given the fact that job satisfaction affects employee turnover and absenteeism, it is a variable that is of practical importance (Hartel et al., 2008; Roxana, 2013). Since the employees’ emotions and behaviors are influenced by those around them (Hartel et al., 2008), it is reasonable to expect that employees’ satisfaction is influenced by the presence of other people in the immediate work environment, namely coworkers and supervisors. Support from coworkers and supervisors creates a positive working environment (Grandey, 2000; Roxana, 2013), which increases job satisfaction. Employees who like each other and have good relationships with each other are more likely to have supportive peer-­ to-­peer communications, greater job satisfaction, and positive relationships with the management (Hartel et al., 2008; Roxana, 2013). In this study, job satisfaction is treated as the dependent variable that reflects the intention to leave the job.

2.2  Perceived Organizational Support Perceived organizational support is the extent to which employees believe that the organization appreciates their contributions and cares about them (Rhoades & Eisenberger, 2002; Kim et al., 2017). Conceptually, perceived organizational support is developed by employees’ tendency to assign human characteristics to their organizations (Levinson, 1965; Kim et al., 2017). Accordingly, we have two hypotheses related to perceived organizational support in this study. H1: Perceived organizational support has a positive effect on perceived coworker support. H2: Perceived organizational support has a positive effect on job satisfaction.

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2.3  Perceived Coworker Support Perceived coworker support is defined as the beliefs employees hold regarding the degree of emotional and instrumental assistance provided to them by their coworkers (Thomas & Sorensen, 2008). It is also defined as employees’ beliefs about the degree to which the quality of helping relationships derived from peers is available (Russell et al., 1987; Kim et al., 2017). Support from coworkers and supervisors is argued to create a positive working environment (Grandey, 2000; Roxana, 2013). An employee’s perception that he or she works in a supportive climate has been found connected to job satisfaction, lowered stress and turnover intentions, and even higher team performance (Grandey, 2000; Roxana, 2013). H3: Perceived coworker support has a positive effect on job satisfaction. H4: Perceived coworker support has a positive mediation role in the relationship between perceived organizational support and job satisfaction.

2.4  Dealing with Job Stress Several studies found that the job satisfaction of employees as well as their performance at work are affected by job stress. Beehr and Newman (1978) argue that stress is present when a person is forced to deviate from normal functioning because of a change in his/her physiological and/or psychological condition, which is a consequence of a specific situation (Ahsan et al., 2009). In addition, from the definition that has been proposed by researchers, job stress was found to be interrelated with job satisfaction (Vinokur-Kaplan, 1991, cited by Ahsan et al., 2009). In the results of their studies, Landsbergis (1988) mentioned that when work stress levels are high, job satisfaction levels will be low (Ahsan et al., 2009). Employees who face difficulties are not able to deal with their feelings and have much stress, which will have a negative impact on their job satisfaction (Sy et al., 2006; Aghdasi et al., 2011). Thus, dealing with stress will positively affect the job satisfaction level of employees. H5: Dealing with job stress has a positive effect on job satisfaction.

3  Methodology and Results 3.1  Data Collection and Participants The secondary data set used in this study was collected by DETE through an exit survey from 702 employees in 12 educational institutions in Queensland, Australia. The survey was developed to identify and examine the opinions and attitudes of employees leaving educational institutions and determine the reasons why employees resign or retire.

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Table 1  The gender distribution in the study Gender Male Female

Frequency 207 389

Percent 34.7 65.3

3.1.1  Participants’ Distribution by Gender Participants’ distribution by gender is shown in Table 1. As shown in Table 1, there are more female participants than male participants. 3.1.2  Participants’ Distribution by Employment Type After gender distribution, the frequency of employment type was analyzed and shown in Table 2. In the sample, we have five employment types: contract/casual, permanent full-­ time, permanent part-time, temporary full-time, and temporary part-time. The two larger types are permanent full-time and temporary full-time; they form 69.5% of the employees who specified their employment type and more than 50% of the overall sample. 3.1.3  Participants’ Distribution by Job Classification For the classification, the results indicate that the participants are classified into nine classes shown in Table 3. The results indicate that the majority of the participants are from classes Administration (AO) (49.2%) and Teacher (including LVT) (33.7%); they form 82.9% of the total valid percentage.

3.2  Ceasing Employment In this part, the factors that contribute to the ceasing of employment as well as the main factor(s) for the decision to cease employment will be addressed. 3.2.1  Contributing Factors for Ceasing Employment The analysis of the contributing factors for the ceasing of employment specified 11 factors, these factors are shown in the Table 4.

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Table 2  The employment type distribution in the study Employment type Contract/casual Permanent full-time Permanent part-time Temporary full-time Temporary part-time

Frequency 71 237 59 177 52

Percent 11.9 39.8 9.9 29.7 8.7

Table 3  The classification distribution in the study Classifications Administration (AO) Apprentice Executive (SES/SO) Operational (OO) Professional officer (PO) Teacher (including LVT) Technical officer (TO) Tutor Workplace training Officer

Frequency 293 2 6 24 33 201 10 16 11

Percent 49.2 .3 1.0 4.0 5.5 33.7 1.7 2.7 1.8

The results show that moving to work in the private sector is the factor that has the highest percentage (14.4%). Also, job dissatisfaction and overall dissatisfaction with the institution are the second and third factors as they got the highest percentages (11% and 9.4% respectively) next to moving to the private sector. The fourth highest factor is career move to the public sector, with a percentage of 8.8%. 3.2.2  Main Factor for Ceasing Employment To see which of the contributing factors is considered the most important factor, participants were asked to choose the main factor among the factors listed. The results are shown in Table 5. The findings show that the participants chose dissatisfaction with the institution and job dissatisfaction as the two most important factors (3.3% and 3.1% respectively).

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Table 4  The contributing factor for ceasing employment Contributing factors Career move – public sector Career move – private Sector Career move – self-employment Ill health Maternity/family Dissatisfaction Job dissatisfaction Interpersonal conflict Study Travel Other None

Frequency 62 101 17

Percent 8.8 14.4 2.4

34 26 66 77 27 16 22 106 46

4.8 3.7 9.4 11.0 3.8 2.3 3.1 15.1 6.6

Table 5  The main factor for ceasing employment results Main factor Career move – private Sector Career move – public sector Career move – self-employment Dissatisfaction with the institution Ill health Interpersonal conflict Job dissatisfaction Maternity/family Other Study Travel Total

Frequency 16 8 4

Percent 2.3 1.1 .6

23

3.3

3 9 22 6 18 2 2 702

.4 1.3 3.1 .9 2.6 .3 .3 100.0

3.3  Participants’ Perspective of the Workplace The last frequency analysis done was about the participant’s perspectives regarding their workplace and its results are shown in Table 6. The results indicate that the majority of participants (62.8%) acknowledge that managers develop a performance and professional development plan (PPDP), although the percentage of participants who ignore the plan is considered high (37.2%). In addition, the participants agreed with a high percentage (90.2%) that the work culture is free from unlawful discrimination. Also, the results indicate that employment equity is respected based on the answers of 87% of the participants.

Manager performance and professional development Workplace plan Answers Frequency Percent No 226 37.2 Yes 382 62.8

Table 6  Perspective about the workplace

Work culture free from unlawful discrimination Frequency Percent 58 9.8 536 90.2

Promoting and practicing principles of employment equity Frequency Percent 75 12.8 512 87.2

Valuing diversity of employees Frequency Percent 98 16.7 488 83.3

Would you recommend the institution as an employer to others? Frequency Percent 165 28.4 416 71.6

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Furthermore, the participants (83.3%) see that the workplace value the diversity of its employees. Also, the results show that the majority of the participants (71.6%) recommend the institution as an employer to others. Even so, the percentage of participants who do not recommend the institution as an employer to others is considered high (28.4%).

3.4  S  pecifying the Variables Affecting Employees’ Turnover Using Exploratory Factor Analysis In order to identify the variables that affect employees’ turnover, we did an exploratory factor analysis (EFA). The results provided by the EFA test show that there are four constructs that should be taken into consideration, and based on the literature review and the variables used in the study, these constructs are job satisfaction, perceived organizational support, perceived coworker support, and dealing with job stress. Table 7 shows the constructs with their relative items as the results of the EFA test.

Table 7  Items of each variable found based on the exploratory factor analysis test Variables Job satisfaction

Perceived organizational support

Perceived coworker support

Dealing with job stress

Items My job was challenging and interesting. I was able to use the full range of my skills in my job. I was able to use the full range of my abilities in my job. I was able to use the full range of my knowledge in my job. My job provided sufficient variety. I feel the senior leadership had a clear vision and direction. The organization recognized when staff did good work. Management was generally supportive of me. Management was generally supportive of my team. I was kept informed of the changes in the organization which would affect me. Staff morale was positive within the Institute. If I had a workplace issue it was dealt with quickly. If I had a workplace issue it was dealt with efficiently. If I had a workplace issue it was dealt with discreetly. I worked well with my colleagues. I was given adequate support and co-operation by my peers to enable me to do my job. There was adequate communication between staff in my unit. I was able to cope with the level of stress and pressure in my job. My job allowed me to balance the demands of work and family to my satisfaction.

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Table 8  Means and reliability tests for the constructs Constructs Perceived organizational support Job satisfaction Perceived coworker support Dealing with stress

Std. N Minimum Maximum Mean deviation 523 1.00 5.00 3.3159 0.96308

Cronbach’s alpha 0.941

589 1.00 586 1.00

5.00 5.00

3.6448 1.02567 4.0506 0.78489

0.936 0.774

600 1.00

5.00

3.8008 0.92629

0.775

3.5  T  esting the Reliability of the Variables Found by Exploratory Factor Analysis To find out whether the identified variables are reliable or not and to see in which way the participants’ answers lean toward, the reliability test and the mean of each construct were analyzed. The results are shown in Table 8. For the reliability of the results, we can see that the values of Cronbach’s alpha tests are all above 0.7 for all the constructs, which means that the constructs are reliable. For the mean values, we see that the answers for perceived organizational support (3.3159) tend to be neutral, while the answers tend to be positive for job satisfaction, perceived coworker support, and dealing with stress (3.6448, 4.0506, and 3.8008 respectively).

3.6  The Effect of Employee Demographics on the Constructs To see whether some criteria make differences in the way the participants see the constructs or not, analysis of variance (ANOVA) test and T-test were done regarding the age, employment type, classification, institutes and gender. The results are shown in Table 9. The ANOVA test between age and the items shows that the results are significant only for perceived organizational support (0.023), which means that age matters in the way the participants see perceived organizational support. In addition, the results between employment type and the items show that the results are significant for both perceived organizational support and dealing with stress (0.030 and 0.002 respectively), and this means that the employees of different types differ in the way they see perceived organizational support and dealing with stress. Also, the ANOVA test between the classification and the items shows that the results are significant for dealing with stress (0.000), which means that classification affects the way the participants see dealing with stress. The results between institutes and items show that result is significant only for job satisfaction (0.042); this means that people in institutes have differences in the way they see job satisfaction. The last test, which was a T-test between gender and the items, shows that results are significant for

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Table 9  ANOVA and T-test results ANOVA test (between groups) Age Constructs Perceived organizational support Job satisfaction Perceived coworker support Dealing with stress

T-test Employment type

Classification Institute

Gender

F Sig. F 2.244 .023 2.698

Sig. F .030 1.684

Sig. (2 Sig. F Sig. t tailed) .100 2.119 .062 −2.175 .030

1.642 .110 2.083 1.768 .081 0.427

.082 0.786 .789 1.929

.616 2.318 .042 −.583 .560 .053 1.321 .254 −2.016 .044

1.130 .341 4.194

.002 6.443

.000 0.114 .989 −.574

.566

Table 10  Correlation matrix between the variables (constructs) Pearson correlations Constructs 1 – perceived organizational support 2 – job satisfaction 3 – perceived coworker support 4 – dealing with stress

1

2

3

.489** .534** .502**

.404** .247**

.458**

4

**Correlation is significant at the 0.01 level (2 tailed)

perceived organizational support and perceived coworker support (0.030 and 0.044 respectively), and this means that men and women differ in the way they see perceived organizational support and perceived coworker support.

3.7  Correlation Between the Constructs of the Study After ANOVA and T-test, a correlation test was done to see the general type of relationship between the constructs. Based on the results of correlation shown in Table 10, all the items are significantly (at the 0.01 level) and positively correlated with each other. For perceived organizational support, it has a medium correlation with the other variables (0.489, 0.534, and 0.502). On the other hand, job satisfaction has a medium correlation with perceived coworker support (0.404) and has a weak correlation with dealing with stress (0.247). In addition, perceived coworker support has a medium correlation with dealing with stress (0.458).

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Table 11  Regression analysis between job satisfaction and the independent variables Variables Step 1: Perceived organizational support Step 2: Perceived organizational support Perceived coworker support Step 3: Dealing with stress N R2 Equation F-value

Job satisfaction R2 change Beta .231 . 0.5 0.268 0.373 .286 .061 .275 702 0.329

T-statistics 12.284 7.939 5.035 6.148

P-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000

3.8  Regression Analysis In order to better understand the intention to leave work, a regression test was done. Based on literature reviews and the results of the descriptive statistics of both contributing factors for ceasing employment and the main factor for ceasing employment previously mentioned in this study, we treated job satisfaction as the dependent variable as it has the main effect on employee turnover. In regression analysis, we will investigate the relation between job satisfaction and the independent variables as well as the mediation role of perceived coworker support in the relation between job satisfaction and perceived organizational support. As we can see in Table  11, there is a positive (Beta  =  0.5) and significant (p-value = 0.000) relation between perceived organizational support and job satisfaction. For mediation, when we added perceived coworker support, which provides a significant (p-value = 0.000) and positive (Beta = 0.286) relation with job satisfaction, the regression coefficient of the relationship between perceived organizational support and job satisfaction decreased to 0.373, which was still significant (p-value = 0.000), which means that perceived coworker support has a partial mediation role between perceived organizational support and job satisfaction. For the relationship between dealing with stress and job satisfaction, there is a positive (Beta = 0.247) and significant (p-value = 0.000) relation between dealing with stress and job satisfaction. Based on regression results, Fig. 1 shows the model used in this study.

4  Conclusion and Discussion Many factors can cause employees to leave their job and move to another organization. In this study, we found that job dissatisfaction is reported as the main reason that motivates an employee to leave his/her job. It is important for the organization

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Perceived Coworker Support

Perceived Organizational Support

Job Sasfacon

Dealing with Stress

Fig. 1  The determinant of job satisfaction’s model

to pay attention to this issue in order to retain employees. The constructs generated in the study, based on the literature review and the exploratory factor analysis, namely, job satisfaction, perceived organizational support, perceived coworker support, and dealing with job stress, are perceived positively by employees and are significantly correlated with each other in a positive way. Furthermore, we found that perceived organizational support is the most sensitive variable to employee demographics (namely, age, employment type, and gender) based on the ANOVA test. In addition, perceived coworker support is only sensitive to the gender of employees, while dealing with stress is sensible to both employment type and classification. As the main reason for leaving a job in this study was employees’ dissatisfaction and basing it on the literature reviews, we treated job satisfaction as the dependent variable that reflects employees’ intention to leave the job. Based on regression tests results, perceived organizational support affects job satisfaction and perceived coworker support significantly, and the presence of perceived coworker support as a mediator between perceived organizational support and job satisfaction did not affect the significant direct effect of perceived organizational support on job satisfaction. Thus, perceived organizational support has a positive direct relation as well as indirect relation with job satisfaction by the mediation of perceived coworker support. Also, regression results show a positive relation between dealing with job stress and job satisfaction. Based on the results, all hypotheses in this study are confirmed. In this regard, the organizations should increase their response to employees and provide more support for them, as well as try to enhance the relationship between employees and help them deal with job stress. These procedures will positively affect employees’ satisfaction with their job, which will reduce dissatisfaction and lead to a higher probability of maintaining employees.

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4.1  Managerial Implications In our case, we found that the lack of job satisfaction is the main reason that motivates employees to leave their jobs, and it is positively affected by organizational support, coworker support, and dealing with stress. Employee turnover is costly and has many negative consequences on the company. It is crucial for organizations to take steps that will have a positive effect on employees’ job satisfaction in order to reduce turnover. In this regard, several actions can be suggested to managers to increase employee satisfaction and improve the factors that affect it to retain employees, especially the most qualified ones. 4.1.1  Organizational Support Organizational support is essential to increase job satisfaction. Organizations should take age, employment type, and gender into consideration when supporting their employees. Managers could have a positive impact on employee satisfaction by introducing flexible schedules, recognizing employees’ success, and giving them feedback continuously. When a company is flexible, it is showing that it values its employees’ time and commitment. As alternative actions, managers can provide telecommuting options to employees, as well as flexible work schedules and generous parental leave policies. For recognition and feedback, managers can recognize an employee by offering a prestigious position in a committee in the organization or by assigning the employee to a task that he/she desires to take if he/she shows commitment and competence in his/her work. On the other hand, even simple feedback can have a great effect on the employee, accompanied by recognition, like a simple “you did a good job,” which would increase his/her satisfaction. Another aspect of organizational support is fairness; it is mainly related to the distribution of jobs and fairness in providing opportunities to employees. Managers should ensure that their employees feel that they are equal and that they have the same chance to get a reward if they give good performance and that they are shown fairness in task distribution, in getting feedback, and in having the opportunity to express themselves or have a chance to speak in meetings. 4.1.2  Coworker Support Organizations should ensure that the relations between employees are suitable and go well for both genders. To get the employees to support each other, managers have to create a positive work environment and adopt values that motivate employees to support each other, in addition to providing training to employees to work as a team. In this regard, managers should appreciate the employees who support others and declare this appreciation to create an environment where support is valued, which will motivate other employees to do the same. In addition, managers should provide

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training workshops to employees about teamwork and its benefits to the team and organizational performance. Also, organizations should state during employee orientation that support is a value that is adopted and should be respected between employees to foster a positive workplace environment. 4.1.3  Stress In order to help employees deal with stress, organizations should address their employees when taking into consideration employment type and classification. Organizations can help employees deal with stress in several ways. Managers should evaluate the description of their employees’ jobs in addition to their workload periodically so that the job description will be modified or the tasks assigned to the employee will be reduced if after the evaluation it is concluded that the workload is high. In addition, managers should avoid giving many tasks to an employee at the same time because unnecessary multitasking is one of the reasons for stress in the workplace. Also, managers should provide training to their employees to help them manage their time at work and be aware of work stress and how to behave when they feel stressed. Furthermore, companies should provide the needed tools and resources that would make the job easier for their employees, which will help them save time and effort and reduce stress at work.

5  Limitations and Future Studies Future research can be carried out taking into account the limitations of this study. The survey that was used to collect data was conducted in Australia. Research could be done in other contexts to check the validity of the findings. The present study identified job satisfaction as the main reason for turnover and analyzed its relationship with three other variables. Future studies could consider additional variables that might be influential to employee turnover. A qualitative study can provide a deeper understanding of reasons that encourage employees to leave their organizations. Such data will be valuable to policy makers for formulating strategies to prevent high levels of employee turnover in educational institutions.

References Aghdasi, S., Kiamanesh, A.  R., & Ebrahim, A.  N. (2011). Emotional intelligence and organizational commitment: Testing the mediatory role of occupational stress and job satisfaction. Procedia. Social and Behavioral Sciences, 29(2011), 1965–1976. Ahsan, N., Abdullah, Z., Fie, D. Y. G., & Alam, S. S. (2009). A study of job stress on job satisfaction among university staff in Malaysia: Empirical study. European Journal of Social Sciences, 8(1), 121–131.

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Beehr, T. A., & Newman, J. E. (1978). Job stress, employee health and organizational effectiveness: A facet analysis, model and literature review. Personnel Psychology, 31, 665–669. Chen, C. (2006). Job satisfaction, organizational commitment, and flight attendants’ turnover intentions: A note. Journal of Air Transport Management, 12(2006), 274–276. Detert, J. R., Treviño, L. K., Burris, E. R., & Andiappan, M. (2007). Managerial modes of influence and counter productivity in organizations: A longitudinal business-unit-level investigation. Journal of Applied Psychology, 92(4), 993–1005. Grandey, A. (2000). Emotion regulation in the workplace. A new way to conceptualize emotional labor. Journal of Occupational Health Psychology, 5(1), 95–110. Griffeth, R. W., Hom, P. S., & Gaertner, S. (2000). A meta-analysis of antecedents and correlates of employee turnover: Update, moderator tests, and research implications for the next millennium. Journal of Management, 26(3), 463–488. Hancock, J. I., Allen, D. G., Bosco, F. A., McDaniel, K. R., & Pierce, C. A. (2011). Meta-analytic review of employee turnover as a predictor of firm performance. Journal of Management, 39(3), 573–603. Hartel, C.  E. J., Gough, H., & Hartel, G.  F. (2008). Work -group emotional climate, emotion management skills, and service attitudes and performance. Asia Pacific Journal of Human Resources, 46(1), 21–37. Kim, H. J., Hur, W. M., Moon, T. W., & Jun, J. K. (2017). Is all support equal? The moderating effects of supervisor, coworker, and organizational support on the link between emotional labor and job performance. Business Research Quarterly, 20(2017), 124–136. Kooij, D. T. A., Jansen, P. G. W., Dikkers, J. S. E., & De Lange, A. H. (2009). The influence of age on the associations between HR practices and both affective commitment and job satisfaction: A meta-analysis. Journal of Organizational Behavior, 31(8), 1111–1136. Landsbergis, P.  A. (1988). Occupational stress among health care workers: A test of the job demands- control model. Journal of Organizational Behavior, 9(3), 217–239. Levinson, H. (1965). Reciprocation: the relationship between man and organizations. Administrative Sciences., 9(4), 370–390. Lishchinsky, O., & Sand Rosenblatt, Z. (2009). Organizational ethics and teachers’ intent to leave: An integrative approach. Educational Administration Quarterly- SAGE, 45(5), 725–758. Locke, E. A. (1976). The nature and causes of job satisfaction. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 1297–1349). Rand McNally. Matz, A. K., Wells, J. B., Minor, K. I., & Angel, E. (2012). Predictors of turnover intention among staff in juvenile correctional facilities: The relevance of job satisfaction and organizational commitment. Youth Violence and Juvenile Justice, 11(2), 115–131. Neal, J. G. (1989). Employee turnover and the exit interview. The Pennsylvania State University, Library trends, 38(1), 32–39. Preenen, P. T. Y., Pater, I. E., Vianen, A. E. M. V., & Keijzer, L. (2011). Managing voluntary turnover through challenging assignments. Group & Organization Management, 36(3), 308–344. Price, J. L. (1977). The study of turnover. Iowa State University Press. University of Michigan. Exit interview form from University of Michigan Libraries. Proudfoot, J. G., Corr, P. J., Guest, D. E., & Dunn, G. (2009). Cognitive-behavioral training to change attributional style improves employee well-being, job satisfaction, productivity, and turnover. Personality and Individual Differences, 46(2009), 147–153. Rhoades, L., & Eisenberger, R. (2002). Perceived organizational support: A review of the literature. Journal of Applied Psychology., 87(4), 698–714. Roxana, A. C. (2013). Social support as a mediator between emotion work and job satisfaction. Procedia – Social and Behavioral Sciences, 84(2013), 601–606. Russell, D. W., Altmaier, E., & Velzen, D. V. (1987). Job-related stress, social support, and burnout among classroom teachers. Journal of Applied Psychology., 72(2), 269–274. Sy, T., Tram, S., & O’Hara, L. A. (2006). Relation of employee and manager emotional intelligence to job satisfaction and performance. Journal of Vocational Behavior, 68(2006), 461–473.

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Takawira, N., Coetzee, M., & Schreuder, D. (2014). Job embeddedness, work engagement and turnover intention of staff in a higher education institution: An exploratory study. Journal of Human Resource Management, 12(1), 1–10. Thomas, W.  H. N., & Sorensen, K.  L. (2008). Toward a further understanding of the relationships between perceptions of support and work attitudes. Group & Organization Management, 33(3), 243–226. Vinokur-Kaplan, D. (1991). Job satisfaction among social workers in public and voluntary child welfare agencies. Child Welfare: Journal of Policy, Practice, and Program, 70(1), 81–91.

Performance Analysis of the Northern and Southern Banking Sectors on Cyprus Island Under the Covid-19 Era Veclal Gündüz

1 

Introduction

The coronavirus pandemic still continues and has had negative impacts on all sectors of the world. Besides their aim to protect people’s health and reduce the spread of the virus, governments have been taking measures to keep financial stability and avoid bankruptcies and unemployment. The aim of this study is to investigate the performance of the banking sectors of Northern and Southern Cyprus during the period of the pandemic. They are examined and compared through the CAMEL analysis based on capital adequacy, asset quality, management, earnings (profitability) and liquidity ratios, which were calculated quarterly for the period 2016–2020. It is found that the effects of the pandemic on banks differed for the two regions, depending on governmental measures and the roles of banks. The Northern banks generated more profits due to the postponements of credits; meanwhile, Southern banks’ interest income was recorded higher compared to their interest expenses. The asset quality of Southern banks weakened because of the high level of non-performing loans (NPLs) recorded after the 2013 crisis. But they are more liquid than those of the Northern banks with the help of European Union (EU) funds. This study gives a set of recommendations on how to improve the financial performance of banks. The two sectors are similar in that they have their fiscal policies, but their monetary policies differ because they have different currencies. The southern part of Cyprus uses the euro (EUR), which is regulated by the European Central Bank. The northern part uses the Turkish lira (TRY), which is controlled by the Central Bank of Turkey. V. Gündüz (*) Bahçeşehir Cyprus University, Nicosia, North Cyprus via Mersin, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 N. Özataç et al. (eds.), New Dynamics in Banking and Finance, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-93725-6_6

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The differences are the sources of the fund aids especially during the crisis periods. The value of money is another discrimination to be considered in view. Also, Southern Cyprus is already a developed country, while Northern Cyprus is still a developing country.

2  Banking Sector in TRNC 2.1  TRNC Banking Legal Legislation The banking activities in the Turkish Republic of Northern Cyprus (TRNC) were formerly regulated by the “Banking Law of the Turkish Republic of Northern Cyprus”, dated 23 January 2001 and numbered 39/2001, and Law 62/2017, which was amended and enforced on 17 November 2017. The current banking law, on the other hand, is implemented with its latest amendments made on 16 June 2020, published in the official newspaper and numbered 110, together with Law 22/2020 in its combined form, including the declarations and regulations published under this law. Apart from banking law, TRNC Central Bank Law, Bills Law, Money and Foreign Exchange Law, Law on Prevention of Money Laundering, Debit Cards and Credit Cards Law, Banking and Insurance Transactions Tax Law, Income Tax Law, Value Added Tax Law, Stamp Law and International Banking Legislation are some of the other applicable laws and declarations.

2.2  Banks Operating in the TRNC In Northern Cyprus, there are 14 private banks, two public banks, five branches of Turkey’s banks as of 31 December 2020. The number of people working in the sector is 3118 as of the same date. The number of branches of the banks in the sector and the number of bank personnel are shown according to their types in the table below. The decrease in the number of branches of private banks as a consequence of the pandemic, as can be seen in Table 1, especially caused a decrease in the number of personnel in all banks. It was reduced by approximately 2.35%, from 3193 in 2019 to 3118  in 2020. This is a typical way of maintaining the profitability levels of banks. They aim to protect their profits by reducing fixed expenses in times of crisis, such as branch expenses (rent, electricity, etc.) and the number of personnel working, particularly those receiving high salaries (Gündüz et al., 2021).

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2.3  N  orthern Cyprus Banking Sector Consolidated Balance Sheet The consolidated balance sheet of the banking sector of Northern Cyprus from 2019 to December 2020, is presented on a quarterly basis (in million TRY), as shown in Table  2. The changes experienced in the last quarter and year are shown in percentages. According to Central Bank’s quarterly report, the concentration in the banking sector, as of December 2020, the share of the largest five banks in terms of asset size in the sector is representing 54.21% and the top ten banks is at the level of 81.10%. When analysing the concentration in the banking sector, as of December 2020, the share of the largest five banks in terms of asset size in the sector is 54.21% and that of the top ten banks is 81.10%. When it comes to loans, the shares of these banks Table 1  Number of bank branches and personnel Banks Public banks Private banks Branch banks Total

Number of banks 31 Dec 20 2 14 5 21

Number of branches 31 Dec 19 31 Dec 20 33 33 160 150 39 39 232 222

Number of personnel 31 Dec 19 31 Dec 20 522 511 2.122 2.071 549 536 3.193 3.118

Source: Central Bank of TRNC Table 2  Northern Cyprus banking sector consolidated balance sheet (million TRY) 2019

Balance sheet (million TRY) Cash and cash equivalents Securities portfolio Total gross loans Other assets Total assets-liabilities Deposits Debt to banks Other liabilities Shareholders’ equity

2020

December March 13.277,2 12.455,7

June 11.800,6

% change Mar 2020 to Dec September December 2020 13.556,6 14.761,7 8.89

2.124,5

2.503,2

3.078,5

3.498,0

3.546,4

1.38

66.93

22.573,4

23.475,6

24.487,2

27.305,2

28.758,3

5.32

27.49

2.829,8 40.804,9

3.566,1 3.724,5 4.196,9 3.534,9 42.001,20 43.090,80 48.556,70 50.601,3

-15.77 4.21

26.75 24.18

32.837,3 2.436,6 1.684,4 3.846,6

34.244,8 2.218,7 1.589,8 3.947,9

4.16 0.54 7.73 5.44

27.26 3.87 8.43 17.55

Source: Central Bank of TRNC

35.247,1 2.209,9 1.615,4 4.018,4

40.117,0 2.517,5 1.685,1 4.237,1

41.787,6 2.531,0 1.815,3 4.467,4

Dec 2019 to Dec 2020 11.18

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are 56.62% for the first five banks and 82.94% for the first ten banks. In terms of deposits, a structure parallel to loans is observed; the share of the first five banks is 55.84% and 82.59% for the first ten banks. The share of the first five banks’ equity is 54.15% in total, while the share of the first ten banks is 82.45%. In terms of debts to banks, the first five banks were recorded with a 69.49% share and the first ten banks with 94.95%.

2.4  Assets and Liabilities and Equity Structure At the end of 2019, gross loans represent the largest share in the total assets of the sector with 55.32%. As of December 2020, this rate has increased to 56.83%. Analysing the percentage distribution of the banking sector’s asset/liability structure for the period December 2019 to December 2020, it is observed that there is a decrease in cash and cash equivalents and an increase in total gross loans compared to 2019. We can interpret this change in 2020 as a consequence of the pandemic and a decrease in the cash cycle in the market. When it comes to liabilities, the percentage of total deposits increased in the same period. The development of total assets based on bank groups is summarised in Table 3 in million TRY.

2.5  Measures for Northern Cyprus Banks Twenty-one banks are operating under the Northern Cyprus Banks Association (KKBB) in order to reduce the effects of financial negativities on their business and individual customers, to eliminate the negativities that may arise from cash flows due to this unexpected situation, to use their existing financial resources for their more urgent needs and to recover again. It has been decided to offer the following additional opportunities to closed businesses and their employees upon request: • Postponement of interests accrued on overdraft accounts at the end of March, April and May until 30 June 2020 • Postponement of debt interests that accrued on 31 March 2020 on loans operating as a debtor current account until 30 June 2020 • Postponement of the payment of loan instalments for a period of 3 months Table 3  Development of total assets based on bank groups (million TRY) Banks Public banks Private banks Branch banks Total

December 19 9.478,7 18.965,2 12.304,6 40.748,5

Source: Central Bank of TRNC

March 20 9.737,6 19.229,7 13.033,9 42.001,2

June 20 10.135,5 19.456,7 13.498,6 43.090,8

September 20 11.064,2 22.369,8 15.122,7 48.556,7

December 20 11.386,1 23.208,8 16.006,4 50.601,3

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In addition, to encourage the use of digital channels, no fee or expense will be charged under any name whatsoever on domestic transfers made via internet banking and payment transactions made through the TRNC Central Bank Electronic Payment System (EOS). As of July 2020 (KKMB, 2020), more measures were made, including the following: • If a borrower finds more suitable conditions in another bank, he will be able to move his debt to that bank. • The borrower can request the restructuring for his loan that he can pay his loan by taking another loan from other bank and the cost will be lower. • The requests of individual loan borrowers who apply through banks via written and/or durable media will be answered within 10  days at the latest, and the requests of commercial loan borrowers will be answered within 30  days at the latest. • The information and documents requested by a borrower who wishes to transfer his loan to another bank via written and/or durable media will be given within two working days at the latest. • If the bank agrees to restructure the borrower’s credit, there will be no charges on the new credit. • In case the loan is requested by the borrower to be transferred to another bank and provided it is included in the contract, the early closing compensation shall not exceed 5 per thousand. • As long as the original amount of the loan is not exceeded, no stamp tax will be collected from the contracts to be issued due to the restructuring. • After the restructuring facilities for credits, the loans will continue to be monitored by the banks in their current loan category. • Necessary arrangements signed by the Central Bank to eliminate the restrictions that may arise regarding the legal limits determined while improving the credit conditions. • Considering that there may be problems with credit card limits, credit card debts have been transformed into instalment loans. • Credit borrowers who want to restructure their loan debt within the scope of the “Covid  – 19 Approach” must apply to their banks by 30 September 2020 at the latest.

3  Southern Banking Sector There are 29 authorised credit institutions in South Cyprus, which include local banks and the branches of international banks. The banking products are deposit, lending activities and international banking products and services. The sector comprises the following: –– Seven authorised local credit institutions

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Three subsidiaries of foreign banks from the EU Member States One subsidiary of a foreign bank from a non-EU country Five branches from the EU Member States Thirteen branches of banks from non-EU Member States and one representative office

Consolidations in the banking industry have risen in the past 3 years (2018–2020) as a result of acquisitions of institutions and a decrease in the number of branches. Cyprus has 326 bank branches as of the end of 2019 (compared to 458 in 2016), with a total of 8548 bank staff working at that time. After being an EU member since November 2014, the Southern Cyprus banking sector started to be under the control of the European Banking Union. According to the provisions of the Single Supervisory Mechanism (SSM) Regulation, three European credit institutions—the Bank of Cyprus, the Hellenic Bank and the RCB Bank—have come under the direct supervision of the European Central Bank. Meanwhile, the subsidiaries of Greek banks in Southern Cyprus are supervised under the SSM because their parent banks are considered systemic in Greece, their home country, based on SSM Regulation provisions. Almost all banks are linked to the SEPA direct debit scheme, which is administered by JCC Payment Systems Limited, a national card aggregator. And in April 2018, a law was enacted on the transposition of the revised Payment Services Directive (PSD2). The banking sector, through the Cyprus Banks Association (ACB), is preparing to address the payment innovations that open banking will bring, providing the necessary increased payment security as well as instant payments. The banking sector in Southern Cyprus consists of local banks, branches of international banks and their subsidiaries, and credit cooperatives. In addition to traditional banking transactions, such as deposit collection and lending, financial and investment consultancy, wealth management, private banking, factoring and insurance services are also offered. Southern Cyprus was admitted to the European Union in 2004, then it switched its currency from the Cyprus lira, which it used until January 2008, to the euro. Throughout 2019, the total bank deposits remained stable at €48.7 billion as confidence in banks was gradually restored. While banks’ leverage continues to decline, the total outstanding loans decreased by €5.6 billion throughout 2019 (down from the 14.3% of 2018) as banks continued their efforts to reduce their NPLs. However, a total of €3.2 billion in new loans were made available to companies and households during the year. The banking sector is making progress in recovering from high NPL levels. In 2019, the total NPL amount decreased by 12.5%, and the NPL ratio at the end of 2019 was recorded at 27.9% (2018: 30.3%).

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3.1  Measures for Southern Cyprus The economy of Southern Cyprus started to deteriorate in 2013. When the first cases of the pandemic were identified in March 2020, it led to the closing of its economic activities until May 2020. Since then, the economy has gradually started to be restored, with most sectors restarting satisfactorily, with the main exception of tourism, due to the ban on flights from key markets such as Russia, the United Kingdom and Israel. However, the second wave of the pandemic and the consequent bans pushed economic activity into a second fall (Alexia et al., 2021). Fiscal assistance has been offered by distributing funds to enterprises, self-­ employed people, and employees. Financial assistance is offered directly to people who match the eligibility conditions and are impacted by the crisis, including the establishment of programmes like the following (Georgiou, 2021): (i) Aid to businesses suffering partial or total cessation of activities (ii) Assistance to self-employed workers (iii) Offering special sick leave and providing an allowance to workers who are unable to attend work due to illness or self-isolation and (iv) Giving special parental leave and providing parents with a stipend to care for their children following the suspension of operations of schools and kindergartens

3.2  Credit Institutions’ Measures The first decree, dated 30 March 2020, suspends the requirement to pay any loan instalments, including interest, on credit facilities given, obtained, or administered by credit institutions (Georgiou, 2021). After the suspension, the following rules are put in place: • The entire suspended interest will be applied to the total loan amount. • Unless otherwise negotiated between the credit institution and the beneficiaries, the suspended loan instalments (capital and interest) will not be due immediately. • The loan payback term will be automatically extended until the whole loan amount is paid (capital and interest). The measures were valid from the 30th of March 2020 until the 31st of December 2020.

3.3  Credit Measures The following are the credit measures (Georgiou, 2021):

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1. The government would offer banks up to €2 billion in guarantee to cover (i) loans extended to enterprises and self-employed workers worth €1,750,000,000 and (ii) a portion of the interest rate worth €250,000,000 owed by natural persons, firms, and self-employed workers. 2. The above-mentioned funds will not be utilised to repay current loans, but they may be used to cover the interest on existing loans that are covered by the government’s programme. 3. Regardless of whether the loan is secured or not, the government guarantees will cover 70% of the potential damages incurred from the said loans, while the banks will cover the remaining 30%. 4. The loan terms will range from 3 to 6 months, with the exception of current accounts, which will be for 1 year. 5. Businesses and self-employed customers will be eligible for loans if they have no non-performing loans as of December 31, 2019. 6. The guarantee is to cover loans that have been or will be given between 2 April and 31 December 2020. 7. Liquidity will be used up to meet the continuous demands of the self-employed and companies, including debt repayment and salary payments. 8. There will be limitations on the maximum amount that can be loaned to any individual or firm, as well as on the purpose of the loan. 9. The programme will only be available to self-employed people and firms if not one of their employees has been fired due to redundancy between the date of the decree’s issuing and 31st of December 2020. 10. Credit interests – examples of interest rates to be applied are as follows: (a) For small and medium-sized businesses (SMEs) (i) For loans of up to 1 year, the rate is 0.75% if with collateral and 1.25% without collateral. (ii) For loans with a term of up to 3 years, the interest rate is 1% with collateral and 1.5% without. (iii) For loans with up to 6 years of maturity, it is 1.5% with collateral and 2% without collateral. (b) For large firms (i) For loans up to 1 year, the rate is 1% if with collateral and 1.5% without collateral. (ii) For loans with a term of up to 3 years, the interest rate is 1.5% with collateral and 2% without. (iii) For loans with a term of up to 6 years, it is 2.5% with collateral and 3% without collateral.

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Table 4  Selected items of the balance sheet of the Northern Cyprus banking sector (in million TRY) Date 31 December 2016 31 March 2017 30 June 2017 30 September 2017 31 December 2017 31 March 2018 30 June 2018 30 September 2018 31 December 2018 31 March 2019 30 June 2019 30 September 2019 31 December 2019 31 March 2020 30 June 2020 30 September 2020 31 December 2020

Total assets 21.134,20 22.105,00 23.232,10 24.602,70 26.115,30 27.698,60 30.051,90 34.956,50 32.980,20 35.452,80 36.894,50 37.855,40 40.748,50 42.001,20 43.090,80 48.556,70 50.601,30

Equity 2.066,20 2.159,20 2.225,80 2.334,30 2.376,80 2.515,70 2.687,20 3.008,10 3.095,10 3.303,70 3.494,10 3.718,50 3.800,40 3.808,50 3.825,20 4.202,60 4.346,30

Deposit 16.635,20 17.652,40 18.494,10 19.672,90 21.098,10 22.352,70 24.131,20 28.163,10 26.441,80 28.449,20 29.572,10 30.224,80 32.837,30 34.244,80 35.247,10 40.117,00 41.787,60

Total loans 12.763,10 13.231,50 13.666,30 14.028,20 15.306,20 16.195,60 17.330,40 19.752,40 18.906,10 19.823,90 20.347,30 20.200,30 22.557,90 23.475,60 24.487,20 27.305,20 28.758,30

NPLs 842,50 820,70 830,30 850,20 874,90 926,00 946,50 982,60 1.044,60 1.085,30 1.102,70 1.140,00 1.266,10 1.491,10 1.480,80 1.495,90 1.555,90

Source: Central Bank of TRNC

4  C  omparison of Northern and Southern Cyprus Banking Sectors 4.1  Financial Data Financial data are presented in the tables below on a quarterly basis, starting from the end of 2016 until the end of 2020. For the items in the balance sheet, the total assets, equity, deposit, gross loans, and non-performing loans in the case of Northern Cyprus are shown in million TRY, while the data for Southern Cyprus are shown in thousand euros (Table 4). The restructuring of loans during the pandemic period, included deferring the loan instalment payments for 3 months within the framework of the measures taken in March 2020, deferring the interest accrued on the overdraft accounts to 30 June with the support of the loan guarantee fund. In addition, all banks have chosen to stand by their customers and facilitate loan payments, especially the unemployed and those SMEs that have difficulty in making loan repayments or that have closed. In 2020, there was no excessive increase in the number of NPLs. In the same period in Southern Cyprus, it was emphasised that the banking sector should support the solution by providing liquidity during the crisis, and the banking industry was seen as the sector that will contribute to the improvement of the economy. The delays in loans and the policy of applying low interest rates caused the recording of a loss in 2020 due to the fact that banks bear some of the risks and it is a non-profit liquidity mechanism (see Tables 5 and 7).

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Table 5  Selected items of the balance sheet of the Southern Cyprus banking sector (in 000 €) Date 31 December 2016 31 March 2017 30 June 2017 30 September 2017 31 December 2017 31 March 2018 30 June 2018 30 September 2018 31 December 2018 31 March 2019 30 June 2019 30 September 2019 31 December 2019 31 March 2020 30 June 2020 30 September 2020 31 December 2020

Total assets 67.337.744 68.148.189 66.852.048 67.199.045 67.646.201 65.418.462 65.567.258 59.412.196 59.620.711 58.385.900 58.995.731 58.715.239 57.954.950 56.880.465 58.301.377 58.818.560 58.753.034

Equity 6.321.379 6.296.550 5.702.486 5.744.898 5.576.458 5.178.355 5.102.349 4.134.341 4.219.245 4.512.863 4.581.841 4.649.412 4.516.491 4.403.120 4.343.205 4.397.330 4.323.233

Deposit 59.697.260 60.341.431 59.541.059 59.822.900 60.280.300 58.411.256 58.784.675 53.870.562 53.951.353 52.400.172 52.937.394 52.514.692 51.966.186 51.027.067 52.411.652 52.803.691 52.803.727

Total loans 47.078.208 47.701.532 46.042.940 44.544.293 43.759.401 42.594.445 40.860.843 37.207.285 37.778.168 37.599.914 37.708.267 38.254.018 36.954.826 37.795.392 38.504.804 38.254.099 37.401.849

NPLs 24.310.929 23.635.668 22.814.545 21.846.774 20.908.450 20.265.549 16.876.663 11.182.212 10.386.265 10.276.201 9.830.233 9.624.240 9.056.135 9.003.058 6.731.403 6.349.030 5.136.170

Source: Central Bank of Cyprus

In Table 6, TRNC data are presented as interest and non-interest income, interest and non-interest expenses and net profit in million TRY, and in Table 7, Southern Cyprus data are presented as interest income and expenses and net profit or loss in thousand euros.

5  Ratio Analysis Both banking sector data have been analysed with the help of the following formulas using data prepared from various reports published on the TRNC Central Bank and Cyprus Central Bank web pages.

5.1  Capital Adequacy Ratio The capital adequacy ratio has been calculated by the Central Bank of TRNC and taken as data. Also the ratio was announced by the Central Bank of Cyprus (CBC) with the financial data (Gündüz, 2020): Capital adequacy ratio = tier 1 (core capital) + tier 2 (contribution capital) / risk-­ weighted assets (credit risk + market risk + operational risk)

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Table 6  Selected income statement items of the Northern Cyprus banking sector (in million TRY) Date 31 December 2016 31 March 2017 30 June 2017 30 September 2017 31 December 2017 31 March 2018 30 June 2018 30 September 2018 31 December 2018 31 March 2019 30 June 2019 30 September 2019 31 December 2019 31 March 2020 30 June 2020 30 September 2020 31 December 2020

Interest revenues 1.588,70

Interest expenditures 465,10

Non-interest revenues 334,80

Non-interest expenditures 519,30

Net profit/ loss 306,70

433,50

156,30

92,10

141,50

104,70

885,70 1.383,60

308,40 802,70

195,50 298,90

295,10 456,90

185,40 297,40

1.891,80

1.101,70

396,90

630,90

407,50

562,50

329,50

108,40

179,90

123,10

1.191,70 2.048,50

689,80 1.165,00

226,40 353,30

373,10 580,30

316,10 596,20

2.999,40

1.762,50

500,30

823,50

689,80

982,50

611,80

161,00

230,40

235,90

2.016,40 3.009,60

1.256,00 1.893,70

320,20 481,10

480,40 734,80

518,10 749,20

3.862,60

2.437,90

674,30

1.057,00

808,90

800,20

449,80

153,90

278,20

183,40

1.534,20 2.364,00

822,10 1227,20

280,10 445,70

557,70 826,30

329,20 513,00

3.303,40

1.715,80

636,80

1.154,50

671,20

Source: Central Bank of Cyprus Table 7  Selected income statement items of the Southern Cyprus banking sector (in 000 €) Date 31 December 2016 31 March 2017 30 June 2017 30 September 2017 31 December 2017 31 March 2018 30 June 2018 30 September 2018 31 December 2018

Interest income 2.556.289 612.236 1.230.112 1.818.513 2.353.978 526.838 1.069.459 1.231.976 1.648.421

Interest expense (889.004) (238.704) (461.761) (693.211) (903.162) (199.776) (399.713) (486.813) (646.031)

Net profit/loss (213.765) (20.825) (635.222) (631.976) (721.396) 153.518 107.475 178.487 143.677 (Continued)

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Table 7 (Continued) Date 31 March 2019 30 June 2019 30 September 2019 31 December 2019 31 March 2020 30 June 2020 30 September 2020 31 December 2020

Interest income 399.527 785.382 1.159.241 1.506.274 334.792 649.198 951.614 1.260.422

Interest expense (136.134) (257.152) (373.844) (477.599) (84.042) (150.533) (209.017) (264.389)

Net profit/loss 107.295 227.154 276.178 171.626 (24.332) (103.040) (32.881) (112.480)

Source: Central Bank of Cyprus Table 8  Capital adequacy rates of the Northern and Southern Cyprus banking sectors Date 31 December 2016 31 March 2017 30 June 2017 30.09.2017 31 December 2017 31 March 2018 30 June 2018 30 September2018 31 December 2018 31 March 2019 30 June 2019 30 September 2019 31 December 2019 31 March 2020 30 June 2020 30 September 2020 31 December 2020

Capital adequacy rate Northern Cyprus 18,24 18,26 18,04 18,27 17,29 17,40 17,57 18,19 17,86 17,79 18,29 19,02 17,05 16,90 16,92 15,69 15,91

Southern Cyprus 16,79 17,30 16,35 16,35 16,29 15,40 15,60 16,51 17,47 18,06 18,77 19,03 19,95 19,34 19,78 20,06 20,29

In Article 46 of the TRNC Banking Law, it is emphasised that the capital adequacy ratio should legally be at least 10%. In addition, the “declaration on the Principles and Procedures Regarding the Measurement, Evaluation and Other Obligations of Banks’ Capital Adequacy Standard Ratio and Leverage Standard Ratio” imposes the obligation to maintain sufficient equity against credit, market and operational risks, specified in the above formula, and to comply with the rate determined by the Central Bank. In Southern Cyprus, credit institutions carry out their activities in accordance with EU Regulation No. 575/2013 and Commission Implementation Regulation No. 680/2014 and related amendments. The regulations regarding capital adequacy

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Northern Cyprus

Dec-20

Sep-20

Jun-20

Mar-20

Dec-19

Sep-19

Jun-19

Mar-19

Dec-18

Sep-18

Jun-18

Mar-18

Dec-17

Sep-17

Jun-17

Mar-17

16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00

Dec-16

Financial Leverage

Southern Cyprus

Fig. 1  Financial leverages of the Northern and Southern Cyprus banking sectors

ratio were last revised on 28 December 2020. The accepted ratio in the EU according to Basel III standards is 8% (Table 8). Examining The capital adequacy ratio of banks, especially the said ratio was almost the same in September 2019. It decreased to 16% in Northern Cyprus at the beginning of 2020 and 20% in the South with an increase by 1%. The ratio of both sectors has a structure above the legal limits. Capital adequacy can also be analysed using financial leverage. The banks can convert their capital to cash whenever they need liquid funds during a financial crisis. In Fig. 1, financial leverage is calculated by using the formula total assets divided by total equity, and it shows the ability of the bank’s near-term financial wealth. Financial leverage is higher in Southern banks than in Northern banks, but it can be easily seen that the two sectors are parallel to each other. According to Basel III, financial leverage must be at least at 3% globally. Throughout the years, it was more than 10% in both sectors.

5.2  Asset Quality Asset quality indicates how well the bank can protect its assets in the face of future aspects. The value of assets can decrease depending on their riskiness. To measure the performance of loans, the following equations were used: • • • •

Total loans/total assets NPA ratio = NPL/total loans Loan loss coverage ratio = NPL provision expense/loans Loan loss provision = NPL provision expense/total NPL

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Asset Quality

Southern NPL Pro Exp/L Northern NPL Pro Exp/L Southern NPL/TL

Sep-20

Dec-20

Jun-20

Dec-19

Mar-20

Jun-19

Sep-19

Mar-19

Sep-18

Dec-18

Jun-18

Dec-17

Mar-18

Jun-17

Sep-17

Mar-17

Northern NPL/TL Dec-16

90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00

Fig. 2  Comparison of the asset quality indicators of the Northern and Southern Cyprus banking sectors

In Fig. 2, it can be seen that during the last years in Southern Cyprus, the banking sector tried to recover and reduce its high NPL levels (Table 9). The analysis of the non-performing loans (NPL) data was collected using the quarterly reports of the NPL. CBC, on the other hand, provides data that it calls nonperforming loans (NPL) and even explains the 90-day delay in loan collection. The loan figures used for NPL calculation differ from those in Table 5. The ratio used for this analysis is as follows: NPL conversion rate = NPL/gross loans The conversion rate is in a very good position in Northern Cyprus compared to Southern Cyprus. However, this rate increased slightly during the pandemic period, particularly in March and June of 2020, when loan delays occurred as a result of government measures. With the effects of the global crisis in Southern Cyprus, collection problems of up to 50% have been experienced in the previous years. But in the last 3 years, this rate has decreased considerably, bringing it to 17.7% as of December 2020.

5.3  Management Efficiency Management efficiency covers the ability of the management to ensure the safe operation of the institution as it complies with the necessary and applicable internal and external regulations. In some studies, the non-performing asset (NPA) ratio is also used to calculate management efficiency as follows: Interest income/interest expense Based on Fig. 3, the Southern banks’ management is more efficient compared to that of Northern banks.

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Table 9  Asset quality ratios of the Northern and Southern Cyprus banking sectors

Date 31 December 2016 31 March 2017 30 June 2017 30 September 2017 31 December 2017 31 March 2018 30 June 2018 30 September 2018 31 December 2018 31 March 2019 30 June 2019 30 September 2019 31 December 2019 31 March 2020 30 June 2020 30 September 2020 31 December 2020

% Total loans/total assets Northern Southern Cyprus Cyprus 60,39 69,91

NPA ratio Loans/deposit Northern Southern Northern Southern Cyprus Cyprus Cyprus Cyprus 6,60 46,37 76,72 78,86

NPL provision/ NPLs Northern Southern Cyprus Cyprus 61,91 42,08

59,86

70,00

6,20

45,02

74,96

79,05

60,97

43,29

58,83

68,87

6,08

44,05

73,90

77,33

60,51

47,13

57,02

66,29

6,06

43,19

71,31

74,46

59,53

47,23

58,61

64,69

5,72

42,48

72,55

72,59

60,12

47,23

58,47

65,11

5,72

41,69

72,45

72,92

56,79

49,17

57,67

62,32

5,46

38,89

71,82

69,51

56,96

48,58

56,51

62,63

4,97

32,03

70,14

69,07

60,58

52,27

57,33

63,36

5,53

30,47

71,50

70,02

68,30

51,55

55,92

64,40

5,47

30,86

69,68

71,76

67,36

52,64

55,15

63,92

5,42

29,99

68,81

71,23

66,47

53,17

53,36

65,15

5,64

28,99

66,83

72,84

64,91

54,27

55,36

63,76

5,61

28,03

68,70

71,11

63,11

55,21

55,89

66,45

6,35

27,80

68,55

74,07

55,63

55,63

56,83

66,04

6,05

22,26

69,47

73,47

55,48

55,33

56,23

65,04

5,48

21,07

68,06

72,45

55,85

55,90

56,83

63,66

5,41

17,70

68,82

70,83

56,49

49,68

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Interest Income/Interest Expense 6.00 4.00

Sep-20

Dec-20 Dec-20

Jun-20

Mar-20

Dec-19

Sep-19

Sep-20

Northern Cyprus

Jun-19

Mar-19

Dec-18

Sep-18

Jun-18

Mar-18

Dec-17

Sep-17

Jun-17

Mar-17

0.00

Dec-16

2.00

Southern Cyprus

Fig. 3  Management quality of the Northern and Southern Cyprus banking sectors

ROE 30.00 20.00

Northern Cyprus

Jun-20

Mar-20

Dec-19

Sep-19

Jun-19

Mar-19

Dec-18

Sep-18

Jun-18

Mar-18

Dec-17

Sep-17

Jun-17

-20.00

Mar-17

0.00 -10.00

Dec-16

10.00

Southern Cyprus

Fig. 4  Return on equity ratios of the Northern and Southern Cyprus banking sectors

5.4  Profit Analysis The rates used for analysing profitability are as follows: Equity profitability ratio (ROE) = net profit/equity Asset profitability ratio (ROA) = net profit/total assets Return on equity and return on assets ratios were calculated negatively in 2020 due to the loss recorded by Southern Cyprus banks. The loss were recorded according to the funding structure of the market and not targeting the profitability. Compared to the previous years, profitability rates in Northern Cyprus banking also decreased due to the pandemic (Figs. 4 and 5 and Table 10).

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ROA 3.00 2.00

Northern Cyprus

Dec-20

Sep-20

Jun-20

Mar-20

Dec-19

Sep-19

Jun-19

Mar-19

Dec-18

Sep-18

Jun-18

Mar-18

Dec-17

Sep-17

-2.00

Jun-17

-1.00

Mar-17

0.00

Dec-16

1.00

Southern Cyprus

Fig. 5  Return on asset ratios of the Northern and Southern Cyprus banking sectors Table 10  Profitability ratios of the Northern and Southern Cyprus banking sectors

Date 31 December 2016 31 March 2017 30 June 2017 30 September 2017 31 December 2017 31 March 2018 30 June 2018 30 September 2018 31 December 2018 31 March 2019 30 June 2019 30 September 2019 31 December 2019 31 March 2020 30 June 2020 30 September 2020 31 December 2020

% Net profit/equity Northern Cyprus 14,84 4,85 8,33 12,74 17,14 4,89 11,76 19,82 22,29 7,14 14,83 20,15 21,28 4,82 8,61 12,21 15,44

Southern Cyprus −3,38 −0,33 −11,14 −11,00 −12,94 2,96 2,11 4,32 3,41 2,38 4,96 5,94 3,80 −0,55 −2,37 −0,75 −2,60

Net profit/total assets Northern Cyprus Southern Cyprus 1,45 −0,32 0,47 −0,03 0,80 −0,95 1,21 −0,94 1,56 −1,07 0,44 0,23 1,05 0,16 1,71 0,30 2,09 0,24 0,67 0,18 1,40 0,39 1,98 0,47 1,99 0,30 0,44 −0,04 0,76 −0,18 1,06 −0,06 1,33 −0,19

5.5  Liquidity Ratios (Fig. 6) The conversion of deposit-to-loan ratio is as follows: Deposit-to-loan ratio = total loans/total deposits

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Northern Cyprus

Sep-20

Dec-20

Jun-20

Dec-19

Mar-20

Jun-19

Sep-19

Dec-18

Mar-19

Jun-18

Sep-18

Mar-18

Dec-17

Jun-17

Sep-17

Southern Cyprus

Dec-16

80.00 78.00 76.00 74.00 72.00 70.00 68.00 66.00 64.00 62.00 60.00

Mar-17

LOANS/DEPOSITS

Liquidity Ratio

Fig. 6  Liquidity ratios of the Northern and Southern Cyprus banking sectors

Especially in 2019 and 2020, the Southern banks’ conversion ratio of deposit to loans increased compared with the Northern banks’. As of December 2020, the loan-to-deposit ratio has been 70.83% in Southern Cyprus and 68.82% in Northern Cyprus.

6  Conclusion The COVID-19 pandemic has caused excessive uncertainty and shock in the global financial system. Of course, after the financial crisis of 2008, the banking sector is in a much better position to deal with this crisis. In particular, the significantly improved capital adequacy of creditor institutions and also the highly satisfactory liquidity they maintain allow the financial system to take in at least a significant degree of macroeconomic turmoil, making credit institutions part of the solution to the problems caused by the pandemic. In this study, capital adequacy was found stronger in Southern banks compared to Northern banks. Meanwhile, the asset quality of Southern banks weakened because of the high level of NPLs recorded after the 2013 crisis. The decisions taken in Cyprus by the central banks and ministries were also immediate and effective. The actions of the authorities aimed to support the economy by ensuring a smooth restoration of economic activities. Furthermore, fiscal measures adopted after the start of the pandemic in March 2020 were in the form of grants and tax breaks and aimed to support the labour market and the disposable income of households and businesses to mitigate the serious economic impact of the pandemic. Comparing the Northern and Southern banking sectors, the management ability of Southern banks is more efficient than that of Northern banks.

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Furthermore, Northern banks generated more profits due to the postponements of credits and increase in interest rates, while Southern banks’ interest income was higher than their interest expenses. Compared to Northern banks, Southern banks are more liquid due to the help of EU funds. The solution to increasing the stability of the banking sector and the efficiency of the central banks is to improve their control monetary policies. The monitoring in the banking sector, especially having close relations with the customers, focusing on their operations are important to have early warning system for the credit risks. With the negative impact of the pandemic in the economy, the customer needs has to be changed during that period and also the usage of FinTech has increased. The banks have to invest more in digitalisation for their products and services, given that mobile or Internet banking usage increased during the pandemic period because of the lockdowns. For the further studies, the financial statements of each bank can be consolidated seperately for the calculation of the other CAMELS ratio analysis. In this context the sensitivity analyse wasn’t included due to the lack of detailed figures.

References Aggregate Cyprus Banking Sector Data, Central Bank of Cyprus, Retrieved on February 19, 2021, from https://www.centralbank.cy/en/licensing-­supervision/banks/ aggregate-­cyprus-­banking-­sector-­data Cyprus’ banking sector: Facts & Figures, Association of Cyprus Banks, Retrieved on February 17, 2021, from https://www.ebf.eu/cyprus/ Gündüz, V., Gülay, G., & Öksüzoğlu, T. Ö. (2021). “Pandemi Döneminde KKTC Bankacılığında Tahsili Gecikmiş Alacaklar Analizi ve Erken Uyarı Sinyalleri” (book chapter). In Finans Üzerine Güncel Yaklaşımlar. Gazi Kitabevi. Gündüz, V. (2020). “Risk Management in Banking Sector” (book chapter). In Management & Strategy. Artikel Akademi. KKMB. (2020). Announcements, Retrieved on April 20, 2021, from http://www.bankalarbirligi. org/SPhERE/cPortal/kkbb/layouts/home.jsp Georgiou, M. (2021). The Cyprus banking sector amid the coronavirus crisis. Retrieved on February 17, 2021, from https://www.karitzis.com/en/news/the-­cyprus-­banking-­sector-­amid-­ the-­coronavirus-­crisis/ppp-­101/100/?utm_source=Mondaq&utm_medium=syndication&utm_ campaign=LinkedIn-­integration Alexia, Y., Pani, K., Artemis, N. (2021). Thematic study measures and decisions taken for addressing its effects COVID-19 pandemic. Retrieved on August 4, 2021, from https://FSD-­Occasional-­ paper-­on-­measures-­re-­COVID-­19.pdf (centralbank.cy)

The Amalgamation of Social Media and Tourism in Ghana Selira Kotoua and Felicity Asiedu-Appiah

1 Introduction Social media platforms have been identified as the most powerful advertising tool, causing changes in the tourism industry (Law et al., 2013; Wöber, 2006). Tourism industries around the world rely on social media platforms to cope with their competition and deal with the high expectations of travellers (Kelly et al., 2016; Sudibya, 2017). It is noted that investment in social media helps promote tourism, so it is important to identify which aspects of social media benefit tourism and improve the industry’s performance (Cohen & Olsen, 2013; Xiang et al., 2015). Existing evidence from relevant tourism literature demonstrates that some specific social media tools may influence tourism destination marketing and promotions (Mai, 2016; Oliveira & Martins, 2011). The creation of websites to sell tourism products is a beneficial tool in the tourism industry. The use of social media has changed the way tourists travel from one destination to another. The Internet has become a major source of information to plan trips (Hoonsawat, 2016; Law et al., 2008; Pesonen & Pasanen, 2017). Many tourists use social media networks to gather information that would impact their intention to visit destination markets (Hoed & Russo, 2017; Lo & Mckercher, 2015). The reputation of a destination is a major priority that influences the decision-­ making process of travellers (Çakar, 2015; Voase, 2012). If a destination market is S. Kotoua (*) Department of Human Resource and Organizational Development, Department of Tourism and Hospitality Management, KNUST Business School, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana F. Asiedu-Appiah Department of Human Resource and Organizational Development, KNUST Business School, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 N. Özataç et al. (eds.), New Dynamics in Banking and Finance, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-93725-6_7

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attractive and beautiful, tourists will choose to visit the location (Dorcic & Komsic, 2017). However, tourist destinations with a bad reputation may lose travellers to other destination options (Kim & Stepchenkova, 2016; Kušen, 2016). Destination markets that heavily depend on tourism for economic growth need a strong and proactive reputation to attract tourism and protect the tourism industry (Marine-Roig & Clavé, 2016). Due to the fact that bad news never stops coming up, it will be wise for a tourism-reliant destination to put a strategy in place to address problems that affect tourists’ decision to visit it. Social media has become a significant tourist service channel that is becoming enormously important among travellers (Narangajavana et al., 2017). Many of them use social media during and after visits to share impressions, read online reviews and view videos and photographs on the Internet. Travellers trust other tourists’ recommendations more than advertisements of any kind. The recent study of Adona and Mafrudoh (2017) reveals that two of the most trusted advertisement resources are personal recommendations and online tourists’ opinions. It further explains that recommendations of reliable sources are more trusted than emails from tourism destination marketers, search engine results and television advertisements (Gossen, 2015; Kušen, 2016). Tourists trust recommendations made by people they know and even advice and suggestions from strangers on the Internet. Adverts and traditional methods of marketing have become strong tools for gaining tourists’ trust in the information searched and deciding whether to visit a destination. In order to make the best use of a destination reputation and social media from tourism organizations more resources should be committed to internet marketing.

1.1 Contribution and Impetus Based on this context, a research model is developed to examine the relationship between tourist expectations, destination reputation, social media and the intention to travel and visit a destination (Harrigan et al., 2017; Jadhav & More, 2010; Kask et  al., 2011). The research model is utilized to specifically investigates traveller satisfaction as a mediator of the effects of tourist expectation, destination reputation and social media on intention to visit (Högberg, 2017; Tseng, 2017). The relationship is tested for a period of one month using data collected from local and international tourists who stayed in Accra, Ghana. This literature aims to contribute to the understanding of the relevant literature on tourism and destination marketing in Ghana. Gaining online recommendations from tourists through social media is a serious challenge for tourism destination marketers (Marchiori et al., 2013). Online destination markets compete to attract local and international tourists. Tourism destinations with a good reputation attract more tourists, thus creating temporal jobs and contributing to the economy (Dowling, 2008). Ghana is a new tourism destination and it intends to create a reputable image among potential tourists throughvarious development (Chen et al., 2017; Mihalache & Mihalache, 2016; Sainaghi et al., 2017).

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The use of the Internet has become common, and the popularity of social media platforms like Facebook, YouTube and Twitter as well as word-of-mouth marketing have been on the rise (Ismagilova et al., 2017; Khan, 2017; Lippka, 2015). Tourism organizations and destination markets across the globe use social media as a tool to communicate with tourists and market tourism products and services (Hoffman & Novak, 2012). The internet as a tool software tool has also encouraged tourists on social media to share information (Kaplan & Haenlein, 2010).

2 Literature Review The use of social media to influence tourist expectation is a common phenomenon among tourism academic scholars (Braunhofer & Ricci, 2017). Social media is part of human intellect and emotions considered as a human digital self that has an impact on tourists’ expectations (Amaro & Duarte, 2017). The theory of information seeking behaviour refers to the way academic scholars search for and use the information on the Internet (Pfaff, 2015; Wilson & Schraefel, 2010). The theory of information seeking was developed by Wilson (1981) and was coined to help academic researchers meet their information searching needs. The tourism destination marketing in Ghana is cluttered, and tourists are exposed to messages of promotions and distinct substitutes of products and services on a daily basis (Egresi, 2015; Diposumarto et al., 2015; Mariani & Amir, 2017). Destination markets are mainly concerned with the selling of tourism products and services, which has currently caught the attention of academic researchers (Jin & Sparks, 2017). The destination brand has become a marketing destination tool because of the advancement in competition among tourism destination marketers (Galí et  al., 2016). The selling of similar products, services and substitute products in tourism destination markets is the basis for the rivalry among destination organizations (Stylos et  al., 2017). Destination markets in Accra promote golden beaches, friendly environments, the local people’s traditional way of life, museums, botanical gardens, nature parks, shopping malls and markets (Kim et al., 2011; Lin & Fu, 2016). Tourist satisfaction has been a central point of tourism destination marketing. The successfulness of a destination market determines visitors’ satisfaction with the intention to visit (Jin & Tu, 2014; Simarmata et al., 2017). The choice of destination market and the consumption of tourism products and services depend on the reputation of the place (Mcaleer, 2017). Various academic scholars compared standards used in the service quality and satisfaction measures of service qualities in different destination markets (Amponsah & Adams, 2017; Kiran, 2017; Kuo-Chien Chang, 2014). In order to understand traveller satisfaction, a basic parameter should be used to examine the performance of products and services at a destination market (Lee, 2016; Veasna et al., 2013). Tourism literature has been used to assess tourist satisfaction, as well as expectations, through various theories (Correia et  al., 2008; Egresi, 2015; Ting Jin et al., 2014). The paradigm for tourism is related to tourists and the behaviour of satisfaction and intention to visit which has been complicated

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to examine; in particular, the reason why tourists feel satisfied with settings where they can enjoy themselves and have fun. The reputation of a destination market is used as a driving force for tourism development. In tourism literature, it is acknowledged that a place’s reputation has an influence on traveller behaviour (Hongmei Zhang et  al., 2016; Kivela, 2006; Tony Tse & Elaine Yulan Zhang, 2013). The reputation of a destination depends on previous visitors’ online recommendations. Tourists believe more in recommendations from previous visitors than in promotions and advertisements from tourism organizations (Gelman & O’Rourke, 2014; Kiráľová & Pavlíčeka, 2015; Shneiderman, 2014). Chen and Tsai (2006) reveal that the ranking of a destination depends on overall traveller satisfaction and the quality of its products and services. A destination’s reputation can further be enhanced by tourists’ future desire to revisit and their readiness to recommend the place to other tourists online. Social media and the Internet have reshaped the manner in which information about tourism destinations is shared, and tourists’ plans to visit (Dewi, 2017). Social media has become popular among destination markets in the tourism business (Davies & Cairncross, 2013). This has rendered other conventional media lacking in both tourism marketing and popularity. A study by Palmer and Lewis (2009) demonstrated that the effectiveness of major stream media channels like television and marketing brochures is now in profound decline, and these media do not provide any profits for destination markets. Social media marketing gains attention through social media websites (Uşaklı et al., 2017). Twitter is a social media platform that helps tourists share messages with others (Dewi, 2017). Facebook, on the other hand, is a full-scale social media network that allows the sharing of information, photos, updates, events and other activities on the Internet (Rahman, 2017). Social media constitute visitors generated websites such as blogs and virtual communities where media files can be shared on Flickr and YouTube (Minazzi, 2014; Tham et al., 2013). These social media websites are an easy way for tourists to review and share travel experiences and comments on the Internet as information for others to benefit from.

2.1 Hypotheses Much study has been conducted in the tourism industry that focused on traveller satisfaction. The expectancy theory proposed by Vroom (1964) relates to tourists’ decisions when selecting a tourism destination to visit. The theory suggests that tourists’ expectations and intending to visit depend on their contentment and how such components can influence visiting intention (Whang et al., 2016; Saleki et al., 2014). Before, tourists used to read about exotic tourism destinations in brochures and magazines, and expectations were based on imagination (Kim & Lee, 2017). The availability of the Internet and social media, however, has made destination expectations easier to know and identify (Jalilvand, 2017). Misleading

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advertisements of destination images can lead to deception and prevent potential tourists from visiting. Based on this theory, the following hypothesis is proposed. H1: Tourists’ expectations have a positive relationship with traveller satisfaction and intention to visit. Traveller satisfaction has always been the central point of tourism destination marketing (Miličević et al., 2016; Zhou, 2014). It is the most important indicator of the performance of various tourism destinations (Prayag, 2009; Tseng, 2017). Traveller satisfaction expressed on social media builds up the reputation of a destination (Sangpikul et al., 2017; Egresi, 2015). Travellers feel a desire to visit a destination because of its reputation formed by previous visitors on social media (Okazaki et al., 2016; Shneiderman, 2014). The use of social media and the availability of the Internet have influenced travellers’ expectations, unlike before (Chiu et al., 2016; Ezeuduji, 2013). The use of search engines and websites for reservations and booking allows travellers easy access to information about various destinations (Filieri et  al., 2017). When travellers are satisfied with a destination’s reputation, they tend to positively communicate their experience to others (Jeuring & Haartsen, 2017; Vega-Vázquez et al., 2016). Based on empirical research by previous academic scholars, the following hypothesis is suggested. H2: Destination reputation has a positive relation to traveller satisfaction and may influence visitor intentions. Social media has a huge influence on traveller satisfaction and desire to visit tourism spots (Tan & Wu, 2016). Tourists go to social media platforms and search engines to help them make informed decisions about visiting a tourism destination (Wong & Yeh, 2009; Singh et  al., 2015). Twitter and WhatsApp messages and YouTube videos are also used to share personal experiences about a tourism destination and provide advice to potential travellers wishing to visit the place (Amaro & Duarte, 2017; Ukpabi & Karjaluoto, 2017). The use of the Internet has changed the way travellers interact with tourism destinations, products and services (Tussyadiah et al., 2017; Lu, 2017). The Internet provides an accurate platform for travellers to directly communicate with tourism service providers in order to gain more information about a destination. H3: Social media has a positive relationship with traveller satisfaction and intention to visit. Other empirical models demonstrate that traveller satisfaction with tourism destination marketing is based on theperception of their psychological mindset (Lai & Hitchcock, 2017; Lin, 2010; Wang et al., 2016). It is further suggested that the purchasing of tourism products depends on fundamental destination marketing concepts and intention to visit (Kwanisai & Vengesayi, 2016; Puh, 2014; Wang et al., 2017). Though replaces despite that research has been done on traveller satisfaction and the utilization of the Internet, this literature is still a gap in the tourism industry in the Sub-Saharan region where Ghana is located. The theory of expectancy/disconfirmation reveals that travellers buy tourism products and services based on their

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Tourist expectation

H: 1 Traveler

Destination reputation

H: 2

H: 4

Intention to visit

satisfaction

H: 3

Social media

Control variables Age Gender Education Job tenure Marital status

Mediation effects H: 1 H: 2 H: 3

Fig. 1  Conceptual research model

expectations of the performance of tourism destinations (Andrades & Dimanche, 2017; Jovicic, 2017; Pasaribu et  al., 2017). The purchasing and consumption of goods and services of tourism destination markets may depend on the experiences and expectations of previous visitors. Negative issues occur when the comparison of expectations fails to measure up. Positive disconfirmation, on the other hand, is observed when a destination market’s performance exceeds tourists’ expectations (Evangelidis & Osselaer, 2017; Ramires et al., 2017). Traveller satisfaction has a positive relationship with destination markets where the relationship is backed by unforgettable experience. For these reasons, the below hypothesis is proposed. H4: Traveler contentment has a useful relation to desire to visit.

2.2 Conceptual Research Model The conceptual research model has several relationships with the variables in Fig. 1. According to the conceptual research model, tourist expectation, destination reputation, traveller satisfaction and social media have a strong impact on traveller satisfaction with a desire of intention to visit tourism destinations.

3 Methodology Data were collected from local and international tourists visiting Accra, Ghana. Eight five-star hotels in Accra were selected for the research, and a convenient sampling technique was utilized. The eight five-star hotels were selected as representatives of the tourism industry in the city and because they have direct contact with tourists. The majority of travellers visiting Accra stay in those hotels, and an email

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was sent to the management of each hotel to ask permission to carry out the study. The permission was granted, and data were obtained from both local and international visitors. The researchers worked with the front desk employees and managers of the hotels for a period of 4 weeks to distribute the research questionnaire directly to the local and international visitors. The respondents were assured of their confidentiality, and the front desk employees in the hotels were friendly and represent the institutions they serve effectively. Warm reception was experienced by tourists, and quality service was offered to enhance the reputation of the hotels to encourage more customers to visit (Okazaki et  al., 2016). Self-administered questionnaires were given to get the opinions of the local and international visitors to the destination. Two hundred fifty-six questionnaires were distributed to the respondents. By the end of the period for obtaining data, 240 questionnaires were collected, representing 0.94% of the response rate.

3.1 Measurement Various measurements collected from relevant literature in tourism and hospitality destination marketing were utilized to define the study constructs. Tourist expectation was measured using five items from the study of Jadhav and More (2010) and Ezeuduji (2013). Five items from the research of Voase (2012) were used to measure destination reputation. Five items each were utilized from Köhler and Gründer (2017) and Li et al. (2017) to measure social media and destination marketing. Five items were employed based on the theory of Truong et al. (2017) and Chen et al. (2016) to measure traveller satisfaction. Five items were applied from the study of Xu et al. (2017) to measure intention to visit. The respondents’ ages and educational levels were measured by using a 5-point scale. Job tenure was similarly measured through the use of a 5-point scale. Gender was coded using two variable components (male = 1 and female = 2). Marital status was coded with the use of two variables as well (married = 0 and single or non-married = 1).

3.2 Data Analysis The measures were placed in an exploratory and confirmatory factor analysis to explore the problems of convergent and discriminant validity (Raykov, 2011). Internal consistency reliability was estimated by applying the commonly accepted cutoff level based on the research of Davenport et  al. (2015), which is 0.70. Hierarchical regression and multiple analysis were utilized to test the relations that exist among the variables. The mediation hypotheses were examined by using the theory of Baron and Kenny (1986). The testing of the mediation through hierarchical multiple regression analysis is common in the tourism and destination marketing literature (Karatepe, 2007–2009; Li Cheng-feng et al., 2013; Santos & Vieira, 2012;

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Tasci, 2007). In order to predict traveller satisfaction, control variables were put into the SPSS software as a first step, then tourists’ expectations and, finally, traveller satisfaction to prognosticate intention to visit. The same procedure was utilized to predict destination reputation and social media. A Sobel test was conducted to examine the importance of the mediating effects. In the analysis to predict traveller satisfaction, the independent variable (tourist expectation) was entered as a first step, including the control variables, and travel satisfaction was entered as a second step. In order to forecast the intention to visit, the independent variable (destination reputation) was entered in the first step, including the control variables; traveller satisfaction in the second step; and intention to visit in the third step. This process was also used to predict social media and the mediation effects. 3.2.1 Main Results The variable measures were first placed into the sequence of exploratory factor analyses by using the principle of components and varimax rotation. It was noted that the items had no loading problems less than the accepted value of 0.50 and were understandably identified with the real factors. It was therefore not necessary to drop items from the various variables to demonstrate the five-factor solution with the eigenvalues that were greater than 1.00 and satisfactorily recorded at 68.2%. The factor loadings for tourists’ satisfaction, destination reputation, social media, traveller satisfaction and intention to visit were all significantly factor loaded. For the approximation to be more well-organized and rigorous, AMOS software was utilized. The results in the first table (Table 1) show a good fit of five-factor model data based on the fit statistics. X2 = 362.05, DF = 182, goodness of fit index (GFI) = 0.91, adjusted goodness of fit index (AGFI) = 0.90, normed fit index (NFI) = 0.92, non-­normed fit index (NNFI) = 0.93, comparative fit index (CFI) = 0.94, root mean square error of approximation (RMSEA)  =  0.07 and standardized root mean square residual (SRMR) = 0.08. Adhiatma and Hendrianti (2019) proposed that convergent validity is evident when all variables are loaded significantly, as shown in Table 1 of this study. The loadings ranged from 0.65 to 0.90, and the average loading was above 0.65. All the eigenvalues were greater than 1.00, and the percentage variance ranged from 14.06 to 32.08, while the alpha values were between 0.90 and 0.92. Discriminant validity was examined on the variables of X2 difference testing of each measure of the constructs by using two-dimensional models for each set of constructs that fit. Variable items that represent each set of constructs were also forcefully put into a single factor solution where the X2 (Chi-square) different tests into significant outcomes for each set of the item measures. Forcing a single-factor solution on two pairs of items constitutes different constructs that may cause the model to decline. The composite scores for every construct were created by calculating the average scores of all items comprised of each specific construct. The outcome of the means, standard deviations and correlations were displayed in Table 2. The results indicate that traveller satisfaction is largely based on social media, destination reputation and tourist expectations.

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Table 1  Item scale reliability and confirmatory factor analysis Scale items Tourist expectation Learn about unique colonial history Increase knowledge about other tourism destinations Experience the Ghanaian culture Relax and rest Experience something different Destination reputation Delivers tourism products and services that match the advertisement Presents accurate information on tourism products Offers satisfying and unforgettable tourism experience Shows strong prospects for future potential growth Outperforms other tourism destination competitors Social media Access to user media such as photos and videos Social media used during the holiday planning process Social media influence on destination marketing, such as accommodation selection Tourists trusting messages from social media Use of social media for tourism destination information search Traveler satisfaction Arrays of shows and exhibitions Eye-catching night life Colourful cultural events and festivals Peaceful and restful atmosphere Very friendly local people Intention to visit Making a booking to the destination was not difficult The prices of hotel accommodation were reasonable The prices of food, drinks, souvenir and handicraft were reasonable

Factor loading

Eigen value 1.78

Percentage variance 32.08

Alpha value 0.91

AVE values 0.67

0.71 0.80 0.69 0.77 0.76 1.60

25.05

0.90

0.58

1.88

21.00

0.92

0.66

1.35

18.01

0.90

0.71

2.08

14.06

0.90

0.68

0.88 0.72 0.81 0.90 0.89

0.79 0.84 0.87

0.86 0.82

0.75 0.86 0.87 0.74 0.70 0.65 0.88 0.69 (continued)

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Table 1 (continued) Factor Scale items loading I am happy that I decided to visit the 0.86 destination I will speak highly of the destination to 0.66 other tourists

Eigen value

Percentage variance

Alpha value

AVE values

Note: Each item was measured based on a five-point scale. Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy  =  0.83; Bartlett’s test of sphericity: approximate chi-square  =  362.05, df  =  182. The total variance explained by all factors is 64.8%. The average variance extracted (AVE) was a little above the accepted value of 0.50 Table 2  Mean STD, correlation of the study variables and alpha values Variables 1 Age 2 Gender 3 Education 4 Job tenure 5 Marital status 6 Tourist expectation 7 Destination reputation 8 Social media 9 Traveler satisfaction 10 Intention to visit 11 Mean 12 Standard deviation (STD)

1 100 0.13 0.15 0.12 0.16 0.09 0.16

2

3

4

100 0.17 0.19 0.18 0.10 0.15

100 0.14 0.11 0.20 0.12

100 0.13 100 0.19 0.23* 0.30* 0.26*

0.13 0.16 0.17 0.03 0.14 0.08 0.14 0.15

5

0.25* 0.28*

6

7

8

9

10

100 0.34** 100 0.24* 0.39** 100 0.39** 0.35** 0.34** 100

0.17 0.19 0.21 0.22* 0.31** 0.27* 3.22 3.51 4.64 2.93 3.89 4.09 1.87 1.47 1.82 2.44 2.36 2.24

0.25* 4.16 2.41

0.32** 0.35* 100 3.95 3.84 4.77 1.99 2.67 2.82

Note: The composite scores for every construct were evaluated by finding the average of each item. The score for tourist expectation, destination reputation, social media and desire to visit were aligned from 1 to 5. Only the scores for traveller satisfaction were from 0 to 6. Gender was coded using the variables male = 1 and female = 0. Job tenure was measured with a 6-point scale * Correlation is significant at 0.05 ** Correlation is significant at 0.01

3.2.2 The Hypothesis Test Results Hypothesis 1 predicted that tourist expectation has a positive relationship with traveller satisfaction and intention to visit. The results in Table  3 reveal that tourist expectation has a positive influence on traveller satisfaction (β = 0.54, P =60.45, GDP per capita >=10062.5, credit rating of 2019>=77.5} ="A" {inflation=67.5} ="AM" {credit rating of 2019>=62.5, unemployment rate=6705} ="BBBP" {unemployment rate=57.5, GDP growth>=0.5} OR {urban population>=65.2, inflation=57.5} ="BBB"

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{total infected cases of COVID-19>=1189, credit rating of 2019>=52.5, inflation>=0.35} OR {total deaths of COVID-19>= 17.908, credit rating of 2019>=52.5, inflation>=0.35} ="BBBM" {credit rating of 2019>=47.5, total infected cases of COVID-19>=1189, unemployment rate=47.5, total deaths of COVID-19>=17.9, unemployment rate=42.5, inflation=2.65} OR {GDP per capita >=3544, government debt=47.5} ="BB" {total infected cases of COVID-19>=400, GDP per capita >=1960.5, credit rating of 2019>=37.5} OR{total deaths of COVID-19>=6, GDP per capita >=1960.5, credit rating of 2019>=37.5} ="BBM" {total infected cases of COVID-19>=400, credit rating of 2019>=32.5, gross saving>=8.5} OR {credit rating of 2019>=32.5, inflation=0.15, credit rating of 2019>=32.5} OR {government debt= 32.5} OR {gross saving= 21.85} ="BM" The application of the decision trees is easy. Only the values of the patterns in question should be available. The user only compares the measures with the values of the given cut-points.

3.2  Overview of Variables Based on A and B Classes Out of the economic and social factors, GDP per capita, government debt, GDP growth, inflation, and gross savings have frequently been out in “A” classes from “AAA” to “AM.” Certainly, GDP per capita is shining among them as the most important factor for “A”- related ratings. None of the “A” classes were affected by pandemic factors in currently announced countries’ CRs by Fitch. By contrast, “Pre-CR” was observed in whole ratings from “AAA” to “BM.” The considerable point of this part is about COVID-19 relevant variables that came out in “speculative grades” from “BBBM” to “BP”. The impressive observation is, as much as we go down through ratings, the value of COVID-19 attributes, cut-points, falls. It shows that coronavirus crises threaten pre-low-rated countries to be downgraded more by the Fitch Rating system during the pandemic. This phenomenon was mentioned in Table 1.

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4  Discussion The current research assesses Fitch Rating’s response to COVID-19 based on ten variables. The main contribution of the research is adding coronavirus factors to the study to analyze whether newly announced CRs have been affected by the pandemic. The outcome of LAD in the form of the decision trees describes the whole 16 rating levels completely at 100% matched-result of the training set that was verified nearly 80% of matched out-put in the test set. To compare 20% of misrated countries with our result, there are some countries which are downgraded or upgraded as follows: Upgraded by Fitch: Germany, Kuwait, and Saudi Arabia have been rated “AAA,” “AA,” “A,” in 2020 that in our result, they got rated “AAP”, “AAM”, “AM” in order. Downgraded by Fitch: Fitch Rating reported, the ranking of Bahrain, Cyprus, Portugal and San Marino in B-related classes as “BP,” “BBBM,” “BBB,” and “BBP,” respectively, in 2020 that they all rated as “A” rating in our result. In comparison with our consequence, Fitch Agency downgraded Bolivia, Costa Rica and Ecuador to one lower level of rating from “BP” to “B” and from “B” to “BM”. To conclude, even in the coronavirus crisis, still economic and social variables are well-defined factors to describe the CR system optimally. However, Pre-CR of countries is the main predictor of the rating system, has been observed within all ratings. COVID-19 variables became apparent among the selected patterns of high yields (lower rating levels)

5  Conclusion The estimation of high debt has been pushed up by virus to the world’s nations, is a threat to downgrade CRs. Certainly, the users of CRAs want to keep their money somewhere safe. Safety may be a principle that returns following the worldwide crisis and Covid-19 shut down. The present study tried to analyze the hidden pattern of the Fitch Rating agency during the pandemic. By application of LAD, the explored rating system was summarized in the form of the decision trees. It can even be used to estimate the ratings of otherwise unrated countries in 2020. Out of the three parts of variables, “Pre-CR” may forecast the next ratings effectively; however, in the case of any crisis, such as COVID-19, it cannot be the only reliable factor where all sections of the economy and social life are closed to be crushed by this pandemic. To sum up the contributions of the study, “Pre-CR” variable beside significant economic and social factors will structure well-organized patterns to describe different levels of Ratings optimally even during a crisis. COVID-19 variables were

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out in “B” class ratings, which were already placed in the speculative grades. Conforming to the result of this research, COVID-19 variables had no impressive effect on Fitch credit rating evaluation in July 2020. It may be studied hereafter to analyze its consequences precisely for a whole year. Consequently, Fitch might intend to downgrade the countries which are in “speculative grades” or “high yields” more.

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The Relationship Between Interest Rates and Inflation: Time Series Evidence from Canada Negar Fazlollahi and Saeed Ebrahimijam

1  Introduction Real interest rates have a dominant role in the economy because of their impact on the level of demand for goods and services by altering the cost of borrowing. According to the literature, by increasing the cost of borrowing of banks, the prime rate of lending to the public increases and the cash position of banks is minimized, which lessens the expected inflation in the economy. Inflation is the tendency of the general prices of goods and services to continuously rise (Barro, 1997; Rose, 1988), which is measured by the consumer price index (CPI). Inflation affects the economy in a negative and positive manner. One of the major positive effects of inflation is related to our study, which concerns maintaining a nominal interest rate above 0, enabling central banks to adjust their interest rates for economic stabilization. Thus, it’s worth paying closer attention to the long-run inflation rate and the associated interest rate when implementing monetary policies. In 1930, Fisher demonstrated the relationship between interest rate and inflation for the first time, explaining that the long-run nominal interest rate is equal to the real interest rate, plus the expected inflation rate. He stated that in the case of real interest rates, which are determined by real factors in the economy, there is a relationship between long-­ run nominal interest rate and expected inflation rate. The Fisher equation is commonly using in central banks for policy making as a behavioral equation. Meanwhile, monetary policies have been altered intensely in the 1990s. Central banks convert their concentration on the exchange rate and money that are intermediate items to policies such as; direct inflation targeting. In 1990, in a survey of 77 N. Fazlollahi (*) · S. Ebrahimijam Department of Banking and Finance, Eastern Mediterranean University, Famagusta, North Cyprus via Mersin 10, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 N. Özataç et al. (eds.), New Dynamics in Banking and Finance, Springer Proceedings in Business and Economics, https://doi.org/10.1007/978-3-030-93725-6_11

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countries, only four of them were using inflation targeting; however, in 1993, the number of countries using it increased to 40. By applying the direct inflation-­ targeting policy, the economy’s performance improved. For 5 years, from 1985 to 1990, inflation decreased from 8.65% to 3.53% on average in 23 developing and industrial countries. For five years, from 1985 to 1990, inflation decreased from 8.65% to 3.53% on average, while the real growth in industrial production increases from 3.2% to 4.28% in 23 developing and industrial countries. However, the real growth AQ5 increases from3.2% to 4.28% in industrial production. Particular in nine countries (Canada, Israel, Australia, Sweden, United Kingdom, Chile, Spain, Finland, and Sweden) where targeted inflation explicitly, leading the inflation to decline on average by more than 7%. Nowadays, central banks around the world are focused more on inflation-­ targeting policies in order to control inflation and create price stability to improve the performance of the economy. Among the world’s central banks implementing inflation targeting, the pioneers are the Bank of Canada, the Swedish Riksbank, the Reserve Bank of New Zealand, and the Bank of England. In Canada, the monetary policy of inflation targeting sets their policy of interest rate in a way to retain inflation at 2% (Bank of Canada, 2020). In order to apply the inflation targeting, the Canadian central bank goes through a transmission mechanism focusing on the joint influence of interest rate and the exchange rate on aggregate demand in the economy (Thiessen, 1995). The aim of this policy is to setting inflation low, steady and foreseeable to serve confidence in the purchasing power of the economy. Historically, inflation targeting starts in February 1991 by the agreement of the Bank of Canada and the government, with the goal of decreasing the inflation rate from 5% to 2% during late 1990 until the end of 1995, and then keep declining inflation to the point that price stability reached. In 1998, Canada reached 2% inflation targeting, however, due to good economic performance decides to keep the 2% inflation rate (Bank of Canada, 2020). Thus, by applying monetary policy the Bank of Canada adjusts short term interest rates accordingly to the changes in inflation, money growth and output. Nowadays, the goal of Canadian monetary policy is to set interest rates in order to reach on average a 2% inflation rate, which is low, stable and predictable. Therefore, in this study, we are focusing on the existence of a long-run relationship between interest rates and inflation rates to support the Canadian government’s monetary policy of inflation targeting.

2  Literature Review The discussion about the relationship between interest rate and inflation rate, firstly raised by Fisher in 1930. He suggested that the real interest rate is equal to the nominal interest rate minus the inflationary expectation rate (Fisher, 1930). The impact of interest rates is on both the level of economic and individual activity (investors and households) where any changes in interest rate may lead to change in the

The Relationship Between Interest Rates and Inflation: Time Series Evidence from Canada 193

economic factors and monetary policies. The earlier studies such as Rose (1988) and Granger and Newbold (1974) state that due to the different orders of integration in the model regressing interest rate on inflation may be spurious (Rose, 1988; Granger & Newbold, 1974). Wijesinghe (2002) finds no cointegration relationship between inflation rates and nominal interest rates in their study using Sri Lankan data. However, several recent empirical studies validate the Fisher hypothesis test by considering time series analysis of the data (Mishkin, 1992; Evans & Lewis, 1995). A number of studies have been conducted to find out the relationship and causality between inflation and interest rate. One of the most important test issues is to investigate the validation of Fisher’s impact in the sample data. Kesriyeli (1994) provides evidence of a long-term relationship between inflation and nominal interest rates in the case of the Turkish economy and confirms the Fisher hypothesis by applying Johansen’s cointegration method. Berument and Jelassi (2002) evaluated monthly data of various countries from 1966 to 1998 to discover the relationship between inflation and nominal interest rates and confirm the Fisher effect. Booth and Ciner (2001) examined the existence of the long-run bivariate one-to-­ one relationship, among the short-term Eurocurrency of interest rate and the inflation rate for the US and Europe. The evidence showed that market participants bind the expected inflation rate to the nominal interest rate. Kose et al. (2012) observed the relationship between nominal interest and expected inflation rates in the context of the Turkish economy for the period 2002–2009. Considering the monetary policy of inflation targeting regime. Their findings illustrate the dependency and influence of monetary policy rates on expected inflation and validate the weakness of the Fisher effect. Jaradat and AI-Hhosban (2014) investigated the casual macroeconomic factors that may influence monetary policies. They found positive relationship between Interest rate and Inflation by Johansen cointegration with bidirectional Granger causality in Jordan, from 1990 to 2012. They assessed the multiple regression model with the use of macroeconomic variables such as economic growth, budget deficit, money supply, and inflation. According to existing theories, an increase in economic growth may cause higher demand for loanable funds and a rise in interest rates (Jaradat & AI-Hhosban, 2014). Meanwhile, growth in money supply may cause less demand for loanable funds and decrease interest rates (Monnet & Webe, 2001). Expanding budget deficit significantly increases interest rates (Laubach, 2009). Umoru and Oseme (2013) discovered the significant negative relationship between expected inflation and interest rate by estimating the Generalized Method of Moment (GMM) method for Nigeria. Julitawaty (2015) applied the autoregressive method and obtained a significant impact of interest rate on inflation rate through Engle-Granger two-step cointegration method during 2013–2014 in Indonesia. Julitawaty proposed that as the interest rate increases, the inflation diminishes and vice versa. According to reports, the objective of the Bank of Canada is to maintain confidence in the purchasing power (value) of money by keeping inflation rates low and stable. Their policy is to hold the interest rate in a range in order to target the inflation rate at 2% on average during the medium term. It is assumed that an increase in

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short-term interest rates may reduce inflation and, hence, sufficiently reduce long-­ term interest rates (Melino, 2012). The purpose of this study is to investigate any relationship and causality between the interest rate and inflation rate of Canada, through contributing to the other macroeconomics factors of the Canadian economy such as; economic growth, budget deficit, and money supply. If the Canadian macroeconomic variables are not cointegrated, then the interest rate will not be influenced by government monetary policies, and no inflation targeting (2%) would be possible (Canada, 2012).

3  Model Specification and Data According to the previous researches that discussed in the introduction section, the interest rate is the function of the country’s macroeconomic factors which is contributing to the proposed model. The basic property of the model is considered as a level equation. The restriction of using the logarithmic form for estimations is due to a large number of negative values in the variables.



Interest rates   0  1 inflation rates   2 growth rates   3 budget deficit   4 money supply   t



(1)

The study sample period covers monthly data from January 1998 to October 2015. The sample contains five macroeconomic factors of the Canadian economy that might have an influence on inflation and interest rates according to the existing literature. The data were extracted from Thomson Reuter’s EIKON database. GDP data source belongs to the statistics Canada, in order to calculate the growth. Money Supply data (M2) is provided by Statistics Canada (CANSIM). Federal government budgetary surplus or deficit, presented by the Department of Finance of Canada uses as a proxy for the budget deficit in the model. Interest rates are Canadian three months treasury bills (end month). Inflation rates calculate from CPI (Consumer Price Index) index changing rate. Both the interest rate and inflation rates explore from the Bank of Canada data source. Figure 1 displays a sudden change in the time series level during the study period. The descriptive statistics of the economic variables of the sample data from 1998 to 2015 are presented in Table 1. The sample includes 214 observations. All the time series except inflation rate are not normally distributed according to their Jarque-­ Bera and probability values.

The Relationship Between Interest Rates and Inflation: Time Series Evidence from Canada 195 Interest Rates

Inflation Rates .012

6

.008

5

.004

4

.000

3

-.004

2

-.008

1

-.012

1998

2000

2002

2004

2006

2008

2010

2012

2014

0

1998

2000

2002

2004

2006

2008

2010

2012

2014

2010

2012

2014

Budget Deficit

Growth Rates

8,000

.015 .010

4,000

.005

0

.000

-4,000 -.005

-8,000

-.010 -.015

1998

2000

2002

2004

2006

2008

2010

2012

2014

-12,000

1998

2000

2002

2004

2006

2008

Money Supply 1,400,000

1,200,000

1,000,000

800,000

600,000

400,000

1998

2000

2002

2004

2006

2008

2010

2012

2014

Fig. 1  Canadian economic factor time series from 1998 to 2015

4  Methodology The methodology of the study begins with a stationarity test, followed by cointegration. Subsequently, the error correction model was carried out to build long-run and short-run dynamic models. Finally, the Granger causality test was applied. Concerning the stationarity test we applied the different methods, due to the characteristics of the time series in the regression model. Cointegration and error correction models are explained in the following sections.

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Table 1  Descriptive statistics of sample data during 1998 to 2015 Mean Median Maximum Minimum Std. dev. Skewness Kurtosis Jarque-Bera J-B probability Sum Sum sq. dev. Observations

Interest rate 2.495374 2.455000 5.670000 0.160000 1.662433 0.272507 1.709926 17.48855 0.000159*** 534.0100 588.6643 214

Inflation 0.001604 0.001774 0.011542 −0.010372 0.003688 −0.167896 3.426504 2.627394 0.268824 0.343236 0.002897 214

Growth rate 0.001899 0.002175 0.012186 −0.013775 0.003592 −0.580850 4.962374 46.37074 0.000000*** 0.406369 0.002749 214

Budget deficit −37.86449 189.0000 5770.000 −9919.000 2547.376 −0.856673 4.446597 44.83479 0.000000*** −8103.000 1.38E+09 214

Money supply 805490.7 732921.5 1364111. 439344.0 286852.5 0.403740 1.772642 19.24600 0.000066*** 1.72E+08 1.75E+13 214

Note: Std. dev. – standard deviation; J-B – Jarque-Bera; sq. dev. – square deviation; *** denotes a 1% significance level

5  Zivot and Andrews Unit Root Test The traditional tests for investigating the presence of unit roots in time series data include the augmented Dickey-Fuller and Phillips-Perron tests (Dickey & Fuller, 1979; Phillips & Perron, 1988). However, these tests cannot capture the presence of structural breaks in the data set. Consequently, these traditional tests may not provide genuine results. In this study, because of the existence of structural breaks, we conduct the Zivot and Andrews (ZA) test for examining unit roots in the data set (Zivot & Andrews, 1992). Zivot and Andrews proposed a new method which considers all of the points as a potential for a probable break point in terms of time, therefore runs different regressions for every possible break date sequentially. While capturing the most significant structural break in the series.







 

k

yt  ˆ  ˆ DU t Tˆi  ˆ t  ˆ yt 1  cˆ j yt  j  eˆt j 1

 

k

yt  ˆ  ˆ t  ˆ DTt Tˆi  ˆ yt 1  cˆ j yt  j  eˆt

 

(2)

j 1

 

(3)

k

yt  ˆ  ˆ DU t Tˆi  ˆ t  ˆ DTt Tˆi  ˆ yt 1  cˆ j yt  j  eˆt j 1

(4)

The ZA model examines the possibility of any shift in the intercept by adopting whether a dummy variable DUt(model 2), or a trend DTt (model 3) or both the intercept and trend (model 4) at time t. For empirical estimations, we use all of the three models. In the above equations, DUt is 1 if t  >  TB, and the others are 0. Also DTt is t − TB if t > TB, and it is 0 if t