Multimarket Contact, Competition and Performance: an Application on Turkish Deposit Banks

doi:10.21203/rs.3.rs-4015795/v1

Purpose- The main purpose of this study is to analyze how multi-market contact affects bank risk and performance. Identifying the factors affecting the risk and performance of banks is very important for the country's economy globally, and determining this may be useful for banks and financial regulators in terms of preparation for possible crises.

Design/methodology/approach- Deposit banks that were continuously operating in the Turkish banking system between 2012 and 2021 were included in the research. The banking industry, when considered as firms offering many homogeneous products and services in various geographic markets, provides a very fertile laboratory environment for testing the theory of mutual avoidance.

Findings- The results show that the process experienced in the Turkish banking sector in recent years has increased the competition between banks. In this process, multi-market contact has a positive effect on the asset quality of banks; It has been observed that it has a negative impact on profitability, performance and bankruptcy risk. Although competition is thought to make banks, as in all businesses, more resistant to financial crises and corruption by improving their corporate governance structures and audit structures, this study concluded that competition negatively affects their financial performance.

Originality/value- There are a few studies in the relevant literature that test multi-market contact and investigate how multi-market contact affects profitability and risk. Therefore, a detailed examination of the impact of the banking sector's multi-market contact on performance and risk will make a significant contribution to the international banking literature.

Banking

Multimarket Contacts

Financial Performance

Risk

Multimarket contact is known to be a factor that weakens competition, especially in an industry with repeated interactions between firms. Multimarket contact between firms and the level of intensity of competition are of interest to the linked oligopoly theory or mutual forbearance theory. According to the linked oligopoly theory, multimarket contact can negatively affects the intensity of competition in local markets. In particular, retaliation can be more extensive if the number of markets in which firms engage is large. Hence, firms may incur losses in one or more markets that are too large to be offset by aggressive profit-enhancing behavior. This effect may lead to a reduction in the overall level of competition. This view has been advocated by Edwards (1955), Feinberg (1984), Alexander (1985), and Hughes and Oughton (1993). On the other hand, there are theoretical studies in the literature that argue that multimarket contact does not reduce competition. Solomon (1970) and Mester (1992) are examples of these studies.

One of the most intensely competitive sectors is the banking sector. The banking sector provides a very fruitful laboratory for testing the theory of mutual avoidance (or connected oligopoly), given that firms offer many identical products and services in various geographical markets. The theory of mutual avoidance argues that firms operating in the same geographic market may be less willing to compete fiercely not only in a particular market but also in all other markets they encounter (Edwards, 1955).

Over the last two decades, the banking systems of most countries have undergone significant structural changes due to deregulation (or liberalization) and the diffusion of new banking technologies. The common response of many countries to deregulation has been consolidation, or bank mergers, which has led to a dramatic reduction in the number of banks in their banking systems. The deregulation process removed geographical constraints by enabling banks to establish diverse branch networks within and outside the country, spanning different geographical markets. As a result, the number of large and geographically diversified banks increased and these banks began to compete with each other in multiple geographic markets simultaneously. This can adversely affect banks' performance, profitability, costs, liquidity, risk, and stability, and is critical for the future of these banks. Therefore, the banking sector needs to determine how banks' competition in more than one geographical market affects bank risk and performance in terms of risk management and sustainable profitability.

Testing the multimarket contact for the banking sector makes sense for two reasons. One is that banks can be defined as firms that sell identical products in many geographic or local markets. The other is that structural reforms, consolidation, and technological advances in the banking sector in many countries over the last two decades have led to changes in the competitive conditions and market structure of the sector. In particular, mergers and acquisitions have facilitated banks to have larger branch networks and increased local markets to compete with each other. Therefore, the banking sector creates a suitable environment for testing the multimarket contact due to the above-mentioned characteristics. Investigating this multimarket contact, which may affect the competitive structure of banks, is very important for analyzing the competitive structure of the sector and its impact on performance and risk, as well as for testing the reliability and soundness of the sector.

This study provides evidence on the relationship between multimarket contact and bank risk and performance using data from the Turkish banking sector over the period 2012–2021. Developments in technology and banks' close follow-up of these developments are the motivation for this study. The development of financial technology in banks and innovations in this field have caused banks to make changes in their strategies. Therefore, this situation may have a positive or negative impact on banks' risks and performance. For this reason we investigate whether the impact of multimarket contact on a bank's risk and performance is moderated by the bank's levels of income diversification. The rest of the paper is structured as follows: Section 2 presents the literature review on the study topic, Section 3 describes the data and methodology, and Section 4 presents the results of the study.

The theory of multimarket contact was first proposed by Edwards (1955). Following this foundational work by Edwards, theoretical literature developed rapidly. The multimarket contact has been empirically investigated in the manufacturing sector (Hughes and Oughton, 1993; Li and Greenwood 2004), the airline industry (Gimeno, 2002; Su and Dresner 2021; Yimga 2023; Asahi 2023), the telecommunications sector (Busse, 2000; Waldfogel and Wulf 2006; Giachetti et al. 2022), hotel industry (Batinić 2015; Deng et al. 2023), the fuel sector (Balaguer and Ripollés 2021), the cement sector (Chicu and Ziebarth 2013) and the financial services sector (Haveman and Nonnemaker, 2000; Molnar et al. 2013; Dao et al. 2021; Cruz- García et al 2021; Hoang et al. 2021; Le and Pham 2022; Ljubownikow et al. 2023).

In the literature studies summarized above, the relationship between competition and performance has been tested in most of the studies. There is no consensus on the effects of competition in the literature, and there are studies that conclude that competition has positive or negative effects. During the MMC literature review, it was observed that researchers measured the intensity of competition using different approaches. Some studies have linked the operation of mutual avoidance to higher profits (Feinberg 1984; Scott 1982), higher prices (Evans and Kessides 1994; Parker and Roller 1997), higher yields (Singal 1996; Gimeno and Woo 1996; and higher market share stability (Martinez 1990; Heggestad and Rhoades 1978).

Most of the studies in the literature have tried to analyze multimarket contact in the banking sectors of developed countries (Coccorese and Pellecchia 2009, De Bonis and Ferrando 2000, Coccorese and Pellecchia 2013). The results of these studies have made multimarket contact more attractive. Most of the studies in the literature deal with sectors in developed countries such as Italy, the USA, and Spain. This study, on the other hand, examines deposit banks operating in Turkey, which is one of the developing countries. Therefore, the first contribution of this study to the related literature is to analyze this issue using data from a developing country that has undergone structural reforms in the banking sector in recent years. The financial system of developing countries such as Turkey relies heavily on the banking sector, and the rapid technological advances in the modern world have put the banking sector in a state of constant development as communication networks have made access to markets almost unlimited. Therefore, a detailed examination of the impact of multimarket contact on performance and risk in the Turkish banking sector would be an important contribution to the international banking literature. Moreover, the behavior of bank managers, customers, investors, and politicians in a developing country, as well as their propensity towards technology, may be different from those of developed countries. Therefore, testing multimarket contact in the context of a developing country's banking system will provide valuable insights. The second contribution of this study is that it adds a different dimension to the few studies that examine the impact of multimarket contact on risk and performance for the banking sector. The fact that there is a relationship between, performance, and risk suggests that mutual avoidance behavior can also occur with a small number of exposures. Only three studies in the literature have analyzed the functional form of this relationship for banking markets (Haveman and Nonnemaker 2000; Kasman and Kasman 2016; Fuentelsaz and Gomez 2006). However, there is no study analyzing the impact of multimarket contact on asset quality and bankruptcy risk for the banking sector in the literature. This study is expected to extend the existing literature by providing a comprehensive and simultaneous analysis of the effects of the three pillars of the requirements of multimarket contact on banks' risk and performance.

A bank's attempt to geographically diversify its scope of operations may lead to retaliation against the bank in other markets where competitors compete. In this context, geographic diversification may have an impact on banks' risk and performance. A significant number of studies have examined the impact of geographic diversification on bank performance. However, geographical diversification can be does not take into account how many competitors banks face in a market on average. Studies examining whether the number of contacts banks have in multimarket contact tends to reduce or increase the level of competition are limited. Moreover, while multimarket contact has been empirically investigated in many industries, research in the banking sector has received little attention (Kasman and Kasman 2016; Le, et al 2019; Le 2020; Hoang, et al 2021).

Banks in Turkey are divided into three groups: deposit, participation, development, and investment. Development and investment banks do not accept deposits and do not compete with other banks. Participation banks, on the other hand, operate on a profit and loss basis and operate on a different basis than deposit banks. At the same time, the number of branches of participation and development and investment banks on a provincial basis is only available for the last year (2021). In this context, the study analyzes 17 deposit banks that accept deposits, compete with other banks, operate on the same basis, and whose branch numbers in all provinces are available. In addition, this deposit banks have a significant share of 86% in the Turkish banking sector according to the total assets of the banking sector as of March 2023 and are in an important position in economic and technological terms (BAT, 2023). Investigating the competitive factors affecting the risk and performance of these banks may be useful for banks and financial regulators in terms of preparing for possible crises.

The Turkish banking sector data between 2012 and 2021 used in the study were obtained from the Banks Association of Turkey (BAT) and FinNet database and are assumed to be up-to-date and accurate. Since the data for 2022 had not yet been published at the time of the application part of the study, the year 2022 was not included in the analysis. The data set was created with the data of 35 deposit banks operating in the Turkish banking sector, but the data of some banks were excluded from the data set due to lack of data. After the banks were excluded from the data set, the analysis was carried out with the data of 17 banks in total. The data set of our study, in which we examine the relationship between risk and performance of banks with multimarket contact, consists of annual data obtained from the December issues published by the BAT between 2012 and 2021, and a total of 10 periods of data are analyzed. As can be seen in Figure 2, the multimarket contact indices were not affected by the Covid-19 pandemic in 2020 and 2021. Therefore, the Covid-19 pandemic is not taken into account in the analysis.

Figure 1. and Figure 2. respectively show the development of the number of branches in the Turkish banking system and the average multi-market contact in the Turkish banking system in the 2012-2021 period.

MMC1 refers to the similarity of competing banks, MMC2 refers to the proportion competing in the same market, and MMC3 refers to the size of competitors, and these are indicators of the multimarket contact. For Turkish deposit banks, the evolution of these indicators for 2012-2021 is presented in Figure 2.

Regarding multimarket contact (MMC1 and MMC2), there was an increase during the 2012-2014 period and a downward trend from 2014 onwards. The increase and decrease in multimarket contact is accompanied by changes in bank branches over the same period.

In the models constructed in the study, bank-specific variables were used along with one main explanatory variable (multimarket contact measure). In the selection of variables, micro indicators that are expected to affect banks were utilized. Return on assets, return on equity, return on equity, return on interest, bankruptcy risk, and asset quality are used as dependent variables; MMC1, MMC2, and MMC3 indices are used as independent variables, and non-interest income diversification index, bank size, capital adequacy and number of employees are used as control variables. For the MMC variable used in the analysis, there are three ways of calculating MMC1, which is calculated by dividing the total number of contacts of a bank by the number of banks it encounters in a particular market, MMC2, which takes into account the similarity between two banks in terms of market shares, and MMC3, which takes into account competitors in terms of size. Two additional measures of the multimarket contact, MMC2 and MMC3, are used to test the robustness of the results. An increase in the values of these variables indicates that the banks in question are exposed to more markets. In the study, the multimarket contact calculated by all three methods is used as an independent variable in separate models. Therefore, 15 models were constructed in total. The models were developed within the framework of the scope and purpose of the study, and the impact of multimarket contact on bank risk and performance was tried to be investigated.

In the broader literature, the multimarket contact is expected to negatively affect banks' performance and risk levels (Degl'Innocenti et al. 2014; Kasman and Kasman 2016; Dao et al. 2021). This is because, over the last two decades, the banking systems of most countries have undergone significant structural changes due to deregulation (or liberalization) practices and the diffusion of new banking technologies. Technological developments have been adapted for use by banks and mobile banking has become widely accepted. Thus, the majority of transactions carried out in bank branches have started to be carried out through mobile banking. This has negatively affected the multimarket contact. The diversification index used in the model, HHI, bank size, capital adequacy, and number of employees are expected to positively affect bank performance and risk levels.

We use model (1) to examine the impact of multimarket contact on bank risk and performance as follows:

We use the performance indicator return on assets (ROA) as the dependent variable. We also use two other performance indicators to control for soundness: Net interest margin (NIM) and return on equity (ROE) (Hoang et al. 2021). As risk variables, we use the risk of bankruptcy (Z-Score) and asset quality (NPL) as dependent variables. The dynamic model was generated taking into account the studies of Wintoki, Linck and Netter (2012) and Muchtar et al. (2018).

Herfindahl-Hirschman Index (HHI - Non-Interest Income Diversification), bank size (SIZE), and Capital adequacy (CAR) calculated as the ratio of shareholders' equity to total assets and number of employees (PERS) were used as bank-specific variables. HHI is calculated as follows:

Where NON is the sum of COM, TRD, OTOP. COM is net fee and commission ıncome, TRD is net trade ıncome, OTOP is income from other operations. The independent variables MMC1 is weighted by the similarity of the banks, MMC2 is weighted by the rate of competition in the same market and MMC3 is weighted by the size of the competitors. Its detailed measurement is given in the Appendix. An increase in the values of the MMC1, MMC2, and MMC3 indices, which are indicators of multimarket contact, suggests that these banks are exposed to more markets and engage in more intense competition. Table I presents descriptive statistics for the variables included in the models.

Table I. Descriptive Statistics

Variables	Mean	Median	Standard Deviation	Minimum	Maximum
Return on Assets (ROA)	1.093	1.199	0.735	-2.210	2.603
Return on Equity (ROE)	11.235	12.559	6.768	-31.424	24.760
Return on Interest (NIM)	0.162	0.044	0.508	0.015	2.783
Insolvency Risk (Z-score)	5.516	4.179	5.949	-1.430	64.876
Asset Quality (NPL)	0.043	0.039	0.0218	0.005	0.148
Multimarket Contact (MMC1)	27.530	32.654	11.085	4.731	40.192
Multimarket Contact (MMC2)	25.881	30.682	10.153	4.656	38.277
Multimarket Contact (MMC3)	1.421	0.95	1.081	0.038	2.933
Herfindahl–Hirschman Index (HHI)	0.554	0.538	0.115	0.341	0.933
Bank Size (SIZE)	10.045	10.816	2.292	3.591	12.174
Number of Personnel (PERS)	3.832	4.079	0.488	2.787	4.410
Capital Adequacy (CAR)	0.16	0.152	0.025	0.121	0.256

Within the scope of the study, there are 170 observations for each variable in the annual periods between 2012 and 2021 for deposit banks operating in Turkey. Return on assets (ROA) average is 1.093, return on equity (ROE) average is 11.235, return on interest (NIM) average is 0.162, bankruptcy risk (Z-score) average is 5.516 and asset quality (NPL) average is 0.043. MMC1, MMC2, 25.881 and MMC3, which are the basis of the research, are found to be 27.530, 25.881 and 1.419, respectively. The MMC1 index ranges from one to the total number of local markets. Theoretically, if a bank has a monopoly in the markets in which it operates, the minimum is zero and the maximum is the number of provinces provided that all banks meet in all markets (provinces). As of 2021, there are 81 provinces in Turkey. The MMC2 index is weighted by an index that measures their similarity (in terms of market share) in all provinces where they meet. The second (weighted) measure of the multimarket contact, the MMC3 index, takes into account the size of competitors. In this context, the MMC2 and MMC3 indices act as robustness checks for the MMC1 index and the multimarket contact indicator values differ from each other. HHI, which indicates the diversity of non-interest income, is calculated as 0.554 on average. Bank size (SIZE) is 10.045, logarithm of headcount (PERS) is 3.832 and capital adequacy (CAR) is 0.160. Table II presents the correlation matrix between independent variables.

Table II. Correlation Matrix between Independent Variables

Correlation matrix for the models for MMC1

	MMC1	HHI	SIZE	PERS	CAR
MMC1	1.000
HHI	0.033	1.000
SIZE	0.142	0.094	1.000
PERS	0.470	0.227	0.209	1.000
CAR	-0.080	-0.016	0.128	-0.226	1.000

Correlation matrix for the models for MMC2

	MMC2	HHI	SIZE	PERS	CAR
MMC2	1.000
HHI	0.036	1.000
SIZE	0.134	0.094	1.000
PERS	0.443	0.227	0.209	1.000
CAR	-0.088	-0.016	0.128	-0.226	1.000

Correlation matrix for the models for MMC3

	MMC3	HHI	SIZE	PERS	CAR
MMC3	1.000
HHI	-0.012	1.000
SIZE	0.134	0.094	1.000
PERS	0.535	0.227	0.209	1.000
CAR	-0.118	-0.016	0.128	-0.226	1.000

According to this analysis, no multicollinearity problem was found between the variables. All values in Table II. are below 90%, therefore, it is concluded that there is no multicollinearity problem among the independent variables used in the analysis.

Table 4 and Table 5 present the estimation results for the models. The fact that lagged dependent variables are significant and positively correlated with the related dependent variables indicates that the use of a dynamic model is appropriate in the current study. The p-values of the AR(2) test and the Hansen test are above 10 percent and satisfy the second-order autocorrelation and overidentification conditions. This indicates that the estimates are valid for statistical inference.

Table IV. Regression results with risk variables and multimarket contact.

	Z-Score						NPL
	10		11		12		13		14		15
	1.099***	0,000	0.773***	0,000	1.004***	0,000
							0,323	0,873	0.329***	0,000	0.374***	0,000
MMC1	-0,026	0,762					-0.001***	0,000
MMC2			-0.084**	0,029					-0.001***	0,000
MMC3					-1.209***	0,000					-0.004***	0,001
HHI	-4.256***	0,000	-2,658	0,171	-2.857**	0,025	0,006	0,725	0,006	0,717	-0,003	0,838
SIZE	-1,402	0,524	-0,031	0,829	-0,099	0,336	0,001	0,936	0,001	0,852	0,001	0,579
PERS	-1.449***	0,000	-5.655**	0,048	-11.370***	0,000	0.007**	0,018	0,007	0,249	0,003	0,483
CAR	-4.116***	0,007	15,511	0,255	-14,862	0,126	0.297***	0,000	0.294***	0,000	0.310***	0,000
AR(1)	-1,666	0,096	-1,766	0,077	-1,499	0,047	-1,69	0,091	-1,691	0,091	-2,085	0,037
AR(2)	0,831	0,406	0,874	0,382	1,356	0,175	-1,204	0,229	-1,199	0,231	-1,239	0,215
Hansen	12,287	0,873	10,857	0,623	12,168	0,351	13,318	0,578	13,325	0,577	14,989	0,452

Note: ***, **, * indicate significance at the 1 percent, 5 percent and 10 percent levels, respectively.

First of all, all three variables representing multimarket contact (MMC1-MMC2-MMC3) are negatively and significantly related to the performance variables ROA, ROE, and NIM, and negatively and significantly related to the risk variables Z-Score and NPL. In other words, according to the results of the study, there is a statistically significant and inverse relationship between ROA, ROE, NIM, and Z-Score and MMC1, MMC2, and MMC3, while there is a statistically significant and same directional relationship between NPL and MMC1, MMC2 and MMC3. As a result of these results, it can be said that banks' competition in multimarket contact decreases their profitability and increases the risk of bankruptcy. This finding is contrary to Coccorese and Pellecchia (2009) and Pilloff (1999) and in line with Kasman and Kasman (2016), Le (2020), Hoang et al. (2021), Dao et al. (2021).

As mentioned earlier, MMC2 and MMC3, which are indicators of multimarket contact, are used to show the strength of the results. Therefore, in all models, the relationship between MMC2 and MMC3 variables and the dependent variables is in the same direction as MMC1, and the results are mutually supportive. Moreover, the return on assets ratio may be biased as it ignores income from off-balance sheet activities. Moreover, banks with higher asset size are more established in the Turkish banking sector and have reached the saturation point in terms of profitability and market share. Therefore, to confirm the results, we also examine the relationship between multimarket contact and return on equity and return on interest. The fact that the results obtained when the dependent variable is replaced with return on equity and return on interest are almost the same as those obtained with return on assets statistically supports the findings in this section.

The results of the study imply that multimarket contact is negatively related to bankruptcy risk and that geographical expansion of banks leads to higher bankruptcy risk and lower bank stability. From this point of view, multimarket contact negatively affects profitability, which in turn poses risks for banks and adversely affects their stability and bankruptcy risk. According to the results of the analysis, it can be said that a bank's greater exposure to competitors in its markets and a wider range of activities lead to increased competition and reduced profitability of banks.

There is a statistically significant and negative relationship between non-interest income diversification, return on assets and bankruptcy risk. Therefore, it can be concluded that in order to keep their profitability high and reduce their bankruptcy risk, banks may focus more on their core activities of collecting deposits and extending them as loans to those who demand funds rather than diversifying their non-interest income. If a bank has a wide range of activities, competition in the market is likely to increase, which may deprive banks of high profitability ratios. In addition, the results of the analysis show that as non-interest income diversification increases, interest profitability is positively affected and increases. Banks that are more geographically spread and engage in more business activities are able to generate more interest income, which in turn positively affects interest profitability.

The negative and significant relationship between bank size and return on assets and return on interest suggests that larger banks encourage new banks to enter the market and therefore may have difficulty in sustaining their high profits. This result suggests that larger (smaller) banks tend to earn lower (higher) profits. Eichengreen and Gibson (2001) argue that the effect of the size of a growing bank on profitability can be positive up to a certain limit. On the contrary, as bank size increases, return on equity is positively affected and increases. This may be interpreted as banks with higher asset size have higher equity capital adequacy and increase their return on equity by making their equity available to those who demand funds.

The results of the analysis show that while the increase in the number of personnel positively affects the return on interest, it negatively affects the bankruptcy risk and the asset quality indicator. The results indicate that staffing strategies are an important factor in banks' market power. The increase in net interest margin due to labor productivity can be interpreted as a positive effect of experienced and technologically savvy staff with hardware, software, service, and information technology skills on banks' interest margins. However, it can be argued that bank personnel extend more loans to achieve the targets set by the banks before the current period, and if the credit risk is not hedged, this may negatively affect banks' non-performing loans and bankruptcy risk.

The negative impact of capital adequacy ratio on return on interest, bankruptcy risk and asset quality suggests that banks with excessively high capital ratios may reduce their profits by ignoring profitable growth opportunities. This finding is in line with Jahankhani and Lynge, 1980; Brewer and Lee, 1986; Karels et al. 1989; Jacques and Nigro, 1997; Blum, 1999; Agusman et al. 2008. In general, a bank with a high capital adequacy ratio is considered safe and more likely to fulfill its financial commitments. However, in this study, the negative relationship between capital adequacy, profitability and risk suggests that banks are not making the best use of their capital.

It is inherent in the structure of the equation that as capital adequacy increases, the ratio of income from capital will decrease. High capitalization may also have a negative impact on bank profitability by increasing fixed and operating costs, which may adversely affect bank development and credit expansion. Managers should take into account that if banks increase their capital adequacy, this will have a negative impact on the asset quality indicator, in other words, it will increase the riskiness of banks. Another explanation for these results may be that banks with high levels of capital are less likely to engage in excessive risk-taking activities. It can be argued that the Basel criteria are designed to protect banks from possible financial crises or recessions by taking into account their financial risks rather than their profitability. Therefore, bank regulators need to focus not only on maintaining high capital adequacy, but also on strategic monitoring and regular assessments to maintain the financial strength and stability of the bank and further enhance the overall performance of the bank, taking into account other requirements under Basel II and Basel III.

This study provides new information on the effects of competition dynamics on bank risk and performance by taking into account multimarket contact. The study examines whether there is an impact of multimarket contact on bank risk and performance in the context of Turkey. The Turkish banking sector, as a sector that has experienced geographical diversification and technological development in recent years, provides a unique environment for examining these relationships. The study includes 17 deposit banks that are in continuous operation in the Turkish banking system between 2012 and 2021 and for which data are available.

Although competition is thought to make banks more resistant to financial crises and corruption by improving their corporate governance structures and audit structures, this study concludes that competition has a negative impact on their financial performance. The increase in the number of branches in the Turkish banking sector may cause banks to lose customers, which may negatively affect their profitability and bankruptcy risk as the product they lend decreases. According to the results of the analysis, banks' competition in multimarket contact has positively affected their asset quality and increased the asset quality of banks. This situation can also be attributed to digitalization and technological development. Because with digitalization, customer information is stored more easily and accurately and the flow of information between banks is faster. The asset quality indicator is expected to be negative as it reflects banks' ability to effectively select and monitor lenders. That is, the quality of risk management is represented by a decline in the NPL ratio. This, in turn, contributed to a decline in the asset quality indicator, NPL, reducing banks' riskiness and improving asset quality.

Now more than ever, technology is shaping the financial ecosystem and the future of banks. At the same time, increasingly digitally sophisticated customers, especially millennials and beyond, are demanding greater convenience and better customer service through mobile or tablet platforms. Moreover, the COVID-19 pandemic has resulted in accelerated adoption of digital technologies in all areas, including financial services. Therefore, to remain competitive in the face of new players, attract customers, and reduce costs, banks see digital transformation as a business priority. Traditionally, branches are the primary distribution channels of banking. However, with the introduction of technology, alternative distribution channels have emerged, initially with automated teller machines (ATMs). Alternative distribution channels, including mobile devices, digital TVs and internet banking, have become appealing to banks because of the advantages they provide (e.g. cost reduction, efficiency and better service quality). Digitalization is driving a paradigm shift in banking towards the intense use of so-called alternative delivery channels. By automating routine tasks and processes, banks can reduce the need for manual labor and improve the speed and accuracy of their services. This has contributed to increased efficiency and cost savings for banks.

As the level of digitalization increases, the base of codified information also expands, leading to the use of various tools for data analytics and decision-making processes. The most prominent of these tools is AI. With the use of AI and many other tools, various sectors are benefiting from increased productivity and profitability, which has economic development implications. Artificial intelligence is becoming increasingly widespread in the field of banking and finance. One of the main effects of the digitalization of the banking sector was the provision of quality, fast, personalized and accessible services, while the other effect was the decrease in the number of branches.

The proliferation of electronic banking transactions has become part of the banking culture. The most recent example of this is the ease of remote account opening, a technological innovation in the banking sector in the wake of the COVID-19 pandemic. Digital transformation has the dual function of enabling banking organizations to offer new service channels through new electronic platforms (e-banking, virtual banking) and service points (e-branches, POS), while reducing operating costs by limiting the number of physical branches they use.

As can be seen from the results of the analysis conducted in this study, the need for branches has decreased with digitalization and the banks' contacts in multimarket contact have negatively affected their profitability and performance, and have decreased the stability and increased the riskiness of the banks. Based on all this, the management of Turkish deposit banks can invest significantly in e-banking infrastructure, saving resources through the degree of acceptance of these technologies by bank customers and the restriction of traditional banking. At the same time, their customers can be encouraged to use more technological products (e.g. mobile banking, internet banking).

The findings of this study are important for financial regulators in terms of promoting economic development, researchers in terms of developing new models, and senior management of banks in terms of developing a healthy banking environment and improving bank performance. As a result of this study, by identifying the impact of the multimarket contact on the long-term performance and riskiness of banks, the decision-making of bank senior management will be facilitated. Digital transformation in the banking sector is a process that requires the redesign of existing methods and affects both the external and internal environment. Therefore, a more diversified banking market in terms of branches may cause losses in banks' profitability and have unintended consequences. In this context, policymakers may re-regulate the opening of new branches, taking into account the bank's profitability and risk, and encourage less geographical diversification. In particular, instead of forcing the bank to incur significant fixed costs by opening branches where there is no customer potential by conducting field research before opening a branch, it can be ensured that the branch reaches the profit transition point in a shorter time by opening branches that will operate more efficiently. Because more multimarket contact can harm banks with digitalization. At the same time, a B2B business model can be developed for banks, thereby increasing business volumes. Banks should also adapt their business models to change their interactions with customers, manage office operations, be competitive, keep abreast of technological developments and be ready for the future. Accordingly, banks need to keep pace with technological developments, taking into account the risks of data and cyber-attacks, and make serious R&D investments in future technologies such as ChatGPT and metaverse. Based on all these, the management of Turkish deposit banks can invest significantly in e-banking infrastructure by saving resources through the degree of acceptance of these technologies by bank customers and the restriction of traditional banking. At the same time, their customers can be encouraged to use more technological products (e.g. mobile banking, internet banking). Bank managers should not ignore these issues when making regulations and new decisions. From this perspective, the results of this study will contribute to the top management focusing on banking sector reforms.

Future studies may examine the relationship between multimarket contact and performance, riskiness, and stability in different sectors. In addition, the effects of the multimarket contact on developed country groups and developing country groups or different country groups can be analyzed comparatively, and thus the results in countries where regulations are implemented can form a basis for future regulations.

Author Contribution

HRE helped discover the topic and created its general outline.BDB provided the data and analyzed itHHE and BDB interpreted the analysis results and made policy inferences.

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No competing interests reported.

APPENDIX.docx

Multimarket Contact, Competition and Performance: an Application on Turkish Deposit Banks

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Abstract

Figures

1. Introduction

2. Literature review

3. Research Methodology

4. Results and Discussion

5. Conclusion

Declarations

Author Contribution

References

Additional Declarations

Supplementary Files

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