INTRODUCTION

Commercial banking is essential in the development of a country. The centrality played by banks in their role as an intermediary as creditor and custodian of society's monetary surpluses, in addition to guaranteeing liquidity among economic agents that promotes the development of productive sectors, makes the performance of the sector a relevant issue for all economic sectors. Banking is also a leading actor in increasing financial inclusion, which drives economic growth and reduces poverty (Palaon et al., 2020). To achieve this scenario is necessary for banks to be profitable and adequately capitalized to guarantee not only their operation but also their expansion. Hence, bank profitability is a recurring theme in the financial literature. Research lines that incorporate multiple determinants into their analyzes with increasingly robust and sophisticated models.

According to the literature consulted, the performance of bank profitability is due to multiple factors that, for practical purposes, are classified into two categories: internal and external factors. The former depends on the management of the senior management of each bank, such as risk indicators, liquidity, efficiency, and profitability, among others. On the other hand, external factors influence profitability but are unrelated to the banks' decisions. Examples of these are changes in the economic environment, such as inflation, economic growth, changes in the regulation of the sector, the market structure in which banks operate, and the development and adoption of technologies.

This article seeks to contribute to the literature by proposing an extension of a model that explains bank profitability based on indicators of operating efficiency and profitability with the incorporation of independent banking access channels to the traditional branch, see Figure 1. These channels are considered independent variables that affect the performance of banks.

In recent decades, the development of Information and Communication Technologies (ICTs) has impacted all economic sectors, and commercial banking is no exception. The different financial innovations adopted by banks have modified the traditional interaction in bank branches with their users (Mbama, 2016; Olavarría et al., 2018).

Users who are increasingly digital demand comfort, speed, and ease in requesting, contracting, and using their financial products and services. To meet this demand, banks have invested in the expansion of alternative banking channels to the branch, such as ATMs (Automatic Teller Machines), POS (Point of Sale Terminals), BM (Mobile Banking), and Commission Agents (also called banking correspondents). This banking infrastructure is supported by Information and Communication Technologies (ICTs), and different investigations have analyzed its relationship with banking performance, finding significant relationships in different channels (Le & Ngo, 2020; Medyawati et al., 2021; Moudud-Ul-Huq & Hossain, 2020).

The articles that analyze the relationship between profitability and efficiency in the banking sector are vast. The relationship between these indicators is inversely significant, indicating that the higher the profitability, the efficiency indicators tend to be lower (Buchory, 2015; Christaria & Kurnia, 2016; Phan et al., 2020). In Mexico, the literature that addresses this relationship is scarce. Rodríguez & Venegas (2010) analyze Mexican banks' profitability and operational efficiency indicators classified by the number of assets they own. They also find evidence that these banks have more significant advantages in the Net Interest Margin and the Net Operating Margin.

The authors Guerrero & Villalpando (2009) analyze the context of the banking sector from the transformation process that began at the end of the last century with the authorization of foreign investment, which generated a change in concentration, being this and the power of the market variables with a strong influence on bank profitability. In the same vein, Maudos & Solisa (2009) find evidence that average operating costs and market power mainly explain the high margins obtained by Mexican banks.

On the other hand, incorporating innovation variables in the proposed model that analyzes bank profitability is theoretically based on the Schumpeterian principle of creative destruction, the diffusion of innovation theory (Rogers et al., 2014), and, in addition, the Theory of the Acceptance of Technology. This Theory affirms that the ease of use of the technology indicates to implement and to learn new systems of information. Therefore, the model emphasizes the ease of use of the new technology affecting the perceived usefulness (Venkatesh & Davis, 2000).

According to Alvarez (1993), innovation is adapting the offer to the demand of the clientele and the markets. However, a broader definition of financial innovation is given by Khraisha & Arthur (2018), who define it as a process carried out by any institution which involves the creation, promotion, and adoption of new (including both incremental and radical) products, platforms and processes or enabling technologies that introduce new forms or changes in the way of carrying out a financial activity. These definitions fit with the current situation in which the banking sector tends to invest more in alternative channels to physical branches as demanded by digital users.

The incorporation of banking access points in financial innovation research is aligned with the term branchless banking. Said concept measures the effect of adopting alternative banking infrastructure on the bank branch. Most empirical research is done in developing countries with large territorial extensions. Another characteristic these countries have in common is that they have many rural communities with low populations, irregular settlements, and a high poverty level on the periphery of large cities. For private commercial banks, it is not profitable to open branches in these areas. To cover this lack of coverage, banks invest in expanding banking access channels, being a viable alternative for banks that meets the needs of users (Magallón et al., 2022). Due to the characteristics of unequal access to banking services in Mexico and given the information to which one has access, the branchless banking construct is considered to measure financial innovation.

With information from the information portfolio of CNBV and through an econometric data panel model, the aim is to elucidate the relationships between the determinants of the proposed model with the performance of the ROA and ROE profitability indicators of the seven leading banks operating in Mexico. No research was found in the Mexican literature incorporating financial channels as explanatory variables of bank profitability, which is the main contribution.

The rest of this article comprises the Literature Review section with a description of empirical articles that analyze the relationship between efficiency indicators and banking infrastructure with bank profitability. The third section presents the methodology of the econometric model and the description of the data collection used. The fourth and fifth sections present our analysis's results, discussion, and conclusions. Finally, topics for future lines of research are proposed.

LITERATURE REVIEW

The banking sector is analyzed from different lines of research in the academic literature in which its history, relationships, and performance are investigated. As mentioned above, this research proposes a model that explains bank profitability with internal determinants of operational efficiency and external determinants that measure banking infrastructure. However, before citing theoretical and empirical research that supports this research, a review of the most critical events in the Mexican banking sector is made to contextualize the evolution of Mexican banking.

Table 1 presents a brief chronology of momentous events in the sector during the last three decades. Although these events are not the only ones, the author considers them the most relevant to explain the consolidation of a healthy and capitalized banking sector. These events have allowed it to face the economic and financial crises presented in this century without the shocks of yesteryear.

Although these events consolidated the sector's finances, they failed to change the concentration level. To exemplify this behavior of the banking market, Graphic 1 presents the percentage of asset possession and income of the Mexican banking system's seven largest banks (G7) in the last two decades.

With data from the CNBV as of December 2020, the G7 had 78% of the sector's assets and 91% of the income. The concentration is similar to 2000, when these banks had 72% of the assets and 92.32% of the profits. Given the weight of the G7, it is easy to infer that the variations in its indicators impact the sector's performance.

Internal determinants

In Mexico, publications that analyze bank profitability with internal factors are scarce. Among them is the one carried out by Rodriguez & Venegas (2012), which classifies Mexican banks into four categories according to the number of assets they own. They analyze these categories through an analysis of liquidity, leverage, and profitability indicators in 2002 and 2009. They conclude that the significant determinants are long-term deposits that allow them to assume more profitable investments and short-term solvency.

Chavarín (2014) analyzes the levels of delinquency in the payments of the credits of the banking clients as not a determinant in profitability. Among his findings, he mentions that the factors that impact profitability are the level of capitalization, the size of the bank, the total level of risk exposure, administration expenses, and the mix of activities. For their part, Ronquillo Rodríguez et al. (2018) conclude that the financial factors that contribute the most to profitability are the financial margin adjusted for credit risks and administration and promotion expenses.

Regarding foreign research on this subject, there is the one carried out by Goddard et al. (2004) proposes a model to measure the profitability of European banks during the nineties and incorporate various elements such as the size of the bank, diversification, risk, efficiency, type of owner and dynamic effects. Efficiency and risk are the determinants that have the most significant effect on profitability. Furthermore, Chortareas et al. (2011) analyze the market where banks operate. Based on 2,500 observations of Latin American banks, they conclude that the efficient structure is the one that supports the profitability of banks in countries with more advanced economies, such as Brazil, Argentina, and Chile.

Neves et al. (2020) analyze efficiency indicators, bank size, macroeconomic variables, and profitability. Sixty-six Portuguese and Spanish banks are analyzed using the generalized method of moments (GMM) and data envelopment analysis (DEA). The latter measures efficiency and concludes that there is a positive and negative nonlinear relationship between the bank's size and its profitability and efficiency levels, respectively. Ercegovac et al. (2020) investigated the banking sector in the European Union during the period 2007-2019. The empirical evidence is in line with the initial assumptions: the efficiency of the banking firm, measured by the cost-income ratio and the delinquency rate, significantly influences bank profitability.

The solidity of the sector is also due to responsible internal management of its financial indicators. The relationship between bank profitability and financial ratios has been documented in the academic field, mainly by European, Asian, and American banking publications. Among the different indicators, the efficiency measurement is a line of research with fewer publications. Efficiency ratios show how well management has sustained revenue growth in the face of rising costs.

External determinants

With the advancement of new information technologies, retail banking competes with new alternative channels to the traditional business model of banks based on the number of branches. Among the first innovations that allowed financial users to carry out transactions outside the bank branch took place in the seventies. They appeared in electronic teller machines, Automatic Teller Machines (ATMs). Some studies that analyze the relationship between these and profitability are mentioned. Based on data from US banks, Massoud et al. (2003) analyze the relationship between ATMs and bank profitability and find a direct relationship between additional charges for using ATMs.

Itah & Emmanuel (2014) study Nigerian banking in which they examine the effect of ATMs, points of sale, and transactions through the Internet on bank profitability, using multiple regression analysis methods by ordinary least squares. The result showed that ATMs and points of sale are positively related to ROE. On the other hand, through a data panel, Medyawati et al. (2021) analyze the relationship between Indonesian banks and access channels such as ATMs, mobile banking, and internet transactions. They conclude that ATMs are not significant in their relationship with ROA.

Mutua (2013) study the Kenyan banking sector, detecting a weak but positive relationship between mobile banking and ROA. The same conclusion was detected by Medyawati et al. (2021). They analyze banks listed on the Indonesian Stock Exchange and find that the relationship between mobile banking and profitability is significant with the ROA ratio. This result is similar to another study conducted in Kenya, which found a significant but weak positive relationship between mobile banking and profitability (Chipeta & Muthinja, 2018). On the other hand, Lebanese banks have no significant relationship with bank profitability (Chaarani & Abiad, 2018).

Regarding the empirical studies that analyze the relationship between profitability and point-of-sale terminals (POS). The one carried out by Le & Ngo (2020) with banks in 23 countries analyzed. It is found that the relationship between POS and bank profitability is significant. Itah & Emmanuel (2014) conclude that point-of-sale terminals positively affect Nigerian banks' profitability, measured by ROE.

In Mexico, the commission agent model's proliferation is due to banks' strategies to have new access points for their users. In addition, the expansion of chains such as Oxxo adds new stores every year (ENIF, 2018). The strategies of the correspondents another essential factor that explains the acceptance of the commission model is the high cost that opening a branch in a distant town implies for banks, such as the bordering areas of large cities and rural towns (Peña & Vázquez, 2012).

In rural Kenya, the installation of two access channels, Mobile banking, and agent banking, is studied by Irura & Munjiru (2013). It concludes that the main factors that encourage the adoption of these financial innovations are: the improvement and guarantee of security, reliability, and trust and the improvement of the propensity to assume risks on the part of SMEs that adopt the technology. In addition to the improvement in the political framework, the telecommunications infrastructure, among others. Some publications analyze banking agents as a factor that drives the performance of commercial banks (Barasa & Mwirigi, 2013; Irura & Munjiru, 2013; Kandie, 2013; Shrader & Duflos, 2014; Waleed & Tahir, 2020).

A large part of the studies that analyze banking access channels has the term Branchless Banking in common. Branchless Banking is a concept that brings together the main innovations in access points outside the bank branch. Its use as a proposal to measure banking innovation is used mainly by developing countries with large territories and marginalized areas where banks have little or no penetration and, therefore, a population excluded from the formal banking system (Ky et al., 2021; Palaon et al., 2020; Zhu et al., 2021). The following section details the Methodology used in this article to assess the relationship between the explanatory variables with profitability.

METHODOLOGY

In order to comply with the Basel III agreements, Mexican banks must publish their financial statements monthly on the CNBV website. Transparency offers an essential source of information that allows interested parties to investigate banks' financial and operational performance. The indicators' values are obtained from secondary sources and monthly financial reports published in the Information Portfolio section. Among the main advantages of working with indicators is establishing a comparable base for all the institutions evaluated regardless of their size, nationality, or membership. The second decade of this century is analyzed using a panel data econometric model (Hill, Griffiths & Lim, 2018). The program used for the processing and analysis of the database is R-studio.

The ratios and financial indexes of profitability and operating efficiency used in the present analysis measure and evaluate the performance of the administrative policies of the senior management of each bank. This index allows us to evaluate the banks' strategies to elucidate the indicators that have the most significant relationship with profitability.

The indicators assume the role of independent variables, and it is explored if they are statistically significant with the dependent variable, ROA and ROE. The financial ratios are obtained from the book "Bank Management and Financial Services" (Rose & Hudgins, 2008), and their formulas and description are shown in Tabla 3. The innovation variables: ATM, POS, NBM, and NMC are the access points that present greater adoption by Mexican users, as observed in the evolution of the data.

Following the specialized literature consulted, and due to the characteristics of the database used, study units (banks) in periods (monthly financial indicators for the period between 2011-2020), the panel model methodology is used. Of data to evaluate profitability, ROA, and ROE. The estimation is made by Ordinary Least Squares (OLS), and the following general equation is used:

The dependent variable is the banking profitability measured by the ROA and ROE indicators. The explanatory variables correspond to the following financial ratios of profitability and operating efficiency: Operating Efficiency (EO), Net Interest Margin (MIN), Net Non-Interest Margin ( MNNI), Asset Turnover (RA), Capital Multiplier (MC), and Profit Margin (MU).

One of the main indicators to assess profitability is ROA. The indicator measures the performance of a company, and in the banking sector, it is widely used due to the uniformity in its calculation, where the assets that are taken into account are: cash, deposits retained by Banxico, cash on hand and instruments with a maturity of the bank, investment, and loans. The ROE indicator evaluates the return of a shareholder who invests his capital in the bank.

The explanatory variables of innovation measure the evolution of the banking infrastructure of access points other than traditional branches. These channels are used as independent variables that are related to bank profitability. Table 3 presents the definition and estimated equation for each indicator. For NMC, it is the number of transactions operated in the commission businesses of bank i at time t. NBM is the number of bank accounts that have the mobile application contracted from bank i at time t. ATMs provide information on the number of ATMs that bank i has at time t. POS is the point of sale terminals bank i have at time t.

According to the data from the CNBV, the evolution of access channels has a constant growth. In the decade analyzed in the present investigation, ATMs grew by 55.51%, translating into 17,725 more ATMs. The POS went from 469,028 units in 2011 to 1,245,003 in 2020, representing a growth of 145.67%. The amounts of transactions in commission businesses had a growth of 846.53%. The most remarkable growth in the analyzed channels is Mobile Banking. With a growth of close to 36 million accounts from 2011 to 2020. On the other hand, bank branches decreased from their highest point reached in 2016. By the end of 2020, the G7 has 7,507, representing a decrease of 5.58%. (Table 2)

In order to select the best data panel model that analyzes the relationship between the dependent variable profitability (ROE and ROA) and the financial and innovation ratios described in Table 3, the methodology described in Figure 2 is followed. Newey West and the selection criteria for panel data models given by the F and Hausman tests are described in the footnotes on this page.

Once this methodology has been carried out, we identify the data panel model that best analyzes the independent variables' data and the ROA and ROE values. With this, the corresponding inferences are obtained to contextualize the banks' strategy from the perspective of efficiency and innovation indicators that are significant in the performance of banks. The results are presented in the next section.

RESULTS

The results of this study are presented following the methodology described in the previous section. In addition, figures and indicators of the banks that make up the G7 are presented to help us understand their importance in the Mexican banking sector. With data as of December 2020, the seven banks analyzed in this investigation owned 78% of the assets and 91% of the system's profits. BBVA Bancomer and Santander, banks of Spanish origin, the largest in the sector, have 22% and 17% of total assets and 35% and 20% of profits (Table 4).

Regarding the access channels, the figures as of December 2020 are present in the following table (Table 5), and it is observed that for the ATMs BBVA Bancomer, Santander, Banorte, and Banamex are the Credit Institutions with the most significant presence, the first account with 22.3% and the other three banks have 16% of the sector each. On the other hand, regarding POS, BBVA and Santander are the leaders, with 33.5% and 22.24%, respectively.

BBVA Bancomer and Banamex are the leaders in Mobile Banking, with more than 14 million accounts each. Regarding the amounts in commission businesses, Banamex has 24.39% of the market, followed by BBVA Bancomer, which has 21.73%.

The behavior of the ROE indicator during the last decade is presented in Graphic 2. It can be observed that the performance of the sector (Total Multiple Banking) presents a similar behavior to that observed by the G7 banks. The same graph also shows the behavior of the ROA indicator of the seven banks. As in the ROE indicator, G7 influences the behavior of the ROA of the Mexican banking sector. This is explained because most of the elements that make up the indicator, net stockholders' equity and income, are contributed by the G7.

A drop is observed in the two indicators in 2020, partly due to the closure of several economic sectors in the first half of 2020. Despite the uncertainty caused by the Covid-19 pandemic, only the HSBC bank presented losses this year of 708 million pesos.

According to the F and Hausman tests, the data panel model that best fits the analysis of the ROE and ROA (Table 6) dependent variables is the data pool, which implies that α and β's are constant for the seven banks analyzed (Table 7).

DISCUSSION AND CONCLUSIONS

The importance of banks' financial performance is an issue of vital importance for a country's economy. Given the interaction of the banking sector with other economic agents, it is essential to follow up on its main indicators. This research proposes to analyze the influence on bank profitability of the profitability and operational efficiency indicators, which measure the efficiency in the administration of its costs concerning its income, of the accounting capital's relationship with respect to the assets between others. In addition, the banking access channels that show significant growth and adoption by the Mexican market are integrated.

The Net Operating Margin (MNO) has a significant relationship with ROA and ROE. The reading of this indicator refers to the differential between income and operating expenses to assets. The higher the indicator, the bank is more efficient in its operations. It is usual for large banks to have a better result, given the economies of scale they manage. This result is in line with the research by Rodríguez & Venegas (2010).

Regarding the indicators that have a significant relationship with the ROA indicator, we find that the indicators:

The noninterest Net Margin, MNNI, has ROA influence but in a negative sense, coinciding with the literature consulted (Rose & Hudgins, 2008). This financial ratio does not indicate the relationship between the difference in income from commissions collected and noninterest expenses (mainly wages and salaries) with assets.

The management of the banks' portfolio refers to the income of the banks in the placement of their assets and is measured by the indicator of Asset Utilization, RA. It has a significant inverse relationship with ROA, which implies a low placement by banks.

Regarding the indicators of banking infrastructure, it is found that electronic teller machines, and ATMs, have a positive but weak influence on ROA. These results align with the research carried out by (Le & Ngo, 2020; Medyawati et al., 2021), who found a positive but weak relationship between ATMs in conjunction with other channels. The result is also consistent with a study by (Clemons, 1990), which concludes that ATMs help banks not to lose market share and not to be more competitive. Conversely, Moudud-Ul-Huq & Hossain (2020) conclude that ATMs have a negative relationship with ROA. On the other hand, Itah & Emmanuel (2014) conclude that ATMs alone negatively influence profitability. However, the relationship is positive if complemented by other innovations, such as Point of Sale Terminals.

Point of Sale Terminals, POS, present a negative relationship with ROA and a very weak effect. This result is in the same sense as the findings found by the investigations (Akhisar et al., 2015; Moudud-Ul-Huq & Hossain, 2020). In the research carried out in 23 countries by Le & Ngo (2020), it was found that POS positively affects bank profitability. On the other hand, Mobile Banking and commission businesses do not have a significant relationship with ROA.

Regarding the determinants that have a significant relationship with the ROE profitability indicator, the commissions charged for the assets held by the banks (MNNI) have a significant negative relationship. In addition, the Equity Multiplier influences profitability. This indicator refers to banks' leverage, whether it is with their capital or debt. It is common for bank management to finance projects with debt, resulting in greater profitability, so care must be taken not to present high levels of indebtedness.

As with the ROA indicator, the innovation indicators, ATM and POS, are related to ROE in the same sense, positive for the first and negative for the second channel. Also, the coefficients have a slight impact on profitability. The relationship is negative for the Mobile Banking channel, with a significance level of up to 0.05. Chipeta & Muthinja (2018) find in their study carried out for banking in Kenya that mobile banking is significant for ROE but in a direct sense. Imamah & Ayu Safira (2021) reach the same conclusion regarding the positive effect mobile banking has on the performance of bank profitability in Indonesia. Regarding the amounts negotiated in the Commission Agent, NMC businesses do not present a significant relationship.

Future Lines research

The CNBV portal presents an accessible database to analyze Mexican banking from different perspectives. In addition, incorporating new reports facilitates access to information on the sector, encouraging academic research in this sector.

Although the access channels present a weak relationship with the performance of the banks, their proliferation represents a virtual object of study to analyze other indicators of the banks, such as the effect on the indicators of efficiency, transactionality, and jobs in the banking sector, among others.

Determine if user satisfaction in using banking access channels contributes to efficiency. It would allow us to determine whether the application of these banking strategies impacts bank profitability.

It is also essential to study the disruption of Neo Banks, whose main characteristic is that they do not have branches that have their business model on their online platforms and mobile applications.

REFERENCES

Akhisar, İ., Tunay, K., & Tunay, N. (2015). The Effects of Innovations on Bank Performance: The Case of Electronic Banking Services. Procedia - Social and Behavioral Sciences, 195, 369–375. https://doi.org/10.1016/j.sbspro.2015.06.336

Alvarez, J. (1993). La banca española Actualidad y perspectivas. Papeles de La Economía Española, 54, 127–138.

BANXICO. (2022). Banxico Ante Ciclos de Ajustes en la Tasa de Fondos Federales. Mexico: Banxico.

Barasa, D. A., & Mwirigi, F. M. (2013). The role of agency banking in enhancing financial sector deepening in emerging markets: lessons from the Kenyan experience. European Journal of Business and Management, 5(21), 19-21

Borja, F. (1995). El nuevo régimen del Banco de México. Revista Comercio Exterior, 45(1), 12-19.

Buchory, H. (2015). Capital, Operational Efficiency and Credit Risk In The Banking Intermediation. Proceedings of the International Conference on Economics and Banking 2015, 5, 141–152. https://doi.org/10.2991/iceb-15.2015.27

Hill, R., Griffiths, W., & Lim, G. (2018). Principles of econometrics. John Wiley & Sons.

Chavarín, R. (2014). Profitability in Mexico. EconoQuantum, 12(1), 97–123.

Chipeta, C., & Muthinja, M. (2018). Financial innovations and bank performance in Kenya: Evidence from branchless banking models. South African Journal of Economic and Management Sciences, 21(1), 1–11. https://doi.org/10.4102/sajems.v21i1.1681

Chortareas, G., Garza, J., & Girardone, C. (2011). Banking Sector Performance in Latin America: Market Power versus Efficiency. Review of Development Economics, 15(2), 307–325. https://doi.org/10.1111/j.1467-9361.2011.00610.x

Christaria, F., & Kurnia, R. (2016). The Impact of Financial Ratios, Operational Efficiency and Non-Performing Loan Towards Commercial Bank Profitability. GATR Accounting and Finance Review, 1(1), 43–50. https://doi.org/10.35609/afr.2016.1.1(6)

Clemons, E. (1990). MAC-Philadelphia national bank’s strategic venture in shared ATM networks. Journal of Management Information Systems, 7(1), 5–25. https://doi.org/10.1080/07421222.1990.11517878

ENIF. (2018). Reporte Nacional de Inclusión Financiera 9. In Consejo Nacional de Inclusión Financiera. Mexico: ENIF.

Ercegovac, R., Klinac, I., & Zdrilić, I. (2020). Bank specific determinants of eu banks profitability after 2007 financial crisis. Management (Croatia), 25(1), 89–102. https://doi.org/10.30924/mjcmi.25.1.5

Fong, C. (1990). Apuntes para una reflexión sobre la privatización bancaria. Carta Económica Regional, 3(15), 50–52.

Girón, A. (1994). La banca comercial de México frente al TLC. Revista de Comercio Exterior, 24(12), 1068–1075.

Goddard, J., Molyneux, P., & Wilson, J. (2004). The profitability of European banks: A cross-sectional and dynamic panel analysis. The Manchester School, 72(3), 363-381. https://doi.org/10.1111/j.1467-9957.2004.00397.x

Guerrero, R., & Villalpando, M. (2009). Rentabilidad, concentración y eficiencia en el sistema bancario mexicano. Trimestre Economico, 76(1), 237–263.

Hernández, F., & López, O. (2001). La banca en México, 1994-2000. Economía Mexicana Nueva Época, 10(2), 363-390.

Imamah, N., & Ayu Safira, D. (2021). Pengaruh mobile banking terhadap profitabilitas bank di bursa efek Indonesia. Profit: jurnal administrasi bisnis, 15(1), 95-103. https://doi.org/10.21776/ub.profit.2021.015.01.10

Irura, N., & Munjiru, M. (2013). Technology Adoption and the Banking Agency in Rural Kenya. Journal of Sociological Research, 4(1), 249–266. https://doi.org/10.5296/jsr.v4i1.3584

Itah, A., & Emmanuel, E. (2014). Impact of Cashless Banking on Banks’ Profitability (Evidence from Nigeria). Asian Journal of Finance & Accounting, 6(2), 301. https://doi.org/10.5296/ajfa.v6i2.6268

Kandie, G. (2013). The effect of agency banking on financial inclusion in Kenya (Doctoral dissertation, University of Nairobi).

Khraisha, T., & Arthur, K. (2018). Can we have a general theory of financial innovation processes? A conceptual review. Financial Innovation, 4(1), 1–27. https://doi.org/10.1186/s40854-018-0088-y

Ky, S., Rugemintwari, C., & Sauviat, A. (2021). Friends or Foes? Mobile money interaction with formal and informal finance. Telecommunications Policy, 45(1). https://doi.org/10.1016/j.telpol.2020.102057

Le, T., & Ngo, T. (2020). The determinants of bank profitability: A cross-country analysis. Central Bank Review, 20(2), 65–73. https://doi.org/10.1016/j.cbrev.2020.04.001

Magallón, H., Galeana, E., & Prado, C. (2022). Banking innovations and their effect on profitability. Mercados y Negocios, 23(47), 25–56. https://doi.org/https://doi.org/10.32870/myn.vi47.7680

Massoud, N. & Scholnick, B. (2003). Is There a Customer Relationship Effect from Bank Atm Surcharges? NYU Working Paper No. S-FI-03-05, Available at SSRN: https://ssrn.com/abstract=1297768

Maudos, J., & Solisa, L. (2009). Determinants of net interest income in the Mexican banking system: an integrated model. Munich Personal RePEc Archive, 15257, 1–31.

Mbama, C. (2016). Digital banking, customer experience and bank financial performance : UK customers’ perceptions. International Journal of Bank Marketing, 36, 230–255.

Medyawati, H., Yunanto, M., & Hegarini, E. (2021). Financial Technology as Determinants of Bank Profitability. Journal of Economics, Finance and Accounting Studies, 3(2), 91–100. https://doi.org/10.32996/jefas.2021.3.2.10

Moudud-Ul-Huq, S., & Hossain, S. (2020). Impact of cashless policy on bank’s profitability: Evidence from a developing economy. International Journal of Financial Engineering, 7(3), 2050034. https://doi.org/10.1142/s2424786320500346

Mutua, R. (2013). Effects of Mobile Banking on the Financial Performance of Commercial Banks in Kenya. Doctoral dissertation, University of Nairobi (Issue 8).

Neves, M., Proença, C., & Dias, A. (2020). Bank Profitability and Efficiency in Portugal and Spain: A Non-Linearity Approach. Journal of Risk and Financial Management, 13(11), 1–19. https://doi.org/10.3390/jrfm13110284

Olavarría, A., Cambra, J., Centeno, E., & Vázquez, R. (2018). Relationship quality as an antecedent of customer relationship proneness: A cross-cultural study between Spain and Mexico. Journal of Retailing and Consumer Services, 42, 78–87. https://doi.org/10.1016/j.jretconser.2018.01.011

Palaon, H., Wiryono, S., & Faturohman, T. (2020). Branchless banking agents: Business satisfaction, continuity, and viability. Cogent Business and Management, 7(1). https://doi.org/10.1080/23311975.2020.1823585

Peña, P., & Vázquez, A. (2012). El impacto de los corresponsales bancarios en la inclusión financiera: una primera evaluación. Estudios Economicos Cnbv, 1, 195–216.

Phan, H., Hoang, T., Dinh, L., & Hoang, D. (2020). The Determinants of Listed Commercial Banks’ Profitability in Vietnam. Journal of Asian Finance, Economics and Business, 7(11), 219–230. https://doi.org/10.13106/jafeb.2020.vol7.no11.219

Rodríguez, A., & Venegas, F. (2010). Indicadores de rentabilidad y eficiencia operativa de la banca comercial en México. Problemas Del Desarrollo, 41(161), 165–191.

Rodríguez, A., & Venegas, F. (2012). Liquidez y apalancamiento de la banca comercial en México. Análisis Económico, XXVII, 73–96.

Rogers, E., Singhal, A., & Quinlan, M. (2014). Diffusion of innovations. In An integrated approach to communication theory and research (432-448). Routledge.

Ronquillo, D., Aguilasocho, D., & Galeana, E. (2018). Factores financieros que determinan la rentabilidad de los bancos que operan en México. Mercados y Negocios, (38), 23–44.

Rose, P., & Hudgins, S. (2008). Measuring and Evaluating the Performance of Banks and Their Principal Competitors. In Bank Management and Financial Services (163–204).

Shrader, L., & Duflos, E. (2014). China: A New Paradigm in Branchless Banking?. Washington: CGAP.

Turrent, D. (2009). Estatización Bancaria en México. Antecedentes, causas y consecuencias. México: Centro de Estudios Espinoza Yglesias.

Turrent, E. (2008). Historia Sintética de la Banca en México. México: Banco de México.

Venkatesh, V., & Davis, F. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204.

Waleed, A., & Tahir, A. (2020). The Impact of Branchless Banking on Promotion. Journal of Finance, Accounting and Management, 11(1), 53.

Zhu, Q., Lyu, Z., Long, Y., & Wachenheim, C. (2021). Adoption of mobile banking in rural China: Impact of information dissemination channel. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2021.101011

Chaarani, H., & Abiad, Z. (2018). The Impact of Technological Innovation on Bank Performance. Journal of Internet Banking and Commerce, 23(3).

Enlace alternativo

http://mercadosynegocios.cucea.udg.mx/index.php/MYN/article/view/7685 (pdf)