Impact of Mobile Banking Adoption on the Technical Efficiency of Commercial Banks in Ethiopia: An Analysis from 2010 to 2022

Impact of Mobile Banking Adoption on the Technical Efficiency of Commercial Banks in Ethiopia: An Analysis from 2010 to 2022 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Mobile Banking Adoption on the Technical Efficiency of Commercial Banks in Ethiopia: An Analysis from 2010 to 2022 Dereje Fedasa Hordofa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3146976/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study investigates the relationship between mobile banking adoption and the technical efficiency of commercial banks in Ethiopia. The research utilizes a stochastic frontier model and analyzes data from 2010 to 2022. Descriptive statistics, pairwise correlations, and regression analysis are employed to examine the impact of mobile banking on technical efficiency. The findings reveal a small but significant positive effect of mobile banking on technical efficiency, aligning with previous literature highlighting the benefits of mobile banking adoption. However, the results also suggest that other factors not accounted for in the model significantly influence technical efficiency. This study contributes to the understanding of the role of mobile banking in enhancing efficiency within the Ethiopian banking sector and emphasizes the need for further research to explore additional determinants of technical efficiency. Accounting Other Business Finance Mobile banking adoption technical efficiency commercial banks Ethiopia impact positive effect 1. Introduction Mobile banking has emerged as a transformative force in the banking industry, revolutionizing the way individual’s access and manage their financial services (Malaquias & Silva, 2020 ; Ngalyuka, 2021 ). In Ethiopia, a country with a rapidly growing economy and a significant unbanked population, mobile banking holds immense potential for driving financial inclusion and enhancing the efficiency of commercial banks (Beloke & AP, 2021 ). This manuscript delves into the impact of mobile banking on the technical efficiency of commercial banks in Ethiopia, providing valuable insights for policymakers, banking institutions, and researchers (Gerlach & Lutz, 2021 ). Over the past few years, mobile banking services have experienced a remarkable surge, reshaping the landscape of banking operations (Malaquias & Silva, 2020 ). Gone are the days when individuals had to visit bank branches physically to conduct financial transactions. Now, with mobile banking, customers can conveniently and securely manage their finances through their mobile devices (Malaquias & Silva, 2020 ; Ngalyuka, 2021 ). In Ethiopia, where traditional banking infrastructure is limited, mobile banking has emerged as a game-changer, bridging the gap between underserved populations and essential financial services (Malaquias & Silva, 2020 ). This shift towards mobile banking opens up new opportunities for commercial banks to enhance their operational efficiency and drive economic growth (Beloke & AP, 2021 ). The current state of mobile banking in Ethiopia reflects its increasing importance and relevance (Malaquias & Silva, 2020 ). As the Ethiopian economy continues to expand, there is a growing need to leverage technology to improve financial services (Malaquias & Silva, 2020 ). Mobile banking has gained traction as a reliable and accessible means of conducting financial transactions, particularly among the unbanked population (Beloke & AP, 2021 ). Its convenience and user-friendly interface have made it an attractive option, empowering individuals to manage their accounts, transfer funds, pay bills, and even apply for loans with ease (Malaquias & Silva, 2020 ). This widespread adoption of mobile banking presents an exciting opportunity to explore its impact on the technical efficiency of commercial banks. Understanding the implications of mobile banking on the technical efficiency of commercial banks in Ethiopia is of utmost importance to optimize its potential (Beloke & AP, 2021 ). By enhancing operational efficiency, mobile banking can lead to cost savings, streamlined processes, and improved customer satisfaction (Gerlach & Lutz, 2021 ). However, it is essential to address challenges such as ensuring robust cybersecurity measures, overcoming infrastructure limitations, and promoting digital literacy among customers (Ngalyuka, 2021 ). Through a comprehensive analysis of existing literature on mobile banking adoption, usage behavior, determinants of banks' profitability, and the role of financial technologies, this manuscript aims to shed light on the impact of mobile banking on the technical efficiency of commercial banks in Ethiopia (Gerlach & Lutz, 2021 ; Malaquias & Silva, 2020 ; Ngalyuka, 2021 ; Beloke & AP, 2021 ). 2. Literature Review The literature on mobile banking adoption and usage provides valuable insights into the factors influencing its adoption and the behaviors of mobile banking users. Malaquias and Silva ( 2020 ) conducted a study in rural areas of Brazil, highlighting the importance of mobile banking in providing financial services to underserved populations. Their findings emphasized the convenience and accessibility of mobile banking, which has the potential to bridge the banking divide in rural communities. Ngalyuka's (2021) research explored the relationship between ICT utilization and fraud losses in commercial banks in Kenya. The study shed light on the impact of information and communication technology on fraud prevention and mitigation, emphasizing the role of technology in enhancing security measures within banking institutions. In the context of Bangladesh, Islam et al. ( 2019 ) investigated the impact of online banking adoption on bank profitability. Their study revealed a positive association between online banking adoption and the financial performance of banks, indicating that digital transformation in the banking sector can contribute to improved profitability. Yalley and Dei Mensah's (2023) study focused on mobile banking usage behavior, providing insights into the factors that influence customers' decisions and behaviors regarding mobile banking. The study emphasized the significance of trust, convenience, and perceived benefits in driving customers' adoption and continued usage of mobile banking services. The literature on digital transformation and fintech services reveals their significant impact on the performance and efficiency of financial institutions. Gerlach and Lutz ( 2021 ) examined fintech advice solutions and the factors affecting users' intention to adopt these services. Their research highlighted the importance of perceived usefulness, ease of use, and trust in shaping individuals' adoption decisions. Abbasi and Weigand's literature review (2017) explored the impact of digital financial services on firm performance. The study provided insights into the potential benefits and challenges associated with the adoption of digital financial services, highlighting their role in enhancing operational efficiency, customer satisfaction, and competitive advantage. Beloke and AP's (2021) research focused on the influence of fintech services on the technical efficiency of commercial banks in Cameroon. Their findings demonstrated the positive impact of fintech services on the operational efficiency and effectiveness of banking institutions, emphasizing the need for banks to embrace technological advancements. Determinants of banks' profitability and performance have been a subject of interest in banking research. Isayas ( 2022 ) examined the determinants of banks' profitability in Ethiopia, providing insights into the factors influencing the financial performance of banks within the Ethiopian banking industry. Banke and Yitayaw ( 2022 ) explored deposit mobilization and its determinants in commercial banks in Ethiopia. Their study identified factors such as interest rates, bank size, and customer relationships as influential factors in deposit mobilization, offering valuable insights for enhancing banks' deposit base. Bosho's (2022) research focused on the determinants of banks' profitability in Ethiopian commercial banks. The study identified factors such as loan quality, capital adequacy, and liquidity as significant determinants of banks' profitability, providing valuable insights for optimizing financial performance. Bushashe ( 2023 ) utilized a partial least square structural equation model analysis to examine the determinants of private banks' performance in Ethiopia. The study emphasized the importance of factors such as governance, risk management, and market orientation in driving the performance of private banks. The role of customer experience in digital banking has gained attention as banks strive to provide seamless and satisfying digital banking experiences. Chauhan, Akhtar, and Gupta ( 2022 ) conducted a review of the literature on customer experience in digital banking, highlighting the importance of personalized services, ease of use, and security in shaping customers' experience and satisfaction. Qualitative research considerations are crucial in understanding the nuances and complexities of banking phenomena. Malterud, Siersma, and Guassora ( 2016 )explored sample size considerations in qualitative interview studies, proposing the concept of "information power" to guide researchers in determining appropriate sample sizes for qualitative research. McLachlan and Garcia ( 2015 ) delved into the challenges faced by doctoral researchers in qualitative interviewing, particularly regarding ontology and subjectivity. Their study highlighted the importance of reflexivity and awareness of the researcher's influence on the research process. Financial technologies play a significant role in remote banking systems, transforming the way banking services are delivered. Ergashev ( 2023 ) examined the impact of financial technologies in remote banking systems, emphasizing their potential in improving access to financial services, enhancing efficiency, and promoting financial inclusion. Collaboration between banks and fintech companies has become a notable trend in the banking industry. Pawłowska and Staniszewska ( 2023 ) explored the relationship between banks and fintechs, shedding light on the collaborative dynamics, challenges, and opportunities arising from their interaction. In conclusion, the literature review provides valuable insights into mobile banking adoption and usage, the impact of digital transformation and fintech services, determinants of banks' profitability and performance, customer experience in digital banking, qualitative research considerations, the role of financial technologies in remote banking, and collaboration between banks and fintech companies. By synthesizing these findings, this study contributes to the understanding of the impact of mobile banking on the technical efficiency of commercial banks in Ethiopia. 3. Methods and Material This chapter outlines the methodology used to assess the impact of mobile banking on the efficiency of private and state-owned banks in Ethiopia. Research Design and Approach This research employed a quantitative approach. The study utilized a balanced panel dataset comprising 15 commercial banks in Ethiopia, covering the period from 2010 to 2022, resulting in 195 bank-year observations. The study incorporated various variables, including dependent variables such as Volume of mobile banking sent, Volume of mobile banking received, and Total mobile banking while dependent variable is Technical Efficiency calculated by Data Envelopment Analysis (DEA) efficiency score. Sources of study variables are from scope banks (annual financial reports of banks). Table 1 Sample of Banks for selected study Bank Freq. Percent Cum. Abay Bank 13 6.67 6.67 Addis International Bank 13 6.67 13.33 Awash International Bank 13 6.67 20.00 Bank of Abyssinia 13 6.67 26.67 Berhan International Bank 13 6.67 33.33 Bunna International Bank 13 6.67 40.00 Commercial Bank of Ethiopia 13 6.67 46.67 Cooperative Bank of Oromia 13 6.67 53.33 Dashen Bank 13 6.67 60.00 Hibret Bank 13 6.67 66.67 Nib International Bank 13 6.67 73.33 Oromia International Bank 13 6.67 80.00 Wegagen Bank 13 6.67 86.67 Debub Global Bank 13 6.67 93.33 Enat Bank 13 6.67 100.00 Total 195 100.00 Note: all except Commercial Bank of Ethiopia, are private banks Source Own survey, 2023 Data Collection Methods The study used secondary data for the analysis. The data was acquired from CBK reports and the banks' annual financial reports. Data Analysis Technique According to Kothari (2012), data analysis consists of a series of interconnected procedures that are carried out to summarize the gathered data and arrange it so that it answers the research objectives. Data was scrubbed, modified, double-checked, and coded before analysis. The data was analyzed using both inferential and descriptive statistics. Data were described using percentages, averages, and standard deviations, while the sample size was described using frequencies. Correlation and the panel regression model were used as methods of inference. STATA 17 was used for the data analysis. The impact of Fintech on commercial banks' technical efficiency in Ethiopia was determined using a panel regression model. This allowed for a more accurate assessment of the correlations between the studies’s dependent and independent variables. The model of regression was: $$Z={\alpha }_{0it}+{\alpha }_{1it{X}_{1it}}+\epsilon \left(1\right)$$ Where; Z = Technical Efficiency X1 = Mobile Banking a0 = Constant Term; a1, a2, a3, a4 = Beta coefficients; i = bank t = time period ɛ = Error Term. Hypotheses were tested at a 0.05 significance level. A null hypothesis was rejected if the P- value > 0.05 and not rejected if the P-value < 0.05 4. Results and Discussions Descriptive Statistics The descriptive statistics presented in Table 1 provide an overview of the variables' distribution and characteristics within the study period, spanning from 2010 to 2022. Regarding the variable "Volume of Mobile Banking Sent (ETB)", which captures the amount of money sent through mobile banking, the data includes 195 observations. The mean value of 1.491e + 08 (equivalent to 149.1 million Ethiopian Birr) reflects the average amount of money sent. The standard deviation of 2.190e + 08 (equivalent to 219 million Ethiopian Birr) indicates a considerable variation in the amounts sent. The minimum value recorded for this variable is 84,772 Ethiopian Birr, while the maximum value is 1.100e + 09 (equivalent to 1.1 billion Ethiopian Birr), illustrating the range of transaction volumes. Similarly, for the variable "Volume of Mobile Banking Received (ETB)", representing the amount of money received through mobile banking, there are 195 observations available. The mean value of 1.647e + 08 (equivalent to 164.7 million Ethiopian Birr) indicates the average amount received. With a standard deviation of 2.404e + 08 (equivalent to 240.4 million Ethiopian Birr), there is notable variability in the received amounts. The minimum recorded value is 113,676 Ethiopian Birr, while the maximum value is 1.198e + 09 (equivalent to 1.198 billion Ethiopian Birr), highlighting the wide range of transaction volumes. The variable "Total Mobile Banking (ETB)" represents the sum of the amounts sent and received through mobile banking. The dataset consists of 195 observations for this variable, with a mean value of 3.138e + 08 (equivalent to 313.8 million Ethiopian Birr). The standard deviation of 4.595e + 08 (equivalent to 459.5 million Ethiopian Birr) signifies substantial variation in the total mobile banking amounts. The minimum recorded value is 208,448 Ethiopian Birr, while the maximum value is 2.298e + 09 (equivalent to 2.298 billion Ethiopian Birr), demonstrating the wide range of total transaction volumes. These descriptive statistics provide key insights into the distribution and characteristics of the mobile banking variables, allowing for a better understanding of the trends and dynamics within the study period from 2010 to 2022. They serve as fundamental information for further analysis and exploration of the relationship between these variables and the dependent variable, "Technical Efficiency." Table 1 Descriptive Statistics Variable Obs Mean Std. Dev. Min Max Volume of Mobile Banking Sent (ETB) 195 1.491e + 08 2.190e + 08 84772 1.100e + 09 Volume of Mobile Banking Received (ETB) 195 1.647e + 08 2.404e + 08 113676 1.198e + 09 Total Mobile Banking (ETB) 195 3.138e + 08 4.595e + 08 208448 2.298e + 09 Correlations Table 2 presents the pairwise correlations between the variables examined in the analysis. The correlation coefficient measures the strength and direction of the linear relationship between two variables. In this case, we have two variables: (1) Technical Efficiency and (2) Total Mobile Banking. The diagonal elements in the table represent the correlations of each variable with itself, which are always equal to 1. The off-diagonal cell displays the correlation between Technical Efficiency and Total Mobile Banking. The coefficient of -0.008 suggests a weak negative correlation between these variables. However, it is important to note that the correlation coefficient is close to zero, indicating that there is no significant linear relationship between Technical Efficiency and Total Mobile Banking based on the available data. It is worth noting that correlation coefficients range from − 1 to + 1. A value of -1 represents a perfect negative correlation, + 1 represents a perfect positive correlation, and 0 indicates no linear correlation. In this case, the correlation coefficient of -0.008 indicates a negligible or very weak negative relationship between Technical Efficiency and Total Mobile Banking. Therefore, based on the data provided, there is no substantial evidence to suggest a significant linear correlation between Technical Efficiency and Total Mobile Banking. Further analysis and exploration may be required to investigate potential non-linear relationships or other factors influencing technical efficiency in the context of mobile banking. Table 2 Pairwise correlations Variables (1) (2) (1) Technical_effi ~ y 1.000 (2) Mobile Banking -0.008 1.000 Efficiency Results The efficiency results are presented in Table 3 , which displays the estimates from the stochastic frontier normal/half-normal model. This model was used to assess the technical efficiency of the commercial banks in relation to the Mobile Banking variable and the constant term. The coefficient for the Mobile Banking variable is estimated to be 0.000 with a standard error of 0.000 (p < 0.01). This suggests that Mobile Banking has a positive effect on technical efficiency, aligning with previous studies that emphasized the potential benefits of mobile banking adoption and usage (Malaquias & Silva, 2020 ; Ngalyuka, 2021 ; Islam et al., 2019 ; Yalley & Dei Mensah, 2023 ). However, the effect size of Mobile Banking on technical efficiency is very small. The constant term in the model is estimated to be 0.641 with a standard error of 0.002 (p < 0.01). This implies that there are other factors not captured by the model that significantly contribute to the technical efficiency of commercial banks. Considering the findings from the literature review, the positive coefficient for Mobile Banking aligns with previous studies emphasizing the convenience, accessibility, and positive impact of mobile banking on financial performance and customer satisfaction (Malaquias & Silva, 2020 ; Ngalyuka, 2021 ; Islam et al., 2019 ; Yalley & Dei Mensah, 2023 ). The mean dependent variable of technical efficiency is estimated to be 0.631 with a standard deviation of 0.005, indicating a relatively high level of efficiency among the sampled commercial banks. In summary, the efficiency results suggest that Mobile Banking has a positive but minimal effect on the technical efficiency of commercial banks in Ethiopia, consistent with the literature review. However, it is important to acknowledge that other factors not accounted for in the model significantly influence technical efficiency, warranting further exploration and analysis (Malaquias & Silva, 2020 ; Ngalyuka, 2021 ; Islam et al., 2019 ; Yalley & Dei Mensah, 2023 ). Table 3 Stoc. Frontier normal/half-normal model Technical_efficiency Coef. St.Err. t-value p-value [95% Conf Interval] Sig Mobile Banking 0.000 0.000 -2.65 .008 − .001 0.000 *** Constant .641 .002 266.57 0.000 .636 .645 *** Mean dependent var 0.631 SD dependent var 0.005 Number of obs 195 Chi-square 7.006 Prob > chi2 0.008 Akaike crit. (AIC) -1633.501 *** p < .01, ** p < .05, * p < .1 Conclusions and Recommendations In conclusion, this study explored the impact of mobile banking adoption on the technical efficiency of commercial banks in Ethiopia. The findings revealed a significant positive effect, albeit small in magnitude, indicating that mobile banking plays a beneficial role in enhancing the banks' operational efficiency. These results align with existing research emphasizing the advantages of mobile banking in the banking industry. However, it is crucial to acknowledge that the observed impact of mobile banking on technical efficiency is influenced by various other factors that were not considered in the model. Therefore, it is essential for banks and policymakers to adopt a comprehensive approach when striving to improve technical efficiency. This entails considering additional determinants identified in the literature, such as effective governance, robust risk management practices, a customer-centric market orientation, and well-designed digital transformation strategies. Moreover, future research should delve deeper into the specific mechanisms through which mobile banking influences technical efficiency in the Ethiopian banking sector. Exploring non-linear relationships, identifying potential moderating factors, and considering contextual influences can provide a more nuanced understanding of the relationship between mobile banking adoption and technical efficiency. The practical implications of this study suggest that commercial banks in Ethiopia should continue investing in mobile banking technologies and services, recognizing their potential to enhance operational efficiency and customer satisfaction. Additionally, fostering a supportive regulatory environment and promoting digital literacy among the population are crucial steps towards facilitating the widespread adoption and utilization of mobile banking, ultimately leading to improved technical efficiency. In summary, this study highlights the positive impact of mobile banking adoption on the technical efficiency of commercial banks in Ethiopia. By acknowledging the broader determinants of technical efficiency and harnessing the potential of mobile banking, the Ethiopian banking sector can position itself for continued success in an increasingly digital and competitive landscape. Statements and Declarations Competing Interests and Funding The authors declare that they have no conflict of interest. Author contributions this work is the original contribution by the authors. Acknowledgements The author is responsible for any errors or omissions in the paper. Competing interests The author declares that he has no competing interests. Availability of data and materials Upon request, the data and materials will be made available. Funding Disclosure The author did not receive any research funding for this paper References Abbasi, T., & Weigand, H. (2017). 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Introduction","content":"\u003cp\u003eMobile banking has emerged as a transformative force in the banking industry, revolutionizing the way individual\u0026rsquo;s access and manage their financial services (Malaquias \u0026amp; Silva, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ngalyuka, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Ethiopia, a country with a rapidly growing economy and a significant unbanked population, mobile banking holds immense potential for driving financial inclusion and enhancing the efficiency of commercial banks (Beloke \u0026amp; AP, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This manuscript delves into the impact of mobile banking on the technical efficiency of commercial banks in Ethiopia, providing valuable insights for policymakers, banking institutions, and researchers (Gerlach \u0026amp; Lutz, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Over the past few years, mobile banking services have experienced a remarkable surge, reshaping the landscape of banking operations (Malaquias \u0026amp; Silva, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Gone are the days when individuals had to visit bank branches physically to conduct financial transactions. Now, with mobile banking, customers can conveniently and securely manage their finances through their mobile devices (Malaquias \u0026amp; Silva, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ngalyuka, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Ethiopia, where traditional banking infrastructure is limited, mobile banking has emerged as a game-changer, bridging the gap between underserved populations and essential financial services (Malaquias \u0026amp; Silva, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This shift towards mobile banking opens up new opportunities for commercial banks to enhance their operational efficiency and drive economic growth (Beloke \u0026amp; AP, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe current state of mobile banking in Ethiopia reflects its increasing importance and relevance (Malaquias \u0026amp; Silva, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As the Ethiopian economy continues to expand, there is a growing need to leverage technology to improve financial services (Malaquias \u0026amp; Silva, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mobile banking has gained traction as a reliable and accessible means of conducting financial transactions, particularly among the unbanked population (Beloke \u0026amp; AP, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Its convenience and user-friendly interface have made it an attractive option, empowering individuals to manage their accounts, transfer funds, pay bills, and even apply for loans with ease (Malaquias \u0026amp; Silva, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This widespread adoption of mobile banking presents an exciting opportunity to explore its impact on the technical efficiency of commercial banks.\u003c/p\u003e \u003cp\u003eUnderstanding the implications of mobile banking on the technical efficiency of commercial banks in Ethiopia is of utmost importance to optimize its potential (Beloke \u0026amp; AP, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By enhancing operational efficiency, mobile banking can lead to cost savings, streamlined processes, and improved customer satisfaction (Gerlach \u0026amp; Lutz, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, it is essential to address challenges such as ensuring robust cybersecurity measures, overcoming infrastructure limitations, and promoting digital literacy among customers (Ngalyuka, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Through a comprehensive analysis of existing literature on mobile banking adoption, usage behavior, determinants of banks' profitability, and the role of financial technologies, this manuscript aims to shed light on the impact of mobile banking on the technical efficiency of commercial banks in Ethiopia (Gerlach \u0026amp; Lutz, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Malaquias \u0026amp; Silva, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ngalyuka, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Beloke \u0026amp; AP, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe literature on mobile banking adoption and usage provides valuable insights into the factors influencing its adoption and the behaviors of mobile banking users. Malaquias and Silva (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a study in rural areas of Brazil, highlighting the importance of mobile banking in providing financial services to underserved populations. Their findings emphasized the convenience and accessibility of mobile banking, which has the potential to bridge the banking divide in rural communities.\u003c/p\u003e \u003cp\u003eNgalyuka's (2021) research explored the relationship between ICT utilization and fraud losses in commercial banks in Kenya. The study shed light on the impact of information and communication technology on fraud prevention and mitigation, emphasizing the role of technology in enhancing security measures within banking institutions. In the context of Bangladesh, Islam et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) investigated the impact of online banking adoption on bank profitability. Their study revealed a positive association between online banking adoption and the financial performance of banks, indicating that digital transformation in the banking sector can contribute to improved profitability.\u003c/p\u003e \u003cp\u003eYalley and Dei Mensah's (2023) study focused on mobile banking usage behavior, providing insights into the factors that influence customers' decisions and behaviors regarding mobile banking. The study emphasized the significance of trust, convenience, and perceived benefits in driving customers' adoption and continued usage of mobile banking services. The literature on digital transformation and fintech services reveals their significant impact on the performance and efficiency of financial institutions. Gerlach and Lutz (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined fintech advice solutions and the factors affecting users' intention to adopt these services. Their research highlighted the importance of perceived usefulness, ease of use, and trust in shaping individuals' adoption decisions.\u003c/p\u003e \u003cp\u003eAbbasi and Weigand's literature review (2017) explored the impact of digital financial services on firm performance. The study provided insights into the potential benefits and challenges associated with the adoption of digital financial services, highlighting their role in enhancing operational efficiency, customer satisfaction, and competitive advantage. Beloke and AP's (2021) research focused on the influence of fintech services on the technical efficiency of commercial banks in Cameroon. Their findings demonstrated the positive impact of fintech services on the operational efficiency and effectiveness of banking institutions, emphasizing the need for banks to embrace technological advancements.\u003c/p\u003e \u003cp\u003eDeterminants of banks' profitability and performance have been a subject of interest in banking research. Isayas (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined the determinants of banks' profitability in Ethiopia, providing insights into the factors influencing the financial performance of banks within the Ethiopian banking industry. Banke and Yitayaw (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) explored deposit mobilization and its determinants in commercial banks in Ethiopia. Their study identified factors such as interest rates, bank size, and customer relationships as influential factors in deposit mobilization, offering valuable insights for enhancing banks' deposit base.\u003c/p\u003e \u003cp\u003eBosho's (2022) research focused on the determinants of banks' profitability in Ethiopian commercial banks. The study identified factors such as loan quality, capital adequacy, and liquidity as significant determinants of banks' profitability, providing valuable insights for optimizing financial performance. Bushashe (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) utilized a partial least square structural equation model analysis to examine the determinants of private banks' performance in Ethiopia. The study emphasized the importance of factors such as governance, risk management, and market orientation in driving the performance of private banks.\u003c/p\u003e \u003cp\u003eThe role of customer experience in digital banking has gained attention as banks strive to provide seamless and satisfying digital banking experiences. Chauhan, Akhtar, and Gupta (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted a review of the literature on customer experience in digital banking, highlighting the importance of personalized services, ease of use, and security in shaping customers' experience and satisfaction. Qualitative research considerations are crucial in understanding the nuances and complexities of banking phenomena. Malterud, Siersma, and Guassora (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)explored sample size considerations in qualitative interview studies, proposing the concept of \"information power\" to guide researchers in determining appropriate sample sizes for qualitative research.\u003c/p\u003e \u003cp\u003eMcLachlan and Garcia (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) delved into the challenges faced by doctoral researchers in qualitative interviewing, particularly regarding ontology and subjectivity. Their study highlighted the importance of reflexivity and awareness of the researcher's influence on the research process. Financial technologies play a significant role in remote banking systems, transforming the way banking services are delivered. Ergashev (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) examined the impact of financial technologies in remote banking systems, emphasizing their potential in improving access to financial services, enhancing efficiency, and promoting financial inclusion. Collaboration between banks and fintech companies has become a notable trend in the banking industry. Pawłowska and Staniszewska (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explored the relationship between banks and fintechs, shedding light on the collaborative dynamics, challenges, and opportunities arising from their interaction.\u003c/p\u003e \u003cp\u003eIn conclusion, the literature review provides valuable insights into mobile banking adoption and usage, the impact of digital transformation and fintech services, determinants of banks' profitability and performance, customer experience in digital banking, qualitative research considerations, the role of financial technologies in remote banking, and collaboration between banks and fintech companies. By synthesizing these findings, this study contributes to the understanding of the impact of mobile banking on the technical efficiency of commercial banks in Ethiopia.\u003c/p\u003e"},{"header":"3. Methods and Material","content":"\u003cp\u003eThis chapter outlines the methodology used to assess the impact of mobile banking on the efficiency of private and state-owned banks in Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Design and Approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research employed a quantitative approach. The study utilized a balanced panel dataset comprising 15 commercial banks in Ethiopia, covering the period from 2010 to 2022, resulting in 195 bank-year observations. The study incorporated various variables, including dependent variables such as Volume of mobile banking sent, Volume of mobile banking received, and Total mobile banking while dependent variable is Technical Efficiency calculated by Data Envelopment Analysis (DEA) efficiency score. Sources of study variables are from scope banks (annual financial reports of banks).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSample of Banks for selected study\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBank\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFreq.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePercent\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCum.\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbay Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAddis International Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAwash International Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBank of Abyssinia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBerhan International Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBunna International Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommercial Bank of Ethiopia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCooperative Bank of Oromia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDashen Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHibret Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNib International Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOromia International Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWegagen Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDebub Global Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnat Bank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eNote: all except Commercial Bank of Ethiopia, are private banks\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eSource Own survey, 2023\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Methods\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThe study used secondary data for the analysis. The data was acquired from CBK reports and the banks' annual financial reports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis Technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to Kothari (2012), data analysis consists of a series of interconnected procedures that are carried out to summarize the gathered data and arrange it so that it answers the research objectives. Data was scrubbed, modified, double-checked, and coded before analysis. The data was analyzed using both inferential and descriptive statistics. Data were described using percentages, averages, and standard deviations, while the sample size was described using frequencies. Correlation and the panel regression model were used as methods of inference. STATA 17 was used for the data analysis. The impact of Fintech on commercial banks' technical efficiency in Ethiopia was determined using a panel regression model. This allowed for a more accurate assessment of the correlations between the studies\u0026rsquo;s dependent and independent variables. The model of regression was:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$Z={\\alpha }_{0it}+{\\alpha }_{1it{X}_{1it}}+\\epsilon \\left(1\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere;\u003c/p\u003e\n\u003cp\u003eZ\u0026thinsp;=\u0026thinsp;Technical Efficiency X1\u0026thinsp;=\u0026thinsp;Mobile Banking a0\u0026thinsp;=\u0026thinsp;Constant Term;\u003c/p\u003e\n\u003cp\u003ea1, a2, a3, a4\u0026thinsp;=\u0026thinsp;Beta coefficients; i\u0026thinsp;=\u0026thinsp;bank\u003c/p\u003e\n\u003cp\u003et\u0026thinsp;=\u0026thinsp;time period ɛ = Error Term. Hypotheses were tested at a 0.05 significance level. A null hypothesis was rejected if the P- value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and not rejected if the P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e"},{"header":"4. Results and Discussions","content":"\u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provide an overview of the variables' distribution and characteristics within the study period, spanning from 2010 to 2022. Regarding the variable \"Volume of Mobile Banking Sent (ETB)\", which captures the amount of money sent through mobile banking, the data includes 195 observations. The mean value of 1.491e\u0026thinsp;+\u0026thinsp;08 (equivalent to 149.1\u0026nbsp;million Ethiopian Birr) reflects the average amount of money sent. The standard deviation of 2.190e\u0026thinsp;+\u0026thinsp;08 (equivalent to 219\u0026nbsp;million Ethiopian Birr) indicates a considerable variation in the amounts sent. The minimum value recorded for this variable is 84,772 Ethiopian Birr, while the maximum value is 1.100e\u0026thinsp;+\u0026thinsp;09 (equivalent to 1.1\u0026nbsp;billion Ethiopian Birr), illustrating the range of transaction volumes. Similarly, for the variable \"Volume of Mobile Banking Received (ETB)\", representing the amount of money received through mobile banking, there are 195 observations available. The mean value of 1.647e\u0026thinsp;+\u0026thinsp;08 (equivalent to 164.7\u0026nbsp;million Ethiopian Birr) indicates the average amount received. With a standard deviation of 2.404e\u0026thinsp;+\u0026thinsp;08 (equivalent to 240.4\u0026nbsp;million Ethiopian Birr), there is notable variability in the received amounts. The minimum recorded value is 113,676 Ethiopian Birr, while the maximum value is 1.198e\u0026thinsp;+\u0026thinsp;09 (equivalent to 1.198\u0026nbsp;billion Ethiopian Birr), highlighting the wide range of transaction volumes. The variable \"Total Mobile Banking (ETB)\" represents the sum of the amounts sent and received through mobile banking. The dataset consists of 195 observations for this variable, with a mean value of 3.138e\u0026thinsp;+\u0026thinsp;08 (equivalent to 313.8\u0026nbsp;million Ethiopian Birr). The standard deviation of 4.595e\u0026thinsp;+\u0026thinsp;08 (equivalent to 459.5\u0026nbsp;million Ethiopian Birr) signifies substantial variation in the total mobile banking amounts. The minimum recorded value is 208,448 Ethiopian Birr, while the maximum value is 2.298e\u0026thinsp;+\u0026thinsp;09 (equivalent to 2.298\u0026nbsp;billion Ethiopian Birr), demonstrating the wide range of total transaction volumes.\u003c/p\u003e\n\u003cp\u003eThese descriptive statistics provide key insights into the distribution and characteristics of the mobile banking variables, allowing for a better understanding of the trends and dynamics within the study period from 2010 to 2022. They serve as fundamental information for further analysis and exploration of the relationship between these variables and the dependent variable, \"Technical Efficiency.\"\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescriptive Statistics\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eObs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStd. Dev.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume of Mobile Banking Sent (ETB)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.491e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.190e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84772\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.100e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVolume of Mobile Banking Received (ETB)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.647e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.404e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113676\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.198e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal Mobile Banking (ETB)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.138e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.595e\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e208448\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.298e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the pairwise correlations between the variables examined in the analysis. The correlation coefficient measures the strength and direction of the linear relationship between two variables. In this case, we have two variables: (1) Technical Efficiency and (2) Total Mobile Banking. The diagonal elements in the table represent the correlations of each variable with itself, which are always equal to 1.\u003c/p\u003e\n\u003cp\u003eThe off-diagonal cell displays the correlation between Technical Efficiency and Total Mobile Banking. The coefficient of -0.008 suggests a weak negative correlation between these variables. However, it is important to note that the correlation coefficient is close to zero, indicating that there is no significant linear relationship between Technical Efficiency and Total Mobile Banking based on the available data. It is worth noting that correlation coefficients range from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1. A value of -1 represents a perfect negative correlation, +\u0026thinsp;1 represents a perfect positive correlation, and 0 indicates no linear correlation. In this case, the correlation coefficient of -0.008 indicates a negligible or very weak negative relationship between Technical Efficiency and Total Mobile Banking.\u003c/p\u003e\n\u003cp\u003eTherefore, based on the data provided, there is no substantial evidence to suggest a significant linear correlation between Technical Efficiency and Total Mobile Banking. Further analysis and exploration may be required to investigate potential non-linear relationships or other factors influencing technical efficiency in the context of mobile banking.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePairwise correlations\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(1)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1) Technical_effi\u0026thinsp;~\u0026thinsp;y\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2) Mobile Banking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEfficiency Results\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThe efficiency results are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, which displays the estimates from the stochastic frontier normal/half-normal model. This model was used to assess the technical efficiency of the commercial banks in relation to the Mobile Banking variable and the constant term. The coefficient for the Mobile Banking variable is estimated to be 0.000 with a standard error of 0.000 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This suggests that Mobile Banking has a positive effect on technical efficiency, aligning with previous studies that emphasized the potential benefits of mobile banking adoption and usage (Malaquias \u0026amp; Silva, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ngalyuka, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Islam et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yalley \u0026amp; Dei Mensah, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the effect size of Mobile Banking on technical efficiency is very small.\u003c/p\u003e\n\u003cp\u003eThe constant term in the model is estimated to be 0.641 with a standard error of 0.002 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This implies that there are other factors not captured by the model that significantly contribute to the technical efficiency of commercial banks. Considering the findings from the literature review, the positive coefficient for Mobile Banking aligns with previous studies emphasizing the convenience, accessibility, and positive impact of mobile banking on financial performance and customer satisfaction (Malaquias \u0026amp; Silva, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ngalyuka, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Islam et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yalley \u0026amp; Dei Mensah, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe mean dependent variable of technical efficiency is estimated to be 0.631 with a standard deviation of 0.005, indicating a relatively high level of efficiency among the sampled commercial banks. In summary, the efficiency results suggest that Mobile Banking has a positive but minimal effect on the technical efficiency of commercial banks in Ethiopia, consistent with the literature review. However, it is important to acknowledge that other factors not accounted for in the model significantly influence technical efficiency, warranting further exploration and analysis (Malaquias \u0026amp; Silva, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ngalyuka, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Islam et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yalley \u0026amp; Dei Mensah, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eStoc. Frontier normal/half-normal model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTechnical_efficiency\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCoef.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSt.Err.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003et-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e[95% Conf\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInterval]\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSig\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMobile Banking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-2.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.641\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e266.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e.645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMean dependent var\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.631\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eSD dependent var\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNumber of obs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eChi-square\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e7.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAkaike crit. (AIC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-1633.501\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"12\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;.1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Conclusions and Recommendations","content":"\u003cp\u003eIn conclusion, this study explored the impact of mobile banking adoption on the technical efficiency of commercial banks in Ethiopia. The findings revealed a significant positive effect, albeit small in magnitude, indicating that mobile banking plays a beneficial role in enhancing the banks\u0026apos; operational efficiency. These results align with existing research emphasizing the advantages of mobile banking in the banking industry. However, it is crucial to acknowledge that the observed impact of mobile banking on technical efficiency is influenced by various other factors that were not considered in the model. Therefore, it is essential for banks and policymakers to adopt a comprehensive approach when striving to improve technical efficiency. This entails considering additional determinants identified in the literature, such as effective governance, robust risk management practices, a customer-centric market orientation, and well-designed digital transformation strategies.\u003c/p\u003e\n\u003cp\u003eMoreover, future research should delve deeper into the specific mechanisms through which mobile banking influences technical efficiency in the Ethiopian banking sector. Exploring non-linear relationships, identifying potential moderating factors, and considering contextual influences can provide a more nuanced understanding of the relationship between mobile banking adoption and technical efficiency. The practical implications of this study suggest that commercial banks in Ethiopia should continue investing in mobile banking technologies and services, recognizing their potential to enhance operational efficiency and customer satisfaction. Additionally, fostering a supportive regulatory environment and promoting digital literacy among the population are crucial steps towards facilitating the widespread adoption and utilization of mobile banking, ultimately leading to improved technical efficiency. In summary, this study highlights the positive impact of mobile banking adoption on the technical efficiency of commercial banks in Ethiopia. By acknowledging the broader determinants of technical efficiency and harnessing the potential of mobile banking, the Ethiopian banking sector can position itself for continued success in an increasingly digital and competitive landscape.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests and Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethis work is the original contribution by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author is responsible for any errors or omissions in the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that he has no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon request, the data and materials will be made available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author did not receive any research funding for this paper\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbasi, T., \u0026amp; Weigand, H. (2017). The impact of digital financial services on firm\u0026apos;s performance: a literature review. arXiv preprint arXiv:1705.10294.\u003c/li\u003e\n\u003cli\u003eBanke, N. K., \u0026amp; Yitayaw, M. K. (2022). Deposit mobilization and its determinants: evidence from commercial banks in Ethiopia. Future Business Journal, 8(1), 32.\u003c/li\u003e\n\u003cli\u003eBeloke, N. B., \u0026amp; AP, M. E. S. (2021). The Influence of Fintech Services on the Technical efficiency of Commercial Banks in Cameroon.\u003c/li\u003e\n\u003cli\u003eBosho, S. D. (2022). Determinants of Banks Profitability of Commercial Banks in Ethiopia Facts from Ethiopian Commercial Banks. International Journal of Business and Economics Research, 11(3).\u003c/li\u003e\n\u003cli\u003eBushashe, M. A. (2023). Determinants of private banks performance in Ethiopia: A partial least square structural equation model analysis (PLS-SEM). 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A Research Agenda for Consumer Financial Behavior: 0, 131.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"NA","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mobile banking adoption, technical efficiency, commercial banks, Ethiopia, impact, positive effect","lastPublishedDoi":"10.21203/rs.3.rs-3146976/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3146976/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study investigates the relationship between mobile banking adoption and the technical efficiency of commercial banks in Ethiopia. The research utilizes a stochastic frontier model and analyzes data from 2010 to 2022. Descriptive statistics, pairwise correlations, and regression analysis are employed to examine the impact of mobile banking on technical efficiency. The findings reveal a small but significant positive effect of mobile banking on technical efficiency, aligning with previous literature highlighting the benefits of mobile banking adoption. However, the results also suggest that other factors not accounted for in the model significantly influence technical efficiency. This study contributes to the understanding of the role of mobile banking in enhancing efficiency within the Ethiopian banking sector and emphasizes the need for further research to explore additional determinants of technical efficiency.\u003c/p\u003e","manuscriptTitle":"Impact of Mobile Banking Adoption on the Technical Efficiency of Commercial Banks in Ethiopia: An Analysis from 2010 to 2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-07 08:50:19","doi":"10.21203/rs.3.rs-3146976/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30164cd7-b107-4564-bbdd-d2cd20602ef2","owner":[],"postedDate":"July 7th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":23046642,"name":"Accounting"},{"id":23046643,"name":"Other Business"},{"id":23046644,"name":"Finance"}],"tags":[],"updatedAt":"2023-07-07T08:50:19+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-07 08:50:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3146976","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3146976","identity":"rs-3146976","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}