FACTORS INFLUENCING THE ADOPTION OF OPEN BANKING BY EMPLOYEES OF COMMERCIAL BANKS IN KENYA ALEX WOKABI REG NO: 156996 A RESEARCH THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTER OF BUSINESS ADMINISTRATION OF STRATHMORE UNIVERSITY MAY, 2025 ii STUDENT’S DECLARATION I declare that this thesis has not been previously submitted and approved for the award of a Master’s degree by this or any other university. To the best of my belief and knowledge, no other person has previously written or published the content herein, with the only exception where reference materials are included in the study. Name of Candidate: ALEX MBUTHIA WOKABI Date: …28th May 2025. Reg. No: 156996 Signature: …………………………... SUPERVISOR’S APPROVAL This research dissertation has been submitted for examination with my approval as the candidate’s supervisor. Signature: … ……Date: …29th May 2025………………………. DR. STELLA NYONGESA LECTURER, STRATHMORE UNIVERSITY iii DEDICATION I dedicate this study to God for the wisdom and strength I drew to complete this research. I also dedicate this work to my family for their never-ending support, encouragement, and understanding throughout this research process. iv ACKNOWLEDGEMENT I thank my supervisor, Dr Stella Nyongesa, for her invaluable encouragement, feedback, and critical insights. Her feedback has played a key role in shaping and refining this study. I also extend my appreciation to my colleagues and lecturers at Strathmore University. Their academic and professional support provided the foundation for this research. Furthermore, I acknowledge the leadership and staff at Strathmore University for ensuring access to a conducive research environment and educational resources. v TABLE OF CONTENTS STUDENT’S DECLARATION ................................................................................................... ii DEDICATION.............................................................................................................................. iii ACKNOWLEDGEMENT ........................................................................................................... iv LIST OF TABLES ..................................................................................................................... viii LIST OF FIGURES ...................................................................................................................... x ABBREVIATIONS AND ACRONYMS .................................................................................... xi ABSTRACT ................................................................................................................................. xii CHAPTER ONE: INTRODUCTION ......................................................................................... 1 1.1. Background to the Study ...................................................................................................... 1 1.1.1. Open Banking ............................................................................................................... 2 1.1.2. Open Banking Adoption ................................................................................................ 4 1.1.3. Factors Influencing Open Banking Adoption ................................................................ 5 1.1.4. The Commercial Banking Sector in Kenya ................................................................... 7 1.2. Problem Statement ............................................................................................................... 9 1.3. Research Aim and Objectives ............................................................................................ 12 1.3.1. General Objective ....................................................................................................... 12 1.3.2. Specific Objectives ..................................................................................................... 12 1.4. Research Questions ............................................................................................................ 13 1.5. Scope of the Study .............................................................................................................. 13 1.6. Significance of the Study ................................................................................................... 13 1.7 Chapter Summary ................................................................................................................ 15 CHAPTER TWO: LITERATURE REVIEW .......................................................................... 15 2.1. Introduction ........................................................................................................................ 15 2.2. Theoretical Framework ...................................................................................................... 16 2.2.1. Technology Acceptance Model (TAM) ...................................................................... 16 2.2.2. Unified Theory of Acceptance and Use of Technology (UTAUT) ............................. 19 2.3. Empirical Literature Review .............................................................................................. 22 2.3.1. Regulatory Support and Open Banking Adoption .......................................................... 22 2.3.2. Technological Infrastructure and Open Banking Adoption ............................................ 25 2.3.3. Digital Literacy in Technology and Open Banking Adoption ........................................ 27 2.3.4. Organisational Readiness and Open Banking Adoption ................................................. 29 vi 2.4. Summary of Literature Gaps .............................................................................................. 31 2.5. Conceptual Framework ...................................................................................................... 36 2.6. Operationalization of the Study’s Variables ...................................................................... 37 2.7. Chapter Summary ............................................................................................................... 39 CHAPTER THREE: RESEARCH METHODOLOGY ......................................................... 40 3.1. Introduction ........................................................................................................................ 40 3.2. Research Philosophy .......................................................................................................... 40 3.3. Research Design ................................................................................................................. 41 3.4. Population and Sampling ................................................................................................... 42 3.4.1. Target Population ........................................................................................................ 42 3.4.2. Sampling Technique .................................................................................................... 42 3.5. Data Collection Tool .......................................................................................................... 44 3.6 Data Collection Procedures ................................................................................................. 46 3.7. Data Quality ....................................................................................................................... 48 3.7.1 Piloting ............................................................................................................................. 48 3.7.2 Reliability ......................................................................................................................... 48 3.7.3 Validity ............................................................................................................................. 49 3.8. Data Analysis ..................................................................................................................... 50 3.9. Ethical Considerations........................................................................................................ 52 3.10. Chapter Summary ............................................................................................................. 52 CHAPTER FOUR: DATA ANALYSIS AND RESULTS ....................................................... 53 4.1 Introduction ......................................................................................................................... 53 4.2 Response Rate ..................................................................................................................... 54 4.3 Demographic Analysis ........................................................................................................ 54 4.3.1 Gender .......................................................................................................................... 54 4.3.2 Age................................................................................................................................ 55 4.3.3 Level of Education ........................................................................................................ 55 4.3.4 Banking Tier ................................................................................................................. 56 4.3.5 Work Experience .......................................................................................................... 57 4.3.6 Department at Work ..................................................................................................... 57 4.4 Descriptive Statistics ........................................................................................................... 58 4.5 Inferential Statistics ............................................................................................................. 59 vii 4.5.1 Multivariate Assumption Tests ..................................................................................... 59 4.6 Correlation Analysis ............................................................................................................ 62 4.7 Regression Analysis ............................................................................................................ 63 4.7.1 The Influence of Regulatory Support on the Adoption of Open Banking.................... 64 4.7.2. The Influence of Technological Infrastructure on the Adoption of Open Banking .... 66 4.7.3 The Influence of Employees’ Digital Literacy on the Adoption of Open Banking ..... 68 4.7.4. The Influence of Organisational Readiness on the Adoption of Open Banking ......... 70 4.8 Overall Regression Model ................................................................................................... 72 4.9 Conclusion ........................................................................................................................... 75 CHAPTER FIVE DISCUSSIONS, CONCLUSIONS, AND RECOMMENDATIONS ....... 76 5.1. Introduction ........................................................................................................................ 76 5.2 Summary of the Study ......................................................................................................... 76 5.3. Discussion of the Findings ................................................................................................. 78 5.3.1. Regulatory Support and Open Banking Adoption ....................................................... 78 5.3.2. Technological Infrastructure and Open Banking Adoption ........................................ 79 5.3.3. Digital Literacy and Open Banking Adoption ............................................................. 80 5.3.4. Organizational Readiness and Open Banking Adoption ............................................. 82 5.4 Conclusion ........................................................................................................................... 83 5.5. Recommendations .............................................................................................................. 84 5.6 Contributions of the Study .................................................................................................. 84 5.6.1. Contribution of the Study to Theory............................................................................ 84 5.6.2. Contribution of the Study to Academia ....................................................................... 85 5.6.3. Contribution of the Study to Policy ............................................................................. 85 5.7. Limitations of the Study ..................................................................................................... 86 5.8. Suggestions for Future Research ........................................................................................ 86 REFERENCES ............................................................................................................................ 87 APPENDIX I: TRANSMITTAL LETTER ............................................................................ 102 APPENDIX II: QUESTIONNAIRE ....................................................................................... 103 APPENDIX III: TARGET POPULATION ........................................................................... 109 APPENDIX 1V: ETHICAL REVIEW APPROVAL ............................................................ 112 APPENDIX V: NACOSTI RESEARCH PERMIT ............................................................... 113 viii LIST OF TABLES Table 2.1. Summary of Literature Gaps........................................................................................ 33 Table 2.2. Operationalization of the Study Variables ................................................................... 37 Table 3.1. Sample Frame and Respondents’ Distribution ............................................................ 44 Table 3.2. Reliability Pilot Statistics............................................................................................. 49 Table 3.3. Reliability Test for All Variables ................................................................................. 49 Table 3.4. Average Variance Extracted (AVE) Pilot Results ....................................................... 50 Table 4.1. Response Rate .............................................................................................................. 54 Table 4.2. Sample Frame and Respondents’ Distribution ............................................................ 54 Table 4.3. Respondents’ Age Results ........................................................................................... 55 Table 4.4. Respondent’s Education Level Results........................................................................ 55 Table 4.5. Banking Tier Results ................................................................................................... 56 Table 4.6. Work Experience Results ............................................................................................. 57 Table 4.7. Department at Work Results ........................................................................................ 57 Table 4.8. Descriptive Statistics Results ....................................................................................... 58 Table 4.9. Collinearity Test .......................................................................................................... 61 Table 4.10. Pearson’s Correlation Analysis Results For All Variable ......................................... 63 Table 4.11.Model Summary Results For Regulatory Support and Bank Adoption ..................... 64 Table 4.12. ANOVA Results For Regulatory Support and Bank Adoption ................................. 65 Table 4.13. Coefficients Results For Regulatory Support and Bank Adoption ............................ 65 Table 4.14. Model Summary For Technological Infrastructure and Bank Adoption ................... 67 Table 4.15. ANOVA Results For Technological Infrastructure and Bank Adoption ................... 67 Table 4.16. Coefficients Results For Technological Infrastructure and Bank Adoption .............. 67 Table 4.17. Model Summary Results For Digital Literacy and Bank Adoption .......................... 68 Table 4.18. ANOVA Results For Digital Literacy and Bank Adoption ....................................... 69 Table 4.19. Coefficients Results For Digital Literacy and Bank Adoption .................................. 69 Table 4.20. Model Summary Results For Organisational Readiness and Bank Adoption ........... 70 Table 4.21. ANOVA Results For Organisational Readiness and Bank Adoption........................ 71 ix Table 4.22. Coefficients Results For Organisational Readiness and Bank Adoption .................. 71 Table 4.23. Model Summary Results For All Variables ............................................................... 72 Table 4.24. ANOVA Results For All Variables ........................................................................... 73 Table 4.25. Coefficients Results For All Variables ...................................................................... 73 x LIST OF FIGURES Figure 2.1. Conceptual Framework .............................................................................................. 36 Figure 4.1. Normality Test ............................................................................................................ 60 Figure 4.2. Homoscedasticity Test................................................................................................ 61 xi ABBREVIATIONS AND ACRONYMS API Application Programming Interfaces BRICS Brazil, Russia, India, China and South Africa CDR Consumer Data Right CMA Competition and Markets Authority EFA Exploratory Factor Analysis GDPR General Data Protection Regulation KBA Kenya Banker’s Association OBIE Open Banking Implementation Entity PEOU Perceived Ease of Use PSD2 Payment Services Directive 2 PU Perceived Usefulness SPSS Statistical Package for the Social Sciences TAM Technology Acceptance Model UK United Kingdom US United States UTAUT Unified Theory of Acceptance and Use of Technology xii ABSTRACT The banking sector is experiencing rapid digital transformation, with open banking emerging as a disruptive technology. Open banking enables third-party financial service providers to access customers’ banking data through Application Programming Interfaces (APIs). While developed nations increasingly embrace open banking, its adoption in Kenya remains limited. Some factors attributed to this limited penetration include an organisation’s readiness, regulatory support or uncertainty, digital literacy, and technological infrastructure. This study explored the factors influencing open banking adoption among employees of Kenya’s commercial banks. Specifically, the study examined how regulatory support, technological infrastructure, digital literacy, and organizational readiness determined open banking adoption. The Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) models were the frameworks that allowed for an understanding of the study’s variables. Adopting a positivist research philosophy, this study employed a descriptive cross-sectional research design. The researcher then collected data from employees working in Kenya’s licensed commercial banks through structured questionnaires using a five-point Likert scale. The sample size of 387 respondents was determined using Yamane’s formula to ensure diverse representation across the three bank tiers. Descriptive and inferential statistical methods, including multiple linear regression, were used to analyse the relationships between the variables. The findings revealed that technological infrastructure and regulatory support significantly impact employees’ intention to adopt open banking, with organizational readiness influencing adoption moderately. Conversely, digital literacy was found to have a weak influence on bank employees’ intention to adopt open banking. The study concluded that banking institutions should prioritise stronger regulatory frameworks, robust technological infrastructure, and organizational readiness for open banking adoption to succeed in Kenya. These findings provide insights to guide policymakers, commercial banks, and regulators in improving the regulatory and technological environment for open banking to flourish in Kenya. Although the study fulfilled its intended purpose, it was limited as its findings were not generalisable to other regions of Kenya, as the focus was on commercial banks in Nairobi County. Another limitation was that the study only focused on a limited set of variables, including regulatory support, technological infrastructure, organisational readiness, and digital literacy. 1 1. CHAPTER ONE: INTRODUCTION 1.1.Background to the Study The United Kingdom (UK) was among the first nations to introduce open banking through the launch of the open banking initiative in 2018. The Competition and Markets Authority (CMA) presented this initiative to ensure increased competition and innovation in the sector (Borgogno & Colangelo, 2020). The initiative specifically gave rise to open banking by requiring the nine largest banks in the UK to open their customer data to third-party service providers through APIs (application programming interfaces). However, the establishment of open banking can be traced to the 1980s when the German Federal Post Office conducted experiments for online banking systems (Schenk, 2024). In Europe, the PSD2 (Revised Payment Services Directive) of 2015 provided a platform for the growth of open banking (Preziuso et al., 2023). Like the CMA initiative, the Payment Services Directive 2 (PSD2) required banks to open their data to third-party service providers. Since 2018, open banking has grown widely, including in the Brazil, Russia, India, and South Africa (BRICS) region (Fang & Zhu, 2023). However, European banks predominantly and currently lead the way in terms of open banking adoption, with inadequate regulatory frameworks limiting penetration in regions like Africa (Nanaeva et al., 2021). Such inadequate regulatory frameworks may also have limited the capacity of commercial banks in Kenya to adopt open banking (Nyawara, 2021). In Africa, the development of open banking can be traced back to the early 2000s with the rise of mobile banking platforms (Regragui, 2022). Consequently, the 2010s witnessed an explosion of FinTech companies across the continent (Ndung’u, 2022). Specifically, FinTech firms increasingly provided services like digital wallets. However, open banking solutions were still absent, unlike the sector’s increased growth in regions like Europe (Preziuso et al., 2023). Even so, Egypt, South Africa, and Nigeria experienced a rise in digital financial services mirroring the open banking trend (Kheira, 2022). Currently, Africa is witnessing more concrete efforts towards the establishment of open banking. For example, the Financial Sector Conduct Authority of South Africa has begun exploring open banking regulations, with such regulations vital for success (Giya et al., 2021; Ziegler, 2021). In Kenya, open banking can be traced to the mobile banking revolution of 2007, with the current mobile banking culture influencing the journey towards open banking (Kimenyi & Ndung’u, 2 2009). M-Pesa, for example, has enabled millions of Kenyans to access financial services (Mbiti & Weil, 2015). FinTech’s growth, especially its rise between 2010 and 2020, has set the stage for the introduction of open banking (Ndung’u & Moturi, 2020). However, open banking is in the early stages of its penetration, with inadequate regulations and the vast growth of mobile banking limiting its expansion (Nyawara, 2021). Even so, the Central Bank of Kenya has recognised the potential for open banking and included this model in its Digital Finance Policy (FinTech News Africa, 2021). Despite this recognition, there is insufficient research on the factors influencing the adoption of open banking by employees of Kenya’s commercial banks. Open banking enables third-party service providers to access consumers' financial data through regulated APIs (Application Programming Interfaces) (Fang & Zhu, 2023). It fosters competition, enhances financial inclusion, and allows banks to offer personalized services. However, the adoption of open banking varies across markets. In developed economies like the UK and Europe, regulatory frameworks such as the PSD2 have facilitated adoption (Preziuso et al., 2023). Conversely, in Kenya, open banking is still in its nascent stages, and this outcome results from regulatory ambiguity, technological constraints, and limited digital literacy among banking staff. Several factors impact the likelihood that employees of Kenya’s commercial banks will embrace open banking. For example, organizational readiness, technological infrastructure, and digital literacy determine how quickly commercial banks adopt open banking. Banks with modern IT infrastructures and digital transformation strategies are more likely to integrate open banking solutions (Ndung’u & Moturi, 2020). On the other hand, customer demand, regulatory or government support, and competitive pressures from FinTechs, also shape the pace of adoption. Kenya’s banking sector has experienced a shift towards digital transformation, and understanding how these factors interrelate is essential to ensure an environment that supports open banking. 1.1.1. Open Banking Various scholars have defined open banking based on the context of their studies. Broby (2021) defines open banking as a medium for sharing financial information between third-party agents and banks. It revolutionises the banking industry by allowing consumers to provide third-party service providers with their financial data. On the other hand, Mansfield-Devine (2016) views open banking as the ability of an organisation to process payments on behalf of its users. For this 3 to work, the users must permit the entity to access the bank’s application programming interfaces (APIs). Further, Unsal et al. (2020) define open banking as using APIs to enhance customer experience by offering them personalised financial products. Conversely, Ziegler (2021) views open banking as the mandated sharing of financial information to ensure increased competitiveness. Similarly, Ramdani et al. (2020) view open banking as a model that permits banks to cooperate with FinTechs by securely sharing users’ financial data. Finally, Mugambi (2022) claims that open banking is becoming the de facto standard for sharing data, where third parties’ access to customers’ bank information allows them to provide personalised financial services. Consumer data exchange is built on application programming interfaces (APIs). APIs allow communication among diverse software components, and consumer consent drives this exchange (Unsal et al., 2020). Third-party service providers are interested in accessing this financial data to provide customised financial services. For example, FinTechs analyse past consumer data to ensure loans are approved quickly. As such, open banking empowers consumers to access financial services and products that traditional banks do not offer (Broby, 2021). The data is also exchanged securely. Indeed, security is essential to ensure consumer data is protected and that privacy and confidentiality are assured. Open banking follows a particular process whereby a consumer’s bank account generates financial data (Broby, 2021). Subsequently, a third-party service provider accesses the data using an API request. Consumers have increasingly embraced open banking, and this sentiment is reflected in the rapid growth and expected adoption by other players in this sector worldwide (Statista Research Department, 2024). The levels of API integration, partnerships between banks and third-party service providers, and the frequency of open banking transactions determine whether banks will adopt open banking (Brodsky et al., 2017; Premchand & Choudhry, 2018). It is vital to measure and analyse these factors, which can help identify the extent of adoption of open banking by employees of Kenya’s commercial banks. For instance, there is an extent to which a commercial bank has embedded open banking APIs, and this extent equates to the level of open banking adoption. Further, third-party service providers must obtain consent from consumers to access their data (Nyawara, 2021). Banks usually have third-party relationships with FinTechs and other third-party service providers. The quality of these partnerships determines the level of open banking adoption within commercial banks. 4 Open banking is a system or model where financial institutions allow third-party providers to access customer data through secure digital platforms, such as APIs (Broby, 2021). APIs facilitate this exchange, which enables external financial service providers to provide advanced tech products. On the other hand, open banking services include real-time payments, automated lending solutions, and enhanced customer financial insights. In this study, open banking refers to the overarching framework of data sharing in the banking sector (Unsal et al., 2020). In contrast, open banking services encompass the specific financial products enabled by open banking. While the two terms are related, they are not interchangeable. This study focuses on the adoption of open banking as a system rather than specific services. 1.1.2. Open Banking Adoption Open banking adoption is the study’s dependent variable. Nguyen (2020) defines it as a bank demonstrating its willingness and readiness to embrace and implement open banking. People evaluate the possibility of accepting and using innovations by thinking logically about their technological readiness and then considering this technology’s perceived usefulness and ease of use (Sivathanu, 2019). Premchand and Choudhry (2018) claim that open banking uptake is based on whether APIs are well integrated and whether banks and third-party service providers cooperate. A major benefit of open banking is ensuring customers access numerous offerings through personalised recommendations and data analytics. Such an advantage may enhance perceived usefulness and drive commercial banks to embrace open banking (Long et al., 2020). The technology acceptance model has been used in extant studies for different banking technologies, including the automated teller machine, telephone, internet, and mobile banking, to research the adoption of a new technology (Sivathanu, 2019; Kaulu et al., 2024). In the context of this study, perceived usefulness represents how much the employees found it useful to adopt open banking, while perceived ease of use signifies the extent to which they consider that adopting open banking is effortless (Kaur & Arora, 2020). Bank employees’ attitudes may determine whether they will accept or reject open banking (Himel et al., 2021). When employees’ attitude towards open banking is favourable, they are likely to support the adoption of open banking. Accordingly, the adoption of open banking is defined as a bank’s likelihood, willingness, and readiness to adopt 5 open banking, and these indicators were derived from Suh and Han (2002), Sivathanu (2019), Himel et al. (2021), and Kaulu et al. (2024) studies. 1.1.3. Factors Influencing Open Banking Adoption Several factors can influence the adoption of open banking across different contexts. An organisation is usually subject to conditions of the internal and external environment, which affect its operations. The external environment presents an organisation with the necessary resources to develop its internal workings (Shatilo, 2019). Further, Gozman et al. (2022) note that external forces shape banking innovation, and these forces compel institutions to adopt emerging technologies. Accordingly, some of these external pressures include regulatory frameworks, competition, technological advancements, and consumer demand. Regulatory policies set the legal framework for data sharing, and these frameworks ensure security and compliance (Remolina, 2019). FinTechs provide considerable competitive pressure, which forces banks to adopt open banking (Omarini, 2018). Additionally, technological advancements create a favourable environment for digital banking (Hefny et al., 2023). Consumer demand is also a key driver as customers increasingly expect seamless and personalized financial services. Internal conditions can also impact an organisation’s strategic decision making (Shatilo, 2019). These internal conditions determine an organisation’s expansion, but that relies on the market or external environment. The top management can control and manage these internal or firm factors at any time (Sadiku, 2022). Engidaw (2021) characterises internal factors as those that emphasize capacity management, entrepreneurship, technological ability, and marketing capabilities. Correspondingly, Sadiku (2022) contends that financial capability, human resources, and technological capacity significantly affect a firm’s performance. This study focuses on regulatory support, technological infrastructure, digital literacy, and organisational readiness, which are regarded as factors that affect open banking adoption. Accordingly, there is a need to analyse a few global, regional, and local studies and their conceptualisation of these factors. Open banking adoption depends on the digital literacy of employees and organisational readiness. In the global context, Hussain and Papastathapoulos (2022) revealed organisational readiness as a considerable element in determining the adoption of new banking technologies. The authors measured organisational readiness using a set of indicators, 6 including change valence, change efficacy, cultural readiness, partnership readiness, and strategic readiness. These indicators were adopted from the literature and measured using a five-point Likert scale. Consequently, the study indicated that change valence and change efficacy are essential for digital transformation in banking. Further, Cetindamar et al. (2021) linked digital banking innovation with digital literacy. The study focused on the adoption of cloud technology, where technology’s use intention and use behaviour were measured using a Likert scale. They noted that examining employees’ digital skills and the presence of digital skills training could aid in determining their digital literacy. On the other hand, Dinckol (2021) investigated whether an association existed between the acceptance of banking technology and the presence of a robust regulatory environment. The data collection process involved 114 semi-structured interviews with challenger banks, FinTechs, regulators, and industry experts. This process was further complemented with archival research. Notably, the study focused on challenges in implementing API standards, experiences regarding partnerships, and challenges regarding open banking adoption. Ultimately, the findings indicated the relevance of effective regulatory standards in open banking adoption. Regionally, the work of Olaolu (2022) generated useful perspectives on the influence of security and banking regulations. Using TAM, the study analysed 250 questionnaires and measured PU, PEOU, and security and banking regulations using five-point Likert scales. The findings supported the work of Dinckol (2021) in advancing the role of a regulatory framework. Further, Ofusu- Ampong (2021) advanced the influence of technological readiness in digital transformation. A mixed-methods approach was used to measure price value, inherent innovativeness, and technology readiness using Likert scales and thematic analysis. In Kenya, Nyawara (2021) explored the regulatory landscape for FinTech adoption. The researcher analysed secondary data sources to uncover the factors influencing FinTechs and open banking. Using a literature review, the study analysed key variables related to the regulatory landscape, including regulatory flexibility, consumer data protection, and innovation incentives. The findings show that a rigid regulatory landscape encouraged digital banking innovation. However, this landscape lacked clear data security measures. 7 Evidently, several factors may determine the adoption of open banking and digital technologies by banking organisations. However, critical literature gaps prevail. For example, the global studies positively link digital innovation with organisational readiness, regulatory frameworks, and digital literacy (Hussain & Papastathapoulos, 2022; Dinckol, 2021; Cetindamar et al., 2021). However, the insights generated lack specificity to open banking adoption within commercial banks. The study by Olaolu (2022) advanced the vitality of security and banking regulations. However, it largely centred on perceived ease of use, while Ofusu-Ampong (2021) did not focus on a specific technology. By focusing on the factors determining the adoption of open banking within Kenyan commercial banks, this study aims to bridge these gaps. In this light, this study examines the specific factors influencing the adoption of open banking, namely regulatory framework, organizational readiness, digital literacy, and technological infrastructure. The current study identified four key factors that may influence the adoption of open banking in Kenya’s commercial banks. The absence of clear open banking regulations in Kenya creates uncertainty. Regulatory policies define data-sharing rules, consumer protection measures, and compliance requirements (Remolina, 2019). Further, banks should have internal capabilities, leadership support, and digital transformation strategies in place (Dinckol, 2021). Organisational readiness determines how well institutions can implement open banking. Additionally, employees should understand and work well with digital tools (Cetindamar et al., 2021). Low digital literacy among banking professionals hinders implementation. Open banking also relies on secure IT systems, cloud computing, and advanced cybersecurity frameworks (Chan et al., 2022). Without proper infrastructure, adoption remains a challenge. These factors may collectively shape how and whether commercial banks can adopt open banking in Kenya. 1.1.4. The Commercial Banking Sector in Kenya Kenya’s banking sector has several critical players, including the regulatory authority, Central Bank of Kenya (CBK), one mortgage refinancing company, one mortgage financing corporation, and 38 licensed commercial banks (Central Bank of Kenya [CBK], 2023). Other key players include money remittance providers, microfinance banks, non-operational bank holding firms, foreign exchange bureaus, and credit reference bureaus (CBK, 2023). The study focuses on the 38 commercial banks licensed by the Central Bank of Kenya (2024). Notably, most of these commercial banks are local or foreign-owned entities. The report included KCB (Kenya 8 Commercial Bank), Equity, NCBA, and Family Bank, which are among the dominant commercial banks in Kenya’s banking sector. While the industry has recently undergone considerable digital transformation, many banks still rely on manual processes (Peter et al., 2021; Koskei, 2020). This attribute may limit the acceptance of open banking. Further, the sector has been disrupted by increased awareness and popularity of sustainable banking systems (Muchiri et al., 2022), consumer acceptance of mobile money platforms (Kingiri & Fu, 2020), and the rise of FinTech organisations (Musamali et al., 2023). These factors increasingly define performance in the market. The regulation of the Kenyan banking sector is provided under a broad regulatory framework by the Central Bank of Kenya (CBK) under the Banking Act Cap 488. Kenya’s commercial banks are supervised and licensed by CBK. The licensed commercial banks are grouped into three tiers depending on the size of market share, the amount of capital available, and the amount of assets held (CBK, 2023). Kenyan commercial banks are grouped into three tiers, Tier I, Tier II, and Tier III, using a composite index that is weighted (Gatuguta & Musau, 2024). The index consists of total deposits, net assets, the number of loan and deposit accounts, and reserves and capital. A bank with an index of 5% or more is regarded as a Tier 1 or large bank. On the other hand, a medium or Tier II bank’s composite index lies between 1% and 5%, whereas banks whose index is less than 1% are referred to as Tier III or small banks (CBK, 2023). As of the end of 2023, nine Tier 1 banks had a total market share of 76.6%, eight Tier II banks’ market share stood at 15%, and twenty two Tier 1 banks held a market share of 8.4% (CBK, 2023). The banking sector plays a pivotal role in Kenya's socio-economic development, contributing approximately 13% to Kenya's GDP and facilitating domestic and international trade through financial intermediation. Kenyans now working or associated with this sector of the economy are over 30,000 directly and indirectly, and the total assets of the sector are worth KES 5.4 trillion as of 2024 (Gatuguta & Musau, 2024). The end of 2023 saw an increase in staff levels to 37,933 from 36,107 at the end of 2022, which represents a 5% rise (CBK, 2023). The most significant increase in the number of employees was evident in Tier 1 banks as they opened more outlets and branches. Extensive branch networks and digital banking solutions, specialized lending programs for SME development, and support of government programs for social welfare and development are the basis for attaining financial inclusion. 9 Despite the evident growth in the banking sector, it is experiencing a number of challenges, including technology integration problems, compliance problems, such as increasing compliance costs, and market dynamics like competing with FinTech. Banks also seem to face challenges with legacy systems and outdated IT infrastructure that are incompatible with modern API integrations necessary for open banking (Babina et al., 2024). However, these challenges are accompanied by opportunities in digital innovation, such as through the adoption of open banking, expansion of mobile banking, opportunities for market expansion through regional integration, as well as opportunities for product development, such as personalized banking and digital lending platforms (CBK, 2023). Therefore, the government should strengthen digital banking regulatory frameworks and encourage innovation through regulatory sandboxes. Moreover, it should create a resilient cybersecurity, while ensuring green banking is supported. The Central Bank of Kenya has outlined ambitious open banking solutions to enhance its adoption nationwide (FinTech News, 2021). However, this acceptance depended on the capacity to mitigate the various challenges that commercial banks experience when adopting open banking. 1.2.Problem Statement Financial information is one of the most regulated data forms and requires tight legislation to ensure its protection. Owing to this, most economies, especially developing ones, are reluctant to adopt new technologies, such as open banking. In 2018, the European Union (EU) implemented the PSD2 (Revised Payment Services Directive 2) as a measure to adjust how financial data exchanges were handled legally (Gozman et al., 2018; Preziuso et al., 2023). Despite this initiative, commercial banks remain hesitant to adopt open banking, and when they do, it is just to comply. Banks perceive such a move as one that may negatively impact their competitive advantage; hence, they run their operations the same way (Heins & Rigopoulos, 2023). Anagnostopoulos (2018) also notes that large banks with a considerable market share are not interested in adopting innovation. Moreover, their technology departments are focused on meeting the requirements for complicated regulations. The legacy systems in most banks also make such a shift difficult and expensive; hence, they view new technology as an unnecessary expenditure (Heins & Rigopoulos, 2023). Additionally, users of banking services are loyal, skeptical of using third-party applications, and only subscribe to or use applications that their banks promote. Therefore, banks’ reluctance and 10 consumers’ trust issues make open banking adoption problematic despite numerous benefits linked to this technology. Although open banking has the potential to revolutionise Kenya’s financial landscape considerably, its penetration in the country remains underexplored (Rutto, 2022; Musamali et al., 2023). Furthermore, despite FinTech and mobile money platforms undergoing significant adoption, open banking in Kenya remains in its embryonic stages (Aicha, 2023). It is evident that Kenya’s financial sector has the potential to be transformed through open banking, but its adoption is slow compared to other integrated markets worldwide, like Europe (Aicha, 2023). Factors like out-of-date technological infrastructure, lack of consumer trust, limited resources, and inflexible regulations limit the adoption of open banking in Kenya (Crosman, 2019; Nyawara, 2021; Rutto, 2022; Nel & Boshoff, 2021). The present study is significant given the emergence of regulatory, technological, and organizational challenges in open banking integration into banks’ operations. Accordingly, this study sought to address the problem of slow adoption of open banking. Such adoption would contribute to the development of personalized financial products, improved customer experiences, and operational efficiency (Organisation for Economic Co-operation and Development [OECD], 2024). Indeed, this problem is critical since it limits the ability of Kenya’s commercial banks to compete internationally, yet the digital financial services environment is evolving rapidly. The implications of the lack of open banking adoption for stakeholders are numerous. If Kenya’s commercial banks fail to embrace open banking, they may lose market share and become uncompetitive, among others, as more global players enter the continent. By excluding open banking services to customers, commercial banks deny them the opportunity to use available financial products (Heins & Rigopoulos, 2023). This will also put pressure on regulatory bodies to develop tight frameworks that support open banking. By doing so, the FinTech firms will miss opportunities to create new business models and partner with traditional banks on new offerings. Globally, the adoption of open banking has gained significant momentum, especially in European regions with clear and robust regulatory frameworks. The inception of the PSD2 in 2015 mandated banks to provide third-party service providers access to consumer data (Gozman et al., 2018). On the contrary, the lack of a unified regulatory framework in North America, especially the United 11 States (US), means the continent has also been slow to welcome open banking development (Farrell, 2022). Regionally, African countries have been even slower in embracing open banking, especially due to the dominance of mobile money solutions and underdeveloped regulatory frameworks (Ahmad et al., 2020; Salami, 2024). In Nigeria, however, regulatory sandbox initiatives show some critical movement towards open banking (Olatunji, 2020). However, the progress has been gradual and minimal. South Africa has also conveyed interest through the Financial Sector Conduct Authority. The state of open banking in these continents portrays the appropriateness of strong regulations for the success of this banking model. Notably, Kenya stands out in Africa as the leading digital financial service hub, especially due to the success of M-Pesa (Jacob, 2016). However, factors such as inadequate infrastructure, unclear regulations (Nyawara, 2021), and consumer issues over data privacy act as key adoption barriers. According to Preziuso et al. (2023), open banking enables personalised financial services and products by allowing third-party providers to analyse a customer’s financial data. This benefit is not adequately realised in Kenya due to the limited penetration of open banking solutions. Furthermore, the inadequate penetration of open banking inhibits Kenyan banks’ capacity to streamline operations and remain competitive in the global market (Rutto, 2022). Molaro (2023) notes that European banks increasingly provide APIs to third parties. On the contrary, banks in Kenya have not fully harnessed this technology. This is partly due to the dominance and consumer preference for mobile money solutions. Subsequently, this outcome limits the capacity for Kenyan commercial banks to remain competitive globally and provide personalised and streamlined services. However, open banking holds the potential for more innovative financial products beyond what mobile money platforms offer (Frank, 2024). As such, the adoption of open banking is critical to the future of Kenya’s banking sector. Open banking has become an increasingly popular research area, attracting considerable attention, especially in current research (Omarini, 2018; Gozman et al., 2018; Chan et al., 2022). However, most research in developed nations focuses on the role of regulations, competition, and consumer trust, and their conceptualisation of key variables only applies to their studies. Conceptually, studies by Sidani and Harb (2023), Al-Issa and Omar (2024), and Pi and Yang (2023 only advanced leadership as the variable impacting the adoption of open banking. Maharaj and Pooe (2021) and Al-Issa and Omar (2024) also did not offer a robust methodological framework for assessing 12 resource availability or readiness in complex banking environments. These studies also employed a qualitative methodology, which relies on people’s experiences and opinions. On the other hand, African studies like Musau et al. (2022) and Dreyer (2022) have explored how open banking promotes financial inclusion in underserved markets. Locally, studies by Rutto (2022) and Nyawara (2021) highlight the slow pace of open banking adoption due to unclear regulations, the supremacy of mobile money platforms, and low digital literacy levels. However, these studies do not comprehensively examine the influence of the study’s key variables. Moreover, they all employed a qualitative methodology, which is subjective and results in generalisation problems. Although studies on open banking are numerous and add to the body of knowledge, significant gaps remain. There is limited understanding of how specific factors influence the acceptance of open banking. Moreover, most of these studies are set in different contexts; hence, their findings cannot be generalised to the Kenyan context. Additionally, most of the studies employed qualitative methodology. Critical gaps are also evident, especially regarding how organisational readiness, regulatory support, technological infrastructure, and employees’ digital literacy are referred to in the literature. Accordingly, this study sought to fill these gaps by improving the conceptual definitions of these factors. It also used a quantitative methodology to address gaps in methodology. Therefore, the goal of this research was to identify the factors influencing open banking adoption by employees of Kenya’s commercial banks. 1.3. Research Aim and Objectives This research aimed to investigate the factors influencing the adoption of open banking by employees of Kenya’s commercial banks. Mainly, it focused on technological infrastructure, employees’ digital literacy, regulatory framework, and organisational readiness. 1.3.1. General Objective To determine the factors influencing the adoption of open banking by employees of commercial banks in Kenya. 1.3.2. Specific Objectives i. To determine the influence of regulatory support on the adoption of open banking by employees of commercial banks. 13 ii. To establish the influence of technological infrastructure on the adoption of open banking by employees of commercial banks in Kenya. iii. To establish the influence of digital literacy among employees in the banking sector on the adoption of open banking by employees of commercial banks. iv. To determine the influence of organizational readiness on the adoption of open banking by employees of commercial banks in Kenya. 1.4. Research Questions This study seeks to answer the following research questions; i. What is the influence of regulatory support on the adoption of open banking by employees of commercial banks in Kenya? ii. What is the influence of technological infrastructure on the adoption of open banking by employees of commercial banks in Kenya? iii. What is the influence of digital literacy among employees on the adoption of open banking by employees of commercial banks in Kenya? iv. What is the influence of organizational readiness on the adoption of open banking in commercial banks in Kenya? 1.5. Scope of the Study The scope of this study was geographically limited to Kenya’s Nairobi County. Specifically, the study aimed to determine whether regulatory support, technological infrastructure, organisational readiness, and employees’ digital literacy levels influence commercial banks’ adoption of open banking in Kenya. The study targeted bank employees who hold management positions to determine the factors affecting open banking adoption. This research particularly targeted users across various demographics, including income levels, age, financial literacy, and educational background. This research employed a mono-method quantitative approach, focusing on surveys as the primary data collection tool. The time scope of this study stretched from September 2024 to May 2025. 1.6. Significance of the Study The study explored the factors influencing the adoption of open banking in Kenya, which is significantly underexplored. For example, Kenya lacks a clear regulatory framework controlling 14 data sharing between banks and third-party entities (Nyawara, 2021). Recent studies have not adequately examined how regulatory frameworks and technological infrastructure impact the uptake of innovations. As such, the findings demonstrate the need to create data privacy policies and laws that protect consumers while allowing banks to introduce new technologies. The findings also provide critical insights for policymakers, especially in crafting more robust regulations supporting the adoption of open banking. Particularly, it informs the introduction of comprehensive regulations on privacy, data sharing, and security within the Kenyan banking environment, owing to the uncertain regulatory environment. The study also indicates the requirement for a common regulation framework and the possibility of implementing a model similar to the PSD2. For industry practitioners, mainly commercial banks, the study sheds light on the factors that should be addressed to adopt open banking successfully. By identifying key barriers and facilitators, the study could guide bank leaders to ensure organizational readiness, invest in technological infrastructure, and train employees to manage banking innovations effectively. The results can enable banks to enhance their competitive edge by embracing open banking and offering customers more personalized and innovative financial products. This outcome may especially be vital for the nation’s underbanked populations. Additionally, the findings may educate Kenyan consumers on the benefits of open banking services. Such benefits include more control over financial data, personalised products, and affordable prices (Chan et al., 2022). Academically, this study fills a significant gap in the literature regarding the adoption of open banking in developing economies, specifically Kenya’s commercial banking sector. Current research has focused on open banking in developed countries, with limited exploration in Kenya. This study aims to contribute new knowledge and insights to the emerging markets' growing financial technology and banking services field. Furthermore, there is limited research on the intersection of the influence of regulatory frameworks, organisational readiness, technological infrastructure, and digital literacy on open banking adoption by employees of Kenya’s commercial banks. Thus, this study is significant for academicians as it addresses this knowledge gap. 15 1.7 Chapter Summary This chapter presented the background of the study, which outlined the evolution of open banking and key factors central to understanding the slow adoption of open banking in Kenya. The problem statement highlighted the gap in existing literature regarding the specific factors influencing open banking adoption in Kenyan commercial banks. In conclusion, the chapter sets the foundation for the study, detailing the research aim and objectives and the importance of addressing the adoption barriers to enhance the competitiveness and innovation capacity of Kenya’s banking sector. 2. CHAPTER TWO: LITERATURE REVIEW 2.1. Introduction The literature review section details and appraises current studies on factors that influence open banking adoption by employees of Kenya’s commercial banks. Particularly, it focuses on understanding global trends in open banking and how and why they depart from the Kenyan market. It then details the TAM (Technology Acceptance Model) and UTAUT (Unified Theory of Acceptance and Use of Technology) models that lay the theoretical foundation for the topic. The 16 literature review also provides conceptual and empirical reviews on regulatory framework, technological infrastructure, digital literacy, and organisational readiness, as well as how these factors influence open banking adoption. This chapter highlights the critical gaps in these studies while including a comprehensive conceptual framework detailing the relationship between the study’s variables. 2.2. Theoretical Framework This section presents two key theories that guided the research and offered an understanding of the phenomenon under study. Specifically, it looks into the application of the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM). Both the UTAUT and TAM models are critical for this study and complement each other. The UTAUT explains the independent variable, whereas the TAM model provides insight into the dependent variable. 2.2.1. Technology Acceptance Model In 1989, Fred Davis formulated this theory, which focuses on two key factors defining technology adoption: perceived ease of use (PEOU) and perceived usefulness (PU) (Khechine et al., 2016). Permatasari and Prajanti define perceived usefulness as the perceived ability of a particular technology to improve or enhance performance (2018). On the contrary, perceived ease of use is the perceived belief that an organisation holds that a new technology would be easy to use (Permatasari & Prajanti, 2018). The technology acceptance model (TAM) assumes that users’ behavioural intention predicts their adoption of a technology, which depends on their perception of its ease of use and usefulness (Marikyan & Papagiannidis, 2024). The model explains the adoption of open banking variable, as it allows commercial banks to understand why their users choose or reject a new technology. In this study, perceived usefulness represented how much the employees would find it useful to adopt open banking. On the other hand, perceived ease of use signified the extent to which the employees considered adopting open banking as effortless. Bank employees’ attitudes, whether positive or negative, influence their reluctance or acceptance of open banking (Himel et al., 2021). When employees’ attitude towards open banking is favourable, they are likely to support the adoption of open banking. 17 Perceived usefulness and perceived ease of use are the key variables in the technology acceptance model (TAM) (Kaur & Arora, 2020). They directly influence banks’ decisions to utilise or adopt open banking platforms. Perceived usefulness is important in markets where FinTech is well established. In such markets, organisations are motivated by the performance gains of open banking (Singh et al., 2020). Commercial banks are more likely to embrace open banking if they believe that they will gain. Open banking elicits benefits like expanded customer offerings through personalised recommendations and data analytics. These benefits are likely to enhance perceived usefulness and may drive commercial banks to accept open banking platforms (Long et al., 2020). For example, some developed countries like the UK have strong regulatory frameworks. In these countries, banks are likely to find value in open banking’s associated convenience and control (Mbama & Ezepue, 2018). On the contrary, banks in Kenya have historically prioritised mobile banking platforms, which provide greater ease of use and simplicity (Ritho & Jagongo, 2015). Further, mobile money platforms, such as M-Pesa, dominate the financial sector (Van Hove & Dubus, 2019). As such, consumers’ perceived ease of use may mean they are more critical in embracing open banking. This notion is relevant because Kenyans are more accustomed to simpler technologies. As such, Kenyan banks may perceive open banking as resource-intensive, which makes them less likely to embrace the technology. Perceived usefulness and perceived ease of use determine open banking adoption (Prastiawan et al., 2021). However, perceived trust and security are also critical in determining the success of open banking. Tiwari and Tiwari (2020) emphasise that companies may not be willing to embrace new technologies if they perceive the move as risky. Similarly, open banking introduces security and data privacy risks, as it involves sharing sensitive consumer information between banks and third-party service providers (Chan et al., 2022). As such, this banking model raises concerns over data security and privacy. The extended technology acceptance model considers perceived security a key factor in consumer trust (Acharya & Mekker, 2022). This outcome will likely influence organisation’s willingness to embrace open banking platforms. In Kenya, this concern is further magnified by regulatory uncertainty. Specifically, Kenya lacks a comprehensive data protection framework regulating the exchange of sensitive consumer data (Nyawara, 2021). As such, commercial banks resist open banking solutions due to the associated risks in data sharing and the existing regulatory gaps. 18 In the global context, Singh et al. (2022) utilise this model to explore the drivers of FinTech adoption. However, this model is expanded to include sub-constructs of the Unified Acceptance of Use of Technology (UTAUT). Consequently, these constructs are classified into technology, adoption, and behaviour. Through this expanded model, the study shows that perceived usefulness and social influence determine the behavioural intention to use FinTech services. Perceived ease of use and social influence determine actual use, while behavioural attributes are affected by digital behaviour and characteristics related to technology. Regionally, Olaolu (2022) used the technology acceptance model to explore digital banking adoption in Nigeria. As such, the TAM-based model included variables that would stimulate digital banking’s use, including perceived use, perceived ease of use, security, and banking regulations. The study administered questionnaires, while Likert scales measured these variables. Alongside the technology acceptance model variables, the study showed that security and banking regulations positively influenced the embracement of open banking services. In Kenya, the work of Wilter (2023) employed the technology acceptance model to show that the adoption of digital financial technology improved banks’ financial performance. The author concluded that the model provided a robust theoretical foundation to comprehend the decision by Kenya’s commercial banks to adopt financial innovation. According to Wilter (2023), the model not only pinpoints the factors that either ensure or hinder adoption but also emphasises the need to understand external factors, such as organisational and social cultural aspects, and how they impact adoption. Despite most scholars referencing the technology acceptance model as ideal for explaining the acceptance of technology, several studies have pointed out that the model does not explain the connection between technology and its actual use and adoption (Ajibade, 2018; Marikyan & Papagiannidis, 2024 ). Further, the TAM model fails to explain the behaviour of users and does not adequately predict acceptance of information communication technology (ICT) (Ajibade, 2018). Even when the studies used the model, it could not present all precursors to social influence or mobile use and facilitating behaviour conditions. Despite having these limitations, the technology acceptance model has presented strong theoretical underpinnings for the last thirty years and can assess and almost accurately predict people’s intention to use technology (Marikyan & Papagiannidis, 2024). In this study, the technology acceptance model is relevant as it explains a person’s thought process when deciding whether or not to adopt a certain technology. The model’s perceived usefulness and ease of use variables were used to explain how banking 19 employees’ attitude about open banking could influence their likelihood to adopt open banking. The technology acceptance model explains how organisations may utilise and embrace new technology (Khatri et al., 2020). Apart from providing a theoretical foundation for the study, the technology acceptance model was primarily used to explain the likelihood of adoption of open banking among Kenya’s commercial banks because it can assess people’s attitudes towards embracing new technology. 2.2.2. Unified Theory of Acceptance and Use of Technology In 2003, Venkatesh, Davis, Morris, and Davis synthesized models derived from the theory of planned behaviour (TPB) and the TAM model to create the unified theory of acceptance and use of technology (UTAUT). The theory proposes four critical factors, including effort expectancy (EE), facilitating conditions (FC), social influence (SI), and performance expectancy (PE), as well as four moderators, such as age, gender, age, experience, and voluntariness (Xue et al., 2024). Accordingly, performance expectancy is viewed as the extent to which a person believes that using a certain technology will enable them to record increased work performance. In contrast, effort expectancy is regarded as how easily a user associates their use of a particular innovation (Venkatesh et al., 2016). Correspondingly, social influence is defined as the extent to which a person perceives that other important people see the need to use the new technology, while facilitating conditions denote the extent to which people believe that technical and organisational infrastructure are available to support the use of the new technology (Xue et al., 2024). The goal of the model was to predict an organization’s behavioural intention to utilise actual technology or technology. Venkatesh et al. (2016) determined that social influence, effort, and performance expectancy influenced behavioural intent to use a technology. On the other hand, facilitating conditions and behavioural intention determined the technology’s use. Indeed, the unified theory of acceptance and use of technology presents a more detailed exploration of additional elements, which may affect open banking’s adoption. The theory was the primary theory used to explain the independent variables, including regulatory support, organisational readiness, technological infrastructure, and employees’ literacy levels. Unlike the technology acceptance model which offers a foundational understanding of how perceived ease of use and perceived usefulness influence technology adoption, the UTAUT model explains the factors that may influence the adoption of open banking. It examines facilitating variables, including social 20 influence and facilitating conditions (Williams et al., 2015). The study focuses on internal and external enablers of open banking adoption, such as organisational readiness, employee digital literacy levels, and regulatory support. Notably, the unified theory of acceptance and use of technology model focuses on social influence, which represents the extent to which decision- makers try to determine whether stakeholders require that they adopt new technologies (Khechine et al., 2016). It provides insights into how external pressures from consumers, rival companies, and regulators shape adoption decisions. In their study, Mensah and Khan (2024) modified the unified theory of acceptance and use of technology model and used it to examine behavioural factors that affect adoption of mobile banking services in China. Despite the Chinese population having smart phones, they were reluctant to embrace mobile banking. The researchers found that effort and performance expectancy, awareness, and perceived economic cost determined whether or not this population would take up mobile banking. Further, Mensah and Khan (2024) concluded that technological infrastructure and government support were positively linked to the population’s intention to uptake mobile services. Similarly, Mayayise (2021)’s study intended to examine the factors that affect the adoption of the practice of bring your own devise (BYOD). The author used a security standard, ISO/IEC 27001, organisational requirement, and a modified UTAUT model as constructs. They found that effort and performance expectancy, awareness and training, and policy existence influence the likelihood of South African employees to adopt BYOD (Mayayise, 2021). However, social influence and organisational requirements were negatively linked to BYOD adoption. Locally, Waithaka et al. (2022)’s study sought to explore the factors that affect researchers in Kenya’s selected public universities’ use and adoption of open access scholarly publishing (OASP). Using a modified UTAUT model, the researchers found that facilitating condition factors, such as management, supportive staff, adequate ICT infrastructure, network literacy, as well as performance expectancy factors like enablement of wider and quicker dissemination and scholars achieving high H-Index influenced the scholars’ OASP uptake. Supervisors’ motivation and recommendations and support by funders, which were under social influence, also influenced the adoption of OASP. Indeed, the empirical studies demonstrate how the UTAUT model explores specific factors that may affect the adoption and use of a novel technology. 21 The unified theory of acceptance and use of technology is relevant to this study. Several studies have applied the model in varied subjects, including e-healthcare technology acceptance (Rouidi et al., 2022, adoption of e-government (Amrouni et al., 2019), and mobile payments and applications (Al-Saedi & Al-Emran, 2021; Kamal & Subriadi, 2021; and Mensah and Khan (2024) among others. Given its extensive application uses, it is ideal for this study as the focus is on determining the factors that influence open banking adoption. Particularly, the UTAUT model is useful in examining organisational settings with multiple stakeholders that determine the decisions to be made (Daka & Phiri, 2019). This model provides a clear and detailed comprehension of adoption dynamics. It considers the direct effect of facilitating aspects and social influence and explores their interaction with contextual factors, such as organisational culture. In Kenya, regulatory bodies may influence society by requiring companies to comply with data security standards, while consumers may demand improved financial services (Nyawara, 2021). On the other hand, facilitating conditions, such as APIs, are critical for adoption. Although the model explains several factors that influence adoption intention, Shachak et al. (2019) and Bayaga and du Plessis (2024) contend that it is narrow and only focuses on the individual. The social, organisational, and technical systems are complex as they encompass organisational culture, governance, and technological aspects. However, the UTAUT model minimises all these factors to a user’s expectations or perceptions. Furthermore, Bayaga and du Plessis (2024) feel that the model majorly focuses on behavioural intention as explaining adoption and fails to consider the technology’s actual usage or implementation. Despite these limitations, the unified theory of acceptance and use of technology is applicable to this study as it delineates social influence and facilitating conditions as factors influencing adoption, which fall under this study’s variables. More importantly, this study integrates the unified theory of acceptance and use of technology and technology acceptance models, enabling a comprehensive analysis of open banking adoption’s drivers. On the one hand, the study focuses on the TAM model, which explains how a person’s perceived usefulness and ease of use of technology impact their decision to adopt this technology. The model is critical to this study as it explains the study’s dependent variable, open banking adoption. On the other hand, the UTAUT model emphasizes organisational and environmental 22 dynamics that come into play to affect adoption decisions (Daka & Phiri, 2019). As such, the study relies on a dual model, which helps address the research’s specific objectives. It helps generate actionable insights into open banking adoption. The UTAUT model specifically emphasizes the broader ecosystem and shows the link between organisational readiness and external pressures. Ultimately, combining these two theories offers a more holistic perspective and ensures that actionable recommendations can be drawn by Kenya commercial banks’ stakeholders. 2.3. Empirical Literature Review This section reviews the current empirical literature on open banking adoption globally, regionally, and locally. It focuses on factors that influence open banking adoption within commercial banks. Specifically, this section reviews the literature on regulatory framework, digital literacy, organizational readiness, and technological infrastructure. 2.3.1. Regulatory Support and Open Banking Adoption The current oversight bodies, regulations, and laws should support open banking for its successful implementation by commercial banks. According to Akyildirima et al. (2024), open banking results in opportunities and challenges that are majorly impacted by a bank’s capability to survive in new regulatory settings and take advantage of technological improvements. Accordingly, regulatory support is the degree to which the government formulates policies and regulations that ensure a compliant and safe environment for open banking adoption (Ofodile et al., 2024). Supportive regulations are likely to encourage banks to adopt open banking. As such, government policies and regulations promote a safe and compliant environment for effective open banking adoption. Supportive regulations promote adoption by reducing perceived risks. However, unsupportive regulations will likely deter banks from adopting open banking platforms. For this research’s purpose, regulatory support encompasses government regulation, which is the support a government authority offers to ensure that organisations adopt technologies, such as open banking. Furthermore, government regulation includes policies that ensure that sensitive data is overseen (Ali & Osmanaj, 2020). Ultimately, the goal of these regulations is to ensure the privacy of consumers is protected and enforce security by guaranteeing integrity, confidentiality, accountability, and availability are upheld. 23 The research conceptualises regulatory support as the need for the government to ensure economic, social, and administrative regulation for the successful adoption of open banking by commercial banks. Accordingly, economic regulation relates to the quality of services and cost, which determines the successful adoption of open banking and enables banks to deliver services efficiently and affordably. On the other hand, social regulation relates to the need to guarantee privacy and security of data in order to protect the bank’s environment (Ali & Osmanaj, 2020). Finally, administrative regulation entails flexibility as well as government and firm-based facilitating conditions that the government provides to encourage the strengthening of banks’ capacity for innovation (Ali & Osmanaj, 2020). The conceptualization of regulatory support is based on research by Ali and Osmanaj (2020). Globally, Zeller and Lynch (2020) investigated the influence of regulatory frameworks on the adoption of open banking. The authors adopted a conceptual analysis approach, whereby they explored regulatory documents, industry reports, and academic literature. The study examined the regulatory impact of the European Union’s PSD2 and Australia’s CDR initiatives. Notably, Zeller and Lynch (2020) adopted a qualitative approach, which was without a sample size. The researchers focused on key variables, including regulatory requirements, market competition, and consumer protection. The study used a comparative analysis method and demonstrated that regulatory-led frameworks encouraged market competition. Even so, these studies faced challenges with compliance costs and consumer scepticism. On the other hand, Brown (2022) adopted a case study approach of nine major UK banks. The study analysed the regulatory effects of CMA’s mandates for open banking. The study used variables like interoperability, data portability, and consumer satisfaction. The document analysis highlighted that the UK’s co- regulatory model had influenced a more competitive financial ecosystem. Both studies suggested that regulatory-led approaches could effectively influence open banking adoption. However, these approaches required strong compliance infrastructure and consumer acceptance. Gozman et al. (2022) examined a more specific view of open banking’s influence on stakeholder roles. Notably, the study focused on stakeholder roles within regulatory frameworks. The researchers interviewed 25 industry stakeholders across various open banking roles. They focused on risk exposure and value opportunities, including efficiency and innovation. Subsequently, the study employed a thematic analysis approach to explore open banking roles. The findings 24 advanced that regulatory clarity is essential for defining responsibilities and minimizing risk (Gozman et al., 2022). Moreover, stakeholders in regulated open banking systems benefited from clear guidelines on responsibilities. Indeed, this suggested that regulatory frameworks should outline distinct roles to support traditional banks and new FinTech companies. Thus, the study highlighted a complex aspect of open banking adoption. Specifically, it conveyed the vitality of well-defined roles for effective open banking integration. Regionally, Giya et al. (2021) were concerned with the potential and challenges of open banking in South Africa. The study employed a secondary data analysis approach to review policy documents, academic literature, and market data. It examined how South Africa’s banking organisations approach regulations surrounding open banking. The researchers included variables like API accessibility, data ownership, data privacy, and competition. Ultimately, they found that the banking market was concentrated and recommended the need for a regulatory-led approach to direct open banking implementation. The study concluded that regulatory frameworks should offer clear guidance on issues that may arise in data security. Correspondingly, Didenko (2017) employed a systematic approach and analysed how Kenya and South Africa regulated FinTechs. The researcher looked into factors like the rule of law, regulatory compliance, and data security. The findings demonstrated that although technological readiness is high, inconsistent enforcement and rule of law issues inhibited adoption. Overall, Didenko (2017) and Giya et al. (2021) advanced the need for regulations to be enforced and improved. These enhancements are essential for open banking growth in Sub-Saharan Africa. In Kenya, Nyawara (2021) focused on the regulatory landscape. The study analysed secondary data sources to evaluate factors influencing FinTechs and open banking. This literature review examined key variables such as regulatory flexibility, consumer data protection, and innovation incentives. Subsequently, the findings conveyed a flexible regulatory environment that encouraged innovation. Even so, this regulatory environment requires clear and comprehensive data protection measures. Ultimately, the inadequate regulatory environment posed considerable risks to consumer privacy. As such, the results demonstrated that more balanced regulations that emphasise innovation and security could influence open banking adoption. The study concluded that a stronger focus on consumer data protection is needed. Additionally, the researcher showed 25 that banks may experience challenges adopting open banking due to a lack of consumer trust. Therefore, there is a need for policies that support data security and innovation. 2.3.2. Technological Infrastructure and Open Banking Adoption Technological infrastructure is pivotal for the implementation and adoption of open banking. Banks require appropriate and adequate infrastructure before starting any project. A technological infrastructure encompasses an infrastructure application or server setup and its content management, data, security, and tools (Nyonje et al., 2018). It also includes operating systems and hardware, development tools for applications, and a management systems platform (Nyonje et al., 2018). Information technology lays the foundation for sharing the capabilities of information technology upon which organisations depend. ICT infrastructure is the shared portion of ICT architecture. Accordingly, this study conceptualises the technology infrastructure variable as the existing IT capabilities within a bank that support data security, integration, and sharing (Sardana & Singhania, 2018; Nyonje et al., 2018). Open banking relies on robust technological infrastructure that ensures data is securely exchanged between systems (Chan et al., 2022). This variable includes system integration capabilities, integration of APIs, and robustness of data security. Nyonje et al. (2018) and Chan et al. (2022)’s studies are used to conceptualise the technological infrastructure variable using the following indicators: robust data security, API compatibility and integration, and ease of data sharing. In the global context, Lin et al. (2024) comprehensively analysed global banking technological and market shifts. The study was set in the United States, where the researchers examined API adoption among banks. Based on the findings, banks that adopted external APIs experienced an increased return on assets (ROA) and data portability advantages. The study’s methodology combined empirical financial analysis from 2007–2022. It focused on API adoption’s impact on key performance metrics. As such, the study made a case for open banking policies. However, the work of Flejterski and Labun (2016) emphasised macro-level trends through a theoretical synthesis. The study specifically leveraged the disruptive innovation and core competencies theories. These theories suggested a shift toward an “opti-channel” model, where FinTech and banks cooperated to maximize customer engagement. Despite these contributions, a methodological gap exists in measuring specific API-related variables. As such, there is a need for studies that offer concrete empirical data on API compatibility and system integration. In this light, 26 this study fills this gap by evaluating the impacts of API infrastructure in a Kenyan banking context. Regionally, the work of Nnaomah et al. (2024) highlighted the regional dynamics of digital banking across Africa. The study employed a comparative analysis between Nigeria and the US. Notably, the study used a mix of secondary data, policy document reviews, and stakeholder interviews. Their findings underscored digital banking’s positive effect on financial inclusion. However, regulatory and infrastructural challenges persisted, especially in Nigeria. The researchers applied a quantitative model with 179 participants in South Africa. The study examined digital banking’s role in underserved communities facing bank branch closures. The study revealed significant barriers limiting digital innovation and adoption. Such barriers included internet connectivity issues, electricity access, and digital literacy (Mdluli, 2022). Both studies lacked an emphasis on technological infrastructure factors like API compatibility and data security robustness. These factors are otherwise central to seamless digital banking. Accordingly, this study assesses the influence of infrastructure indicators, including API compatibility, on open banking adoption within Kenya. In Kenya, a study by Ooko and Muchelule (2024) provided essential empirical insights into digital banking. The study analysed data from 42 licensed banks in Nairobi and focused on service automation and integration technologies. Notably, the findings demonstrated that these elements significantly impacted the integration of digital banking. The study used a descriptive survey design and a composite indicator to statistically show the importance of these technologies. However, the study was limited in estimating compatibility of APIs. Similarly, Mulee (2019) focused on the association between digital innovation and economic growth. The study used quarterly data over ten years. It examined variables like mobile money transfer volume and internet banking transactions. The results generated an R-squared value of 0.992, implying that the study strongly correlated digital transaction growth with economic impact. However, the study did not demonstrate how API compatibility impacted digital integration. Thus, the proposed study aims to address these methodological and theoretical gaps by including measurable factors like API compatibility and evaluating their effects in the context of open banking adoption. 27 Evidently, a literature gap is evident regarding the specific role of technological infrastructure indicators, such as data security, API compatibility, and integration. Global and regional studies focused on the theoretical and widescale effects of API adoption (Lin et al., 2024; Nnaomah et al., 2024). However, few studies addressed these infrastructural aspects statistically or by targeting a specific region, such as Kenya. Furthermore, empirical data was lacking. For example, scarce data demonstrates the relationship between infrastructural indicators and financial inclusion in open banking adoption. Therefore, this study bridges the gaps of previous research by employing a quantitative approach that includes empirical analysis of Kenya’s commercial banking data and infrastructure. Furthermore, the study emphasises security robustness and system integration. In this way, the study provides a more comprehensive framework for understanding the role that technological infrastructure plays in open banking adoption by Kenya’s commercial banks. 2.3.3. Digital Literacy in Technology and Open Banking Adoption Digital literacy encompasses employees’ abilities or competencies in using innovations or digital technologies when working, which could ultimately enhance the use of these technologies at the organisational level (Vuorikari et al., 2016; Cetindamar et al., 2021). In this study, digital literacy was regarded as expressions like learning, knowledge, and competence instead of the past perception of literacy, which was limited to a person’s capability to use printed texts, write, and read in different contexts (Cetindamar et al., 2021). Competence depicts an individual’s capacity to be knowledgeable about something and demonstrate it. For this study’s purpose, digital literacy is referred to as employees’ competence in working with technology, familiarity with digital tools, and willingness to learn or be trained on innovations. Accordingly, the digital literacy variable consists of employees’ levels of training, their familiarity, understanding of digital banking tools, and employee adaptability, which were adopted from studies by Khoja et al. (2007), Lokuge et al. (2018), and Cetindamar et al. (2021). In the global context, Cetindamar et al. (2021) provided important insights into digital literacy’s effects on the embracement of new technologies. The study specifically focused on the influence of this factor on the use of cloud technology within Australian organizations. By utilizing the theory of planned behaviour (TPB), the study demonstrated that employees’ attitudes, subjective norms, and perceived behavioural control influence the adoption of digital tools. As such, the study conveyed the importance of digital literacy. However, the study’s focus on the Australian context 28 limits its direct relevance to the Kenyan banking sector. Similarly, Bansal (2020) explored how employee training contributed to the adoption of digital banking services in India. Notably, the data collection process comprised 412 questionnaires, which were subsequently analysed using SPSS (The Statistical Package for the Social Sciences). Subsequently, the study emphasised the importance of training in reducing operational costs and enhancing service usability. However, the study predominantly focused on the unorganized sector in India. Further, it lacked specificity regarding tool familiarity or adaptability, a critical research gap that this research aims to fill. At the regional level, Okoro (2024) explored the role of digital literacy within the context of the banking sector in Africa. Notably, the study examined the role of digital human resource practices in Nigerian banks. The study employed a document analysis of nonfiction books, academic journals, and print encyclopaedias. Consequently, the findings suggested that digital literacy and skill development are vital for improving recruitment, training, and employee development (Okoro, 2024). These outcomes were critical to digital innovation. The study’s emphasis on broader human resource practices provided useful insights. Even so, the research did not focus on the specific factors that directly influenced the adoption of open banking systems. The study overlooked factors such as tool familiarity or employee adaptability. Meanwhile, Kagoya and Yapkoreny (2024) investigated the relationship between financial literacy and technology adoption in Uganda. To attain its objectives, the researchers employed a cross-sectional and quantitative research design with a sample size of 108 employees and analysed the data using SPSS. The study found that financial literacy strongly correlated with the adoption of banking technology. However, it overlooked the broader scope and role of digital literacy. Evidently, the study did not integrate the specific aspects of employee adaptability and tool familiarity. In effect, this leaves a gap in understanding how employee digital literacy influences the adoption of open banking systems. Locally, Okuku (2024) offered some insights into the adoption of digital technologies in Kenya’s banking sector. However, these insights were not directly tied to open banking adoption. Through a qualitative literature review, the researcher highlighted the importance of talent development and skill enhancement in Kenyan banks. The study focused on the relevance of these factors, particularly in response to disruptions in mobile banking and FinTech innovations. It identified digital literacy as a key factor in improving strategic planning. However, the research did not 29 address the role of digital literacy in adopting specific systems like open banking. Okuku (2024)’s focus on mobile banking also restricted its applicability to the broader scope of digital banking systems. On the other hand, Kimathi (2024) examined the impact of digital organizational innovations on the financial performance of Kenyan banks. Notably, the study selected a sample size of 315 employees across 39 commercial banks in Kenya and used descriptive statistics to analyse data. Ultimately, the findings suggested a positive correlation between digital innovations and financial outcomes. However, the study did not delve into how digital literacy affects the adoption of open banking systems. 2.3.4. Organisational Readiness and Open Banking Adoption Organisational readiness for open banking is influenced by how well the firm is prepared in terms of resources and technology. For a bank to demonstrate resource readiness, it should be flexible in addressing its banking technological needs. On the other hand, an organisation demonstrates IT readiness by its ability to adopt open banking through IT infrastructure (Taganoviqa et al., 2024). Organisational readiness is also dependent on cultural and partnership readiness. Partnership readiness is an organisation’s association with external stakeholders that support open banking, while cultural readiness is an organization’s valuable central values that lead to the adoption of open banking (Lokuge et al., 2018; Taganoviqa et al., 2024). When the organisation’s members are aligned with embracing and implementing organizational change and top management works on leading this initiative, organizational readiness is higher. For this study’s purpose, organizational readiness’ operational definition is a bank’s level of preparedness to adopt new technologies by having supportive management, adequate resources, technology, and displaying cultural readiness through its organizational culture. Therefore, the indicators for the variables included cultural readiness, top management support, resource availability, and IT readiness, which were adopted from Lokuge et al. (2018) and Taganoviqa et al. (2024)’s studies. Sidani and Harb’s (2023) study provided a global perspective on innovation in banking. It focused on the influence of transformational leadership in Lebanon’s financial services industry. Notably, the study employed qualitative interviews with senior executives to collect data. Consequently, the findings revealed that intellectual stimulation and inspirational motivation are key to fostering banking innovation. However, other leadership dimensions, such as idealized influence and individualized consideration, had limited impact. Similarly, Pi and Yang (2023) explored how 30 board culture influenced bank innovation across China’s state-owned banks. Using a sample of A- share listed banks, their quantitative analysis found that cultural diversity on the board significantly impacted innovations. As such, the findings suggested that innovation drivers are influenced by board diversity and a low power distance. This outcome illustrated the importance of Eastern cultural values like collectivism and risk aversion in banking innovation. Both studies conveyed the vitality of organisational culture and leadership in influencing technology adoption. However, they did not examine how these variables intersect with open banking adoption. Specifically, the studies did not focus on the needs and challenges that may arise in specific technological environments, such as open banking. In the regional context, Al-Issa and Omar (2024) examined the roles of digital leadership, innovative culture, and techno-stress inhibitors. The study focused on how these factors promote digital innovation within Libyan banks. Most notably, the study emphasised the need for digital engagement among bank employees. Using structural equation modelling on survey data from five banks, their study identified techno-work engagement’s mediating role and technostress’s moderating role in digital innovation. As such, the findings underscored the significance of leadership and digital readiness in fostering innovation. On the other hand, Maharaj and Pooe (2021) focused on the challenges South African banks faced during digital transformation. The study found that integrating dive