Exploring Factors that Influence Artificial Intelligence Adoption in Banks and Credit Unions

by

Vijaya S. Tumma

W
A dissertation submitted in partial fulfillment
of the requirements for the degree of
IE
Doctor of Business Administration
Muma College of Business
University of South Florida
EV

Co-Major Professor: Sajeev Varki, Ph.D.

Co-Major Professor: Aharon Yoki, D.B.A.
Jean Kabongo, Ph.D.
PR

Joann Quinn, Ph.D.

Douglas E. Hughes, Ph.D.

Date of Approval:
October 25, 2024

Keywords: AI, Financial Institutions, UTAUT, Technology

Copyright © 2024, Vijaya S. Tumma

 DEDICATION

To my dearest husband, your unwavering support, patience, and love have been my

anchor throughout this journey and always. Your belief in me, even during the most challenging

moments, has given me the strength to persevere. Thank you for being my constant source of

encouragement, my sounding board, and my greatest cheerleader. This achievement is as much

W
yours as it is mine, and I am forever grateful to walk this path with you by my side.
IE
To my dearest parents and brother, your unwavering love has been the cornerstone of my

journey. You have been my inspiration and instilled in me the values of resilience, humility, and
EV
the pursuit of knowledge. Your endless support and belief in my potential have carried me

through every challenge during this journey and triumph.

To my beloved children, your unconditional love has been a constant source of

PR

motivation and strength. You remind me daily of the beauty in learning and the importance of

perseverance. This work is a testament to the dreams I hope to inspire in you—that with

dedication and hard work, anything is possible. To my beloved Dolce, Stich, and Lola, whose

unwavering love and companionship have been a constant source of inspiration.

To the credit union community, whose unwavering commitment to empowering members

has been a constant source of inspiration.

 To the technology leaders and innovators who strive to balance progress with purpose,

and to my colleagues and mentors, who have offered invaluable guidance and support throughout

this journey.

W
IE
EV
PR
 ACKNOWLEDGMENTS

I would like to express my deepest gratitude to my dissertation co-chairs, Dr. Sajeev

Varki and Dr. Aharon Yoki, whose mentorship, wisdom, and steadfast support were instrumental

in guiding this research to completion. Their complementary perspectives and expertise have

profoundly shaped my journey. I acknowledge my distinguished dissertation committee

W
members: Dr. Jean Kabongo, Dr. Joann Quinn, and Dr. Douglas Hughes. Their insightful
IE
feedback, challenging questions, and constructive criticism have significantly elevated the

quality of this research.

EV
My heartfelt appreciation goes to my fellow classmates Wen Wei, Lisa Hammond and

Pete Shaw who served on my committee. Their collaborative spirit and peer support made this

journey intellectually stimulating as well as personally rewarding. Their camaraderie and

PR

academic insights have been invaluable throughout this process.

I would like to express my sincere gratitude to all the research participants who

generously shared their time, experiences, and insights. Without their willing participation and

candid contributions, this research would not have been possible.

 TABLE OF CONTENTS

List of Tables .................................................................................................................................. iv

List of Figures ..................................................................................................................................v

Abstract .......................................................................................................................................... vi

Chapter One: Introduction ...............................................................................................................1

W
Statement of Purpose and Contribution to Knowledge........................................................2
Theoretical Foundation ........................................................................................................3
Research Questions .............................................................................................................4
IE
Researcher Bias and Assumptions .......................................................................................5

Chapter Two: Literature Review .....................................................................................................7

Banks and Credit Unions ....................................................................................................7
EV
Credit Unions’ Growth Strategies ........................................................................................9
AI in Banking .....................................................................................................................10
Unified Theory of Acceptance and Use of Technology .....................................................13
Potential AI Opportunities for Credit Unions ....................................................................15
AI Implementation Guidelines ...........................................................................................16
PR

Challenges ..........................................................................................................................17

Chapter Three: Methodology .........................................................................................................18

Research Design and Framework ......................................................................................18
Qualitative Phase ...............................................................................................................19
Qualitative Data Analysis ......................................................................................21
Intercoder Reliability .............................................................................................25
Quantitative Phase .............................................................................................................28
Quantitative Data Analysis ....................................................................................30
Data Integration .................................................................................................................31
Limitations .........................................................................................................................32

Chapter Four: Results ....................................................................................................................34

Qualitative Interview Results.............................................................................................34
Contextual Findings ...............................................................................................36
Performance Expectancy .......................................................................................38
Effort Expectancy ..................................................................................................45

i
 Social Influence .....................................................................................................49
Facilitating Conditions ...........................................................................................53
Consumer Survey Results ..................................................................................................57
Statistical Analysis Based on UTAUT Theory.......................................................59
Descriptive Statistics ..............................................................................................60
Linear Regression ..................................................................................................61
Model Summary.....................................................................................................63
Coefficients ............................................................................................................63
Summary of Results ...........................................................................................................64

Chapter 5: Discussion ....................................................................................................................66

Summary of Key Findings .................................................................................................67
Performance Expectancy (PE) Implications for Consumers .............................................67
Performance Expectancy (PE) Implications for Financial Institutions..............................69
Managerial Implications ........................................................................................70
Unified understanding of AI Benefits ....................................................................71
Organic Growth .....................................................................................................71

W
Develop AI Advocates ...........................................................................................71
Measure Success ....................................................................................................72
Align Goals and Objectives ...................................................................................72
IE
Effort Expectancy (EE) Implications .................................................................................73
Social Influence Implications on Consumer ......................................................................74
Social Influence Implications on Financial Institutions.....................................................75
Facilitating Conditions Implications ..................................................................................77
EV
Compliance ............................................................................................................77
Technology and Infrastructure ...............................................................................79
Resources ...............................................................................................................80
Practical Contributions.......................................................................................................81
Academic Contribution ......................................................................................................82
PR

Limitations and Future Research .......................................................................................83

Conclusion .........................................................................................................................83

References ......................................................................................................................................85

Appendix A: Study Invitation ........................................................................................................96

Appendix B: Informed Consent – Interviews ................................................................................97

Appendix C: Informed Consent – Survey ......................................................................................99

Appendix D: Survey Questions ...................................................................................................101

Appendix E: Interview Questions ................................................................................................102

Appendix F: Coding Results ........................................................................................................104

ii
Appendix G: AI Policy ................................................................................................................107

Appendix H: Performance Expectancy – AI Areas Summary.....................................................111

Appendix I: Additional Interview Quotes....................................................................................114

W
IE
EV
PR

iii
 LIST OF TABLES

Table 1: AI Governance Areas .............................................................................................16

Table 2: Interview Participant Details..................................................................................21

Table 3: Sample Intercoder Rating ......................................................................................26

Table 4: Interviewees Based on Financial Institutions ........................................................37

W
Table 5: Thematic Representation from Credit Unions and Banks .....................................38

Table 6:
IE
Performance Expectancy – Summary of Categories .............................................40

Table 7: Effort Expectancy – Summary of Categories ........................................................47

EV
Table 8: Social Influence – Summary of Categories ...........................................................50

Table 9: Facilitating Conditions – Summary of Categories .................................................54

Table 10: Aggregated Form of Survey Responses .................................................................60

PR

Table 11: Descriptive Statistics ..............................................................................................61

Table 12: Model Summary – Behavioral Intention................................................................63

Table 13: Coefficients ............................................................................................................64

Table 14: Summary of Results ...............................................................................................64

Table E1: Interview Questions .............................................................................................102

Table F1: Coding Results .....................................................................................................104

Table H1: Performance Expectancy and AI Areas................................................................ 111

Table I1: Additional Interview Quotes ................................................................................ 114

iv
 LIST OF FIGURES

Figure 1: Open Codes Sample ...............................................................................................23

Figure 2: Axial Codes Sample...............................................................................................24

Figure 3: Themes ...................................................................................................................25

Figure 4: Interviewees Based on Roles .................................................................................37

W
Figure 5: Codes Generated for UTAUT Themes ...................................................................38

Figure 6:
IE
Overview of PE Categories ....................................................................................39

Figure 7: PE Breakdown Between Banks and Credit Unions ...............................................39

EV
Figure 8: Overview of EE Categories ...................................................................................46

Figure 9: EE Breakdown between Banks and Credit Unions................................................46

Figure 10: Overview of SI Categories .....................................................................................49

PR

Figure 11: SI Breakdown Between Banks and Credit Unions ................................................50

Figure 12: Overview of FC Categories ...................................................................................53

Figure 13: FC Breakdown Between Banks and Credit Unions ...............................................54

Figure 14: Demographic Summary .........................................................................................58

Figure 15: AI Usage Among All Respondents ........................................................................58

Figure 16: AI Recommendations from Respondents ..............................................................59

Figure 17: Residuals vs. Dependent Variable ..........................................................................62

v
 ABSTRACT

The importance of Artificial Intelligence (AI) is exploding in the banking sector, fueled

by enhanced productivity, improved efficiencies, and personalized services to the consumers. For

credit unions, the adoption of AI technologies presents opportunities and challenges. This

research explores the factors influencing AI adoption in the banking sector through the lens of

W
Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This study aims to
IE
explore the influence of key aspects of UTAUT model, Performance Expectancy (PE), Effort

Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) on the intention of AI
EV
adoption among credit unions, banks, and their consumers. This research combines qualitative

insights gathered from semi-structured interviews with leaders in the banking sector and

quantitative data from consumer surveys.

PR

vi
 CHAPTER ONE:

INTRODUCTION

Artificial intelligence (AI) technology is transforming every walk of life, and it is

empowering banking organizations to completely redefine how they operate, establish innovative

products and services, and most importantly, impact customer experience interventions (Malali,

W
& Gopalakrishnan, 2020). In this context, credit unions that have traditionally focused on
IE
personalized, consumer-centric services have been slower to adopt the latest technologies

compared to other financial institutions (Dow, 2006). The focus of this research is to investigate
EV
how AI is used in the banking sector for the benefit of their consumers and employees for

sustainable growth and leverage these best practices within credit unions and banks.

AI is an area of computer science that emphasizes the creation of intelligent machines

PR

that work and react like humans and enables the machines to reason and perform sophisticated

mental tasks (Korteling et al., 2021). The extent of AI technologies in the banking industry will

continue to increase as more entrants and the growth of new large language models become more

available to consumers and business (Biswas et al., 2020). McKinsey's (2021) estimates suggest

AI can potentially deliver up to $1 trillion of additional value each year for global banking

(Biswas et al., 2020).

The focus on personalization in consumers’ daily experiences sets a higher standard for

financial institutions to deliver similar personalized experiences to their consumers and

1
employees. By leveraging AI solutions, financial institutions can create unique and high-quality

experiences by personalization (Boustani, 2022). Clydesdale and Yorkshire Banking Group

(CYBG) is a medium-size bank in the United Kingdom, and their digital strategy includes an

application that uses AI to help manage their customer’s accounts (Burgess, 2018). This AI

application allows the customer to open an account in 11 minutes, learns the patterns of usage to

predict fund depletion in their accounts and suggests ways to avoid unnecessary bank charges

(Burgess, 2018). The customer relationship is healthy if financial institutions fulfill their

consumer’s needs and expectations, which change frequently (Satheesh & Nagaraj, 2021).

AI leverages technology to expand services and reach consumers regardless of their

W
geographical location. Nigeria’s United Bank for Africa’s (UBA) has a banking chatbot, called
IE
Leo, that helps customers with several transactions, such as transferring money, paying bills,

buying airtime, and checking account balances (mTransfersHQ, 2018). Customers can remotely
EV
chat with Leo on various channels via WhatsApp, Facebook messenger, and Apple business chat

and the chatbot responds immediately (Kshetri, 2021). However, credit unions are unable to meet

the consumers demands in a similar manner, and these challenges stem from various factors
PR

including their operational models, member expectations, and technological advancements.

Statement of Purpose and Contribution to Knowledge

The purpose of this study is to investigate attributes that influence financial institutions’

adoption of Artificial Intelligence (AI) with a focus on applying the Unified Theory of

Acceptance and Use of Technology (UTAUT). As AI continues to change the financial services

landscape, credit unions encounter challenges and opportunities to integrate these tools to

improve consumer experiences, streamline operations, and enhance decision-making process.

However, there is a lack of understanding of performance expectations, effort expectations,

2
social influences, and facilitating conditions that influence AI adoption in the context of credit

unions and other financial institutions.

This study aims to fill this gap by researching how the UTAUT determinants affect AI

adoption among employees and consumers of financial institutions, such as credit unions, and

identify the enablers and barriers of this adoption. This research seeks to provide practical

insights that can guide credit unions and other financial institutions in effectively leveraging AI

technologies for operational efficiencies, to enhance consumer engagement, and to maintain a

competitive edge. This study seeks to contribute to wider academic discourse on AI adoption in

the financial sector.

W
As a technology leader in a credit union, the researcher’s motivation is rooted in
IE
uncovering how AI can be strategically implemented in credit unions to increase their value

proposition without compromising their core values. As a practitioner and researcher focused in
EV
the credit union industry, the researcher took this opportunity to bridge the gap between credit

unions and their AI technology adoption.

Theoretical Foundation
PR

This study’s theorical foundation is the Unified Theory of Acceptance and Use of

Technology (UTAUT) model. UTAUT is a comprehensive model that combines elements from

various technology acceptance theories to explain and predict user acceptance of technology

(Venkatesh et al., 2003). UTAUT incorporates four constructs that are determinants of user

acceptance and user behavior: Performance Expectancy, Effort Expectancy, Social Influence,

and Facilitating Conditions (Venkatesh et al., 2003). UTAUT also has four moderate variables:

gender, age, experience, and voluntariness of use (Morris et al. 2005). The research design

explores the research questions through the lens of the four main UTAUT constructs.

3
 When applying UTAUT to AI adoption in credit unions the following key elements are

considered:

1. Performance Expectancy (PE) is defined as the degree to which an individual believes

that using a system will enhance their job performance (Venkatesh, 2022). The goal is to

assess how credit unions perceive the advantages their consumers and employees would

gain with AI implementations.

2. Effort Expectancy (EE) is the degree of ease associated with the use of the system

(Venkatesh, 2022). The effort expectancy can be improved when concerns regarding the

difficulty or ease of the AI systems are addressed.

W
3. Social Influence (SI) refers to an individual’s perception that others believe he or she
IE
should use the new system (Venkatesh, 2022). The objective is to understand the social

factors influencing AI acceptance within credit unions.

EV
4. Facilitating Conditions (FC) refers to an individual’s belief that an organizational and

technical infrastructure exists to support use of the system (Venkatesh, 2022). When

infrastructure and support for AI adoption is evaluated in the credit union, it lays down
PR

the foundation for the facilitating conditions for AI adoption.

The UTAUT model provides the necessary framework to explore the complex factors that

influence AI adoption in credit unions and actionable insights for enhancing this integration.

Research Questions

The goal of this research is to study the usability of AI in the banking industry and how

these practices may be applied in credit unions and banks. This study is guided by the research

questions below:

RQ1: What UTAUT factors influence financial institutions’ intention for AI adoption?

4
 RQ2: What UTAUT factors influence financial institutions consumers intention for AI

adoption?

These research questions help to understand attributes involved with AI adoption in banks

and credit unions from perspectives of various leaders in the financial institutions.

The interview results drive the analysis of the survey information gathered from

consumers in the banking sector. Hypothesis statements for the quantitative data analysis are

based on the most significant UTAUT factors from the interview results:

H1: PE has a stronger influence than EE and FC on consumers behavioral intention for

AI adoption.

W
H2: EE and FC have a stronger influence than SI on consumers behavioral intention for

AI adoption.
IE
The units of analysis for this study are financial institutions and their consumers.
EV
Consumers provided their insights regarding their usage of AI tools for their financial needs

through a survey. Organizational insights for these research questions are provided by individuals

involved in AI related planning or AI projects within their organizations. The interview and
PR

survey questions are based on UTAUT constructs.

Researcher Bias and Assumptions

A potential challenge considered during the qualitative research design was the

researcher’s experience in the technology area and view of AI as beneficial, potentially

amplifying the positive aspects of AI integration. Selection bias can also arise from focusing on

participants who are inclined towards AI technology, which may result in skewed understanding

of AI adoption attitudes. To ensure objectivity and comprehensiveness, the researcher focused on

seeking diverse perspectives from technology leaders, executive leaders, and leaders in the

5
business areas who influence AI adoption in their organizations. In addition, survey information

was gathered anonymously from the consumers in the banking sector for more accurate and

unbiased responses.

W
IE
EV
PR

6
 CHAPTER TWO:

LITERATURE REVIEW

The financial landscape is rapidly evolving and the adoption of advanced technologies,

such as AI, has become crucial for financial institutions to maintain competitiveness, efficiency,

and consumer satisfaction (Deloitte, n.d.). Credit unions are more consumer-centric financial

W
institutions than commercial banks (McKillop & Wilson, 2011), and integrating AI incorporates
IE
embracing technological advancements and aligning these modern technologies with their core

values of personalized service, trust, and community (Garg, 2024). As financial institutions
EV
explore the rapidly evolving landscape of AI solutions, it is important to understand the factors

that influence AI adoption for successful implementation and sustainability. The literature review

aims to explore the existing operational variations between banks and credit unions, AI adoption
PR

in banking and other financial services, key constructs of UTAUT and how they apply in the

context of the financial industry and challenges as well as carriers to AI adoption in banking

sector.

Banks and Credit Unions

In 2012, the number of credit unions and commercial banks were nearly equal in the

United States; however, banks in the aggregate held $13 trillion in assets and credit unions held

$1 trillion in assets (Anderson & Liu, 2013). The literature review revealed numerous factors that

contribute to the dominance of banks over credit unions.

7
 Credit unions’ unsophisticated management styles are a predominant cause for their lack

of growth (Gutenberg et al., 2014; Turner, 1996). Management in credit unions often

demonstrates an attitude of moral superiority to banks due to their non-profit nature, which may

cause them to overlook the necessity of employing efficient practices to increase their market

penetration (Gutenberg et al., 2014). Credit unions must understand the consumer’s demand for

their products, make best use of their competitive advantage, and create increased value for their

consumers to compete in the changing banking marketplace (Gutenberg et al., 2014).

Consumers in a credit union do not rely on credit unions for their primary financial

services and potentially invest savings in large retail banks due to credit unions’ technological

W
inefficiencies (Turner, 1996). The designation “Primary financial institution” is considered
IE
desirable as consumers typically first think of this institution when increasing their current

relationship or shopping for new financial products (Turner, 1996).

EV
Economies of scale in the banking industry occur when the cost per dollar of loans or

assets declines as the number of loans or assets increases, and an efficient bank operates at the

lowest cost per dollar of assets or loans (Jacewitz et al., 2020). Credit union’s smaller size
PR

restricts them from attaining profits through economies of scale (Almehdawe et al., 2021; Turner,

1996; Lu & Swisher, 2020). Economies of scale are driven by the increased use of information

technology in banking as well as by regulatory changes (Wheelock & Wilson, 2012). In addition,

there is evidence of scale economies in bank holding companies, which is attributed to

technological advances (Hughes & Mester, 2013).

Differences in the composition of product portfolios of credit unions are expected to have

implications in their growth performance (Goddard et al., 2002). Employing cross sectional and

panel methodologies, Goddard et al. (2002) analyzed the patterns of U.S. credit union growth

8
and identified that larger financial institutions, such as banks, are moving towards new areas of

business and becoming more diversified. State chartered credit unions in this competitive

environment are unable to operate across state lines and constrain their growth potential

(Goddard et al., 2002).

Contrary to the popular belief that credit union’s consumers are key to their growth,

credit union’s consumers potentially have a vested interest in keeping the credit union in their

community, even if the credit union can increase their earnings by shifting their business to other

densely populated centers (Maiorano et al., 2016). This mentality of a close-knit community is

driven by the credit union’s mission and value, which have a distinct niche (Maiorano et al.,

W
2016). This mentality motivates the consumers to be comfortable in their niche market and are
IE
unwilling to take the financial risks associated with reducing their niche market (Maiorano et al,

2016),
EV
Several other factors were identified that impact credit unions’ growth performance.

Macro-economic variables, such as interest rates, fiscal policies, consumer behaviors, mergers,

and acquisitions, were identified as significant factors to attain new consumer growth
PR

(Almehdawe et al., 2021; Lu & Swisher, 2020). As noted by Almehdawe et al. (2021),

compliance and regulatory requirements can substantially affect a credit union’s financial

performance; the need for credit unions to find solutions to protect their consumers and

simultaneously follow regulatory requirements are challenging tasks(Almehdawe et al., 2021).

Credit Unions’ Growth Strategies

When financial expertise, marketing expertise, and development capital available from a

bank are combined with market and community acceptance, a sense of financial commitment

with its members and a low cost of operation from a credit union is created (Turner, 1996).

9
Credit union innovations are important in sustaining an organizations’ financial performance and

raise their competitive strength (Chepkwei, 2018). The need for improved efficiency is exerting

pressure on the credit unions to develop into more competitive entities and understand strategic

innovation management practices that lead to success (Kalay & Lynn, 2015). Consumer-focused

initiatives, such high quality services and favorable rates, eventually attract customers

(Almehdawe, 2021; Lu & Swisher, 2020). When a strong service culture is created, it has a

substantial impact on attracting consumers to the credit unions, which can be achieved through

the adoption of technological innovations for efficiencies (Allred, 2001; Duncan & Elliot, 2004;

Turner, 1996). When credit unions integrate their values and traditions into a high-tech

W
development such as Artificial Intelligence (AI), it can solidify their position in the marketplace

(Thowfeek et al., 2020).

IE
AI in Banking
EV
The banking sector has been one of the leading adopters of AI technology to reduce

operating costs and accelerate the inclusion of banking services (Tulcanaza-Prieto et al., 2023).

AI brings transformation in governance, economy, and society in developing countries (Kshetri,

PR

2019). According to the World Bank Group (2017), about 1.7 billion people are unbanked around

the world. Traditional banks are unwilling and reluctant to serve this population due to high

transaction costs and inefficient processes (Kshetri, 2019). AI is rapidly developing and creating

social, economic, and political transformation in these developing economies; this shift has been

driven by matured AI algorithms, increased competition, growth in AI investment, and changes

in consumers’ preferences for digital financial products facilitated by AI (Kshetri, 2021).

Countries such as Mexico, Nigeria, Indonesia, Malaysia, Chile, and Brazil are using AI

10
technologies to improve efficiency, lower the risks, and reduce operating costs to improve lower-

income population’s access to financial services (Kshetri, 2021).

Deutsche Bank used AI speech recognition technology to listen to employee

conversations with clients to improve efficiency and ensure employees comply with regulations

(Burgess, 2018). Deutsche Bank also used AI to identify potential customers (Burgess, 2018). In

addition, it is important that banks maintain the relationship with their existing customers

(Wulandari, 2022). Customer loyalty to the bank can be improved if the banks provide high

quality services at lower prices (Kishada et al, 2016). An AI model for assessing customer

loyalty in the banks will help management successfully develop and implement customer loyalty

W
strategies (Kishada et al., 2016).
IE
The banking sector is implementing chatbots to develop stronger customer-brand

relationships, and deliver contextual information to customers (Trivedi, 2019). Chatbot is a

EV
software system that can chat or interact with a human user in a natural language such as English

(Shawar & Atwell, 2007). AI-enabled chatbots in the banking sector offer personalized customer

service, assist in transaction processing, customer education, prevent fraud, and can offer other
PR

products and services through upsell and cross-sell (Singh et al., 2018; Trivedi, 2019).

Banking customers believe using online banking websites increases the potential for

phishing attacks and identity theft, which can lead to trust concerns with the banking relationship

(Aburrous et al., 2010). Phishing websites are malicious platforms disguised as legitimate

institutions that can steal a customer’s personal account information; this data and identity theft

occurs when a customer attempts to access their account while believing the site is legitimate

(Aburrous et al., 2010). An intelligent phishing website detection system based on AI will reduce

11
the need for human intervention and enhance the precision and performance of the phishing

detection websites (Aburrous et al., 2010).

One of the most effective uses of AI in financial services is identification of fraudulent

activity (Burgess, 2018). AI-based fraud monitoring systems oversee customers’ real-time

transactions to identify potential fraudulent patterns based on known patterns from previous

fraudulent transactions (Burgess, 2018). AI supports customer credit approval by predicting

potential customer credit in the approval process and avoiding situations such as bankruptcy as

well as fraud activities (Moro et al., 2015). HSBC Bank detected possible money laundering

activities by searching transaction patterns which reduced their false-positive investigations by

W
20%; this process made efficient use of their expensive resources (Burgess, 2018). HSBC also
IE
assessed risk using AI simulations to understand their trading positions and associated risks

(Burgess, 2018).
EV
Banks are increasingly adopting cloud computing technologies to create flexible and

agile banking environments to respond to business needs (Asadi et al, 2017). Several cloud

security issues exist in the banking industry; these include lack of standards and Service Level
PR

Agreements (SLAs), lack of transparency, regulatory and compliance requirements, malicious

activity, security issues, and cyber-attacks, etc. (Elzamly et al., 2017). Artificial Neural Networks

(ANN) is a modeling technique inspired by the human nervous system that allows learning by

example from representative data describing a physical phenomenon or a decision process

(ScienceDirect, n.d.). By using ANN algorithms in AI, critical cloud computing security issues

can be predicted (Elzamly et al., 2017). Ortiz et al (2016) studied another aspect of ANN to

improve the physical banking system security and prevent robberies in banks and ATMs.

12
 Liquidity risks in banks can be interpreted as the ability to quickly turn an asset without

capital loss, interest penalty, or the risk of inability to raise funds in the financial market (Vento

& La Ganga, 2009). A simplistic AI model using endogenous factors can address loan-based

liquidity risk prediction issues in banks (Tavana et al., 2018). This model presents the efficiency,

accuracy, and flexibility of AI and Machine Learning methods to measure the ambiguous

occurrences related to bank liquidity risks (Tavana et al., 2018).

AI is the driving force behind digital technologies in modern banking (Sathish & Renu,

2024). This research studies the adoption of AI in the banking sector through the lens of the

Unified Theory of Acceptance and Use of Technology (UTAUT) to understand the underlying

W
theoretical foundations.
IE
Unified Theory of Acceptance and Use of Technology

UTAUT is a model that combines elements from several technology acceptance theories
EV
to explain and predict user acceptance of technology (Venkatesh et al., 2003). UTAUT was

formulated with four constructs that play a significant role as direct determinants of user

acceptance and user behavior: Performance Expectancy (PE), Effort Expectancy (EE), Social
PR

Influence (SI), and Facilitating Conditions (FC) (Venkatesh et al., 2003), and up to four variables

that moderate various relationships - gender, age, experience, and voluntariness of use (Morris et

al. 2005).

Several studies consider UTAUT as the theoretical basis to research technology adoption.

An individual’s intention to adopt mobile banking is influenced by performance expectancy,

social influence, perceived financial cost, and credibility (Yu. 2012). In a study conducted by

Roh et al., (2023), they concluded that PE, EE, and SI enhance user attitudes toward adopting

13
 robo-advisors. They also showed strong evidence that attitude and facilitating conditions increase

user intentions to adopt robo-advisors (Roh et al., 2023).

Research indicates that performance expectancy, effort expectancy, and social influence

are consistent and significant predictors of behavioral intention to adopt mobile and internet

banking services (Bhatiasevi, 2016; Dendrinos & Spais, 2023; Mensah & Khan, 2024).

However, some studies found contradictory results regarding the influence of certain UTAUT

constructs. For example, while perceived financial cost and facilitating conditions were not

supported as significant factors in Thailand (Bhatiasevi, 2016), they were discovered to be

important in China (Mensah & Khan, 2024). This instance highlights the importance of

W
considering contextual differences when applying the UTAUT model.
IE
Several studies extended the UTAUT model by incorporating additional constructs to

explain banking technology adoption. These include perceived credibility, perceived

EV
convenience (Bhatiasevi, 2016), awareness, and government regulations (Mensah & Khan,

2024). Trust and security concerns were identified as critical factors, especially in the context of

Fintech adoption (Jafri et al., 2023). The integration of consumption values and motivational
PR

factors provides further insights into mobile banking adoption (Dendrinos & Spais, 2023).

UTAUT is a robust framework that can be applied to understand banking technology

adoption. Researchers found UTAUT beneficial for adapting and extending the model to capture

context-specific factors (Ayaz & Yanartaş, 2020). The literature suggests a comprehensive

approach considering technological, individual, social, and institutional factors most effective in

explaining and predicting the adoption of digital banking services across different markets

(Souiden et al., 2020; Yuliana & Aprianingsih, 2022).

14

Reproduced with permission of copyright owner. Further reproduction prohibited without permission.