Factors Affecting Electronic Banking Adoption in Barbados

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Factors Affecting Electronic Banking Adoption in Barbados Factors Affecting Electronic Banking Adoption in Barbados

Jacqueline Delores Bend

Walden University

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Walden University

College of Management and Technology

This is to certify that the doctoral study by

Jacqueline Delores Bend

has been found to be complete and satisfactory in all respects,

and that any and all revisions required by

the review committee have been made.

Review Committee

Dr. Deborah Nattress, Committee Chairperson, Doctor of Business Administration

Faculty

Dr. Roger Mayer, Committee Member, Doctor of Business Administration Faculty

Dr. David Moody, University Reviewer, Doctor of Business Administration Faculty

Chief Academic Officer and Provost

Sue Subocz, Ph.D.

Walden University

2020

Abstract

Factors Affecting Electronic Banking Adoption in Barbados

Jacqueline Delores Bend

MBA, University of Leicester, 2014

BCOMM, Nipissing University, 2007

Doctoral Study Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Business Administration

Walden University

December 2020

Abstract

The low rate of customers’ adoption of electronic banking services affects retail banks’

profitability. The operating cost for a financial transaction performed by bank tellers

averages US$1.07 compared to US$0.01 using electronic banking channels. It is

paramount for retail banking leaders to understand the factors influencing customer

adoption of electronic banking to sustain competitive advantage. Grounded in the

technology acceptance model framework, the purpose of this quantitative correlational

study was to examine the relationship between perceived usefulness (PU), perceived ease

of use (PEOU), and customer adoption of electronic banking in Barbados. The validated

technology acceptance model survey instrument was used to collect 72 responses from

bank account holders living in Barbados who owned a mobile smartphone or a computer

and used electronic banking services (mobile or online banking). A multiple regression

analysis confirmed that the model as a whole was able to significantly predict customer

adoption of electronic banking services: F(2, 69) = 123.503, p < .001. Both PU and

PEOU were statistically significant with PEOU (t = 6.249, p < .01, β = .574) accounting

for a higher contribution to the model than PU (t = 3.883, p < .01, β = .357). A key

recommendation is that retail banking leaders provide customers with educational

resources to aid in increasing their usage of electronic banking services. The implications

for positive social change include an improved understanding of electronic banking

services to residents, increased awareness of the availability of electronic banking

services to retail banking customers, and expanded access to affordable financial services

for individuals in Barbados.

Factors Affecting Electronic Banking Adoption in Barbados

Jacqueline Delores Bend

MBA, University of Leicester, 2014

BCOMM, Nipissing University, 2007

Doctoral Study Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Business Administration

Walden University

December 2020

Dedication

My favorite quote is “the sky is the limit,” but I cannot do it alone! Successful

completion of this doctoral study is proof of that fact, so I would like to dedicate this

work to God, who gives me the strength to do all things possible. I also dedicate this

study to my children and cheerleaders, Shanique and Shano, who encouraged me to

pursue my life-long goal and rewarded me when I took the leap of faith to start the

degree. You cheered me on when I felt overwhelmed with competing priorities and

celebrated each milestone with me along the journey. To my mother, Brenda Watson, for

her continuous support and prayers, thank you so much! To my siblings, Kayrene,

Gregory, Beverley, Nigel, and Kenville, I rarely saw you, but thank you for keeping in

touch! To my church family at Christ is the Answer Family Church and ministry group,

Daughters of Worship International, who prayed me through each day and shouldered

some of my responsibilities while I studied, thank you! Finally, I dedicate this study to

Fitzroy Bardouille, my coach, friend, and mentor, for your unwavering support, guidance,

and encouragement; you kept me focused on the destination! Thank you all!

Acknowledgments

This achievement was possible because of the reliable support system behind me!

I thank God for keeping me in good health, strength, and will. To my chair, Dr. Deborah

Nattress, I thank you for keeping me focused throughout my dissertation, for your

leadership, encouragement, and sharing your personal experiences to show that I was not

alone when I felt overwhelmed. To my SCM, Dr. Roger Mayer, for your direction and

support, thank you! To my URR, Dr. David Moody, for your critique and encouragement

that helped me to produce quality work, thank you! I would also like to thank my

program director’s representative, Dr. Al Endres, for supporting my work.

I acknowledge and thank my colleagues from the 8100 and 9000 courses and

faculty from previous courses for their valuable feedback. To the Writing Center staff,

especially Claire who challenged me to take my writing skills to the next level with each

submission, thank you! To other family members, friends, and work colleagues who have

helped me in various ways along this journey, a sincere thank you!

Table of Contents

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

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

Section 1: Foundation of the Study ..................................................................................... 1

Background of the Problem ........................................................................................... 1

Problem Statement ......................................................................................................... 2

Purpose Statement ......................................................................................................... 2

Nature of the Study ........................................................................................................ 3

Research Question ......................................................................................................... 4

Hypotheses .................................................................................................................... 4

Theoretical Framework ................................................................................................. 4

Operational Definitions ................................................................................................. 5

Assumptions, Limitations, and Delimitations ............................................................... 6

Assumptions ............................................................................................................ 6

Limitations ............................................................................................................... 7

Delimitations ........................................................................................................... 7

Significance of the Study ............................................................................................... 8

Contribution to Business Practice ........................................................................... 9

Implications for Social Change ............................................................................... 9

A Review of the Professional and Academic Literature ............................................... 9

Application to the Business Problem .................................................................... 10

The Technology Acceptance Model ...................................................................... 11

Rival Theories ....................................................................................................... 18

Measurement ......................................................................................................... 24

Other Factors Affecting E-Banking Adoption ...................................................... 28

Electronic Banking Adoption ................................................................................ 34

E-Banking Adoption in Barbados ......................................................................... 37

The Impact of Electronic Banking and Banks’ Profitability Performance ............ 39

E-Banking Adoption and Strategic Planning ........................................................ 40

Methodologies Used in Research on E-banking Adoption ................................... 43

Transition ..................................................................................................................... 45

Section 2: The Project ....................................................................................................... 46

Purpose Statement ....................................................................................................... 46

Role of the Researcher ................................................................................................. 46

Participants .................................................................................................................. 48

Research Method and Design ...................................................................................... 50

Research Method ................................................................................................... 50

Research Design .................................................................................................... 52

Population and Sampling ............................................................................................. 54

Ethical Research .......................................................................................................... 56

Data Collection Instruments ........................................................................................ 59

Data Collection Technique .......................................................................................... 65

Data Analysis ............................................................................................................... 67

Study Validity .............................................................................................................. 75

iii

Transition and Summary ............................................................................................. 77

Section 3: Application to Professional Practice and Implications for Change .................. 79

Introduction ................................................................................................................. 79

Presentation of the Findings ........................................................................................ 80

Demographic Statistics .......................................................................................... 83

Reliability and Validity Test ................................................................................. 83

Tests of Assumptions ............................................................................................ 84

Descriptive Statistics ............................................................................................. 87

Inferential Results .................................................................................................. 88

Applications to Professional Practice .......................................................................... 91

Implications for Social Change ................................................................................... 93

Recommendations for Action ...................................................................................... 94

Recommendations for Further Research ..................................................................... 98

Reflections ................................................................................................................... 99

Conclusion ................................................................................................................. 100

References ....................................................................................................................... 102

Appendix A: Survey E-Banking Adoption ...................................................................... 161

Appendix B: Permission to Adopt TAM Survey Instrument .......................................... 163

List of Tables

Table 1. Post Hoc Analysis for Customer Adoption of E-Banking .................................. 82

Table 2. Demographic Statistics by Age ........................................................................... 83

Table 3. Correlation Coefficient Among Study Predictor Variables ................................. 85

Table 4. Means and Standard Deviations for Study Variables .......................................... 88

Table 5. Regression Analysis Summary for Predictor Variables ...................................... 90

List of Figures

Figure 1. Power as a function of sample size .................................................................... 56

Figure 2. Normal probability plot (P-P) of the regression standardized residuals ............ 86

Figure 3. Scatterplot of the standardized residuals ............................................................ 87

Section 1: Foundation of the Study

Electronic banking or e-banking is an alternative concept to traditional banking

that researchers claim is advantageous to both banks and their customers (Patel & Patel,

2017). Bank leaders promote e-banking to reduce operating expenses, create efficiency,

and promote customer retention, whereas customers enjoy the convenience, accessibility,

and availability (Patel & Patel, 2017). As a developing country, Barbados is in its infancy

stages of electronic banking and bank leaders are faced with challenges to convert a

culture of cash intensive banking to a cashless environment.

Background of the Problem

In recent years, innovation technology advances in the banking industry created a

paradigm shift from traditional branch-based banking to electronic or e-banking services

(Mishra & Singh, 2015; Rad, Rasoulian, Ali, Mahmoad, & Sharifipour, 2017; Sanchez-

Torres, Canada, Sandoval, & Alzate, 2018). Akhisar, Tunay, and Tunay (2015) noted that

retail banking leaders could eliminate 40% percent of the branch-based transaction costs

if they transitioned customers to lower-cost e-banking services. Retail banking leaders,

therefore, sought to develop strategies to promote e-banking services to increase

profitability and sustain competitive advantage, however the customer adoption rate

remained low (Akhisar et al., 2015; Belas, Koraus, Kombo, & Koraus, 2016). My

doctoral research study provided strategies to help retail banking leaders in Barbados

understand the relationship between perceived usefulness (PU), perceived ease of use

(PEOU), and customer adoption of electronic banking services. The study also provided a

predictive model to help retail banking leaders in Barbados reduce high-cost branch-

based transactions with the adoption of lower-cost electronic banking services.

Problem Statement

The low rate of customers’ adoption of e-banking services affects retail banks’

profitability (Mansour, 2016). The operating cost for a financial transaction performed by

bank tellers in 2011 totaled US$1.07 compared to US$0.01 using e-banking channels

(Chandio, Irani, Zeki, Shah, & Shah, 2017). The general business problem was that banks

lose money when customers do not use e-banking services. The specific business

problem was that some retail banking leaders in Barbados do not understand the

relationship between perceived usefulness (PU), perceived ease of use (PEOU), and

customer adoption of e-banking services.

Purpose Statement

The purpose of this quantitative correlational study was to examine the

relationship between PU, PEOU, and customer adoption of e-banking services in

Barbados. The predictor variables were PU and PEOU. The dependent variable was e-

banking adoption. The target population was retail banking customers in Barbados with

access to smartphones, tablets, laptops, or desktop computers who had at least one bank

account. The implications for social change included the potential to provide an improved

understanding of e-banking services to Barbadian residents, to increase awareness of the

availability of e-banking services to retail banking customers in Barbados, and to create

access to affordable financial services for individuals in Barbados.

Nature of the Study

Researchers use three methods to conduct their studies: (a) quantitative, (b)

qualitative, and (c) mixed methods (Fricker, 2016). According to Saunders, Lewis, and

Thornhill (2015), researchers use a quantitative methodology to examine the relationships

between variables and apply closed-ended questions to test hypotheses. In this study, I

used a quantitative method because I intended to examine the possible relationship

between two independent variables (PU, PEOU), and one dependent variable (electronic

banking adoption). Researchers adopt a qualitative methodology to explore a central

phenomenon and gather information using open-ended questions (Rutberg & Bouikidis,

2018). I did not attempt to explore a central phenomenon; therefore, a qualitative method

was not appropriate for this study. Mixed method researchers adopt both quantitative and

qualitative methodologies in a single study (Saunders et al., 2015; Yin, 2018). I did not

use a mixed method approach because I did not require qualitative data in this study.

I chose a correlational design for this study. Researchers use correlations to

examine the relationship between two or more variables (Saunders et al., 2015). The

correlational design was appropriate for this study because the purpose of the study was

to examine the relationship between a set of predictor variables and a dependent variable.

I did not choose experimental or quasiexperimental designs because they are appropriate

when researchers attempt to assess a degree of cause and effect (Rutberg & Bouikidis,

2018; Saunders et al., 2015). The primary objective of this study was to examine the

correlational relationship between predetermined variables; therefore, the experimental or

quasiexperimental designs were not appropriate for this study.

Research Question

RQ: What is the relationship between PU, PEOU, and customer adoption of

electronic banking in Barbados?

Hypotheses

: There is no statistically significant relationship between PU, PEOU, and

customers’ adoption of e-banking services.

: There is a statistically significant relationship between PU, PEOU, and

customers’ adoption of e-banking services.

Theoretical Framework

Davis (1986) developed the technology acceptance model (TAM) as an extension

of the theory of reasoned action (TRA) previously proposed in 1975 by Fishbein and

Ajzen (Illia, Ngniatedema, & Huaug, 2015; Mansour, 2016; Mansour, Eljelly, &

Abdullah, 2016; Marakarkandy, Yajnik & Dasgupta, 2017; Ozlen & Djedovic, 2017;

Patel & Patel, 2018). Davis argued that people adopt technology primarily because of the

functions it performs and secondly because of the ease or difficulty with the system

performing these functions (Mansour, 2016; Mansour et al., 2016; Ozlen & Djedovic,

2017; Patel & Patel, 2018). TAM is concerned with PU, individuals’ belief that the use of

technology will improve their job performance; and PEOU, individuals’ belief that use of

such technology would require minimal effort (Mansour et al., 2016; Marakarkandy et

al., 2017; Patel & Patel, 2018; Shaikh & Karjaluoto, 2015; Sharma, Govindaluri, &

Balushi, 2015).

Theorists of TAM confirmed the model as a powerful and parsimonious concept

and sought to provide bank leaders, local governments, marketing professionals, and

Internet banking service providers with strategies to enhance their e-banking platforms,

features, and benefits to increase the rate of customers’ adoptions (see Aboobucker &

Bao, 2018; Mansour et al., 2016; Marakarkandy et al., 2017; Patel & Patel, 2018; Shaikh

& Karjaluoto, 2015; Sharma et al., 2015). As it related to this quantitative correlational

study, I applied TAM as the theoretical framework because it aligned with my objective

to examine the factors that influence customer adoption of e-banking services in

Barbados.

Operational Definitions

Branch-based banking: Branch-based banking refers to financial activities that

customers perform over the counter (OTC) in the retail banking industry (Gaservic,

Vranjes & Drinic, 2016).

Competitive advantage: Competitive advantage is a firm’s differential position

through access to resources, markets, and opportunities for its products or services (Arbi,

Bukhari, & Saadat, 2017)

Electronic banking: Electronic banking is a technology-enabled self-service

channel for customers in the banking industry to perform financial transactions via the

Internet, mobile, telephone, or automated teller machines (ATMs; Rad et al., 2017).

Innovation technology: Innovation technology refers to the process of generation,

acceptance, and implementation of new technological changes to improve services,

systems, or processes (Perez, Popadiuk, & Cesar, 2017).

Perceived ease-of-use (PEOU): Perceived ease-of-use is the extent to which a

consumer believes that using electronic banking is effortless (Davis, 1989).

Perceived usefulness (PU): Perceived usefulness represents the degree to which a

consumer believes that using the technology will increase performance (Davis, 1989)

Transaction cost: Transaction cost refers to the operational expenses associated

with retail banks performing customer financial OTC activities (Akhisar et al., 2015).

Assumptions, Limitations, and Delimitations

In the following subsection, I discussed the assumptions, limitations, and

delimitations of this study. Assumptions are beliefs that are true but unverifiable

(Bryman, 2016; Leedy & Ormrod, 2015; Marshall & Rossman, 2014). Limitations are

factors that the researcher has no control over but could negatively influence the study

(Leedy & Ormrod, 2015; Marshall & Rossman, 2014; Yin, 2018). Delimitations are the

elements the researcher outlined as the scope of the research (Ensslin, Dutra, Ensslin,

Chaves, & Dezem, 2015; Leedy & Ormrod, 2015; Marshall & Rossman, 2014).

Assumptions

Assumptions are the researcher’s underlying and inherent beliefs that are true but

unverifiable and usually direct the research study (Leedy & Ormrod, 2015; Marshall &

Rossman, 2014; Yin, 2018). Assumptions are also the factors that a researcher may not

have control over but form part of the study (Cerniglia, Fabozzi, & Kolm, 2016; Leedy &

Ormrod, 2015). In this quantitative correlation study, I had the following assumptions: (a)

the participants voluntarily participated in the survey; (b) each participant responded to

the survey questions honestly, independently, and anonymously; (c) the participants’

responses to the survey questionnaire were accurate representations of the opinions of all

electronic banking customers in Barbados; (d) the participants were knowledgeable in

electronic banking services, and (e) the opinions of the selected participants represented

the views of other electronic banking customers in the retail banking industry in

Barbados.

Limitations

Limitations are factors that could negatively impact the quality of the study

(Adewunmi, Koleoso, & Omirin, 2016; Friedman, Fireworker, & Nagel, 2017; Leedy &

Ormrod, 2015). In correlational studies, the researcher investigates the interactions of

variables but cannot prove cause and effect (Altman & Krzywinski, 2015; Leedy &

Ormrod, 2015). There were four limitations in this study. The first limitation was that

participants could withdraw from the survey at any time; therefore, the valid responses

may not be a representation of the population. The second limitation was the use of the

survey technique with closed-ended responses of participants. Thirdly, the study focused

on retail banking customers in Barbados that might not have represented the views of

electronic banking customers in other customer groups, financial institutions, or countries

within the Caribbean region. Lastly, this study might become irrelevant due to technology

advances in electronic banking in response to environmental factors or increased

customer demands.

Delimitations

Delimitations refer to the restrictions the researcher introduced in the study as

well as the scope and boundaries that governed the research (Ensslin et al., 2015; Jolley

& Mitchell, 2010; Leedy & Ormrod, 2015). Delimitations of my study included the

following: (a) I did not address customer reaction to electronic banking services; (b) I

collected data from customers who met specific criteria; (c) the study did not include

other factors such as new products, changes to existing technologies, regulations, or

environmental changes that could influence the conversion of customers to electronic

banking services, and (d) the findings of this study might be only applicable to Barbados.

This decision may have limited my ability to investigate more themes to support my

research or identify additional issues impacting retail banking leaders’ abilities to

increase customer adoption of electronic banking by focusing on one geographic area.

Significance of the Study

Over the past 2 decades, there has been a rapid growth in technology innovation

in the banking industry. Retail banking leaders continue to invest in Internet banking,

mobile banking, point-of-sale, and automated teller machines to offer customers

convenient ways to conduct their financial activities while reducing operating expenses

associated with branch-based transactions (Akhisar et al., 2015; Alkailani, 2016; Shaikh

& Karjaluoto, 2015). However, the success rate of converting customers to electronic

banking has been slower than bank leaders expected (Akhisar et al., 2015; Alkailani,

2016; Olufemi & Ezekiel, 2017; Shaikh & Karjaluoto, 2015). The significance of this

study was three-fold: (a) to help retail banking leaders, marketers, and technology

developers improve the design and implementation strategies for their electronic banking

services to increase customers’ adoption; (b) to enhance the existing customers’

experiences with electronic banking services; and (c) to provide information to attract

non-users to adopt electronic banking services.

Contribution to Business Practice

This study was significant to business practice because I provided information to

potentially help retail banking leaders understand customers’ expectations of electronic

banking services and develop strategies to address customers’ concerns in Barbados. I

also provided a model that might help retail banking leaders reduce high-cost branch-

based transactions with the adoption of lower-cost electronic banking services that could

increase profitability and sustain competitive advantage.

Implications for Social Change

The implications for social change included the potential to provide an improved

understanding of electronic banking services to Barbadian residents, to increase

awareness of the availability of electronic banking services to retail banking customers in

Barbados, and to create access to affordable financial services for individuals in

Barbados. Increased usage of electronic banking services could stimulate the progression

of Barbados into an environmentally friendly society with a reduction in the paper used to

perform branch-based transactions.

A Review of the Professional and Academic Literature

A literature review is a synthesis of published literature on known and unknown

information about a research topic (Saunders et al., 2015). A literature review is either

narrative, which critiques a topic but does not provide information on the selection

criteria for the studies, or systematic, where studies are selected based on the research

topic (Onwuegbuzie & Weinbaum, 2017). I began the literature review with a systematic

review of the extant literature on TAM, the predictor variables (PU and PEOU) of the

study, and prior research on TAM and e-banking adoption. The section contained a

detailed discussion on the rival theories and measurement. I discussed other factors that

could affect customer adoption of e-banking, provided an overview of the dependent

variable (e-banking adoption), examined innovation technology and transformational

leadership, and innovation technology and strategic planning. I concluded with an

explanation of innovation technology and banks’ profitability performance and other

diversification strategies to increase banks’ profitability. The literature review contained

218 peer-reviewed references, with 201 (92.20%) published within the past 5 years as of

2019.

I explored the Walden University online library as the primary source for articles

relevant to my research problem. I also accessed other electronic databases including,

EBSCOHost’s Business Complete, Proquest’s ABI/INFORM Complete, Proquest

Central, Emerald Management Journals, SAGE Journals, and Google Scholar. My search

was limited mainly to peer-reviewed articles within 5 years as of 2019. I used search

terms such as technology acceptance model, TAM, technology adoption, electronic

banking, e-banking, mobile banking, Internet banking, online banking, electronic banking

in Barbados, and innovation technology.

Application to the Business Problem

The purpose of this quantitative correlational study was to examine the

relationship between PU, PEOU and customer adoption of e-banking services in

Barbados. The null hypothesis was that there is no statistically significant relationship

between PU, PEOU, and customers’ adoption of e-banking services. The alternative

hypothesis was that there is a statistically significant relationship between PU, PEOU,

and customers’ adoption of e-banking services. The targeted population was comprised of

adult individuals residing on the island of Barbados. The implications of this study for

positive social change included the potential to provide an improved understanding of

electronic banking services to Barbadian residents, to increase awareness of the

availability of electronic banking services to retail banking customers in Barbados, and to

create access to affordable financial services for individuals in Barbados. Increased usage

of electronic banking services could stimulate the progression of Barbados into an

environmentally friendly society with a reduction in the paper used to perform branch-

based transactions.

The Technology Acceptance Model

Researchers use the TAM model to examine the impact of technology on human

behavior (Chauhan, 2015; Sinha & Mukherjee, 2016). Davis (1986) developed the TAM

model to analyze the impact of external factors on internal beliefs, attitudes, and

intentions (Chauhan, 2015; Illia et al., 2015; Mansour, 2016; Priya et al., 2018). The

TAM is rooted in cognitive psychology and is an extension of Fishbein and Ajzen’s

(1975) TRA. Fishbein and Ajzen claimed that users’ attitudes influenced their behavioral

intentions and subjective norms in accepting information technology (Mansour et al.,

2016; Marakarkandy et al., 2017; Ozlen & Djedovic, 2017; Patel & Patel, 2018; Sinha &

Mukherjee, 2016). Davis hypothesized that people accept technology because of the

functions it performs (PU) and the ease or difficulty of the system performing these

functions (PEOU; Mansour, 2016; Mansour et al., 2016; Ozlen & Djedovic, 2017; Patel

& Patel, 2018).

TAM is concerned with two constructs: PU, referred as the extent to which

individuals believe that the use of technology will improve their job performance, and

PEOU, known as the degree to which individuals believe that use of such technology

would require minimal effort (Mansour et al., 2016; Marakarkandy et al., 2017; Patel &

Patel, 2018; Shaikh & Karjaluoto, 2015; Sharma et al., 2015). Theorists of TAM viewed

the model as a powerful and parsimonious concept and sought to provide bank leaders,

local governments, marketing professionals, and Internet banking service providers with

strategies to enhance their e-banking platforms, features, and benefits to increase the rate

of customers’ adoptions (see Aboobucker & Bao, 2018; Alalwan, Dwivedi, Rana &

Williams, 2016; Mansour et al., 2016; Marakarkandy et al., 2017; Patel & Patel, 2018;

Shaikh & Karjaluoto, 2015; Sharma et al., 2015).

Perceived usefulness (PU). Within the TAM framework, PU is associated with

productivity and performance (Priya et al., 2018; Ramos, Ferreira, de Freitas, &

Rodrigues, 2018). Users perceive systems to be useful when they use the technology to

improve their job performance and productivity (Davis, 1989; Patel & Patel, 2018; Priya

et al., 2018). PU plays an important role in strategic decision making as it relates to the

development of the features and functionalities of a system. Priya et al. (2018) purported

that users perceive a system to be useful when they think a positive relationship between

its usefulness and performance. Fellow researchers claimed that PU is a significant factor

that affects user acceptance of information technology (see Alkailani, 2017; Mansour et

al., 2016; Marakarkandy et al., 2017; Rodrigues, Oliveira, & Costa, 2016). In the context

of e-banking, if customers believe that online and mobile banking services are useful,

they will accept them as alternative options to traditional banking.

According to Marakarkandy et al. (2017), the most useful feature of online

banking is its 24-hour availability, Alkailani, (2016) supported this claim and highlighted

that the ease of processing transactions and increased financial transparency were

additional benefits of online banking usefulness. Priya et al. (2018) found that customers

perceived mobile banking useful because it was low cost, convenient, and easy to

conduct banking. For the purpose of this study, I adopted Liebana-Cabanillas, Munoz-

Leiva, Sanchez-Fernandez, and Viedma-del Jesus’s (2016) and Rodrigues et al.’s (2016)

definitions of PU as the degree to which a bank’s customer perceives that the use of a

business application makes it easier to purchase or sell financial products. Bank leaders

could benefit from increased financial performance with reduced overhead expenditures

associated with facilitating branch-based banking.

Perceived ease of use (PEOU). The theorists of the TAM framework posited that

PEOU is a significant determinant of user acceptance by influencing the attitudes of users

to adopt new technologies (Patel & Patel, 2018; Priya et al., 2018; Ramos et al., 2017).

Researchers claimed that the concept of PEOU is an important factor in the acceptance of

information technology and is significant when demonstrated through PU of the systems

(Alkailani, 2016; Liebana-Cabanillas et al., 2016; Mansour et al., 2016; Rodrigues et al.,

2016). According to Davis (1989), PEOU may be an antecedent of PU rather a direct

determinant of technology usage. In the context of e-banking, PEOU means hassle-free

use of online and mobile banking services while finding the experience to be enjoyable

(Alkailani, 2016; Liebana-Cabanillas et al., 2016; Ramos et al., 2017; Rodrigues et al.,

2016; Sinha & Mukherjee, 2016). Mansour et al. (2016) and Rodrigues et al. (2016)

stated that if users perceived e-banking easy to use, they would be more likely to adopt

the services. Priya et al. (2018) supported this claim and noted that when customers

perceive mobile services to be easy, they feel less threatened using the applications.

In previous studies on TAM, researchers found that PEOU had a direct positive

impact on the adoption of e-banking or predicted customer adoption of e-banking (Patel

& Patel, 2018). Other researchers did not identify a significant relationship between

PEOU and technology adoption (Marakarkandy et al., 2017; Patel & Patel, 2018). To

increase the use of mobile banking, researchers recommended that bank leaders and

developers should design the applications with user-friendly interfaces, graphical layouts,

and intuitive navigation to address potential user deterrents with using small screens to

perform banking activities (Priya et al., 2018; Ramos et al., 2017). In this study, I

examined the effect of PEOU on the adoption of e-banking services in Barbados.

TAM literature on e-banking adoption. There is a plethora of research on TAM

and e-banking adoption. Researchers who use TAM as the theoretical framework

attempted to examine PU and PEOU as factors that affect customer intention to adopt e-

banking services (Bambore & Singla, 2017; Mortensona & Vidgen, 2016; Vejacka &

Stofa, 2017). Magotra (2016) conducted a study on mobile banking adoption in India.

The results from data collected on 413 respondents showed that PU and PEOU were

significant factors affecting the adoption of mobile banking. Similarly, Rahi, Ghani, and

Alnaser (2017) designed an empirical study using TAM as the theoretical framework to

explore the factors affecting Internet banking adoption in Pakistan. Rahi et al. analyzed

data from 265 respondents and found that PU and PEOU were important to increasing the

customer adoption rate. In subsequent studies, researchers used the TAM framework to

examine the significance of PU and PEOU on customer adoption of e-banking and

supported previous findings.

George (2018) examined the effects of PU and PEOU on the perceptions of users

to adopt Internet banking in Kerala, India. George used the random and convenience

sampling methods and collected data from 406 respondents. The results showed that PU

and PEOU had a direct effect on the use of Internet banking. Chandio et al. (2017)

investigated the constructs of the TAM framework to understand and predict user

behavioral intention to use online banking in Pakistan. Chandio et al. collected 310

responses, and the analysis showed that the impact of PU on intended behavior was more

significant than the effect of PEOU, but individual behavior intention to adopt online

banking was dependent on both PU and PEOU. I will, therefore, adopt the TAM

framework to examine the significance of PU and PEOU on customer adoption of e-

banking in Barbados.

Limitations of the TAM. Teo et al. (2015) found that several researchers

commented on the limitations of TAM. The theorists stated that the TAM framework did

not include economic, demographic, or external variables that might affect customer

intention to adopt technology (Venkatesh & Davis, 2000). Ajibadi (2018) argued that

TAM is concerned with personal use but did not address acceptance of technology in the

context of business, educational institutions, or organizations. Ozlen and Djedovic

(2017), in their study on online banking acceptance in Bosnia, claimed that TAM lacked

incorporating social structure and acceptance. Other researchers claimed that the TAM

alone did not have adequate explanatory power to predict customers’ intentions to adopt

e-banking (Teo et al., 2015). Chechen, Yi-Jen, and Tung-Heng (2016), Rfieda and Mira

(2018), and Sinha and Mukherjee (2016) found that a combined model using TAM and

diffusion of innovation (DOI) had a better explanatory power instead of the TAM

constructs. The TAM is cross-sectional, which researchers highlighted as a limitation in

their studies on e-banking adoption (Chandio et al., 2017; George, 2018; Patel & Patel,

2018; Priya et al., 2018; Yaseen & El Qirem, 2018). Similarly, researchers cited the

generalization and validity of the findings in their studies using the TAM framework as a

limitation of their research (Aboobucker & Bao, 2018; Chandio et al., 2017; Priya et al.,

2018; Ramos et al., 2018; Yaseen & El-Qirem, 2018). Researchers therefore sought to

address the limitations of the TAM.

In prior studies on e-banking adoption, researchers cited several limitations of the

TAM framework to predict customer intention to adopt e-banking services and included

other external variables to strengthen the model (Chauhan, 2015; George, 2018; Kaushik

& Rahman, 2015; Novita, 2017; Rawashdeh, 2015; Shaikh & Karjaluoto, 2015;

Cristovao-Verissimo, 2016; Yaseen & El Qirem, 2018). The variables most frequently

examined included perceived risk, security, and trust (Alkailani, 2016; Mansour, 2016;

Liebana-Cabanillas et al., 2017; Sinha & Mukherjee, 2016). Sharma et al. (2015)

extended the TAM model to include service quality and trust in their study to explore the

determinants of Internet banking adoption. The authors collected data from 110 Internet

banking users to analyze and concluded that PU, PEOU, service quality, and trust were

significant determinants of Internet banking adoption (Sharma et al., 2015). Normalini

(2019) extended TAM to include quality dimensions and attitude as independent

variables to understand the impact of quality on customer intention to continue using

Internet banking in Malaysia. The results from 413 respondents showed that PU, PEOU,

quality dimensions, and attitude were significant determinants of intention to continue

using Internet banking. Danurdoro and Wulandari (2016) surveyed 96 students to explore

the effects of PU, PEOU, subjective norm, and experience on students’ intention to adopt

Internet banking in Indonesia. The results showed that PEOU and experience

significantly affected students’ intention to adopt Internet banking. Conversely, the

impact of PU and subjective norm was insignificant. Ozlen and Djedovic (2017)

extended TAM to include perceived system security and perceived system quality.

Despite researchers’ efforts to modify the TAM to strengthen their findings, limitations

remained evident.

To address the limitations of TAM, researchers recommended future studies

include moderating factors such as gender, age, and income (Cocosila & Trabelsi, 2016;

Danyali, 2018; Liebana-Cabanilla et al., 2016; Ramos et al., 2018). Some researchers

recommended a longitudinal design approach to better understand the causality of

variables (Aboobucker & Bao, 2018; Chandio et al., 2017; George, 2018; Patel & Patel,

2018; Priya et al., 2018; Yaseen & El Qirem, 2018). Future research should include a

wider population to improve the generalizations of results (George & Kumar, 2015;

Sharma et al., 2015; Tseng, 2015) and additional independent constructs (Aboobucker &

Bao, 2018; Choudrie, Junior, McKenna, & Richter, 2018; George, 2018; Illia et al., 2015;

Mansour, 2016; Marakarkandy et al., 2017). Despite the above limitations, several

researchers validated the TAM framework as a suitable model for investigating

technology acceptance. Therefore, I used the TAM framework to examine the

significance of PU and PEOU on customer adoption of e-banking services in Barbados.

Rival Theories

Researchers used other theories to examine the factors that influence customer

adoption of e-banking. Maduku (2017) noted that prominent alternate theoretical

frameworks included the theory of planned behavior (TPB; Ajzen, 1985), diffusion of

innovation (DOI; Roger, 1995), extended TAM (TAM2; Davis et al., 1992), unified

theory of acceptance and use of technology (UTAUT; Venkatesh, Morris, Davis, &

Davis, 2003), and extended unified theory of acceptance and use of technology

(UTAUT2; Venkatesh, Thong, & Xu, 2012). Critics of TAM also challenged theorists to

develop frameworks comprised of additional constructs to increase the strength of the

correlation and predictability between predictor variables and user adoption of

technology (Ajibadi, 2018; Ozlen & Djedovic, 2017; Teo et al., 2015). Alternate theories,

therefore, play an important role in helping researchers determine the factors that affect

customer adoption of e-banking.

Theory of planned behavior (TPB). Ajzen (1985) developed the TPB as an

improvement to the TRA previously proposed in 1975 by Fishbein and Ajzen that

explained human behavior. Ajzen argued that behavioral intention is the antecedent of

behavioral adoption (Gourlan, Bord, & Cousson-Gelie, 2019). Ajzen posited that three

constructs influence behavioral intention, namely, attitude towards the behavior (ATT),

subjective norm regarding the behavior (SN), and perceived control over the behavior

(PBC; Asadi, Hussin, & Saedi, 2016; Umer, Qazil, & Makhdoom, 2018). ATT could be

favorable or unfavorable feelings towards adoption, SN responding to social pressures

from others to adopt, and PBC highlights an individual’s ability to adopt (Chiu, Bool, &

Chiu, 2017; Gourlan et al., 2019; Yu, 2015). These assumptions were tested by

researchers to validate the theorists’ arguments.

Researchers use the TPB to predict human behavior in the fields of information

technology and environmental issues (Kamrath, Rajendran, Nenguwo, Afari-Sefa, &

Broring, 2018; Koul & Eydgahi, 2017), healthcare (Gourlan et al., 2019; Sussman &

Gifford, 2019), and entrepreneurial discipline (Anjum, Sharifi, Nazar, & Farrukh, 2018;

Gonzales, Jaen, Topa, & Moriano, 2019). Other researchers, however, cited limitations

with the TPB model that included the assumed linear relationship between the constructs

and the direct impact of intentions on physical activity (Gourlan et al., 2019). Yadav,

Chauhan, and Pathak, (2015) examined customer behavioral intentions to adopt Internet

banking and found a combination of the TPB and TAM models was a suitable approach.

In this study, I did not intend to examine behavioral intention; therefore, TPB was not

appropriate.

Diffusion of innovation theory (DIT or DOI). Roger (1995) developed the DOI

also called the epidemic model of adoption to explain how users adopt or reject

innovations for products and services through social influence or social contagion

(Chiyangwa & Alexander, 2016; Yapp, Balakrishna, Yeap, & Ganesan, 2018). DOI has

five characteristics that influence the adoption of innovation: (a) relative advantage, (b)

compatibility, (c) complexity, (d) trialability, and (e) observability (Chiyangwa &

Alexander, 2016; Yapp et al., 2018). The general assumption of DOI is that the

innovations are superior to, and replace, previous systems or models (Mullan, Bradley, &

Loane, 2017), and researchers tend to use either the preadoption or postadoption process

to validate their assumptions regarding user adoption of technology (Estrella-Ramon,

Sanchez-Perez & & Swinnen, 2015; Montazemi & Qahri-Saremi; 2015). Researchers

found that DOI applied to groups instead of individuals, with specific focus on timelines

(Chiyangwa & Alexander, 2016). I examined factors that influence individuals’ adoption

of technology, therefore, the DOI model was not suitable for this study.

Extended TAM (TAM2). Davis et al. (1992) developed the TAM2 to explain the

causal relationship between users’ internal beliefs: PU, PEOU, and perceived enjoyment

(PE), attitude, intentions, and usage behavior. TAM2 supplemented the limitation with

the TAM explanatory power regarding intrinsic motivators, such as trust, security, PE,

social influences, brand equity, and past use experience, and user acceptance of hedonic-

oriented information systems (Cheng, 2015; Chi, 2018; Li, Chung, & Fiore, 2017; Wang

& Goh, 2017). Davis et al. argued that PE and PU mediated the influence of PEOU, and

the three variables are relevant for users to adopt computer-based technology. Cheng

(2015) supported Davis et al.’s claim and found that both extrinsic factors (PU and

PEOU) and the intrinsic motivator (PE) influenced user intention to adopt m-learning.

Researchers used the TAM2 model to examine causal relationships between user

acceptance of technology in the fields of e-commerce and m-commerce (Chi, 2018; Li et

al., 2017; Yadav & Mahara, 2019), e-learning (Akman & Turhan, 2017; Chang, Hajiyev,

& Su, 2017; Cheng, 2015; To & Tang, 2019), healthcare (Ducey & Coovert, 2016),

gaming (Wang & Goh, 2017; Wang & Sun, 2016), and e-banking (Alkailani, 2016;

Goran & Jovana, 2017; Mansour, 2016; Patel & Patel, 2018) among various

demographical groups. Haider, Changchun, Akram, and Hussain (2016) adopted the

TAM2 in their study on e-banking because it was extensively used in recent studies and

therefore appropriate for their research on customer intention to adopt mobile banking. I

did not examine the relationship between intrinsic motivators as factors that influence

customer adoption of e-banking services in Barbados; therefore, the TAM2 theory was

not appropriate for this study.

Unified theory of acceptance and use of technology (UTAUT). Venkatesh et

al., (2003) developed the UTAUT model to increase the predictive power for behavioral

intention (BI) toward adoption of technology (Teo et al., 2015; Wang, Cho, & Denton,

2017). The UTAUT model is a combination of eight established information systems

theories, namely, TRA, TAM, IDT, TPB, motivational theory (MM), a combination of

the TAM and TPB models, a model of PC utilization (MPCU), and social cognition

theory (SCT) (Tan & Lau, 2016; Teo et al., 2015; Maduku, 2017). The UTAUT model

has four core constructs: (a) performance expectancy (PE), (b) effort expectancy (EE), (c)

social influence (SI) to determine customer behavioral intention, and (d) facilitating

condition (FC) to determine use behavior (Lee, Lin, Ma, & Wu, 2017; Tan & Lau, 2016;

Teo et al., 2015; Venkatesh et al., 2003). The model also incorporates four moderating

variables: gender, age, experience, and voluntariness of use (Tan & Lau, 2016).

Venkatesh et al. claimed that the UTAUT model has the highest explanatory power and

capability of explaining 70% of the variance in BI to adopt and 50% in of the variance in

technology use (Maduku, 2016; Yaseen & El Qirem, 2018). Therefore, the UTAUT

model appeared to be a suitable framework to examine technology acceptance.

Researchers adopted the UTAUT model in various technology acceptance studies

in e-banking (Bhativasevi, 2016; Savic & Pesterac, 2018), e-commerce (Blaise, Halloran,

& Munch-Nick, 2018; Sanchez-Torres et al., 2017), and e-health (Kurila, Lazuras, &

Ketikidis, 2016). While the UTAUT model has been extensively used in current studies,

researchers modified the model to include other variables such as computer anxiety

(Cimperman, Brencic, & Trkman, 2016; Nysveen & Pedersen, 2016) and trust (Warsame

& Ireri, 2018) to increase the predictability of customer behavioral intention to adoption

technology. Other researchers added security, risk, and task-technology fit (Tarhini, El-

Masri, Ali, & Seranno, 2015), or perceived credibility, cost, and convenience (Bhatiasevi,

2016) to determine the degree of influence other variables had on customer adoption of

technology. In recent studies, researchers combined UTAUT with the TAM model to

better explain user intentions to adopt new technology (Ali & Arshad, 2016). Kuila et al.

recommended the UTAUT model because they found that PE and EE within the model

were similar to PU and PEOU in the TAM model, therefore, I did not use the UTAUT

model in this study.

Extended unified theory of acceptance and use of technology (UTAUT2).

Venkatesh et al. (2012) created the UTAUT2 model to study technology acceptance and

use in a customer context. The UTAUT2 model has three additional new constructs: (a)

habit, (b) hedonic motivation, and (c) price value to determine customer usage of

technology to the existing four variables in the UTAUT model (Farah, Hasni & Abbas,

2018; Yaseen & Al Qirem, 2018). The UTAUT2 model also incorporates individual,

technological, and environmental components to understand what drives individual

intention to adopt systems (Farah et al., 2018). The theorists claimed that the UTAUT2

model enhanced the variance explained in technology use from 40% to 52% in the

UTAUT model to 56% to 74% for behavioral intention (Gharaibeh & Arshad, 2018;

Yaseen & Al- Qirem, 2018). Thus, the UTAUT2 model was appropriate for examining

several variables affecting behavioral intention to accept technology.

Researchers adopted the UTAUT2 model in various technology acceptance

studies in e-banking (Alalwan et al., 2018; Baabdullah, Alalwan, Rana, Kizgin, &

Dwivedi, 2019; Farah et al., 2018), m-commerce (Blake, Neuendorf, LaRosac, Luming,

Hudzinski, & Hu, 2017; Shaw & Sergueeva, 2019), and mobile learning (El-Masri &

Tarhini, 2017). According to Farrah et al. (2018), the UTAUT2 model could strengthen

the predictive power of customer technology adoption if it included trust and perceived

risk. There are mixed researchers’ views on the application of the UTAUT2 model to

research in the adoption of e-banking. Yaseen and Al Qirem (2018) claimed that there

was limited use of the UTAUT2 model in e-banking adoption studies, while Sanchez-

Torres et al. (2017) noted that the UTAUT2 model was extensively used in e-banking

adoption. According to Tak and Pankar (2016), there is limited research on the use of the

UTAUT2 model in studies on adoption of health apps, mobile payments, and mobile

learning. For this study, I did not use the UTAUT2 model as the theoretical framework

because I did not intend to examine customer behavioral intentions to adopt e-banking

services in Barbados.

Measurement

The adoption of a suitable measurement tool will impact the outcome of a study.

When researchers choose to develop surveys using references from the extant literature, it

signifies ownership of the survey design, but the process could be time-consuming, and

the instruments must be tested using a pilot study to ensure that the reliability and validity

of the instrument are not compromised (Clements & Boyle, 2018; Shaw & Sergueeva,

2019). Ramos et al. (2018) found that survey instruments are the most popular

measurement tool for data collection in quantitative studies. In prior research on customer

adoption of technology, researchers used web-based surveys (Olasina, 2015; Sharma et

al., 2015) and paper-based surveys (Al-Jabari, 2015; Farah et al., 2018; Maduku, 2017;

Tan & Lau, 2016) as the primary data collection instruments. Several researchers used

the TAM survey in their studies (Malaquias & Hwang, 2019; Mansour, 2016; Patel &

Patel, 2018; Priya et al., 2018) while others used previous researchers’ questionnaires to

customize their surveys (Alkailani, 2016; Kampakaki & Spyros, 2016; Teo et al., 2015;

Tseng, 2015).

TAM survey. Davis (1989) developed the TAM survey instrument to support his

TAM theory. Davis designed the TAM survey to test the significance of the constructs:

PU and PEOU, on customer adoption of technology. Davis claimed that the TAM survey

adequately captured the reasons why customers adopted new technology. The survey

consists of two scales with six questions each for both constructs, for a total of 12

questions. One scale consists of a score relative to the impact of the PU inclusive of (a)

speed of systems, (b) systems’ performance, (c) productivity, (d) effectiveness of

systems, (e) ease of doing tasks, and (f) useful of systems. The other scale consists of the

attributes for PEOU: (a) meets needs of user, (b) easy to understand, (c) flexible, (d)

improves user skills, and (e) easy to use (Davis, 1989; Malaquias & Hwang, 2019; Priya

et al., 2018). The questions aligned to the TAM theory appear on an ordinal scale, with a

7-point Likert scale with responses ranging from extremely likely to extremely unlikely. In

a cross-section of studies on customer adoption of technology in e-commerce, education,

transportation, and cloud computing, researchers used the questions on PU and PEOU in

the TAM survey (Bollinger, Mills, White, & Kohyama, 2015) or extended the TAM

survey to include questions for additional variables (Mansour, 2016; Patel & Patel, 2018).

The TAM survey also formed the basis for several research studies on customer adoption

of electronic banking in developed and developing countries (Akhisar, Tunay, & Tunay,

2015; Sanchez-Torres et al., 2018). The TAM survey was, therefore, relevant for this

study.

The reliability of the TAM survey instrument has a Cronbach’s alpha value of

.843 (Olufemi & Ezekiel; 2013). Priya et al. (2018) used the TAM survey to examine the

factors that affected mobile banking adoption among 269 young Indian consumers. The

scale had composite reliability >0.7, which met the recommended minimum requirement

of .70 (Heale & Twycross; 2015). Malaquias and Hwang (2019) used the TAM survey to

conduct a comparative study on the determinants of customer adoption of e-banking in

Brazil and the United States. The authors found the scale to have internal reliability with

a lower bound of 0.075 and upper bound of 0.092. While most researchers used the TAM

survey in their studies on customer adoption of technology, others used a combination of

questionnaires from the extant literature to design their surveys (Abbasi, Kamran, &

Akhtar, 2017; Alkailani, 2016; Teo et al., 2015; Tseng, 2015).

Customized online surveys. Researchers who used the TAM theory as their

foundational framework designed surveys from the extant literature instead of using the

TAM instrument to determine the factors that influenced customer adoption of

technology (Abbasi et al., 2017; Dakduk, Ter, Santala, Molina, & Malave, 2016; Lee,

Yang & Johnson, 2017; Mokhtar, Katan, & Hidayat-ur-Rehman, 2018; Wang, Wang &

Wu, 2015). Conversely, other researchers who chose various theoretical frameworks to

adopt questions to design their surveys, for example, Asadi, Nilashi, Husin, and

Yadegaridehkordi (2017) designed a survey with 33 questions extracted from eight

studies, and Roy (2017) adopted questions from four previous studies on customer

adoption of technology to develop a survey to examine customer adoption of app-based

cab services. Likewise, Sarmah, Rahman, and Kamboj (2017) developed their survey

from a selection of nine studies.

In prior studies on customer adoption of e-banking services, researchers opted to

design surveys using questions from the extant literature (Alkailani, 2016; Malaquias &

Hwang, 2019; Olufemi & Ezekiel, 2017; Pamungkas & Kusuma, 2017). Asadi et al.

(2017) found that using self-developed surveys influenced by questions adapted from

previous research strengthened the validity of their study. Researchers also used paper-

based surveys to examine customer adoption of technology.

Paper-based survey. Researchers used paper-based surveys for data collections

instead of online surveys because of restrictions, security risks, or preference (Al-Jabari,

2015; Bollinger et al., 2015). In prior research on customer adoption of technology,

researchers designed paper-based surveys from the extant literature and adopted similar

methodologies used for online surveys to measure the reliability and validity of the

paper-based survey instruments (Al-Jabari, 2015; Braekman et al., 2018). Researchers

also used the analysis of data collected from paper-based surveys to discuss the findings

of their studies (Al-Jabari, 2015).

Researchers claimed that a benefit from distribution of paper-based surveys is that

they are available to respond to participants’ queries unlike the impersonal nature of

online surveys (Agarwal, Paswan, Fulpagare, Sinha, Thamarangsi, & Rani, 2018; Al-

Jabari, 2015; Farah et al., 2018; Maduku, 2017; Tan & Lau, 2016). Al-Jabari (2015)

conducted a study to understand the factors that influence customer intention to adopt

mobile banking in Saudi Arabia. The author used a paper-based survey to collect data

from 253 participants. The findings showed that compatibility had a significant influence

on customer intention to adopt mobile banking, while perceived risk was a deterrent.

Paper-based surveys, however, require manual data entry of participants’ responses into a

statistical software for data analysis, whereas online surveys could be exported

electronically for data analysis (Agarwal et al., 2018: Braekman et al., 2018). Therefore,

in this study, I used a web-based survey to collect data using the TAM instrument to

examine the impact of the two constructs (PU and PEOU) on customer adoption of e-

banking in Barbados.

Other Factors Affecting E-Banking Adoption

Several variables affect customer adoption of e-banking adoption. In this study, I

examined the relationship between PU and PEOU on customer adoption of e-banking in

Barbados. From my review of the extant literature researchers modified the TAM model

to include other variables from various theoretical frameworks, such as DOI, UTAUT,

TAM2, and UTAUT2, to determine their significance on e-banking adoption (Joachim,

Spieth, & Heidenreich, 2018; Laukkanen, 2016; Oruc & Tatar, 2017; Sharma, Mangla,

Luthra, Al-Salti, 2018). The most extensively researched variables were trust, risk or

perceived risk, social influence, and self-efficacy. I discussed the above variables and

their possible influence on e-banking adoption.

Trust. While trust is not an independent variable of this study, it has been widely

researched as a factor that could negatively impacts customer adoption of e-banking and

e-commerce (Asni, Nasir, Yunus, & Nurdasila, 2018; Kaabachi, Mrad, & O’Leary, 2018;

McNeish, 2015; Szopinksi, 2016). Trust is a complex, multidisciplinary concept applied

to building relationships between institutions and individuals in the fields of psychology,

health, sports, finance, and e-commerce (Malaquais & Hwang, 2016; Wang,

Ngamsiriudom, & Hsieh, 2015). Pamungkas and Kusuma (2017) claimed that researchers

are yet to agree a definition for trust due to its complexity. Yu and Asgarkhani (2015)

support this claim noting that trust is a multidimensional concept, and defined trust in e-

banking as a customer’s willingness to conduct online transactions with the belief that

banks will fulfil their obligations, despite having the ability to monitor the banks’ actions.

Damghanian, Zarei, and Kojuri (2016) noted that customers’ trust in banks increase if

they believe that banks have the required knowledge, experience, and skills, and consider

their interests when performing transactions. Researchers use four constructs:

competence, integrity, benevolence, and predictability to assess trust (Skvarciany &

Jurevicience, 2018; Yu & Asgarkhani, 2015).

In prior studies on e-banking adoption, researchers modified various theoretical

frameworks on technology acceptance to include trust as a variable to determine if it

significantly impacted customer behavioral intention to adopt e-banking (Salem, Baidoun

& Walsh, 2019; Sharma & Sharma, 2019). Chiu et al. (2017) collected and analyzed data

from 314 non-users of mobile banking in the Philippines and found that trust significantly

influenced non-user behavioral intention to adopt mobile banking. Malaquais and Hwang

(2017) examined the impact of trust as an antecedent to the utilitarian (online banking,

online shopping), and hedonic values (entertainment) of mobile devices in Brazil.

Malaquais and Hwang collected data from 1,080 respondents trust had a positive

relationship utilitarian values for customers who used their mobile devices for online

banking and e-commerce. Conversely, the relationship between hedonic values and trust

was insignificant. Yu and Asgarkhani (2015) conducted a comparative study on trust in e-

banking in Taiwan and New Zealand sampling 510 and 122 respondents respectively, and

found that despite cultural differences, customer trust is a significant factor in adoption of

e-banking services. Trust, therefore, may affect customer adoption of e-banking services

in Barbados.

Risk or perceived risk. Researchers claimed that risk is a barrier to the adoption

of any innovation (Serener, 2019). Risk is defined as an individual’s tolerance towards

potential losses. The higher the possibility of losses, the more risk an individual perceives

(Arora & Kaur, 2018). Risk cannot be measured objectively, therefore theorists focused

on individual perceived risk (Akram, Malik, Shareef, & Goraya, 2019). Bauer (1960)

developed the perceived risk theory (PRT) to explain the customer risk-increasing or risk-

decreasing behavior (Khedmatgozar & Shahnazi, 2018). Perceived risk consists of

several dimensions: performance, physical, financial, psychological, privacy, security,

and social (Arora & Kaur, 2018; Khedmatgozar & Shahnazi, 2018; Marafon, Basso,

Espartel, de Barcello, & Rech, 2017). In the context of e-banking, perceived risk is

associated with customer uncertainty or lack of trust in e-banking systems, the higher the

uncertainty, the less-likely they will purchase or adopt electronic products or services

(Tandon, Kiran, & Sah, 2018; Wei, Wang, Zhu, Xue, & Chen, 2018).

In prior studies on e-banking adoption, researchers purported that perceived risk

had a negative influence on customer adoption (Jansen, van Schaik, 2018). Chauhan,

Yadav, and Choudhary (2018) conducted a study on the impact of perceived risk on

Internet banking adoption in India. Chauhan et al. used data collected from 487

respondents, and found that perceived risk, negatively impacted adoption of Internet

banking. Khedmatgozar and Shahnazi (2018) examined the dimensions of perceived risk

on 442 corporate clients’ intention to adopt Internet banking in Iran, and found that

performance, financial, privacy, security, time, and social risks negatively impacted the

clients’ intention to adopt Internet banking. While perceived risk is not an independent

variable in this study, Marafon et al. (2017) posit that perceived risk is implied in the PU

construct of the TAM model, demonstrating confidence in the usefulness of technology.

Thus, perceived risk may affect adoption of e-banking in Barbados.

Social influence. While social influence is not an independent variable in this

study, the concept has been widely researched as a factor that affects customer adoption

of e-banking (Adapaa & Roy, 2017; Chaouali, Yahia, & Souiden, 2016; Matsuo, Minami,

& Matsuyama, 2018). Kelman (1958) developed the social influence theory to explain the

influence of members in a social network on each other’s attitudes or behaviors. Kelman

claimed that individual’s beliefs, attitudes, and behaviors are influenced by compliance,

internalization, and identification (Chaouali et al., 2016). Social influence is also known

as social norms or normative pressure and is defined as an individual’s perception that

important persons believes he or she should use a new technology system (Chaouali et

al., 2016; Oliviera, Thomas, Baptista, & Campos, 2016; Ventatesh et al., 2003). Social

influence is also the pressure of significant others (family, friends, colleagues) on an

individual to adopt a specific behavior or innovation (Farah et al. 2018; Makanyeza,

2017). Social influence is usually associated with TPB and consists of two factors:

subjective norm and critical mass.

In prior research on e-banking, researchers examined the impact of social

influence as a predictor variable on customer adoption and reported positive correlation

between social influence and customer behavioral intention to adopt the new technology

(Kaabachi et al., 2018; Sitorus, Govindaraju, Wiratmadja, & Sudirman, 2018). Matsuo et

al. (2018) collected data from 616 respondents in Japan and found that social influence

directly and indirectly affected the innovation resistance of experienced and

inexperienced users of Internet banking. Mortimer, Neale, Hasan, and Dunphy (2015)

modified the TAM model to include social influence as a variable in their comparative

study on mobile banking adoption in Thailand and Australia. Mortimer et al. analyzed

responses from 348 respondents and concluded that social influence has a significant

impact on mobile banking adoption in Thailand and to a lesser extent in Australia.

Makanyeza (2017) also identified social influence has a factor that influence mobile

banking adoption in Zimbabwe after analysis of data collected from 232 customers in the

banking industry. Rahi, Ghani, Alnaser, and Ngah (2018) used the UTAUT model in

their study on customer behavioral intentions to adopt Internet banking in Malaysia. Rahn

et al. collected data from 398 users and concluded that social influence was a significant

influence on Internet banking adoption. Therefore, social influence may affect customer

adoption of e-banking services in Barbados.

Self-efficacy. While self-efficacy is not an independent variable in this study,

researchers investigated the variable as a factor that affects customer behavioral

intentions to adopt of e-banking (Susanto, Chang, & Ha, 2016). Bandura (1986)

developed the concept of self-efficacy as part of his social cognitive theory which sought

to explain how people learn and interact with others. The key concepts of self-efficacy

are efficacy expectations and response outcome expectations. Shao (2018) defined self-

efficacy as an individual’s belief in his or her capabilities to use technology in diverse

situations. The stronger an individual’s self-efficacy, the more persistent to achieve an

outcome (Mohammadi, 2015). Orazi and Pizzetti (2015) also highlighted self-efficacy as

an individual’s coping response to threats.

Marakarkandy et al. (2017) in their empirical study on enabling Internet banking

adoption in India, claimed that there is limited research on self-efficacy in TAM,

however, other researchers found that self-efficacy affected customer adoption of online

banking (Mohammadi, 2015). Singh and Srivastava (2017) examined six factors

(computer self-efficacy, PEOU, trust, security, social influence, perceived financial cost)

to predict intention to adopt mobile banking in India, and from analysis of the data from

855 respondents, found that computer self-efficacy was the second most important factor

to influence customer intention to adopt mobile banking. Likewise, Alalwan et al. (2016)

in their study on the influence of PU, trust, self-efficacy on Jordanian customers to adopt

telebanking, collected data from 323 respondents and concluded that self-efficacy had a

significant influence on customers’ behavioral intentions, PU and trust to adopt

telebanking. Alalwan et al. (2016) conducted a similar study on customer behavioral

intention to adopt mobile banking in Jordan and from analysis of 343 responses, found

that self-efficacy as a significant factor in adoption of mobile banking. Davis (1989)

claimed that perceived self-efficacy is similar to PU, whereas other researchers suggested

to use self-efficacy as an antecedent to PEOU (Mohammadi, 2015). Thus, in this study,

the self-efficacy might be a factor that affect e-banking adoption in Barbados.

Electronic Banking Adoption

Senior retail banking leaders implemented technology-based applications, such as

Internet banking, mobile banking, point-of-sale terminal (POS), and automated teller

machines (ATMs) to meet the customer demands, reduce high branch-based transactions,

and sustain competitive advantage (Akhisar et al., 2015; Belas et al., 2016; Dyer,

Godfrey, Jensen, & Bryce, 2016; Mishra & Singh, 2015; Rad et al., 2017; Sanchez-

Torres et al., 2018). Akhisar et al. (2015), Sanchez-Torres et al. (2018) and van der Boor,

Oliveira, and Veloso (2014) agreed that technology innovation helped banking executives

to successfully introduce low-cost electronic banking in developed countries. However,

Akhisar et al. (2015) and van der Boor et al. (2014) concluded that there was a limited

success in developing countries because of an inadequate technology infrastructure or

ineffective leadership strategies to transition customers to electronic banking therefore

having a negative impact of technology innovation on the banks’ profitability.

Online banking. Online banking is a web-based application developed by banks

and other financial institutions to offer customers fast and easy access to financial

services and transactions (Chandio et al., 2017; Mujinga, Eloff, & Kroeze, 2018; Sharma

& Lenka, 2015). Kiljan, Simoens, de Cock, van Eekelen, and Vranken, (2016) noted that

online banking became accessible in the late 1990s and grew exponentially from desktop

computers to mobile devices. Bank leaders also view online banking as a cost-effective

alternative to traditional in-branch services (Chandio et al., 2017).

Researchers found that security is one of the primary customers’ concerns with

online banking due to threats of cybercrimes (Alghazo, Kazmi, & Latif, 2017; Ali, 2019).

Bank leaders, therefore, continue to invest millions annually to improve their online

banking infrastructure to increase customer adoption and address concerns about

password integrity, data encryption, privacy, and security (Kilgan et al., 2016; Malik &

Oberoi, 2017). Alghazo et al. (2017) also encouraged bank leaders to help users protect

themselves again cyber threats by implementing mandatory password changes, complex

password constructs, and frequent updates to their browsers. In this study on e-banking

adoption, I intended to examine online banking as one of the key components.

Mobile banking. Mobile banking provides an innovated way for customers to

conduct banking transactions (funds transfers, enquiring services, instant payments, and

bill payments) using a mobile device (Gupta, 2018; Jamshidi, Keshavarz, Kazemi, &

Mohammadian, 2018; Tam & Oliveira, 2017) especially the unbanked (Mustafa, 2015) or

those who are constrained by distance and transportation issues (Amran, Mohamed, &

Yusuf, 2018; Yadav, 2016). Gupta found that the decreasing costs of mobile handsets in

India might further increase the mobile penetration rate beyond 90%, while Trialah et al.

(2018) noted that mobile banking users in Indonesia reached 80% which was above

global average.

Some researchers claimed that despite the high penetration of mobile phones,

customer adoption of mobile banking is low (Kansal & Changanti, 2018). Masrek,

Mohamed, Daud, and Omar (2014) found that trust was the primary issue affecting

customer adoption of mobile banking. Shareef, Baabdullah, Dutta, Kumar, & Dwivedi

(2018) supported Masrek et al.’s claim and argued that while communication with service

providers and security were concerns of customers, trust remained the primary issue for

mobile banking adoption. Other researchers purported that bank leaders could benefit

from mobile banking by promoting better efficiency and service quality (Malaquias &

Hwang, 2019). I examined mobile banking as one of the e-banking services in this study.

Automatic teller machines (ATM). The ATM is created from a combination of

technology and electronics (Gumus, Apak, Gumus, Gumus, & Gumus, 2015; Narteh,

2015). Banker leaders posit the ATM as a convenient alternative to in-branch banking for

customers to pay bills, transfer funds, make deposits, and withdraw cash (Farajzadeh,

2015; Ram & Goyal, 2016). The technological advances in ATMs evolved over past

decade to convert traditional ATMs into smart ATMs, multivendor ATMs, and ATMs for

the visual impaired (Hota & Mishra, 2018; Korwatanasakul, 2018).

Despite the benefits of ATMs, there were factors that impacted customer

adoption. While Tade and Adeniyi (2017) acknowledged the benefits of ATMs, they

found a high degree of fraud associated the machines that impacted the financial industry

in India. In their study on elderly customers adoption of ATMs, Huang, Yang, Yang, and

Taifeng (2019) found that that introduction of graphics as a learning mechanism did not

increase customer usage. Researchers also highlighted ATMs were largely inefficient

(Farajzaheh, 2015). In this study I did not examine ATMs as an e-banking service.

Point-of-sale terminals (POS). The POS terminal is a standalone electronic

device or integrated system where customer swipe a debit or credit card to pay merchants

for goods and services without using cash (Farajzadeh, 2016; Olufemi & Ezekiel, 2017;

von Solms, 2016). In their comparative study on customer adoption of electronic

payments in China and Germany, Korella and Li (2018) found that credit and debit

transactions at POS terminals were the dominant payment method, whereas in China,

consumers adopted non-bank options such as Alipay and WeChat at POS terminals.

Similarly, von Solms, noted that POS terminals were the most frequently used method for

cashless transactions in South Africa. Conversely, Farajzadeh found that of the 600 POS

terminals sampled from the Bank Melli Iran, only 24 or 4% were efficient. Researchers

also claimed that there should be ongoing maintenance, such as regular inspections and

renewal of components for POS terminals, to prevent customer dissatisfaction and losses

for the merchants (Fukushige, Murai, & Kobayashi, 2018). Despite the challenges

associated with POS terminals, researchers argued that researchers and practitioners

could use the data from the terminals as inputs to predict consumer consumption (Duarte,

Rodrigues, & Rua, 2017). I did not examine customer adoption of POS terminals in this

study.

E-Banking Adoption in Barbados

Retail banking is no longer a concept associated with brick and mortar institutions

where customers visit to perform financial transactions or inquiries OTC. With the

evolution of e-banking, bank leaders boast of providing low-cost convenient banking

services for their customers to do business anytime and anywhere they chose to bank,

however, customer adoption rates in developing countries remained low (Al-Ajam &

Nor, 2015; Ozuem, Howell & Lancaster, 2018). Barbados is one of the developing

countries in the 15 CARICOM member states with a land mass of 166 square miles, a

population size of 286,000, and fiscal deficit of 3% of GDP (Central Bank of Barbados,

2018; The World Bank, n.d). Barbados has an active banking industry comprising of a

central bank, commercial banks, merchant banks, finance companies, trust companies,

credit unions, insurance companies, asset management firms, and a stock exchange in its

infancy stage (Ghartey, 2018; Wood & Clement, 2015). However, the country’s financial

development and economic growth outlook remain weak (Central Bank of Barbados,

2019). Researchers recommended that government officials implement incentives to

enhance its financial market development and reverse the negative rate (Ghartey, 2018).

Like most developing countries in the region, Barbados’ banking industry is rapidly

evolving with the introduction of digital technology, but there is limited research on e-

banking adoption, (Robinson & Moore, 2011).

In 1995, the Central Bank of Barbados issued a report outlining issues anticipated

with the introduction of Internet banking in Barbados from a regulators’ perspective

which included lack of examiner training to effectively supervisor e-banking activities,

hidden costs implications for customers, facilitation of unregulated services, and

computer fraud (Bayne, 1995). In subsequent studies on e-banking in the Caribbean,

researchers found that Barbados was one of four countries that recorded the highest usage

of Internet banking (Robinson & Moore, 2011). Researchers also commented on

Barbados’ ability to advance strategies to implement e-commence (Molla, Taylor, &

Licker, 2006) and digital currency using bitcoin (Wood & Brathwaite, 2016). In 2017,

Barbados recorded 120,471 fixed phone subscribers and 329,565 mobile phone

subscriptions, but the adoption rates remain relatively low in comparison 81,761 Internet

users (The World Bank, n.d.). I sought to address the gap in research on e-banking

adoption in Barbados and contribute to the extant literature on the topic of e-banking

adoption.

The Impact of Electronic Banking and Banks’ Profitability Performance

Technology innovation in the banking industry became a topic of interest for

researchers as leaders attempted to respond to the changes due to globalization.

Historically, banks were perceived as conservative institutions, highly regulated by

government agencies, and demonstrated little or interests in changing the technology

platforms (Akhisar et al., 2015). The strategic decisions to implement technology-based

applications to reduce in-branch operational costs and risks associated with manual

processing was considered revolutionary to a certain extent. Studies conducted on

developed and developing countries, therefore, produced varying results on the impact of

technology-based applications on the profitability performance of banks by geography

and customer demographics (Akhisar et al., 2015; van der Boor et al., 2014).

Empirical evidence from extant literature highlighted the success of technology

innovation on the performance of banks in developed countries (Akhisar et al., 2015;

Sanchez-Torres et al., 2018; van der Boor et al., 2014). The studies conducted on banks

in the United States of America and Europe revealed that technology-based banking

services, such as Internet banking increased the assets quality of banks thereby increasing

profitability (Akhisar et al., 2015). On the other hand, van der Boor et al. (2014) research

highlighted increase profitability for the banks located in France that introduced mobile

banking services their customers. There were mixed findings, however, in developing

countries. Researchers conducted studies on banks in Jordan, Romania, India, Pakistan

(Akhisar et al., 2015) and on banks in Kenya (van der Boor et al., 2014) and concluded

that there were mixed results. Akhisar et al. (2015) found that the technology

infrastructure and lack of customer familiarity with electronic banking adversely

impacted the implementation of electronic banking. Additionally, some customers were

reluctant to use the electronic banking services primarily due to unfamiliarity with the

technology and their preference for face-to-face banking (Akhisar et al., 2015). In

developing countries, therefore, the impact of technology-based banking was positive for

most countries and negative for other countries.

E-Banking Adoption and Strategic Planning

Northouse (2016) and Saxena (2014) claimed that transformational leaders have a

unique vision for the future of their organizations and motivate followers to be creative

and innovative. Dyer et al. (2016) noted that transformational leaders possess the ability

to influence organizational culture by aligning key stakeholders: employees, customers,

shareholders, suppliers, and governments to achieve competitive advantage and increase

economic profitability. Likewise, McCleskey (2014) agreed with fellow researchers and

concluded that transformational leaders possessed the characteristics to increase their

followers’ efforts at innovation by removing obstacles that negatively impact creativity.

Transformational leaders therefore portrayed the qualities to promote innovation through

effective strategic planning.

Some researchers claimed that technology innovation is fundamental to a bank’s

economic performance (Savino, Messeni Petruzzelli, & Albino, 2017) whereas others

argued that strategic planning is important to an organization gaining or sustaining a

competitive advantage (Ali Madhi, Abbas, Mazar, & George, 2015; Dyer et al., 2016;

Kaiser & Egan, 2013; Styles & Goddard, 2014). The strategic planning process helps

leaders formulate and effectively implement innovation technology. Felype Neis, Pereira,

and Maccari (2016) concluded that senior bank executives align their strategic planning

with their organizational structure to facilitate the implementation of innovation.

Kiziloglu (2015) supported Felype Neis et al. (2016) findings but expanded their

argument to recommend that leaders in the banking industry considered organizational

learning capabilities to help with implementing innovation.

Technology readiness remain an important enabler in the strategic planning

objectives for e-banking adoption (Gupta & Garg, 2015). Gupta and Garg (2015) found

that optimism and innovativeness were key drivers for technological readiness, while

discomfort and insecurity were the main inhibitors of technological readiness. In other

studies, on e-banking adoption, researchers highlighted that e-banking was a business

strategy to provide quicker, easier, and more reliable financial services to customers

(Ozuem et al., 2018). Ozuem et al. (2018) conducted a study to examine the themes

underlying strategic planning in a technology-induced environment in Nigeria, and found

that efficiency, usability, control, and security were significant drivers for the

implementation of Internet banking. Al-Ajam and Nor (2015) investigated the challenges

with Internet banking adoption in Yemen, and concluded that bank managers’

understanding of the factors that influence customers’ behavioral intentions to adopt

Internet banking would help facilitate their strategic market planning objectives.

While there remained a gap in extant research on the relationship between

innovation and organizational performance, some researchers recommended inclusion of

other factors such as culture, communication, leadership styles, regulatory, and external

environmental factors as elements to incorporate in future research (Felype Neis et al.,

2016; Sanchez-Torres et al., 2018). The inclusion of the above variables would help

leaders determine the overall effectiveness of strategic planning on innovation and the

performance of banks (Felype Neis et al., 2016). Implementing effective strategic

planning would also help banking executives implement the right diversification

strategies relative to innovation.

Leaders should continuously explore strategies to sustain a competitive

advantage. To reduce high-cost branch-based transactions, leaders should explore

diversification strategies either to supplement their revenue. However, not all

diversification strategies benefited the banks’ performance. Jouida, Bouzgarrou, and

Hellara (2017) concluded that activity diversification: investment banking, venture

capital, and underwriting insurance as alternative solutions to increase revenue, but the

above activities reduced banks’ performance. On the other hand, Mustafa (2015)

introduced business model innovation as another diversification strategy which involved

partnering with telecommunication companies to offer mobile banking to unbanked

communities to increase profitability. Unlike Jouida et al. (2017), Mustafa’s findings

were inconclusive but argued that a business model innovation as a diversification

strategy could only be successful if there is an established interdependence model among

stakeholders.

From the literature research, I observed that the reduction in high-cost branch-

based transactions is a specific business problem facing banking executives, and

historically there was empirical evidence of success in developed countries and most

developing countries. Other innovation diversification strategies failed to increase

profitability, but banking executives are encouraged to develop business strategies that

will positively impact their banks’ revenues. In this study, I focused on understanding the

factors that affect adopt e-banking services in Barbados using the TAM as the contextual

framework. My aim was to contribute to the gap in the extant literature.

Methodologies Used in Research on E-banking Adoption

Several factors affect e-banking adoption, therefore, utilizing various research

methods: quantitative, qualitative, or mixed-methods, to help researchers determine the

relationship between variables. A quantitative method is applicable when researchers

seek to test hypotheses, examine causal relationships between or amongst variables, and

predict outcomes using surveys and questionnaires (Else-Quest & Hyde, 2016; Park &

Park, 2016). Researchers extensively used quantitative method to examine factors

affecting user adoption of e-banking (Belas et al., 2015; Kurila et al., 2016; Padmaja et

al., 2017; van Tonder et al., 2018; Zallaghi, 2018). Sanchez-Torres et al. (2018) examined

the impact of trust, performance expectancy, effort expectancy, and government support

on e-banking adoption in Colombia. The authors found that trust, performance

expectancy, and effort expectancy had a positive impact on e-banking adoption. the

impact of government support was insignificant (Sanchez-Torres et al., 2018). Likewise,

Marunyane and Yuanqiong (2018) examined the effect of counter-conformity,

motivation, website social feature, ease of use, and e-customer service on consumer

intention to adopt e-banking in China. The authors concluded that counter-conformity,

website social feature, and e-customer service were significant factors that influenced e-

banking adoption. In this study, I examined the factors that affect e-banking adoption in

Barbados, therefore, a quantitative method was appropriate.

Researchers use a qualitative method to understand the perspectives of

participants or situations by gathering data through non-statistical approaches including

interviews or direct observations (Park & Park, 2016). Qualitative researchers identify

underlying reasons and motivations to provide insights for a problem, generate ideas for

later quantitative research, and uncover trends in thoughts and opinions (Park & Park,

2016; Peticca-Harris, deGama, & Elias, 2016). Patel and Brown (2016) conducted a

qualitative phenomenological study to investigate the factors that influenced the choice of

adopting of a particular banking channel. The authors found that comparative advantages

of channels, compatibility with personal preferences, and transactions being performed

customers, sub-consciously and consciously influenced customers evaluation of the

channel before selection (Patel & Brown, 2016). A qualitative method was not suitable

for this study because I did not attempt to understand the underlying reasons or

perceptions why customers adopt e-banking services in Barbados.

Mixed methods studies include a combination of quantitative and qualitative

research methods and used to investigate phenomena at the micro and macro levels

(Whiteman, 2015). In mixed method studies, researchers collect statistical data using

elements of quantitative method (surveys, questionnaires), and nonstatistical data using

elements of qualitative method (interviews, observations) for analysis (Johnson, 2015;

Mauceri, 2016). Shetty and Sumalatha (2015) conducted a mixed method empirical study

to collect customers’ opinions on e-banking adoption, its importance, and problems

associated with e-banking in Brahmavar. The findings showed that customers were

satisfied with the convenience of e-banking but dissatisfied with the threat of fraud and

bank related errors (Shetty & Sumalatha, 2015). In this study, a mixed methods approach

was not suitable because I did not undertake a qualitative methodology

Transition

In this quantitative correlational study, I intended to examine the relationship

between PU, PEOU, and e-banking adoption. In section 1, I provided an overview of the

foundational aspects of the study that included the problem and purpose statements, the

nature of the study, and the research question. Section 1 also contained the theoretical

framework, the significance of the study, and the literature review relative to the research

question. Section 2 contained the role of the researcher, the study participants, the

methodology, and design for this study, as well as the ethical procedures. The section

also contained a detailed discussion on the population sampling, data collection

instruments, data organization, and analysis techniques, concluding with reliability and

validity. Section 3 covered the presentations of findings, application to professional

practice, implications for social change, recommendations for action, recommendations

for further research, reflections, and conclusion.

Section 2: The Project

The project section of the study begins with a reinstatement of the purpose of the

study. I described my role as the researcher and the selection criteria for prospective

participants. I discussed the research methodology and design and justified my selection

of a quantitative method and a correlational design to examine the relationship between

PU, PEOU, and customer adoption of digital banking services in Barbados. Section 2 also

contains a discussion on the population and sampling, data collection instruments, and the

techniques that l used to collect, organize, and analyze the data. I concluded the section

with a discussion on the validity and reliability of the instruments used in the study.