QUANTITATIVE RESEARCH PROSPECTUS TEMPLATE FIRST DRAFT (FOR CLED900/987)

LIBERTY UNIVERSITY

JOHN W. RAWLINGS SCHOOL OF DIVINITY

THE WORD MADE DIGITAL: LEVERAGING ARTIFICIAL

INTELLIGENCE TO INCREASE

BIBLE ENGAGEMENT

A Dissertation Presented in Partial Fulfillment

Of the Requirements for the Degree

Doctor of Philosophy

Henry Lee Jackson III

Liberty University, Lynchburg, VA

2023

THE WORD MADE DIGITAL: LEVERAGING ARTIFICIAL

INTELLIGENCE TO INCREASE

BIBLE ENGAGEMENT

by Henry Lee Jackson III

A Dissertation Presented in Partial Fulfillment

Of the Requirements for the Degree

Doctor of Philosophy

Liberty University, Lynchburg, VA

2023

APPROVED BY:

__________________________________

Gary Bredfeldt, Ph.D., Dissertation Supervisor

__________________________________

Stephen Grusendorf, Ph.D., Second Reader

ABSTRACT

The purpose of this descriptive study was to understand whether a relationship exists between an

individual's behavioral intention to use a Bible-based chatbot that leverages AI to create human-

like engagement with Scripture and the constructs of performance expectancy, effort expectancy,

perceived enjoyment, and perceived risk, controlling for gender, age, and experience among

registered users of the Inductive Bible Study App. Data was collected through an online survey

and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), multi-group

analysis (MGA), and homogeneity-of-slopes analysis of covariance (ANCOVA). While this

quantitative descriptive study validated the correlation between each of the four reflective

constructs and the formative construct (behavioral intent), the data suggests that perceived

enjoyment maintains the strongest link to behavioral intent. In addition, the moderators appear to

indicate that the strongest correlation to behavioral intent is found in communities of younger

males with no prior exposure to chatbots. The results of this study provide useful insights into

how individuals perceive and make decisions about using technology for religious or spiritual

purposes, and how these perceptions may differ based on demographic factors. Additionally, the

results inform the development and implementation of similar AI-based tools in religious or

spiritual contexts and provide insights into how leaders in these contexts can effectively utilize

technology to engage with their communities.

Keywords: Artificial Intelligence, Bible-based chatbot, Scripture engagement, Unified

Theory of Acceptance and Use of Technology

Liberty University has permission to reproduce and disseminate this document in any form by

any means for purposes chosen by the University, including, without limitation, preservation or

instruction.

Dedication

To my beloved wife, Vanessa, who has been my rock throughout this journey. Your

unwavering support and understanding have allowed me to focus on my studies, even when it

meant skipping out on essential household chores. Thank you for being my partner in life and for

helping make everything smooth in my world outside of school.

To my son, Henry IV, who has been my inspiration in more ways than one. Your goal of

graduating high school by the age of 13 has pushed me to strive for greatness and to never give

up on my dreams. Thank you for being my motivation and for always believing in me.

To my parents, Henry and Lamona Jackson, who have been my biggest supporters since

childhood. Thank you for always being there to listen when I expressed frustration and for

providing constant encouragement throughout this journey.

To my co-laborers in Christ, Bishop Oliver, Dr. Letra Smith, Dr. Darrell Hall, Joyce

Holmes, Joyce Moorman-Owens, and Dr. Garcy Bruce-Linton, who served alongside me in

ministry and have encouraged and inspired me throughout this journey.

To my ancestors, who were once forbidden to read and write by anti-literacy laws, yet

persevered and went on to make profound contributions to the world. I feel indebted to you and

am grateful for the sacrifices you made.

Finally, to the wonderful professors at Liberty University who have helped disciple me

through its well-formed doctoral program. Thank you for your instruction as well as for

modeling intellectual curiosity. Your guidance and support have been invaluable.

This dissertation is dedicated to all of you who have helped me along the way. Your

support and encouragement have been instrumental in my success. Thank you.

Acknowledgments

I would like to express my deepest gratitude to Dr. Bredfeldt, my dissertation supervisor,

who has encouraged and guided me throughout this journey. Your valuable insights and

feedback have been crucial in shaping my research and have helped me to overcome the many

challenges I have encountered along the way.

I would also like to extend my sincere appreciation to my Second Reader, Dr.

Grusendorf. Your keen insights have helped to shape my study in many invaluable ways. Your

critical feedback and suggestions have helped me to strengthen the weak areas of my research,

and I am forever grateful for your guidance and support.

I would like to further extend a special thank you to Dr. John Dyer for trusting me

enough to share insights from his book before publication that were relevant to my study. Your

contributions have been invaluable, and I am grateful for the opportunity to have been able to

learn from your wisdom and expertise.

I would also like to acknowledge the Holy Spirit, who has given me wisdom and

guidance throughout this journey. I am reminded of the Scripture in James 1:5, "If any of you

lacks wisdom, you should ask God, who gives generously to all without finding fault, and it will

be given to you."

And I must also express my gratitude to the study participants, who have given their time

and energy to make this research possible. Your contributions have been instrumental, and I am

grateful for the opportunity to have learned from your perspectives.

Finally, I would like to express my appreciation to all those who have helped me in any

way during the course of my research. I am grateful for all of your support and guidance.

       
 
 
7 
Table of Contents 
ABSTRACT .....................................................................................................................................3 
Copyright .........................................................................................................................................4 
Dedication ........................................................................................................................................5 
Acknowledgments............................................................................................................................6 
List of Tables .................................................................................................................................11 
List of Figures ................................................................................................................................12 
List of Abbreviations .....................................................................................................................13 
CHAPTER ONE: RESEARCH CONCERN .................................................................................14 
Introduction ........................................................................................................................14 
Background to the Problem ...............................................................................................15 
Statement of the Problem ...................................................................................................20 
Purpose Statement ..............................................................................................................23 
Research Questions ............................................................................................................24 
Assumptions and Delimitations .........................................................................................24 
Research Assumptions ...........................................................................................24 
Delimitations of the Research Design....................................................................25 
Definition of Terms............................................................................................................26 
Significance of the Study ...................................................................................................28 
Summary of the Design .....................................................................................................28 
Research Population...............................................................................................28 
Sampling Techniques .............................................................................................29 
Methodological Design ..........................................................................................29 

       
 
 
8 
CHAPTER TWO: LITERATURE REVIEW ................................................................................32 
Overview ............................................................................................................................32 
Theological Framework for the Study ...............................................................................32 
Theoretical Framework for the Study ................................................................................45 
Related Literature...............................................................................................................55 
Rationale for Study and Gap in the Literature ...................................................................72 
Profile of the Current Study ...............................................................................................73 
CHAPTER THREE: RESEARCH METHODOLOGY ................................................................75 
Research Design Synopsis .................................................................................................75 
The Problem ...........................................................................................................76 
Purpose Statement ..................................................................................................77 
Research Questions and Hypotheses .....................................................................78 
Research Design and Methodology .......................................................................79 
Population ..........................................................................................................................81 
Sampling Procedures .........................................................................................................81 
Limits of Generalization ....................................................................................................82 
Ethical Considerations .......................................................................................................83 
Proposed Instrumentation ..................................................................................................85 
Validity ..................................................................................................................86 
Reliability ...............................................................................................................88 
Research Procedures ..........................................................................................................89 
Data Analysis and Statistical Procedures ...........................................................................91 
Data Analysis .........................................................................................................91 

       
 
 
9 
Statistical Procedures .............................................................................................93 
Chapter Summary ..............................................................................................................95 
CHAPTER FOUR: FINDINGS .....................................................................................................96 
Overview ............................................................................................................................96 
Research Questions ............................................................................................................96 
Hypothesis..........................................................................................................................97 
Compilation Protocol and Measures ..................................................................................97 
Demographic and Sample Data .........................................................................................99 
Data Analysis and Findings ...............................................................................................99 
Research Question One ........................................................................................103 
Research Question Two .......................................................................................103 
Research Question Three .....................................................................................104 
Research Question Four .......................................................................................104 
Research Question Five .......................................................................................105 
Univariate Tests ...............................................................................................................106 
Evaluation of the Research Design ..................................................................................109 
CHAPTER FIVE: CONCLUSIONS ...........................................................................................111 
Overview ..........................................................................................................................111 
Research Purpose .............................................................................................................111 
Research Questions ..........................................................................................................111 
Research Conclusions, Implications, and Applications ...................................................119 
Conclusions ..........................................................................................................119 
Theoretical Implications ......................................................................................121 

       
 
 
10 
Empirical Implications .........................................................................................122 
Practical Applications ..........................................................................................122 
Research Limitations .......................................................................................................124 
Further Research ..............................................................................................................126 
Summary ..........................................................................................................................128 
REFERENCES ............................................................................................................................131 
APPENDICES .............................................................................................................................152 
Appendix A: IRB Approval .............................................................................................152 
Appendix B: Introductory Email to Participants .............................................................153 
Appendix C: Consent Document .....................................................................................154 
Appendix D: Follow-Up Email to Participants ................................................................157 
Appendix E: Proof of Permission - UTAUT Instrument .................................................158 
Appendix F: ChatGPT Bible Prompt Responses .............................................................159 
Appendix H: Expert Panel - Email of Inquiry .................................................................163 
Appendix I: Expert Panel - Guidance ..............................................................................164 
Appendix J: Permission Statement ..................................................................................166 
 
   

List of Tables

Table 1 U.S. Scripture Engagement by Year (in Millions) ........................................................... 57

Table 2 “Not a Bible User” by Generation .................................................................................. 58

Table 3 Constructs and Survey Questions .................................................................................... 92

Table 4 Loadings of the PLS-SEM Measurement Model .............................................................. 98

Table 5 Construct Reliability and Average Variance Extracted .................................................. 99

Table 6 Descriptive Statistics...................................................................................................... 100

Table 7 Path Coefficients of Constructs and Moderating Effects............................................... 102

Table 8 Results of homogeneity-of-slopes ANCOVA analyses ................................................... 107

Table 9 All Results: PLS-SEM, SEM-MGA, and homogeneity-of-slopes ANCOVA................... 108

List of Figures

Figure 1 Diffusion Curve .............................................................................................................. 65

Figure 2 Research Model .............................................................................................................. 94

List of Abbreviations

Analysis of Covariance (ANCOVA)

Artificial Intelligence (AI)

Assemblies of God Fellowship (AGF)

Behavioral Intent (BI)

Effort Expectancy (EE)

Inductive Bible Study (IBS)

Multi-Group Analysis (MGA)

Natural Language Processing (NLP)

New King James Version (NKJV)

Optical Character Recognition (OCR)

Partial Least Squares Structural Equation Modeling (PLS-SEM)

Perceived Enjoyment (PEN)

Perceived Risk (PR)

Performance Expectancy (PE)

Personally Identifiable Information (PII)

Research Question (RQ)

Technology Acceptance Model (TAM)

Theory of Planned Behavior (TPB)

Unified Theory of Acceptance and Use of Technology (UTAUT)

United States (US)

Virtual Reality (VR)

Wireless Fidelity (WIFI)

CHAPTER ONE: RESEARCH CONCERN

Introduction

Leadership is the process of guiding and directing a group or organization toward a

specific goal or objective (Burns, 1978; Mann, 2000). It involves the ability to inspire and

motivate individuals to work together toward a common purpose (Kurtzman, 2010). Leadership

can come in many forms, including formal and informal roles, and it can be exercised by

individuals or groups (Bolman & Deal, 2017).

Technology can also play a role in leading individuals, particularly in the context of

business and marketing (Țicău & Hadad, 2021). For example, technology can be used to

influence consumer behavior and decision-making through targeted advertising and personalized

recommendations (Wut et al., 2021). Additionally, technology can be used to enhance

communication and collaboration within organizations, facilitating the work of leaders and teams

(Citrin & DeRosa, 2021).

In recent years, technology has become an increasingly prevalent force in the lives of

individuals. A study found that Americans devote almost five hours per day to screen time

(Milliot, 2016). From smartphones to social media, technology has shaped the way individuals

communicate and engage with the world. As one considers the role of leadership in their life, it is

important to recognize that in this context, too, technology has become a powerful force in

shaping human behaviors and values.

However, when it comes to the Bible, the engagement experience still consists largely of

looking at words on a page, whether they be printed or digital (Tsuria et al., 2021). While

technology has changed the way we engage with the world, it has yet to fully revolutionize the

way we engage with Scripture. This is despite the fact that technology has the potential to

enhance our engagement with the Bible in ways that were previously unimaginable.

Recently, the author attended his first Bible study service in virtual reality (VR) and

caught a glimpse of the potential of the immersive experience that can result from the

unconventional integration of technology with the teachings of the Bible. While at home,

wearing a VR headset, the researcher attended “VR Church,” pastored by DJ Soto. On this

particular Sunday, the study covered Jesus’ being tempted in the wilderness (Matt. 4:1-11). For

this lesson, an environment was created to allow attendees to walk virtually through the

wilderness during the study. As the lesson reached points such as the devil taking Jesus to a high

mountain (Matt. 4:8), attendees were led up a mountain and given the opportunity to peer off the

edge to see a representation of the “kingdoms of the world” (Matt. 4:8).

This study evaluates the factors that influence the adoption of modern technology,

specifically Artificial Intelligence (AI) natural language processing, in relation to its potential to

enhance Scripture engagement. It explores how AI can be used to create new and innovative

ways of interacting with the Bible, and how it can help individuals connect with Scripture in a

deeper and more meaningful way. By leveraging the full weight of technological advances, the

researcher believes that individuals can grow closer to Christ through Scripture engagement.

This study is intended to inspire individuals to consider how the modern affordances of

technology can be leveraged to lead individuals to Christ.

Background to the Problem

Scripture declares that the entire biblical canon is God-breathed (2 Tim. 3:16). The

Bible is the only book that can make a lost sinner wise for salvation, refute lies, restore sinners,

and educate in righteousness (Lenski, 1937). Further, it serves as a practical guide for

equipping believers to competently carry out every good work (2 Tim. 3:17). Hutchings

(2017b) argues:

Evangelical Christians regard the Bible as a special source of knowledge and as a

medium of encounter with God's living voice. Through frequent reading, Evangelicals

expect to achieve more biblical lifestyles and characters and a closer relationship with

the God who speaks to them through the text. (pp. 212-213)

Hence, it is arguable that words found in the Bible are the most valuable counsel

available to humanity. As such, any effort to increase its widespread engagement would be

beneficial to the masses.

In the Apostle John's opening to his account of the life of Jesus, he aptly connects Christ

to the Scriptures by stating, "the Word became flesh and dwelt among us" (John 1:14 ESV).

Vincent adds, "flesh" was simply a "new mode of being" (1887, p. 51). Throughout history,

God has shown a clear inclination to communicate with humanity using various creative

methods, including inspiration, visions, dreams, oral tradition, papyrus scrolls, and stone tablets

(Ex. 31:18). John emphasizes the embodiment of the living Word in human form as the most

powerful means of conveying a message directly to its recipients, irrespective of the medium

employed. In an effort to provide clarity regarding the synergy between Christ and what was

previously regarded as "the Law of the Prophets," Jesus states, "I have not come to abolish

them but to fulfill them" (Matt. 5:17). Letham asserts, "The verb became (egeneto) denotes the

Word entering into something new. The Word himself remained the same, but the environment

into which he went was an experience that was different" (2013, p. 118).

The potency of Scripture in the life of believers is best depicted in the hands of the

omnipotent. As a testament to Christ's ultimate power, the gospel of Mark recounts an instance

when demons ask that Jesus not harm them (Mark 5:6-7). After commanding them to leave the

man, the demons plead with Jesus, seeking permission to be allowed to dwell in a herd of pigs

instead (Mark 5:8-13). Instead of relying on His inherent power, when tempted by the Devil in

the wilderness (Matt 4:1-11, Mark 1:12-13, Luke 4:1-13), Jesus uses the Word as His sole

defense against "the God of this world" (2 Cor. 4:4). Moore observes how Jesus counters each

Satanic lure by reciting a passage of Scripture (R. D. Moore, 2011).

For centuries, engagement with the Scriptures has served as the backbone of Christian

life. In 2016, researchers examined the extent to which Bible engagement correlates to moral

behavior among Christian youth and adults (Ovwigho et al., 2016). In a study of private

spiritual practices, a random sample of 2,431 respondents established a strong link between

Bible engagement and moral fortitude. Respondents' propensity to drink in excess, engage in

promiscuity, gambling, and the viewing of pornography were evaluated in relation to their

interaction with Scripture. The study showed, "those who read or listen to the Bible at least four

times a week are the least likely to participate in any of the four specific behaviors examined"

(Ovwigho et al., 2016, p. 236).

Unlike common literature, the Bible is believed to be distinguished by its divine author's

precepts which are intended to establish universal morals. Vozzola defines morality as

"principles of right and wrong actions and judgments" (2014, p. 3). In her book, Moral

Development, she delves into the history of moral development theory; specifically, how an

individual's perspective of right and wrong evolves over time. The aim of this research is to

understand how artificial intelligence can be leveraged to establish God's Word as the moral

compass at each stage in human development, as opposed to being defined by society.

It is crucial to acknowledge the central role of the Holy Spirit in the transformative

power of the Word of God. As highlighted in John 16:7-11, it is not merely the act of reading

the Bible that leads to transformation, but the Spirit's application of God's truth in the life of the

believer. The Holy Spirit plays a vital role in illuminating and applying the teachings of

Scripture, guiding individuals into a deeper understanding of Jesus Christ, who is the ultimate

representation of God in a Christian’s life (John 14: 25-26).

1 Corinthians 2:9-14 begins by quoting from Isaiah 64:4, which declares that no one can

know God's thoughts except God's own Spirit. Paul then goes on to say that God has given

believers His Spirit so that they can know the wonderful things He has freely given them. He

also says that the things of God are foolish to those who do not have the Holy Spirit. Therefore,

while Scripture serves as a conduit through which the Spirit testifies about the Son of God

(John 5:39), it is through a lifestyle characterized by obedience to Jesus that genuine moral

transformation and spiritual growth take place (John 14:23, Rom. 12:2, 1 John 2:6).

Just as engagement with the Scriptures has been found to have a significant influence on

the life of Christians, personalization through artificial intelligence enables marketers to

influence consumer behaviors (Kumar et al., 2019). When referring to artificial intelligence,

Kumar et al. consider the "broad idea that computers, through the use of software and

algorithms, can think and perform tasks like humans" (2019, pp. 135–136). Artificial

intelligence, through personalization, has the potential to actively mold human lifestyles across

various facets of daily life (Kumar et al., 2019).

Bible engagement serves as the first theme that resounds in the literature being studied.

Electronic devices have a way of luring users to interact with them for hours each day without

their awareness. When referring to Bible apps, Hutchings notes, "these apps are attempting to

train users into habits of regular Bible engagement through systems of easy access, planned

routines, frequent prompts, pleasant rewards and opportunities to invest, personalize and share"

(Hutchings, 2014, p. 168). He compares the work of the YouVersion Bible app to ministry

offered by the Foreign Bible Society in 1804 and The Gideons in 1908, arguing that "reading

the Bible generates an opportunity for the reader to be transformed by the power of God

through the text" (Hutchings, 2014, p. 169).

The marriage of theology and technology has been flourishing for decades. Hutchings

(2015) noted the contributions of faith-based IT firms:

A small industry of Christian computing companies in the 1980s and 1990s produced

digitized commentary texts, analytical software, early e-reader devices and eventually

websites, seeking to take advantage of emerging digital technologies to encourage

greater access to the Bible and invite an increasingly computer-literate society to take

an interest in the Christian message. (p. 424)

Just as the printing press enabled greater access to the Bible, so the researcher sees

mobile devices as a medium for reaching further into the everyday life of individuals with the

transformational words of Scripture.

Without their being aware of it, individuals are being shaped by technology. Social

media companies are facing ethical challenges regarding the dissemination of false information

disguised as genuine news (Moravec et al., 2022; S. (Ada) Wang et al., 2022). "Persuasive

Technology" is defined as any technology that is "intentionally designed to change the user's

attitude, behavior or beliefs often by giving the user feedback of his actions (or omissions) and

by trying to 'suggest' to him a desired pattern of behavior" (Spahn, 2012). When surveyed,

representatives of leading Bible technology companies listed the following as driving forces

behind their work: "seeking to make the bulky printed text more accessible, easier to

understand, more attractive to new audiences, more frequently read, and easier to study"

(Hutchings, 2017b, p. 212). When YouVersion users were asked, "Do you find you turn to the

Bible more because it's available on your mobile devices?", three-fourths of users (77%)

responded "yes" (YouVersion, 2013). "Engagement and commitment are pursued through

persuasive content, but also in more implicit ways, including the structure of the interface and

the actions the user is invited to perform" (Hutchings, 2017b, p. 213).

Bogost (2007) categorized the approach of persuasive technology into the following

headings:

1. “Reduction” of a desired shift in behavior to simple tasks

2. “Tunnelling” the user through a series of pre-determined actions

3. “Tailoring” information to the user's needs

4. “Suggestion” of behaviors at opportune moments

5. “Self-monitoring” of progress

6. “Surveillance” of progress

7. “Conditioning” the user by offering targeted rewards and punishments

Simply stated, Spahn defines artificial intelligence (AI) as "intelligence demonstrated

by machines" (2012, p. 92). Even though AI is in widespread use today, the topic is generally

relegated to the realm of science fiction. De Bruyn (2020) argues that as communities adopt

these technologies, they are no longer considered artificial intelligence. He challenges readers

to consider how Optical Character Recognition (OCR), a technology introduced in the '70s, is

no longer considered AI. In the present work, the researcher has sought to synergize the themes

of "Bible engagement," "persuasive technology," and "artificial intelligence" to explore factors

influencing the adoption of a natural language processing chatbot used to reconnect those who

would otherwise be disengaged from the Bible in an ethical and theologically accurate manner.

Statement of the Problem

Since 2011, the American Bible Society has engaged in an ongoing research project that

examines the state of the Bible among U.S. adults (Fulks & Plake, 2020). According to their

2020 report, 60% of American adults have never read the Bible or read it only rarely (three to

four times a year or less). In addition, the latest findings show that a staggering 46% of U.S.

adults are “Bible Disengaged” (Fulks & Plake, 2020, p. 31). “Bible Disengaged people rarely

seek out the Bible, tending to encounter it through others, rather than by choice” (Fulks & Plake,

2020, p. 35).

Scriptural engagement, or the study and application of Scripture, has been proven to

greatly impact the lives of believers in Christ (Ovwigho et al., 2016). It can serve as a source of

guidance and direction, helping individuals to understand God's will and plan for their lives

(Fuhr & Köstenberger, 2016). By studying Scripture, believers can gain a deeper understanding

of God's character and nature, as well as the principles and teachings that are central to the

Christian faith (Francis & ap Siôn, 2016). Engaging with Scripture can also strengthen one's faith

and provide comfort during difficult times (Gorichanaz, 2016). Additionally, reading and

meditating on Scripture can deepen one's relationship with God and help them to grow in their

spiritual walk (Collins, 2014). Overall, scriptural engagement plays a vital role in shaping the

beliefs, values, and actions of believers in Christ.

With the advent of mobile devices, individuals have gained the freedom to do things on

the go that could previously only be accomplished at a stationary location (Chayutimaphan et al.,

2022). Unfortunately, most digital Bible applications merely transfer the text from printed form

to digital form, leveraging very few of the affordances of modern technology to increase

scriptural engagement (Tsuria et al., 2021). Bible applications lack personalization, which is an

essential aspect of modern technology that can help increase scriptural engagement (Opoku et

al., 2020).

Advancements in the use of artificial intelligence continue to augment every aspect of life

(De Bruyn et al., 2020). Businesses are leveraging artificial intelligence to better serve clients,

while social media and streaming services use the tool to increase viewership of content

(Brittain, 2020). Time spent online has increased at an alarming rate, leaving some psychologists

to speculate about the long-term effects of this inordinate amount of time spent using technology

on current and future generations (Hutchinson, 2019). Considering this present-day situation, the

researcher on this project has explored whether artificial intelligence could be leveraged to help

individuals grow closer to God.

In November 2022, a chatbot called ChatGPT was launched with widespread acclaim and

adoption (Shrivastav, 2023). ChatGPT is a large language model developed by OpenAI. It is

based on the GPT (Generative Pre-trained Transformer) architecture and is trained on a wide

range of internet texts, including websites, articles, and books (Hughes, 2023). It is capable of

generating human-like text and can be used for a variety of natural language processing tasks

such as language translation, question answering, and text summarization (Pavlik, 2023).

Recently, it has been launched as a service to provide users with natural language processing

capabilities for their own applications (Castelvecchi, 2022). This allows developers to integrate

the model into their own software and use its capabilities to improve the user experience in their

applications (Hachman, 2023). The launch of the ChatGPT service is significant, as it makes this

powerful technology more widely available and accessible to developers and businesses

(Shrivastav, 2023).

There currently does not exist any research exploring individuals' behavioral intention to

use a Bible-based chatbot that leverages the natural language processing (NLP) capabilities of AI

to create human-like engagement with Scripture. This is an area that has yet to be explored and is

a gap in the current literature. It would be valuable to understand how individuals perceive the

use of a Bible-based chatbot and how it may impact their engagement with Scripture. In this

study, factors such as performance expectancy, effort expectancy, perceived enjoyment, and

perceived risk were examined to understand the behavioral intentions of individuals toward

adopting the usage of Bible-based chatbots as one of their means of spiritual formation. These

findings also enable mobile Bible application developers to make more informed decisions about

how to design and develop such applications to better meet the needs and preferences of end-

users.

Purpose Statement

The purpose of this descriptive study was to understand whether a relationship exists

between an individual's behavioral intention to use a Bible-based chatbot leveraging the natural

language processing (NLP) capabilities of artificial intelligence (AI) to create human-like

engagement with Scripture and the constructs of performance expectancy, effort expectancy,

perceived enjoyment, and perceived risk, as adapted from the Unified Theory of Acceptance and

Use of Technology (UTAUT) framework, controlling for gender, age, and experience for

registered users of the Inductive Bible Study App. Artificial intelligence is already at work in the

world, driving behaviors that advance the agendas of various organizations (De Bruyn et al.,

2020). Due to several factors, which will be explored in the literature review, a gap exists in

considering the power of artificial intelligence toward spiritual formation through Bible

engagement. Since the UTAUT framework has been used in hundreds of studies to quantify

factors contributing to individuals' intent and use and usage behavior of technology, this

theoretical foundation will serve as a guide for the study (Tamilmani et al., 2021).

Research Questions

The following research questions guided this study:

RQ1. What influence does performance expectancy have on an individual’s behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create human-like engagement with Scripture?

RQ2. What influence does effort expectancy have on an individual’s behavioral intention

to use a Bible-based chatbot that leverages the natural language processing capabilities of AI to

create human-like engagement with Scripture?

RQ3. What influence does perceived enjoyment have on an individual’s behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create human-like engagement with Scripture?

RQ4. What influence does perceived risk have on an individual’s behavioral intention to

use a Bible-based chatbot that leverages the natural language processing capabilities of AI to

create human-like engagement with Scripture?

RQ5. How do gender, age, and experience moderate the relationship among the UTAUT

framework constructs of performance expectancy, effort expectancy, perceived enjoyment,

perceived risk, and the behavioral intention to use a Bible-based chatbot that leverages the

natural language processing capabilities of AI to create human-like engagement with Scripture?

Assumptions and Delimitations

Research Assumptions

This research assumes that Bible engagement is a beneficial spiritual practice to the life

of a Christian. Over the span of eight years, the Center for Bible Engagement examined the

spiritual development of over 100,000 individuals worldwide (Cole & Ovwigho, 2012). Aside

from an inherent increase in feelings of peace and joy, participants who reported engaging with

Scripture four or more times a week also exhibited a considerable decrease in their likelihood

of yielding to temptations such as gossiping, lying, overeating, or overspending. A consistent

finding across these individual studies is that regularly engaging with the Bible four or more

times a week emerges as the most robust and reliable predictor of spiritual growth (Cole &

Ovwigho, 2012).

The UTAUT framework is widely regarded as a valid theory for explaining user

acceptance and usage of technology. This is due to the numerous studies that have used

UTAUT to understand and predict the adoption and usage of technology products (Tamilmani

et al., 2021). These studies have provided strong evidence for the validity of UTAUT,

demonstrating its ability to accurately predict user behavior and explain why some technology

products are adopted while others are not (Kamboj & Joshi, 2021; Liu et al., 2019; Tak &

Panwar, 2017). The theory's broad range of predictors and its ability to incorporate various

individual and situational factors make it an ideal framework for understanding and predicting

behavioral intention to use a Bible-based chatbot that leverages AI to create human-like

engagement with Scripture.

This research assumes a primary focus on Christian individuals, as the Inductive Bible

Study app is intentionally designed and targeted toward this specific demographic. Within this

particular context, the term "Christian" refers to an individual who embraces the teachings and

beliefs of Christianity, expressing faith in Jesus Christ as the Son of God (Rom. 10:9-10) and

endeavoring to align their life with the values and principles outlined in the Bible (2 Tim. 3:16-

17). Although this research does not exclude individuals from different religious or non-

religious backgrounds, it assumes a predominant focus on the perspectives of Christian users

due to the app's intended audience.

Delimitations of the Research Design

Research into individuals' behavioral intention to use a Bible-based chatbot that leverages

the natural language processing (NLP) capabilities of AI to create human-like engagement with

Scripture has several delimitations. These delimitations are important to consider in order to

understand the scope and limitations of the study. This focus may impact the degree to which the

conclusions can be made, which generalize other populations.

1. The research was delimited to registered users of the Inductive Bible Study app.

2. The study focused only on the behavioral intention of individuals to use a Bible-based

chatbot that leverages the natural language processing (NLP) capabilities of AI to

create human-like engagement with Scripture.

3. The research evaluated performance expectancy, effort expectancy, facilitating

conditions, and perceived risk as constructs of the UTAUT theory, and no other

potential factors that may influence usage behavior.

4. The study considered only the natural language processing capabilities of AI, and no

other AI-based features were incorporated.

Definition of Terms

The following terms and definitions are given as clarification for this study.

1. Artificial Intelligence (AI): A branch of computer science that aims to create

intelligent machines that work and learn like humans. AI research includes

developing algorithms and statistical models that enable computers to perform tasks

that typically require human intelligence, such as visual perception, speech

recognition, decision-making, and language translation. These intelligent systems can

be trained on large data sets, allowing them to improve their performance over time.

AI can also be divided into weak AI and strong AI, weak AI is designed to perform a

specific task, whereas strong AI is intended to have general cognitive abilities like

human (Kumar et al., 2019).

2. Bible Engagement: Refers to the various ways in which individuals interact with the

Bible. This can include reading, studying, meditating on, memorizing, and applying

the teachings and principles of the Bible in one's daily life. The goal of Bible

engagement is to gain a deeper understanding of God's word and to apply its

teachings to one's life in order to grow in one's faith and relationship with God

(Ovwigho et al., 2016).

3. Chatbot: A chatbot is a computer program designed to simulate conversation with

human users, especially over the internet. They can be integrated into messaging

platforms, mobile apps, and websites to provide customer service, answer frequently

asked questions, and automate repetitive tasks. Chatbots use natural language

processing (NLP) and machine learning algorithms to understand and respond to user

input (Sugumar & Chandra, 2021).

4. Effort Expectancy: One of the key constructs of the UTAUT framework. It refers to

the perceived level of ease associated with using a technology product. Effort

expectancy is considered a critical predictor of technology adoption and usage

because individuals are more likely to adopt and use a product if they perceive it to be

easy to use (S. Hu et al., 2020). In other words, the lower the effort required to use a

technology product, the more likely individuals will adopt and use it.

5. Natural language processing (NLP): A subfield of artificial intelligence and

computer science that focuses on the interaction between computers and humans in

natural language. It involves developing algorithms, models, and tools that enable

computers to understand, interpret, and generate human language. This includes tasks

such as speech recognition, language translation, sentiment analysis, text

summarization, and more. The goal of NLP is to enable computers to process and

analyze large amounts of natural language data and to make it easier for people to

interact with computers using natural language (Y. Kang et al., 2020).

6. Perceived Enjoyment: Refers to an individual's assessment of how enjoyable or

satisfying it is to use a technology product. It is believed to play a key role in

determining an individual's intention to continue using a technology and is considered

one of the strongest predictors of technology acceptance and usage. The perceived

enjoyment construct of the UTAUT framework highlights the importance of

designing technology products that are not only functional, but also enjoyable and

satisfying to use, as this is crucial in encouraging continued adoption and usage

(Chao, 2019; De Cicco et al., 2020).

7. Perceived Risk: Refers to an individual's perceived uncertainty and concerns about

the negative outcomes associated with using a new technology. This UTAUT

framework construct is a key predictor of technology adoption and usage and includes

various dimensions, such as financial risk, performance risk, psychological risk, and

social risk (Farzin et al., 2022).

8. Performance Expectancy: Refers to an individual's belief that using a particular

technology will help them achieve their goals or improve their performance.

According to the UTAUT framework, this construct represents one of the key factors

influencing the adoption and usage of technology. It is based on the idea that users are

more likely to adopt and use a technology if they perceive that it will help them

achieve their desired outcomes, such as increased productivity, efficiency, or

convenience (Magsamen-Conrad et al., 2020; Popova & Zagulova, 2022).

9. Persuasive Technology: A field of study that focuses on the design and development

of technology-based systems and products that aim to change people’s attitudes and

behaviors. The goal of persuasive technology is to create products that influence

individuals to make decisions, perform actions, or adopt habits that are beneficial to

their well-being or the well-being of others (Bogost, 2007; Spahn, 2012).

10. UTAUT Framework: A comprehensive framework for understanding and predicting

the acceptance and usage of technology. It combines and extends several existing

theories to create a comprehensive model that incorporates various individual and

situational factors to explain and predict technology acceptance and usage behavior

(Venkatesh et al., 2003).

Significance of the Study

The researcher believes that the potential impact of the study is as far-reaching as the

impact of the printing press on Bible access. As society races toward a post-Christian worldview

and individuals are inundated with technology that is specifically designed to consume hours of

time, Christians must seek ways to harness the power of digital engagement through artificial

intelligence for good. Just as the human experience with the Bible has transitioned from oral

tradition to printed text and now digital text, the researcher believes that artificial intelligence

provides the greatest opportunity for the next level of interactivity with the Bible.

Summary of the Design

Research Population

The chosen research population for this study comprises registered users of the Inductive

Bible Study app. This selection was made for several reasons. Firstly, these users represent a

diverse demographic, encompassing a wide range of age groups, experience levels of

technology, and denominational affiliations. By including an expansive sample, the study aims to

capture an understanding of factors that influence the behavioral intention to use a Bible-based

chatbot. Additionally, the decision to focus on users of this particular app is justified by their

demonstrated interest in leveraging technology to enhance their faith journey. The usage of the

app itself suggests a willingness to explore innovative tools and platforms for Bible engagement,

making this population an ideal choice in which to examine the variables of performance

expectancy, effort expectancy, perceived enjoyment, and perceived risk in relation to the

chatbot's adoption.

Sampling Techniques

The study employed an a priori approach to determine the sample size required for the

structural model. Through careful consideration, it was determined that an optimal number of

848 responses would be necessary to achieve the desired statistical power level of 0.8. To reach

this target, a total of 40,960 registered users of the Inductive Bible Study app were invited to

participate in the study. Within a span of two weeks, 1,036 individuals (2.5% response rate)

completed the survey, forming the final sample for analysis.

The determination of the sample size was crucial, as it directly influenced the accuracy of

estimates and the study's ability to draw meaningful conclusions based on the collected data

(Norouzian, 2020). By ensuring an adequate sample size, the study aimed to minimize sampling

errors and enhance the reliability of the findings. A medium anticipated effect size of 0.3 was

considered in the calculation, which denotes the magnitude of the relationship between the

variables under investigation (H. Kang, 2021). Furthermore, the researcher set a probability level

of 95% for the three observed variables associated with the five latent variables in the structural

model, ensuring a robust level of confidence in the study's outcomes.

Methodological Design

The study’s methodological design employed a quantitative survey research

methodology, aiming to investigate the relationship between various factors and behavioral

intention to use a Bible-based chatbot. The survey instrument utilized in this study was designed

to gather data on variables such as performance expectancy, effort expectancy, perceived

enjoyment, perceived risk, and behavioral intention. By adopting a quantitative approach, the

study sought to obtain numerical data that could be analyzed statistically to derive meaningful

insights and draw conclusions (Leedy & Ormrod, 2019).

To guide the analysis and interpretation of the data, the study applied the UTAUT

framework (Venkatesh et al., 2003). This widely recognized framework provides a

comprehensive theoretical foundation for understanding technology adoption and usage

behavior. By employing the UTAUT framework, the study aimed to explore how the selected

variables influenced behavioral intention to use the Bible-based chatbot, while also considering

factors such as performance expectancy, effort expectancy, perceived enjoyment, and perceived

risk.

The data collected through the survey was subjected to analysis using Partial Least

Squares Structural Equation Modeling (PLS-SEM), a statistical technique suitable for analyzing

complex models with both observed and latent variables (F. Hair Jr et al., 2014). PLS-SEM

enables researchers to assess relationships, test hypotheses, and evaluate the significance of

various factors in the proposed model (Shmueli et al., 2019). Additionally, the study employed

multi-group analysis (MGA) to examine potential differences across demographic groups within

the sample (Yen et al., 2022). Moreover, homogeneity-of-slopes analysis of covariance

(ANCOVA) was employed to explore any covariate effects and to enhance the robustness of the

findings (Bartlett, 2020; Hedges et al., 2023). These analytical techniques allowed for a

comprehensive investigation of the research questions and provided a deeper understanding of

the relationship between the variables of interest.

This chapter has provided an overview of the study, starting with the background and

context of the research. It underlines the problem of decreasing scriptural engagement,

introduces the concept of a Bible-based chatbot that leverages artificial intelligence, and lays the

groundwork for the study of factors that influence an individual’s behavioral intention to use a

Bible-based chatbot. The significance of the study is emphasized, highlighting its potential to

enhance the understanding of technology-driven Bible engagement. Transitioning to the

literature review, the subsequent chapter of this dissertation will provide a comprehensive

literature review, delving into existing research and theories related to technology adoption,

Bible engagement, and the UTAUT framework to establish both a theological and theoretical

foundation for the study.

CHAPTER TWO: LITERATURE REVIEW

Overview

The first step in seeking to contribute to the body of knowledge in a particular discipline

is to understand the scholarship of others (Efron & Ravid, 2019; Galvan & Galvan, 2017). It is

the goal of this literature review to provide a structured survey of the relevant literature related to

the topic of this study. The literature review begins by providing the theological framework with

an investigation of concepts such as the importance of scripture-life integration, challenges of

studying Scripture, and the practical nature of Scripture. After establishing a biblical basis for the

study, the literature review surveys the UTAUT theoretical framework (Venkatesh et al., 2003).

When reviewing this theoretical framework, its origins, recent applications, extensions, and

criticisms are examined. Last, a thorough review of related literature is synthesized focusing on

Bible engagement, digital religion, innovation resistance, and persuasive technology. The

rationale for the study is also discussed along with the identification of gaps in the literature.

Theological Framework for the Study

Engaging with the Bible is regarded by some Christians as a positive and potentially

transformative practice (Ford, 2019b). The believer's spiritual vitality relies on the Word of God

as its life-giving essence (Cole & Ovwigho, 2012). Blessing follows the one who finds joy in the

precepts of God so much that they are constantly considering them (Ps. 1:2). This theological

survey considered the challenges to studying Scripture. Scripture holds a unique position, as its

words originate from the mind of the Creator (2 Tim. 3:15), where both divine and human

authors, under the influence of the Holy Spirit, have contributed to its composition (2 Peter

1:21). Furthermore, the Holy Spirit plays a pivotal role in guiding the understanding of its

teachings (1 Cor. 2). Considering the fact that God’s thoughts are higher than the thoughts of the

beneficiaries of Scripture (Isa. 55:8-9), individuals may encounter challenges in studying God’s

Word.

Despite these challenges, Scripture, when understood and applied correctly is extremely

practical in the life of Christians. This section of the literature review considers the creativity

God has already used in the delivery of His Word to mankind. The reader will find that God has

not limited Himself throughout human history by any means in presenting His principles in the

most creative and compelling ways. It is in this light that this research suggests that mankind is

granted creative liberty when accurately presenting God’s Word in modern contexts through

innovative channels.

The purpose of this descriptive study was to understand whether a relationship exists

between an individual's behavioral intention to use a Bible-based chatbot that leverages the

NLP capabilities of AI to create human-like engagement with Scripture and the constructs of

performance expectancy, effort expectancy, perceived enjoyment, and perceived risk, adapted

from the UTAUT framework and controlling for gender, age, and experience among

registered users of the Inductive Bible Study App. Therefore, it is imperative that a firm

theological footing be established to demonstrate the study’s alignment with Scripture. To

accomplish this feat, the theological framework used has explored the importance of

Scripture-life integration, challenges to studying Scripture, the practical nature of Scripture,

and the creative delivery methods of God’s Word.

Importance of Scripture-Life Integration

There are many subjects and books worthy of study, but none with the source and

potential impact offered by the Bible. The Words found in Scripture originate in the mind of the

all-knowing (1 John 3:20), everlasting (Ps. 90:2) God of all creation. It is obvious that a delicious

meal serves no purpose unless it is consumed. Likewise, Scripture is most beneficial to believers

when it is studied and leveraged as a guide for maneuvering through life. Fuhr & Köstenberger

(2016) assert, “those who devote their lives to the study of this one book— which, unlike any

other work, is the divinely inspired Word of God with the power to transform our hearts and

lives— will never be the same” (p. 2).

Discipleship Mandate

Scripture offers discipleship as the chief instrument for helping individuals grow in their

faith (Acts 2:42). Christlikeness is not formed by merely assigning converts particular passages

of scripture to read (Ovwigho et al., 2016). Spiritual disciplines such as prayer, fasting, and

scripture memorization are outstanding ways to grow in one’s faith, but being done in isolation

could result in misdirection. Ogden (2019) maintains, “discipling is an intentional relationship in

which we walk alongside other disciples in order to encourage, equip and challenge one another

in love to grow toward maturity in Christ” (p. 3).

Discipleship fosters the growth of the individual and the global community of Christ.

Simply put – disciples make disciples. English (2020) asserts, “to be a disciple of Jesus is to

make other disciples of Jesus. Multiplication is baked into the definition of what it means to be a

disciple” (p. 3). Discipleship is different from mere mentoring in that the goal of discipleship is

to collectively shed the trappings of societal formation in exchange for conformity to the image

of God (Col. 3:10). The core training curriculum for discipleship is the Bible (2020). Inside this

book, believers find a clear mandate for scripture-life integration through discipleship.

Great Commission. On a mountainside in Galilee, Jesus delivers his final marching

orders to his followers. Jesus charges his co-laborers:

Go therefore and make disciples of all the nations, baptizing them in the name of the

Father and of the Son and of the Holy Spirit, teaching them to observe all things that I

have commanded you; and lo, I am with you always, [even] to the end of the age. (Matt.

28:19-20 NKJV)

Herein, Jesus stresses the assignment (“make disciples”), context (“all nations”), and

assurance (“I am with you”).

The act of teaching is inseparable from the task of making disciples. “The spread of the

gospel and building communities for teaching, learning, and growth is the heart of the Great

Commission that Jesus gave to his original disciples” (Goodrich & Strauss, 2019, p. 156).

Wilson (2018) contends that every Christian is called to aid in the mission of the global church,

which is prescribed in the Great Commission. He further notes that since the charge was

originally rendered, it has never been withdrawn. Therefore, followers of Christ are expected to

diligently devote their hands and hearts to the task of making disciples in the local, regional,

national, and international contexts. At its core, “disciple making is helping people to trust and

follow Jesus” (Harrington & Patrick, 2017, p. 38).

Teach Children. As adults, the importance of providing for the physical needs of

children is an obvious necessity. Beyond their physical needs, parents and caregivers must not

neglect the spiritual needs of children. Otherwise, successive generations will be physically

healthy but spiritually malnourished. In his final instructions prior to Israel’s entrance their

promised land (Deut. 6:1), in regard to the commandments of God, Moses contends, “You shall

teach them diligently to your children, and shall talk of them when you sit in your house, when

you walk by the way, when you lie down, and when you rise up” (Deut. 6:7). In response to this

directive, Israel’s families were expected to have an ongoing dialogue with their children

regarding the precepts of God.

Hillman & Edwards maintain that “parents bear the primary responsibility for the

spiritual growth and development of their children” (2018, p. 122). After first learning and

practicing the statutes of God, they must incorporate these lessons into the daily routine of

engagement with their children. Even though the discipleship of children can be augmented by

the children and youth programs offered by the local church, these were never intended to

replace the responsibility of in-house ministry. The limited time spent in children’s ministry will

never replace the daily opportunities for biblical inculcation afforded to involved parents

(Hillman & Edwards, 2018).

Csinos (2020) suggests, “children are not simply passive consumers of theology; they

actively generate theological meaning for themselves” (p. 3). Children’s theology is shaped by

the building blocks of their life experiences. Whether those experiences are good or bad, they

serve as the framework for their understanding of who God is in their life. Nunnery postulates,

“the Hebrew word translated for us as ‘teach them diligently’ literally means ‘to whet or

sharpen’ and conveys the idea of instilling an idea, attitude, or habit by persistent instruction”

(2018, p. 60). Nunnery further maintains that Moses is instructing Israel to prepare their children

with the same level of intricate care they would their weapons prior to battle.

Howe & Strauss (2000) observe that Millennials, the next succeeding generation,

maintain a unique position in comparison to prior youth generations. Further, “they are more

numerous, more affluent, better educated, and more ethnically diverse” (Howe & Strauss, 2000,

p. 4). As Jesus looked at the flailing multitudes, he was struck with compassion (Matt. 9:36).

Jesus compares the crowds to shepherdless sheep – individuals with no guides. Thus, he turns to

the disciples and contends, “the harvest truly is plentiful, but the laborers are few” (Matt. 9:37).

Modern context presents another opportunity for an abundant harvest, but the underlying

question is how diligently the parents of succeeding generations have raised their children “in the

training and admonition of the Lord” (Eph. 6:4).

Generational Impact

Since Moses was so diligent to express the primary importance of teaching upcoming

generations the precepts of God, one must consider the ramifications of failing to do so. The

narrative of Israel’s journey into the mysteries of slavery begins with a transition of leadership

(Ex. 1:6-8). The children of Israel are described as being affluent in number and might (Ex. 1:7).

Unfortunately, the generation of Joseph died in the community of Israel (Ex. 1:6), all while a

transition of leadership occurred in Egypt (Ex. 6:8). Based on Egypt’s new leader’s ignorance of

the blessed relationship between Israel and Egypt, he began to view and treat the children of

Israel as a threat (Ex. 1:9-10). In this historical case study, readers catch a glimpse of the

devastating impact of a lack of transfer of knowledge in existing relationships.

The book of Judges depicts the ramifications of neglecting to transfer the knowledge of

God to succeeding generations. “When all that generation had been gathered to their fathers,

another generation arose after them who did not know the LORD nor the work which He had

done for Israel” (Judg. 2:10). Juxtaposed against Joshua’s generation, this current generation is

described in terms of what they are lacking – knowledge of the Lord (Boda et al., 2017).

Beldman adds, “‘to know’ / עדי (yādaʿ) goes beyond mere cognition; it denotes intimacy with the

object of ‘knowing,’ as is evident so often in the Bible when a husband ‘knows’ his wife and a

baby is conceived” (2020, p. 63)!

Over the past decade, the collaborative research of the American Bible Society and Barna

Group has provided profound insight into the intersection of faith and culture in America. The

output of their years of work is recorded in their seminal work, titled State of the Bible. In the

2021 edition, succeeding generations (Gen Z & Millennials) are found to be the least engaged

with Scripture (Cooper et al., 2021). Shockingly, despite their lack of interaction with the Bible,

they self-assess as having the most comprehensive understanding of its contents. Simply put, the

upcoming generations devote little to no time toward reading or studying scripture, yet they

believe that they have a firm grasp of its principles and statutes. This unfortunate scenario

certainly depicts the perfect formula for a repeat of the times presented in the book of Judges.

Model

Aside from written instruction, one of the best ways to ensure that one’s training is as

effective as possible is to see someone model the preferred behavior. When learning a new

language, it is important to hear it spoken by someone who speaks it natively (Tsang, 2023).

Likewise, Jesus serves as the model of discipleship; He speaks discipleship fluently. As the

author of all Scripture, Jesus is perfectly suited to serve as an example in living a life to the

standards of holiness He dictates (Harrington & Patrick, 2017). Harrison and Patrick (2017)

contend, “Jesus didn’t just give us a message, he also gave us a method” (p. 40).

As a follower of Christ, Paul beckons readers to imitate him so long as he continues to

model the behavior found in Christ (1 Cor. 11:1, 4:16; Phil. 3:17; 1 Thess. 1:6). As believers live

in ways that point people to Christ and His teachings, it is alright to offer oneself as an example

of biblical principles being put into practice (Schreiner, 2018). Schriener (2018) adds that the

urging of others to imitate them must be born out of a love for God and His people.

Challenges of Studying Scripture

This review has demonstrated the profound significance that Scripture carries in the life

of every believer. One's ability to study and comprehend Scripture stands as one of the building

blocks of a disciple’s life. The study of Scripture will prove beneficial to all, but that does not

mean that it is an easy feat. Fuhr & Köstenberger (2016) contend, “This is the challenge of Bible

study: its particulars are often complex but never vexing; its message is simple but not simplistic;

the study of Scripture requires hard work— in fact, it entails a lifelong journey— and yet along

that pathway of discovery you’ll find enrichment and growth from day one” (p. 3). They further

suggest that students of the Bible must wrestle with foreign history and cultural norms, in

addition to a variety of literature genres (Fuhr & Köstenberger, 2016; Segovia & Tolbert, 1995).

Comprehension

It is one step to read the Bible, but its jewels are nestled in its comprehension. Without

comprehension, holy writ merely amounts to a collection of words. A glaring example found in

Scripture of one reading but not understanding is the case of the Ethiopian Eunuch (Acts 8:26-

40). Garland finds, “the Scripture captures his imagination, but its full meaning escapes him”

(2017, p. 103). Francis & Jones observe that Philip’s first step toward spiritual accompaniment

was to listen for the opportunity to help (2020). Garland expounds, “Christians must be ready to

interpret the Scriptures to those drawn to the Bible but bewildered by it” (2017, p. 101).

This chain of events, orchestrated by the Holy Spirit, was made possible by the

combination of the humility of the Eunuch, which was met by a prepared Philip (Averkīĭ et al.,

2017). As should always be the case, the Eunuch’s understanding of Scripture resulted in His

desire to respond – seeking baptism (Acts 8:36-38). Without assistance over the hurdle of

comprehension, the Eunuch would not have had a solid footing in which to comply (Francis &

Jones, 2020). In their research on empirical theology and biblical hermeneutics, Francis and ap

Siôn (2016) highlight the value of the “Reader Perspective” approach to biblical interpretation by

arguing:

The reader perspective may conceptualize the revelatory capacity of scripture

materializing not through unlocking the deep (and possibly hidden) meaning embedded

in the text by the author, but rather through the dynamic interactions or dialogue between

the reader and the text. (p. 25)

In yet another instance in Scripture, readers are introduced to two disciples who are

actually experiencing scripture being fulfilled, yet they failed to grasp its meaning (Luke 24:13-

32). Had it not been for the insights shared by Jesus, along their journey to Emmaus, these

disciples would have missed the significance of the events unfolding in their midst. After

listening to their disagreement, Jesus pinpoints that the individuals have a two-fold dilemma.

They have both doubt in Christ’s resurrection capability (Luke 24:25) and ignorance of the

Scriptures (Wolter et al., 2017). Christ’s ability to make sense of the Scriptures compelled the

disciples to hurry to Jerusalem to tell their friends (Cross, 2015). While reflecting on the feeling

of having the Scriptures explained to them, they described it as a burning in their hearts (Luke

24:32), which was an old metaphor to express genuine excitement (Wolter et al., 2017).

Apathy

An underlying challenge to the study of Scripture is to mistake the chief aim of study to

be that of comprehension. The apostle James admonishes believers to “be doers of the word”

(James 1:22). James seems to imply that the word should take root in the lives of believers and

ultimately bear fruit in our actions (Nielson, 2019). “To open our ears to ascertain the meaning of

a passage is insufficient. The true purpose of Bible study is for the whole person to be changed in

every area of our lives” (Osborne, 2019, p. 41). The ideal response to understanding scripture is

best exemplified by the commitment made by Israel upon hearing the word of the Lord declared

by Moses (Ex. 24:3).

The Westminster Confession of Faith states:

These good works, done in obedience to God’s commandments, are the fruits and

evidences of a true and lively faith: and by them believers manifest their thankfulness,

strengthen their assurance, edify their brethren, adorn the profession of the gospel, stop

the mouths of the adversaries, and glorify God, whose workmanship they are, created in

Christ Jesus thereunto, that, having their fruit unto holiness, they may have the end,

eternal life (Bray, 2019, p. 449).

Practical Nature of Scripture

Even though students of Scripture may sometimes find its words difficult to comprehend,

they are nonetheless useful and relevant to one’s current circumstances. The Bible offers

practical advice on topics such as personal relationships, ethics, morality, and spirituality, as well

as providing comfort and hope in times of hardship and loss (Copan, 2016). Additionally, its

historical and cultural context offers insights into the human experience and the timeless wisdom

it contains is often regarded as universal and applicable to contemporary life (Gorichanaz,

2016). Fuhr & Köstenberger (2016) surmise:

There is profound irony in the fact that students earn PhDs writing learned tomes on

narrow areas of biblical research (for no one person could possibly master all the fields

related to the study of Scripture), while a five-year-old can understand the basic message

of the gospel and be saved. (p. 3)

Usefulness

In his second letter to Timothy, Paul continues to equip and encourage his understudy.

After alerting him to the downward spiral of the human condition (2 Tim. 3:13), Paul

admonishes his mentee to continue in the faith that has been taught to him since an early age (2

Tim 3:14-15). Next, Paul uncovers the source and significance of the curriculum in which

Timothy has thus been schooled (2 Tim. 3:16-17). “‘All Scripture’ refers to the Bible as a whole.

Every book, every passage is God-breathed. Every genre— prose, poetry, history, and ethics— is

God’s Word” (Doriani & Phillips, 2020, p. 13h).

Stott notes that the underlying Greek word “theopneustos” points to Scripture being

breathed by God (Stott, 2021). The collaboration between God as the divine author and human

writers is evident in the composition of Scripture, where the words originate from the mind of

God and are expressed through the literary contributions of human beings under the guidance of

the Holy Spirit (2 Peter 1:21; 2 Tim. 3:16). The divine nature of Scripture results in profitability

in the areas of teaching, reproof, correction, and training in righteousness (Doriani & Phillips,

2020). While an understanding of the natural world can be observed and classified through

scientific research, the Bible is the only means of gaining insight regarding salvation (Stott,

2021). “At all events, it is only by a careful study of Scripture that the servant of God may be

thoroughly equipped for every good work” (Stott, 2021, p. 81).

Relevance

Arguably the most practical and powerful example of the direct use of Scripture is found

in Jesus’ fending off the temptations of Satan in the wilderness (Matt. 4:1-11). In this passage,

Moore (2011) observes that each of the progressive enticements involves a test of the established

character of God. In response to each of Satan’s desperate lures, Christ responds and thus

overcomes every temptation through direct knowledge of the word of God (Wolter et al., 2017).

Green encourages believers to prepend the phrase “it is written” to every response offered to the

enemy’s lies (2020, p. 71).

Some may question the relevance of Scripture due to its dated origins. It is true that the

Bible does not speak directly to social media and the various aspects of technology that dominate

modern culture. Even though Satan may change the bait, he is still seeking to hook believers to

arguments and presumptions that run counter to the knowledge of God (2 Cor. 10:5). Vanhoozer

(2005) compares the Bible to a script wherein the director has given the actors the freedom to

improvise within established bounds.

Creative Delivery Methods of God’s Word

The theological framework now concludes with an examination of the creativity that God

has leveraged in order to deploy His unfailing word to humanity. Just as Bezaleel and Aholiab

were recruited for their mastery and skill when building the sanctuary (Ex. 36:1), so has God

chosen the perfect strategies and individuals at the right time to convey His truth.

Oral

In days of old, God would endow human individuals, called prophets, with His

authoritative message to mankind (Heb. 1:1). These prophets would then communicate God's

message to the people and serve as a guide for understanding God's will and instructions.

Hebrews 1:1 makes mention of the variety of times and ways in which God used individuals.

The words of prophets were taken so seriously that the punishment for one found to be false was

death (Deut. 18:20). According to Peterson, Schnabel, and Perrin (2020), prophetic utterances

played a crucial role in Israel's relationship with God, encompassing promises, praises,

predictions, warnings, commands, and encouragements.

Oral communication was not limited to that of humans. As Balaam was tempted by

requests to use his prophetic utterances for personal gain (Num. 22:18-19), he became blind to

God’s impending judgment. After several failed diversion attempts through the rerouting of his

donkey, God “opened the mouth of the donkey” (Num 22:28) in order to get Balaam’s attention.

Consider the breadth of creativity God was willing to go through in briefly extending the vocal

capabilities of an animal to transmit His word to a human (2017). “The irony is evident: the

prophet-for-hire—the would-be seer—is governed by his contract for business, but it is his ass

who can see what is really at stake” (Briggs, 2018, p. 233).

Media

Oftentimes the terms media and technology are understood from a limited and modern

perspective. Campbell and Gardner (2016) expand the scope of technology’s definitions by

suggesting that it encompasses any tool that enables humanity to gain a better understanding of

itself or the world. Thus, “the Bible itself can be seen as a tangible expression of technology and

media in that it is typically a human-created physical artifact (though other mediated

representations are possible) that has been produced by special knowledge” (H. Campbell &

Garner, 2016, p. 29).

Moses chronicles the experience of receiving handwritten tablets of stone directly from

God (Ex. 31:18). Hutchings (2017b) traced the format of the Christian Bible throughout the ages

from the scroll, to the letter, to the codex, to the printed book. Hong (2020) points out that the

first use of the Gutenberg press in 1455 was to produce the Bible in bulk. Even now, as digital

Bibles gain a dominant share of the preferred method for reading Scripture, Christians are

wrestling with the potential ramifications of migrating from paper to screen (Hutchings, 2017b).

Person

Has God shown any restraint in delivering the Word to fallen man? In Christ, one cannot

say that the Almighty Himself was unwilling to hand-deliver His crown jewel to humanity. As

the apostle John marvels at the incarnation of Christ, hoping to bridge the connection between

Christ and God’s Word, he states, “the Word became flesh and dwelt among us, and we beheld

His glory, the glory as of the only begotten of the Father, full of grace and truth” (John 1:14).

Theologians have observed that the direct correlation between the Word’s incarnation as Jesus

does not negate the fact that he was fully human (Charlesworth & Pruszinski, 2019; Crisp, 2016).

“Although present in a transient manner in the Old Testament prophets, the Logos becomes

incarnate in Christ, and thus provides a point of mediation between God and humanity”

(McGrath, 2017, p. 14). Christ represents the perfect and final representation of God’s Word to

mankind, perfectly fulfilling all previous revelations (Cockerill, 2012; Crisp, 2016).

Theological Framework Summary

At the beginning of this section, the researcher highlighted the interconnected nature of

Scripture and life for believers. This co-joining was seen to be mandated in Scripture in the Great

Commission as well as in Moses’ charge to parents to teach their children while engaging in the

routine of life. The review outlined the core challenges to studying Scripture as being in the areas

of comprehension and apathy. Further, despite the Bible being a dated text, this review

demonstrated that its precepts remain useful and relevant in modern times. Last, the various

methods God has used to relay His Word to mankind were surveyed, culminating in the

incarnation of His Son.

Theoretical Framework for the Study

Since the term “information technology” was coined in a 1958 Harvard Business Review

article (Leavitt & Whisler, 1958, p. 11), researchers have been on a quest to understand its

impact on users. In particular, one area of investigation has focused on user acceptance or

adoption rates. If organizations could accurately predict the adoption rate of various information

technology products prior to investing millions of dollars in development and promotions,

significant resources could be saved. On the front end, if organizations were able to design

products in such a way as to increase the likelihood of acceptance, they could avoid countless

failed IT projects. The theoretical framework chosen to guide this study was UTAUT (Venkatesh

et al., 2003).

The purpose of this descriptive study was to understand whether a relationship exists

between an individual's behavioral intention to use a Bible-based chatbot that leverages AI to

create human-like engagement with Scripture and the constructs of performance expectancy,

effort expectancy, perceived enjoyment, and perceived risk, controlling for gender, age, and

experience among registered users of the Inductive Bible Study App. As the next generation of

evangelical Christian millennials engages less with the Bible (Cooper et al., 2021), this research

has uncovered an opportunity to capitalize on technology that is proven to have a significant

impact on the behavior of individuals in the marketplace (De Bruyn et al., 2020; Kumar et al.,

2019). In an effort to validate the theoretical framework undergirding this research, the origins,

applications, extensions, and criticism of UTAUT will be explored.

Origins

When the research team began to develop the UTAUT framework, several models

already existed that sought to reliably predict the acceptance rate of new IT systems (Venkatesh

et al., 2003). The most prominent of these were: Theory of Reasoned Action, Technology

Acceptance Model (TAM), the Motivational Model, the Theory of Planned Behavior (TPB), a

model combining the TAM and the TPB, the model of PC Utilization, the Innovation Diffusion

Theory, and Social Cognitive Theory. With so many competing theories, each with unique

acceptance determinants and a wide range of variance, the UTAUT research team sought to

establish a unified view that would deliver more dependable predictions (Venkatesh et al., 2003).

In comparison to the eight models that were examined, which predicted user acceptance

rates between 17% and 53%, UTAUT explained 70% of the variance in user acceptance of

information technology solutions (Tamilmani et al., 2021). Venkatesh et al. (2003) note,

UTAUT thus provides a useful tool for managers needing to assess the likelihood of

success for new technology introductions and helps them understand the drivers of

acceptance in order to proactively design interventions (including training, marketing,

etc.) targeted at populations of users that may be less inclined to adopt and use new

systems (p. 426).

Hence, UTAUT was deemed the most appropriate model for the present research to

consider the probability of acceptance of an app that offers biblical literature in a way that

leverages artificial intelligence.

The UTAUT team was able to successfully narrow down the vast list of determinants of

system usage to the following four constructs, which play a vital role in use behavior –

performance expectancy, effort expectancy, social influence, and facilitating conditions

(Venkatesh et al., 2003). In the following paragraphs, each of these determinants will be

explored.

Performance Expectancy

One of the first key determinants of use behavior revolves around the user’s perceived

benefit from the product. Performance expectancy is defined as “the degree to which using a

technology will provide benefits to consumers in performing certain activities” (Venkatesh et al.,

2012, p. 159). In essence, if an individual believes that a new system will augment their ability to

accomplish a goal, whether in work or leisure, their probability of usage correlates with the

anticipated gains in efficiency that are perceived by the prospective user (Abbad, 2021;

Marangunić & Granić, 2015; Safi et al., 2018).

Effort Expectancy

Next, prospective adopters of a particular technology are concerned with how much

effort will be required to leverage a system’s benefits. Effort expectancy is defined as “the

degree of ease associated with consumers' use of technology” (Venkatesh et al., 2012, p. 159).

For instance, if users were required to submit to an hour-long authentication process prior to

accessing the internet, early adoption of the internet would have been hampered by effort

expectancy. In order to achieve optimal levels of behavioral intention, according to UTAUT,

newly introduced systems must be perceived as easy-to-use or user-friendly (Miguel Cruz et al.,

2022; Tamilmani et al., 2021).

Perceived Enjoyment

Perceived enjoyment is one of the factors included in the UTAUT framework. This

construct refers to an individual's evaluation of the pleasure or enjoyment that they expect to

experience while using a specific technology (Venkatesh et al., 2003). According to the UTAUT

model, perceived enjoyment is believed to have a positive impact on an individual's behavioral

intention to use a technology, as well as on their actual use behavior (Abbad, 2021;

Nikolopoulou et al., 2021; Venkatesh et al., 2016). This means that if an individual perceives that

using a technology will be enjoyable, they are more likely to intend to use it and to actually use

it. Perceived enjoyment can be influenced by various factors such as design, usability, and the

type of task being performed. A well-designed, user-friendly technology that is well-suited to the

task at hand is more likely to be perceived as enjoyable to use.

Perceived Risk

Perceived risk is a construct included in the UTAUT framework. This construct refers to

an individual's assessment of the potential negative consequences associated with using a

specific technology (A. Bere, 2014; Habib & Hamadneh, 2021). According to the UTAUT

model, perceived risk is believed to have a negative impact on an individual's behavioral

intention to use a technology, as well as on their actual use behavior (S. Hu et al., 2020; Kamboj

& Joshi, 2021). This means that if an individual perceives that using a technology will expose

them to significant risks or negative consequences, they are less likely to intend to use it and less

likely to actually use it.

Perceived risk is a crucial aspect to consider in the context of technology adoption and

usage. It encompasses factors such as uncertainty, trust, privacy concerns, financial risks, and

safety risks (Klaver et al., 2021; Marangunić & Granić, 2015). When individuals perceive higher

levels of risk associated with using a technology, it can have a deterrent effect on their

behavioral intention to use it and their actual usage behavior. Uncertainty about the technology,

lack of trust in the company or platform, and concerns about privacy and data security can

contribute to a higher perceived risk. Therefore, understanding and addressing perceived risk is

important in the design and implementation of technology-based interventions, such as the Bible-

based chatbot in this study, to mitigate concerns and increase user acceptance and engagement.

Application

The above review has revealed the necessity of a unified theory for understanding the

likelihood of acceptance and use of technology. In the following sections, a review of related

applications of UTAUT will be explored. While there have been numerous studies of systems

that leverage the constructs of the UTAUT to estimate usage or adoption rates, the review of

applications presented in this study will be limited to mobile apps. These digital online systems

are the best match to the system evaluated in this study. UTAUT studies were thus considered

that explore shopping, physical activity, and travel-based apps.

Shopping

When seeking to uncover the preexisting conditions that lead users to app-based shopping

in India, the constructs of UTAUT were leveraged to survey 350 mobile app shoppers in Delhi

(Tak & Panwar, 2017). Data from the study provided conclusive evidence that hedonic and

habitual behaviors were strong indicators of user adoption of mobile apps for shopping. These

findings correspond to the prior work of Liao et al. (2006) and Venkatesh et al. (2012). The

hedonic motivation was added to the list of motivators in the UTAUT 2 extension, in which

intrinsic motivators were considered valid factors (Tamilmani et al., 2021). Hedonic motivation

is “the fun or pleasure derived from using technology and it is an important determinant of

consumer’s technology acceptance and use” (Tamilmani et al., 2019).

During the coronavirus pandemic, Zhao and Bacao (2020) leveraged UTAUT to explore

the determining factors of individuals using food delivery apps. This quantitative study

investigated 532 users of food delivery apps and found satisfaction to be the most significant

underlying factor in the continual usage of apps used by individuals who are shopping for food.

Even though UTAUT was the primary framework used in this study, Expectancy Confirmation

Model (Bhattacherjee, 2001) and Task-Technology Fit Model (Goodhue & Thompson, 1995)

were used as well.

Physical Activity

In 2019, JMIR mHealth and uHealth published the findings of a study that used UTAUT

to investigate students’ intention to use physical activity apps (Liu et al., 2019). After conducting

a cross-sectional study of 1,704 university students in Guangzhou, China, researchers found that

4 out of 10 (41.8%) of the participants intended to use physical activity apps. UTAUT constructs

allowed researchers to reveal that performance expectancy, effort expectancy, and social

influence were among the leading factors influencing app usage. These findings will ultimately

aid in attempts to reverse the declining trends in physical exercise among university students in

China.

Researchers continue to use UTAUT to understand user intentions and behavior toward

apps, but many augment the UTAUT to suit their particular needs. When seeking to understand

the motivational factors that influence the continual usage of mobile sports apps, researchers

established a modified version of UTAUT to study 267 collect students (S. Lee et al., 2017). As

a result, they were able to identify effort expectancy as one of the core UTAUT constructs that

contributed to sports app usage. Based on their extension of the theory, it was found that social

utility and entertainment were significant factors as well.

Travel

Research involving the apps used in the travel industry has uncovered issues that relate to

technology adoption (Ahmad et al., 2021; Rasheed Gaber & Elsamadicy, 2021). Fong et al.

(2017) assert, “if users perceive that mobile apps offer superior performance to alternatives (e.g.,

websites), are easy to use, align with the opinions of other important people, and are equipped

with sufficient support, then they will reuse them” (p. 339). As this research provides

recommendations founded in data, the industry as a whole is better able to meet the needs of its

consumers. Likewise, the current study has leveraged insights on user adoption to develop ways

of engaging with Scripture.

Extension

As alluded to in the various applications of UTAUT, many researchers are building on its

solid framework in order to extend its usage deep into particular industries or areas of interest. It

is not necessary to develop a new theory when subtle nuances will add needed clarity. It is

through these extensions that the theoretical framework can be further refined in order to achieve

optimal levels of accuracy. Considerations of such extensions into the areas of learning,

purchasing, and services are explored next.

Learning

When considering the influence of learning value on learning management system use,

researchers extended UTAUT (Ain et al., 2016; Kari & Kosa, 2023). By replacing the UTAUT2

construct of price value with learning value, researchers were able to obtain relevant insights

about predictors of student intentions toward the use of learning management systems. Learning

value is described as “the students’ positive perceptions about learning from LMS” (Ain et al.,

2016, p. 1311).

The question of whether or not eReaders will eventually replace print books has plagued

brick and mortar retailers. When studying behavioral patterns related to the choice of eReaders

over print books, Yoo & Roh (2016) used UTAUT in combination with cognitive judgmental

processes to uncover valuable insights. The following attributes were added based on the context

of their research: job relevance, result demonstrability, and output quality. As a result, the data

was able to demonstrate the significant influence that output quality has on job relevance and

performance expectancy (original construct).

For years, visually impaired individuals have used canes and guide dogs to maintain a

level of independence in society. Technology was proposed that would enable visually impaired

individuals to learn to navigate unfamiliar environments (Chaudary et al., 2017). The approach

would involve the visually impaired individual being guided by a remote person via a video

transmission. In order to study the perceived acceptance rate adoption, blind individuals were

studied using a modified version of UTAUT which included attitude and anxiety as additional

attributes (Chaudary et al., 2017)

Purchasing

As the world changes due to global warming and climate change, some consumers are

seeking to transition to electric vehicles. Researchers have therefore leveraged UTAUT to

determine consumer purchase intentions for electric vehicles (Degirmenci & Breitner, 2017).

Based on the subject of study, the original constructs were augmented to include attitude toward

electric vehicles, environmental performance, range confidence, and profession. Degirmenci &

Breitner (2017) concluded, “environmental performance of EVs was found to be a stronger

predictor than price value and range confidence” (p. 256). As a result, marketing teams would be

wise to focus on the environmental performance aspects of new products in order to increase the

likelihood of success in the marketplace.

The capabilities of smartphones continue to expand into every aspect of human existence

(Tak & Panwar, 2017). As retailers make advances toward the elimination of cash payment

options, mobile phones are being used to achieve proximity-based payment options. Here again,

UTAUT was used to study user perceptions and intentions regarding this new technology. In

order to supplement the base theoretical framework, researchers added the following constructs:

privacy and financial risks, product involvement, and purchase decision involvement (Tak &

Panwar, 2017). The insights unveiled in this research assist retailers as they develop strategies to

promote this new alternative payment method. A similar study seeking to address the relatively

low number of mobile devices that are used for online purchases added the following constructs

to UTAUT: perceived risk, trust, mobile affinity, innovativeness (Marriott & Williams, 2016).

Services

Despite the stringent security requirements of financial institutions, consumers are

demanding that more of their services be available online, accessible via their mobile apps.

Researchers used a version of UTAUT that added brand name as a construct to assess the

importance of branding in consumer adoption of mobile banking services (Mahfuz et al., 2016).

The study found that the bank's brand name had the most significant impact on behavioral

intention.

In years past, there were only a few prominent players in the social media arena. As more

social networking services are offered by different organizations, research became necessary in

order to examine how “users’ intentions to share their experiences with products and brands are

formed through these applications” (Herrero et al., 2017, p. 209). A modified version of

UTAUT, in which privacy concerns were added as a construct, provided the theoretical

framework to classify data from 537 tourists. The results of the study showed that privacy

concerns did not have a significant influence on individuals’ intent to share content on social

media (Herrero et al., 2017).

The instance of an extension of UTAUT that most relates to the study in question is one

that used the Consumer Acceptance and Use of Information Technology theory (Venkatesh et

al., 2012). This modification was created to address consumer behavioral intent toward using

technology. In research using that theory, e-Government services that offered web

personalization via recommender systems were examined to determine their ability to influence

user behavior (Krishnaraju et al., 2016). Web personalization was added as a modifying

construct. Krishnaraju et al. (2016) concluded, “our analyses show that personalizing the Web by

self-reference and content relevance has a significant moderator role in influencing the

relationship between determinants of intention to use and behavioral intention in certain cases”

(p. 579). Similarly, in the present study, artificial intelligence algorithms supported Scripture-

reading recommendations based on interfacing with the user.

Criticism

Due to its extensible nature, few studies have found evidence to critique UTAUT. One

such study sought to identify the drivers for adopting blockchain technologies (Li, 2020).

Beginning with one of UTAUT’s root theories, the Technology Acceptance Model (TAM), Li

(2020) argued that its predictive accuracy was insufficient to warrant its use. When considering

UTAUT, the R

was considerably higher, but Li asserts that the “excessive number of variables

would also make UTAUT impractical for real-life research” (p. 253). In addition, a study

involving UTAUT in health information technology found that the base theory determinants

explained a marginal variance in eHealth literacy (Magsamen-Conrad et al., 2020). As a result,

they advise scholars to consider using variables that were previously excluded, such as attitude.

Theoretical Framework Summary

A robust theoretical framework plays a vital role in establishing the credibility and

foundation of research endeavors (Galvan & Galvan, 2017). The preceding section of the

literature review delved into a comprehensive exploration of the UTAUT framework,

encompassing its historical origins, contemporary applications, extensions, and critical

assessments. By surveying the UTAUT framework, a comprehensive understanding of its

conceptual underpinnings and relevance in examining the factors influencing the acceptance and

utilization of technology have been provided.

Related Literature

The theological framework adopted in this study serves as a guiding foundation, ensuring

a biblical perspective is integrated into the research. Complementing this, the theoretical

framework incorporates established constructs to provide a structured framework for organizing

the study. The literature review explored a range of relevant topics including biblical

engagement, digital religion, resistance to innovation, and persuasive technology. By delving

into the existing literature, this section has expanded the current knowledge base and tapped into

the wealth of scholarly work available on these subjects. The review critically examined the

current understanding of these topics and their relevance to the objectives of this study,

contributing to a comprehensive exploration of the research area.

Bible Engagement

Many definitions of Bible engagement exist, yet none is universally adopted (Collins,

2014). Fretwell (2016) provides the most concise summary of Bible engagement, which

includes receiving, reflecting, and responding to God’s Word. Fretwell includes both reading and

listening to Scripture under the umbrella of receiving. He views reflecting as one’s own

pondering on the meaning of the words. Pondering may involve conducting in-depth word study,

consulting commentaries, or simply listening to what the Holy Spirit has to say on a given

passage (Gorichanaz, 2016). Afterward, all viable forms of biblical engagement must challenge

the recipient to respond in accordance with God’s revelation (Fuhr & Köstenberger, 2016).

Essential for Spiritual Formation

Everyone, from the babe in Christ to the veteran saint, must be in a perpetual quest to

grow in their faith (Howard, 2018). The main objective of spiritual formation, Copan (2016)

argues, “has to be knowing and loving God in Jesus as deeply and intimately as possible and

bringing all of our life into alignment with his life” (p. 38). One of the fundamental steps towards

spiritual growth is to engage with God’s word found in Scripture (Lu, 2021). Through scriptural

engagement, God’s servants receive the training necessary for “every good work” (2 Tim. 3:17).

In contrast, without the intense study of scripture, one would essentially be lacking the necessary

training to carry out God’s bidding.

For nearly a decade, the Center for Bible Engagement studied the spiritual formation of

over 100,000 individuals around the world (Cole & Ovwigho, 2012). Their study found that

individuals who engage the Bible four or more times per week experience more victory in

overcoming temptations such as alcoholism, lust, anger, gossip, and lying. In addition to its

effect on external behaviors, negative internal beliefs such as bitterness and a lack of forgiveness

were found significantly less in individuals who regularly engaged with Scripture. Cole &

Ovwigho (2012) advocate, “engaging the Bible most days of the week is critical to grow in the

Christian faith” (p. 6). The data from their research is reflective of individuals aged eight to more

than 80 with samples taken from over 20 countries.

State of the Bible

The most comprehensive research regarding the state of the Bible is the result of a

partnership between Barna and the American Bible Society (Cooper et al., 2021). Their seminal

study titled State of the Bible analyzes vast datasets on how Americans perceive and engage with

Scripture. Since the early stages of their research, they have noticed a growing skepticism toward

the Bible by adults. The research team landed on the following terms to categorize scripture

engagement: “Scripture engaged,” “Movable middle,” and “Bible disengaged” (Cooper et al.,

2021, p. xi).

Table 1

U.S. Scripture Engagement by Year (in Millions)

Behavior Profile

2018

2019

2020

2021

Bible Disengaged

136

120

119

100

Movable Middle

Scripture Engaged

Scripture engaged represents a combination of their earlier groupings, “Bible Centered”

and “Bible Engaged,” which describe individuals who interact with the Bible ranging from

regularly to frequently (Fulks & Plake, 2020). The newly formed “Movable Middle” is a

combination of the prior categories, “Bible Friendly” and “Bible Neutral,” which describe

individuals who periodically read their Bibles between once per week and once per month. Last,

“Bible Disengaged” is defined as “people [who] interact with the Bible infrequently, if at all, and

it has a minimal impact on their lives” (Fulks & Plake, 2020, p. 32). Further, their research

uncovered an age demographic gap in relation to the Bible.

Table 2

“Not a Bible User” by Generation

Generation

Proportion

Gen Z (18-20)

37.7%

Millennial (21-35)

39.3%

Gen-Xer (35-54)

45.3%

Boomer (36-54)

49.1%

Elder (74+)

54.4%

Cooper et al. (2021) found that in terms of Bible-reading frequency by generation,

individuals describing themselves as “Not a Bible User” ranged from 37.7% among 18-20-year-

olds to a staggering 54.4% among 74+-year-olds. Based on these numbers, one can clearly see

that a large percentage of the population has lost or never possessed an interest in the Bible. Just

as Jesus’ parable of the lost coin portrays God’s love for individuals (Luke 15:8-10), so believers

must be concerned about the millions of individuals who are “Bible Disengaged.” Even though

the Barna and American Bible Society research team have sought to render valid data, the

researcher must note that the findings are from self-attested surveys (Cooper et al., 2021). It

would otherwise be quite difficult to conduct observations to determine the exact level of

engagement a large sample of individuals has with the Bible over the course of a month.

Digital Millennials

One glimmer of hope uncovered in the State of the Bible research is the fact that

“Scripture engagement is significantly higher on average for Millennials than for any other

generation” (Fulks & Plake, 2020, p. 42). Unfortunately, Stanton (2017) contends, “while there

are, no doubt, numerous exceptions, young people across the Western world display low levels

of Bible engagement and biblical literacy” (p. 34). Hamidović et al. (2019) point out that

relatively few Millennials engaged with Scripture directly, arguing that their most common form

of Bible interaction is through a mediator. When speaking of a mediator, Hamidović is referring

to indirect Biblical engagement through preaching, teaching, social media, podcasts, blog posts,

etc.

The popularity of digital Bibles (52%) among Millennials narrowly beat their preference

for print (48%) (Cooper et al., 2021, p. 45). Younger generations prefer the ability to read on the

go or, better yet, easily integrate their time with the Bible into their already busy schedules

(Ford, 2019a). Despite increased ease of use, Ford’s (2019a) research revealed a growing

indifference toward the Bible by digital Millennials. Six out of 10 (62%) identified as having no

relationship with the Bible or rarely engaging with it. Even when presented on the never-ending

scroll feeds of social media, over half (53%) of the participants acknowledged that they chose to

ignore Biblical passages.

Digital Religion

After examining the significance and current state of Bible engagement, the literature

review now focuses on the migration of religious practices to digital realms. This section

explores various aspects, such as the emergence of religious apps, the digitization of sacred texts,

virtual religious practices, and the consequential transformations in religious authority. By

delving into these topics, the aim is to investigate the existing landscape of digital religion and its

implications for religious institutions. This review is directly aligned with the overarching

objective of the present study, as it seeks to explore the extent to which technology can be

harnessed to facilitate and enhance Bible engagement. By considering the advancements and

challenges within digital religion, this section contributes to a comprehensive understanding of

the potential boundaries and opportunities for leveraging technology in support of increased

engagement with the Bible.

Religious Apps

Rinker et al (2016) define apps as “computer programs that are either free or available for

purchase from places like the Apple iTunes store” (p. 2). A religious app is an app designed for

the purpose of enabling faith adherents to engage in practices that promote spiritual growth

(Bellar, 2017). As businesses leverage the robust benefits of technological advancements,

religious institutions are growing open to the use of religious apps to connect in meaningful ways

with congregants (Arthur & Rensleigh, 2015). In 2019, venture funding for religious apps

exceeded $18 million (Crain’s Chicago Business, 2021).

The range of digital affordances offered by religious apps is continually expanding to

support the needs of religious communities. For instance, the Tithe.ly (2021) app helps ministries

by providing a secure platform for online giving. Based on a firm belief in the power of prayer,

Echo Prayer’s (2021) app enables users to deepen their prayer experience while facilitating

community through online prayer groups. Abide (2021), a global startup company/app

comprised of former Google employees, leverages its religious app to fulfill its mission of

enabling “the world to experience the peace of Christ through Biblical meditation”.

Digitized Sacred Text

The cornerstone of many religions is sacred texts that establish the foundation of their

belief system. For example, Christians turn to the Bible as their source of holy writ. Meanwhile,

Muslims adhere to the teachings of the Quran and the Hadiths, Hindus honor the Vedas and the

Upanishads, and Buddhists revere the Tipitaka. With the advent of technologies such as the

internet and mobile devices, religious institutions are leveraging these platforms as a means to

easily distribute their sacred texts to adherents (Hutchings, 2014). Followers are appreciative of

the ability to access their religious materials on the go (Bellar, 2017; Hutchings, 2014).

In a recent comparative study of 100 iTunes mobile applications that host the sacred texts

from five major world religions, Tsuria et al. (2021) found that the sacred texts could be placed

in one of the following categories: transferred, mediated, or transformed (p. 19). Their study

classified apps that simply presented the sacred text as is, without any noteworthy enhancements,

as transferred. The second category, mediated, is similar to transferred with the only difference

being the inclusion of a few digital affordances such as audio options. The last, transformed,

refers to apps that take more advantage of technological advancements to enhance the user

experience (Tsuria et al., 2021, p. 75). YouVersion (2013) is an example of an app that

transformed a sacred text using technology with features such as lesson plans, in-app sharing,

verse-image creation, etc.

Further, the study uncovered that the transforming strategy was the least used by app

developers, speculating, “perhaps because religious developers and users prefer apps that remind

them of their offline engagement with sacred texts” (Tsuria et al., 2021, p. 74). The research

team suggests further research be conducted exploring the reasoning behind the limited use of

the transforming app design strategy. They hypothesized, “app developers believed that the

translation of the text forms a stronger link between the online and offline text than the

transformation of the text, which alters both the online and potentially the offline understanding

of the text” (Tsuria et al., 2021, p. 75). This is because translation (or transferring) preserves the

original meaning and context of the text, whereas transformation can change the intent and

interpretation of the text. One of the proposed fears of the transformation of a sacred text is the

fear that doing so would result in profanation (Rinker et al., 2016; Tsuria et al., 2021).

Virtual Religious Practices

When the term “ecclesiology” was first introduced, its primary focus was on church

architecture rather than Christian principles (Kärkkäinen, 2021). In light of recent paradigm

shifts in culture prompted by the need to facilitate global communication, teams and

communities are figuring out ways to effectively connect virtually in meaningful ways

(Afflerbach, 2020). These adjustments are challenging pastors to reconsider the meaning of the

anchor text, “not forsaking the assembling of ourselves together” (Heb. 10:25 KJV), often used

to admonish members not to neglect regular offline church attendance (H. A. Campbell &

DeLashmutt, 2014). The Economist (2020) argues that virtual worship experiences date back as

early as the 1920s, when American preachers began to leverage the radio for televangelism.

In his book, Creating Church Online, Hutchings (2017a) explores the rituals, community,

and various forms of media employed to facilitate online worship experiences. Beginning with a

survey of the history of the online church, Hutchings traces the launch of the first online church

back to 1985. Hutchings records that the first online church’s founders sought to create a worship

environment that was free from distractions, hoping to allow genuine worship “in spirit and

truth” (John 4:24). Hutchings observed that all online churches included in his study facilitated

community, belonging, and prayer in one form or another (Hutchings, 2017a, p. 3). Included

among the case studies referenced in the book is Life.Church, which boasts over 100,000 weekly

participants (Hutchings, 2017a, p. 252).

Even before the world was forced to grapple with the far-reaching effects of a global

pandemic, religious institutions were turning to digital technologies to extend their reach into the

lives of believers (Arthur & Rensleigh, 2015; H. A. Campbell & DeLashmutt, 2014; Donaldson,

2009). During the health crisis of 2020/21, many ministries suspended physical gatherings in

order to reduce the spread of disease (Camarines & Camarines, 2021). Churches that were not

prepared, delayed, or were apprehensive toward making the abrupt shift from offline to online

religious environments struggled to meet the spiritual needs of congregants during a stressful

season of life (Camarines & Camarines, 2021).

Religious Authority

Technology has brought about the emergence of digital leaders, challenging the ranks of

traditional religious elites (Abdel-Fadil, 2017; McCully, 2014). These influential digital leaders

have gained prominence in shaping religious beliefs and practices (Zaki, 2017). With increased

access to information, individuals now have the ability to customize and personalize their

spirituality according to their own needs and preferences (McClure, 2016). This shift has

compelled religious elites to adapt in order to maintain their authority and relevance in the digital

age (Grusendorf, 2018).

In online churches, visitors may encounter “new rituals, new kinds of community, new

styles of leadership and new challenges to old structures of authority” (Hutchings, 2017a, p. 4).

Proponents of digitized religion are leery of religious authority shifting from scholars and

conventional publishers to “informal networks, celebrity pastors, and those with a reason and

means to push their own personal translation“ (Olsen, 2014). There is a valid concern that the

established, centralized religious governance structures of the past are being replaced with a

decentralized model with blurred lines of credibility when it comes to authentic religious leaders

(Hutchings, 2014; Rajan & Kundu, 2019).

While there are many instances of religious authority being undermined by digital

religious practices, an India-based study provided a case study on how religious organizations

can jump ahead of the curve and prevent rogue online leadership from taking the reins (Rajan &

Kundu, 2019). In their study of the Assemblies of God Fellowship (AGF), a youth-focused

fellowship based in Ahmedabad, Gujarat, they found that the ministry was able to circumvent

shifts in religious authority by embracing technology and using everything from various social

media platforms to streaming content to stay fully engaged with congregants. In response to their

unique strategy, the article observed, “the offline authority remains intact despite the increasing

emphasis on building an online presence of the church. Its structure of authority in the virtual

world is similar to and dependent on how it functions in the physical world” (Rajan & Kundu,

2019, pp. 67–68). In this instance, digital religion has helped to validate AGF’s religious

authority through the use of online shared content.

Innovation Resistance

The preceding section highlighted the profound influence of digitization on various

aspects of society, including sacred religious domains. While religious institutions strive to adapt

and provide digitized offerings to cater to their communities, the adoption of innovation is often

met with resistance (Kaminski, 2011). This section focuses on exploring the impact of

innovation resistance by examining key concepts such as the diffusion of innovation theory, the

adoption of e-books and digital Bibles, and consumer decision-making patterns. By surveying

these areas, the aim is to shed light on the factors and dynamics that contribute to resistance

toward embracing innovation within the religious context. Understanding these dynamics is

crucial for the present study, as it investigates the adoption and utilization of a Bible-based

chatbot, aiming to identify potential barriers and devise strategies to mitigate resistance and

enhance acceptance.

Diffusion of Innovations Theory

Just because an advancement in technology addresses a known problem in society, it

cannot be assumed that everyone will embrace it and leverage its benefits (Weil, 2018).

American sociologist Everette Rogers (1995) developed the Diffusion of Innovations Theory to

explain this phenomenon. Rogers' research uncovered that individuals in society fall into one of

five groups based on the time it takes them to adopt an innovation (Crespin-Mazet et al., 2021).

His research aided in the successful launch of new innovations by informing organizations of the

necessity of developing unique marketing to target each group differently (Bianchi et al., 2017).

Figure 1

Diffusion Curve

Note. This figure illustrates the relationship between types of adopters classified by innovativeness and their location

on the adoption curve. From Diffusion of Innovations (5th ed., p. 281), by E. M. Rogers, 2003 by Free Press.

Innovators are the first group to learn about and adopt a particular innovation (Ali et al.,

2019). These risk-takers easily embrace change and like being at the forefront of new

technological breakthroughs. Innovators serve as influencers to the larger community because

they are able to share their first-hand experience of using a product or service (Pashaeypoor et

al., 2016). Early adopters follow the innovators, not wanting to be first in line but eager to accept

some risk when it comes to embracing new innovations. The key contribution of the early

adopters is to help build up enough momentum to shift an innovation from being a fad to one that

is embraced by the majority (Weil, 2018).

The majority groups are broken into two subsections – early majority and late majority.

Combined, these groups represent over 60% of society (Mohammadi et al., 2018). The early

majority are observant of new innovations and will monitor their progress and user adoption until

they are convinced that it will add genuine benefit to their lives. The late majority are a bit more

resistant to change, waiting patiently for the results of product testing to ensure that it is proven

safe before embarking on the uncertainty of trying something new (Min et al., 2019). Individuals

in the late majority are influenced by a combination of real user experiences and qualitative test

results (Bianchi et al., 2017; E. Rogers, 1995).

Finally, the last group to adopt a new innovation are the Laggards (Crespin-Mazet et al.,

2021). Laggards are the most resistant to change and the most challenging to influence through

traditional marketing channels. Laggards either wait until an innovation is nearly unavoidable

prior to adoption (Pashaeypoor et al., 2016), or else remain steadfast in their conviction to reject

an innovation despite widespread adoption in society. As a result, one should not expect 100%

adoption of any new innovation, no matter how profound the benefit. Also, in order to achieve

mainstream adoption of technology, marketers must use messaging and platforms that speak to

each of the adoption groups (Pashaeypoor et al., 2016; E. Rogers, 1995).

In Print vs. Digital Reading

Hong (2020) observes, “the book publishing industry is rapidly increasing the availability

of digitized content from printed sources due to the popular use of smartphones, tablets, PCs, and

e-book devices” (p. 24). Despite their heavy investment into the digitization of bodies of work,

some e-book businesses are finding that customers remain resistant to the transition from

hardcopy to digital versions of their favorite texts. Milliot (2016) associates this phenomenon

with what he describes as “digital fatigue” (p. 4). While it may be assumed that “digital natives”

prefer reading texts on digital platforms, research suggests that educators must make both print

and digital media available in order to support optimal adolescent reading engagement. Digital

natives are defined as individuals who are raised during an era of digital technology, and as a

result are inculcated with the use of technology from an early age (Kirschner & De Bruyckere,

2017).

Reading is defined as the “meaningful decoding and comprehension of text” (Loh & Sun,

2019, p. 663) regardless of the format in which the material is presented. Reading in print refers

to the act of a reader possessing a physical hard-copy material, usually paper, to consume

information. Digital reading refers to reading that is performed on a vast array of different

devices such as online, laptop, tablet, or smartphone (Yoo & Roh, 2016). When studying

adolescent preferences regarding reading in-print versus digital reading, the data revealed that

the content being read had a direct impact on the individual's format preference (Loh & Sun,

2019).

The nature of the current research project is related to digital Bibles. A study of 173

randomly selected Koreans found that subjective norms regarding religious practices have a

positive influence on innovation resistance for embracing digital Bible resources (Hong, 2020).

Therefore, if individuals whom one considers influential, whether in their home or who maintain

religious authority, embrace or reject the use of digital Bibles, then this will have a direct

influence on the individual’s acceptance or rejection of digital Bibles (Copan, 2016). Other

influential factors at work are perceived usefulness and perceived ease of use (Hutchings,

2017b).

Consumer Decision Making

There are some innovations that are so counter-cultural that if individuals were merely

told their features ahead of time, they would be quickly dismissed as infeasible. Such is the case

with the prediction of ride-sharing platforms such as Uber and Lyft. When researching the

factors that influence consumer adoption of the Uber mobile application, Min et al. (2019)

identified that relative advantage, compatibility, observability, and social influence had a direct,

positive impact on consumers' adoption of the mobile application.

Relative advantage speaks to the perceived convenience and gains that individuals

believe they will experience as a result of adopting a particular technology (Lin & Chen, 2012).

Compatibility refers to the innovation's alignment with the prospective user's values and moral

code (Kaminski, 2011). Observability speaks to the ability to monitor the innovation’s impact on

society as others leverage its perceived benefits (E. M. Rogers, 2003). Last, social influence is

identified by the extent to which a group of individuals exercises influence over another person’s

behavior (Kelman, 1958).

Persuasive Technology

Having explored the various factors contributing to resistance toward technological

innovations, the focus of this literature review now turns to examining the persuasive nature of

certain technological innovations. Persuasion itself is a neutral concept, with its moral

implications determined by the intent behind it (Falk & Scholz, 2018). Some technologies are

specifically designed to influence user behavior through persuasive techniques (Spahn, 2012).

For instance, fitness devices like FitBit employ move alerts to encourage sedentary individuals to

engage in physical activity (Szabo-Reed et al., 2017). In this section, the review will delve into

the realm of artificial intelligence and personalized messaging to explore their roles within the

context of persuasive technology. By examining these areas, the aim is to gain insights into how

these technologies leverage persuasion and how they can potentially enhance engagement and

behavior change, contributing to a comprehensive understanding of the subject matter.

Artificial Intelligence

When computers are endowed with programming that allows them to perform operations

or make decisions in a manner such as humans would otherwise perform them, they are said to

be leveraging artificial intelligence (AI) (Kumar et al., 2019). Complex algorithms and

mathematical functions are generally the building blocks of artificial intelligence (De Bruyn et

al., 2020). Artificial intelligence-enabled technology is, in some cases unknowingly, being used

in everything from finance, social media, gaming, security, etc. (Healey, 2020). The term

artificial intelligence is applied universally to a variety of levels, but a deeper dive into the

various dimensions of AI will help provide clarity regarding their nuances.

Generalized learning, reasoning, and problem-solving are the essential ingredients of

determining whether or not a technology possesses artificial intelligence (Astorino et al., 2019).

Generalized learning refers to the object's ability to adapt to new, previously inexperienced

obstacles (Brezina, 2020). Reasoning is measured by the object’s ability to arrive at a logical

conclusion or make a reasonable prediction based on its currently known information or data

(Astorino et al., 2019; Brezina, 2020). Problem-solving is the extent to which the device can

leverage algorithms, heuristics, or conduct root-cause analysis to derive an optimal solution

(Brezina, 2020).

Most AI implementations are classified into one of the two the following categories:

weak/narrow AI or strong AI (Astorino et al., 2019). Narrow AI is labeled based on its scope of

capability (Brezina, 2020; Healey, 2020). For instance, a system with weak/narrow AI may be

able to route callers to the proper personnel or voicemail box based on the input of a series of

prompts. But the same device could not be used to diagnose an illness or to determine how to

address a customer support issue. The narrow AI-based system can be used to reliably and

efficiently route calls.

At the moment, examples of strong AI are primarily found in the pages of science fiction

(Szollosy, 2017). The movie Terminator depicts a strong AI machine that is sent to prevent a

cyborg assassin from executing the future mother of humanity's greatest leader. Self-awareness is

a key advantage that strong AI machines have over weak ones (Astorino et al., 2019; Szollosy,

2017). Unfortunately, their self-awareness renders them unpredictable. This is the point at which

strong AI machines, in science fiction, choose whether or not humans are dispensable (Szollosy,

2017).

The present research has explored the potential of AI to help both believers and non-

believers better understand the Word of God and, through this comprehension, draw them nearer

to God. Similar to how narrow AI facilitates meaningful connections between organizations and

their clients, AI has the potential to enhance comprehension of the Bible. Another aspect to

consider is whether individuals within the faith community will be hesitant to adopt such a tool

due to preconceived notions about the role of technology in relation to sacred texts. While

examining literature on this topic, the researcher in this study used an AI-based music app,

Pandora, which curates music based on user's preferences.

Personalized Messaging

Personalized messaging refers to the practice of creating specific messages that are

directed toward individuals with the aim of influencing their behavior. In the context of

marketing, this could involve crafting messages that are tailored to convince customers to

repeatedly purchase a company's products. The goal is to create a message that speaks directly to

the individual and persuades them to act in a certain way (Malins et al., 2020). In order to

personalize a message, individuals must first know something about their chosen demographic.

In the current era of social media, one does not have to leap through great hoops to learn about

prospective clients, as they voluntarily provide more than enough information publicly on which

to build a profile (Krishnaraju et al., 2016). According to Barth and DeJong (2017), the privacy

paradox refers to the phenomenon wherein people do not behave logically when it comes to

personal information. For instance, on social media, some individuals have been found to

regularly share the intimate details of their lives with complete strangers, while keeping

important secrets from those who are closest to them.

It takes very little information to build a reliable personality profile that could be used to

predict behavior. Graves & Matz (2018) found that evaluating 65 liked Pages on Facebook could

help understand someone’s personality better than their friends. Further, by examining 120

Pages, one could understand the individual better than their family members. After examining

250 liked pages, algorithms have the capacity to understand an individual’s personality better

than their significant other.

Through artificial intelligence-based algorithms, marketers are able to craft messages that

are specifically tailored to their prospective clients, resulting in more predictable responses from

a higher percentage of recipients (Hirsh et al., 2012; Kumar et al., 2019). The fundamental

question arises: Can the message of the Bible be effectively conveyed to believers in a manner

that fosters lasting and positive life transformations? Rather than utilizing persuasive

communication solely for commercial purposes, how would society react to encountering the

Word of God at the opportune moment, presented in a compelling way that motivates them to

embody its teachings?

Related Literature Summary

This literature review section commenced with an exploration of pertinent literature

concerning Bible engagement, emphasizing its significance for spiritual development. The State

of the Bible study (Cooper et al., 2021) was highlighted as a key reference, offering insights into

the current landscape. Subsequently, the review delved into the realm of digital religion,

examining the influence of religious apps and virtual religious practices. Within this context,

digitization's impact on sacred texts and religious authority was thoroughly investigated.

In recognizing that resistance to innovation exists despite its evident societal benefits, the

review incorporated the Diffusion of Innovations theory as a framework to comprehend the

varying rates of innovation adoption. This was exemplified in discussion of the preferences of

individuals regarding traditional hard copy reading versus digital formats. Lastly, this section

explored persuasive technology and its implementation through artificial intelligence and

personalized messaging, acknowledging its potential influence within the technological

landscape.

Rationale for Study and Gap in the Literature

The primary aim of this descriptive study was to investigate the potential relationship

between an individual's behavioral intention to utilize a Bible-based chatbot, which leverages

AI's natural language processing capabilities to facilitate immersive engagement with Scripture,

and a series of metrics for understanding intentionality. Specifically, the study examined the

influence of performance expectancy, effort expectancy, perceived enjoyment, and perceived

risk, derived from the UTAUT framework, while controlling for gender, age, and experience

among registered users of the Inductive Bible Study App. To achieve a comprehensive

understanding, this research drew from multiple disciplines within the social sciences. Education

was pertinent, as Bible engagement serves as an educational process that imparts divine

principles for life, while leadership was relevant due to the expectation for Christians to become

both disciples and disciple-makers. Furthermore, a solid understanding of computer science was

necessary to explore topics like artificial intelligence and app design.

Despite the presence of well-established bodies of work within each of these social

science disciplines, the existing literature revealed a notable gap when it came to exploring the

transformed design model of Bible apps. This design model challenges app developers to

embrace the full range of technological affordances to facilitate spiritual growth (Rinker et al.,

2016; Tsuria et al., 2021). While speculations were made regarding the reasons behind the

reluctance of app developers to fully leverage technology for fostering individuals' closeness to

God (Tsuria et al., 2021), conclusive findings were lacking and left as a subject for future

research (H. A. Campbell et al., 2014; Hutchings, 2014; Tsuria et al., 2021). The present study

addresses this gap by shedding light on the factors that may influence an individual's decision to

utilize a Bible-based chatbot powered by AI for immersive Scripture engagement.

Profile of the Current Study

The current study was conducted to examine the relationship between behavioral

intention to use a Bible-based chatbot leveraging AI for enhancing Bible engagement,

controlling for performance expectancy, effort expectancy, perceived enjoyment, and perceived

risk among registered users of the Inductive Bible Study app. The purpose of the study was to

understand the factors influencing individuals' willingness to adopt and utilize the chatbot, with

the aim of enhancing their engagement with Scripture.

This descriptive study employed a quantitative research methodology. A survey

instrument was designed to collect data on the relevant variables from the participants. To recruit

participants, an invitation was distributed to the registered users of the Inductive Bible Study

app. A total of 1,036 individuals completed the survey within a two-week timeframe. The survey

was administered electronically, allowing participants to conveniently respond to the

questionnaire. The collected data served as the basis for the subsequent analyses and

investigation of the research questions.

The findings of this study contribute to the existing literature on technology adoption and

Bible engagement, providing insights into the factors that influence behavioral intention to use a

Bible-based chatbot. This profile of the current study serves as a transition to Chapter 3, which

outlines the research methodology employed in the study, including data collection procedures,

measurement instruments, and statistical analyses.

CHAPTER THREE: RESEARCH METHODOLOGY

The Literature Review revealed that scriptural engagement is on the decline in America

and all over the world. It also showed that scriptural engagement has a huge impact on the lives

of believers in Christ. However, with the effects of fast-advancing technology impacting every

industry, mobile applications give individuals the freedom to do things on the go that could

previously only be accomplished at a stationary location. Unfortunately, the literature review

also revealed that most Bible applications merely transfer the text from printed form to digital

form, leveraging very few of the affordances of modern technology to increase scriptural

engagement. The solution to this must was drawn from data and then analyzed. Therefore, the

research context, data collection methods and instructions, and approach to data analysis in this

study are explored next. Future researchers that may seek to build on this study will thus have a

clear understanding of the inner workings of the study (Goertz & Mahoney, 2012).

Research Design Synopsis

Quantitative research revealed the relationship between behavioral intention to use a

Bible-based chatbot that leverages the natural language processing (NLP) capabilities of AI to

create human-like engagement with Scripture, controlling for performance expectancy, effort

expectancy, perceived enjoyment, and perceived risk among registered users of the Inductive

Bible Study app. The study used a quantitative survey research methodology and applied the

UTAUT framework. The target sample size for the study was 848 registered users of the

Inductive Bible Study app. Upon extending invitations to 40,960 individuals, 1,036 respondents

participated in the survey.

The study used a questionnaire to gather data from registered users of the Inductive Bible

Study app. The questionnaire included questions related to behavioral intention, performance

expectancy, effort expectancy, perceived enjoyment, and perceived risk. The data collected from

the survey was analyzed using partial least squares structural equation modeling (PLS-SEM).

This study provides valuable insights for Christian leadership by investigating how

individuals perceive and value the use of technology for engaging with Scripture. By examining

the relationship between behavioral intention and factors such as performance expectancy, effort

expectancy, perceived enjoyment, and perceived risk, the study reveals what motivates

individuals to use a Bible-based chatbot and what factors might discourage them from doing so.

By controlling for factors such as gender, age, and experience, the study provides information

about how these demographic factors may influence an individual's behavioral intention toward

using the chatbot, which can be used to target communication and marketing efforts to specific

demographics. Overall, the study provides valuable insights for leaders in the field of

technology-based religious engagement, helping them to understand how to increase adoption

and engagement with Bible-based chatbots among their target audience.

The Problem

In a longitudinal study spanning the past decade, the American Bible Society has

chronicled the grim reality that scriptural engagement is on the decline in America (Fulks et al.,

2022). Other studies have revealed that scriptural engagement has a profound impact on the lives

of believers in Christ (Ovwigho et al., 2016). With the advent of mobile devices, mobile

applications give individuals the freedom to do things on the go that could previously only be

accomplished at a stationary location (Chayutimaphan et al., 2022). Unfortunately, most Bible

applications merely transfer the text from printed form to digital form, leveraging very few of the

affordances of modern technology to increase scriptural engagement (Tsuria et al., 2021).

Advancements in the use of artificial intelligence continue to augment every aspect of life

(De Bruyn et al., 2020). Businesses are leveraging artificial intelligence to better serve clients,

while social media and streaming services use it to increase viewership of content (Brittain,

2020). Time spent online has increased at an alarming rate, leaving some psychologists to

speculate about the long-term effects of the inordinate amounts of time spent on technology on

current and future generations (Hutchinson, 2019). In light of the current situation, the present

researcher seeks to explore whether artificial intelligence could be leveraged to help individuals

grow in their faith.

Purpose Statement

The purpose of this descriptive study was to understand whether a relationship exists

between an individual's behavioral intention to use a Bible-based chatbot that leverages the

natural language processing (NLP) capabilities of artificial intelligence (AI) to create human-like

engagement with Scripture and the constructs of performance expectancy, effort expectancy,

perceived enjoyment, and perceived risk, adapted from the UTAUT framework, controlling for

gender, age, and experience among registered users of the Inductive Bible Study App. Artificial

intelligence is already at work in the world, driving behaviors that advance the agendas of

various organizations (De Bruyn et al., 2020). Due to several factors, which have been explored

in the literature review, a gap exists in consideration of directing the power of artificial

intelligence toward spiritual formation through Bible engagement. Since the UTAUT framework

has been used in hundreds of studies to quantify factors contributing to individuals' intent and

use and usage behavior of technology, this theoretical foundation guided the present study

(Tamilmani et al., 2021).

Research Questions and Hypotheses

Research Questions

The following research questions guided this study:

RQ1. What influence does performance expectancy have on an individual’s behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create human-like engagement with Scripture?

RQ2. What influence does effort expectancy have on an individual’s behavioral intention

to use a Bible-based chatbot that leverages the natural language processing capabilities of AI to

create human-like engagement with Scripture?

RQ3. What influence does perceived enjoyment have on an individual’s behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create human-like engagement with Scripture?

RQ4. What influence does perceived risk have on an individual’s behavioral intention to

use a Bible-based chatbot that leverages the natural language processing capabilities of AI to

create human-like engagement with Scripture?

RQ5. How do gender, age, and experience moderate the relationship between the

UTAUT framework constructs of performance expectancy, effort expectancy, perceived

enjoyment, perceived risk, and the behavioral intention to use a Bible-based chatbot that

leverages the natural language processing capabilities of AI to create human-like engagement

with Scripture?

Research Hypotheses

H1:

Performance expectancy significantly affects behavioral intention to use a Bible-

based chatbot that leverages the natural language processing capabilities of AI to create human-

like engagement with Scripture.

H2:

Effort expectancy significantly affects behavioral intention to use a Bible-based

chatbot that leverages the natural language processing capabilities of AI to create human-like

engagement with Scripture.

H3:

Perceived enjoyment significantly affects behavioral intention to use a Bible-based

chatbot that leverages the natural language processing capabilities of AI to create human-like

engagement with Scripture.

H4:

Perceived risk significantly affects behavioral intention to use a Bible-based chatbot

that leverages the natural language processing capabilities of AI to create human-like

engagement with Scripture.

H5a to H5d:

Gender moderates the relationships in H1 to H4.

H6a to H6d:

Age moderates the relationships in H1 to H4.

H7a to H7d:

Experience moderates the relationships in H1 to H4.

Research Design and Methodology

Building from a clearly stated problem and questions, the researcher considers the most

appropriate research design and method. DeBose (2018) likens the research design to the plan

one has to address the research question, whereas the research method is the approach leveraged

in executing the plan. Moreover, she contends that leveraging the appropriate research design

will help ensure that the gathered data will help uncover the answer to the researcher's questions

in the most efficient manner.

Out of the available methodological choices, based on the aims of the research and

successful implementation in precedent literature, this researcher chose a monomethod

quantitative approach for the research. A monomethod refers to the researcher choosing to

implement a singular method rather than a combination of methods throughout the study

(Blessing & Chakrabarti, 2009). Creswell and Creswell (2018) note, "Quantitative research is an

approach for testing objective theories by examining the relationship among variables" (p. 3).

Typically, quantitative data is structured numerically and is analyzed using statistical techniques

(Mukherjee, 2019). Survey research lends well to the quantitative method, as data is collected

from a pool of participants via a well-structured questionnaire (Arnab, 2017).

Survey research extends from academia to business, allowing organizations to better

understand their customers to adjust their strategies to better meet their needs (Crawford, 2022).

Pollsters leverage surveys to gain insights regarding public attitudes and political opinions or to

predict voting patterns (Vis & Stolwijk, 2021). In any context, gathering the data is merely part

of the initial phase of survey research (Hendra & Hill, 2019). The data must be analyzed, and

conclusions are then rendered (Avemegah et al., 2021). In academia, survey research is often

used as the basis for validating or rejecting a hypothesis (Abbitt & Boone, 2021). Whereas in the

marketplace, the findings of survey research are often presented to executive leadership within

organizations to facilitate data-based decision-making (Wohlin et al., 2021).

Researchers must not make the mistake of undervaluing the complexity of survey

research, considering it as merely asking a series of questions (Lavrakas, 2008). Even though

answering the survey may require minimal effort on the part of the respondent, the expertise

required to execute each phase of survey research is considerable (Z. Moore et al., 2021). Leedy

and Ormrod (2019) contend, "a survey design makes critical demands on the researcher that, if

not carefully addressed, can place the entire research effort in jeopardy" (p. 152). The survey

method is executed in research via several models, including face-to-face interviews, telephone

surveys, focus groups, online surveys, or panel surveys (Arnab, 2017; Lavrakas, 2008).

Regardless of the delivery method, survey-based "approaches focus on carefully measuring (or

experimentally manipulating) a parsimonious set of variables to answer theory-guided research

questions and hypotheses" (Creswell & Creswell, 2018, p. 147).

As opposed to either randomly selecting a method or selecting one based on the

researcher's familiarity with a particular method, it is best to survey existing literature in order to

evaluate the strengths and weaknesses of methods that have been applied to answer similar

research questions (DeBose, 2018). This research project investigated the factors that influence

user adoption of a "transformed" (Tsuria et al., 2021, p. 75) Bible interface that leverages an AI-

based chatbot to increase Bible engagement. Since the researcher was unable to find such a

platform or studies in this context at the time of the study, the researcher had to explore studies

that sought to understand the factors that would influence the adoption of technology concepts

such as autonomous vehicles (Farzin et al., 2022) or chatbot usage in emerging sectors (Sugumar

& Chandra, 2021; Terblanche & Kidd, 2022).

Since it was the researcher's goal to complete the dissertation within the calendar year, it

was apparent that an extended longitudinal time horizon study would not be pertinent. Instead,

the cross-sectional time horizon was chosen for this research based on its use in similar studies

and the scope of the current study. While the factors influencing user adoption of AI-based

technologies that enhance Bible engagement may change over time as society becomes more

acclimated to the affordances offered by AI, the study's scope was limited to the present moment.

Population

The population for this research consisted of registered users of the Inductive Bible Study

app. Individuals typically connect with Inductive Bible Study (IBS) LLC to grow in their faith by

studying the Bible inductively using their favorite tablet or mobile device. The researcher

leveraged a pool of 40,960 registered users of the Inductive Bible Study app, providing ample

population size from which to recruit respondents. The researcher developed the Inductive Bible

Study app and thus has access to the contact information of its user community.

Sampling Procedures

The collection of data in research typically falls within two source categories – primary

and secondary (Larsen et al., 2020). Some studies combine data from both primary and

secondary sources in order to achieve optimal levels of insight (Odiase et al., 2019). Studies

based on primary source data often request participants to self-report using an online survey

(Avemegah et al., 2021). Among a survey of related studies, self-reporting surveys were found to

be the primary choice and best-practice method of data collection (Boyle et al., 2017).

Some studies base their findings on pools of participants available from online survey

websites (Park & Woo, 2022). Considering how the outcome of the current study seeks to

understand factors influencing individual interest in Bible engagement using an AI-based

interface, the convenience sample of registered users of the Inductive Bible Study app was a

likely choice. Convenience sampling is commonly used in non-probability sampling (Stratton,

2021).

When collecting data for research, selecting the appropriate sample size is important (H.

Kang, 2021). Kang (2021) urges all research teams to include a statistician who can evaluate the

appropriate sample size. The sample size is defined as the number of participants included in a

particular study (Norouzian, 2020). The correct sample size increases the accuracy of estimates

and improves a study's ability to make assumptions based on the data (X. Wang & Ji, 2020). This

study adopted an a priori approach to calculating sample size for the structural model (Soper,

2022). With a medium anticipated effect size of 0.3 (Farhat et al., 2022), the desired statistical

power level of 0.8 (Kalia et al., 2021), five latent variables, and three observed variables at a

probability level of 95% (Agrawal, 2021; Hamouda, 2021), the minimum required sample size to

detect effect is 150. The study distributed surveys to Inductive Bible Study LLC subscribers in

hopes of reaching an optimal number of 848 responses. Invitations to participate in the study

were extended to 40,960 individuals via email. Over a two-week timespan, 1,036 individuals

completed the survey.

Limits of Generalization

There are several limitations to generalizing the study of individuals' behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create a life-like engagement with Scripture:

1. Sample bias: The population of registered users of the Inductive Bible Study App may

not be representative of the general population, which could limit the generalizability of

the study findings.

2. Self-selection bias: Individuals who choose to use the Inductive Bible Study App may

differ in meaningful ways from those who do not use the app, which could also limit the

generalizability of the study findings.

3. Limited generalization of results:

a. The results of the study may only be generalizable to a specific population of

users with similar characteristics, such as similar religious backgrounds.

b. The results of the study may be limited to the specific population of users of the

app, and may not be generalizable to other populations.

c. The results of the study may be limited to the specific time period of the study,

and may not be generalizable to other time periods.

Ethical Considerations

When conducting research that involves human participants, special consideration must

be given to the ethics pertaining to the study (Leedy & Ormrod, 2019). This research complied

with the IRB process by compiling and submitting a packet for review prior to conducting any

research. Only after approval and integration of any relevant feedback did the researcher begin

gathering data from human subjects.

The first principle of ethics to consider is the participant's right to choose whether or not

to participate in the study (Azakir et al., 2020). Researchers are not allowed to use deceptive

tactics in order to gain data from subjects in studies (Edwards, 2020). In order to address this

ethical concern, participants were provided with an overview of the study in the form of a letter

and introductory video. After being provided an overview of the study, the prospective

participant was presented with a formal request to participate, giving them the freedom to decide

without any fear of consequences. Only after providing consent were participants routed to the

online survey.

The next ethical concern addressed was the goal of providing minimal harm and maximal

benefit (Scott et al., 2021). When conducting research online, there is always the threat of

participant information being leaked (Arnab, 2017). In order to mitigate this risk, the survey

information was collected anonymously. No personally identifiable information (PII) was

contained in the survey instrument. The PII information used for recruitment purposes only was

stored on a secure server that maintains up-to-date encrypted data transmission protocols. The

benefit to participants was the opportunity to be exposed to and provide valuable input to an app

concept that the researcher hopes to develop after the research is complete.

Another ethical concern of research is the minimization of bias (Hendra & Hill, 2019). A

researcher's beliefs, values, and expectations can introduce bias into the research (Creswell &

Creswell, 2018). Such bias could be demonstrated by the researcher choosing to investigate the

relationship between variables of interest while ignoring others (Abbitt & Boone, 2021). In

addition, one's conclusions may be limited to findings that support one’s particular preference

(DeBose, 2018). While it is impossible to eliminate all bias, the strategy to mitigate the risk of

bias in this research was to enlist a panel of non-participants to review and provide feedback

regarding the survey instrument prior to distribution (Avemegah et al., 2021). The panel

consisted of individuals who are experts in social science research, theology, and artificial

intelligence.

The researcher admits to having a preexisting appreciation for the value of Scripture

engagement. Scripture has shaped the researcher's worldview and provides the framework by

which the researcher’s life is governed. He currently serves as a Youth Pastor at a Baptist church

in Atlanta, where he uses Scripture as the primary vehicle to steer students toward a relationship

with Jesus from a young age. With this in mind, the objective nature of quantitative research will

prove helpful in allowing the researcher the opportunity to critically analyze the data.

Proposed Instrumentation

Data collection for this study will be conducted using a survey questionnaire (Arnab,

2017). The questionnaire was administered to subscribed users of the Inductive Bible Study app.

The survey instrument was designed in such a way that the data collected would provide

empirical data to support the latent variables being measured, which highlight the factors most

relevant in one's behavioral intent to use a Bible-based chatbot that leverages artificial

intelligence to increase scriptural engagement. The instrument used is a modification of the

original instrument used in the first UTAUT study (Venkatesh et al., 2003). Because of the

unique nature of the technologies being studied, subsequent research based on the UTAUT

framework commonly modifies the original instrument in order to contextualize the

questionnaire (Balakrishnan et al., 2022; Hsu & Peng, 2022; S. Hu et al., 2020).

Each question related directly to the research questions, with each RQ being represented

through three separate questionnaire items. An independent panel was established to ensure the

questions correlated to the research questions and the research problem. The questionnaire was

designed to be easy for the participants to complete because of their busy schedules (Anjaria,

2022). The participants were asked to complete the survey within a two-week timespan. The time

window was gleaned through experience from the field test. All questions had clear directions

and wording (Jebb et al., 2021). Participants were provided examples of how to fill out the

survey correctly, especially in any areas that could potentially cause misunderstandings (Kaplan

et al., 2021). The questions were developed to be objective and straightforward questions that did

not present any bias or lead participants to answer in specific ways (Dourado et al., 2021). The

questionnaire allowed this study to use data provided through the sample population to be

described numerically, which revealed the quantitative description of trends, attitudes, and

opinions within the population (Dourado et al., 2021).

The questionnaire was designed utilizing the Likert method to assess the strength of

factors that would influence an individual's adoption of a Bible-based chatbot that leverages

artificial intelligence to increase Scripture engagement. The questionnaire introduced a statement

with seven available responses ranging from Strongly Agree to Strongly Disagree (Jebb et al.,

2021). The values corresponding to responses on the seven-point Likert scale were as follows:

Strongly Agree = 7, Agree = 6, Somewhat Agree = 5, Neither Agree or Disagree = 4, Somewhat

Disagree = 3, Disagree = 2, and Strongly Disagree = 1 (Anjaria, 2022). In addition, there were

questions to obtain demographic information such as gender and age, which will serve as

moderating variables in the study (Balakrishnan et al., 2022).

After completing the demographic questions, participants in the study were presented

with a video simulating the exchange found in Appendix F with ChatGPT. The purpose of the

video was to provide participants with an understanding of how interacting with a chatbot can

provide answers to inquiries related to Christianity or the Bible. This exposed them to the

concept of using a chatbot as a tool for exploring their faith. Using prototypes or mockups in

studies is a common practice to help participants grasp concepts that may not be readily

accessible at the time of the study (Terblanche & Kidd, 2022).

Validity

A survey instrument structured as a closed-ended questionnaire was used to test the

proposed research hypotheses. Researchers must objectively evaluate the validity of an

instrument prior to deployment to ensure that it has the optimal chance of achieving its desired

goals. Connell et al. (2018) define validity as "the extent to which a measure captures what it is

intended to measure" (p. 1893). Due to the unique nature of the technology and theology

community, this study employed both face and content validity for the survey instrument. Face

validity involves gathering a group of experts to evaluate the instrument in regard to its intended

audience and its ability to obtain information from the community (Frantz & Holmgren, 2019).

In contrast, content validity helps ensure that the questions included in the survey correlate well

with the constructs being assessed in the study (Terwee et al., 2022).

The researcher established an expert panel specializing in technology, theology, and

research. The panel was asked to evaluate the questions based on their clarity, organization,

grammar, and bias (Arnab, 2017). Feedback from their evaluation was captured based on the

methodology outlined in Appendix I. Ultimately, these steps helped the instrument achieve an

optimal level of clarity and reduced the influence of bias.

In addition, after obtaining feedback from the expert panel, the researcher requested

seven personal acquaintances, who are registered users of the Inductive Bible Study app, to help

in employing a field test. These individuals were representative of the sample pool solicited for

participation in the actual study (Lavrakas, 2008). However, none of these individuals were

members of the expert panel. Field tests are used by researchers to evaluate the ability of new or

modified questions to obtain accurate information from survey respondents (Avemegah et al.,

2021). The field test contained evaluator questions that were not present in the actual survey

instrument that inquired regarding clarity, perceived bias, completion time, and ease of use

(Hendra & Hill, 2019). In addition to incorporating the feedback provided by the field testers, the

details of the survey instrument and any associated solicitation communications were evaluated

by the IRB of Liberty University (Stark, 2012). All advised corrections or modifications were

reviewed and addressed prior to the utilization of the instrument.

Reliability

Reliability is an important concept to measure when utilizing an instrument to gather data

used in a study. According to Crossman (2020), "reliability is the degree to which a measurement

instrument gives the same results each time that it is used, assuming that the underlying thing

being measured does not change" (para. 1). Cronbach's Alpha is a commonly used measure to

objectively evaluate an instrument's reliability (Alkhadim, 2022). The underlying statistics in

Cronbach's Alpha measure an instrument's internal consistency with values ranging between 0

and 1 (Gavidia & Mariño, 2021). Internal consistency measures the interrelationship between the

constructs used in the questionnaire to determine the extent to which they obtain consistent

results (Ventura-León & Peña-Calero, 2020).

When calculating Cronbach's Alpha, groups of questions that relate to each concept are

measured independently, allowing the researcher to assess the impact of each set of questions on

the credibility of the entirety of the questionnaire (Barbera et al., 2021). Calculating this on the

survey as a whole could skew the results, giving the appearance of an unacceptable value due to

the heterogeneous nature of the survey's constructs (Gavidia & Mariño, 2021). The goal of

calculating the questionnaire's Cronbach's Alpha was to identify the strengths and weaknesses of

the instrument (Barbera et al., 2021). For question sets that received a low score, the researcher

interpreted the value as a poor interrelation among the questions (Olvera Astivia et al., 2020).

Conversely, the researcher acknowledges that having a survey instrument's alpha be too high

might suggest that some questions could be eliminated due to redundancy (Sijtsma & Pfadt,

2021). Except for the perceived risk construct, the survey questions successfully demonstrated

construct reliability, as indicated by a Cronbach's Alpha ranging from 0.70 to 0.90 (Ventura-

León & Peña-Calero, 2020).

Research Procedures

The quantitative research sample population consisted of registered users of the Inductive

Bible Study app. The researcher is the founder of the Inductive Bible Study LLC. Its flagship

product, the Inductive Bible Study app allows individuals to study the Bible using the inductive

method on their favorite mobile device. The database consists of 40,960 registered users. This

study distributed surveys to the Inductive Bible Study LLC email list with the expectation that at

least 150 responses would be achieved. Over the span of two weeks, 1,036 individuals completed

the online survey.

This study applied a convenience sampling technique. Convenience sampling is

commonly used in non-probability sampling (Stratton, 2021). Considering how the outcome of

this study sought to understand factors influencing individual interest in Bible engagement using

an AI-based interface, the convenience sample of registered users of the Inductive Bible Study

app was most appropriate.

The theoretical framework that supported the study sometimes contains a base survey

instrument used in the original study (S. Hu et al., 2020, p. 2; Nordhoff et al., 2020). A UTAUT-

based survey was used, which contained two primary parts: demographics and UTAUT model

constructs (Venkatesh et al., 2003). Prior to using the modified version of the survey in the study,

the study obtained approval from the authors of the original survey and place the associated

communications in Appendix E.

TypeForm was used to deliver the survey. This online tool delivers "surveys that feel less

like interrogations and more like conversations" (Typeform, 2022). Given the importance of

aesthetics for a survey inquiring about an app concept, it was important to leverage a tool that

has the built-in capability to present content in an appealing way. The email addresses of

registered users of the Inductive Bible Study application were already contained within the

Mailchimp email marketing platform. IBS-registered users received an email inviting them to

take part in a study. The content of that email is located in Appendix B.

The survey instrument was imported into and tested in the TypeForm platform. Clicking

on the link found within the email launched an internet browser window that initiated the

TypeForm survey. Initial survey screens introduced prospective participants to the study by

providing a brief overview of the objectives immediately, followed by a prompt that solicits

whether or not the prospective respondent would like to participate in the study. If the individual

indicated that they would not like to participate, they were promptly routed to a thank you page

that allowed them to gracefully exit the process (Arnab, 2017). If the individual opted to

complete the survey, a message thanking them for their participation was presented at the

conclusion of the survey.

After email invitations were sent to prospective participants, the researcher allowed two

weeks for them to respond (Sue & Ritter, 2012). If the optimal response rate was not achieved, a

reminder email would have been sent, and an additional two weeks was granted to submit a

response (Abbitt & Boone, 2021). The reminder email would have been sent to all prospective

participants because the survey's anonymous nature prevented the researcher from knowing who

had submitted responses during the initial period. Fortunately, the 1,096 responses received

within the initial two weeks were ample enough to allow the researcher to move forward.

Afterward, all data gathered during the designated timeframe, excluding any associated

participant identifiers (such as email address), was exported into Microsoft Excel format for

further analysis.

Data Analysis and Statistical Procedures

Sue and Ritter (2012) argue, "The plan for data analysis begins with survey objectives,

continues with suitable questions on the questionnaire, and ends with specification of the

statistical tests, if any, to be performed" (p. 140). In this section, the researcher discloses the

techniques utilized in analyzing the data as well as the associated statistical procedures that were

employed to describe factors influencing an individual's behavioral intention to use a Bible-

based chatbot that leverages artificial intelligence to increase Scripture engagement.

Data Analysis

Upon initial review of the survey results, the study saw the total number of valid

responses submitted in tablature and compared that to the a priori calculated sample size. Survey

data was also cross-tabulated based on the initial research questions, paying special attention to

moderating variables such as age, gender, and experience. During this analysis phase, the

researcher looked for patterns that may be present in the data. The initial analysis uncovered the

gender and age breakdown of respondents to the questionnaire.

Additionally, survey data were screened to identify potential duplicates and incomplete

submissions. The risk of obtaining incomplete submissions was minimized based on TypeForm’s

ability to designate questions as required. Incomplete submissions are more common when

instruments are deployed using paper. Had the researcher needed to extend the response

timeframe due to an inadequate number of responses, then responses submitted within the initial

response window would have been compared to those submitted in the follow-up timeframe to

assess response similarities or differences.

Based on a modified version of the UTAUT framework, performance expectancy, effort

expectancy, perceived enjoyment, and perceived risk were identified as latent determinants that

influence an individual's behavioral intention to use a Bible-based chatbot that leverages the

natural language processing (NLP) capabilities of AI to create human-like engagement with

Scripture. The following table maps the constructs to the survey questions.

Table 3

Constructs and Survey Questions

Construct

Survey Question

Performance

Expectancy (PE)

PE1

Having access to an AI-based Bible chatbot would motivate me to engage

with Scripture more often.

PE2

Using an AI-based Bible chatbot would enhance my effectiveness when

engaging with Scripture.

PE3

I would find an AI-based Bible chatbot useful for applying Scripture to my

life.

PE4

I would find an AI-based Bible chatbot useful for my spiritual growth.

Effort Expectancy

(EE)

EE1

Overall, I believe an AI-based Bible chatbot would be easy to use.

EE2

I would find it easy to communicate with an AI-based Bible chatbot.

EE3

Learning to use an AI-based Bible chatbot to engage with Scripture would

be easy for me.

EE4

I would find it easy to engage with Scripture using an AI-based Bible

chatbot.

Perceived

Enjoyment (PEN)

PEN1

I would find it enjoyable to interact with an AI-based Bible chatbot.

PEN2

The actual process of using an AI-based Bible chatbot would be pleasant.

PEN3

I would have fun using an AI-based Bible chatbot.

PEN4

I would enjoy using the AI-based Bible chatbot more than reading the Bible

conventionally.

Perceived Risk

(PR)

PR1

I feel that confidentiality concerns inhibit my willingness to use an AI-

based Bible chatbot for engaging with Scripture.

PR2

I believe my privacy would be adequately protected when using an AI-

based Bible chatbot.

PR3

I do not trust an AI-based Bible chatbot as a means for engaging with

Scripture.

PR4

I believe it would be risk-free to use an AI-based Bible chatbot.

Construct

Survey Question

Behavioral Intent

(BI)

BI1

I intend to use an AI-based Bible chatbot in the future if one becomes

available.

Demographic (D)

Age:

Under 18

18-24

25-34

35-44

45-54

55-64

65 or older

Prefer not to say

Gender:

Male

Female

Non-binary

Prefer not to say

Experience - Have you ever engaged with a chatbot? (y/n)

Experience - Have you ever interacted with an advanced human-like

chatbot such as ChatGPT? (y/n)

Statistical Procedures

Partial least squares structural equation modeling (PLS-SEM) was used to investigate the

model presented in Figure 1 for the analysis of cross-sectional survey data (F. Hair Jr et al.,

2014). Partial least squares (PLS) modeling was performed using the SEMinR package in R (v.

4.3.0). Various research studies have engaged PLS and have established it as a prominent

analysis modeling technique (Mohamad et al., 2020; Nordhoff et al., 2020). Structural equation

modeling (SEM) is not limited to one statistical technique; instead, it is a general modeling

framework incorporating a number of different multivariate techniques (Amini &

Alimohammadlou, 2021). SEM is typically used when research questions involve complex,

multi-faceted constructs (Sarstedt et al., 2014). SEM is also helpful when research questions

focus on the direct and indirect effects of independent variables on dependent variables (Shmueli

et al., 2019). PLS allows researchers to analyze relationships among variables simultaneously

(Leong et al., 2020).

Figure 2

Research Model

Possible moderating effects of age were tested within SEM using multi-group analysis

(MGA) where the sample was divided into two age groups (above and below the median age)

and the path coefficient was compared between the two separately fitted SEM models

(Terblanche & Kidd, 2022). Gender and experience moderation were tested using MGA as well

(Yen et al., 2022).

The moderating effects were investigated further by conducting univariate tests (Q.

Zhang et al., 2021). Homogeneity-of-slopes analysis of covariance (ANCOVA) was used for

gender, age, and experience (Terblanche & Kidd, 2022). The findings of the univariate approach

were compared with the findings from the SEM-MGA model. These analyses were conducted

using R (Popova & Zagulova, 2022). A 5% significance level (p <.05) was used as a guideline

for rejecting hypotheses (Terblanche & Kidd, 2022).

Chapter Summary

Throughout history, God has chosen creative methods to relay His message of truth to

mankind (Opoku et al., 2020). As society continues to make great strides in the Information Age,

with inventions such as social media, mobile devices, and WIFI becoming commonplace (Oliver,

2020), this study considers how artificial intelligence could be leveraged to increase Scripture

engagement. The literature review demonstrated that most Bible applications on mobile devices

merely transfer the physical text to a digital format (Tsuria et al., 2021). This study advances the

possibility of pushing the envelope with artificial intelligence in hopes of shifting the growing

tide of biblical disengagement.

This chapter outlines the methodology that was used to orchestrate a study using a

quantitative descriptive research design to test hypotheses regarding determinants that influence

an individual's behavioral intention to use a Bible-based chatbot that leverages artificial

intelligence to increase scriptural engagement. A closed-ended questionnaire that capitalizes on

the past performance of the Likert scale was used to gauge the pulse of registered users of the

Inductive Bible Study app. Considering the success of similar studies that have successfully

implemented a similar strategy based on the UTAUT framework (Tamilmani et al., 2021), the

researcher views the executed implementation method as optimal for this study.

CHAPTER FOUR: FINDINGS

Overview

This study adopted a quantitative descriptive research design to evaluate the relationship

between behavioral intention of using a Bible-based chatbot that leverages the natural language

processing capabilities of AI to create human-like engagement with Scripture and the constructs

of performance expectancy, effort expectancy, perceived enjoyment, and perceived risk among

registered users of the Inductive Bible Study app. The data was collected using a survey

questionnaire whereby the UTAUT framework was applied. This study sent out email invitations

to 40,960 registered users of the Inductive Bible Study app, but only 1,036 registered users

completed the survey. This represents 2.5% of the total population. The data collected from the

survey was analyzed using partial least squares structural equation modelling in R software. The

study questions and hypotheses to be explored are highlighted below.

Research Questions

RQ1. What influence does performance expectancy have on an individual’s behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create human-like engagement with Scripture?

RQ2. What influence does effort expectancy have on an individual’s behavioral intention

to use a Bible-based chatbot that leverages the natural language processing capabilities of AI to

create human-like engagement with Scripture?

RQ3. What influence does perceived enjoyment have on an individual’s behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create human-like engagement with Scripture?

RQ4. What influence does perceived risk have on an individual’s behavioral intention to

use a Bible-based chatbot that leverages the natural language processing capabilities of AI to

create human-like engagement with Scripture?

RQ5. How do gender, age, and experience moderate the relationship between the

UTAUT framework constructs of performance expectancy, effort expectancy, perceived

enjoyment, perceived risk, and the behavioral intention to use a Bible-based chatbot that

leverages the natural language processing capabilities of AI to create human-like engagement

with Scripture?

Hypothesis

H1:

Performance expectancy significantly affects behavioral intention to use a Bible-

based chatbot that leverages the natural language processing capabilities of AI to create human-

like engagement with Scripture.

H2:

Effort expectancy significantly affects behavioral intention to use a Bible-based

chatbot that leverages the natural language processing capabilities of AI to create human-like

engagement with Scripture.

H3:

Perceived enjoyment significantly affects behavioral intention to use a Bible-based

chatbot that leverages the natural language processing capabilities of AI to create human-like

engagement with Scripture.

H4:

Perceived risk significantly affects behavioral intention to use a Bible-based chatbot

that leverages the natural language processing capabilities of AI to create human-like

engagement with Scripture.

H5a to H5d:

Gender moderates the relationships in H1 to H4.

H6a to H6d:

Age moderates the relationships in H1 to H4.

H7a to H7d:

Experience moderates the relationships in H1 to H4.

Compilation Protocol and Measures

The hypotheses and research questions were tested using PLS-SEM carried out by the

SEMinR package in R. PLS-SEM was used for analysis because of its ability to determine the

relationships and strengths in the latent variables as well as how the research model (Figure 1)

explains the constructs of interest being tested (F. Hair Jr et al., 2014; Sarstedt et al., 2021).

Performance expectancy (PE), effort expectancy (EE), and perceived enjoyment (PEN) were

treated as reflective constructs, perceived risk (PR) as a formative construct, and behavioral

intent (BI) as a single-item construct. These tests were used to support or reject the hypotheses

with the main consideration being p-values. In cases where the p-value is below 0.01, a

hypothesis is supported, while when it is above 0.01, the hypothesis is rejected. These tests were

done using validated models to ensure accurate results were rendered.

Loading was also of great importance to this analysis, and the recommended loading was

0.708. Therefore, any variable with a loading of less than 0.708 was removed. In this case PEN4,

PR1, and PR3 were removed, since they had lower loading than the recommendation (Table 2).

PR1 and PR3 did not significantly contribute toward explaining perceived risk. Therefore,

removal of PR1 and PR3 solved the AVE problem and improved Cronbach’s Alpha.

Average variance extracted was also acceptable for each of those constructs (>0.5) (Table

3). In this case, performance expectancy, effort expectancy, perceived enjoyment, perceived risk,

and behavioral intent all have AVE of greater than 0.5. The discriminant validity was assessed

using the heterotrait-monotrait ratio. 95% confidence intervals were calculated for the ratios and

if the upper limit was >1, then the two constructs were considered to be highly correlated to one

another. The levels of the upper limits were less than one (Table 2), therefore, we can conclude

that all constructs sufficiently discriminated from one another.

Table 4

Loadings of the PLS-SEM Measurement Model

Latent Variable

Item

95% lower

95% upper

p-value

Performance Expectancy

PE1

0.92

0.90

0.93

<0.01

Performance Expectancy

PE2

0.93

0.92

0.94

<0.01

Performance Expectancy

PE3

0.91

0.89

0.93

<0.01

Performance Expectancy

PE4

0.95

0.96

<0.01

Effort Expectancy

EE1

0.87

0.84

0.89

<0.01

Effort Expectancy

EE2

0.89

0.87

0.91

<0.01

Effort Expectancy

EE3

0.89

0.87

0.91

<0.01

Effort Expectancy

EE4

0.89

0.87

0.91

<0.01

Perceived Enjoyment

PEN1

0.93

0.91

0.94

<0.01

Perceived Enjoyment

PEN2

0.94

0.93

0.95

<0.01

Perceived Enjoyment

PEN3

0.92

0.90

0.94

<0.01

Perceived Enjoyment

PEN4

0.65

0.61

0.69

<0.01

Perceived Risk

PR1

(reversed)

0.03

-0.07

0.14

0.27

Perceived Risk

PR2

0.89

0.85

0.92

<0.01

Perceived Risk

PR3

(reversed)

0.67

0.59

0.74

<0.01

Latent Variable

Item

95% lower

95% upper

p-value

Perceived Risk

PR4

0.72

0.64

0.78

<0.01

Behavioral Intent

BI1

1.00

<0.01

Table 5

Construct Reliability and Average Variance Extracted

Construct

Cronbach's alpha

Composite

reliability

Average variance

extracted (AVE)

Performance

Expectancy

0.95 (0.95)

0.96 (0.96)

0.86 (0.86)

Effort Expectancy

0.91 (0.91)

0.94 (0.94)

0.78 (0.78)

Perceived Enjoyment

0.88 (0.94)

0.92 (0.96)

0.75 (0.88)

Perceived Risk

0.55 (0.65)

0.70 (0.80)

0.44 (0.59)

Behavioral Intent

1.00 (1.00)

Note: Numbers in brackets are results after PEN4, PR1, and PR3 were removed for low loading and low weight,

respectively.

Demographic and Sample Data

Demographic information was categorized as per age, gender, and experience. Age had

the following categories: under 18, 18-24, 25-34, 35-44, 45-54, 55-64, 65 or older, and those

who preferred not to disclose their ages. In terms of gender, the respondents who were involved

in this study were classified into the following categories: male, female, non-binary, and others

who opted not to disclose their gender. Lastly, regarding experience, this was categorized based

on respondents who had ever engaged with a chatbot and those who had interacted with an

advanced human-like chatbot such as ChatGPT. The study invited 40,960 registered users of the

Inductive Bible Study app to participate, but only 1,036 registered users completed the survey.

This represents 2.5% of the total population, which still enabled the study to draw meaningful

conclusions from the survey data.

Data Analysis and Findings

Descriptive data analytics mainly focuses on measures of central tendencies such as

mean, median, mode, and standard deviation (Kaur et al., 2018). This study will therefore focus

100

on these measures to come up with the descriptive statistics as shown in Table 4.

Table 6

Descriptive Statistics

Original

Est

Bootstrap

mean

Bootstrap

T Stat

2.5% CI

97.5% CI

PE-EE

0.694

0.021

32.744

0.650

0.734

PE-PEN

0.864

0.865

0.012

73.928

0.841

0.887

PE-PR

0.848

0.850

0.022

37.776

0.805

0.896

PE-BI

0.809

0.808

0.014

56.258

0.780

0.835

EE-PEN

0.837

0.836

0.017

48.616

0.800

0.869

EE-PR

0.744

0.028

26.801

0.688

0.798

EE-BI

0.720

0.019

37.747

0.681

0.755

PEN-PR

0.840

0.841

0.022

37.455

0.795

0.884

PEN-BI

0.847

0.846

0.012

70.942

0.823

0.870

PR-BI

0.842

0.844

0.021

39.519

0.802

0.886

As shown in Table 4 above, performance expectancy to effort expectancy had an original

estimate of 0.694, mean of 0.694, standard deviation of 0.021, and a t-score of 32.744. In the

analysis, we therefore use the t-value to determine the influence of performance expectancy to

effort expectancy. This is because a large t-value indicates that the constructs are different, while

a small one indicates similarity between the constructs. This means that performance expectancy

is 32.744 away from 0 (mean), and thus will not have any relationship to effort expectancy.

Performance expectancy to perceived enjoyment had an original estimate of 0.864, mean

of 0.865, standard deviation of 0.012, and a t-score of 73.928. Performance expectancy has a

73.928 difference from 0 (mean), which is very large. This implies that performance expectancy

and perceived enjoyment are totally different from each other, and the latter construct will not

influence perceived enjoyment. Performance expectancy to perceived risk had an original

estimate of 0.848, mean of 0.850, standard deviation of 0.022, and a t-score of 37.776. The

analysis shows that perceived enjoyment is 37.776, which is extremely large, a huge difference

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from 0 (mean). This implies that perceived enjoyment has no influence on behavioral intention.

Performance expectancy to behavioral intention had an original estimate of 0.809, mean of

0.808, standard deviation of 0.014, and a t-score of 56. 258. The t-value was used to determine

the influence of performance expectancy to behavioral intention. This means that performance

expectancy has a 56.258 difference from 0 (mean), and therefore the construct will not influence

behavioral intention.

Effort expectancy to perceived enjoyment had an original estimate of 0.837, mean of

0.836, standard deviation of 0.017, and a t-score of 48.616. The analysis shows that effort

expectancy is 48.616, which is a big difference from 0 (mean). Effort expectancy thus has no

influence on perceived enjoyment. Effort expectancy to perceived risk had an original estimate

of 0.744, mean of 0.744, standard deviation of 0.028, and a t-score of 26.801. Effort expectancy

is 26.801, which is a significant difference from 0 (mean), meaning that it has no influence on

the perceived risk. Effort expectancy to behavioral intention had an original estimate of 0.720,

mean of 0.720, standard deviation of 0.019, and a t-score of 37.747. Effort expectancy has a

difference of 37.747 from 0 (mean). This implies that effort expectancy does not influence an

individual’s behavioral intention.

Perceived enjoyment to perceived risk had an original estimate of 0.840, mean of 0.841,

standard deviation of 0.022, and a t-score of 37.455. Effort expectancy has a difference of 37.455

from 0 (mean). This means that perceived enjoyment does not influence perceived risk.

Performance enjoyment to behavioral intention had an original estimate of 0.847, mean of 0.846,

standard deviation of 0.012, and a t-score of 56.258. This implies that performance expectancy at

70.942 has a huge difference from 0 (mean), and therefore it does not influence an individual’s

behavioral intention. Perceived risk to behavioral intention had an original estimate of 0.842,

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mean of 0.844, standard deviation of 0.021, and a t-score of 39.519. This means that perceived

risk has a difference of 39.519 from 0 (mean) and does not impact an individual’s behavioral

intention.

The research questions and hypothesis were used as the basis of the data analysis. This

was achieved by describing the null hypothesis and identifying the corresponding statistical test.

Also included were assumption tests, analysis, alpha level, effect size, and an explanation of

whether or not the null hypothesis was rejected or not. Table 7 shows the path coefficients of the

constructs tested and their moderating effects.

Table 7

Path Coefficients of Constructs and Moderating Effects

Path

Hypothesis

Path coefficient

p-value

PE -> BI

0.28

<0.01

EE -> BI

0.09

<0.01

PEN -> BI

0.40

<0.01

PR -> BI

0.18

<0.01

PE*gender -> BI

H5a

0.31 (male)

0.24 (female)

0.17

EE*gender -> BI

H5b

0.10 (male)

0.04 (female)

0.11

PEN*gender -> BI

H5c

0.44 (male)

0.33 (female)

0.05

PR*gender -> BI

H5d

0.12 (male)

0.33 (female)

<0.01

PE*age -> BI

H6a

0.35 (young)

0.21 (old)

0.03

EE*age -> BI

H6b

0.13 (young)

0.07 (old)

0.14

PEN*age -> BI

H6c

0.32 (young)

0.46 (old)

0.03

PR*age -> BI

H6d

0.20 (young)

0.16 (old)

0.26

PE*ExpD3 -> BI

H7a

0.38 (yes)

0.18 (no)

<0.01

EE*ExpD3 -> BI

H7b

0.11 (yes)

-0.02 (no)

0.02

PEN*ExpD3 -> BI

H7c

0.31 (yes)

0.53 (no)

<0.01

PR*ExpD3 -> BI

H7d

0.18 (yes)

0.23 (no)

0.17

PE*ExpD4 -> BI

H7a

0.25 (yes)

0.34 (no)

0.08

EE*ExpD4 -> BI

H7b

0.30 (yes)

0.01 (no)

<0.01

PEN*ExpD4 -> BI

H7c

0.30 (yes)

0.34 (no)

0.27

PR*ExpD4 -> BI

H7d

0.12 (yes)

0.27 (no)

<0.01

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Research Question One

The null hypothesis states that performance expectancy has a significant impact on the

behavioral intention to use a Bible-based chatbot that utilizes AI's natural language processing

capabilities to create a human-like engagement with Scripture. Conversely, the alternative

hypothesis suggests that performance expectancy does not affect the behavioral intention to use

such a chatbot. This study utilized a significance level (alpha) of 0.05 to determine whether to

reject or fail to reject the null hypothesis. When the p-value is less than 0.05, the null hypothesis

is not rejected, while when the p-value is greater than 0.05, the null hypothesis is rejected. The

absence of moderating effects on the path for performance expectancy indicates a significant

effect on behavioral intention (p<0.05), thereby supporting hypothesis H1. The strength of

performance expectancy's influence on behavioral intention is 0.28, indicating a strong impact.

Therefore, the null hypothesis is not rejected in this scenario, as concluded by the researcher.

Research Question Two

The null hypothesis states that effort expectancy significantly affects behavioral intention

to use a Bible-based chatbot that leverages the natural language processing capabilities of AI to

create human-like engagement with Scripture. The alternate hypothesis, however, states that

effort expectancy does not affect behavioral intention to use a Bible-based chatbot that leverages

the natural language processing capabilities of AI to create human-like engagement with

Scripture. As is customary, the researcher began by setting the significance level (alpha), which

is typically 0.05. If the p-value is greater than 0.05, the null hypothesis will be rejected; if it is

less than 0.05, the null hypothesis will not be rejected. The effort expectancy path lacks

moderating effects, meaning it has a significant effect on behavioral intent (p < 0.05), thus

supporting hypothesis H2. The strength of EE's effect on BI is 0.09, which indicates that its

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influence on BI is the weakest. The null hypothesis was not rejected, as the p-value is less than

0.05.

Research Question Three

The null hypothesis for research question three states that perceived enjoyment

significantly affects behavioral intention to use a Bible-based chatbot that leverages the natural

language processing capabilities of AI to create human-like engagement with Scripture. The

alternate hypothesis, however, states that perceived enjoyment does not affect behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create human-like engagement with Scripture. The researcher set the level of

significance at 0.05, meaning that if the p-value is greater than 0.05, perceived enjoyment will

not influence behavioral intention, thus rejecting the null hypothesis. In this case, it is observed

that the p-value is less than 0.05, meaning that perceived enjoyment has a significant effect on

behavioral intention. Therefore, the null hypothesis was not rejected. The path for perceived

enjoyment lacks moderating effects, meaning it has a significant effect on behavioral intent (p <

0.05), thus supporting hypothesis H3. The strength of PEN's effect on BI is 0.40, which indicates

that its influence on BI is very strong.

Research Question Four

The null hypothesis anticipates that perceived risk significantly affects behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create human-like engagement with Scripture. The alternate hypothesis, however, states

that perceived risk does not affect behavioral intention to use a Bible-based chatbot that

leverages the natural language processing capabilities of AI to create human-like engagement

with Scripture. The researcher set the level of significance (alpha) at 0.05, meaning that if the p-

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value is greater than 0.05, it suggests that perceived risk does not affect behavioral intention,

therefore rejecting the null hypothesis. In this case, since the p-value is less than the significance

level of 0.05, it can be concluded that perceived risk has a significant impact on behavioral

intention. Therefore, the null hypothesis was not rejected. The path for perceived risk lacks

moderating effects, meaning it has a significant effect on behavioral intent (p < 0.05), thus

supporting hypothesis H4. The strength of PR's effect on BI is 0.18, which indicates that its

influence on BI is moderately strong.

Research Question Five

The null hypothesis states that gender moderates the relationships in H1 to H4, while the

alternate hypothesis observes that gender does not moderate the relationships in H1 to H4.

PE*gender -> BI means gender has a moderating effect on performance expectancy as it relates

to behavioral intent. In this case, the path coefficients mean the same thing, but for just one

subgroup at a time. The p-values for these moderating effects test whether the path coefficients

for males are significantly different from those for females. A p-value < 0.05 means there is a

significant difference between the path coefficients of males and females (or young vs. old, or

yes vs. no).

By setting the alpha level at 0.05, the researcher investigates whether gender either

moderates the relationships in H1 to H4 or does not have any effect. In this case, gender will

moderate the relationships in H1 to H4 if it has a p-value of less than 0.05, which would cause

the researcher to fail to reject the null hypothesis. On the other hand, if the p-value is greater than

0.05, this means that gender does not moderate the relationships in H1 to H4, thus rejecting the

null hypothesis. Since this is categorical data, the most appropriate test normally used is the chi-

square test for independence (McBeath, 2020).

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The analysis of gender showed that hypotheses H5a, H5b, and H5c were rejected,

because the p-values for each hypothesis were greater than 0.05. However, hypothesis H5d was

supported, as the p-value for this hypothesis was less than 0.01. This means that there was a

significantly stronger relationship between perceived risk and behavioral intent among females

than among males. The analysis of age showed that hypotheses H6a and H6c were supported,

while hypotheses H6b and H6d were rejected. The path coefficients for hypothesis H6a (young =

0.35; old = 0.21) indicate that the relationship between performance expectancy and behavioral

intent is stronger for people younger than 55 than it is for people 55 and older (p = 0.03).

Hypothesis H6c was also supported, as the p-value for this hypothesis was less than 0.03. This

means that there is a stronger relationship between perceived enjoyment and behavioral intent

among older people than among younger people.

The analysis of experience showed that hypotheses H7a and H7c were supported by D3,

but not by D4. However, the path coefficients for hypothesis H7a were opposite for D3 and D4,

with the yes answer being higher for D3 but lower for D4. This makes it difficult to interpret the

results for hypothesis H7a, and it is probably safest to reject this hypothesis. Hypothesis H7d was

supported by D4, but not by D3. Again, it is probably safest to reject this hypothesis.

Hypothesis H7b was accepted by both indicators, and the path coefficients were higher

for the yes answer in both cases. This means that there was a significantly stronger relationship

between effort expectancy and behavioral intention for people with chatbot experience than

people without chatbot experience.

Univariate Tests

The univariate moderation tests are discussed in this section. For each case (gender, age,

experience D3, experience D4), homogeneity-of-slopes analysis of covariance (ANCOVA) was

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conducted. Age data, being categorical rather than continuous, was divided into two categories:

below median (<55 years old) and above median (55+ years old).

Table 8

Results of homogeneity-of-slopes ANCOVA analyses

Dependent

variable

Moderator

Covariate

Moderator x

covariate

interaction

p-value

Behavioral Intent

Gender

Performance Expectancy

1,1022

= 2.31

0.12

Behavioral Intent

Gender

Effort Expectancy

1,1022

= 1.66

0.21

Behavioral Intent

Gender

Perceived Enjoyment

1,1022

= 6.64

0.01

Behavioral Intent

Gender

Perceived Risk

1,1022

= 16.10

<0.01

Behavioral Intent

Age

Performance Expectancy

1,1026

= 3.93

<0.05

Behavioral Intent

Age

Effort Expectancy

1,1026

= 0.73

0.39

Behavioral Intent

Age

Perceived Enjoyment

1,1026

= 6.15

0.01

Behavioral Intent

Age

Perceived Risk

1,1026

= 0.85

0.35

Behavioral Intent

ExpD3

Performance Expectancy

1,1026

= 14.71

<0.01

Behavioral Intent

ExpD3

Effort Expectancy

1,1026

= 5.28

0.02

Behavioral Intent

ExpD3

Perceived Enjoyment

1,1026

= 11.74

<0.01

Behavioral Intent

ExpD3

Perceived Risk

1,1026

= 0.18

0.67

Behavioral Intent

ExpD4

Performance Expectancy

1,1026

= 1.25

0.26

Behavioral Intent

ExpD4

Effort Expectancy

1,1026

= 25.41

<0.01

Behavioral Intent

ExpD4

Perceived Enjoyment

1,1026

= 0.42

0.52

Behavioral Intent

ExpD4

Perceived Risk

1,1026

= 10.15

<0.01

In the MGA, there was a moderating effect of gender on PR->BI. The homogeneity-of-

slopes ANCOVA confirms that females have a stronger relationship for PR->BI as compared to

males (p < 0.01). In the MGA, it was also very close in showing that males have a stronger

relationship between perceived enjoyment and behavioral intent (p = 0.05). The homogeneity-of-

slopes ANCOVA confirms this fact, as it shows there is a stronger relationship between

perceived enjoyment and behavioral intent for males than for females (p = 0.01).

In the MGA, age had a moderating effect on performance expectancy and perceived

enjoyment. This is evident from homogeneity-of-slopes ANCOVA. There is a stronger

relationship between performance expectancy and behavioral intent among younger people than

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older people (p < 0.05). There is a stronger relationship between perceived enjoyment and

behavioral intent for older people than younger people (p = 0.01).

In the MGA, Experience D3 had a moderating effect on performance expectancy, effort

expectancy, and perceived enjoyment. The homogeneity-of-slopes ANCOVA proofs this fact. In

the MGA, Experience D4 had a moderating effect on effort expectancy and perceived risk. This

is also supported by the homogeneity-of-slopes ANCOVA.

Table 9

All Results: PLS-SEM, SEM-MGA, and homogeneity-of-slopes ANCOVA

Path

Hypo

thesis

Path coefficient

ANCOVA

interaction

ANCOVA

p-value

SEM

p-value

Hypothesis

supported?

PE -> BI

0.28

<0.01

supported

EE -> BI

0.09

<0.01

supported

PEN -> BI

0.40

<0.01

supported

PR -> BI

0.18

<0.01

supported

PE*gender -

> BI

H5a

0.31

(male)

0.24

(female)

1,1022

= 2.31

0.12

0.17

rejected

EE*gender -

> BI

H5b

0.10

(male)

0.04

(female)

1,1022

= 1.66

0.21

0.11

rejected

PEN*gender

-> BI

H5c

0.44

(male)

0.33

(female)

1,1022

= 6.64

0.01

0.05

supported

PR*gender -

> BI

H5d

0.12

(male)

0.33

(female)

1,1022

= 16.10

<0.01

supported

PE*age ->

H6a

0.35

(young)

0.21 (old)

1,1026

= 3.93

<0.05

0.03

supported

EE*age ->

H6b

0.13

(young)

0.07 (old)

1,1026

= 0.73

0.39

0.14

rejected

PEN*age ->

H6c

0.32

(young)

0.46 (old)

1,1026

= 6.15

0.01

0.03

supported

PR*age ->

H6d

0.20

(young)

0.16 (old)

1,1026

= 0.85

0.35

0.26

rejected

PE*ExpD3 -

> BI

H7a

0.38 (yes)

0.18 (no)

1,1026

= 14.71

<0.01

supported

EE*ExpD3 -

> BI

H7b

0.11 (yes)

-0.02 (no)

1,1026

= 5.28

0.02

supported

PEN*ExpD3

-> BI

H7c

0.31 (yes)

0.53 (no)

1,1026

= 11.74

<0.01

supported

PR*ExpD3 -

> BI

H7d

0.18 (yes)

0.23 (no)

1,1026

= 0.18

0.67

0.17

rejected

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Path

Hypo

thesis

Path coefficient

ANCOVA

interaction

ANCOVA

p-value

SEM

p-value

Hypothesis

supported?

PE*ExpD4 -

> BI

H7a

0.25 (yes)

0.34 (no)

1,1026

= 1.25

0.26

0.08

rejected

EE*ExpD4 -

> BI

H7b

0.30 (yes)

0.01 (no)

1,1026

= 25.41

<0.01

supported

PEN*ExpD4

-> BI

H7c

0.30 (yes)

0.34 (no)

1,1026

= 0.42

0.52

0.27

rejected

PR*ExpD4 -

> BI

H7d

0.12 (yes)

0.27 (no)

1,1026

= 10.15

<0.01

supported

Evaluation of the Research Design

One of the main strengths of the quantitative descriptive design used in this study is that

it enabled the researcher to obtain the data in a systematic manner, making it easier to analyze as

well, thus increasing the results reliability and validity. Moreover, the structured nature of the

design facilitates easy future study replication. The numerical data that was acquired from the

population studied was also precise and comprehensive and it enabled the study to draw

meaningful conclusions, measure different constructs (performance expectancy, effort

expectancy, perceived enjoyment, and perceived risk, and behavioral intention), and identify the

relationships and patterns that existed between each. However, the design was also limiting as it

did not offer an in-depth comprehension of the underlying causes in the observed relationships.

This design may be revised by including more data collection methods other than surveys to

capture more detailed data on the sample and constructs being tested. Qualitative methods, for

instance, may be used alongside quantitative data to capture more information that cannot be

obtained using quantitative methods alone.

One notable aspect of this research is the utilization of convenience sampling.

Convenience sampling was employed as a practical and efficient method for selecting

participants. Given the constraints of time and resources, this sampling technique allowed for the

collection of sufficient data without the need to survey the entire population of approximately

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40,960 registered users. While convenience sampling may limit the generalizability of the

findings, it provided a feasible approach considering the available resources and timeframe. The

selected participants may not fully represent the diverse characteristics and demographics of the

population, but the study aimed to gather valuable insights within the given limitations.

However, a significant limitation of the research pertained to the collection and analysis

of demographic information, particularly regarding age groups. The age distribution of the

participants was categorized into two groups: below 55 years and above 55 years, due to the data

being provided in a categorical format. This categorization may lead to potential generalizations,

as the needs and preferences of individuals below 18 years of age may differ significantly from

those above 55 years. Moreover, it is not practical to classify everyone below 55 years as

"young." Therefore, to address this limitation and provide more nuanced insights, it is

recommended to revise the research design by collecting age information in a continuous form.

This approach would allow for a more detailed examination of the behavioral intention to use a

Bible-based chatbot within each age category, ensuring that the unique issues and considerations

specific to different age groups are adequately addressed and no pertinent issues are overlooked

due to broad categorization.

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CHAPTER FIVE: CONCLUSIONS

Overview

This chapter gives a thorough examination of the study's results and their implications.

The goal of the research was to better understand the factors that affect people's behavioral

intentions to utilize a Bible-based chatbot that uses AI's natural language processing skills to

simulate human interaction with Scripture. The chapter is structured according to the research

questions, offering a thorough examination for each one. This section concludes with a

comprehensive examination of the study findings, implications, and applications. The discussion

examines the study implications on a theoretical, empirical, and practical level, focusing on the

ways in which the research adds to the body of previously published work. Furthermore,

suggestions for future study are presented to guide research in the field, taking into account

various demographics, testing instruments, theoretical frameworks, and limitations.

Research Purpose

The purpose of this research is to examine the variables affecting a person's behavioral

intention to utilize a Bible-based chatbot that uses artificial intelligence's natural language

processing skills to generate human-like engagement Scripture.

Research Questions

RQ1. What influence does performance expectancy have on an individual’s behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create human-like engagement with Scripture?

The first question sought to determine the impact of performance expectancy on a

person's behavioral intention to utilize a Bible-based chatbot that uses AI's natural language

processing skills to generate human-like interaction with Scripture. Users' behavioral intentions

to utilize a technology are heavily influenced by their expectations of the technology's

performance. Performance expectation in this research relates to users' assessments of the

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effectiveness of the Bible-based chatbot in fostering their engagement with Scripture. It includes

users' opinions regarding the chatbot's capacity to offer precise and meaningful interpretations,

respond to their inquiries, and provide tailored suggestions.

The link between performance expectation and behavioral intention is also supported by

theoretical frameworks like the Technology Acceptance Model (TAM) and the UTAUT, which

note that individuals are more inclined to embrace and make use of a new technology if they feel

that it will assist them in accomplishing their goals (Abbad, 2021; Marangunić & Granić, 2015).

The results of this research show that what individuals expect about the chatbot's performance

have a positive effect on their likelihood of using it. Users are more likely to indicate a

willingness to utilize the chatbot if they think it can provide them an enriching and

engaging experience with Scripture. This result is in agreement with the findings of prior

research, which emphasize the relevance of users' perceptions of the usefulness of a technology

in influencing whether or not they would accept and make use of it (Abbad, 2021; Safi et al.,

2018).

Furthermore, research observes that users’ past experiences with related technologies or

AI-driven apps affect their expectations for performance (Cheng & Jiang, 2020). Thus, if users

have previously had favorable encounters with chatbots or AI-powered platforms, they may have

greater expectations about the functioning of the Bible-based chatbot that was investigated in this

research. Conversely, users with limited experience with AI-powered applications may have

reduced initial performance expectations (Cheng & Jiang, 2020).

When examining this study’s findings as contrasted to those conducted earlier, it is clear

that several models of the adoption of technology show that an individual’s intention and what

they choose to do align with performance expectations. For instance, research on virtual learning

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showed that the opinions of learners on the usefulness of the software and applications to

facilitate this activity impact their intention or desire to utilize it (Abbad, 2021). Those who

believe the applications will promote learning and help them thrive academically are more likely

to engage and use them consistently (Gurban & Almogren, 2022). The impact of performance

expectation on behavioral intention has also been shown in research looking at the acceptability

of AI-based virtual assistants and chatbots (Mohd Rahim et al., 2022; Van Bussel et al., 2022).

Research on virtual customer service has shown that a user's inclination to utilize a virtual

assistant is favorably influenced by their opinion of the assistant's competency and capacity to

provide correct and pertinent information (S. Zhang et al., 2021). Users are more likely to

interact with a virtual assistant when they believe that it can meet their requirements and provide

necessary and satisfactory assistance (S. Zhang et al., 2021).

RQ2. What influence does effort expectancy have on an individual’s behavioral intention

to use a Bible-based chatbot that leverages the natural language processing capabilities of AI to

create human-like engagement with Scripture?

The second study question investigates how expectations about effort affect a person's

propensity to interact with a Bible-based chatbot that uses AI's natural language processing skills

to simulate engagement with Scripture. Effort expectancy is the perceived simplicity and

convenience of use affiliated with a particular technology (Abbad, 2021). In the context of this

research, it pertains to users' perceptions of how simple and convenient it is to engage with the

Bible-based chatbot to acquire knowledge about Scripture.

According to the results of this research, users' behavioral intentions to use the Bible-

based chatbot are greatly affected by their expectations of the amount of effort required to utilize

it. Users are more likely to indicate a desire to use a chatbot if they believe it to be easy to

use, intuitive, and needing little effort to explore and find pertinent Scripture-related material.

This confirms the findings of previous studies that have shown how much usability and

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simplicity may influence people's willingness to embrace new technologies (Miguel Cruz et al.,

2022). The connection between effort expectancy and behavioral intention is further supported

by theoretical frameworks like TAM and UTAUT (Abbad, 2021; Marangunić & Granić, 2015).

These concepts suggest that people are more likely to accept and utilize a technology when they

believe it to be simple to use and need little effort to engage with.

Numerous studies have looked at how users' expectations of effort affect their intentions

to embrace new technology. For example, studies conducted on consumer online purchasing

behaviors shows that views on the effort needed to undertake the task majorly affected individual

incidence of conducting purchases online (Daroch et al., 2021; Raman, 2014). People are more

likely to have a positive behavioral intention when they believe that online shopping platforms

are simple to use and involve minimal effort in the purchase process (Petcharat &

Leelasantitham, 2021; Raman, 2014). Similar results on the impact of effort expectancy on

behavioral intention have also been documented by research looking at the acceptability of

chatbot technology (Misischia et al., 2022). For instance, research on chatbots in the context of

customer service has shown that users' perceptions of a chatbot's utility and ease of use have a

positive effect on their willingness to utilize it (Misischia et al., 2022). Users are more inclined to

seek answers or assistance from a chatbot if they view engagement with it to be natural and

unobtrusive.

RQ3. What influence does perceived enjoyment have on an individual’s behavioral

intention to use a Bible-based chatbot that leverages the natural language processing capabilities

of AI to create human-like engagement with Scripture?

The third study question looks at how perceived enjoyment affects a person's behavioral

intention to utilize a Bible-based chatbot that uses AI's natural language processing skills to

create human-like interaction with Scripture. The concept of perceived enjoyment describes how

a person perceives the pleasure, entertainment, and satisfaction they get from utilizing a piece of

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technology (Abbad, 2021). In the case of this research, it refers to the users' impressions of the

chatbot as a tool that makes engaging with Scripture fun and interesting.

The results of this research show a substantial correlation between reported enjoyment and

behavioral intention to utilize Bible-based chatbot. Users are more likely to show a desire to

utilize a chatbot if they find it entertaining and engaging. This finding is consistent with

antecedent research pointing out the importance of user enjoyment in technology acceptance and

adoption (Safi et al., 2018). The perceived satisfaction of using a technology might favorably

impact users' desire to utilize it according to theoretical frameworks like the Technology

Acceptance Model and the Hedonic or Utilitarian Model (Abbad, 2021; Nikolopoulou et al.,

2021). People are more likely to interact with a technology and discover ways to integrate it into

their routines if they consider the technology to be enjoyable and captivating.

The impact of perceived enjoyment on users' desire to utilize technology in diverse

circumstances has been the subject of several research studies. For example, studies conducted in

on mobile apps have shown that users' perceptions of the pleasure they gain from using a mobile

app have a considerable influence on their intentions to continue using it (Nedra et al., 2019;

Nikolopoulou et al., 2021). When consumers have a favorable experience with the app, the

probability that they will continue to use it increases.

Similar results addressing the impact of perceived pleasure on behavioral intention have

also been revealed by research looking at the adoption of virtual reality technology. It has been

established via research carried out in the fields of virtual reality gaming, tourism, and

education that users' perceptions of enjoyment have a major impact on their intentions to use

virtual reality applications (Kari & Kosa, 2023; H.-W. Lee et al., 2021; Santos Garduño et al.,

2021). When consumers get the impression that the virtual reality experience they are having is

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engaging, immersive, and thrilling, they are more likely to interact with it and explore its

capabilities.

RQ4. What influence does perceived risk have on an individual’s behavioral intention to

use a Bible-based chatbot that leverages the natural language processing capabilities of AI to

create human-like engagement with Scripture?

The fourth study question explores how perceived risk affects a person's behavioral

intention to utilize a Bible-based chatbot that makes use of AI's natural language processing

skills to create human-like interaction with Scripture. A person's subjective evaluation of the

risks or possible drawbacks related to implementing and using a certain technology is known as

perceived risk (Abbad, 2021). In the context of this study, it refers to the impressions that

individuals have regarding the challenges and risks linked with the use of chatbots that are based

on the Bible.

The findings show that a statistically significant negative correlation exists between

individual perception of risk and intention or choice to use chatbots that are Bible-based. Users

are less inclined to express their intention to use the software if they think there are more risks

involved in doing so. These results are consistent with other research results that highlight the

significance of perceived risk as a barrier to the acceptance and implementation of

new technologies (Habib & Hamadneh, 2021; Klaver et al., 2021). UTAUT and the Technology

Acceptance Model both recognize the significance of users' perceptions of risk in determining

whether or not they will embrace and utilize a new technology (Abbad, 2021; Marangunić &

Granić, 2015). People are less likely to interact with technology and will exhibit a reduced

interest in using it when they perceive a greater degree of risk, particularly regarding issues about

security, privacy, reliability, or negative effects.

Studies that have been carried out in various technological settings have repeatedly

indicated that perceived risk has an influence on the behavior of users in terms of acceptability

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and implementation. E-commerce research, for example, has highlighted that individual

perceptions of the challenges or risks linked with buying goods online or carrying out

transactions online, including issues surrounding personal information, privacy, or goods quality,

greatly impact their frequency of conducting online purchases (Daroch et al., 2021; Raman,

2014). Lower intentions and less frequent use of e-commerce platforms are often caused by

higher perceived risks.

Similar conclusions on the impact of perceived risk on users' behavioral intention have also

been revealed by research looking at the acceptability of mobile apps and social media platforms

(Nedra et al., 2019). Users who are aware of possible threats to their privacy, inappropriate uses

of their data, or adverse effects on their social lives are more likely to have a lower desire to

utilize the technologies in consideration (Barrett-Maitland & Lynch, 2020).

RQ5. How do gender, age, and experience moderate the relationship between the

UTAUT framework constructs of performance expectancy, effort expectancy, perceived

enjoyment, perceived risk, and the behavioral intention to use a Bible-based chatbot that

leverages the natural language processing capabilities of AI to create human-like engagement

with Scripture?

The fifth research question examines the moderating effects of gender, age, and

experience on the relationship between the UTAUT framework's constructs (performance

expectancy, effort expectancy, perceived enjoyment, and perceived risk) and their behavioral

intention to use a Bible-based chatbot that uses AI's natural language processing capabilities to

create engagement with Scripture that is similar to that of a human. This study's results give

important insights into how these demographic characteristics might impact the link between

UTAUT constructs and users' behavioral intentions.

The analysis showed that gender significantly modifies the correlation between the

UTAUT components and behavioral intention. More specifically, we discovered that males are

more susceptible to the effects of performance anticipation and perceived enjoyment on

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behavioral intention than females. This indicates that men may be more motivated to use a

chatbot based on the Bible because they put a larger value on the perceived advantages and

satisfaction obtained from doing so. However, when it comes to reading Scripture online, women

may be impacted by other influences or have different tastes. These gender disparities underline

the need to take into account gender-specific techniques when marketing the chatbot to various

user groups.

Other studies that have been conducted previously have also considered gender as a

major determining factor in individual choice to utilize a specific software or technology (Cai et

al., 2017; Goswami & Dutta, 2016). Evidence shows that males and females possess distinct

attitudes and behaviors when it comes to technology, which may be determined by their

perceived usefulness of the technology, how easy it is to use, convenience, and their likelihood

of embracing it (Cai et al., 2017). Developers and designers should adjust their interventions and

techniques to successfully engage both male and female consumers by taking into account these

discrepancies.

The moderating influence of age on the connection between UTAUT components and

behavioral intention was another aspect of our research that was investigated. The findings show

that the associations between the UTAUT components and behavioral intentions are

considerably affected by age. Compared to older users, younger users have much greater

connections between the UTAUT components and behavioral intention. This shows that younger

people may be more receptive to trying out new technologies and may see the chatbot as having

more positive outcomes in terms of advantages, enjoyment, and risks. In contrast, senior

individuals may have distinct preferences or encounter greater obstacles when implementing new

digital tools. Our findings are consistent with other research that found age-related differences in

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technology acceptability as a moderator. While older people could show greater opposition or

caution, younger people often accept technology at faster rates and have more favorable opinions

regarding it (De Cicco et al., 2020; Vaportzis et al., 2017). Our data corroborates these patterns,

thus highlighting the significance of taking age into account while building and marketing the

usage of Bible-based chatbots. For instance, customized user interfaces or training courses

designed for certain age groups might improve user engagement and experience (Thangavel et

al., 2022).

Our research also looked at the moderating influence of experience. The findings showed

that all of the UTAUT components and behavioral intention are highly moderated by experience.

An individual's previous experience with related technologies or fields impacts their impressions

of new technologies as well as their intent to utilize these technologies (Butz et al., 2022). When

contrasted with those with less experience, users with greater experience had higher connections

between the UTAUT components and behavioral intention. This shows that people who have

expertise with or familiarity with comparable technology may perceive more advantages, find it

simpler to use, and like using the chatbot more (De Cicco et al., 2020). However, less

experienced users may confront more challenges or uncertainty, decreasing their chatbot usage

intention (De Cicco et al., 2020). These results highlight the need to take into account users'

existing experience and offer relevant help and advice to improve chatbot adoption and use

(Dinh & Park, 2023).

Research Conclusions, Implications, and Applications

Conclusions

This study’s results addressing the first research question point to a significantly

favorable relationship existing between expectations of performance and behavioral intention.

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This suggests that people are more likely to use the Bible-based chatbot if they believe it to be

successful in fostering compelling and human-like interactions with Scripture. This confirms

what other studies have shown about the role of perceived usefulness in influencing people to

accept new technologies. This research therefore contributes to an understanding of the

importance of performance expectation in Bible-based chatbots by validating and building upon

previous research findings.

The analysis of research question two showed a substantial correlation between

behavioral intention and effort expectancy. This shows that people who think the Bible-based

chatbot is simple to use and requires little work are more likely to express an intention to utilize

it. This result is in line with other research emphasizing how important user friendliness is to the

adoption of new technologies. The present analysis underlines the relevance of addressing

consumers' perceived ease of use while building and advertising Bible-based chatbots by

correlating current findings.

The answers to study question three show a strong, positive correlation between

perceived enjoyment and intention. This suggests that those who find the Bible-based chatbot

entertaining and interesting will be more inclined to utilize it. This confirms what has been found

in other research: that hedonic considerations have a significant influence in determining which

technologies people choose to embrace. By supporting and adding to previous research, this

study shows how important it is for Bible-based chatbots to be fun and have interesting aspects

that encourage people to use them.

A substantial negative correlation between perceived risk and behavioral intention is

found by analyzing research question number four. This seems to imply that persons who

perceive more risks in utilizing a Bible-based chatbot are far less inclined to have the desire to

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use it. This confirms the conclusions of other studies that identified concern over new

technologies as a significant obstacle to their widespread use. By proving that perceived risk has

a negative effect, our study shows how important it is to address and reduce users' worries about

privacy, data security, and possible negative effects in order to get more people to embrace and

utilize Bible-based chatbots.

The results of study question number five show that gender, age, and experience

significantly moderate the link between behavioral intention and the UTAUT components

(performance expectation, effort expectancy, perceived enjoyment, and perceived risk). These

findings stress the need for marketers and developers of Bible-based chatbots to take into

account the varied needs and interests of potential users. The efficacy and acceptability of the

chatbot may be increased by developing tactics that take into account gender-specific

preferences, age-related challenges and motivations, and users' past experiences.

Theoretical Implications

This study's results have significant theoretical implications in the area of technological

acceptability and adoption, especially in the context of Bible-based chatbots. This research

extends the UTAUT theoretical framework and shows that it is applicable to the field of religious

technology adoption. According to the research results here, the UTAUT measurements of

performance expectancy, effort expectancy, perceived enjoyment, and perceived risk are relevant

in influencing users' behavioral intention to utilize Bible-based chatbots.

This study’s results likewise contribute to the theoretical knowledge by showing the

moderating impact of age, gender, and experience on the UTAUT constructs. This underscores

the importance of considering user inclinations and attributes during the design of technology, as

these factors play a major role in determining how effectively new technology is embraced. By

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understanding that different user groups have different wants and expectations, academics and

practitioners can make more detailed theoretical models that better describe the complexity of

how people embrace technology in various demographic categories.

Empirical Implications

This research contributes to the empirical evidence regarding the factors that influence

the use of Bible-based chatbots. The finding that substantial correlations exist between factors

such as performance expectation, effort expectancy, perceived enjoyment, perceived risk, and

behavioral intention add to the expanding body of study on the topic of technology adoption in

the religious field. The results provide more evidence that the acceptability and use of Bible-

based chatbots are heavily influenced by users' opinions of their usefulness, simplicity of use,

enjoyment, and risks.

The research also clarifies how gender, age, and experience have moderating impacts.

These empirical findings bring to light the importance of individualized methods in the design

and marketing of technological products. Bible-based chatbots may be more successful and

widely accepted if their approaches are tailored to account for differences in gender preferences,

age-related challenges and motivations, and users' past experiences.

Practical Applications

The research highlights the importance of enhancing digital literacy among parishioners.

The study reveals a significantly stronger relationship between effort expectancy and behavioral

intention for individuals with chatbot experience compared to those without. This finding

suggests that church leaders should actively provide opportunities for parishioners to learn about

and utilize digital technologies. Strategies such as offering digital literacy classes, workshops, or

providing resources on how to effectively engage with AI-powered Bible chatbots can empower

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congregants in the realm of digital literacy. Building upon the church's historical contributions to

literacy and translation efforts, embracing the potential of Bible-based chatbots can further

increase engagement with Scripture, making it more accessible to a wider audience.

AI-powered Bible chatbots offer an opportunity to reach new audiences within the

church. The research indicates a stronger relationship between performance expectancy and

behavioral intent among younger individuals. This finding suggests that AI-powered Bible

chatbots can effectively engage and connect with younger parishioners. Forward-thinking church

leaders who prioritize the next generation can utilize these chatbots to offer dynamic Bible

studies, provide spiritual guidance, and foster connections with individuals seeking to deepen

their understanding of the Bible.

AI-powered Bible chatbots have the potential to complement traditional Bible study

methods. This study's findings confirm a strong relationship between performance expectancy

and behavioral intent, indicating that AI-powered chatbots can effectively supplement existing

approaches to Bible study. Church leaders can utilize these chatbots to provide supplementary

resources, enhancing the study experience for individuals. Additionally, AI-powered chatbots

offer a unique avenue for people to engage with Scripture in a different and interactive manner.

By incorporating these chatbots into their programs, church leaders can enrich the Bible study

experience and cater to diverse learning preferences.

It is crucial for church leaders to persist in researching the use of AI-powered Bible

chatbots. While the present study has provided valuable insights, its scope was limited, leaving

many aspects of AI-powered chatbot utilization unexplored. To gain a comprehensive

understanding of the technology's benefits and risks, church leaders should actively engage in

further research. By conducting additional studies and investigations, they can uncover new

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possibilities, explore potential challenges, and make informed decisions regarding the

implementation of AI-powered Bible chatbots.

Church leaders have a crucial responsibility in addressing ethical considerations and

providing guidance on the use of AI and technology in relation to Scripture. They should actively

educate congregants about potential risks, foster critical thinking, and offer resources to navigate

ethical dilemmas that may arise when utilizing AI-based tools for religious purposes. In a context

where the use of AI is increasing while the reading of Scripture is decreasing, it becomes

essential for parishioners and pastors to discern and vet materials to minimize potential risks.

Church leaders need to carefully consider the ethical implications associated with AI-powered

Bible chatbots. Although this study revealed that users are more inclined to use a Bible chatbot

when they perceive it as low-risk, ethical concerns, including bias and discrimination, still exist

regarding the use of AI-powered technology.

Research Limitations

The participants for the current research were selected using convenience sampling,

which is important to acknowledge in evaluating the study's methodology. While convenience

sampling has its advantages in terms of accessibility and feasibility, it is important to recognize

its limitations. Convenience sampling may introduce biases and limit the generalizability of the

findings (Z. Hu & Qin, 2018). The researcher's access to the population as the creator of a Bible

study app influenced participant selection, potentially resulting in a sample that may not fully

represent the wider population of prospective Bible-based chatbot users. Therefore, caution must

be exercised when extrapolating the study's results beyond the specific sample. Future studies

could consider alternative sampling strategies such as cluster sampling or simple random

sampling to enhance representativeness and improve the generalizability of findings.

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The study's ability to provide an exhaustive understanding is constrained by the

utilization of a quantitative descriptive design. Although this approach enables the gathering of

extensive data and offers an accurate depiction of the perspectives and behaviors of the people

who took part, it may not be able to capture the complexities and subtleties that qualitative

research methodologies can give. To acquire a deeper comprehension of participants' experiences

and perspectives, future research may consider including qualitative methodologies, like focus

groups or interviews.

The measuring tools used to gather data are critical in quantitative research. The current

research used surveys to collect information on participants' views and goals. The social

desirability bias affects self-report surveys, causing respondents to provide answers they believe

to be socially acceptable as opposed to ones that accurately represent their genuine opinions or

actions (Durmaz et al., 2020). While confidentiality and anonymity were used to reduce social

desirability bias, its possible influence on the results should be considered. To supplement self-

report measures and reduce bias, researchers can investigate the actions and experiences of

chatbot users using other techniques for collecting data, like behavioral assessments or objective

use statistics.

The fact that the research was carried out in a specific setting may also restrict how

broadly the results may be applied to other contexts or groups. Participants’ views and actions

regarding Bible-based chatbots may be influenced by elements including cultural differences,

technical capabilities, or religious convictions. Researchers should exercise caution when

applying these findings to other areas, and they should consider doing replication studies in a

range of circumstances to assess the reliability and generalizability of the information collected.

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Cross-sectional analysis, which entails the collection of data from one point in time, was

used (Setia, 2016). This method poses limitations, as it renders it difficult to determine variables’

causal relationships and to determine changes that have occurred throughout time (Setia, 2016).

The shifting patterns of behavioral intention and utilization of Bible-based chatbots would be

better understood through longitudinal designs that collect data at various time intervals. Future

study should include longitudinal methodologies to investigate the temporal nature of variable

connections. Researchers may give an accurate assessment of the study results by taking into

account the limitations linked to the methodology used. Comprehending and resolving these

limitations will help to steer future study and enhance understanding in the area of Bible-based

chatbot adoption.

Further Research

During the course of this study, several noteworthy avenues for future research have

emerged. These areas warrant further exploration and investigation, as they have the potential to

contribute valuable insights to the existing body of knowledge. The identified opportunities for

further research are as follows:

1. Future research might examine the generalizability of the results to other groups, as the

present study concentrated on a particular demographic. It would be beneficial to look at

how Bible-based chatbots are adopted by various groups, including those with different

ages, religious affiliations, or cultural backgrounds, in order to gain more understanding

of possible differences in attitudes, intention, and motivations. This could make it easier

to pinpoint the exact factors that affect how well these technologies are adopted and used

by different demographic groups.

2. The limitations that were mentioned previously should serve as a foundation for further

study, which should aim to tackle and overcome these constraints. The external validity

and generalizability of the results might be improved if the studies were conducted with

bigger sample sizes, different settings, and representative populations. To get a complete

picture of the user experiences, motives, and challenges associated with Bible-based

chatbot adoption, researchers should investigate using mixed-methods methodologies that

integrate both qualitative and quantitative data.

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3. Future research may look at alternative instruments, as the present study used recognized

tools to measure concepts like performance expectancy, effort expectancy,

perceived enjoyment, and perceived risk. Developing and validating new assessment

instruments to analyze users' attitudes and intents regarding Bible-based chatbots might

improve measurement accuracy and dependability. To gain rich and complex

perspectives on individual experiences and opinions, researchers might also look into the

usage of qualitative techniques like in-depth interviews or focus groups.

4. The study's theoretical underpinning was the UTAUT framework. Nevertheless, there

could be more relevant frameworks that might improve comprehension of the

factors affecting the use of Bible-based chatbots. To acquire a deeper understanding of

user intents and behavior, future study may include additional factors from

relevant theories, trust, social influence, or compatibility. Researchers have the potential

to unearth new insights and make contributions toward the creation of a more

comprehensive theoretical model in this field if the theoretical framework is expanded.

5. Future study may utilize longitudinal methods, which gather data over a lengthy period of

time, to gain insight into the changing patterns of user intentions and behavior. This

would make it possible to track and analyze changes in people's perspectives, goals, and

actions concerning adoption over time. Longitudinal designs have the ability to not only

give insights into the characteristics that influence the ongoing usage of Bible-based

chatbots but also to discover any possible obstacles or hurdles that may occur over the

course of time.

6. Comparative investigations that contrast the adoption of Bible-based chatbots with other

technological interventions or other methods of interacting with religious texts might

provide insightful information. Researchers may determine the special benefits and

limitations of chatbot applications and provide guidance for the creation of more efficient

and user-friendly tools by contrasting the efficacy, user experiences, and outcomes of

Bible-based chatbots with conventional approaches or alternative technological options.

7. Concerns about ethics and privacy grow more pressing as Bible-based chatbots

increasingly harness artificial intelligence and natural language processing skills. Future

studies should examine the ethical consequences of utilizing chatbots to engage in

religious activity, taking into account concerns like privacy of information, algorithmic

bias, and the possibility of manipulation. Analyzing users' views of these ethical

problems, as well as setting rules or procedures to promote ethical, courteous, and

respectful utilization of chatbot technology in religious contexts, are subjects that should

be researched further.

8. Further investigation may concentrate on the design features of Bible-based chatbots in

order to improve the user experience and increase engagement. Investigating things like

chatbot personality, conversational style, graphical presentation, and interaction can help

in the design of chatbot experiences that are more interesting and persuasive. The

creation of more user-focused chatbot experiences may also be influenced by an

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awareness of users' preferences for customization choices, personalization features, and

content that is tailored to specific requirements.

9. Future studies may examine how users' religious experiences, spiritual development, and

well-being are affected by Bible-based chatbots. Examining how chatbots affect users'

reading of the Bible, prayer habits, and general spiritual growth might reveal both the

potential advantages and drawbacks of these technological affordances. Researchers

might utilize qualitative methodologies to document the transformational features of

chatbot interactions and investigate how they connect with users' own beliefs and values.

10. User obstacles and resistance. Investigating user resistance and challenges to the

adoption of Bible-based chatbots may give insights into the factors that impede their

acceptability and utilization. Skepticism and worries about technology replacing

interpersonal relationships, as well as religious objections, are a few examples of topics

that research might look into. If developers and implementers understand the fundamental

causes for resistance, it may help them address these issues and build techniques to lessen

resistance, which will eventually drive greater adoption and engagement with chatbot

technology.

11. Future research may also look into how Bible-based chatbots could help people get

involved in their communities and connect with each other. Investigating chatbot

interface elements like group interactions, shared conversations, or collaborative learning

might improve social relationships and provide chances for collective religious

experiences. New strategies for religious participation in the digital era may be developed

by learning how chatbot technology might aid in community building.

Summary

The purpose of the current research was to examine the factors affecting people's

behavioral intentions to utilize a Bible-based chatbot that uses artificial intelligence's natural

language processing skills to simulate human interaction with Scripture. The UTAUT framework

served as the theoretical underpinning for the research's quantitative descriptive approach. Data

from a broad sample of individuals were gathered via convenience sampling. The study's results

offer insight into the moderating impacts of demographic characteristics and give useful insights

into the primary drivers of people' behavioral intention to utilize a Bible-based chatbot.

The first major takeaway was that expectations about performance, effort, and perceived

enjoyment have a big impact on behavior. The research indicated that people had a higher

intention to utilize a Bible-based chatbot if they believe it has a high-performance expectation,

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meaning that it enables them to successfully engage with Scripture. Moreover, people's

behavioral intention rises when they believe the chatbot to be enjoyable and simple to use (effort

expectancy). These results are consistent with previous studies and highlight the significance of

taking into account the perceived advantages, simplicity of use, and satisfaction that chatbot

systems provide to their users when developing and advertising these systems.

The second major takeaway was that behavioral intention is adversely impacted by

perceived risk. The research found that people's perceptions about risks connected with utilizing

the chatbot based on the Bible affect their behavioral intentions. It was revealed that having a

higher degree of perceived risk was connected with having less of an intention to utilize the

chatbot. This result underlines how important it is to address those concerns users have about

their security, privacy, and the reliability of the information the chatbot provides. Transparency,

privacy protections, and unambiguous communication may all go a long way toward winning

over users' confidence and encouraging them to interact with the chatbot.

To better understand the significance of the findings, we will examine a scenario from

real life. Sarah, a consistent churchgoer, was originally hesitant to use the Bible-based chatbot

because of doubts over its authenticity and veracity. Sarah's assessment of performance

and effort expectancy, and her satisfaction, improved after she discovered its beneficial effects

on others and made use of its informative and entertaining features. The chatbot presented her

with an opportunity to learn more about the Bible in a manner that was both easy and

convenient. Sarah's behavioral intention to engage with the chatbot increased as she evaluated

the level of risk associated with using it to be lower. This highlights the potential

transformational impact of chatbot systems that are effectively designed and trustworthy.

Chatbot developers can therefore increase user adoption and encourage meaningful interaction

130

with Scripture by meeting users' requirements, guaranteeing simplicity of use, and eliminating

perceived risks.

131

REFERENCES

A. Bere. (2014). Exploring determinants for mobile learning user acceptance and use: An

application of UTAUT. 2014 11th International Conference on Information Technology:

New Generations, 84–90. https://doi.org/10.1109/ITNG.2014.114

Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning

systems in developing countries. Education and Information Technologies, 26(6), 7205–

7224. https://doi.org/10.1007/s10639-021-10573-5

Abbitt, J. T., & Boone, W. J. (2021). Gaining insight from survey data: An analysis of the

community of inquiry survey using Rasch measurement techniques. Journal of

Computing in Higher Education, 33(2), 367–397. https://doi.org/10.1007/s12528-020-

09268-6

Abdel-Fadil, M. (2017). Identity Politics in a Mediatized Religious Environment on Facebook:

Yes to Wearing the Cross Whenever and Wherever I Choose. Journal of Religion in

Europe, 10(4), 457–486. https://doi.org/10.1163/18748929-01004001

Abide. (2021). About – Abide. Abide. https://abide.co/about/

Afflerbach, T. (2020). Hybrid virtual teams in shared services organizations: Practices to

overcome the cooperation problem. Springer. https://go.exlibris.link/vS4ZrzCK

Agrawal, S. R. (2021). Digital pollution and its impact on the family and social interactions.

Journal of Family Issues, 42(11), 2648–2678.

https://doi.org/10.1177/0192513X20985558

Ahmad, W., Kim, W. G., Choi, H.-M., & Haq, J. U. (2021). Modeling behavioral intention to use

travel reservation apps: A cross-cultural examination between US and China. Journal of

Retailing and Consumer Services, 63. https://doi.org/10.1016/j.jretconser.2021.102689

Ain, N., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning

management system use: An extension of UTAUT2. Information Development, 32(5),

1306–1321. https://doi.org/10.1177/0266666915597546

Ali, M., Raza, S. A., Puah, C. H., & Amin, H. (2019). Consumer acceptance toward takaful in

Pakistan: An application of diffusion of innovation theory. International Journal of

Emerging Markets, 14(4), 620–638. https://doi.org/10.1108/IJOEM-08-2017-0275

Alkhadim, G. S. (2022). Cronbach’s Alpha and semantic overlap between items: A proposed

correction and tests of significance. Frontiers in Psychology, 13, 815490.

https://doi.org/10.3389/fpsyg.2022.815490

Amini, A., & Alimohammadlou, M. (2021). Toward equation structural modeling: An

integration of interpretive structural modeling and structural equation modeling. Journal

of Management Analytics, 8(4), 693–714.

https://doi.org/10.1080/23270012.2021.1881927

132

Anjaria, K. (2022). Knowledge derivation from Likert scale using Z-numbers. Information

Sciences, 590, 234–252. https://doi.org/10.1016/j.ins.2022.01.024

Arnab, R. (2017). Survey sampling theory and applications. Elsevier/AP, Academic Press, an

imprint of Elsevier. https://go.exlibris.link/9QDx2hvl

Arthur, J., & Rensleigh, C. (2015). The use of online technologies in the small church. SA

Journal of Information Management, 17(1). https://doi.org/10.4102/sajim.v17i1.630

Astorino, S., Simmonds, M., Puget, J.-F., & Sanders, R. E. (2019). Artificial intelligence:

Evolution and revolution. MC Press, LLC. https://go.exlibris.link/wyW4cpwn

Avemegah, E., Gu, W., Abulbasher, A., Koci, K., Ogunyiola, A., Eduful, J., Li, S., Barington,

K., Wang, T., Kolady, D., Perkins, L., Leffler, A. J., Kovács, P., Clark, J. D., Clay, D. E.,

& Ulrich-Schad, J. D. (2021). An examination of best practices for survey research with

agricultural producers. Society & Natural Resources, 34(4), 538–549.

https://doi.org/10.1080/08941920.2020.1804651

Averkīĭ, A. of J., Kotar, N., & Permi

︠

︡

kov, V. (2017). The Acts of the Apostles: Vol. II. Holy

Trinity Seminary Press, Holy Trinity Monastery. https://go.exlibris.link/thtZHY3Z

Azakir, B., Mobarak, H., Al Najjar, S., El Naga, A. A., & Mashaal, N. (2020). Knowledge and

attitudes of physicians toward research ethics and scientific misconduct in Lebanon. BMC

Medical Ethics, 21(1), 39. https://doi.org/10.1186/s12910-020-00475-5

Balakrishnan, J., Abed, S. S., & Jones, P. (2022). The role of meta-UTAUT factors, perceived

anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based

services? Technological Forecasting and Social Change, 180, 121692.

https://doi.org/10.1016/j.techfore.2022.121692

Barbera, J., Naibert, N., Komperda, R., & Pentecost, T. C. (2021). Clarity on Cronbach’s Alpha

use. Journal of Chemical Education, 98(2), 257–258.

https://doi.org/10.1021/acs.jchemed.0c00183

Barrett-Maitland, N., & Lynch, J. (2020). Social media, ethics and the privacy paradox. In C.

Kalloniatis & C. Travieso-Gonzalez (Eds.), Security and Privacy From a Legal, Ethical,

and Technical Perspective. IntechOpen. https://doi.org/10.5772/intechopen.90906

Barth, S., & de Jong, M. D. T. (2017). The privacy paradox – Investigating discrepancies

between expressed privacy concerns and actual online behavior – A systematic literature

review. Telematics and Informatics, 34(7), 1038–1058.

https://doi.org/10.1016/j.tele.2017.04.013

Bartlett, J. W. (2020). Robustness of ANCOVA in randomized trials with unequal

randomization. Biometrics, 76(3), 1036–1038. https://doi.org/10.1111/biom.13184

Beldman, D. J. H. (2020). Judges. William B. Eerdmans Publishing Company.

https://go.exlibris.link/1yR7zSGv

133

Bellar, W. (2017). Private practice: Using digital diaries and interviews to understand

evangelical Christians’ choice and use of religious mobile applications. New Media &

Society, 19, 111–125. https://doi.org/10.1177/1461444816649922

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-

confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921

Bianchi, M., Di Benedetto, A., Franzò, S., & Frattini, F. (2017). Selecting early adopters to foster

the diffusion of innovations in industrial markets: Evidence from a multiple case study.

European Journal of Innovation Management, 20(4), 620–644.

https://doi.org/10.1108/EJIM-07-2016-0068

Blessing, L. T. M., & Chakrabarti, A. (2009). DRM, a design research methodology. Springer.

https://doi.org/10.1007/978-1-84882-587-1

Boda, M. J., Schwab, G. M., & Boda, M. J. (2017). Judges, Ruth. Zondervan.

https://go.exlibris.link/TfXcWHwF

Bogost, I. (2007). Persuasive games: The expressive power of videogames. MIT Press.

Bolman, L. G., & Deal, T. E. (2017). Reframing organizations: Artistry, choice and leadership

(6th ed.). Jossey-Bass.

Boyle, L. H., Whittaker, T. A., Eyal, M., & McCarthy, C. J. (2017). What really happens in

quantitative group research? Results of a content analysis of recent quantitative research

in JSGW. The Journal for Specialists in Group Work, 42(3), 243–252.

https://doi.org/10.1080/01933922.2017.1338812

Bray, G. L. (2019). Documents of the English reformation 1526-1701 (3rd ed.). James Clarke.

https://go.exlibris.link/qwmxC53f

Brezina, C. (2020). Artificial intelligence and you. Rosen YA. https://go.exlibris.link/V05jn7xF

Briggs, R. (2018). Theological hermeneutics and the book of Numbers as Christian scripture.

University of Notre Dame Press. https://go.exlibris.link/HYfQZnzy

Brittain, C. C. (2020). Artificial intelligence: Three challenges to theology. Toronto Journal of

Theology, 36(1), 84–86. https://doi.org/10.3138/tjt-2020-0033

Burns, J. M. (1978). Leadership. Harper & Row.

Butz, B., Kloep, L., & Kriegesmann, B. (2022). User experience reevaluation and diffusion of

technology in the context of compulsory usage illustrated by the example of

telepsychotherapy—A literature review. DIGITAL HEALTH, 8, 205520762211344.

https://doi.org/10.1177/20552076221134448

Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: A meta-analysis.

Computers & Education, 105, 1–13. https://doi.org/10.1016/j.compedu.2016.11.003

134

Camarines, T. M., & Camarines, J. C. M. (2021). Religious coping and the use of religious apps

during COVID-19 pandemic. Journal of Public Health (Oxford, England), Journal

Article. https://doi.org/10.1093/pubmed/fdab230

Campbell, H. A., Altenhofen, B., Bellar, W., & Cho, K. J. (2014). There’s a religious app for

that! A framework for studying religious mobile applications. Mobile Media &

Communication, 2(2), 154–172. https://doi.org/10.1177/2050157914520846

Campbell, H. A., & DeLashmutt, M. W. (2014). Studying technology and ecclesiology in online

multi-site worship. Journal of Contemporary Religion, 29(2), 267–285.

https://doi.org/10.1080/13537903.2014.903662

Campbell, H., & Garner, S. (2016). Networked theology: Negotiating faith in digital culture.

Baker Academic.

Castelvecchi, D. (2022). Are ChatGPT and AlphaCode going to replace programmers? Nature

(London), Journal Article. https://doi.org/10.1038/d41586-022-04383-z

Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An

application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652.

https://doi.org/10.3389/fpsyg.2019.01652

Charlesworth, J. H., & Pruszinski, J. G. R. (2019). Jesus research: The gospel of John in

historical inquiry. Bloomsbury T&T Clark. https://go.exlibris.link/hqBjwL9q

Chaudary, B., Paajala, I., Keino, E., & Pulli, P. (2017). Tele-guidance based navigation system

for the visually impaired and blind persons. In K. Giokas, L. Bokor, & F. Hopfgartner

(Eds.), EHealth 360° (pp. 9–16). Springer International Publishing.

Chayutimaphan, K., Manuyakorn, W., Siwarom, S., Arnin, J., Kanchongkittiphon, W.,

Pachasidchai, C., Kiewngam, P., Jotikasthira, W., & Sawatchai, A. (2022). Benefits of

mobile application in food allergy patients and caregivers. Journal of Allergy and

Clinical Immunology, 149(2), AB116. https://doi.org/10.1016/j.jaci.2021.12.400

Cheng, Y., & Jiang, H. (2020). How do AI-driven chatbots impact user experience? Examining

gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of

Broadcasting & Electronic Media, 64(4), 592–614.

https://doi.org/10.1080/08838151.2020.1834296

Citrin, J. M., & DeRosa, D. M. (2021). Leading at a distance: Practical lessons for virtual

success. Wiley.

Cockerill, G. L. (2012). The epistle to the Hebrews. William B. Eerdmans Publishing Company.

Cole, A., & Ovwigho, P. C. (2012). Bible engagement as the key to spiritual growth: A research

synthesis. Center for Bible Engagement.

135

Collins, P. (2014). Defining “scripture engagement.” Bible Gateway.

https://www.biblegateway.com/resources/scripture-engagement/sidebar/defining-

scripture-engagement

Connell, J., Carlton, J., Grundy, A., Taylor Buck, E., Keetharuth, A. D., Ricketts, T., Barkham,

M., Robotham, D., Rose, D., & Brazier, J. (2018). The importance of content and face

validity in instrument development: Lessons learnt from service users when developing

the Recovering Quality of Life measure (ReQoL). Quality of Life Research, 27(7), 1893–

1902. https://doi.org/10.1007/s11136-018-1847-y

Cooper, L., Fulks, J., Farquhar Plake, J., & Youngblood, A. (2021). State of the Bible: USA

2021. American Bible Society.

Copan, V. A. (2016). Changing your mind: The Bible, the brain, and spiritual growth. The

Lutterworth Press. https://go.exlibris.link/fhNyN5gv

Crain’s Chicago Business. (2021, April 16). Chicago company behind Catholic prayer app

raises $12 million. Crain’s Chicago Business.

https://www.chicagobusiness.com/technology/chicago-company-behind-catholic-prayer-

app-raises-12-million

Crawford, R. (2022). Seeing the bigger picture: Why market research history matters?

International Journal of Market Research, 64(2), 187–199.

https://doi.org/10.1177/14707853211067701

Crespin-Mazet, F., Goglio-Primard, K., Havenvid, M. I., & Linné, Å. (2021). The diffusion of

innovation in project-based firms – linking the temporary and permanent levels of

organisation. The Journal of Business & Industrial Marketing, Journal Article.

https://doi.org/10.1108/JBIM-01-2020-0066

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed

methods approaches (5th ed.). Sage.

Crisp, O. (2016). The word enfleshed: Exploring the person and work of Christ. Baker

Academic. https://go.exlibris.link/k1gj1LY5

Cross, J. R. (2015). The stranger on the road to Emmaus; Who was the man? What was the

message? GoodSeed International.

Crossman, A. (2020). The meaning of reliability in Sociology. ThoughtCo.

https://www.thoughtco.com/reliability-definition-3026520

Csinos, D. M. (2020). Little theologians: Children, culture, and the making of theological

meaning. McGill-Queen’s University Press. https://go.exlibris.link/2npRy1nB

Daroch, B., Nagrath, G., & Gupta, A. (2021). A study on factors limiting online shopping

behaviour of consumers. Rajagiri Management Journal, 15(1), 39–52.

https://doi.org/10.1108/RAMJ-07-2020-0038

136

De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K.-U., & von Wangenheim, F. (2020).

Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive

Marketing, 51, 91–105. https://doi.org/10.1016/j.intmar.2020.04.007

De Cicco, R., Silva, S. C., & Alparone, F. R. (2020). Millennials’ attitude toward chatbots: An

experimental study in a social relationship perspective. International Journal of Retail &

Distribution Management, 48(11), 1213–1233. https://doi.org/10.1108/IJRDM-12-2019-

0406

DeBose, K. (2018, September 21). Research methods guide: Research design & method.

Virginia Tech. https://guides.lib.vt.edu/researchmethods/design-method

Degirmenci, K., & Breitner, M. H. (2017). Consumer purchase intentions for electric vehicles: Is

green more important than price and range? Transportation Research Part D: Transport

and Environment, 51, 250–260. https://doi.org/10.1016/j.trd.2017.01.001

Dinh, C.-M., & Park, S. (2023). How to increase consumer intention to use Chatbots? An

empirical analysis of hedonic and utilitarian motivations on social presence and the

moderating effects of fear across generations. Electronic Commerce Research.

https://doi.org/10.1007/s10660-022-09662-5

Donaldson, B. (2009, November 19). Top 10 reasons to start an online church. Church Leaders.

https://churchleaders.com/pastors/pastor-articles/138871-top-10-reasons-to-start-an-

online-church.html

Doriani, D. M., & Phillips, R. D. (2020). 2 Timothy & Titus. P & R Publishing.

Dourado, G. B., Volpato, G. H., de Almeida-Pedrin, R. R., Pedron Oltramari, P. V., Freire

Fernandes, T. M., & de Castro Ferreira Conti, A. C. (2021). Likert scale vs visual analog

scale for assessing facial pleasantness. American Journal of Orthodontics and

Dentofacial Orthopedics, 160(6), 844–852. https://doi.org/10.1016/j.ajodo.2020.05.024

Durmaz, A., Dursun, İ., & Kabadayi, E. T. (2020). Mitigating the effects of social desirability

bias in self-report surveys: Classical and new techniques. In M. L. Baran & J. E. Jones

(Eds.), Advances in Library and Information Science (pp. 146–185). IGI Global.

https://doi.org/10.4018/978-1-7998-1025-4.ch007

Echo Prayer. (2021). Echo—Prayer App for iPhone and Android. https://new.echoprayer.com/

Edwards, J. R. (2020). The peaceful coexistence of ethics and quantitative research. Journal of

Business Ethics, 167(1), 31–40. https://doi.org/10.1007/s10551-019-04197-6

Efron, S. E., & Ravid, R. (2019). Writing the literature review: A practical guide. The Guilford

Press.

English, J. T. (2020). Deep discipleship: How the church can make whole disciples of Jesus.

B&H Publishing Group. https://go.exlibris.link/TDw4BdFx

137

F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares

structural equation modeling (PLS-SEM): An emerging tool in business research.

European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128

Falk, E., & Scholz, C. (2018). Persuasion, In. Annual Review of Psychology, 69(Journal Article),

329–356. https://doi.org/10.1146/annurev-psych-122216-011821

Farhat, K., Aslam, W., Arif, I., & Ahmed, Z. (2022). Does the dark side of personality traits

explain compulsive smartphone use of higher education students? The interaction effect

of dark side of personality with desirability and feasibility of smartphone use. IIM

Kozhikode Society & Management Review, 11(1), 62–74.

https://doi.org/10.1177/22779752211000479

Farzin, I., Mamdoohi, A. R., & Ciari, F. (2022). Autonomous vehicles acceptance: A perceived

risk extension of nified theory of acceptance and use of technology and diffusion of

innovation, evidence from Tehran, Iran. International Journal of Human–Computer

Interaction, 0(0), 1–10. https://doi.org/10.1080/10447318.2022.2083464

Fong, L. H. N., Lam, L. W., & Law, R. (2017). How locus of control shapes intention to reuse

mobile apps for making hotel reservations: Evidence from Chinese consumers. Tourism

Management (1982), 61, 331–342. https://doi.org/10.1016/j.tourman.2017.03.002

Ford, D. G. (2019a). The Bible and digital millennials. Routledge, Taylor & Francis Group.

Ford, D. G. (2019b). The transformative potential of Bible engagement for ordinary Catholics.

Practical Theology, 12(5), 513–525. https://doi.org/10.1080/1756073X.2019.1609253

Francis, L. J., & ap Siôn, T. (2016). Empirical theology and biblical hermeneutics: Exploring

lessons for discipleship from the road to Emmaus (Luke 24: 13-35). Journal of Empirical

Theology : JET, 29, 24–44. https://doi.org/10.1163/15709256-12341000

Francis, L. J., & Jones, S. H. (2020). The mysterious case of the Ethiopian eunuch: An empirical

and psychological examination in biblical hermeneutics. Mental Health, Religion &

Culture, 1–13. https://doi.org/10.1080/13674676.2020.1764514

Frantz, A., & Holmgren, K. (2019). The Work Stress Questionnaire (WSQ) – reliability and face

validity among male workers. BMC Public Health, 19(1), 1580.

https://doi.org/10.1186/s12889-019-7940-5

Fretwell, M. (2016, April 12). 3 components of Bible engagement. Biblical Leadership.

https://www.biblicalleadership.com/blogs/3-components-of-bible-engagement/

Fuhr, R. A., & Köstenberger, A. J. (2016). Inductive Bible study: Observation, interpretation,

and application through the lenses of history, literature, and theology. B&H Academic.

Fulks, J., Peterson, R., & Plake, J. F. (2022). State of the Bible: USA 2022. American Bible

Society.

138

Fulks, J., & Plake, J. F. (2020). State of the Bible 2020. American Bible Society.

Galvan, J. L., & Galvan, M. (2017). Writing literature reviews: A guide for students of the social

and behavioral sciences (7th ed., Vol. 1). Routledge.

https://doi.org/10.4324/9781315229386

Garland, D. E. (2017). Acts. Baker Books. https://go.exlibris.link/SwcV2mBR

Gavidia, J. L., & Mariño, C. J. (2021). Reliability of the instrument with Cronbach’s Alpha and

validation of the hypothesis by ANOVA. 16.

Goertz, G., & Mahoney, J. (2012). A tale of two cultures: Qualitative and quantitative research

in the social sciences. Princeton University Press. https://go.exlibris.link/Kdp1Bly4

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance.

MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689

Goodrich, J. K., & Strauss, M. L. (2019). Following Jesus Christ: The New Testament message

of discipleship for today. Kregel Publications.

Gorichanaz, T. (2016). Experiencing the Bible. Journal of Religious & Theological Information,

15(1–2), 19–31. https://doi.org/10.1080/10477845.2016.1168278

Goswami, A., & Dutta, S. (2016). Gender differences in technology usage—A literature review.

Open Journal of Business and Management, 04(01), 51–59.

https://doi.org/10.4236/ojbm.2016.41006

Graves, C., & Matz, S. (2018, May 2). What marketers should know about personality-based

marketing. Harvard Business Review. https://hbr.org/2018/05/what-marketers-should-

know-about-personality-based-marketing

Green, M. (2020). The message of Matthew: The kingdom of heaven (Revis). IVP Academic, an

imprint of Inter-Varsity Press.

Grusendorf, S. (2018). Diversified power: The rise of new leaders in Bourdieu’s religious field.

8(1).

Gurban, M. A., & Almogren, A. S. (2022). Students’ actual use of e-learning in higher education

during the COVID-19 pandemic. SAGE Open, 12(2), 215824402210912.

https://doi.org/10.1177/21582440221091250

Habib, S., & Hamadneh, N. N. (2021). Impact of perceived risk on consumers technology

acceptance in online grocery adoption amid COVID-19 pandemic. Sustainability, 13(18),

10221. https://doi.org/10.3390/su131810221

Hachman, M. (2023). ChatGPT is the dazzling, scary future of AI chatbots. PC World, Journal,

Electronic, 20–24.

139

Hamidović, D., Clivaz, C., & Bowen Savant, S. (Eds.). (2019). The power of visual culture and

the fragility of the text. In Ancient Manuscripts in Digital Culture (pp. 30–49). BRILL.

https://doi.org/10.1163/9789004399297_004

Hamouda, M. (2021). Purchase intention through mobile applications: A customer experience

lens. International Journal of Retail & Distribution Management, 49(10), 1464–1480.

https://doi.org/10.1108/IJRDM-09-2020-0369

Harrington, B., & Patrick, J. (2017). The disciple maker’s handbook: Seven elements of a

discipleship lifestyle. Zondervan. https://go.exlibris.link/gjX1Yf6l

Healey, J. (2020). Artificial intelligence: Vol. 450.;450; The Spinney Press.

https://go.exlibris.link/JPjKBnJ0

Hedges, L. V., Tipton, E., Zejnullahi, R., & Diaz, K. G. (2023). Effect sizes in ANCOVA and

difference‐in‐differences designs. British Journal of Mathematical & Statistical

Psychology, 76(2), 259–282. https://doi.org/10.1111/bmsp.12296

Hendra, R., & Hill, A. (2019). Rethinking response rates: New evidence of little relationship

between survey response rates and nonresponse bias. Evaluation Review, 43(5), 307–330.

https://doi.org/10.1177/0193841X18807719

Herrero, Á., San Martín, H., & Garcia-De los Salmones, M. del M. (2017). Explaining the

adoption of social networks sites for sharing user-generated content: A revision of the

UTAUT2. Computers in Human Behavior, 71, 209–217.

https://doi.org/10.1016/j.chb.2017.02.007

Hillman, G. M., Jr, & Edwards, S. G. (2018). Invitation to educational ministry: Foundations of

transformative Christian education. Kregel Academic.

https://go.exlibris.link/WbG5myPV

Hirsh, J. B., Kang, S. K., & Bodenhausen, G. V. (2012). Personalized persuasion: Tailoring

persuasive appeals to recipients’ personality traits. Psychological Science, 23(6), 578–

581. https://doi.org/10.1177/0956797611436349

Hong, S. C. (2020). Digital Bible and innovation resistance. Journal of Media and Religion, 19,

24–34. https://doi.org/10.1080/15348423.2020.1728187

Howard, E. B. (2018). A guide to Christian spiritual formation: How scripture, spirit,

community, and mission shape our souls. Baker Academic, a division of Baker

Publishing Group.

Howe, N., & Strauss, W. (2000). Millennials rising: The next great generation. Vintage Books.

Hsu, C.-W., & Peng, C.-C. (2022). What drives older adults’ use of mobile registration apps in

Taiwan? An investigation using the extended UTAUT model. Informatics for Health and

Social Care, 47(3), 258–273. https://doi.org/10.1080/17538157.2021.1990299

140

Hu, S., Laxman, K., & Lee, K. (2020). Exploring factors affecting academics’ adoption of

emerging mobile technologies-an extended UTAUT perspective. Education and

Information Technologies, 25(5), 4615–4635. https://doi.org/10.1007/s10639-020-10171-

Hu, Z., & Qin, J. (2018). Generalizability of causal inference in observational studies under

retrospective convenience sampling. Statistics in Medicine, 37(19), 2874–2883.

https://doi.org/10.1002/sim.7808

Hughes, A. (2023). ChatGPT: Everything you need to know about OpenAI’s GPT-3 tool. BBC

Science Focus.

Hutchings, T. (2014). Now the Bible is an app: Digital media and changing patterns of religious

authority. In K. Granholm, M. Moberg, & S. Sjö (Eds.), Religion, Media, and Social

Change. (pp. 143–161). Routledge.

Hutchings, T. (2015). E-Reading and the Christian Bible. Studies in Religion, 44(4), 423–440.

https://doi.org/10.1177/0008429815610607

Hutchings, T. (2017a). Creating Church Online: Ritual, Community and New Media: Vol. 8;8.;

Routledge. https://doi.org/10.4324/9780203111093

Hutchings, T. (2017b). Design and the digital Bible: Persuasive technology and religious

reading. Journal of Contemporary Religion, 32(2), 205–219.

https://doi.org/10.1080/13537903.2017.1298903

Hutchinson, A. (2019). New study shows that text messaging is the most popular form of digital

interaction in emerging markets. Social Media Today.

https://www.socialmediatoday.com/news/new-study-shows-that-text-messaging-is-the-

most-popular-form-of-digital-int/561709/

Jebb, A. T., Ng, V., & Tay, L. (2021). A review of key Likert scale development advances:

1995–2019. Frontiers in Psychology, 12, 637547.

https://doi.org/10.3389/fpsyg.2021.637547

Kalia, P., Kaushal, R., Singla, M., & Parkash, J. (2021). Determining the role of service quality,

trust and commitment to customer loyalty for telecom service users: A PLS-SEM

approach. The TQM Journal, 33(7), 377–396. https://doi.org/10.1108/TQM-04-2021-

0108

Kamboj, S., & Joshi, R. (2021). Examining the factors influencing smartphone apps use at

tourism destinations: A UTAUT model perspective. International Journal of Tourism

Cities, 7(1), 135–157. https://doi.org/10.1108/IJTC-05-2020-0094

Kaminski, J. (2011). Diffusion of Innovation Theory. Canadian Journal of Nursing Informatics,

6(2), 1–6.

141

Kang, H. (2021). Sample size determination and power analysis using the G*Power software.

Journal of Educational Evaluation for Health Professions, 18, 17.

https://doi.org/10.3352/jeehp.2021.18.17

Kang, Y., Cai, Z., Tan, C.-W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP)

in management research: A literature review. Journal of Management Analytics, 7(2),

139–172. https://doi.org/10.1080/23270012.2020.1756939

Kaplan, A. D., Sanders, T. L., & Hancock, P. A. (2021). Likert or not? How using Likert rather

than biposlar ratings reveal individual difference scores using the godspeed scales.

International Journal of Social Robotics, 13(7), 1553–1562.

https://doi.org/10.1007/s12369-020-00740-y

Kari, T., & Kosa, M. (2023). Acceptance and use of virtual reality games: An extension of

HMSAM. Virtual Reality. https://doi.org/10.1007/s10055-023-00749-4

Kärkkäinen, V.-M. (2021). An introduction to ecclesiology: Historical, global, and interreligious

perspectives (Second). IVP Academic.

Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of

Academic Medicine, 4(1), 60. https://doi.org/10.4103/IJAM.IJAM_7_18

Kelman, H. C. (1958). Compliance, identification, and internalization: Three processes of

attitude change. The Journal of Conflict Resolution, 2(1), 51–60.

https://doi.org/10.1177/002200275800200106

Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker.

Teaching and Teacher Education, 67(Journal Article), 135–142.

https://doi.org/10.1016/j.tate.2017.06.001

Klaver, N. S., Van De Klundert, J., Van Den Broek, R. J. G. M., & Askari, M. (2021).

Relationship between perceived risks of using mhealth applications and the intention to

use them among older adults in the Netherlands: Cross-sectional study. JMIR MHealth

and UHealth, 9(8), e26845. https://doi.org/10.2196/26845

Krishnaraju, V., Mathew, S. K., & Sugumaran, V. (2016). Web personalization for user

acceptance of technology: An empirical investigation of E-government services.

Information Systems Frontiers, 18(3), 579–595. https://doi.org/10.1007/s10796-015-

9550-9

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial

intelligence in personalized engagement marketing. California Management Review,

61(4).

Kurtzman, J. (2010). Common purpose: How great leaders get organizations to achieve the

extraordinary. John Wiley & Sons.

142

Larsen, N. E., Venkadasalam, V. P., & Ganea, P. A. (2020). Prompting children’s belief revision

about balance through primary and secondary sources of evidence. Frontiers in

Psychology, 11, 1503. https://doi.org/10.3389/fpsyg.2020.01503

Lavrakas, P. J. (Ed.). (2008). Encyclopedia of survey research methods. SAGE Publications.

Leavitt, H. J., & Whisler, T. L. (1958). Management in the 1980’s. Harvard Business Review,

36(6), 41.

Lee, H.-W., Kim, S., & Uhm, J.-P. (2021). Social virtual reality (VR) Involvement affects

depression when social connectedness and self-esteem are low: A moderated mediation

on well-being. Frontiers in Psychology, 12, 753019.

https://doi.org/10.3389/fpsyg.2021.753019

Lee, S., Wang, S., & Kim, S. (2017). Motivation factors influencing intention of mobile sports

apps use by applying the unified theory of acceptance and use of technology(UTAUT).

International Journal of Applied Sports Sciences : IJASS, 29(2), 115–127.

https://doi.org/10.24985/ijass.2017.29.2.115

Leedy, P. D., & Ormrod, J. E. (2019). Practical research: Planning and design (12th ed.).

Pearson.

Lenski, R. (1937). The interpretation of St. Paul’s Epistles to the Colossians, to the

Thessalonians, to Timothy, to Titus and to Philemon. Lutheran Book Concern.

Leong, L.-Y., Hew, T.-S., Ooi, K.-B., & Chong, A. Y.-L. (2020). Predicting the antecedents of

trust in social commerce – A hybrid structural equation modeling with neural network

approach. Journal of Business Research, 110, 24–40.

https://doi.org/10.1016/j.jbusres.2019.11.056

Letham, R. (2013). The message of the person of Christ: The word made flesh. InterVarsity

Press.

Li, J. (2020). Blockchain technology adoption: Examining the fundamental drivers. Proceedings

of the 2020 2nd International Conference on Management Science and Industrial

Engineering, 253–260. https://doi.org/10.1145/3396743.3396750

Liao, C., Palvia, P., & Lin, H.-N. (2006). The roles of habit and web site quality in e-commerce.

International Journal of Information Management, 26(6), 469–483.

https://doi.org/10.1016/j.ijinfomgt.2006.09.001

Lin, A., & Chen, N.-C. (2012). Cloud computing as an innovation: Percepetion, attitude, and

adoption. International Journal of Information Management, 32(6), 533–540.

Liu, D., Maimaitijiang, R., Gu, J., Zhong, S., Zhou, M., Wu, Z., Luo, A., Lu, C., & Hao, Y.

(2019). Using the unified theory of acceptance and use of technology (UTAUT) To

investigate the intention to use physical activity apps: Cross-sectional survey. JMIR

MHealth and UHealth, 7(9), e13127–e13127. https://doi.org/10.2196/13127

143

Loh, C. E., & Sun, B. (2019). “i’d still prefer to read the hard copy”: Adolescents’ print and

digital reading habits. Journal of Adolescent & Adult Literacy, 62(6), 663–672.

https://doi.org/10.1002/jaal.904

Lu, J. I. (2021). Educational models of spiritual formation in theological education:

Introspection‐based spiritual formation. Teaching Theology & Religion, 24(1), 28–41.

https://doi.org/10.1111/teth.12560

Magsamen-Conrad, K., Wang, F., Tetteh, D., & Lee, Y.-I. (2020). Using technology adoption

theory and a lifespan approach to develop a theoretical framework for Ehealth literacy:

Extending UTAUT. Health Communication, 35(12), 1435–1446.

https://doi.org/10.1080/10410236.2019.1641395

Mahfuz, M. A., Hu, W., & Khanam, L. (2016). The influence of cultural dimensions and website

quality on m-banking services adoption in Bangladesh: Applying the UTAUT2 model

using PLS. 14.

Malins, S., Biswas, S., Patel, S., Levene, J., Moghaddam, N., & Morriss, R. (2020). Preventing

relapse with personalized smart‐messaging after cognitive behavioural therapy: A proof‐

of‐concept evaluation. British Journal of Clinical Psychology, 59(2), 241–259.

https://doi.org/10.1111/bjc.12244

Mann, H.-J. (2000). Leadership. Naval Forces, 21(5), 28.

Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from

1986 to 2013. Universal Access in the Information Society, 14(1), 81–95.

https://doi.org/10.1007/s10209-014-0348-1

Marriott, H. R., & Williams, M. D. (2016). Developing a theoretical model to examine consumer

acceptance behavior of mobile shopping. In Y. K. Dwivedi, M. Mäntymäki, M. N.

Ravishankar, M. Janssen, M. Clement, E. L. Slade, N. P. Rana, S. Al-Sharhan, & A. C.

Simintiras (Eds.), Social Media: The Good, the Bad, and the Ugly (Vol. 9844, pp. 261–

266). Springer International Publishing. https://doi.org/10.1007/978-3-319-45234-0_24

McBeath, A. (2020). Doing Quantitative Research with Statistics. In S. Bager-Charleson & A.

McBeath (Eds.), Enjoying Research in Counselling and Psychotherapy (pp. 161–173).

Springer International Publishing. https://doi.org/10.1007/978-3-030-55127-8_9

McClure, P. K. (2016). Faith and Facebook in a pluralistic age: The effects of social networking

sites on the religious beliefs of emerging adults. Sociological Perspectives, 59(4), 818–

834. https://doi.org/10.1177/0731121416647361

McCully, W. (2014). Gleaning the fields of change: Adoption of information and communication

technologies in religious organizations. Journal of Communication & Religion, 37(4),

82–98. Education Source.

McGrath, A. E. (2017). Theology: The basics (4th ed.). Wiley Blackwell.

https://go.exlibris.link/Kbnn55dM

144

Miguel Cruz, A., Lopez Portillo, H. P., Daum, C., Rutledge, E., King, S., & Liu, L. (2022).

Technology acceptance and usability of a mobile app to support the workflow of health

care aides who provide services to older adults: Pilot mixed methods study. JMIR Aging,

5(2), e37521. https://doi.org/10.2196/37521

Milliot, J. (2016). As e-book sales decline, digital fatigue grows. The Publishers Weekly,

263(25), 4.

Min, S., So, K. K. F., & Jeong, M. (2019). Consumer adoption of the Uber mobile application:

Insights from diffusion of innovation theory and technology acceptance model. Journal

of Travel & Tourism Marketing, 36(7), 770–783.

https://doi.org/10.1080/10548408.2018.1507866

Misischia, C. V., Poecze, F., & Strauss, C. (2022). Chatbots in customer service: Their relevance

and impact on service quality. Procedia Computer Science, 201, 421–428.

https://doi.org/10.1016/j.procs.2022.03.055

Mohamad, N. I., Ismail, A., & Nor, A. M. (2020). The relationship between management support

in training programs and motivation to perform task with motivation to learn as mediator.

Logforum, 16(3), 431–446. https://doi.org/10.17270/J.LOG.2020.458

Mohammadi, M. M., Poursaberi, R., & Salahshoor, M. R. (2018). Evaluating the adoption of

evidence-based practice using Rogers’s diffusion of innovation theory: A model testing

study. Health Promotion Perspectives, 8(1), 25–32. https://doi.org/10.15171/hpp.2018.03

Mohd Rahim, N. I., A. Iahad, N., Yusof, A. F., & A. Al-Sharafi, M. (2022). AI-based chatbots

adoption model for higher-education institutions: A hybrid PLS-SEM-Neural Network

modelling approach. Sustainability, 14(19), 12726. https://doi.org/10.3390/su141912726

Moore, R. D. (2011). Tempted and tried: Temptation and the triumph of Christ. Crossway.

Moore, Z., Harrison, D. E., & Hair, J. (2021). Data quality assurance begins before data

collection and never ends: What marketing researchers absolutely need to remember.

International Journal of Market Research, 63(6), 693–714.

https://doi.org/10.1177/14707853211052183

Moravec, P. L., Kim, A., Dennis, A. R., & Minas, R. K. (2022). Do you really know if it’s true?

How asking users to rate stories affects belief in fake news on social media. Information

Systems Research, 33(3), 887–907. https://doi.org/10.1287/isre.2021.1090

Mukherjee, S. P. (2019). A guide to research methodology: An overview of research problems,

tasks and methods (1st ed.). CRC Press. https://doi.org/10.1201/9780429289095

Nedra, B.-A., Hadhri, W., & Mezrani, M. (2019). Determinants of customers’ intentions to use

hedonic networks: The case of Instagram. Journal of Retailing and Consumer Services,

46, 21–32. https://doi.org/10.1016/j.jretconser.2018.09.001

145

Nielson, J. (2019). James: Portrait of a living faith. P & R Publishing.

https://go.exlibris.link/kDDyfhg1

Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance

expectancy and technological pedagogical knowledge affect teachers’ intention to use

mobile internet. Computers and Education Open, 2, 100041.

https://doi.org/10.1016/j.caeo.2021.100041

Nordhoff, S., Louw, T., Innamaa, S., Lehtonen, E., Beuster, A., Torrao, G., Bjorvatn, A., Kessel,

T., Malin, F., Happee, R., & Merat, N. (2020). Using the UTAUT2 model to explain

public acceptance of conditionally automated (L3) cars: A questionnaire study among

9,118 car drivers from eight European countries. Transportation Research Part F: Traffic

Psychology and Behaviour, 74, 280–297. https://doi.org/10.1016/j.trf.2020.07.015

Norouzian, R. (2020). Sample size planning in quantitative L2 research: A pragmatic approach.

Studies in Second Language Acquisition, 42(4), 849–870.

https://doi.org/10.1017/S0272263120000017

Nunnery, L. (2018). Teach them diligently: Raising children of promise. New Leaf Publishing

Group, Inc. https://go.exlibris.link/5y8MmBn6

Odiase, O., Wilkinson, S., & Neef, A. (2019). South African community in Auckland: Natural

hazards and the resilience to a potential disaster event. Disaster Prevention and

Management: An International Journal, 29(3), 390–404. https://doi.org/10.1108/DPM-

02-2019-0052

Ogden, G. (2019). Discipleship essentials. InterVarsity Press. https://go.exlibris.link/7x3vmc9t

Oliver, W. H. (2020). The Bible in the fourth industrial revolution: ‘What’s in it for me?’ HTS

Teologiese Studies / Theological Studies, 76(4). https://doi.org/10.4102/hts.v76i4.6020

Olsen, T. (2014, March 6). Hacking the Bible. ChristianityToday.Com.

https://www.christianitytoday.com/ct/2014/march/bible-in-original-geek.html

Olvera Astivia, O. L., Kroc, E., & Zumbo, B. D. (2020). The role of item distributions on

reliability estimation: The case of Cronbach’s coefficient Alpha. Educational and

Psychological Measurement, 80(5), 825–846. https://doi.org/10.1177/0013164420903770

Opoku, D., Owusu Okyireh, R., & Siaw Kissi, P. (2020). Consumer intention towards the use of

Bible application. American Journal of Education and Learning, 5(1), 72–86.

https://doi.org/10.20448/804.5.1.72.86

Osborne, G. R. (2019). James: Verse by verse. Lexham Press. https://go.exlibris.link/6T43LPvc

Ovwigho, P. C., Cole, A. R., & Myatt, A. (2016). Private spiritual practices: Bible engagement

and moral behavior. Journal of Psychology and Christianity, 35(3), 233–241. ProQuest

Central.

146

Park, J., & Woo, S. E. (2022). Who likes artificial intelligence? Personality predictors of

attitudes toward artificial intelligence. The Journal of Psychology, 156(1), 68–94.

https://doi.org/10.1080/00223980.2021.2012109

Pashaeypoor, S., Ashktorab, T., Rassouli, M., & Alavi-Majd, H. (2016). Predicting the adoption

of evidence-based practice using “Rogers diffusion of innovation model.” Contemporary

Nurse : A Journal for the Australian Nursing Profession, 52(1), 85–94.

https://doi.org/10.1080/10376178.2016.1188019

Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative

artificial intelligence for journalism and media education. Journalism & Mass

Communication Educator, Journal Article, 107769582211495.

https://doi.org/10.1177/10776958221149577

Petcharat, T., & Leelasantitham, A. (2021). A retentive consumer behavior assessment model of

the online purchase decision-making process. Heliyon, 7(10), e08169.

https://doi.org/10.1016/j.heliyon.2021.e08169

Peterson, D., Schnabel, E. J., & Perrin, N. (2020). Hebrews: An introduction and commentary

(Vol. 15). Inter-Varsity Press. https://go.exlibris.link/WrY8tKfC

Pitkänen, P. (2017). A commentary on Numbers: Narrative, ritual, and colonialism. Taylor and

Francis. https://doi.org/10.4324/9781315201740

Popova, Y., & Zagulova, D. (2022). UTAUT model for smart city concept implementation: Use

of web applications by residents for everyday operations. Informatics, 9(1), 27.

https://doi.org/10.3390/informatics9010027

Rajan, B., & Kundu, D. (2019). Churching online: A study on religious authority and tourism in

a digital age. Atna - Journal of Tourism Studies, 14(1), 47–82.

https://doi.org/10.12727/ajts.21.4

Raman, P. (2014). Factors influencing women consumers’ buying behavior towards online

shopping in India. Journal of Contemporary Management Research, 8(2), 23–56.

ProQuest Central.

Rasheed Gaber, H., & Elsamadicy, A. M. (2021). What drives customers to continue using ride-

sharing apps during the COVID-19 pandemic? The case of Uber in Egypt. Cogent

Business & Management, 8(1), 1944009.

https://doi.org/10.1080/23311975.2021.1944009

Rinker, C. H., Roof, J., Harvey, E., Bailey, E., & Embler, H. (2016). Religious apps for

smartphones and tablets: Transforming religious authority and the nature of religion.

Interdisciplinary Journal of Research on Religion, 12(4).

Rogers, E. (1995). Diffusion of Innovations (4th ed.). Simon & Schuster.

Rogers, E. M. (2003). Diffusion of Innovations (Fifth edition). Free Press.

147

Safi, S., Thiessen, T., & Schmailzl, K. J. (2018). Acceptance and resistance of new digital

technologies in medicine: Qualitative study. JMIR Research Protocols, 7(12), e11072.

https://doi.org/10.2196/11072

Santos Garduño, H. A., Esparza Martínez, M. I., & Portuguez Castro, M. (2021). Impact of

virtual reality on student motivation in a high school science course. Applied Sciences,

11(20), 9516. https://doi.org/10.3390/app11209516

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation

Modeling. In C. Homburg, M. Klarmann, & A. E. Vomberg (Eds.), Handbook of Market

Research (pp. 1–47). Springer International Publishing. https://doi.org/10.1007/978-3-

319-05542-8_15-2

Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial least squares

structural equation modeling (PLS-SEM): A useful tool for family business researchers.

Journal of Family Business Strategy, 5(1), 105–115.

https://doi.org/10.1016/j.jfbs.2014.01.002

Schreiner, T. R. (2018). 1 Corinthians: An introduction and commentary: Vol. 7;7.; IVP

Academic, an imprint of InterVarsity Press. https://go.exlibris.link/SGKBdTHd

Scott, A. M., Chalmers, I., Barnett, A., Stephens, A., Kolstoe, S. E., Clark, J., & Glasziou, P.

(2021). ‘The ethics approval took 20 months on a trial which was meant to help

terminally ill cancer patients. In the end we had to send the funding back’: A survey of

views on human research ethics reviews. Journal of Medical Ethics, 47(12), e90–e90.

https://doi.org/10.1136/medethics-2020-106785

Segovia, F. F., & Tolbert, M. A. (Eds.). (1995). Reading from this place. Fortress Press.

Setia, M. (2016). Methodology series module 3: Cross-sectional studies. Indian Journal of

Dermatology, 61(3), 261. https://doi.org/10.4103/0019-5154.182410

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M.

(2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict.

European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-

2019-0189

Shrivastav, U. (2023). What is ChatGPT? Business World (India), Journal, Electronic.

Sijtsma, K., & Pfadt, J. M. (2021). Part II: On the use, the misuse, and the very limited

usefulness of Cronbach’s Alpha: Discussing lower bounds and correlated errors.

Psychometrika, 86(4), 843–860. https://doi.org/10.1007/s11336-021-09789-8

Soper, D. S. (2022). A-priori sample size calculator for structural equation models (4.0).

https://www.danielsoper.com/statcalc

148

Spahn, A. (2012). And lead us (not) into persuasion: Persuasive technology and the ethics of

communication. Science and Engineering Ethics, 18(4), 633–650.

https://doi.org/10.1007/s11948-011-9278-y

Stanton, G. (2017). The glory of kings: Dialogical practices of Bible engagement with teenagers

in a culture of expressive individualism. St. Mark’s Review, 240, 34–53.

Stark, L. J. M. (2012). Behind closed doors: IRBs and the making of ethical research. The

University of Chicago Press.

Stott, J. R. W. (2021). The message of 2 Timothy: Guard the gospel (Revis). Intervarsity Press.

https://go.exlibris.link/TGCjQpDW

Stratton, S. J. (2021). Population research: Convenience sampling strategies. Prehospital and

Disaster Medicine, 36(4), 373–374. https://doi.org/10.1017/S1049023X21000649

Sue, V. M., & Ritter, L. A. (2012). Conducting online surveys (2nd ed.). Sage.

https://go.exlibris.link/NkFcgJZn

Sugumar, M., & Chandra, S. (2021). Do I desire chatbots to be like humans? Exploring factors

for adoption of chatbots for financial services. 42.

Szabo-Reed, A. N., Willis, E., Ptomey, L., Washburn, R., & Donnelly, J. (2017). Fitbit And

Actigraph: A Comparison Of Physical Activity And Sedentary Time In

Overweight/obese Adults 1353 Board #28 June 1 9 00 AM - 10 30 AM. Medicine and

Science in Sports and Exercise, 49(5S), 366.

https://doi.org/10.1249/01.mss.0000517883.22655.db

Szollosy, M. (2017). Freud, Frankenstein and our fear of robots: Projection in our cultural

perception of technology. AI & Society, 32(3), 433–439. https://doi.org/10.1007/s00146-

016-0654-7

Tak, P., & Panwar, S. (2017). Using UTAUT 2 model to predict mobile app based shopping:

Evidences from India. Journal of Indian Business Research, 9(3), 248–264.

https://doi.org/10.1108/JIBR-11-2016-0132

Tamilmani, K., Rana, N. P., Prakasam, N., & Dwivedi, Y. K. (2019). The battle of brain vs.

Heart: A literature review and meta-analysis of “hedonic motivation” use in UTAUT2.

International Journal of Information Management, 46, 222–235.

https://doi.org/10.1016/j.ijinfomgt.2019.01.008

Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory

of Acceptance and Use of Technology (UTAUT2): A systematic literature review and

theory evaluation. International Journal of Information Management, 57.

https://doi.org/10.1016/j.ijinfomgt.2020.102269

149

Terblanche, N., & Kidd, M. (2022). Adoption factors and moderating effects of age and gender

that influence the intention to use a non-directive reflective coaching chatbot. SAGE

Open, 12(2), 16.

Terwee, C. B., Elders, P. J. M., Langendoen-Gort, M., Elsman, E. B. M., Prinsen, C. A. C., van

der Heijden, A. A., de Wit, M., Beulens, J. W. J., Mokkink, L. B., & Rutters, F. (2022).

Content validity of patient-reported outcome measures developed for assessing health-

related quality of life in people with type 2 diabetes Mellitus: A systematic review.

Current Diabetes Reports, 22(9), 405–421. https://doi.org/10.1007/s11892-022-01482-z

Thangavel, G., Memedi, M., & Hedström, K. (2022). Customized information and

communication technology for reducing social isolation and loneliness among older

adults: Scoping review. JMIR Mental Health, 9(3), e34221. https://doi.org/10.2196/34221

The Economist. (2020). Our father, who art in cyberspace: Virtual worship. The Economist,

435(9189), 46.

Țicău, I. R., & Hadad, S. (2021). Technological determinism vs. Social shaping of technology.

The influence of activity trackers on user’s attitudes. Management Dynamics in the

Knowledge Economy, 9(2), 147–163. https://doi.org/10.2478/mdke-2021-0011

Tithe.ly. (2021). Church Giving: Mobile App, Online & Text Giving for Churches.

https://get.tithe.ly/

Tsang, A. (2023). “The best way to learn a language is not to learn it!”: Hedonism and insights

into successful EFL learners’ experiences in engagement with spoken (listening) and

written (reading) input. TESOL Quarterly, 57(2), 511–536.

https://doi.org/10.1002/tesq.3165

Tsuria, R., Bellar, W., Campbell, H., & Cho, K. J. (2021). Transferred, mediated or transformed:

Considering the design, features, and presentation of sacred text mobile applications.

Journal of Contemporary Religion, 36, 57–78.

https://doi.org/10.1080/13537903.2021.1878633

Typeform: People-Friendly Forms and Surveys. (2022). Typeform. https://www.typeform.com/

Van Bussel, M. J. P., Odekerken–Schröder, G. J., Ou, C., Swart, R. R., & Jacobs, M. J. G.

(2022). Analyzing the determinants to accept a virtual assistant and use cases among

cancer patients: A mixed methods study. BMC Health Services Research, 22(1), 890.

https://doi.org/10.1186/s12913-022-08189-7

Vanhoozer, K. J. (2005). The drama of doctrine: A canonical-linguistic approach to Christian

theology (First). Westminster John Knox Press. https://go.exlibris.link/zkBLmSfj

Vaportzis, E., Giatsi Clausen, M., & Gow, A. J. (2017). Older adults perceptions of technology

and barriers to interacting with tablet computers: A focus group study. Frontiers in

Psychology, 8, 1687. https://doi.org/10.3389/fpsyg.2017.01687

150

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information

technology: Toward a unified view. MIS Quarterly, 27(3), 425.

https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information

technology: Extending the unified theory of acceptance and use of technology. MIS

Quarterly, 36(1), 157. https://doi.org/10.2307/41410412

Venkatesh, V., Thong, J., & Xu, X. (2016). Unified Theory of Acceptance and Use of

Technology: A Synthesis and the Road Ahead. Journal of the Association for Information

Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428

Ventura-León, J., & Peña-Calero, B. N. (2020). The world should not revolve around Cronbach’s

alpha ≥ .70. Adicciones, 33(4), 369. https://doi.org/10.20882/adicciones.1576

Vincent, M. (1887). Word studies in the New Testament (Vol. 2). Charles Scribner’s Sons.

Vis, B., & Stolwijk, S. (2021). Conducting quantitative studies with the participation of political

elites: Best practices for designing the study and soliciting the participation of political

elites. Quality & Quantity, 55(4), 1281–1317. https://doi.org/10.1007/s11135-020-01052-

Vozzola, E. C. (2014). Moral development: Theory and applications. Routledge.

Wang, S. (Ada), Pang, M.-S., Pavlou, P., University of Houston, Baruch College, & Temple

University. (2022). Seeing is believing? How including a video in fake news influences

users’ reporting of fake News to social media platforms. MIS Quarterly, 45(3), 1323–

1354. https://doi.org/10.25300/MISQ/2022/16296

Wang, X., & Ji, X. (2020). Sample Size Estimation in Clinical Research. Chest, 158(1), S12–

S20. https://doi.org/10.1016/j.chest.2020.03.010

Weil, A. R. (2018). Diffusion of innovation. Health Affairs, 37(2), 175–175.

https://doi.org/10.1377/hlthaff.2018.0059

Wilson, D. J. (2018). Pipeline: Engaging the church in missionary mobilization. William Carey

Press. https://go.exlibris.link/YJQf5Zn9

Wohlin, C., Papatheocharous, E., Carlson, J., Petersen, K., Alégroth, E., Axelsson, J.,

Badampudi, D., Borg, M., Cicchetti, A., Ciccozzi, F., Olsson, T., Sentilles, S.,

Svahnberg, M., Wnuk, K., & Gorschek, T. (2021). Towards evidence‐based decision‐

making for identification and usage of assets in composite software: A research roadmap.

Journal of Software: Evolution and Process, 33(6). https://doi.org/10.1002/smr.2345

Wolter, M., Coppins, W., & Heilig, C. (2017). The gospel according to Luke: Luke 9:51-24 (Vol.

2). Baylor University Press. https://go.exlibris.link/W9gn4dG7

151

Wut, E., Ng, P., Leung, K. S. W., & Lee, D. (2021). Do gamified elements affect young people’s

use behaviour on consumption-related mobile applications? Young Consumers, 22(3),

368–386. https://doi.org/10.1108/YC-10-2020-1218

Yen, H., Liao, Y., & Huang, W. (2022). Walking and cycling for health: A multi‐group analysis

of path models between genders. Journal of Advanced Nursing, 78(11), 3721–3732.

https://doi.org/10.1111/jan.15322

Yoo, D. K., & Roh, J. J. (2016). Use and uptake of e-books in the lens of unified theory of

acceptance and use of technology. 20th Pacific Asia Conference on Information Systems,

PACIS 2016.

http://www.scopus.com/inward/record.url?scp=85011035326&partnerID=8YFLogxK

YouVersion. (2013). Now the Bible is an app – infographic.

https://blog.youversion.com/2013/07/now-the-bible-is-an-app-infographic/

Zaki, M. (2017). The pragmatics of Arabic religious posts on Facebook: A relevance-theoretic

account. Research in Language, 15(1), 37–60. https://doi.org/10.1515/rela-2017-0002

Zhang, Q., Fan, M.-X., Han, X.-G., Liu, Y.-J., He, D., Liu, B., & Tian, W. (2021). Risk factors

of unsatisfactory robot-assisted pedicle screw placement: A case-control study.

Neurospine, 18(4), 839–844. https://doi.org/10.14245/ns.2142560.180

Zhang, S., Meng, Z., Chen, B., Yang, X., & Zhao, X. (2021). Motivation, social emotion, and the

acceptance of artificial intelligence virtual assistants—Trust-based mediating effects.

Frontiers in Psychology, 12, 728495. https://doi.org/10.3389/fpsyg.2021.728495

Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food

delivery apps during 2019 novel coronavirus pandemic period? International Journal of

Hospitality Management, 91. https://doi.org/10.1016/j.ijhm.2020.102683

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APPENDICES

Appendix A: IRB Approval

April 6, 2023

Henry Jackson

Gary Bredfeldt

Re: IRB Exemption - IRB-FY22-23-1200 The Word Made Digital: Leveraging Artificial Intelligence to Increase

Bible Engagement

Dear Henry Jackson, Gary Bredfeldt,

The Liberty University Institutional Review Board (IRB) has reviewed your application in accordance with the

Office for Human Research Protections (OHRP) and Food and Drug Administration (FDA) regulations and finds

your study to be exempt from further IRB review. This means you may begin your research with the data

safeguarding methods mentioned in your approved application, and no further IRB oversight is required.

Your study falls under the following exemption category, which identifies specific situations in which human

participants research is exempt from the policy set forth in 45 CFR 46:104(d):

Category 2.(i). Research that only includes interactions involving educational tests (cognitive, diagnostic,

aptitude, achievement), survey procedures, interview procedures, or observation of public behavior

(including visual or auditory recording).

The information obtained is recorded by the investigator in such a manner that the identity of the human

subjects cannot readily be ascertained, directly or through identifiers linked to the subjects.

Your stamped consent form(s) and final versions of your study documents can be found under the

Attachments tab within the Submission Details section of your study on Cayuse IRB. Your stamped consent

form(s) should be copied and used to gain the consent of your research participants. If you plan to provide

your consent information electronically, the contents of the attached consent document(s) should be made

available without alteration.

Please note that this exemption only applies to your current research application, and any modifications to

your protocol must be reported to the Liberty University IRB for verification of continued exemption status.

You may report these changes by completing a modification submission through your Cayuse IRB account.

If you have any questions about this exemption or need assistance in determining whether possible

modifications to your protocol would change your exemption status, please email us at i[email protected].

Sincerely,

G. Michele Baker, PhD, CIP

Administrative Chair

Research Ethics Office

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Appendix B: Introductory Email to Participants

Subject:

Participate in a Study on Bible-based Chatbots: Share Your Thoughts and Influence the Future of

Scripture Engagement

Body:

Dear Potential Participant,

As a graduate student in the School of Divinity at Liberty University, I am conducting research

as part of the requirements for a Doctor of Philosophy degree. The purpose of my research is to

understand factors that might influence an individual's use of a Bible-based chatbot (a computer

program that is trained to communicate like a person) that creates a human-like engagement with

Scripture. I am writing to invite you to join my study.

Participants must be 18 years or older. Participants will be asked to complete an anonymous,

online survey. It should take approximately 10 minutes to complete the online survey.

Participation will be completely anonymous, and no personal, identifying information will be

collected.

To participate, please click here (https://tinyurl.com/biblechatbotsurvey) to complete the study

survey.

A consent document is provided on the first page of the survey. The consent document contains

additional information about my research. Because participation is anonymous, you do not need

to sign and return the consent document. After you have read the consent form, please click the

link to proceed to the survey. Doing so will indicate that you have read the consent information

and would like to take part in the survey.

Sincerely,

Henry Jackson III

PhD Candidate, Liberty University

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Appendix C: Consent Document

Consent

Title of the Project: The Word Made Digital: Leveraging Artificial Intelligence to Increase

Bible Engagement

Principal Investigator: Henry Jackson III, Doctoral Candidate, School of Divinity, Liberty

University

Invitation to be Part of a Research Study

You are invited to participate in a research study. Participants must be 18 years or older. Taking

part in this research project is voluntary.

Please take time to read this entire form and ask questions before deciding whether to take part in

this research project.

What is the study about and why is it being done?

The purpose of this study is to understand factors that might influence an individual's use of a

Bible-based chatbot (a computer program that is trained to communicate like a person) that

creates a human-like engagement with Scripture.

What will happen if you take part in this study?

If you agree to be in this study, I would ask you to complete an online, anonymous, multiple

choice questionnaire that will take no more than 10 minutes.

How could you or others benefit from this study?

Participants should not expect to receive a direct benefit from taking part in this study.

Benefits to society include valuable insights for leaders in the field of technology-based religious

engagement, helping them to understand how to increase adoption and engagement with Bible-

based chatbots among their target audience.

What risks might you experience from being in this study?

The expected risks from participating in this study are minimal, which means they are equal to

the risks you would encounter in everyday life.

How will personal information be protected?

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The records of this study will be kept private. Research records will be stored securely, and only

the researcher will have access to the records.

• Participant responses will be anonymous.

• Data will be stored on a password-locked computer. After three years, all electronic

records will be deleted.

How will you be compensated for being part of the study?

Participants will not be compensated for participating in this study.

Is the researcher in a position of authority over participants, or does the researcher have a

financial conflict of interest?

The researcher serves as founder at Inductive Bible Study LLC. To limit potential or perceived

conflicts, data collection will be anonymous, so the researcher will not know who participated.

This disclosure is made so that you can decide if this relationship will affect your willingness to

participate in this study. No action will be taken against an individual based on his or her

decision to participate or not participate in this study.

Is study participation voluntary?

Participation in this study is voluntary. Your decision whether to participate will not affect your

current or future relations with Liberty University or Inductive Bible Study LLC. If you decide to

participate, you are free to not answer any question or withdraw at any time prior to submitting

the survey without affecting those relationships.

What should you do if you decide to withdraw from the study?

If you choose to withdraw from the study, please exit the survey and close your internet browser.

Your responses will not be recorded or included in the study.

Whom do you contact if you have questions or concerns about the study?

The researcher conducting this study is Henry Jackson III. You may ask any questions you have

now. If you have questions later, you are encouraged to contact him at [removed for

publication]. You may also contact the researcher’s faculty sponsor, Dr. Gary Bredfeldt, at

[removed for publication].

Whom do you contact if you have questions about your rights as a research participant?

If you have any questions or concerns regarding this study and would like to talk to someone

other than the researcher, you are encouraged to contact the IRB. Our physical address is

Institutional Review Board, 1971 University Blvd., Green Hall Ste. 2845, Lynchburg, VA,

24515; our phone number is 434-592-5530, and our email address is [email protected].

156

Disclaimer: The Institutional Review Board (IRB) is tasked with ensuring that human subjects

research will be conducted in an ethical manner as defined and required by federal regulations.

The topics covered and viewpoints expressed or alluded to by student and faculty researchers

are those of the researchers and do not necessarily reflect the official policies or positions of

Liberty University.

Your Consent

Before agreeing to be part of the research, please be sure that you understand what the study is

about. You can print a copy of this document for your records. If you have any questions

about the study later, you can contact the researcher/study team using the information provided

above.

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Appendix D: Follow-Up Email to Participants

Subject:

Reminder: Participate in a Study on Bible-based Chatbots

Body:

Dear Potential Participant,

As a graduate student in the School of Divinity at Liberty University, I am conducting research

as part of the requirements for a Doctor of Philosophy degree. The purpose of my research is to

understand factors that might influence an individual's use of a Bible-based chatbot (a computer

program that is trained to communicate like a person) that creates a human-like engagement with

Scripture. Two weeks ago, an email was sent to you inviting you to participate in a research

study. This follow-up email is being sent to remind you to complete the survey if you would like

to participate and have not already done so. The deadline for participation is [Date].

Participants must be 18 years or older. Participants will be asked to complete an anonymous,

online survey. It should take approximately 10 minutes to complete the online survey.

Participation will be completely anonymous, and no personal, identifying information will be

collected.

To participate, please click here (https://vtcscu1mkad.typeform.com/to/O0rJdiQt) to complete

the study survey.

A consent document is provided on the first page of the survey. The consent document contains

additional information about my research. Because participation is anonymous, you do not need

to sign and return the consent document. After you have read the consent form, please click the

link to proceed to the survey. Doing so will indicate that you have read the consent information

and would like to take part in the survey.

Sincerely,

Henry Jackson III

PhD Candidate, Liberty University

158

Appendix E: Proof of Permission - UTAUT Instrument

Permission was requested from the lead researcher, Dr. Viswanath Venkatesh, via the following

online form:

https://docs.google.com/forms/d/e/1FAIpQLSfQCjqH-

dDzhDmZNfzLYRlNSUHjKLVbMHgP4PnzR4rAbM5n9A/viewform

Submission

Request Submission Date: January 22, 2023

Based on the use and adaptation of content from following paper:

Venkatesh, V., Thong, J., & Xu, X. (2016). Unified Theory of Acceptance and Use of

Technology: A Synthesis and the Road Ahead. Journal of the Association for Information

Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428

Approval Response

From: vvenkateshautoreply

Date: Sunday, January 22, 2023 at 6:16 AM

To: Jackson, Henry

Subject: Permission Granted

Dear Henry Jackson III,

Thank you for your interest. Your permission to use content from the paper is granted. Please

cite the work appropriately. Note that this permission does not exempt you from seeking the

necessary permission from the copyright owner (typically, the publisher of the journal) for any

reproduction of any materials contained in this paper.

Sincerely,

Viswanath Venkatesh

Eminent Scholar and Verizon Chair of Business Information Technology

Director, Executive Ph.D., http://executivephd.pamplin.vt.edu/

Website: http://vvenkatesh.com

159

Appendix F: ChatGPT Bible Prompt Responses

The following prompts provide an example of a real exchange with a natural language

processing (NLP) AI chatbot. The exchange is with OpenAI’s ChatGPT Plus version of its

software. The chat occurred on March 4, 2023. Each response was rendered instantly. Visit

https://chat.openai.com/chat to have a conversation with the latest human-like chatbot.

Participants in the study will be given the option of watching a recorded reenactment of

the exchange below with ChatGPT (https://tinyurl.com/chatgptbibleresearch), after they have

answered demographic questions. The video is intended to give participants a sense of how

interacting with a chatbot can provide answers to inquiries related to Christianity or the Bible.

This will expose them to the concept of using a chatbot as a tool for exploring their faith.

Prompt

ChatGPT Response

According to the

Bible, how do I get

to heaven?

According to the Bible, there is one way to get to heaven: through faith

in Jesus Christ. The Bible teaches that all people have sinned and fall

short of God's glory (Romans 3:23) and that the punishment for sin is

death (Romans 6:23). However, God has provided a way for us to be

saved from this punishment.

The Bible says that God sent His only Son, Jesus Christ, to die on the

cross for our sins (John 3:16). Through faith in Jesus, we can be

forgiven of our sins and receive eternal life in heaven (Romans 10:9-

10). This means that we must confess our sins to God, believe that

Jesus died for our sins and rose again, and accept Him as our Lord and

Savior (Acts 16:31).

In addition to faith, the Bible also teaches that we should live a life that

is pleasing to God (1 Thessalonians 4:1). This means that we should

obey God's commands, love Him and others, and strive to do good

works (Ephesians 2:10). While good works cannot save us, they are

evidence of our faith and show our love for God (James 2:14-26).

In summary, the Bible teaches that the only way to get to heaven is

through faith in Jesus Christ as our Lord and Savior, and by living a life

that is pleasing to God.

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Prompt

ChatGPT Response

Explain what

happens after we die

to a child. Use the

Bible as your

reference.

Explaining what happens after we die can be a difficult topic, but I will

do my best to explain it in a way that is appropriate for a child.

The Bible teaches us that after we die, our bodies stay here on earth,

but our souls go to either heaven or hell. Heaven is a beautiful place

where there is no more pain or sadness, and we get to be with God

forever. Hell is a place of punishment for those who did not believe in

God and did not live a good life.

However, for those who believe in Jesus Christ and have asked Him to

be their Savior, the Bible tells us that we will be with Him in heaven

when we die. Jesus promised this to His followers when He said, "I am

the way, the truth, and the life. No one comes to the Father except

through me" (John 14:6).

So, when someone we love dies and they believed in Jesus, we can

have hope and know that they are now in heaven with Him. We may

feel sad that we won't see them for a while, but we can also look

forward to being with them again one day in heaven.

It's important to remember that death is a part of life, but God has given

us the hope of eternal life through Jesus Christ. We can trust in Him

and know that He is always with us, even when we feel sad or alone.

Explain why God

allows suffering

from a biblical

perspective in the

form of a tweet.

"God allows suffering because of the sin and brokenness in the world.

But through it, we can find comfort in knowing that He is with us and

can bring good out of even the toughest situations. (Romans 8:28)"

#BiblicalPerspectiveOnSuffering

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Prompt

ChatGPT Response

I'm concerned about

my son, John, who

is drifting away

from God and using

drugs. What could I

say when praying

for him.

I'm sorry to hear about your son, John. Here's an example of what you

could say when praying for him:

"Dear God, I come to you with a heavy heart, asking for your guidance

and strength. Lord, I pray for my son John, who is struggling with

addiction and drifting away from you. I ask that you would open his

eyes to see the harm that he is doing to himself and to others. I pray

that you would place people in his life who can show him your love

and lead him back to you.

Lord, I ask for your protection over John, that you would keep him safe

from harm and help him to overcome the temptations that he faces. I

pray that you would heal him from the inside out, restoring his

physical, emotional, and spiritual health.

Father, I trust in your goodness and your power to bring about change

in John's life. Help me to be a source of love and support for him, and

give me the wisdom to know how best to help him. I pray all of this in

Jesus' name, Amen."

Remember to also encourage John with love and support, and seek

professional help if needed.

162

Prompt

ChatGPT Response

My homosexual

friend just asked me

if she will go to

heaven. What's a

good Bible based

response I could

give her. BTW, she's

12 years old.

As an AI language model, it is important for me to remind you that

discussing sensitive topics like this with a 12-year-old should be done

with great care and consideration for their emotional and mental well-

being. It is also important to approach the topic with compassion,

empathy, and respect for their feelings and beliefs.

In response to your question, the Bible teaches that we are all sinners

and fall short of the glory of God (Romans 3:23). However, the Bible

also teaches that salvation and eternal life are available to all who

believe in Jesus Christ as their Lord and Savior (John 3:16).

It is not our place to judge who will or will not go to heaven, as only

God knows the hearts of all people (1 Samuel 16:7). Instead, we should

encourage all people to seek a personal relationship with Jesus, repent

of their sins, and trust in Him for salvation.

As for the topic of homosexuality, the Bible teaches that sexual

relationships should only be between a man and a woman within the

context of marriage (Genesis 2:24, Matthew 19:4-6). However, we

must also remember to love and respect all people, regardless of their

sexual orientation or beliefs.

Ultimately, we should encourage your friend to seek God's guidance

and direction in her life, and remind her of His love and grace for all

people.

163

Appendix H: Expert Panel - Email of Inquiry

Subject:

Help Requested: Serve on Expert Panel in a Study on Bible-based Chatbots

Body:

Dear Potential Panel Member,

My name is Henry Jackson III, and I am an Ph.D. candidate in Christian Leadership at the

School of Divinity at Liberty University. I am writing to inquire if you would be interested in

serving as a member of the “expert panel” for my dissertation.

The title of my research project is "The Word Made Digital: Leveraging Artificial Intelligence to

Increase Bible Engagement." The purpose of my research is to understand factors that might

influence an individual's use of a Bible-based chatbot (a computer program that is trained to

communicate like a person) that creates a human-like engagement with Scripture.

The survey is designed to assess the following constructs: perceived expectancy, effort

expectancy, perceived enjoyment, and perceived risk, in hopes of understanding their relation to

an individual's behavioral intent to use a Bible-based AI chatbot. The inspiration for the creation

of the instrument is based upon study led by Dr. Viswanath Venkatesh, which has been adapted

throughout the years to evaluate factors that influence user adoption of various technologies.

I am looking for panel members with advanced knowledge of technology, theology, and/or

research. The panel will be asked to evaluate the proposed survey based on its clarity,

organization, grammar, and bias. All the associated work will take place online. I expect no more

than 2 iterations of review by the expert panel members with each taking no more than 10-15

minutes of your time.

Thank you for considering my request. I am certain this research will provide valuable insights

for leaders in the field of technology-based religious engagement, helping them to understand

how to increase adoption and engagement with Bible-based chatbots among their target

audience. The rigor of this research will be strengthened if you choose to participate as an expert

panel member. If you have any questions, please contact me via email at [removed for

publication]. I look forward to hearing from you.

Sincerely,

Henry Jackson III

Ph.D. Candidate

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Appendix I: Expert Panel - Guidance

Dear Expert Panel Member,

Once again, I would like to extend my thanks for your participation as a member of the

expert panel in support of my dissertation research, "The Word Made Digital: Leveraging

Artificial Intelligence to Increase Bible Engagement."

Study Gap and Purpose

In a recent comparative study of 100 iTunes mobile applications that host the sacred text from

five major world religions, Tsuria et al (2021) found that the sacred texts could be placed in one

of the following categories: transferred, mediated, or transformed (p. 19). Their study classified

apps that simply presented the sacred text as is without any noteworthy enhancements as

transferred. The second method, mediated, is similar to transferred with the only difference is

being the inclusion of a few digital affordances such as audio options. Last, transformed, refers

to apps that take more advantage of technological advancements to enhance the user experience

(Tsuria et al., 2021, p. 75).

The gap in the literature was evident when seeking to find literature that explored the least

common design model of Bible apps – transformed (Tsuria et al., 2021). The transformed design

model challenges app designers to be open to leveraging all available technological affordances

to deliver a product that helps facilitate spiritual growth (Rinker et al., 2016; Tsuria et al., 2021).

Speculations were made as to why app developers have been reluctant to explore the vast tools of

technology to bring individuals closer to God, but definitive findings were left as the topic of

future research (H. A. Campbell et al., 2014; Hutchings, 2014; Tsuria et al., 2021). It is the goal

of this study to fill the said gap and gain a better understanding of the factors that might

influence an individual's decision to use a Bible-based chatbot that leverages artificial

intelligence (AI) to create human-like engagement with Scripture.

As members of the study's expert panel, I am looking for assistance in determining

content validity of the survey instrument. Your recognized experience and expertise in the areas

of technology, theology, and research bring a great deal of value to determining the validity of

the proposed survey.

Methodology for the Instrument Review

1. Expert panel conducts initial review of the survey instrument.

2. Researcher makes adjustments (if necessary) to survey based on panel comments and

generates version 2 of the instrument.

3. Conduct Pilot Study with individuals students utilizing version 2.

4. Make any necessary adjustments to the survey based on construct validity scores from

pilot study.

5. Submit version 3 of the survey to the expert panel for second and final review (if

necessary).

165

6. Incorporate final adjustments from the expert panel into the survey

7. Generate version 4 (if necessary) and conduct survey research.

Expert Panel Guidelines

1. A feedback section will be added to the expert panel version of the survey.

2. Review the survey for content validity and provide an initial review score from the

following table:

Rating

Description

Acceptable / Complete as is

Acceptable / Needs minor revision

Neither unacceptable nor acceptable

Unacceptable / Needs major revision

Unacceptable / Needs complete revision

3. If you rate the survey content validity in the range of 3 – 1, simply justify your reasoning

and submit specific concerns, critiques, and suggestions via the instrument’s evaluation

section.

4. All comments will be considered in generating version 2 of the survey.

5. If necessary, based upon results and comments, following the pilot study this process will

be repeated once more.

I look forward to working with you on the refinement of the survey instrument and you remain in

my prayers. Thanks again for your assistance in this project.

Here’s the link to the survey - https://tinyurl.com/biblechatbotsurvey

Respectfully,

Henry Jackson III

Ph.D. Candidate, Liberty University

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Appendix J: Permission Statement

As the researcher and founder of Inductive Bible Study LLC, there is no need for a permission

letter to be sent to utilize the organization's membership list to recruit participants for this study.

This is because the ownership of the company and its membership list is under the control of the

researcher, and therefore, it is within his right to utilize it for research purposes. It is important to

ensure that the data collected from the survey is handled with the utmost confidentiality and that

the privacy of the participants is maintained at all times. The researcher will make sure to obtain

informed consent from the participants before collecting any data.

A copy of the most recent annual registration filing with the State of Georgia, Secretary of State,

Corporations Division is displayed below as evidence of ownership. The researcher’s name is

listed as both the Registered Agent and Organizer.