T.C.
YEDITEPE UNIVERSITY
GRADUATE SCHOOL OF SOCIAL SCIENCES
CONSUMERS ACCEPTANCE OF GENERATIVE ARTIFICIAL
INTELLIGENCE BASED ON CHATBOT USE
AYŞE YEŞİM MUTLU
Istanbul, 2024
YEDITEPE UNIVERSITY
GRADUATE SCHOOL OF SOCIAL SCIENCES
CONSUMERS ACCEPTANCE OF GENERATIVE ARTIFICIAL
INTELLIGENCE BASED ON CHATBOT USE
RESEARCH IN MARKETING
By
AYŞE YEŞİM MUTLU
In Partial Fulfillment
of the Requirements for the Degree of
Doctor of Philosophy
in
Faculty of Economics and Administrative Sciences
Department of Business Administration
Supervisor
Prof. Dr. Yusuf Can Erdem
Jurors
Prof. Dr. Tülin Ural
Doç. Dr. Ayşe Sevencan
Doç. Dr. Erkan Yıldız
Dr. Esra Nur Gökhan
Istanbul – 2024
ii
DECLARATION OF ORIGINALITY
I hereby declare that this thesis is my own work and that all information in this thesis has
been obtained and presented following academic rules and ethical conduct. I have fully cited
and referenced all material and results as required by these rules and conduct, and this thesis
study does not contain any plagiarism. The necessary permissions have been obtained if any
material used in the thesis requires copyright. No material from this thesis has been used to
award another degree.
To the best of my knowledge and belief, it contains no material previously published or
written by another person nor material accepted for the award of any other degree except
where due acknowledgment has been made in the text.
I accept all kinds of legal liability that may arise in cases contrary to these situations.
Ayşe Yeşim Mutlu
iii
ABSTRACT
CONSUMERS ACCEPTANCE OF GENERATIVE ARTIFICIAL
INTELLIGENCE BASED ON CHATBOT USE
The impacts of emotions, anthropomorphism, cognitive appraisals, social influence and
motivation on acceptance of users play important role in the usage of the artificial
intelligence software. The challenge for generative artificial intelligent software is to
evaluate users’ emotions, cognitions and perceptions. Measurements of performance
expectancy, effort expectancy, anthropomorphic characteristics, intrinsic motivation, social
influence, and emotions are the prerequisite to understand users’ willingness to accept the
use of these software. To interpret users’ acceptance while the users are interacting with the
generative artificial intelligence chatbot software, Unified Theory of Acceptance and Use
of Technology Model and Cognitive Appraisal Theory are used as theoretical foundation.
The model was empirically tested using data collected from online survey. Generative
Artificial Intelligence software based on users’ acceptance were analyzed with PLS_SEM
along with the influencing factors of users’ willingness to use.
The findings indicated that emotion is powerful antecedent of acceptance the use of these
software, and supports the proposed three level appraisal of use acceptance model for
Generative AI Software.
Keywords: Generative Artificial Intelligence, Unified of Acceptance and Use of Technology,
Cognitive Appraisal Theory, Emotion, Social Influence, Performance Expectancy,
Anthropomorphism, Effort Expectancy, Intrinsic Motivation
iv
ÖZET
ÜRETKEN YAPAY ZEKA TABANLI SOHBET BOTU KULLANIMININ
TÜKETİCİLER İÇİN KABULÜ
Duygular, antropomorfizm, bilişsel değerlendirme, sosyal etki ve motivasyonun
kullanıcıların kabulü üzerindeki etkileri, yapay zeka yazılımlarının bir hizmet olarak
kullanımında önemli rol oynamaktadır. Üretken yapay zeka yazılımlarının zorluk çektiği
alanlar, başta kullanıcıların duygularını anlamak; motivasyonlarını, çevrelerinin üzerlerinde
yarattığı etkiyi kavramak ve bilişsel değerlendirmelerini olumlu olarak, etkileyebilmektir.
Performans beklentisi ve efor beklentisi, antropomorfik karakteristikler, içsel motivasyon,
sosyal etki ve duyguların ölçümü, kullanıcıların bu yazılımların kullanımını kabul etme
isteklerini anlamak için önemli birer faktördür. Kullanıcıların üretken yapay zeka yazılımları
ile etkileşim halindeyken kabullerini yorumlamak için Birleşik Kabul ve Teknoloji
Kullanımı Modeli Teorisi ve Bilişsel Değerlendirme Teorisi temel olarak kullanılmıştır.
Model, çevrimiçi bir anket çalışması ile toplanan veriler kullanılarak ampirik olarak test
edilmiştir. Kullanıcıların kabulüne ilişkin Üretken Yapay Zeka yazılımları, çevrimiçi
ortamda fiili kullanım verileri toplanmış, kullanıcıların istekliliğini etkileyen faktörler
Yapısal Eşitlik Modellemesi ile analiz edilmiştir.
Araştırmanın öne çıkan bulguları, duyguların üretken yapay zeka sohbet yazılımlarının
kullanımını kabul etmede en önemli neden olduğunu göstermektedir. Bu çalışma, kullanılan
Bilişsel Değerlendirme Teorisi bazlı araştırma modelini desteklediğini saptamıştır.
Anahtar Kelimeler: Üretken Yapay Zeka, Birleşik Kabul ve Teknoloji Kullanım Modeli,
Bilişsel Değerlendirme Teorisi, Duygu, Sosyal Etki, İçsel Motivasyon, Performans
Beklentisi, Antropomorfizm, Efor Beklentisi
v
ACKNOWLEDGEMENTS
PhD. program has been uncompleted journey in my life since I had to leave PhD. in Science
in 1992. The passion that I carry on in whole my life for IS, and my career lead to have this
thesis. I always try to understand the progress of artificial intelligence since studying on my
MSc thesis.
While doing this thesis, my objective is to provide the review of previous findings on how
people perceive artificial intelligence and the inferences regarding measurement of usage
intention towards generative artificial intelligence.
I am grateful to Prof. Dr. Yusuf Can Erdem for all his support , since the beginning of PhD.
program.
His continuous support and encouragement have been valuable in each stage of PhD.
program.
In short, I feel lucky to have met the invaluable academicians. I would like to express my
special thanks to Doç. Dr. Erkan Yıldız and Prof. Dr. Tülin Ural.
I want to dedicate my thesis to my family supporting me in every steps of my life.
Ayşe Yeşim Mutlu
vi
TABLE OF CONTENTS
DECLARATION OF ORIGINALITY………………………………..……………………ii
ABSTRACT......................................................................................................................... iii
ÖZET .................................................................................................................................... iv
ACKNOWLEDGEMENTS ................................................................................................... v
LIST OF FIGURES ........................................................................................................... viii
LIST OF TABLES ................................................................................................................ ix
LIST OF ABBREVIATIONS ................................................................................................ x
1 INTRODUCTION .............................................................................................................. 1
2 LITERATURE REVIEW AND THEORITICAL BACKGROUND ................................. 4
2.1
Technology Acceptance and Adoption ...................................................................... 4
2.1.1 Performance Expectancy and Effort Expectancy ......................................................... 5
2.1.2 Acceptance of Artificial Intelligence Technologies ..................................................... 5
2.2
Social and Relational aspects in the use of Artificial Intelligence ............................ 7
2.2.1
Emotion .............................................................................................................. 7
2.2.2
Anthropomorphic Characteristics ...................................................................... 8
2.2.3
Social Influence ............................................................................................... 10
2.2.4
Intrinsic Motivation ......................................................................................... 11
3 DEVELOPMENT OF THE RESEARCH MODEL AND HYPOTHESIS ...................... 19
3.1
Willingness to accept the use of Generative AI software ........................................ 19
3.2
Three Level Process of Technology Adaptation ...................................................... 19
3.2.1 Primary Appraisal .................................................................................................... 20
3.2.3 Outcome stage.......................................................................................................... 26
vii
4 TESTING AND VALIDATION OF THE MODEL ........................................................ 28
4.1
Research Design and Methodology ......................................................................... 28
4.1.1 The Measurement instrument and questionnaire design.......................................... 28
4.1.2 Sampling Design and Data Collection ..................................................................... 31
4.2
Data Analysis and Results ....................................................................................... 32
4.2.1 Analysis of Measurement Model ............................................................................. 33
Internal Consistency Reliability and Convergent Validity ...................................... 34
Discriminant Validity .............................................................................................. 50
4.2.2. Desciptive Statistics ................................................................................................. 43
4.2.3. Evaluation of the Structural Equation Model ............................................................ 43
Coefficient Retermination, Effect Size, Predictive Power Coefficient ................. 48
Significance of Path Coefficients ............................................................................ 48
Specific Indirect Effects........................................................................................... 48
Analysis of Demographic Groups............................................................................ 52
5 DISCUSSION OF RESULTS ......................................................................................... 56
6 CONCLUSIONS ............................................................................................................. 59
REFERENCES .................................................................................................................... 62
APPENDIX A……………………………………………………………………………..69
viii
LIST OF FIGURES
Figure 1
Proposed Research Model……………………………………………………27
Figure 2
Measurement Model………………………………………………………….34
Figure 3
Structural Equation Model……………………………………………………43
Figure 4
Revised Research Model……………………………………………………...52
ix
LIST OF TABLES
Table 1
Literature Review…………………………………………………………….13
Table 2
Constructs, Items and Sources in the Study …………………………………29
Table 3
Demographic Attributions of Respondents…………………………………..32
Table 4
Results of Measurement Model………………………………………………36
Table 5
Cross Loadings……………………………………………………………….38
Table 6
Discriminant Validity Results - Fornell-Larcker criterion……………………39
Table 7
Discriminant Validity Results - Heterotrait-Monotrait Ratio criterion……….40
Table 8
Descriptive Statistics of Items and Constructs………………………………..42
Table 9
Structural Model VIF, R2, F2, Q2 Values……………………………………..45
Table 10
Significance of Path Coefficients……………………………………………..47
Table 11
Specific Indirect Effect Coefficients………………………………………….48
Table 12
Summary of the Hypothesis…………………………………………………...50
Table 13
Demographic Group Analysis Results – Age…………………………………54
Table 14
Demographic Group Analysis Results – Gender………………………………55
x
LIST OF ABBREVIATIONS
AI
Artificial Intelligence
AGI
Artificial General Intelligence
AIDUA
Artificial Intelligence Device Use Acceptance
AVE
Average Variance Extracted
ASP
Automated Social Presence
CASA
Computers are Social Actors
CAT
Cognitive Appraisal Theory
CPA
Cognitive Appraisal Theory
CR
Composite Reliability
f2
Effect size coefficients
HTMT
Heteotrait-Monotrait
IDT
Innovation Diffusion Theory
IS
Information Systems
LLM
Large Language Model
NGO
Non-Governmental Organizations
PLS-PM
Partial Least Square Path Modeling
PLS-SEM
Partial Least Square Structural Equation Modeling
Q2 Value
Predictive Power Coefficients or Predictive Relevance
R2 Value
Coefficient Determination
SRIW
Service Robot Integration Willingness
UTAUT
Unified Theory of Acceptance and Use of Technology
SDT
Self Determination Theory
SPSS
Statistical Package for the Social Sciences
STDEV
Standard Deviation
TAM
Technology Acceptance Model
TBP
Theory of Planned Behavior
TRA
Theory of Reasoned Action
USD
United States Dollars
VIF
Variance Inflation Factor
1
1
INTRODUCTION
As a subdomain of computer science, artificial intelligence encloses with machine
learning and deep learning. These disciplines focus on creating AI software inspired by
human brain decision-making processes. Deep learning algorithms use neural networks
which are programmable mathematical structures to learn from large amounts of data. They
have multiple layers of nodes that are interconnected to each other. Deep learning algorithms
make decisions with probabilistic approach and enable unsupervised learning (IBM, 2024).
Unsupervised learning provides the multiplier effect to create context specific models from
a large amount of unlabeled data. These models are extended in last few years, it can be
language, audio, visual and even context specific (Nvidia,2024).
Many generative AI models is created for text at the beginning and is considered to be
most studied field. Language-based generative models are generated and named as large
language models (LLMs). LLMs are used for broad type of tasks, including writing essay,
software development, translation, and giving support to learning. OpenAI's chatbot is the
first product of text domain which is opened to public in November 2022. The emergence
of ChatGPT is accepted to be a significant milestone in this domain. It has most advanced
large language model, and has the ability to write poems, create jokes, and generate essays
that are similar to human work. The new version of ChatGPT has more further features such
as interacting with human naturally and even understanding emotions and responding the
empathy (IBM, 2024; Openai, 2024)
Generative AI models are available for music, audio, and speech. Models are being
able to develop songs and pieces of audio clips with text inputs, distinguish objects in videos
and play a musical accompaniment for different video footage, and even compose music
(Nvidia, 2024)
One of the most popular applications and software of generative AI is within the visual
domain. These are the composition of three-dimensional images, avatars, videos, graphs,
and other figures. These models can generate lifelike characters for virtual or augmented
reality, develop three-dimensional video games, logo design, and enhance or modify existing
images and even artistic installations. Sora is one of the important examples for this domain,
which creates video with realistic characters, animated illustrations and images by prompting
distinctive features as a text (Openai, 2024).
2
The tremendous progress of artificial intelligence is experienced by the users in last
two years. After emergence of generative AI, researchers and engineers are working on
artificial general intelligence (AGI). Former chief scientist of OpenAI, Ilya Sutskever
explored the potential of AGI. He explained that digital brains would become as good and
even better than biological brains in the near feature. He notified that AGI would have
dramatic and unimaginable impact on every single area of human activities (TED, 2023).
According to the latest available data, the global AI market, is currently valued at
196.63 billion USD in 2024 (NEXTMSC, 2024). Forecasts project that by 2032, the AI
market is expected to soar to 2.575 trillion USD, illustrating a substantial increase of
approximately more than ten times of current size (Precedence Research, 2024).
A chatbot, commonly referred to as a conversational bot, is an AI software that
simulates human conversation through text or voice using the internet. Its purpose is to
address simple inquiries, provide product suggestions, and offer customer support, allowing
organizations and businesses to streamline operations, enhance efficiency, and save time
through automation. Recent advancements have led to the creation of more sophisticated
chatbots that utilize deep learning algorithms to respond to complex queries and issues.
Various types of chatbots exist, including menu-based, keyword-based, rule-based, and
voice-based bots.
In this domain, as mentioned earlier, one of the leading Artificial Intelligence Research
Center OpenAI which they have generative models using deep learning, which leverages
large amounts of data to train the model to perform a task. OpenAI can think with 100 million
parameters and understand complex graphics, have advance capabilities like writing an email,
essay and summary creation, editing the text, acting as a teacher and writing code. OpenAI gave
their models to the companies for next generation applications (Openai, 2024). Likewise,
OpenAI, there is another important generative large language models Gemini created by Google
and opened to public in 2023. Google Gemini AI’s language generation capabilities can be
augmented with additional data sources, such as social media, news, articles, or product reviews,
to provide more context and insights to its responses (Google, 2024).
Research Questions and Objectives
The acceptance and usage of generative artificial intelligence software imply that users
embrace new technology innovations. In this research, it is questioning "How to measure
factors that affect users’ intentions interacting with these generative chatbot software”.
3
Previous studies have focused mostly on simple type of chatbot software. They searched for
the factors of users' emotions through expected performance and effort, anthropomorphism,
motivation, and social items which affect adoption of simple AI device or software. AI
technology is evolving and advances rapidly, some of generative AI chatbot solutions
opened to public as a draft for testing purposes since November 2022. Countries, European
Union, world economic communities and NGO’s create their strategies including AI,
develop new regulations and try to solve ethical issues according to AI.
This research proposes to evaluate unified factors of the acceptance and usage of
generative artificial intelligence software. It maintains the following parts;

Analyze the existing literature about technology acceptance and social/relational
determinants in terms of AI users’ behavior

Analyze the previous studies which gives some guidance while determining
strategies and implementing AI device or software,

Expose the factors of users' emotions through expected performance and efficiency,
anthropomorphism, motivation, and social items which effects adoption of innovation,
This research aims to search about all the previous findings and inferences in Turkey.
4
2
2.1
LITERATURE REVIEW AND THEORITICAL BACKGROUND
Technology Acceptance and Adoption
In this study, prior researches especially applicable to Information Systems and
especially Artificial Intelligence related to consumer adoption and behavior are examined.
When concern of researches is to investigate the behavioral intention and use behavior of
new technologies, the dominance of Unified Theory of Acceptance and Use of Technology
(UTAUT) in the literature and the underlying fundamental theory, Technology Acceptance
Model (TAM) (Davis, 1986; Davis 1989; Venkatesh et al., 2003). There is chronological
explanation of related theories in this section.
The first popular consumer behavior theory of reason action model, is Theory of
Planned Behavior (TBP) emerged by Fishbein and Ajzen (1975). TBP is fundamentally
social psychology model, and most essential and effective theory of individual users’
behavioral. According to this model, behaviors engendered by the behavioral intention and
behavioral intention engendered by attitudes and subjective norms. Attitudes generated by
the beliefs and subjective norms generated by the normative beliefs and motivations (ibid).
In essence, it is a general model is used heavily to explain and predict different type of
behaviors (Sheppard,1988).
Then Davis (1989) introduced the TAM users’ acceptance and usage of technology.
He composed TAM by using TBP adding two variables; these are extended and “perceived
usefulness” and “perceived ease of use”. TAM suggests that “perceived usefulness” and
“perceived ease of use” are two primary factors in explaining “users’ adoption intention”
(Taylor & Todd, 1995; Davis, Bagozzi and Warshaw, 1989). Since its initial publication, the
TAM has been validated by numerous scholars across various areas and has been widely
utilized in technology adoption researches over the past decades. It has been continuously
studied and extended, the second version was introduced by Venkatesh and Davis (1996) by
adding subjective norm and job relevance and Venkatesh and Moore (2000) by adding
gender and experience.
5
TAM was developed to explain and predict the adoption of information technologies
within the business context. Although the model was originally developed for information
technologies, it is also used to explain the acceptance of other technological innovations.
2.1.1 Performance Expectancy and Effort Expectancy
About two decades ago Venkatesh, Davis and Morris reviewed and studied on the
technology acceptance theories and models and they synthesized these models into the new
theory (Venkatesh et. al, 2013). They introduced “Unified Theory of Acceptance and Use of
Technology” (UTAUT) (Venkatesh et al., 2003). UTAUT has four key constructs;
“performance expectancy, effort expectancy, social influence, and facilitating conditions and
four moderators; age, gender experience and voluntariness”. These first three key factors
were used to influence “behavioral intention” and then “behavioral intention” and
“facilitating conditions” were decisive the usage. On top of UTAUT, Venkatesh, Thong and
Xu (2012) modified the theory and gave particular attention to consumer use context and
developed UTAUT2 (Venkatesh et al., 2012). They adapted the original constructs and
definitions from UTAUT to applied to acceptance and use of consumer technology.
“Performance expectancy” is defined as the degree of the benefits which is provided for
consumers in performing certain activities, “”effort expectancy” is the degree of easiness of
use for consumers, and “facilitating conditions” address the consumers’ perceptions
regarding to the resources and supports to perform a behavior (Venkatesh et al.,2003).
UTAUT2 is expanded to include hedonic construct by using Motivation Theory (Vallerand
1997, Deci 1971).
2.1.2 Acceptance of Artificial Intelligence Technologies
Technology acceptance models have traditionally emphasized the adoption of
functional usage of computer technologies, basic online services, and traditional software
tools, with researchers concentrating on the factors that drive customers' intention to use
these platforms in a conventional manner. Lu et. al (2019) argue that some core factors such
as “perceived ease of use” and “perceived usefulness” may not capture all aspects of AI
software usage. When it comes to using AI software, consumers are likely to be aware of
whether a service is provided by a human or by AI, as well as the superior level of service
that AI software can offer. Therefore, Lu et al. (2019), developed a scale to measure
“customers’ willingness to accept the use of AI service” (SRIW) in the long term.
6
They examined previous ample researches new innovations usage of Apple pay,
Google Glass, virtual reality technologies. They found that personality trait influence
consumers’ satisfaction, and adoption of technologies can also be associated by the
consumers within a social domain (van Doorn et al., 2017). New technology enabled services
innovates customer experience by integrating digital, physical, and social domains (Bolton
et al.,2018). Lu et. al (2019) created new theoretical framework by collecting important
factors which consumers think valuable to them. SRIW scale includes key factors identified
from previous technology acceptance theories but also customizes features of service bots,
and measures via qualitative interviews, and focus groups. The study has revealed six
dimensions.
These
are
“performance
expectancy,
“facilitating
conditions”,
“anthropomorphism”, “social influence”, “intrinsic motivation” and “emotions”.
“Performance expectancy”,
“facilitating conditions”, and intrinsic motivation” are
UTAUT2 based dimensions. Triggering emotions associated with AI devices or software are
the main item in various studies (van Doorn et al., 2017; 2019). Anthropomorphism was the
first time introduced as an important dimension of a technology acceptance model in their
study (Lu et al., 2019).
Customer who uses AI software or devices does not only interest task performance,
considers also the social and emotional interaction and affiliation (van Doorn et al. , 2017,
2019). However, none of the existing technology acceptance research has captured the
different roles of AI software or device. Whereas, there is a need for comprehensive model
depicts
psychological route that conduct customers’ readiness to accept the use of AI
software. Gursoy et al. (2019) proposed a theoretical model of “AI Device Use Acceptance”
(AIDUA) by using “Cognitive Appraisal Theory”(CAT) (Lazarus, 1991a; Lazarus, 1991b)
and the “Cognitive Dissonance Theory”(CDT) (Festinger,1962). AIDUA applies the
cognitional-motivational-relational process that customers follow in defining their intention
to use of AI device or software. Replacing human entity with AI devices is argumentative
subject whether consumer will accept or refuse to use AI software or robots (Huang and
Rust, 2018). Hence, supporting and opposing factors coexist and interact to shape users’
behavior. Therefore, their study delineates decision stages that result in both positive and
negative outcomes within their framework. It is based on UTAUT2 model, and largely used
SRIW scale. Facilitating conditions are extracted in AIDUA model, because there is no
sufficient experience to answer these conditions and is more relevant actual behavior on the
7
field rather than the decision making (Venkatesh et al. ,2012) . Venkatesh et al. (2003)
found that facilitating conditions were meaningful for older people who has substantial
experience. In this research, SRIW scale and AIDUA model has been used heavily (Lu et
al. , 2019; Gursoy et al., 2019).
2.2 Social and Relational aspects in the use of Artificial Intelligence
2.2.1 Emotion
The model of CAT developed by psychologist Richard Lazarus (1991a, 1991b) is
referenced in this research. Lazarus focused on the relation of cognition, motivation and
emotion in his researches. CAT focuses on the role of motivational, cognitional and
relational appraisals of individuals whether it is conscious or not. In continuation, it
examines how the emotion arises, what is the emotion process, what kind of (relational,
cognitional and motivational) causes of the emotions, and appraisals patterns on individual
behavior. This theory introduced emotion process, cognitive-relational-motivational
analysis of each fundamental emotions and molecular analysis of appraisal pattern for
emotions. Key points of the appraisal pattern are “primary appraisal, secondary appraisal,
and emotional” response. In the initial stage of cognitive appraisal, individuals evaluate the
significance of an event or situation. This involve determining whether the situation is
congruent to one's goals and if it has any personal meaning or implications (Lazarus, 1991b).
Following primary appraisal phase, individuals engage in secondary appraisal. This
phase involves assessing their ability to cope with or manage the situation. Here, individuals
evaluate their resources, strategies, and potential outcomes, which can determine the
emotional response. The emotions experienced by individuals are a result of their primary
and secondary appraisals. For instance, if a situation is appraised as threatening and one feels
ill-equipped to handle it, anxiety or fear may result.
Cognitive-motivational-relational theory of emotion has a great power to guide the
researches, predict about how emotion is formed and how it shapes subsequent adaptations.
In continuance, it helps us reason towards in the back from any pattern of emotion to its
causality. Such information can help us work to change emotional patterns that arise from
faulty appraisals and coping patterns that are clinically nonfunctional or potentially
destructive.
8
Another related research with this research, the study of Pelau et al. (2021) which
examines “empathy and anthropomorphic characteristics in acceptance of artificial
intelligence”. Their research is “the role of psychological anthropomorphic characteristics,
perceived empathy, and interaction quality in the acceptance and trust of AI devices”. They
analyzed mediation effects of “interaction quality” and “perceived empathy” on relation
between “anthropomorphic characteristics” and “acceptance and trust of AI device” and
found that significant mediation for both dimensions. They concluded that a human-like AI
device has higher acceptance and trust when they show empathy and in certain level of
interaction quality in relationship with consumer (ibid).
2.2.2 Anthropomorphic Characteristics
Anthropomorphism refers to the tendency to attribute human-like characteristics,
motivations, intentions, or emotions to the real or imagined behavior of non-human entities
(Epley et al., 2007). Epley et. al, emerged Three Factor Theory of Anthropomorphism to
introduce framework to understand process of anthropomorphism. They focused on three
psychological determinants, the first one is the accessibility and applicability of
anthropocentric knowledge. Accessibility of anthropocentric knowledge is mental cognition,
and impacts perceptions and interactions. Applicability of anthropocentric knowledge
impacts the individual’s interpretation of non-humans’ behavior and decision-making
process and can help social interactions with non-human entities. Second determinant is the
motivation
to
explain
and
understand
the
behavior
of
non-human
entities.
Anthropomorphism provides an intuitive and ready method for reducing uncertainty. The
third determinant is the need for social engagement and affiliation. People need to establish
and maintain social connection with others and connecting with non-human entities easily
satisfy the needs of human. This theory provides the framework for understanding process
of anthropomorphism and gives the prediction when people anthropomorphize or not, when
non-human entities are treated as humanlike or not (ibid).
From the AI technology point of view, van Doorn et al.(2017), studied on humanoid
robot providing service, and classified abilities of technologies to engage customers with a
social degree. They introduced the new concept of “automated social presence” (ASP) which
technology enables customers considering the existence of non-human social entity to the
literature. Automated underlines the technology displaces human as social entities. They
classified ASP from low to high, what is today with existing technology, and what would it
9
be with the emerging technology. They first proposed that customers’ inference of “warmth”
and “competence” related to ASP mediates its effect on “customer service outcomes”.
Higher (vs. lower) levels of ASP reveals higher levels customers’ inference on “warmth”
and “competence”; respectively, higher levels of “warmth” and “competence” leads to better
“customer service outcomes”. Secondly, they argued that higher (vs. lower) levels of ASP
reveals higher levels of customers’ inference on “receptiveness,
attractiveness, and
manipulability”; and leads to better “customer service outcomes”. Thirdly they suggest that
the positive effects of high (vs. low) ASP on “warmth” and “competence” are larger when
consumers interact with higher (vs. lower) levels of “anthropomorphism”. The positive
effect of high (vs. low) ASP on “perceived receptiveness”, “attractiveness”, and
“manipulability” are larger when “anthropomorphism” of the technology is high (vs. low).
Lastly, they underline the moderating role of “technology readiness”. They proposed that
consumer’s “technology readiness” as a moderator created higher “receptiveness”,
“attractiveness”, and “manipulability” of ASP, and higher impact on “warmth” and
“competence” of ASP (ibid).
However, Ackerman’s study (2016) underlines the disadvantages of social robots,
especially in the dimension of impact of employment, user acceptance and trust, ethical and
privacy concerns, technical limitations, and lack of emotional intelligence. He emphasizes
the phenomenon of social robots is a kind of uncanny valley issue. Uncanny value was
hypothesized that human response to a human-like robot would suddenly change from
empathy to disgust when the robot approached but failed to achieve a realistic appearance
(Mori, 1970). According to Ackerman, the introduction of social robots in various job roles
raises concerns about potential job displacement or changes in employment patterns. There
is already an ongoing and threatening debate about the balance between robotic automation
and preserving job opportunities for humans. In addition to job displacement, social robots
may face resistance from users who are unfamiliar with or distrustful of robotic technology.
He addressed that building trust and ensuring that users feel comfortable interacting with
social robots to present significant challenges. He argued that the integration of social robots
raises ethical issues, particularly related to privacy and data security. Social robots often
collect and process personal information, which can lead to concerns about data misuse or
unauthorized access. He suggests that there are also ethical questions about the impact of
robots on human relationships and the potential for dependency on robotic companionship
(ibid).
10
2.2.3 Social Influence
Social Impact Theory (SIT) (Latane, 1981) stems that individuals or groups influence
the thoughts or actions of others through their behavior, complying with the group norms
based on importance of the group. The theory posits that “social influence” depends on three
primary factors: “strength”, “immediacy”, and “number of sources” exerting the influence.
Latane's (1981) initial principle suggests that when multiple social sources influence a target
individual, the impact felt by the target is determined by a combined effect of importance,
proximity, and the number of people involved. In other words, the impact experienced by
an individual is a multiplicative function of the “strength”, “immediacy”, and “number of”
people affecting him or her. Second principle is “immediacy” refers to the proximity in time
and space between the influence source and the target. Influence tends to be stronger when
the source is physically closer to the target and when the interaction is immediate rather than
delayed. The third principle is “strength”; the impact of the influence source (person or
group) is stronger if the source is perceived as authoritative, important, or credible. The
“strength” can be influenced by the status, power, or expertise of the source. It is reasonable
to predict that the greater the status, the more immediate the influence, and the larger the
number of influencing individuals, the greater impact an individual is likely to experience
(ibid).
One of the fundamental theories is related to social groups is Social Identity Theory
(SIDT), developed by Tajfel and Turner (1979). They introduced that individuals take their
self-concept from their social group. They examined how individuals categorize themselves
and others into groups and how these categorizations affect their behavior and perceptions.
The theory primarily explores the psychological processes that underlie group dynamics,
intergroup relations, and the formation of social identities. They suggested that the various
groups individuals were part of, such as social class, family, or a football team, played a
significant role in shaping their sense of pride and self-worth. This theory focuses on the
positive effect of “social identity” on individuals, highlighting key concepts like belonging,
purpose, self-value, and identity. Belonging to a group can foster a sense of connection,
unity, and provide individuals with reassurance that they are not alone in their shared
experiences or views. Purpose denotes that group affiliations frequently entail shared
objectives or missions that offer guidance and intention to individual members. Another vital
concept, self-worth, highlights that being part of a group can boost self-esteem as individuals
derive pride from the group's achievements and a favorable group reputation. And the final
11
one is identity, derived from groups that offer a structure for understanding oneself in the
context of the community, can help individual to define who is him/her based on shared
qualities, values, or objectives.
2.2.4 Intrinsic Motivation
The versions of TAM and previous researches related technology acceptance models
before UTAUT2, indicates that “perceived usefulness” is a major determinant and predictor
of intentions to use the computers. Thus, the effect of enjoyment on “intention to use” has
not been questioned. Davis, Bagozzi and Warshaw (1992) reported their concerns about the
related effects of “enjoyment” and “usefulness” on “intention to use” of computers at their
workspace. The researchers discovered that “enjoyment,” as a form of “intrinsic
motivation”, significantly influenced individuals' “intentions to use” word processing
software and business graphics programs. Additionally, they observed that “enjoyment”
played a mediating role in the relationship between “usage intentions” and “perceived output
quality”, as well as “perceived ease of use”. They drew several conclusions on designing
computer programs that are not only more functional but also more engaging to enhance
user acceptance (Davis, et al., 1992).
The first version of UTAUT encompass only determinant of extrinsic motivation and
focusing on the utility value, named as performance expectancy, which was identified as a
significant determinant influencing the “intention to use” in the UTAUT(Venkatesh et
al.,2003). Venkatesh et al. (2012), enhanced extrinsic motivation by the intrinsic and hedonic
components in UTAUT2. Hedonic component represents an intrinsic extension according to
Motivation Theory (Deci 1971; Vallerand, 1997). Deci explored that the complex interplay
between intrinsic and extrinsic motivators and laid the groundwork for future research into
how autonomy-supportive environments can enhance motivation by fulfilling intrinsic
psychological needs.
Vallerand's “Hierarchical Model of Intrinsic and Extrinsic Motivation” introduces a
framework to classify the researches on types of motivation and integrate the researches
with motivational determinants. He explores that “intrinsic motivation”, “extrinsic
motivation” and “amotivation” are important concepts to explain large part of human
behavior, represents human experience and they create important and varied consequences.
His study explored that intrinsic motivation refers to activity and the pleasure and
12
accomplishment based on participation. On the other hand, extrinsic motivation is not
engaged in the activity. He assessed these concepts with all their variables and indicators
in global, contextual and situational levels with mediators; these are autonomy, competence
and relatedness. Study shows that perceptions of competence, autonomy, and relatedness
mediates the effects of social elements on motivation. These results explain that there are
various social effects on “intrinsic motivation” and “extrinsic motivation”. Framework
includes that these motivations cause outcomes into cognitive, affective, and behavioral
categories. Most of the positive consequences are developed by intrinsic motivation, most
of the negative consequences are developed by extrinsic motivation and amotivation as
defined in self-determination theory (Deci and Ryan, 1985a, 1980).
Vallerand and his colleagues (Vallerand et al., 1989, 1992, 1993) posited three types
of “intrinsic motivation”. First one is “intrinsic motivation to know”, which can be described
as to be in an activity while learning, exploring, or trying to understand something new.
Another type is “intrinsic motivation toward accomplishments”, which focuses on activity
while person is attempting to surpass herself/himself, or to accomplish or create something.
The third type is to “experience stimulation” occurs when an individual participates in an
activity to enjoy pleasant sensations primarily linked to one's senses. Vallerand also
highlights the global, situational, and contextual factors, affects to foster autonomy and
creates intrinsic the level of motivation. (Vallerand, 1995; 1997).
From the marketing point of view, emotions played an important role in the AI
software consumption experience. As it is explained before, some researchers had the
discoveries on the hedonic needs of consumers focusing on the image sensory including
taste, sound, scent, and visual image, fantasy and emotional arousal of consumer experience
(Holbrook and Hirschman, 1982a). They (1982b) presented a novel model for
comprehending consumer behavior, contrary to traditional utilitarian approach. Based on
their perspective, consumers exhibit hedonic approach and their activities driven not by
specific objectives (Hoffman and Novak, 1996). Throughout the consumption journey, the
consumers have enjoyment and entertainment independent from the final products or
services (Babin et al.,1994). Instantly, creating best good-looking image of herself/himself
in an augmented reality with AI software; can simulate the personal image that person wants
to have and to be in. In this case, consumers prioritize the hedonic experience over the act
13
of purchasing. Hedonic determinants motivate the adoption of new technology by providing
inner satisfaction, arousal and emotion.
Literature review summary, related theories and researches are given in Table 1.
Table 1 Literature review summary
Core
Concepts
Theories
Core
Construct
Authors
Findings
Technology
Acceptance
and Adoption
Theory of Reasoned Action
(TRA):
Attitude
toward
Behavior
Ajzen and
Fishbein
(1975)
An individual’s positive
and negative feelings
assessed and converted to
the behavior
TRA is fundamentally
social psychology model,
and most essential and
effective theory of
individual users’ behavioral
The Person’s behavior
related to social network
Subjective
Norm
Technology
Acceptance Model (TAM)
Perceived
Usefulness
TAM is the first theory
specific to IS. Perceived
usefulness and perceived
ease of use are main
determinants in explaining
individual users’ intention
Perceived
Ease of Use
The level of personal
believes that usage of
technology is effortless.
Subjective
Norm
Adapted from TRA/TPB
Theory of Planned Behavior
(TPB):
is based on TRA by adding
perceived behavioral
control. It is additional
construct to determine
intention toward behavior
Decomposition and
crossover effects of TPB :
In contrast to TPB, similar
to TAM, decomposes
attitude, subjective norm
and perceived behavioral
control into its underlying
belief structure within
technology adoption
contexts.
Attitude
toward
Behavior
Davis,
1992
Ajzen,
1991
The level of personal
believes that technology
enhances his or her
performance.
Adapted from TRA
Subjective
Norm
Adapted from TRA
Perceived
Behavioral
Control
Created control for
perceived easiness or
difficulty, on intention
toward behavior
Perceived
Behavioral
Control
Taylor and
Todd,
1995
Perceptions of internal
and external limitations
of behavior
14
Core
Concepts
Theories
Core Construct
Authors
Findings
Technology
Acceptance
and
Adoption
TAM2 : additional
theoretical constructs
including social influence
processes and cognitive
instrumental processes to
the model
Job relevance
Venkatesh
and Davis,
2000
individual believes that
using technology,
enhances job
performance
Subjective norm
Adopted from TRA
Voluntariness
and Experience
Moderating variables
Use of innovation
provides gains to one’s
status in social group
Image and Social
Influence
Output Quality
and Result
Demonstrability
Cognitive Dissonance
Theory(CDT):
Individuals attempt to
rationalize conflicting
attitudes, beliefs or
behaviors. They may fail to
rationalize and the
inconsistency is still
continuing to exist, and this
dissonance leads to
psychological discomfort
Innovation Diffusion
Theory (IDT):
is the innovation-diffusion
process as “an uncertainty
reduction process”. It
proposes five
characteristics of
innovations that aim to
decrease uncertainty about
the innovation
User Acceptance of
Information Technology
(UTAUT):
This unified model is based
on TAM
It is useful tool to assess
the success of new
technologies.
It is for organizational
context.
Tangible results of
innovation and degree
of matching system
performance and job
goals effect
One of the important
theories in social
psychology. Cognitive
dissonance plays a role
in judgments, decisions
and evaluations. Helps
to understand how
conflicting beliefs
impact the decisionmaking process.
Cognitive
Dissonance,
Inconsistency,
Change belief,
Reduce
dissonance
Festinger,
1962
5 constructs
Rogers,
2003
Scale of IDT is used to
measure innovation
diffusion
Venkatesh
, Morris
and Davis,
2003
Identifies significant
determinants of
intention and usage
behavior.
Relative
Advantage,
Compatibility,
Trialability,
Observability,
Complexity/Sim
plicity
Social Influence,
Facilitating
Conditions,
Performance
Expectancy,
Effort
Expectancy ,
Moderators:
Gender, Age,
Experience,
Voluntariness of
use
15
Core
Concepts
Scales/Research
Core Construct
Authors
Findings
Technology
Acceptance
and Adoption
UTAUT2:
New Constructs:
It is for consumer
context. Incorporates
three constructs into
UTAUT; hedonic
motivation, price value,
and habit.
Measure moderating
effects of : age, gender,
and experience on
behavioral intention
Hedonic
Motivation
Venkatesh
Thong and
Xu, 2012
Enhanced constructs in
UTAUT important in
determining technology
acceptance and use
Acceptance
of Artificial
Intelligence
Technologies
Service
AI
Device
Integration Willingness
is based on UTAUT2
model, scale for AI device
Has six
constructs
Proposed a new
theoretical model by
using Cognitive
Appraisal Theory
Human-like AI
Research analyzes
three dimensions :
interaction quality,
empathy,
anthropomorphic
characteristics, in order to
gain trust and acceptance
Social and
Relational
aspects
Emotion
Price Value
Habit
Adapted from
UTAUT2
AI Device Use
Acceptance (AIDUA):
Cognitive Appraisal
Theory (CAT):
focuses on the role of
motivational, cognitional
and relational appraisals
of individuals
Assess the significant
impact of pricing on
consumers’ technology
use.
New Construct:
Anthropomorphi
sm
Constructs
adapted from
UTAUT2,
Model is adapted
from Cognitive
Appraisal Theory
Lu, Chai
and
Gursoy,
2019
Experience moderates
the effect of behavioral
intention
the first scale for AI
devices which
encompasses
anthropomorphism as
critical determinant
Gursoy,
Chi, Lu
and
Nunkoo,
2019
Depicts 3-step process
(cognition- motivationemotion ) that
customers use for
appraisals toward
acceptance of AI
Empathy,
Interaction
Quality,
Anthropomorphi
c Characteristics
Pelau,
Dabija,
and Ene,
2021
Higher acceptance,
when human-like AI
has the capability of
empathy and a certain
level interaction quality
Empathy has an
ultimate
mediation role between
anthropomorphism and
interaction
quality.
Emotion
Lazarus,
1991a,
1991b
Introduced emotion
process, cognitiverelational-motivational
analysis of each
fundamental emotions
and analysis of
appraisal pattern for
emotions.
16
Core
Concepts
Theories/Researches
Core
Construct
Authors
Findings
Social and
Relational
aspects
Causes and Consequences
of Emotions on Consumer
Behavior
Outcome
desirability
Watson and
Spence, 2007
Four appraisals are
defined implicating
specific emotions and
their effects on
consumer behavior.
Deci,1971
Impact on the
understanding of
motivation, leading to
the broader
development of SDT
Agency
Emotion
Intrinsic
and Hedonic
Motivation
Identifying the cause(s) of
emotions has clear practical
importance to understand
consumer behavior.
Fairness
Certainty
Effects of externally
mediated rewards on
intrinsic motivation
Intrinsic
Motivation
Self Determination
Theory(SDT)
Intrinsic and
Extrinsic
Motivation
First research to examine
intrinsic and extrinsic
motivation in human
subjects
Deci and
Ryan,
1980,1985
Autonomy,
Competence
and
Relatedness
Hierarchical Model of
intrinsic and extrinsic
motivation
Intrinsic and
Extrinsic
Motivation
Vallerand,
1995
and1997
Analysis of consumption
experience pursuing toward
fantasies, feelings and fun
Behavior,
Cognition,
Affect
Holbrook
and
Hirschman,
1982
Defines Hedonic
Consumption with
Experimental Aspects
Hedonic
Mental
Constructs
Hirschman
and
Holbrook
1982
Measuring Hedonic and
Utilitarian Shopping Value
Hedonic and
Utilitarian
Value
Babin,
Darden and
Griffin, 1994
Scale for assessing
consumer perceptions of
both hedonic and utilitarian
values
SDT analyzes and
inferences on relations
with motivation types
and SDT constructs
Posited the types of
intrinsic motivation.
Analysis of global,
situational, and
contextual factors,
affecting autonomy
and creating the level
of intrinsic
motivation.
Introduced a new
model to understand
consumer behavior,
contrary to traditional
utilitarian approach
Usefulness of the
hedonic perspective in
consumer behavior
the fundamental
aspects of hedonic
shopping value: pure
enjoyment,
excitement,
captivation, escapism,
and spontaneity
17
Core Concepts
Theories/Researches
Core
Construct
Authors
Findings
Social and
Relational
aspects
Social Impact Theory
Social Impact
Latane, 1981
Stems that individuals
or groups influence
the thoughts or actions
of others through their
behavior, complying
with the group norms
based on importance
of the group.
Social Identity,
Self Esteem
Tajfel and
Turner,1979
Emerged that people's
self-esteem is tied to
the social standing of
their social group
Accessibility
and
applicability
anthropocentric
knowledge,
Motivation to
explain and
understand the
behavior of
nonhuman
entities, Desire
for social
contact and
affiliation
Social
Cognition,
Psychological
ownership,
Technology
Readiness
Epley, Waytz
and
Cacioppo,
2007
Provides the
framework for
understanding process
of anthropomorphism
and gives the
prediction when
people
anthropomorphize or
not, when non-human
entities are treated as
humanlike or not.
van Doorn,
Noble,
Hulland, and
Grewal 2017
1. customers’
inference on warmth
and competence
related to ASP
mediates on customer
service outcomes.
2. higher levels of
ASP reveals higher
levels of customers’
inference on
receptiveness,
attractiveness, and
manipulability
consumer
3. Technology
readiness as a
moderator: higher
receptiveness,
attractiveness, and
manipulability of
ASP, and higher
impact on warmth and
competence of ASP
specifies the effect of
other persons on an
individual
Social
Influence
Social and
Relational
aspects
Anthropomorp
hic
Characteristics
Social Identity Theory
Explains the cognitive
processes and social
conditions behind the
intergroup behaviors
A Three-Factor Theory of
Anthropomorphism
Introduce whether people
are likely to
anthropomorphize nonhuman entities or not
Emergence of Automated
Social Presence(ASP)
Presents conceptual
framework that draws
relation between ASP,
and social cognition.
Service outcomes is
mediated by social
cognition and
psychological ownership,
and moderation of
technology readiness
18
Computers are Social
Actors Theory (CASA)
HumanComputer
Interaction
Nass, Steuer,
and Tauber,
1994
Presents individuals’
interactions with
computers are social
Anthropomorphism in
Service Provision
Examines Meta-Analytic
framework which includes
antecedents, functional
and relational mediators
and moderators of
anthropomorphism
Nobody wants Social
Robot :Underlines the
disadvantages of social
robots, especially in the
dimension of impact of
employment, user
acceptance and trust,
ethical and privacy
concerns, technical
limitations, and lack of
emotional intelligence.
Brand anthropomorphism
scale
Blut, Wang,
Wünderlich
and Brock,
2021
Social Impact
Ackerman,20
16
Stems that individuals
or groups influence
the thoughts or actions
of others through their
behavior, complying
with the group norms
based on importance
of the group.
Appearance,
Golossenko
Pilai and
Aroean, 2020
For marketing sector,
it is a scale to be
perceived human-like
brand image. This
scale can be used as
an identification tool,
to measure the degree
of brand is perceived
as human-like.
Add social disposition
construct in assessing
anthropomorphism of
smartphone users.
Moral virtue,
Has four dimensions to
verify appearance, these
dimensions create better
anthropomorphic score
Smartphones as Social
Actors
Verified and add to the
theory of sociality
determinant of
anthropomorphism in the
computer technology
perspective, and the
CASA paradigm
Must smart objects look
human: Examines impact
of anatomical
anthropomorphism on the
acceptance of the
product, measured by its
perceived usefulness,
easiness, and use
intentions.
Social Norms can be
applied to the
computers, Computers
are social actors.
Human-computer
interaction is socialpsychological.
Anthropomorphism
impacts customer’s
intention to use a
robot. The impact
depends on robot
type, service type and
functional/relational
mediators
Cognitive
Experience,
Conscious
Emotionally
Social
disposition
Anatomical
Anthropomorp
hism
Wang, 2017
Goudey and
Bonnin, 2016
Partially
anthropomorphic
robots are more
accepted than the
other robots as their
appearance is humanlike
19
3 DEVELOPMENT OF THE RESEARCH MODEL AND
HYPOTHESIS
3.1
Willingness to accept the use of Generative AI software
To measure users’ willingness to use Generative AI software, SRIW scale developed
by Lu et al. (2019) examined in this study. They identified six main determinants of
“consumers’ willingness to use AI device”. These are “performance efficacy” (“performance
expectancy”
and
“self-efficacy”
are
combined),
“intrinsic
motivation”,
“anthropomorphism”, “social influence”, “effort expectancy”, and “emotions”. These
determinants were examined to have foreseeability, interactions among them, and their
influence on customers’ willingness has been tested once by AIDUA model (Gursoy et al,
2019). According to CAT, during the decision-making process “cognition” and “motivation”
in “emotion” plays important role. There are three stages of appraisals in the model. The
model originates from CAT and AIDUA with some changes and applied to this study to
measure intention of using Generative AI software. It is analyzing behavioral appraisal and
creating emotions and emotion leads to behavioral intentions. In essence, as per the theory,
individuals' reactions to a stimulus are fundamentally shaped by the emotions they undergo
subsequent to a cognitive appraisal of the stimulus conducted at three hierarchical levels
(Lazarus, 1991a; Lazarus, 1991b).
3.2
Three Level Process of Technology Adaptation
This research utilized AIDUA (2019) and Lazarus’s “cognition-motivation-emotion”
framework (1991a) and integrated SRIW scale (Lu et al. , 2019) to examine “users’
willingness to accept the use of generative AI software”. It unfolds in a three-level process:
individuals commence by evaluating the significance (primary appraisal), weighing
behavioral alternatives (secondary appraisal), and eliciting emotions in response to stimuli
that drive behavioral intentions (outcome). In the primary appraisal phase, customers gauge
the pertinence and significance of AI device utilization during interactions, considering three
factors: “social influence”, “intrinsic motivation”, and “anthropomorphism”. Initially,
individuals assess the primary appraisal through these factors. Subsequently, they scrutinize
the benefits of AI software. Their secondary appraisal is based on the “performance
20
expectancy” and “effort expectancy” of AI devices (Lazarus, 1991a). As a result of the
secondary appraisal, they generate “emotions” towards generative AI software. The
“emotions” of individuals determine their level of willingness. This study also examines
mediation relations between constructs of primary appraisals and secondary appraisals
through “emotion”. While analyzing mediation relations, also direct relations of constructs
in primary appraisals and “emotion” are examined.
3.2.1
Primary Appraisal
In this stage, users first evaluate that the generative AI software is relevant and
congruent to themselves. Relevance is the dimension whether the stimulus is related to the
consumer. Congruence shows that the consistency between stimulus and individuals’
values, aims and beliefs. If the stimulus is perceived as unlikely to return to any outcomes
or lack of relevance, then generation of emotion will be failed (Lazarus, 1991b). In the
proposed model, based on SRIW scale (Lu et al., 2019), three determinants are defined as
constructs. These are “social influence” (Venkatesh et al., 2012), “intrinsic motivation”
(Brown and Venkatesh, 2005; Venkatesh 2012), “anthropomorphism” (van Doorn et
al.,2017; Lu et al.,2019). During this phase of the process, consumers' assessment of AI
software usage is influenced by their social group norms, “intrinsic motivation”, and
“anthropomorphism”. Their assessment reflects whether the use of AI software aligns with
their norms, and if the evaluation is positive, it is probable that they will progress to the
second appraisal stage (Lazarus, 1991a).
Social Influence indicates that the level of congruency of the user with social norms
while his/her social group believes to use AI software. SIT (Latane, 1981) advise that user
is more likely to follow the group norms, if the group is important for the individual.
Furthermore, SIDT (Tajfel & Turner,1979) suggests that people's self-esteem is tied to the
social standing of their social group. This means that people are motivated to maintain a
positive image of the groups they belong to, and adopting to group’s behavioral norms
increased the attachment level. A positive social identity creates a number of positive
consequences, such as increased self-esteem, happiness, and satisfaction. Therefore,
consumers’ social network has positive opinions and attitudes toward AI software, then
consumers will have positive efficacy for their social identity. This efficacy is expected to
21
lead the consumer to adhere to their group's norms by exhibiting a positive attitude. Drawing
upon theoretical and empirical insights, the following hypothesis is formulated :
Hypothesis 1: “Social influence is positively related to performance expectancy of
Generative AI software”
Effort Expectancy of customer attitudes presents the perceived level of difficulty of
associated with using AI software. According to the Lazarus’s framework, “social norms”
is argued to influence “effort expectancy”. There are theories and empirical studies that are
researched on social norms and perceived difficulty of technology. Users’ identification
with a community played significant role on users’ intention while using technology with
difficulty level (Nass et al., 1994; Hall and Heningsen 2008; Hsu and Lin, 2008). If
community has positive opinions which generative AI software has low level of difficulty,
then this opinion affects customer evaluation. Based on theoretical and empirical insights
the following hypothesis is formulated:
Hypothesis 2: “Social influence is negatively related to effort expectancy of Generative
AI software”
There are previous researches validated direct effect of “social influence” on
“emotion” (Latane, 1981, Lazarus, 1991a; Lazarus, 1991b). This study examines the relation
of social influence and emotion.
Hypothesis 3: “Social influence has direct impact on generation of Emotion”
“Intrinsic motivation” refers to received pleasure and joy during the interaction with AI
software. Previous researches stem that hedonic motivation and intrinsic motivation is the
main driver of technology use (Brown and Venkatesh, 2005; Venkatesh et al., 2012).While
the generative AI software gives service to the customer, they apply humanlike interactions
by satisfying entertainment, and personal interest (Fryer et al.,2017). Therefore, increased
intrinsic motivation of a customer towards new technologies, like AI software for daily use,
results in a stronger behavioral intention from the user, consequently promoting usage
behavior. Consequently, it is derived that customers who have intrinsic motivations toward
AI software are likely to have positive attitudes.
22
Hypothesis 4: “Intrinsic motivation is positively related to performance expectancy of
Generative AI software”
There is a large number of researches in psychology argued that relation between
motivation and task difficulty impacts justifies the effort to perform the task. Studies
confirmed that a connection between “motivation” and the level of perceived difficulty
associated with a task (Gendolla and Wright, 2005). Furthermore, individuals are more
likely to persist on difficult tasks when they have a high sense of self-efficacy, or belief in
their ability to succeed (Bandura,1977; Gursoy et al, 2019). Thus, highly motivated
customers are more likely to perceive that using of AI software is easy. Consequently, the
following hypothesis is proposed.
Hypothesis 5: “Intrinsic motivation is negatively related to effort expectancy of
Generative AI software”
There is a large number of researches in psychology and marketing validated relation
between
motivation and emotion (Lazarus, 1991a; Lazarus, 1991b; Vallerand, 1997;
Holbrook and Hirschman, 1982; Babin et al., 1994). This study also examines the power of
intrinsic motivation on emotion.
Hypothesis 6: “Intrinsic motivation has direct impact on generation of Emotion”
Anthropomorphism refers to the act of attributing human-like qualities, behaviors, or
mental attributes to non-human entities, including objects, brands, animals, and
technological devices (Golossenko et al. 2007). Attributing physical characteristics or
behaviors that represent distinctive mental states specific to human beings, such as
rationalizing, moral decision-making, goal setting, and emotional experiences (Golossenko
et al., 2020, Kim and McGill, 2011). Several studies focused on the role of anthropomorphic
characters in the acceptance and trust of AI (Wang, 2017, Blut et. al 2021). The acceptance
of AI software goes beyond mere efficiency, fascination, and satisfaction; it also
encompasses deeper social, emotional, and empathetic dimensions. Previous studies found
out that
“anthropomorphism” is a critical determinant to understand customers’ usage
behavior for AI solutions (Lu et al, 2019;van Doorn et al.,2017; Gursoy et al., 2019).
23
Generative AI software designed to look and behave like humans, which could make people
worry about the uniqueness of humanity. According to the threat to distinctiveness
hypothesis, people are more likely to be concerned about social robots when they are
perceived to be too similar to humans. This is because when the boundaries between humans
and machines become blurred, it can make people feel like self-identity as humans is being
threatened (Ackerman, 2016). Thus, humanoid features of AI solutions can be perceived as
a threat to customers’ self-identity (Ackerman, 2016; Gursoy, 2019). They react to these
solutions with objection by perceiving negative performance.
Hypothesis 7: “Anthropomorphism is negatively related to performance expectancy
of Generative AI software”
Customers may show the similar reaction to reject to the use of AI software because
they make an assumption that interaction with AI software needs more knowhow or skills
compare to human. They may need to be more dependent to their social beliefs and norms
about using anthropomorphic features, as this could lead to negative perceptions of
humanized service. Therefore, the hypothesis is formulated that anthropomorphic features
may create additional effort on consumers’ perception (Kim and McGill, 2011; Gursoy et
al., 2019).
Hypothesis 8: “Anthropomorphism is positively related to effort expectancy of
Generative AI software”
There are previous researches examine direct effect of anthropomorphism on emotion
(Blut et.al., 2021; Pelau et al., 2021; van Doorn et al., 2019, Golossenko et. al, 2020; Kim
and McGill, 2011; Aggarwal and McGill, 2012). This study aims to explore the role of
anthropomorphism on emotion, whether it is strong or not.
Hypothesis 9: “Anthropomorphism has direct impact on generation of Emotion”
3.2.2
Secondary appraisal
In this phase, individuals typically evaluate decision options and the emotional
consequences of each decision. Users will weigh the efforts and gains of utilizing AI
software based on their “performance expectancy ” and “effort expectancy” of the AI devices
(Venkatesh et al., 2012), thereby shaping their emotions towards the AI software. This
24
research underscores that “performance expectancy” and “effort expectancy” serve as
central constructs that customers are prone to utilize in evaluating the efforts and benefits of
AI software usage. These constructs are emerged as crucial determinants of users' emotions,
as indicated by the SRIW scale (Lu et al., 2019).
Users
established attitudes towards AI software are likely to influence the
"performance expectancy" and "effort expectancy" factors. Users’ negative appraisal of AI
software, formed following the “primary appraisal” process, will be amplified by a higher
level of “effort expectancy” and attenuated by a higher level of “performance expectancy”.
“Effort expectancy” reinforces users' pre-existing negative perceptions, while “performance
expectancy” diminishes these negative attitudes. Additionally, a customer's positive
appraisal resulting from the “primary appraisal” will be weakened by a higher level of “effort
expectancy” and strengthened by a higher level of “performance expectancy” (Gursoy et al.,
2019).
According to the CDT (Festinger, 1962), when individuals have conflicting attitudes,
beliefs or behaviors, they made to attempt to rationalize, they may fail to rationalize and
inconsistency causes cognitive dissonance, then the psychological discomfort occurs. After
making choice individuals seek evidence to confirm their decision and to reduce dissonance.
In the same way, users who generate positive attitudes as a result of primary appraisal stage,
intend to preserve their evaluations during the secondary appraisal stage. If users are
convinced on using AI software because of accurate, reliable, fast and consistent (Lu et al.,
2019), then “performance expectancy” is obtained and positive emotion will be generated
(Gursoy et al., 2019).
Hypothesis 10: “Performance Expectancy has a positive impact on generation of
positive emotions toward the willingness to accept the use of Generative AI software”
“Social influence” can be mediated by cognition and cognitive appraisals subsequently
lead to emotional response based on CAT. Lazarus depicts that basic emotions are generated
after the cognitive appraisals, not only the personal situations also social groups with whom
people are identified, and also to ideas or ideologies. He explained that even the individuals
have not been attacked themselves, they react as though they were, as if they had a personal
stake in what is happening (Lazarus, 1991b; Bagozzi et al.,1992).
25
Hypothesis 11: “Performance Expectancy mediates on relation between Social
Influence and Emotion”
Intrinsically motivated hedonic enjoyment is important for consumer researchers.
When performance of technology is driven by intrinsic motivation, success is anticipated to
elicit positive emotional reactions (Holbrook et al., 1984; Vallerand, 1995; Vallerand 1997).
Hypothesis 12: “Performance Expectancy mediates on relation between Intrinsic
Motivation and Emotion”
In study of Pelau et al.(2021), examining the impact of anthropomorphic features,
perceived empathy, and interaction quality on AI device or software acceptance, it is found
that interaction quality serves as a mediator in this process. It acts as a link between
anthropomorphic traits and acceptance, suggesting that a human-like AI software is more
likely to be accepted if it demonstrates empathy and interaction quality by the user (ibid).
Hypothesis
13:
“Performance
Expectancy
mediates
on
relation
between
Anthropomorphism and Emotion”
However, the interaction with AI software may also create significant communication
problem or fear on customers or require more cognition skills, then customer may confuse
and think about complexity of AI software (Lu et al., 2019; Thompson, Higgins, & Howell,
1991). This could potentially raise the level of effort needed. which may increase the amount
of effort required. Consequently, if customers perceive that utilizing AI software will
demand excessive effort, it may lead to the generation of negative emotions(Lazarus,
1991b).
Hypothesis 14: “Effort expectancy has a negative impact on generation of emotions
toward the willingness to accept the use of Generative AI software”
Identically, “effort expectancy” is the part of cognition phase. Based on CAT, in the
primary appraisal phase, social influence affects cognition and it leads to emotional response
(Lazarus, 1991b; Bagozzi et al. , 1992). Based on this relation, we can formulate the
following hypothesis:
26
Hypothesis 15: “Effort Expectancy mediates on relation between Social Influence and
Emotion”
Performance and emotional reactions are influenced by various intermediary
perceptions. Complexity, in particular, holds significant relevance for intrinsically motivated
technologies. The perception of complexity is expected to fluctuate in response to success
or failure. Ultimately, perceived complexity is likely to serve as a mediating factor
(Holbrook et al., 1984; Vallerand, 1995; Vallerand 1997).
Hypothesis 16: “Effort Expectancy mediates on relation between Intrinsic Motivation
and Emotion”
Pelau’s study (2021) defines interaction quality as “based on items that measured the
quality of the provided information”. Facilitating conditions of use and interaction are the
parts of the information quality. Thus, interaction quality which is between anthropomorphic
characteristics and acceptance, embraces effort expectancy (Pelau et al.,2021). Based on this
relation, we can formulate the following hypothesis:
Hypothesis 17: “Effort Expectancy mediates on relation between Anthropomorphism
and Emotion”
3.2.3
Outcome stage
Watson and Spence (2007) reviewed CAT. Their purpose is to explore how
consumption scenarios can be emotionally charged, and pinpointing the reasons behind these
emotions holds significant practical value in comprehending consumer behavior.
Pleasantness, goal consistency, fairness, and certainty are the appraisals that found to be
capable of implicating specific emotions such as pleasant surprise, anger, joy their effects
on consumer behavior (ibid). According to these findings, following the secondary appraisal
phase, particular emotions regarding the usage of AI software will emerge, subsequently
influencing customers' readiness to accept the utilization of AI software. This determination
includes users’ future attitude toward AI software. In line with the CAT and other theoretical
evaluations, users harboring positive emotions towards AI software are more inclined to
embrace the use of AI software. (Lazarus, 1991;Watson & Spence, 2007;Gursoy et al.,
2019).
27
Hypothesis 18: “Emotion is positively related to customers’ willingness to accept the
use of AI software”
The proposed model is the integration of AIDUA (Gursoy et al., 2029) and CAT
(Lazarus, 1991) is shown in Figure 1.
Figure 1
Proposed Research Model
Primary Appraisal
Secondary Appraisal
Outcome Stage
Social
Influence
Performance
Expectancy
Intrinsic
Motivation
Emotion
Effort
Expectancy
Anthropomo
rphism
Demographics
Gender, Age, Education
Source: Author A.Y.Mutlu (2024)
Willingness
to accept the
use of
GenAI
28
4 TESTING AND VALIDATION OF THE MODEL
This research primarily focuses on influencing factors of user acceptance in use of
generative AI. It is combined with AIDUA and UTAUT2 to model user acceptance in the
chatbot software.
4.1
Research Design and Methodology
4.1.1 The Measurement instrument and questionnaire design
The data is collected online survey using a questionnaire. The measurement items were
used from the scales where the validity and the reliability accepted in many studies. To
measure the key constructs “anthropomorphism”, “social influence”, “intrinsic motivation”,
“performance expectancy”, and “emotion”, the SRIW scale was used. The scale developed
by Lu et al. (2019) is originally measure “willingness to use artificial intelligence robot
device” in service context. There has been no other scale before Lu and his colleagues to
conceptualize “willingness to use” for service robots with multi-dimensional approach. The
scale provides a set of significant predictors that consumers consider when determining
whether to adopt and use a new technology. It has been developed in United States and
adapted to Turkish (Özkan et al. , 2020) and used in many researches especially related
intelligent devices since 2019.
To measure willingness to accept the generative AI software, the scale of UTAUT2
was used. Behavioral intention construct with 3 items developed by Venkatesh et al.,(2012)
was used. It is also used for the same measurement in AIDUA research (Gursoy et al., 2019).
The constructs are measured on a five-point Likert scale : “strongly disagree, disagreement
to some extent, uncertain, agree to some extent, and strong agreement” (Likert, 1932).
The questionnaire starts with the screening question which controls the usage of
participants’ generative AI software. If the participant answer is positive then continues to
the questionnaire. The second part of the survey is to measure willingness to use generative
artificial intelligence chatbot software usage. It consists of 34 items measuring the 7
constructs. Table 2, summarizes the definitions of all constructs and items and gives
supporting literature sources.
29
Table 2
Constructs, Items and Sources in the Study
Construct
Source
Abbreviation
Item
Performance
Lu et al , 2019
PE1
Expectancy
SRIW
Generative AI chatbot software are more
accurate than human beings
PE2
Information provided by generative AI chatbot
software is more accurate with less human
errors
Information provided by generative AI chatbot
software such as robots are more consistent
Lu et al , 2019
SRIW
Lu et al , 2019
PE3
SRIW
Lu et al , 2019
PE4
Generative AI chatbot software are more
dependable than human beings
PE5
Service provided by generative AI chatbot
software is more predictable than human
service.
I am able to avoid inefficient personal contacts
if I use generative AI chatbot software.
SRIW
Lu et al , 2019
SRIW
Lu et al , 2019
PE6
SRIW
Intrinsic
Lu et al , 2019
Motivation
SRIW
Lu et al , 2019
IM1
is fun
IM2
Interacting with generative AI chatbot software
is entertaining
IM3
I find the interaction with generative AI chatbot
software to be enjoyable
IM4
The actual process of interacting with
generative AI chatbot software would be
pleasant
The actual process of interacting with
generative AI chatbot software would be
pleasant
Interactions with generative AI chatbot software
will take too much of my time.
SRIW
Lu et al , 2019
SRIW
Lu et al , 2019
SRIW
Lu et al , 2019
IM5
SRIW
Effort
Lu et al , 2019
Expectancy
SRIW
Lu et al , 2019
EE1
EE2
Working with generative AI chatbot software
will be so difficult to understand and use
EE3
It will take me too long to learn how to interact
with generative AI chatbot software
EE4
Generative AI chatbot software will be
intimidating to me.
E1
Bored: Relaxed
E2
Melancholic/Contented
SRIW
Lu et al , 2019
SRIW
Lu et al , 2019
SRIW
Emotion
Lu et al , 2019
Interacting with generative AI chatbot software
SRIW
Lu et al , 2019
SRIW
30
Lu et al , 2019
E3
Despairing: Hopeful
E4
Unsatisfied: Satisfied
E5
Annoyed: Pleased
SI1
Utilizing generative AI chatbot software will be
status symbol in my social networks (e.g.,
friends, family and co-workers)
People in my social networks (e.g., friends,
family and co-workers) who would utilize
generative AI chatbot software will have more
prestige than those who won’t
People in my social networks (e.g., friends,
family and co-workers) who would utilize
generative AI chatbot software will have a high
profile
People whose opinions that I value would prefer
that I utilize generative AI chatbot software
during a service transaction
People who are important to me would
encourage me to utilize generative AI chatbot
software during a service transaction
People who influence my behavior would want
me utilize generative AI chatbot software during
service transactions
Generative AI chatbot software will have a
mind of their own
SRIW
Lu et al , 2019
SRIW
Lu et al , 2019
SRIW
Social
Lu et al , 2019
Influence
SRIW
Lu et al , 2019
SI2
SRIW
Lu et al , 2019
SI3
SRIW
Lu et al , 2019
SI4
SRIW
Lu et al , 2019
SI5
SRIW
Lu et al , 2019
SI6
SRIW
Lu et al , 2019
A1
SRIW
Lu et al , 2019
A2
Generative AI chatbot software will have
consciousness
A3
Generative AI chatbot software will have their
own free will
A4
Generative AI chatbot software will experience
emotions
A5
Generative AI chatbot software will have
intentions
W1
I am willing to receive generative AI chatbot
software services
W2
I will feel happy to interact with generative AI
chatbot software
W3
I am likely to interact with generative AI chatbot
software
SRIW
Lu et al , 2019
SRIW
Lu et al , 2019
SRIW
Lu et al , 2019
SRIW
Willingness to
accept use of
Generative AI
Software
Venkatesh et.
al., 2012
Venkatesh et.
al., 2012
Venkatesh et.
al., 2012
31
The last part of the questionnaire includes standard demographic questions based on
gender, age, level of education. The demographic differences are used as moderators.
The study of Özkan et al. (2020), SRIW scale is translated to the Turkish language
and complied with the local culture according to the adaptation method suggested by Brislin
(1980). It encompasses validation and reliability of Turkish version. The questionnaire in
Turkish language is taken from the study of Özkan et al.(2020) and adjusted in the context
of this study and the final version of questionnaire is checked by the Turkish teacher.
4.1.2 Sampling Design and Data Collection
Sampling population is the individuals who are mainly in Istanbul, Sakarya and
Kocaeli. The reason to choose Istanbul is more importantly carries the identities of each
regions and cities in Turkey. The study’s ideal population was unreachable, there is no legal
report or statistics which give the usage of AI software or technologies in Turkey. This study
is sent to participants as a survey of Google Forms (web-based questionnaire) through the
social network (LinkedIn) and via e-mail to collect data for quantitative testing of the
proposed model. The questionnaire reached out more than 1000 respondent. Data is collected
from participants who already use before or intention to use generative AI software. The
screening question is “Do you ever use Generative Artificial Intelligence Chatbot software”.
Based on the answer of the screening question, the respondent who has no usage is neglected.
Because of these reasons, convenience and judgmental sampling methods are used in this
study. Within these conditions, 346 participants are answered the survey. The data collection
phase was around four months between September 2023 to January 2024. There are no
concerns such as missing data, and suspicious response patterns (e.g., “straight lining” or
“inconsistent answers”) (Hair et al, 2017).
Table 3 shows the demographic questions, frequencies and percentages. The answers
have been gathered 346 respondents. Their age is between 18 to 55 plus years.
32
Table 3
Demographic Attributes of Respondents
Frequency
Percent
(%)
Gender
Female
142
41.04
Male
204
58.95
18-25
160
46.24
26-34
61
17.63
35-44
47
13.58
45-54
36
10.40
55- and above
42
12.14
High School
122
35.26
University
159
45.95
Post Graduate and
65
18.79
Age
Education Level
above
The data indicates that the 64.74 percent of the respondent had at least university
education, other respondent are university students. 46.24 percent of respondent are the ages
between 18 and 25. 41.04 percent of respondents are female and 58.95 percent is male.
4.2
Data Analysis and Results
Statistical analysis has been a core tool used by social science researchers for over a
hundred years. Advancements in computer hardware and software have led to a considerable
expansion in the application of statistical methods, especially in recent times where
accessible technology interfaces have made a wider array of methods available. Initially,
researchers relied on simpler univariate and bivariate analyses to interpret data and
relationships. However, the growing complexity of relationships explored in contemporary
social science research necessitates the adoption of more sophisticated multivariate data
analysis techniques (Hair et. al, 2017).
33
In this study, Structural Equation Modeling (SEM) is used to do multivariate data
analysis. SEM enables researchers to incorporate unobservable variables measured
indirectly by indicator variables. They also facilitate accounting for measurement error in
observed variables. One of the types of SEM is partial least squares SEM primarily used to
develop theories in exploratory research. It does this by focusing on explaining the variance
in the dependent variables when examining the model. PLS-SEM has been selected as a
multivariate analysis method to do exploratory research and considering complexity level of
the model. It is crucial to search for patterns in the data, particularly when there is minimal
or no prior understanding of how the variables are interrelated (Hair et. al, 2017). In this
study, research model is examined using by latest version of SmartPLS (Version 4.1.0.3).
SmartPLS is a powerful and user-friendly tool for conducting SEM and path
analysis. It is created and updated by Hair et al. who are significant scholars in the field of
research methodology and statistical analysis, particularly in the context of SEM and Partial
Least Squares Path Modeling (PLS-PM). It is particularly valuable for complex statistical
analyses and allows to investigate relationships between multiple variables in their research
models. It provides valuable insights into causal relationships, mediating factors, and
moderation effects within a model. Additionally, SmartPLS offers a range of advanced
features and tools that make it a versatile software for conducting sophisticated data analysis
and hypothesis testing (Smartpls, 2024).
4.2.1 Analysis of Measurement Model
Initially, the assessment of the measurement model is completed. Before analyzing
the research model, validity and reliability assessment of the constructs were conducted. As
part of the validity and reliability assessments, internal consistency reliability, convergent
validity, and discriminant validity were assessed.
Internal consistency reliability refers to the degree of agreement or intercorrelation
between multiple items that are intended to measure the same construct within a research
instrument. (Hair et. al, 2017). Convergent validity aims to evaluate the extent to which
different measures that are theoretically expected to be related are, in fact, positively
correlated. It assesses whether multiple measures of the same construct converge or come
together in measuring the intended construct. By demonstrating convergent validity, it is
34
confirmed that different indicators designed to measure the same underlying construct are
indeed measuring that construct effectively. Strong convergent validity enhances the validity
of the research, indicating that the research instrument is accurately capturing the intended
construct (Hair et al., 2017).
The measurement model is shown in Figure 2.
Figure 2
Measurement Model
Internal Consistency Reliability and Convergent Validity
For internal consistency reliability is assessed using Cronbach's Alpha and Composite
Reliability (CR) coefficients. The traditional criterion for internal consistency is Cronbach’s
alpha, which provides an estimate of the reliability based on the intercorrelations of the
observed indicator variables. According to Hair et al. (2017) recommend the usage of CR
coefficients. CR coefficients indicates that the latent variables in the model are reliable and
reflective of the construct they are meant to be represent.
35
Convergent validity was evaluated based on factor loadings and Average Variance
Extracted (AVE) values. A common measure to establish convergent validity on the
construct level is the average variance extracted (AVE). This criterion is defined as the grand
mean value of the squared loadings of the indicators associated with the construct (Hair et
al., 2017).
Factor loadings are expected to be ≥ 0.70; Cronbach's Alpha and Composite Reliability
coefficients are expected to be ≥ 0.70; and the Average Variance Extracted value is expected
to be ≥ 0.50 (Hair et al., 2019; Hair et al., 2022). The results of the measurement model are
shown in Table 4.
As per Hair et al. (2022), factor loadings should be ≥ 0.70. The authors recommend
eliminating items with factor loadings below 0.40 from the measurement model.
Additionally, items with factor loadings between 0.40 and 0.70 should be removed if the
AVE (Average Variance Extracted) or CR (Composite Reliability) values of the relevant
variable fall below the threshold.
The factor loadings of the fourth and fifth items of anthropomorphism, the fourth item
of effort expectancy, the fifth item of intrinsic motivation, the fifth and sixth items of
performance expectancy and the first, second, and third items of social influence were
calculated to be below the threshold. However, since the AVE (Average Variance Extracted)
and CR (Composite Reliability) values of the respective variables were above the threshold,
the mentioned items were not removed from the measurement model (Hair et al.,2010;
2017).
Internal consistency reliability has been achieved as evidenced by the Cronbach's
Alpha coefficients of the constructs ranging from 0,747 to 0.883; and the Composite
Reliability (CR) coefficients ranging from 0.854 to 0.914. Convergent validity can be stated
to be achieved given that the factor loadings of the constructs ranging from 0,477 to 0.869;
and the Average Variance Extracted (AVE) values ranging from 0.511 to 0.681.
36
Table 4
Results of Measurement Model
Construct
Anthropomorphism
Emotion
Effort Expectancy
Intrinsic
Motivation
Performance
Expectancy
Social Influence
Willingness to
Accept the Use of
Generative AI
Items
A1
A2
A3
A4
A5
E1
E2
E3
E4
E5
EE1
EE2
EE3
EE4
IM1
IM2
IM3
IM4
IM5
PE1
PE2
PE3
PE4
PE5
PE6
SI1
SI2
SI3
SI4
SI5
SI6
W1
W2
W3
Factor
Loadings
0,757
0,899
0,861
0,655
0,595
0,838
0,846
0,797
0,772
0,870
0,733
0,850
0,821
0,633
0,836
0,814
0,898
0,872
0,645
0,825
0,807
0,807
0,784
0,477
0,490
0,578
0,615
0,641
0,860
0,873
0,842
0,843
0,864
0,728
Cronbach’s
Alfa
CR
AVE
0,821
0,871
0,581
0,756
0,847
0,584
0,883
0,914
0,681
0,872
0,909
0,669
0,792
0,857
0,511
0,840
0,879
0,556
0,747
0,854
0,662
37
Discriminant Validity
In determining discriminant validity, the study employed cross-loadings, following the
Fornell and Larcker (1981) criterion, and the HTMT criterion proposed by Henseler et al.
(2015) were used. The cross-loadings are shown in Table 5, the Fornell and Larcker (1981)
results are presented in Table 6, and the HTMT coefficients are provided in Table 7.
Cross-loadings serve as an initial method to evaluate the discriminant validity of items.
Typically, an item's loading on its corresponding construct should exceed its loading on any
other constructs (i.e., its cross-loadings). After reviewing the cross-loadings presented in
Table 5, it was found that there were no instances of items correlating with statements
measuring different constructs (Hair et al., 2017).
38
Table 5
Cross Loadings
A1
A2
A3
A4
A5
E1
E2
E3
E4
E5
EE1
EE2
EE3
EE4
IM1
IM2
IM3
IM4
IM5
PE1
PE2
PE3
PE4
PE5
PE6
SI1
SI2
SI3
SI4
SI5
SI6
W1
W2
W3
Anthropo
morphism
Effort
Expectancy
Emotion
Intrinsic
Motivation
Performance
Expectancy
Social
Influence
“Willingness
to Accept
the Use of
Generative
AI”
0,757
0,899
0,861
0,655
0,595
0,032
0,180
0,114
0,182
0,101
0,099
0,177
0,139
0,214
0,044
0,033
0,036
0,032
0,042
0,213
0,191
0,221
0,212
0,075
0,015
0,131
0,190
0,166
0,121
0,097
0,139
0,038
0,187
-0,014
0,042
0,238
0,274
0,069
0,076
-0,373
-0,217
-0,331
-0,176
-0,294
0,733
0,850
0,821
0,633
-0,313
-0,220
-0,270
-0,336
-0,250
0,072
0,114
0,151
0,108
0,057
-0,231
-0,009
0,087
-0,072
-0,190
-0,153
-0,096
-0,328
-0,213
-0,319
0,120
0,111
0,082
0,216
-0,018
0,838
0,846
0,797
0,772
0,870
-0,203
-0,232
-0,203
-0,378
0,347
0,330
0,394
0,394
0,406
0,222
0,185
0,201
0,238
0,205
0,331
0,134
0,154
0,250
0,378
0,368
0,365
0,534
0,706
0,469
0,079
0,017
-0,030
0,116
0,040
0,382
0,390
0,404
0,326
0,398
-0,287
-0,325
-0,272
-0,166
0,836
0,814
0,898
0,872
0,645
0,141
0,143
0,100
0,130
0,207
0,352
0,162
0,156
0,315
0,350
0,359
0,399
0,483
0,472
0,381
0,182
0,208
0,168
0,157
0,086
0,257
0,297
0,313
0,259
0,258
0,044
0,047
0,040
0,022
0,250
0,196
0,208
0,184
0,231
0,825
0,807
0,807
0,784
0,477
0,490
0,189
0,231
0,277
0,342
0,365
0,310
0,199
0,367
0,201
0,184
0,126
0,072
0,197
0,148
0,301
0,368
0,371
0,271
0,319
-0,112
-0,120
-0,079
-0,077
0,351
0,288
0,332
0,312
0,388
0,290
0,278
0,309
0,283
0,189
0,284
0,578
0,615
0,641
0,860
0,873
0,842
0,337
0,400
0,241
0,088
0,030
0,031
0,244
0,021
0,564
0,608
0,608
0,512
0,650
-0,264
-0,298
-0,205
-0,253
0,516
0,379
0,493
0,440
0,396
0,184
0,199
0,141
0,219
0,153
0,425
0,157
0,125
0,300
0,395
0,392
0,337
0,843
0,864
0,728
39
The Fornell-Larcker (1981) criterion serves as a secondary method for evaluating
discriminant validity. It involves comparing the square root of the Average Variance
Extracted (AVE) values with the correlations between latent variables. In particular, the
square root of each construct's AVE should exceed its highest correlation with any other
construct for discriminant validity to be established.
Table 6
Discriminant Validity Results - Fornell-Larcker criterion
Anthropomorphism
Anthropomorphism
Effort Expectancy
Emotion
Intrinsic
Motivation
Performance
Expectancy
Social Influence
Willingness to
Accept the Use of
Generative AI
Effort
Expectancy
Emotion
Intrinsic
Motivation
Performance
Expectancy
Social
Influence
(0,762)
0,212
0,146
(0,764)
-0,341 (0,825)
0,046
-0,345
0,462
(0,818)
0,218
0,174
0,050
-0,128
0,337
0,398
0,263
0,412
(0,715)
0,394
(0,745)
0,104
-0,339
0,716
0,550
0,328
0,411
Willingness
to Accept
the Use of
Generative
AI
(0,814)
Upon reviewing the table, it is noted that the values in parentheses represent the square
roots of the Average Variance Extracted (AVE), whereas the other coefficients denote the
correlations between constructs. It is observed that the square root of the AVE for each
construct surpasses its correlation coefficients with other constructs in its own row and
column, indicating the fulfillment of the Fornell-Larcker criterion for discriminant validity
(Fornell, and Larcker, 1981).
Henseler et al. (2015) propose assessing the heterotrait-monotrait ratio (HTMT) of the
correlations. HTMT is the ratio of the between-trait correlations to the within-trait
correlations. HTMT is the mean of all correlations of indicators across constructs measuring
different constructs relative to the (geometric) mean of the average correlations of indicators
measuring the same construct (Henseler et al., 2015). Technically, the HTMT approach is
an estimate of what the true correlation between two constructs would be, if they were
perfectly measured. This true correlation is also referred to as disattenuated correlation. A
40
disattenuated correlation between two constructs close to 1 indicates a lack of discriminant
validity (Hair et al.,2017).
Table 7
Discriminant Validity Results - Heterotrait-Monotrait Ratio criterion
Anthropomorphism
Effort
Expectancy
Emotion
Intrinsic
Motivation
Performance
Expectancy
0,241
0,177
0,090
0,405
0,422
0,523
0,275
0,246
0,230
0,172
0,393
0,425
0,307
0,452
0,465
0,189
0,464
0,856
0,674
0,390
Social
Influence
Willingness
to Accept
the Use of
Generative
AI
Anthropomorphism
Effort Expectancy
Emotion
Intrinsic Motivation
Performance
Expectancy
Social Influence
Willingness to
Accept the Use of
Generative AI
0,468
As per Henseler et al. (2015), the HTMT Ratio compares the geometric mean of
correlations among items within the same construct to the average correlations among items
from different constructs. Henseler et al. recommended that the HTMT value should be
under 0.90 for conceptually similar concepts and under 0.85 for distinct concepts. It is noted
that the HTMT values provided in Table 7 are below the specified threshold value, indicating
compliance with the suggested criteria. With reference to the cross-loadings, Fornell-Larcker
criterion, and HTMT criterion, it can be affirmed that discriminant validity has been
achieved (Hair et al., 2017).
4.2.2 Descriptive Statistics
Within IBM SPSS Statistics, the descriptive procedure computes a select set of basic
descriptive statistics for the constructs and its items to analyze the normality of data
distribution (SPSS, 2024). These statistics can be listed as, valid responses, mean of each
item, sum of the constructs, standard deviation of each item, skewness and kurtosis.
Skewness assesses the extent to which a variable’s distribution is symmetrical. If the
distribution of responses for a variable stretches toward the right or left tail of the
distribution, then the distribution is characterized as skewed. Kurtosis is a measure of
whether the distribution is too peaked (a very narrow distribution with most of the responses
41
in the center). When both skewness and kurtosis are close to zero, the pattern of responses
is considered a normal distribution (Hair et al., 2017).
Descriptive Statistics of items and constructs are analyzed in IBM SPSS Statistics
Version 29.0.1.1. Items of “intrinsic motivation”, “emotion”, and “willingness to accept use
of generative AI” are valued more distinct from indecision and indicates
positive
determination. Skewness value ranging from -1 to +1 is viewed as excellent, while a range
of -2 to +2 is generally considered acceptable (Hair et al., 2022). General rule is that a
kurtosis above +2 suggests an overly peaked distribution, whereas below -2 indicates a flat
distribution (Hair et al., 2022). Hair et al.(2010), and Bryne (2010) suggested that data is
deemed to be normally distributed when kurtosis falls within the range of -7 to +7.
42
Table 8
Descriptive Statistics of Items and Constructs
Item
PE1
PE2
PE3
PE4
PE5
PE6
Mean
Effort Expectancy
EE1
EE2
EE3
EE4
Mean
Social Influence
SI1
SI2
SI3
SI4
SI5
SI6
Mean
Intrinsic Motivation
IM1
IM2
IM3
IM4
IM5
Mean
Anthropomorphism
A1
A2
A3
A4
A5
Mean
Emotion
E1
E2
E3
E4
E5
Mean
Willingness to Accept W1
Use of Generative AI W2
W3
Mean
Construct
Performance
Expectancy
Mean
Std. Deviation Skewness Kurtosis
2.971
.816
.118
-.689
3.043
.905
-.133
-.923
3.069
.930
-.138
-.794
2.916
.852
.105
-.556
3.442
.843
-.605
-.484
3.595
.906
-.615
-.026
3.173
1.983
.730
.836
1.452
1.824
.731
.914
1.261
1.853
.717
.795
1.042
2.277
1.121
.716
-.324
1.984
3.078
.985
-.249
-.691
2.564
1.015
.421
-.549
3.566
.985
-.835
.173
3.555
.843
-.698
-.132
3.451
.878
-.600
-.358
3.237
.908
-.369
-.542
3.242
3.942
.759
-.863
1.432
3.766
.791
-.793
1.311
3.864
.759
-1.089
2.404
3.853
.745
-1.193
2.739
3.630
.839
-.668
.648
3.811
2.691
1.147
.128
-1.095
2.246
1.061
.654
-.416
1.991
.937
.782
-.003
2.564
1.198
.251
-.980
2.853
1.124
-.064
-.902
2.469
3.575
.739
-.347
.072
3.546
.609
-.062
-.319
3.694
.649
-.493
.757
3.468
.698
.294
.425
3.468
.664
.028
-.205
3.550
3.910
.655
-.589
1.439
3.538
.750
-.585
.468
4.075
.655
-.825
2.754
3.841
43
4.2.3. Evaluation of the Structural Equation Model
The structural equation model designed to test the study hypotheses is depicted in Figure 3.
Figure 3
Structural Equation Model
SmartPLS Version 4.1.0.3 statistical software is used to analyze data (Ringle et al.,
2022; Yıldız, 2021). Various assessments were conducted to evaluate the relationships in the
structural model, including collinearity assessment, determination of coefficients, f2 effect
sizes, and predictive power Q2. The analysis involved running the PLS algorithm to compute
collinearity, path coefficients, R2 values, and f2 effect sizes, while the PLSpredict analysis
was used to calculate Q2. Furthermore, bootstrapping with 10,000 subsamples was
performed to determine the significance of the PLS path coefficients through t-values. The
research findings, encompassing Variance Inflation Factor (VIF), Coefficient Determination
(R2 Value), effect size coefficients (f2), and Q2 values, are detailed in Table 9.
Collinearity Assessment
To assess the level of collinearity, tolerance is computed which represents the amount
of variance of construct not explained by the other constructs in the same block. VIF is
defined as the reciprocal of the tolerance and quantifies the severity of collinearity among
the indicators in a measurement model. The VIF is directly related to the tolerance value
(VIFi = 1/tolerancei) (Hair et al., 2017).
44
According to Hair et al. (2022), VIF coefficients being below 5 indicates that there is
no multicollinearity problem for the variables. When examining the VIF coefficients in
Table 9, it is observed that the coefficients are below 3. Following the findings, it can be
inferred that there are no indications of multicollinearity among the variables.
Coefficient Determination R2 Value
R2 Value is a measure of the model’s predictive power and is calculated as the squared
correlation between a specific endogenous construct’s actual and predicted values. The
coefficient represents the exogenous latent variables’ combined effects on the endogenous
latent variable. That is, the coefficient represents the amount of variance in the endogenous
constructs explained by all of the exogenous constructs linked to it. Because the R2 is the
squared correlation of actual and predicted values and, as such, includes all the data that
have been used for model estimation to judge the model’s predictive power, it represents a
measure of in-sample predictive power (Sarstedt, Ringle, Henseler, & Hair, 2014).
When examining the R2 values obtained for the model, it is found that willingness to
accept the use of generative AI is explained by 51%, “emotion” by 36%, and “performance
expectancy” by 19% and “effort expectancy” 17%.
45
Table 9
Structural Model VIF, R2, F2, Q2 Values
Construct
VIF
Anthropomorphism
Intrinsic Motivation
R2
1,032
Effort Expectancy
1,206
f2
0,064
0,171
0,118
Social Influence
1,241
0,001
Anthropomorphism
1,032
0,030
Intrinsic Motivation
Performance
Expectancy
1,206
0,191
0,016
Social Influence
1,241
0,099
Effort Expectancy
1,225
0,094
Performance
Expectancy
1,255
0,046
Anthropomorphism
Emotion
1,119
0,354
0,020
Intrinsic Motivation
1,383
0,067
Social Influence
1,369
0,030
Emotion
Willingness to Accept
the Use of Generative
AI
1,000
Q2
0,512
1,051
0,145
0,169
0,249
0,285
Effect Size f2
In addition to evaluating the R2 values of all endogenous constructs, the change in the
R2 value when a specified exogenous construct is omitted from the model can be used to
evaluate whether the omitted construct has a substantive impact on the endogenous
constructs. This measure is referred to as the ƒ2 effect size (Hair et al., 2017).
Effect size coefficients (f2) are considered small if they are 0.02 or above, medium if
they are 0.15 or above, and large if they are 0.35 or above (Cohen, 1988). According to
Sarstedt et al. (2017), when the coefficient is below 0.02, it is not possible to speak of an
effect. When examining the effect size coefficients (f2), it is observed that emotion has a
large effect size on “willingness to accept the use of generative AI”; and “social influence”,
“intrinsic motivation” and “performance expectancy” has a small effect size on “emotion”;
“intrinsic motivation” has close to medium effect size compare to others,
46
“anthropomorphism” has a small effect size on “effort expectancy”, “social influence” has
a small but better effect size compare to others on “performance expectancy”.
Predictive Power Coefficients Q2 Value
2
In addition to evaluating the magnitude of the R values as a criterion of predictive accuracy,
2
it is examined Stone-Geisser’s Q value (Geisser, 1974; Stone, 1974). This measure is an
indicator of the model’s out-of-sample predictive power or predictive relevance. When a
PLS path model exhibits predictive relevance, it accurately predicts data not used in the
2
model estimation. In the structural model, Q values larger than zero for a specific reflective
endogenous variable indicate the path model’s predictive relevance for a particular
dependent construct and suggest that the path model demonstrates predictive power
capability (Hair et al., 2022). As the Q2 values in Table 9 are greater than zero, it can be
stated that the research model has predictive power for the constructs of “willingness to
accept the use of generative AI”, “emotion”, “performance expectancy” and “effort
expectancy”.
47
Significance of Path Coefficients
The direct effects are detailed in Table 10.
Table 10
Significance of Path Coefficients
Estimate
β
STDEV
T statistics
P values
0,233
0,060
3,862
0,000
0,121
0,054
2,231
0,026
0,159
0,052
3,082
0,002
-0,273
0,053
5,166
0,000
0,029
24,378
0,000
-0,344
0,055
6,271
0,000
0,245
0,063
3,858
0,000
0,123
0,057
2,181
0,029
0,194
0,049
3,993
0,000
-0,027
0,053
0,502
0,616
Social Influence -> Emotion
0,164
0,058
2,810
0,005
Social Influence ->
Performance Expectancy
0,315
0,051
6,208
0,000
Path
Anthropomorphism ->
Effort Expectancy
Anthropomorphism ->
Emotion
Anthropomorphism ->
Performance Expectancy
Effort Expectancy ->
Emotion
Emotion ->
Willingness to Accept the
Use of Generative AI
Intrinsic Motivation ->
Effort _Expectancy
Intrinsic Motivation ->
Emotion
Intrinsic Motivation ->
Performance Expectancy
Performance Expectancy ->
Emotion
Social Influence -> Effort
Expectancy
0,716
Hypothesis
H8
Supported
H9
Supported
H7
Rejected
H14
Supported
H18
Supported
H5
Supported
H6
Supported
H4
Supported
H10
Supported
H2
Rejected
H3
Supported
H1
Supported
Upon examining the effects in Table 8, it is observed that the constructs of
“anthropomorphism” (β=0.233; p<0.01), “intrinsic motivation” (β=-0.344; p<0.01) have
statistically significant effects on “effort expectancy”; the constructs of “intrinsic
motivation” (β=-0.123; p<0.01) and “social influence” (β=-0.315; p<0.01) have statistically
significant effects on “performance expectancy”; and the constructs of “performance
expectancy”
(β=0.194;
p<0.01),
“effort
expectancy”
(β=-0.273;
p<0.01),
“anthropomorphism” (β=0.121; p<0.01), “intrinsic motivation” (β=0.245; p<0.01), and
48
“social influence”
have statistically significant effects on emotion; the construct of
“emotion”” (β=0.716; p<0.01) has a statistically significant effect on willingness to accept
the use of generative AI”. According to these results, the study’s hypotheses H1, H3, H4,
H5, H6, H8, H9, H10, H14 and H18 are supported. However, findings indicated a nonsignificant negative relationship between social influence” and “effort expectancy” (β =
−.027, p = .616). The construct of “anthropomorphism” (β=0.159; p<0.01) has a positive
significant effect on performance expectancy but not the negative relation as stated
hypotheses H7. Thus, hypotheses H2 and H7 were rejected.
Specific Indirect Effects
The results for indirect effects are presented in Table 11.
Table 11
Specific Indirect Effect Coefficients
Constructs
Anthropomorphism ->
Effort _Expectancy ->
Emotion
Social _Influence ->
Performance _Expectancy > Emotion
Intrinsic _Motivation ->
Effort _Expectancy ->
Emotion
Anthropomorphism ->
Performance _Expectancy > Emotion
Social _Influence -> Effort
_Expectancy -> Emotion
Intrinsic _Motivation ->
Performance _Expectancy > Emotion
Estimate β
STDEV
T statistics
-0,064
0,021
3,030
0,061
0,019
3,243
0,094
0,022
4,172
0,031
0,014
2,234
0,007
0,015
0,503
H17
0,002 Supported
H11
Supported
0,001
H16
Supported
0,000
H13
Supported
0,026
H15
0,615 Rejected
0,024
0,013
1,817
H12
0,069 Rejected
P values Hypothesis
Upon examining the values in Table 9, it is observed that the indirect effects of
“anthropomorphism” (β=-0.064; p<0.01) and “intrinsic motivation” (β=0.094; p<0.01) on
effort expectancy through the “emotion” constructs are significant. In addition to that the
indirect effects of “anthropomorphism” (β=-0.031; p<0.01) and “social influence” (β=0.061;
p<0.01) on “effort expectancy” through the “emotion” constructs are significant. However,
the indirect effect of the “social influence” on “effort expectancy” through “emotion” and
49
the indirect effect of “intrinsic motivation” on “performance expectancy” is through
“emotion” statistically insignificant (p>0.05).
Presence of a mediation effect is considered when the effects of exogenous variables
on the mediator constructs and the effects of the mediator constructs on the endogenous
constructs are significant (indirect effects) (Zhao et al. ,2010). Therefore, it can be mentioned
that there are mediation effects since the effects of “anthropomorphism” and “intrinsic
motivation” on “effort expectancy”, and the effect of “effort expectancy” on “emotion” are
significant. In addition to that, it can be mentioned that there are mediation effects since the
effects of “anthropomorphism” and “social influence” on “performance expectancy”, and
the effect of “performance expectancy” on “emotion” are significant.
Due to the identification of mediation effects, the types of mediation were examined
by in line with Yıldız’s (2021) mediation tree, and mediation analysis procedure (Hair et
al.,2017).
The indirect effect on the path Anthropomorphism → Effort Expectancy → Emotion
is significant, and the direct effect on the path Anthropomorphism → Emotion is significant,
with the path coefficients is negative. Therefore, it indicates that “effort expectancy”
construct has a competitive partial mediation role in the relationship from
“anthropomorphism” to “emotion”.
The indirect effect on the path Social Influence → Performance Expectancy →
Emotion is significant, and the direct effect on the path Social Influence → Emotion is
significant, with the path coefficients is positive. Therefore, it indicates that “effort
expectancy” construct has a complementary partial mediation role in the relationship from
“social influence” to “emotion”.
The indirect effect on the path Anthropomorphism → Performance Expectancy →
Emotion is significant, and the direct effect on the path Anthropomorphism → Emotion is
significant, with the path coefficients is positive. Therefore, it indicates that “performance
expectancy” construct has a complementary partial mediation role in the relationship from
“anthropomorphism” to “emotion”.
50
The indirect effect on the path Intrinsic Motivation → Effort Expectancy → Emotion
is significant, and the direct effect on the path Intrinsic Motivation → Emotion is significant,
with the path coefficients is positive. Therefore, it indicates that “effort expectancy”
construct has a complementary partial mediation role in the relationship from “intrinsic
motivation” to “emotion”.
Based on the obtained findings, the study’s hypotheses H18, H10, H13 and H14 are
supported while hypothesis H9 and H12 are rejected. It indicates that “effort expectancy”
has no mediation role in the relationship from “social influence” to “emotion”. In addition
to these results, it indicates that “performance expectancy” has no significant mediation role
in the relationship from “intrinsic motivation” to “emotion”.
The whole list of Hypothesis is given in Table 12 and based on supported hypotheses the
revised model is shown in Figure 4.
Table 12
Summary of the Hypothesis
Hypothesis
Results
H1: “Social Influence is positively related to Performance Expectancy of Supported
Generative AI software.”
H2: “Social Influence is negatively related to
Effort Expectancy of Rejected
Generative AI software.”
H3: “Social Influence has direct effect on Emotion”
Supported
H4: “Intrinsic Motivation is positively related to Performance Expectancy Supported
of Generative AI software.”
H5: “Intrinsic Motivation is negatively related to Effort Expectancy of Supported
Generative AI software.”
H6: “Intrinsic Motivation has direct effect on Emotion”
Supported
H7: “Anthropomorphism is negatively related to Performance Expectancy Rejected
of Generative AI software.”
H8: “Anthropomorphism is positively related to perceived Effort Supported
Expectancy of Generative AI software.”
H9: “Anthropomorphism has direct effect on Emotion”
Supported
51
H10: “Performance Expectancy has a positive impact on generation of Supported
positive emotions toward the willingness to accept the use of Generative AI
software.”
H11: “Performance Expectancy mediates on relation between Social Supported
Influence and Emotion”
H12: “Performance Expectancy mediates on relation between Intrinsic Supported
Motivation and Emotion”
H13:
“Performance
Expectancy
mediates
on
relation
between Rejected
Anthropomorphism and Emotion”
H14: “Effort Expectancy has a negative impact on generation of emotions Supported
toward the willingness to accept the use of Generative AI software.”
H15: “Effort Expectancy mediates on relation between Social Influence and Rejected
Emotion”
H16: “Effort Expectancy mediates on relation between Intrinsic Motivation Supported
and Emotion”
H17: “Effort Expectancy mediates on relation between Anthropomorphism Supported
and Emotion”
H18: “Emotion is positively related to customers’ willingness to accept the Supported
use of Generative AI software.”
52
Figure 4
Revised Research Model
Primary Appraisal
Secondary Appraisal
Outcome Stage
Social
Influence
Performance
Expectancy
Intrinsic
Motivation
nn
Emotion
Effort
Expectancy
Willingness
to accept the
use of
GenAI
Anthropo
morphism
m
Demographics
Gender, Age, Education
Source: Author A.Y.Mutlu
Analysis of Demographic Groups
Within SPSS Statistics, t-tests and ANOVA analyses are usually used to test the
differences between perception related to the indigenous constructs among the compared
groups, whereas in PLS, group-based testing of the model can be conducted. The testing of
paths in the model has been performed for the entire dataset (Yıldız, 2021).
Group analysis of demographic attributes: age and gender are examined in SmartPLS.
First analysis is to investigate the significance in the structural model across two customer
segments: first group is the age between 18 and 25, the second group is the age between 26
and 54. The first group consisted of 160 respondents, while the second group included 144
respondents. The results are given in Table 13.
The results for the group of 18_25_age and 26_54_age in the following paths:
Significant positive effect of “anthropomorphism” on “effort expectancy” for the
group of 18_25_age, whereas nonsignificant effect for the group of 26_54_age. It shows that
anthropomorphic features create additional effort on perception of young adults.
53
Significant positive effect of “anthropomorphism” on “performance expectancy” for
the group of 18_25_age whereas nonsignificant effect for the group of 26_54_age. It
indicates that humanoid features of AI solutions are not perceived as a threat to young adults.
Significant positive effect of “anthropomorphism” on “emotion” for the group of
18_25_age whereas nonsignificant effect for the group of 26_54_age. Significant positive
effect of “social influence” on “emotion” for the group of 18_25_age whereas nonsignificant
effect for the group of 26_54_age. Direct effect of “anthropomorphism” and “social
influence” on “emotion” is stronger for the young adults.
In addition to that, significant positive effect of “performance expectancy” on
“emotion” for group of 26_54_age whereas nonsignificant effect for the group of 18_25_age
is observed. It shows that performance expectancy has strong effect on emotion for older
millennials and early and late middle age.
“Social influence” has strong significant effect on “performance expectancy” for the
group of 26_54_age. It indicates that level of congruency of the user with social norms while
his/her social group believes to use generative AI software for older millennials and early
and late middle age.
54
Table 13
Demographic Group Analysis Results - Age
Anthropomorphism ->
EffortExpectancy
Anthropomorphism ->
Emotion
Anthropomorphism ->
PerformanceExpectancy
Effort Expectancy ->
Emotion
Emotion -> Willingness
to Accept the Use of
Generative AI
Intrinsic _Motivation ->
EffortExpectancy
IntrinsicMotivation ->
Emotion
IntrinsicMotivation ->
PerformanceExpectancy
PerformanceExpectancy
-> Emotion
SocialInfluence ->
EffortExpectancy
SocialInfluence ->
Emotion
SocialInfluence ->
PerformanceExpectancy
Original
18_25_age
Original
26_54_age
STDEV
18_25_age
STDEV
26_54_age
t value
18_25_age
t value
26_54_age
p value
18_25_age
p value
26_54_age
0,311
0,128
0,091
0,119
3,425
1,076
0,001
0,282
0,190
0,083
0,086
0,073
2,212
1,130
0,027
0,258
0,240
0,116
0,105
0,069
2,286
1,691
0,022
0,091
-0,304
-0,243
0,072
0,078
4,242
3,102
0,000
0,002
0,709
0,719
0,043
0,038
16,529
19,014
0,000
0,000
-0,325
-0,377
0,077
0,080
4,202
4,719
0,000
0,000
0,284
0,224
0,072
0,099
3,970
2,268
0,000
0,023
0,121
0,088
0,102
0,073
1,192
1,199
0,233
0,230
0,090
0,287
0,077
0,068
1,177
4,236
0,239
0,000
-0,001
-0,053
0,094
0,076
0,006
0,702
0,995
0,483
0,206
0,110
0,085
0,082
2,435
1,334
0,015
0,182
0,179
0,433
0,081
0,071
2,202
6,084
0,028
0,000
The second analysis is to investigate the significance in gender: first group is the
female the second group is the male. The first group consisted of 142 respondents, while
the second group included 204 respondents. The results are given in Table 14.
The results for the group of males in the following paths:
Significant positive effect of “anthropomorphism” on “effort expectancy” for the
group of males, whereas nonsignificant effect for the group of females. It indicates that
anthropomorphic features create additional effort on perception of males.
Significant positive effect of “intrinsic motivation” on “performance expectancy” for
the group of males, whereas nonsignificant effect for the group of females. It indicates that
males received pleasure and joy during the interaction with AI software and leads to
positive performance expectation.
Significant negative effect of “effort expectancy” on
“emotion” for the group of
males whereas nonsignificant effect for the group of females. It indicates that complexity
of AI software creates negative emotions for males.
55
Table 14
Demographic Group Analysis Results - Gender
Anthropomorphism
-> Effort
Expectancy
Anthropomorphism
-> Emotion
Anthropomorphism
-> Performance
_Expectancy
Effort Expectancy
-> Emotion
Emotion ->
Willingness to
Accept the Use of
Generative AI
IntrinsicMotivation
-> Effort
_Expectancy
IntrinsicMotivation
-> Emotion
IntrinsicMotivation
-> Performance
_Expectancy
Performance
_Expectancy ->
Emotion
Social _Influence ->
EffortExpectancy
SocialInfluence ->
Emotion
Social _Influence ->
Performance
_Expectancy
Original
female
Original
male
STDEV
female
STDEV
male
t value
female
t value
male
p value
female
p value
male
0.164
0.282
0.096
0.085
1.696
3.300
0.090
0.001
0.193
0.105
0.075
0.083
2.591
1.267
0.010
0.205
0.176
0.130
0.084
0.066
2.097
1.957
0.036
0.050
-0.140
-0.328
0.075
0.068
1.864
4.835
0.062
0.000
0.706
0.723
0.041
0.040
17.318
17.985
0.000
0.000
-0.400
-0.313
0.085
0.072
4.689
4.334
0.000
0.000
0.274
0.211
0.077
0.084
3.555
2.522
0.000
0.012
0.123
0.149
0.097
0.071
1.269
2.102
0.204
0.036
0.337
0.125
0.060
0.076
5.573
1.648
0.000
0.099
-0.060
0.002
0.094
0.068
0.637
0.022
0.524
0.982
0.169
0.189
0.079
0.081
2.130
2.342
0.033
0.019
0.240
0.361
0.089
0.066
2.680
5.466
0.007
0.000
The results for the group of females in the following paths:
Significant positive effect of “anthropomorphism” on “performance expectancy”,
whereas nonsignificant effect for the group of males indicates that humanoid features of AI
solutions are not perceived as a threat to females.
Significant positive effect of “anthropomorphism” on “emotion”, whereas
nonsignificant effect for the group of males
indicates
that direct effect of
anthropomorphism on emotion is stronger for females.
Significant positive effect of “performance expectancy” on “emotion”, whereas
nonsignificant effect for the group of males indicating that performance expectancy has
strong effect on emotion for females.
56
5 DISCUSSION OF RESULTS
The analysis of findings provides robust support for the proposed model. Users'
assessment shows that utilizing generative AI chatbot software is shaped significantly by
their “intrinsic motivation”, “social influence” and “anthropomorphism” during the
“primary appraisal” phase. This initial appraisal stage leads to an initial impact on the use of
generative AI software and significantly influences users' assessments in the subsequent
appraisal phase. During the “secondary appraisal” phase, the evaluation of “performance
expectancy” and especially “effort expectancy” becomes pivotal in shaping users'
“emotions”. Ultimately, at the “outcome stage”, users' “emotions” play an extensive role for
identifying their intentions regarding the use of generative AI software. The results confirm
that individuals' “willingness to accept generative AI software use” is influenced by
Lazarus’s “cognition-motivation-emotion” model (1991a).
The findings reveal that a positive effect of “intrinsic motivation” on “performance
expectancy” and a negative highest effect on “effort expectancy” which suggest that
“intrinsic motivation” is the most powerful construct predicting “effort expectancy” among
three determinants (β = -.344, β = .123) (Table 10). This result iterates the result of the
previous study AIDUA result that “intrinsic motivation”, such as “perceived fun”,
“enjoyment” and “entertainment”, has substantial role. It indicates that users perceived that
usage of generative AI software enjoyable, and interesting, and are inclined to think lower
level of effort required. The explanation behind this inference is that the higher level of
“technology readiness” can suppress the “perceived effort”. Furthermore , based on CDT
(Festinger, 1962), users who have a high level of “intrinsic motivation” for using this
software are more likely to perform an evaluation of less concerns about the performance of
generative AI software. This can be attributed to the notion that a higher level of technology
readiness may suppress the perceived effort needed for using generative AI software.
Another important
finding revealed that there is a significant positive effect of
“anthropomorphism” on “performance expectancy”. This finding couldn’t be found in the
previous study (Gursoy et al., 2019). It validates the results of previous studies, which
suggest that the more the humanlike features of a new technology leads to better the product
performance (Gollosenko et al., 2020; van Doorn et al., 2017, Aggarwal et al, 2012; Kim
and McGill, 2011).
57
Furthermore, direct
effect of “anthropomorphism” on “emotion” is positive.
Moreover, the total effect of “anthropomorphism” on “emotion” is decreased because of
the competitive mediating role of the “effort expectancy”. These findings are unveiled that
the argument of
“only negative emotion on users’ behavioral outcomes by
anthropomorphism” cannot be validated for generative AI software. The previous argument
on the “identity-threat imposed by the human-appearance” (Ackerman, 2016)
is not
validated in this study. The reason can be the differences of AI device and generative AI
software. Users' perceptions of generative AI software are not so anthropomorphized as to
threaten their identities.
Another important finding is
the significant effect of “social influence” on
“performance expectancy” rather than “effort expectancy”. It means that, when users adhere
to their group norms, they are more inclined to emphasize the advantages of the software
rather than the efforts required. “social influence” can impact users’ decision making process
for variety of reasons such as to be comply, to internalize group beliefs. This influence
depends on the context (Venkatesh and Morris, 2000). Non-significant negative relation
with “effort expectancy” can be explained that users may not worry about the effort as long
as generative AI solutions are congruent with their group of norms. This research underlines
that “social influence” is an essential determinant based on user assessment for
the
performance benefits of AI software, leading positive emotions and positive behavioral
intention.
Findings point that both “performance expectancy” and “effort expectancy” are critical
constructs that are used by customers to appraise the use of generative AI software and serve
critical role to determine users’ emotions toward their “willingness to accept the use” of it.
According to the descriptive statistics, “performance expectancy” has no distinct value as
positive determinant, and for group of male and young adults have no significant effect of
“performance expectancy” on “emotion”. This finding highlights that young male users
gave priority to consumed effort, while evaluating usage behavior.
Furthermore, the findings also reveal that in the total effect of “intrinsic motivation”
on “emotion”, mediating role of “effort expectancy” is highest effect %27.5 (Table 10, Table
11). Also, in the total effect of “social influence” on “emotion”, mediating role of
“performance expectancy” is %27.
58
Results indicates that “intrinsic motivation” has highest significant direct effect on emotion
(β = .245) (Table 10, Table 11).
The model explained 19% (p < .001) of the variance in “performance expectancy”,
17% (p < .001) of the variance in “effort expectancy”, 35% (p < .001) of the variance in
“emotion”, 51% (p < .001) of the variance in “willingness to accept the use of generative AI
software”. Model explanation reveals that there are other undefined predictors on users’
“emotion”, ”performance expectancy” and “effort expectancy” (Table 9).
Finally, the generation of positive “emotions” extensively exposes users’ “willingness
to accept the use of generative AI software” (β = .716). It proves that Lu et al.(2019)’s
findings as a most determinative predictive of user’s willingness (Table 10).
59
6 CONCLUSIONS
This study is developed and tested cognitive-motivation-emotion
model of
Lazarus(1991a, 1991b) combining with UTAUT2 (Venkatesh et al., 2003) to explain users’
willingness to accept generative AI chatbot software usage. Generative AI solution usage is
extremely young topic and this study provides the identification of significant elements on
this topic. Primary appraisal stage identifies users’ evaluation of the importance and
relevance of AI software use; the secondary appraisal stage focuses on users’ evaluation of
the perceived benefits and the perceived costs of using AI software, which results in
generation of emotions toward the use of generative AI software. The outcome stage reflects
the impact of emotions on users’ willingness to accept.
Intrinsic motivation is the most powerful construct predicting “effort expectancy”
among three determinants during the primary appraisal stage and strong indirect effect in
secondary appraisal phase. These results indicate that intrinsic motivation, such as perceived
fun, enjoyment and entertainment has substantial role while giving decision to use generative
AI software. The users are chasing to have pleasing, enjoyable, and satisfied solutions.
Emotion as a most determinative predictor of user’s willingness, the generative AI
solutions able to show some friendship, empathy and positivity to the users. These are the
users’ needs and desires. Therefore, if companies are able to develop solutions according to
user’s desires and needs then generative AI will get more users’ acceptance. Prime example
to this issue is that OpenAI announced recently the GPT-4o. Open-AI realized this issue
and made important progress with GPT-4o. It has more natural human and machine
interactions, capability of sensing human feelings and showing empathy to human.
The findings point out that the “ only negative emotion on users’ behavioral outcomes
by anthropomorphism” is not validated. Only 17% of responders have apprehension with
anthropomorphism. Moreover, young adult responders have no fear or discomfort for
humanlike interaction. The arguments about “identity-threat imposed by the humanappearance” and “uncanny valley” concept (Mori, 1970) might not be applicable to
Generative AI software. One of the reasons behind these results might be generative AI
60
software are not embodied (not in visible form, it is only text and voice) yet. That’s why
users do not consider these solutions can be threaten their identities.
Theoretical contribution
For the theoretical perspective, the main contribution of this study is to apply
cognitive-motivation-emotion model to the usage acceptance of generative AI software
Previous studies have focused mostly on simple type of chatbot software. They searched for
the factors of users' emotions through expected performance and effort, anthropomorphism,
motivation, and social items which affect adoption of simple AI device or software. This
study offers a model to examine Generative AI chatbot software. It explores the relationship
between anthropomorphism, social influence and intrinsic motivation with emotion.
Limitation and feature research directions
Large number of different AI related studies and AI related topics are reviewed for
this study. Based on the researchers’ reviews and previous studies, it is determined that there
are limited theoretical and conceptual models that can be used to determine the attitude
generation process toward the use advance AI solutions. In other words, according to the
previous studies there is still limited understanding of how users generate attitude for the
use of acceptance especially generative AI solutions.
This study, based on the SRIW scale, examined small number of determinants. Results
show that intrinsic motivation and anthropomorphism explained only 17% of the total
variance for effort expectancy, and social influence has no effect on effort expectancy.
Moreover, intrinsic motivation, social influence and anthropomorphism explained only 19%
of the total variance for performance expectancy. Therefore, adding other predictors also in
to the first appraisal phase which influence gains as performance expectancy, and efforts as
effort expectancy might be useful. Possible determinants can be empathy or technology
readiness to explain performance and effort expectancy. Future studies should investigate
the other variables that can be added to the current model in order to increase the model's
predictive power. This might be especially useful if the future studies can identify other
factors to predict the emotion.
61
The convenience sampling and judgmental sampling methods are used in this study,
sampling data is collected from participants who already use in the past generative AI
software. Participants has answered the question based on their prior experience. Generative
AI solutions are evolving with unexpected velocity since November 2022, and these
solutions is becoming more common in people’s daily-life. Therefore, future studies can
analyze the determinants without using judgmental and convenience sampling.
62
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APPENDIX A: QUESTIONNAIRE IN TURKISH
Üretken Yapay Zeka Sohbet Robotu Uygulamalarının Kullanımı
Değerli Katılımcı,
2022 den itibaren üretken “yapay zeka sohbet robotu” prototip uygulamaları günlük ve iş
yaşamımıza girmiştir. Bu araştırma bu uygulamaların, yaşamımızdaki yeri ve rolünü
irdelemeyi hedefler. Yapay zeka sohbet robotu uygulamaları, öğrenme ve biliş anlayışımıza
yakınlaşmış doğal dil işleme modelleri olup, büyük veri ölçeği ve hesaplama kullanılarak
eğitilmiştir. Bu uygulamalar soyutlama, kavrama, görme, kodlama, matematik, tıp, hukuk,
insan güdülerini ve duygularını anlama ve daha fazlasını içeren çeşitli alanlarda ve
görevlerde dikkate değer beceriler göstermeye başlamıştır.
Bugüne kadar kullandığınız, denediğiniz ya da kullanmayı düşündüğünüz bu
tür uygulamalar konusunda düşüncelerinizi öğrenmeye yönelik hazırlamış olduğum bu
anketi; çeviri, döküman hazırlama, özet hazırlama, problem çözme, bilgi alma, görsel/sunu
oluşturma gibi ihtiyaçlarınızı dikkate alarak doldurmanızı rica ediyorum.
Bu araştırmaya vereceğiniz katkı için şimdiden teşekkür ederim.
Aşağıdaki soruları Yapay Zeka Sohbet Robotu kategorisine girebilecek (ChatGPT,
Google Bard, Microsoft Copilot ve Meta LLaMa vb. gibi) yada benzer üretken
yapay zeka uygulamaları için yanıtlayınız
Kesinlikle
Katılmıyorum
Katılmıyorum
Kararsızım
Katılıyorum
Kesinlikle
Katılıyorum
1.Yapay Zeka Sohbet Robotu uygulamaları tarafından sağlanan
bilgiler daha doğrudur.
O
O
O
O
O
2.Bu uygulamalar tarafından sağlanan hizmetler daha az hata taşır.
O
O
O
O
O
3.Bu uygulamalar tarafından sağlanan bilgiler daha tutarlıdır.
O
O
O
O
O
4.Bu uygulamalar tarafından sağlanan hizmetler daha güvenilirdir.
5.Bu uygulamalar tarafından sağlanan hizmetler daha tahmin
edilebilirdir.
6.Bu uygulamaları kullanırsam verimsiz kişisel temasları
önleyebilirim.
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
7.Bu uygulamaları kullanmak çok fazla zamanımı alıyor.
8.Bu uygulamaları anlamanın ve kullanmanın çok zor olacağını
düşünüyorum.
9.Bu uygulamalar ile etkileşim kurmayı öğrenmek çok zamanımı
alır.
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
10.Yapay Zeka Sohbet Robotu uygulamaları beni korkutuyor.
11.Çevremdeki insanlar için bu uygulamaları kullanmak, iş ve
arkadaş çevrelerinde olumlu bir izlenim yaratmalarını sağlar.
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
17.Bu uygulamalar ile etkileşim kurmak, benim için eğlenceli.
O
O
O
O
O
18.Bu uygulamalar ile etkileşim kurmak, insanları eğlendirir.
O
O
O
O
O
19.Bu uygulamalar ile etkileşimi zevkli buluyorum.
O
O
O
O
O
20.Bu uygulamalar ile etkileşime girme süreci keyiflidir.
O
O
O
O
O
12 Çevremde bu uygulamaları kullanan insanlar,
kullanmayanlardan daha fazla itibara sahiptirler.
13.Çevremde bu uygulamalardan faydalanan insanlar, güncel
teknolojik ve pratik beceriler kazanırlar.
14.Fikirlerine değer verdiğim kişiler,, bana bu uygulamaları
kullanmamı önerirler.
15.Benim için önemli olan insanlar; beni, bu uygulamalardan
faydalanmaya teşvik ederler.
16.Davranışlarımda etkisi olan insanlar, bu uygulamaları
kullanmamı isterler.
70
21.Bu uygulamalar ile anlaşabildiğimi düşünüyorum.
22.Yapay Zeka Sohbet Robotu uygulamalarının kendilerine ait bir
zihinleri vardır.
O
O
O
O
O
O
O
O
O
O
23.Bu uygulamalar bir bilince sahiptirler.
O
O
O
O
O
24.Bu uygulamaların kendi özgür iradeleri vardır.
O
O
O
O
O
25.Bu uygulamalar duyguları bile hissedebilecekler.
O
O
O
O
O
O
O
O
26.Bu uygulamaların niyetleri olacaktır.
27.Bu uygulamaları kullanırsam, şu şekilde hissedeceğim:
Huzursuz / Rahat
28.Bu uygulamaları kullanırsam, şu şekilde hissedeceğim: Mutsuz/
Mutlu
29.Bu uygulamaları kullanırsam, şu şekilde hissedeceğim:
Memnuniyetsiz / Memnun
30.Bu uygulamaları kullanırsam, şu şekilde hissedeceğim:
Umutsuz/Umutlu
31.Bu uygulamaları kullanırsam, şu şekilde hissedeceğim:
Huzursuz/Keyifli
O
O
Çok
Huzursuz
Huzursuz
Kararsız
Rahat
Çok Rahat
Çok Mutsuz
Mutsuz
Nötr
Mutlu
Hiç Memnun
Kalmadım
Memnun
Kalmadım
Kararsızım
Memnun
Kaldım
Çok Mutlu
Çok
Memnun
Kaldım
Çok Umutsuz
Umutsuz
Nötr
Umutlu
Çok Umutlu
Huzursuz
Nötr
Keyifli
Katılmıyorum
Kararsızım
Katılıyorum
Çok Keyifli
Kesinlikle
Katılıyorum
Çok
Huzursuz
Kesinlikle
katılmıyorum
32. Yapay Zeka Sohbet Robotu uygulamalarından hizmet almayı
istiyorum.
O
O
O
O
O
33. Bu uygulamalar ile etkileşimden dolayı 4 hissedeceğim.
O
O
O
O
O
34. Büyük olasılıkla, bu uygulamalar ile etkileşim kuracağım.
O
O
O
O
O
Cinsiyetiniz
Kadın
O
Erkek
O
En son mezun olduğunuz eğitim kurumu
Lise
O
Yüksek Lisans
O
Üniversite
O
Doktora
O
18-25
O
45-54
O
26-34
O
55 ve Üzeri
O
35-44
O
Yaşınız