Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 21, No. 2, 2025 

 

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How Unified Factors Impact AI Usage Intention in the 
Workplace: A Study from Employee Perspective 

Asima Asif, Muneeb Hussain, You Jun 

School of Economics and Management Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China 

 

Abstract: The rapid advancement of Artificial Intelligence (AI) presents significant opportunities for enhancing workplace 
productivity and efficiency. As AI technologies continue to evolve, understanding the factors that influence employees' inten tion 

to adopt AI becomes increasingly crucial. This study investigates the psychological mechanisms underlying AI usage intention 

among employees, with a specific focus on three key predictors: Innovation Preference (IP), Social Influence (SI), and 

Performance Expectancy (PE). Additionally, Attitude toward AI is examined as a mediating variable, while Anxiety and Firm 

Reputation are explored as moderators that may affect these relationships. To examine these relationships, the study employed  a 

structured, valid, and reliable questionnaire to gather data from employees in Pakistan. A total of 610 valid responses were 

collected and analyzed using hierarchical regression and moderation -mediation analysis. The results demonstrate that IP, SI, and 

PE significantly predict Attitude toward AI, which in turn has a strong impact on the Intention to use AI. The mediating role of 

Attitude was confirmed across all three predictors (IP, SI, and PE), indicating that a favorable attitude is a key psychologi cal 

pathway through which these factors influence AI adoption intentions. The study's findings highlight the importance of fostering 

positive employee attitudes toward AI to promote its adoption in organizational settings. Furthermore, the moderation analysi s 

reveals that Anxiety significantly moderates the relationship between Social Influence and Attitude, while Firm Reputation 

moderates the link between Attitude and Intention to use AI. These results emphasize the need for organizations to consider b oth 

psychological and contextual factors—such as emotional responses and perceived organizational credibility —when 

implementing AI technologies. Overall, the study contributes valuable insights into the social and psychological dynamics tha t 

shape technology adoption decisions in the workplace. 

Keywords: AI Usage Intention; Unified Factors; AI in Workplace; Employee’s AI Use; UTAUT Theory. 

 

1. Introduction 

1.1. Background 

With the rapid development in Artificial Intelligence (AI), 

its use in the workplace has the potential to dramatically 

increase productivity and efficiency  marking a pivotal shift in 

organizational strategies and operations in response to the 

digital age [12]. AI technologies have evolved at an 

unprecedented pace, providing new solutions that have the 

potential to transform the way in which employees perform 

their work.  However, as AI continues to proliferate, 

understanding how employees perceive, accept, and intend to 

use these technologies within their workplaces is important. 

Employee’s intention to use AI is shaped by personal 

preferences, social influences, and perceived benefits, 

underscoring the importance of gaining insights from the 

employees' perspective. AI’s ability to provide a new way to 

revolutionize the workplace through the automation of 

routine work, the provision of data-driven insights, and the 

encouragement of more effective workflows is tremendous. 

The effective use of AI in the workplace, however, has a 

strong dependence on the intention of employees to use it. 

Organizations, candidates, and employees view AI in 

different ways. For organizations, AI is a way to streamline 

their work, cut down costs, and gain a competitive advantage. 

Candidates see AI as a tool that automates the process of 

getting into the right job role and connecting them with the 

right job opportunities. Employees see AI as a way to 

augment their work performance, cut down on mundane work, 

and increase job satisfaction. In spite of the tremendous 

scholarly interest that AI has generated, it has a perceptible 

gap in understanding employees' intentions to use AI 

technologies. Previous research has directed its focus on the 

technological capabilities as well as organizational benefits of 

AI and has given less importance to the psychological factors 

that govern individuals' acceptance and use of AI in the 

workplace. Filling this gap is essential to devising strategies 

for the stimulation of the adoption of AI by employees and 

thereby increasing the effectiveness of AI implementations in 

organizational environments. 

The key problem that this research aims to tackle is the lack 

of knowledge about what is driving employees' intention to 

use artificial intelligence (AI) in the workplace. More 

specifically, the research examines the effect of performance 

expectancy, social influence, and innovation preference on 

the intention to use AI among employees, with attitude as 

mediating variables, and anxiety and firm reputation as a 

moderating variable. The research attempts to fill up this gap 

by delivering an understanding of the psychological processes 

through which AI usage intention is generated from an 

employee perspective. 

Knowledge about the working of AI usage intention from a 

psychological perspective assumes great significance for 

organizations that wish to adopt AI technologies successfully. 

The research would not only help in identifying new 

situational variables that are influencing AI adoption, but also 

contribute to determining the full picture of the determinants 

of intention to use AI among employees. The research is 

important from the perspective that it is the first of its kind 

that aims at delving into AI usage intention among employees 

in Pakistan and, thus, would offer a different kind of 

dimension about AI adoption taking place in a developing 

country setup. Moreover, the research would facilitate 



 

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comparison of AI usage intentions among employees in 

Pakistan and China that would generate further insights into 

the cultural and contextual discrepancies across AI adoption. 

1.2. Problem Statement 

Despite the rapid advancement and potential benefits of AI 

technologies, there remains a significant gap in understanding 

the factors that influence employees' intention to use AI in the 

workplace. Current literature often focuses on technological 

aspects or managerial perspectives, neglecting the 

psychological and social dimensions from the employees' 

viewpoint. This gap in knowledge poses a barrier to the 

effective implementation and acceptance of AI technologies. 

To bridge this gap, this study aims to ex plore the unified 

factors that impact AI usage intention from an employee 

perspective. 

1.3. Research Questions 

This study aims to address the following research questions: 

Q1: How do unified factors impact AI usage intention 

among employees? 

Q2: How does attitude moderates the relationship between 

unified factors and AI usage intention? 

Q3: How does anxiety moderate the relationship between 

unified factors, and attitude? 

Q4: How does firm reputation moderate the relationship 

between attitude and AI usage intention? 

1.4. Research Significance 

This study has several potential contributions:  

Understanding Psychological Mechanics: This study aims 

to uncover the psychological mechanisms underlying AI 

usage intentions among employees, providing deeper insights 

into their attitudes. 

Identifying New Situational Variables: The research 

identify new variables that influence AI usage intention, 

contributing to the broader literature on technology 

acceptance. 

First AI Usage Intention Study in Pakistan: This study is 

the first to investigate AI usage intention among employees in 

Pakistan, offering valuable country -specific insights and 

contributing to the global discourse on AI usage intention. 

2. Theory and Literature Review 

The research is grounded theoretically on the Unified 

Theory of Acceptance and Use of Technology (UTAUT). This 

theory is a combination of different theories [5]. The UTAUT 

model, definitively articulated by [39], argues four key 

constructs that play a part in user acceptance as well as usage 

behavior: performance expectancy, effort expectancy, social 

influence, and facilitating conditions. The current research 

works on the first half of the theory, i.e. performance 

expectancy, social influence, and innovati on preference 

(instead of effort expectancy), and their effect on AI usage 

intention. Intention to use AI is the main focus of this study 

from employee’s perspective. Performance expectancy refers 

to the degree of which an individual believes that using AI 

will lead him or her to achieve gains in job performance which 

strongly influences AI usage intention [2]. Performance 

expectancy is expected to have a positive effect on AI usage 

intention. Employees who see AI as a tool that is helpful in 

boosting their job performance are more likely to be willing 

to use it. Social influence captures the degree to which an 

individual believes that important others think that he or she 

should use AI. Social influence is expected to have a positive 

effect on AI usage intention. When employees believe that 

their peers, supervisors, or even the organizational culture are 

behind the use of AI, they are more likely to adopt the 

technology. Given Pakistan's collectivist society, where group 

norms and relationships hold sway, so cial influence plays a 

pivotal role in shaping AI usage intention and acceptance [10].  

Innovation preference, which replaces effort expectancy, 

captures an individual's predisposition to using new 

technologies and innovations. Innovation preference is 

hypothesized to have a positive effect on AI usage intention. 

Employees who are inclined to  move in the direction of the 

adoption of new technologies are more likely to be willing to 

use AI. For example, in industries such as construction and oil 

and gas, the adoption of AI is influenced by its perceived 

functional value and reliability [12]. Si milarly, trust in AI 

tools, like chatbots, can stimulate innovative applications in 

daily business operations. Understanding how AI can be 

innovatively used is crucial for organizations to enhance work 

practices and operational efficiency [23]. Attitude [17] is used 

as mediating variables in this study. Attitude is how 

employees personally feel about AI, whether they like or 

dislike it. It focuses on their individual feelings and opinions 

about using AI at work, without being influenced by their 

preference for new ideas, what others think, or how useful 

they believe AI will be for their job performance. [14], 

including feelings of excitement, anxiety, or fear. Positive 

affective states, may assist in the greater likelihood of AI 

usage intention, whereas negative affective states are likely to 

act as barriers.  

Firm reputation is hypothesized to play a moderating role 

between the mediators (attitude) and the usage intention of AI. 

A strong firm reputation may contribute to the trust and 

confidence of employees in AI-based technologies and thus 

extend the positive effect of attitude on the usage intention of 

AI. A positive firm reputation can reinforce employees' 

positive attitudes towards AI adoption, thereby enhancing 

overall AI usage intention within the workplace.  Many of 

the researches show that AI has influenced almost all aspects 

of business activities [19]. However most of the employees 

do not intend to use AI in the workplace because they believe 

companies cannot fulfill the ethical obligations needed for 

meaningful work [1] specially employees in Pakistan.  

2.1. Innovation Preference 

Innovation preference refers to the degree to which an 

individual enjoys using new and innovative technologies. In 

the context of AI adoption, this concept captures how willing 

employees are to embrace AI tools within their work 

environment. Existing literature highlights innovation 

preference as a critical driver of technology adoption behavior 

[27]. Employees who exhibit a strong preference for 

innovation tend to be early adopters, motivated by the 

potential benefits AI can provide, such as streamlining t asks, 

automating routine processes, and improving decision -

making. This aligns closely with their intrinsic desire for 

efficiency and productivity enhancement. Research by [24] 

emphasizes that individuals with a higher innovation 

preference are more adaptable to technological changes, 

viewing AI as an opportunity for continuous learning and 

professional development rather than as a threat to job 

security.  

This positive orientation toward AI supports the formation 

of favorable attitudes and a stronger intention to use AI in 



 

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their roles. Despite the recognized importance of innovation 

preference in general technology adoption, few studies have 

specifically examined its role in AI adoption from the 

employees’ perspective, particularly in workplace contexts. 

This gap is significant because employees are the primary 

users who operationalize AI technologies. By focusing on 

employees’ innovation preference, this study contributes to 

the theoretical understanding of AI adoption by linking an 

individual’s inclination towards innovatio n with their 

attitudes and intention to use AI. Integrating this construct 

within the Unified Theory of Acceptance and Use of 

Technology (UTAUT) framework enhances the explanatory 

power of the model, addressing emotional and cognitive 

dimensions specific to AI. Thus, the inclusion of innovation 

preference enriches the theoretical framework and provides a 

nuanced insight into the human factors that facilitate AI 

adoption in organizations. 

2.2. Social Influence 

Social influence refers to the extent to which an 

individual’s decisions to use technology are affected by others 

around them. In the context of AI adoption, social influence 

captures how much employees are swayed by colleagues, 

supervisors, or external factors—such as media and societal 

trends—in their choice to use AI technologies at work. This 

construct is especially significant in the Pakistani workplace, 

where cultural norms and social dynamics heavily shape 

technology acceptance behaviors [7]. Employe es tend to rely 

on social cues and the behavior of influential figures within 

their professional networks when deciding whether to 

embrace new technologies like AI. 

Previous research highlights that the attitudes and 

behaviors of peers and leaders within organizations play a 

vital role in encouraging or discouraging AI adoption [15]. 

Employees who perceive their social environment as 

supportive and positive toward AI are more likely to develop 

favorable attitudes and stronger intentions to use AI tools. 

This is partly driven by the human tendency to conform to 

social norms and expectations to maintain group cohesion. 

Conversely, skepticism or resistance from key indivi duals 

within the workplace can create barriers to AI acceptance and 

usage. 

Moreover, informal communication channels—such as 

word-of-mouth and social interactions—serve as important 

sources of information and reassurance, helping employees 

form opinions about AI based on their peers’ experiences. 

These social processes amplify the influence of social norms 

on employees’ attitudes and intentions toward AI adoption. 

Despite the acknowledged importance of social influence in 

technology acceptance, few studies have explored its role 

specifically in the context of AI usage intentions from the 

employee perspective, especially within collectivist cultures 

like Pakistan. 

This study addresses that gap by integrating social 

influence into the UTAUT framework to better explain how 

social factors interact with individual attitudes to shape AI 

adoption. By focusing on employees’ social environment and 

cultural context, the research extends theoretical 

understanding of technology acceptance and highlights the 

critical role social influence plays in shaping AI usage 

intentions in the workplace. 

2.3. Performance Expectancy 

Performance expectancy refers to the degree to which an 

individual believes that using a technology will improve their 

job performance. In the context of AI adoption, this concept 

captures employees’ perceptions of how AI tools can enhance 

their productivity, efficiency, and overall work quality. 

Performance expectancy is widely recognized as a 

fundamental factor shaping employees’ attitudes and 

intentions toward adopting new technologies [18]. When 

employees believe that AI can simplify tasks, reduce errors, 

and support more informed decision -making, they are more 

motivated to integrate AI into their daily workflows. 

Research has demonstrated that employees are particularly 

receptive to AI technologies that provide tangible benefits, 

such as AI-driven analytics tools that enable faster and more 

accurate data analysis, helping users identify patterns and 

trends that may otherwise remain hidden. Such technologies 

empower employees by enhancing their capabilities rather 

than threatening job security, which helps to reduce resistance 

to AI adoption. This perception of AI as an enabler rather than 

a replacement is crucial for fostering a positive attitude 

toward its use. 

Additionally, organizational efforts to communicate the 

practical advantages of AI adoption —such as increased 

productivity, improved accuracy, and better decision 

support—play a vital role in shaping employees’ performance 

expectancy. When these benefits are clearly articulated, 

employees develop stronger intentions to use AI, reinforcing 

the importance of performance expectancy as a key predictor 

in technology acceptance models [4]. 

Despite the extensive literature on performance expectancy 

in general technology adoption, relatively few studies have 

focused specifically on AI adoption from the employees’ 

perspective. This study addresses that gap by incorporating 

performance expectancy within the UTAUT framework to 

better explain how employees’ beliefs about AI’s impact on 

their job performance influence their attitudes and intentions. 

This theoretical integration advances understanding of the 

cognitive factors driving AI adoption and underscores the 

importance of highlighting AI’s practical value to promote its 

use in organizational settings. 

2.4. Attitude 

Attitude refers to employees’ overall evaluation of AI in the 

workplace, capturing their feelings of favorability or 

unfavorability toward using AI technologies. It reflects an 

emotional and cognitive response shaped by perceptions of 

AI’s relevance, usefulness, and impact on their work. As a 

psychological construct, attitude plays a crucial role in 

shaping employees’ intentions to adopt and use AI, acting as 

a mediator that links external influences—such as innovation 

preference, social influence, and performance expectancy—

to behavioral outcomes [17]. This mediating role of attitude 

is well established in foundational technology acceptance 

theories like the Theory of Planned Behavior [45] and the 

Technology Acceptance Model [33], where it consistently 

predicts behavioral intention. 

In the context of AI adoption, a positive attitude toward AI 

enhances the likelihood of its use, while negative feelings, 

such as fear or skepticism, can hinder adoption. Employees 

who view AI as beneficial and relevant to their work are more 

inclined to integrate it into their daily tasks. Conversely, 

negative attitudes may arise from concerns about job security, 

complexity, or lack of understanding, which can act as 

barriers to AI acceptance. Existing literature highlights the 

importance of fostering positive attitudes through 



 

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organizational efforts such as effective communication, 

training programs, and addressing employee concerns 

directly [14]. 

Despite the broad recognition of attitude’s importance in 

technology acceptance, there remains limited research 

focusing on attitude as a mediating factor specifically in AI 

adoption from the employee perspective. This study fills that 

gap by emphasizing attitude’s mediating role within the 

UTAUT framework to better understand how employees’ 

perceptions and beliefs translate into AI usage intentions. By 

doing so, it advances theoretical knowledge on how affective 

and cognitive evaluations influence AI adopt ion behaviors in 

the workplace, highlighting attitude as a key lever for 

organizations seeking to encourage AI use. 

2.5. Anxiety 

Anxiety is widely recognized as a significant emotional 

factor influencing employees’ behavior toward adopting new 

technologies, including artificial intelligence (AI) in the 

workplace. Existing research consistently highlights anxiety 

as a negative emotional response that can create barriers to 

technology acceptance by causing feelings of uncertainty, fear, 

and resistance [21]. Specifically, anxiety related to AI often 

emerges from the fear of the unknown, concerns about 

potential job displacement, and the perceived complexity of 

AI systems [20]. These fears may result in employees feeling 

overwhelmed and reluctant to engage with AI, despite 

recognizing its potential benefits. This emotional barrier is 

critical because it can weaken the positive impact of k ey 

drivers such as innovation preference, social influence, and 

performance expectancy on employees’ intention to use AI [3]. 

For example, even employees who prefer innovation or trust 

social cues to adopt AI may hesitate if anxiety is high, 

reducing the overall likelihood of AI usage. 

This study builds on this understanding by hypothesizing 

that anxiety moderates the relationships between innovation 

preference, social influence, performance expectancy, and AI 

usage behavior. By doing so, it extends existing technology 

adoption models, such as the Technology Acceptance Model 

and Unified Theory of Acceptance and Use of Technology, 

which often overlook emotional responses like anxiety. The 

theoretical significance lies in recognizing anxiety not just as 

a direct predictor but as a contextual factor that shapes how 

other beliefs influence behavior. Furthermore, literature 

emphasizes that organizations can actively reduce anxiety 

through interventions like training, transparent 

communication, and creating a supportive environment that 

aligns AI adoption with career security and growth [9]. 

Incorporating anxiety as a moderating variable in this study 

highlights the importance of addressing employees’ 

emotional responses to maximize AI acceptance, thereby 

offering practical insights for organizat ions aiming to 

implement AI technologies effectively. This focus on anxiety 

enriches the theoretical framework by linking cognitive, 

social, and emotional dimensions of technology adoption in 

the workplace, which is particularly relevant in the emerging 

context of AI use among employees. 

2.6. Reputation 

Firm reputation plays a crucial role in shaping employees’ 

trust and confidence in adopting AI technologies in the 

workplace. A strong and positive reputation signals to 

employees that the organization is reliable, ethical, and 

committed to innovation, which can extend and strengthen the 

positive effects of employees’ cognitive and emotional 

responses on their intention to use AI [13]. Even when 

employees may have mixed or negative attitudes toward AI 

itself, their positive attitude toward the firm can enco urage 

continued engagement and loyalty, influencing their behavior 

in subtle but important ways. Existing research shows that AI 

is transforming nearly every aspect of business activities, but 

despite its potential, many employees remain reluctant to use 

AI at work due to concerns that organizations may fail to meet 

ethical standards or provide meaningful and responsible work 

environments [1]. This gap highlights the importance of firm 

reputation as a key factor in fostering positive attitudes 

toward AI.  

Organizations known for transparency, ethical practices, 

and innovation tend to cultivate more favorable employee 

attitudes toward AI adoption, which in turn increases their 

usage intention [16]. On the other hand, negative perceptions 

about AI’s reliability or doubts about organ izational motives 

can damage employee trust, weakening the relationship 

between attitude and AI usage intention. Therefore, firm 

reputation acts as a critical moderator in the technology 

adoption process, influencing how employees’ attitudes 

translate into their actual intention to use AI in the workplace. 

The theoretical significance of including firm reputation in 

this study lies in its ability to bridge organizational -level 

factors with individual technology acceptance, enriching 

traditional models that often focus narrowly on personal 

attitudes or technological attributes. By considering 

reputation, this research provides a more holistic 

understanding of AI adoption that accounts for the broader 

organizational context in which employees operate. 

2.7. AI Usage Intention 

AI usage intention refers to the degree to which an 

employee plans or intends to use AI technologies in their job 

tasks. This construct is central to understanding what drives 

employees to adopt AI in the workplace. In organizational AI 

adoption research, AI usage intention acts as a key mediator 

that connects employees’ attitudes and perceptions with their 

actual behavior of integrating AI into daily work routines [6]. 

Existing literature shows that employees’ intention to use AI 

is influenced by various factors, including how useful and 

easy to use they perceive AI to be [8]. When employees see 

AI as helpful in simplifying their work, improving 

productivity, and aiding decision -making, they are more 

motivated to use it. On the other hand, if they view AI a s 

complicated, unreliable, or not fitting well with their tasks, 

their intention to adopt AI decreases, leading to resistance or 

reluctance.  

The usability and ease of use of AI systems also play a 

significant role in shaping usage intention. AI tools that are 

user-friendly, intuitive, and well-integrated with employees’ 

existing workflows encourage higher intention to use. In 

contrast, poorly designed or complex AI systems may 

discourage employees from adopting them. Furthermore, 

employees’ attitudes towards AI, which are influenced by 

factors such as innovation preference, social influence, and 

performance expectancy, are critical determinants of their AI 

usage intention [11]. Positive attitudes towards AI correspond 

with greater willingness to explore and use AI technologies in 

everyday work, while negative attitudes contribute to 

resistance or rejection. Overall, AI usage intention reflects a 

complex combination of individual perceptions, attitudes, and 

behaviors, all shaped by personal, organizational, and 



 

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contextual influences [22]. Understanding these relationships 

is theoretically significant because it helps organizations 

identify which factors to target to increase employee 

willingness to use AI, thereby supporting successful AI 

integration and maximizing its potential benefits in the 

workplace [25]. 

3. Theoretical Framework  

This research employs the Unified Theory of Acceptance 

and Use of Technology (UTAUT) as its theoretical foundation. 

UTAUT explains user intentions and subsequent technology 

usage behavior. The proposed model integrates UTAUT with 

additional constructs relevant to AI adoption.  

3.1. Hypothesis Development 

Artificial Intelligence (AI) technologies are transforming 

workplaces across industries by automating tasks, enhancing 

decision-making, and improving efficiency. Despite this rapid 

growth, there remains a significant gap in research regarding 

employees’ intentions to use AI tools at work. Most existing 

studies on technology adoption focus broadly on 

organizational or managerial perspectives, or on general 

technology use, without specifically examining the 

employees’ viewpoint concerning AI adoption in the 

workplace. This is an important omission because employees 

are the actual users who decide whether and how AI tools are 

incorporated into daily work processes. Understanding the 

factors that influence employees’ intention to adopt AI is 

therefore critical for successful implementation and 

maximizing AI’s benefits. 

To address this gap, this study constructs a research model 

grounded in the well-established Unified Theory of 

Acceptance and Use of Technology (UTAUT), which 

explains user intentions and technology use behavior based 

on factors like performance expectancy  and social influence 

[40]. UTAUT has been widely applied to study various 

technologies, but AI poses unique cognitive, emotional, and 

organizational challenges that UTAUT alone cannot fully 

capture. Hence, this study extends UTAUT by including 

additional constructs relevant to AI, such as innovation 

preference, anxiety, and firm reputation, to provide a more 

comprehensive explanation of AI adoption from the employee 

perspective. 

Innovation Preference reflects an employee’s tendency to 

embrace and try out new technologies. Prior research has 

shown that individuals with a high preference for innovation 

are more likely to adopt new tools because they are motivated 

by curiosity and the desire to improve their work practices [2]. 

For example, studies in industries like construction and oil 

and gas highlight that innovation preference drives the use of 

AI to improve operational reliability and efficiency [12]. 

However, no prior study has directly linked innovation 

preference to employees’ intention to use AI in the workplace, 

especially in the context of developing countries like Pakistan. 

Therefore, this research hypothesizes: 

H1: Innovation preference significantly influences AI 

usage intention. 

Social Influence is another core UTAUT construct that has 

been demonstrated to affect technology adoption decisions, 

particularly in collectivist cultures where social norms and 

peer opinions carry great weight [40]. In Pakistan, where 

interpersonal relationships strongly  impact workplace 

behavior, social influence plays a key role in shaping 

employees’ attitudes and intentions toward AI adoption [10]. 

Despite this, most previous AI adoption studies have not 

explicitly examined social influence from the emp loyee 

viewpoint. This study fills that gap by proposing:  

H2: Social influence significantly influences AI usage 

intention. 

Performance Expectancy — the belief that AI will improve 

job performance — has consistently been shown to be one of 

the strongest predictors of technology acceptance across 

numerous studies [2], [40]. Employees who perceive AI as a 

tool that can enhance productivity, automate repetitive tasks, 

and improve decision-making are more likely to intend to use 

it. Yet, existing AI research has rarely focused on employees’ 

performance expectancy specifically. This study integrates 

this factor into the model to assess its effect on AI adoption 

intentions: 

H3: Performance expectancy significantly influences AI 

usage intention. 

Attitude towards AI represents employees’ positive or 

negative feelings about using AI tools. While UTAUT does 

not explicitly include attitude as a core construct, many 

technology adoption studies highlight its importance as a 

mediator between external factors (e.g., innovation 

preference, social influence, performance expectancy) and 

behavioral intention [17]. Attitude captures the emotional and 

cognitive response to AI, which can either facilitate or hinder 

adoption. Fear, anxiety, or distrust can lead to  negative 

attitudes, while enthusiasm and perceived benefits promote 

positive attitudes [14]. This study therefore considers attitude 

as a key mediator to explain how employees’ beliefs and 

social environment translate into AI usage intentions:  

H4a: Attitude mediates the relationship between innovation 

preference and AI usage intention. 

H4b: Attitude mediates the relationship between social 

influence and AI usage intention. 

H4c: Attitude mediates the relationship between 

performance expectancy and AI usage intention. 

Anxiety towards AI is an emotional response that reflects 

employees’ fear, uncertainty, or discomfort with using AI 

tools. While UTAUT acknowledges facilitating conditions 

and effort expectancy, it does not adequately address 

emotional barriers like anxiety, which have been shown in 

other studies to negatively impact technology adoption [2]. 

Anxiety can weaken or distort the effects of innovation 

preference and performance expectancy on attitude, as even 

motivated and performance-focused employees might 

hesitate to use AI if they feel anxious. Additionally, anxiety 

may strengthen the effect of social influence because anxious 

employees tend to seek reassurance and guidance from peers 

or supervisors [17]. Prior research has mostly overlooked 

anxiety’s moderating role in AI adoption, especially in 

workplace settings. This study thus hypothesizes:  

H5a: Anxiety moderates the relationship between 

innovation preference and attitude. 

H5b: Anxiety moderates the relationship between social 

influence and attitude. 

H5c: Anxiety moderates the relationship between 

performance expectancy and attitude. 

Firm Reputation represents employees’ perceptions of their 

organization’s trustworthiness, ethical standards, and support 

for innovation. Research suggests that when employees trust 

their organization and believe it values ethical conduct, they 

feel more confident adopting new technologies, including AI 

[19]. This is especially relevant in contexts like Pakistan 

where concerns about organizational ethics and reliability can 



 

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hinder technology acceptance [1]. Despite this, firm 

reputation has rarely been examined as a moderator in AI 

adoption models. This study contributes theoretically by 

integrating firm reputation as a factor that strengthens the link 

between positive attitude and AI usage intention: 

H6a: Firm reputation moderates the relationship between 

attitude and AI usage intention. 

3.2. Conceptual Model 

The conceptual model Figure 3.1 consists of three 

independent variables: Innovation Preference, Social 

Influence, and Performance Expectancy, which influence the 

dependent variable, AI Usage Intention. Attitude serves as a 

mediating variable, linking the indep endent variables to the 

intention to use AI. Furthermore, Anxiety acts as a moderating 

variable between the independent variables and Attitude, 

potentially altering how the independent variables influence 

employees' attitudes towards AI. Additionally, Firm  

Reputation is hypothesized to moderate the relationship 

between Attitude and AI Usage Intention, potentially 

enhancing or weakening the impact of employees' attitudes on 

their intention to use AI. This model aims to provide a 

comprehensive understanding o f the factors influencing AI 

adoption in the workplace, incorporating cognitive, emotional, 

and organizational factors to explain AI usage intention. 

 
Figure 3.1. Conceptual Framework 

4. Methodology 

4.1. Measurement Instrument 

To test our hypotheses, we developed a questionnaire by 

adapting scales previously employed in the specialized 

literature. Innovation Preference was assessed using four 

items adapted from [41]. Social Influence was measured with 

three items from [42]. Performance Expectancy was 

evaluated using three items based on [5]. Attitude was 

measured using three items from [5]. Anxiety was assessed 

using three items from [43]. Firm Reputation was measured 

with three items from [37]. AI Usage Intention was measured 

with three items from [44]. A five-point Likert scale ranging 

from 1 (strongly disagree) to 5 (strongly agree) was used to 

measure all variables in this study. Table 4.1 shows all the 

scales. 

 

 

 

 

 

 

 

Table 4.1. Measurement Scales 

 

4.2. The Sampling 

The sample was collected from Pakistan using google 

questionnaire. Questionnaire was screened strictly and those 

participants were strictly eliminated who failed to pass the 

screening questions, whose answers were regular and whose 

filling time was either too short or too long. 610 valid 

responses were collected in total. Prior power analysis was 

done to determine the sample size. G*Power was used with 

the following settings: alpha: 0.05; effect size: 0.15; and 

power: 0.95. The sample size needed for this study calculated 

by G power is 146 and the sample size of this study thus met 

the requirement. Table 4.2 presents the demographic 

characteristics of the participants. 

4.2.1. Gender Distribution 

The sample consists of 610 respondents, with a gender 

distribution showing that the majority of participants are male 

(56.9%, n = 347) while female respondents make up 43.1% 

(n = 263) of the sample (M = 1.43, SD = 0.496). This indicates 

a slight male predominance, although the gender distribution 

remains relatively balanced overall. 

4.2.2. Age Distribution 
The majority of participants are in the 20 -30 years age 

group (42.5%, n = 259), followed by those aged 31 -40 years 

(40.5%, n = 247). Smaller proportions of respondents fall into 

the under 20 years (6.1%, n = 37) and above 50 years (1.6%, 

n = 10) categories (M = 2.58, SD = 0.807). This shows a 

predominant participation from younger adults, which is 

consistent with trends in technology adoption, where younger 

populations tend to be more engaged with emerging 

innovations such as artificial intelligence (AI). 

4.2.3. Educational Background 
The majority of respondents are Bachelor’s degree holder 

(34.9%, n = 213), followed by those with a Master’s degree 

(29%, n = 177). A smaller percentage of participants possess 

a Diploma (15.7%, n = 96) or PhD (3.3%, n = 20) (M = 2.86, 

SD = 1.115). The data shows a significant representation of 

individuals with higher educational qualifications, 

particularly at the Bachelor’s and Master’s levels. Higher 



 

34 

educational attainment is often associated with greater 

professional experience and access to better-paying jobs, 

which could influence the participants' familiarity with or 

intention to engage with technologies like AI. 

4.2.4. Salary Distribution 
The salary distribution reveals that the majority of 

participants earn between 40,001 -60,000 (32.5%, n = 198), 

followed by those earning in the range of 20,000 -40,000 

(16.9%, n = 103) and 60,001 -80,000 (18%, n = 110). A 

smaller proportion of respondents report earning more than 

100,000 (9%, n = 55) (M = 3.52, SD = 1.354). These results 

indicate a broad spectrum of income levels, with the most 

respondents earning within the mid -range salary bracket. 

Furthermore, higher levels of education are likely linked with 

higher income levels, suggesting that individuals with 

advanced degrees or qualifications are more likely to occupy 

higher-paying roles. 

4.2.5. Job Experience 

When examining job experience, the largest proportion of 

respondents (31.3%, n = 191) have between 3 -4 years of work 

experience, followed by those with 1 -2 years (26.9%, n = 164). 

Smaller proportions report having 5 -6 years (17.5%, n = 107), 

7-8 years (13.9%, n = 85), or 8-10 years or more (10.3%, n = 

63) of experience (M = 2.50, SD = 1.300). This suggests that 

the majority of participants are relatively early in their careers, 

with a significant proportion having 1 -4 years of experience. 

This trend is consistent with the assumption that younger 

individuals, who tend to have less job experience, are more 

engaged with technological advancements like AI. 

 

Table 4.2. Demographics of Participants 

 

5. Results 

5.1. Reliability and Validity 

SPSS and Smart PLS was used to test data reliability. The 

reliability and validity analysis of the constructs reveals 

strong internal consistency and robust measurement 

properties demonstrated by the Cronbach’s alpha, Composite 

Reliability (CR), and Average Variance Extracted (AVE) 

values. The Cronbach’s alpha values for all constructs range 

from 0.838 to 0.896, exceeding the accepted threshold of 0.7. 

This indicates excellent internal consistency, signifying that 

the items within each construct are consist ently measuring the 

same underlying concept. High Cronbach's alpha values 

suggest that the measurement scales are reliable and provide 

consistent results across different samples. These results 

indicate that the scales used to measure each construct are 

reliable, ensuring the validity of the findings. The Composite 

Reliability (CR) values for all constructs fall between 0.863 

and 0.917, further supporting the internal consistency of the 

constructs. CR is a more robust indicator of reliability in the 

context of structural equation modeling, as it considers the 

error variance of the individual items. Values exceeding the 

threshold of 0.7 suggest strong internal consistency and 

reliability, confirming that the constructs measured in this 

study are stable and dependable. The Average Variance 

Extracted (AVE) values for the constructs range from 0.678 

to 0.788, all of which are above the recommended threshold 

of 0.5, indicating good convergent validity. AVE is an 

indicator of the amount of variance captured by the items 

within a construct. Higher AVE values signify that the 

construct is well represented by its indicators and that the 

items share significant common variance. These results shows 

that the constructs measured are valid, as the items are 

sufficiently capturing the intended latent constructs. The 

factor loadings for the items across all constructs are strong, 

ranging from 0.805 to 0.896, and all exceed the minimum 

threshold of 0.7. Standardized factor loadings reflect the 

strength of the relationship between the observed variables 

(items) and the latent constructs they represent. Higher factor 

loadings indicate a strong connection between the items and 

their respective constructs, further confirming that the items 

are valid indicators of the latent constructs they measure. 

Table 5.1 shows reliability and validity. 

 

Table 5.1. Reliability and Validity 

 

 

The Innovation Preference construct has factor loadings 

ranging from 0.835 to 0.862 for its items (IP1 = 0.862, IP2 = 

0.835, IP3 = 0.843, IP4 = 0.858). These values reflect a strong 

relationship between the items and the construct, and the 

corresponding t-values were highly significant, further 

validating the robustness of the measurement model. The 

Cronbach’s alpha for Innovation Preference is 0.884, the 

Composite Reliability is 0.912, and the Average Variance 

Extracted is 0.721, all of which suggest high reliability and 

convergent validity for this construct. 

The Social Influence construct has factor loadings ranging 

from 0.856 to 0.872 (SI1 = 0.870, SI2 = 0.872, SI3 = 0.856), 

indicating that the items are strong and consistent indicators 

of the latent construct. The corresponding Cronbach’s alpha 

for Social Influence is 0.870, the Composite Reliability is 

0.900, and the AVE is 0.750, all of which reflect high internal 

consistency and convergent validity. 



 

35 

For Performance Expectancy, the factor loadings ranges 

from 0.825 to 0.886 (PE1 = 0.886, PE2 = 0.825, PE3 = 0.826). 

The Cronbach’s alpha for Performance Expectancy is 0.840, 

the Composite Reliability is 0.883, and the Average Variance 

Extracted is 0.715, suggesting strong internal consistency and 

convergent validity. 

For Attitude, the factor loadings for the items (AT1 = 0.835, 

AT2 = 0.884, AT3 = 0.869) are all above the 0.7 threshold, 

indicating a strong relationship between the items and the 

construct. The Cronbach’s alpha for Attitude is 0.880, the 

Composite Reliability is 0.897, and the Average Variance 

Extracted is 0.744, suggesting excellent reliability and good 

convergent validity. 

For Anxiety, the factor loadings are slightly lower 

compared to other constructs, ranging from 0.817 to 0.862 

(A1 = 0.862, A2 = 0.856, A3 = 0.817). Despite this, the 

Cronbach’s alpha is 0.838, the Composite Reliability is 0.882, 

and the Average Variance Ex tracted is 0.678, indicating 

acceptable reliability and convergent validity for this 

construct. 

The Intention construct has factor loadings ranging from 

0.805 to 0.839 (I1 = 0.839, I2 = 0.827, I3 = 0.805), with 

Cronbach’s alpha at 0.896, Composite Reliability at 0.863, 

and Average Variance Extracted at 0.714, which suggests 

strong internal consistency and convergent validity for this 

construct. Firm Reputation construct has the highest factor 

loadings, ranging from 0.879 to 0.898 (FR1 = 0.898, FR2 = 

0.879, FR3 = 0.887), with Cronbach’s alpha of 0.895, 

Composite Reliability of 0.917, and Average Varian ce 

Extracted of 0.788. These values indicate excellent reliability 

and good convergent validity for Firm Reputation. 

The results of the reliability and validity analyses shows 

that the constructs are both reliable and valid. The Cronbach’s 

alpha, Composite Reliability, and Average Variance Extracted 

values all exceed the recommended thresholds, confirming 

that the constructs are robust and consistent. The factor 

loadings and t-values further validate the strong relationships 

between the items and their respective constructs.  

 

Table 5.2. Mean, SD and Correlation 

 

 

The correlations among the constructs are displayed in 

Table 5.2. The Innovation Preference construct shows 

significant positive correlations with Social Influence (r = 

0.305, p < 0.01), Attitude (r = 0.402, p < 0.01), Intention (r = 

0.382, p < 0.01), and Firm Reputation (r = 0.290, p < 0.01). A 

negative correlation is observed with Anxiety (r = -0.177, p < 

0.01), indicating that higher innovation preference is 

associated with lower anxiety levels. Social Influence is 

positively correlated with Innovation Preference (r = 0.305, p 

< 0.01), Attitude (r = 0.285, p < 0.01), Intention (r = 0.360, p 

< 0.01), and Firm Reputation (r = 0.153, p < 0.01). A negative 

correlation is observed with Anxiety (r = -0.148, p < 0.01), 

suggesting that social influence has a stronger posit ive impact 

on attitudes and intentions, while also negatively correlating 

with anxiety. 

The Performance Expectancy (PE) construct shows 

moderate positive correlations with Innovation Preference (r 

= 0.178, p < 0.01), Attitude (r = 0.179, p < 0.01), Intention (r 

= 0.434, p < 0.01), and Firm Reputation (r = 0.171, p < 0.01). 

It exhibits a negative correlation with Anxiety (r = -0.206, p 

< 0.01), indicating that higher performance expectancy is 

linked to more favorable attitudes and intentions, as well as 

lower anxiety levels. Attitude is positively correlated with 

Innovation Preference (r = 0.40 2, p < 0.01), Social Influence 

(r = 0.285, p < 0.01), Performance Expectancy (r = 0.179, p < 

0.01), Intention (r = 0.315, p < 0.01), and Firm Reputation (r 

= 0.273, p < 0.01). A negative relationship is found with 

Anxiety (r = -0.136, p < 0.01), highlighting the importance of 

attitude in fostering stronger intentions and a positive 

perception of the firm. The Intention construct shows strong 

positive correlations with Innovation Preference (r = 0.382, p 

< 0.01), Social Influence (r = 0.360, p < 0.01), Perfo rmance  

Expectancy (r = 0.434, p < 0.01), and Attitude (r = 0.315, p < 

0.01). A negative correlation is also observed with Anxiety (r 

= -0.422, p < 0.01), suggesting that higher intentions to act 

are linked to lower anxiety levels. Anxiety has negative 

correlations with all other constructs, notably Innovation 

Preference (r = -0.177, p < 0.01), Performance Expectancy (r 

= -0.206, p < 0.01), Attitude (r = -0.136, p < 0.01), Intention 

(r = -0.422, p < 0.01), and Firm Reputation (r = -0.165, p < 

0.01), indicating that higher anxiety levels are associated with 

lower preferences, performance expectations, attitudes, 

intentions, and perceptions of firm reputation. Firm 

Reputation is positively correlated with Innovation 

Preference r = 0.290, p < 0.01), Social Influ ence (r = 0.153, p 

< 0.01), Performance Expectancy (r = 0.171, p < 0.01), 

Attitude (r = 0.273, p < 0.01), and Intention (r = 0.299, p < 

0.01). A negative correlation is observed with Anxiety (r = -



 

36 

0.165, p < 0.01), suggesting that a higher perception of firm 

reputation is linked to more positive responses across the 

other constructs and lower anxiety levels. All correlations are 

statistically significant at the 0.01 level (2 -tailed), 

highlighting the robustness and reliability of these 

relationships. 

5.2. Multicollinearity Check 

The multicollinearity of the constructs was assessed by 

examining the Tolerance and Variance Inflation Factor (VIF) 

values. Tolerance values greater than 0.1 and VIF values less 

than 10 generally indicate that multicollinearity is not a 

concern. The Innovation Preference construct shows a 

Tolerance value of 0.759 and a VIF of 1.318, indicating no 

significant multicollinearity with other constructs. For Social 

Influence, the Tolerance value is 0.868 and the VIF is 1.153, 

suggesting that it also does not suffer from multicollinearity. 

The Performance Expectancy construct has a Tolerance value 

of 0.918 and a VIF of 1.090, both of which are well within the 

acceptable range, indicating no multicollinearity issues.  

The Attitude construct has a Tolerance value of 0.779 and 

a VIF of 1.284, further supporting the absence of 

multicollinearity. The Anxiety construct shows a Tolerance 

value of 0.920 and a VIF of 1.086, suggesting that it is not 

affected by multicollinearity. Finally, Firm Reputation 

exhibits a Tolerance value of 0.868 and a VIF of 1.152, also 

indicating no multicollinearity concerns. The Tolerance and 

VIF values in Table 5.3 for all constructs are well within 

acceptable thresholds, indicating that multicoll inearity is not 

a significant issue in this dataset. 

 

Table 5.3. Multicollinearity Check 

 

5.3. Testing Direct Effects 

The regression analysis reveals several key insights into the 

factors influencing employees' intention to use AI. The model, 

which includes eight independent variables—Job Experience, 

Social Influence, Performance Expectancy, Gender, 

Innovation Preference, Qualification, Salary, and Age—

explains about 36.7% of the variance in employees' intention 

to use AI, as indicated by the R² value of 0.367. This suggests 

that while the predictors explain a significant portion of the 

variance, other unaccounted factors may also contribute to AI 

adoption intentions. The Adjusted R² value of 0.359, which 

accounts for the number of predictors in the model, further 

supports the model’s fit. In terms of overall significance, the 

ANOVA test yields an F-statistic of 43.568 and a p-value of 

0.000, which confirms the model is statistically significant 

and that the relationship between the predictors and the 

dependent variable (AI usage intention) is not due to chance. 

The regression results in Table 5.4 highlight the significant 

predictors of AI usage intention. The analysis of the direct 

effects provides a comprehensive understanding of how 

various factors influence an individual’s intention (IN) to use 

or adopt a particular system. The findings reveal that 

Performance Expectancy (PE) exerts the strongest positive 

influence on intention, with a unstandardized coefficient (B) 

of 0.364 and a highly significant p -value (p < 0.001). This 

indicates that individuals who believe that the system will 

improve their job performance or productivity are much more 

likely to form an intention to use it. The strong t -value (10.396) 

further reinforces the robustness of this effect. Similarly, 

Social Influence (SI) demonstrates a significant and positive 

relationship with intention (B = 0.244, p < 0.001), suggesting 

that individuals who perceive encouragement, support, or 

expectations from others (such as peers, supervisors, or social 

networks) are more inclined to intend to use the system. This 

aligns with theories emphasizing the power of social norms 

and peer pressure in shaping technology adoption behaviors. 

Another important factor, Innovation Preference (IP), also 

shows a significant and positive effect on intention (B = 0.275, 

p < 0.001). This finding implies that individuals who are more 

open to trying new ideas, technologies, or innovations are 

more likely to form a strong intention to use the system. The 

positive relationship indicates that fostering a culture of 

innovation can enhance adoption intentions among users. On 

the other hand, Age exhibits a negative and significant effect 

on intention (B = -0.198, p = 0.042). This suggests that as 

individuals age, their intention to adopt or use the system 

slightly decreases. While the effect is not as strong as other 

variables, it highlights that younger individuals tend to have 

higher adoption intentions, possibly due to greater familiarity 

with technology, less resistance to change, or higher 

perceived adaptability. Conversely, several demographic 

factors—Gender, Salary, Qualification, and Job Experience—

do not significantly predict intention. Gender shows a small, 

positive but non-significant effect (B = 0.110, p = 0.206), 

indicating that being male or female does not substantially 

influence adoption intentions in this sample. Similarly, Salary 

(B = 0.064, p = 0.129) and Job Experience (B = 0.059, p = 

0.238) also have non-significant effects, suggesting that an 

individual’s income level or years of experience do not 

meaningfully shape their intention to use the system. 

Furthermore, Qualification has a small, negative but non -

significant effect (B = -0.048, p = 0.291), indicating that 

educational background does not play a significant role in 

forming intention in this context. 

Overall, the model explains approximately 36.7% of the 

variance in intention (R² = 0.367), which is a moderate level 

of explanatory power for a behavioral model. The F-statistic 

(43.568, p < 0.001) indicates that the overall model is 

statistically significant, meaning that the combination of 

predictors provides a meaningful explanation for variations in 

intention. In summary, the findings highlight that fostering 

perceptions of usefulness (performance expectancy), 

leveraging social influence, and encouragin g openness to 

innovation are the key factors in driving intention to adopt a 

system, while demographic characteristics such as gender, 

salary, qualification, and job experience play a much smaller 

or negligible role. 

 

 

 

 

 



 

37 

Table 5.4. Direct Effects Testing 

 

 

5.4. Testing Mediating Effects 

The mediation analysis reveals significant mediating 

effects for all three paths, demonstrating that Attitude acts as 

a mediator between the independent variables (Innovation 

Preference, Social Influence, and Performance Expectancy) 

and Intention. 

5.4.1. Attitude Mediation Between Innovation Preference 

and Intention 

The mediating role of Attitude (AT) in the relationship 

between Innovation Preference (IP) and Intention (IN) was 

examined using hierarchical regression and bootstrapped 

mediation analysis shown in Table 5.5. In the first stage, 

regression results demonstrated that IP had a statistically 

significant and positive effect on Attitude (β = 0.4325, SE = 

0.0383, t = 11.290, 95% CI [0.3572, 0.5077], p < .001). This 

suggests that individuals with higher levels of innovation 

preference tend to form more favorable attitudes. The model 

including IP and the control variables (age, gender, salary, 

education, and job experience) explained approximately 

18.63% of the variance in Attitude (R² = 0.1863), and the 

model was statistically significant (F = 23.003). 

In the second stage, the dependent variable Intention (IN) 

was regressed on both the independent variable IP and the 

mediator AT, along with the same control variables. The 

results revealed that IP remained a significant predictor of 

Intention (β = 0.3524, SE = 0.0422, t = 8.344, 95% CI [0.2695, 

0.4354], p < .001), and Attitude also significantly predicted 

Intention (β = 0.1817, SE = 0.0408, t = 4.454, 95% CI [0.1016, 

0.2618], p < .001). The inclusion of Attitude in the model led 

to a reduction in the direct effect of IP on Intention, which is 

indicative of a partial mediation effect. This model accounted 

for 22.16% of the variance in Intention (R² = 0.2216), and the 

overall model fit was statistically significant (F = 24.477). 

 

Table 5.5. Mediating Role of AT between IP and IN 

 

 

To further validate the mediation, a bootstrapping analysis 

with 5,000 resamples was performed to estimate the indirect 

effect of IP on IN through Attitude shown in Table 5.6. The 

indirect effect was found to be statistically significant 

(indirect effect = 0.0786, SE = 0.0207, 95% CI [0.0392, 

0.1205], p < .001), as the confidence interval did not include 

zero. This provides robust support for the mediating role of 

Attitude. The findings suggest that individuals who have a 

stronger preference for innovation are more likely to develop 

a positive attitude, which subsequently enhances their 

intention. Therefore, Attitude acts as a crucial psychological 

mechanism that partially explains how innovation preference 

translates into behavioral intentions. 

 

Table 5.6. Indirect Effect of IP on IN through AT 

 

 

5.4.2. Attitude Mediation Between Social Influence and 
Intention 

The study further investigated the mediating effect of 

Attitude (AT) in the relationship between Social Influence (SI) 



 

38 

and Intention (IN) shown in Table 5.7. In the first regression 

model, SI significantly predicted Attitude (β = 0.3684, SE = 

0.0371, t = 9.938, 95% CI [0.2955, 0.4412], p < .001), 

indicating that greater perceived social influence is associated 

with more positive attitudes. This model, which also included 

the control variables (age, gender, salary, education, and job 

experience), accounted for 18.79% of the variance in Attitude 

(R² = 0.1879) and was statistically significant (F = 23.200).  

In the second model, which regressed Intention on both SI 

and AT (while controlling for the same demographic factors), 

both predictors were found to be significant. Social Influence 

continued to significantly predict Intention (β = 0.2787, SE = 

0.0421, t = 6.623, 95% CI [0.1961, 0.3612], p < .001), while 

Attitude also significantly contributed to Intention (β = 

0.1813, SE = 0.0411, t = 4.409, 95% CI [0.1009, 0.2616], p 

< .001). Notably, the coefficient for SI decreased when 

Attitude was included in the model, suggesting partial 

mediation. This model explained 20.35% of the variance in 

Intention (R² = 0.2035), and the overall model was 

statistically significant (F = 21.842). 

 

Table 5.7. Mediating Role of AT between SI and IN 

 
 

To confirm the mediation effect, a bootstrapping procedure 

with 5,000 resamples was used to test the indirect effect of SI 

on IN via AT shown in Table 5.8. The bootstrapped indirect 

effect was statistically significant (indirect effect = 0.0668, 

SE = 0.0156, 95% CI [0.0390, 0.1001]), as the confidence 

interval did not include zero. These results provide strong 

evidence for the mediating role of Attitude in the relationship 

between Social Influence and Intention. In essence, the 

findings suggest that individuals who perceive higher levels 

of social influence are more likely to develop positive 

attitudes, which in turn increase their intention, thereby 

supporting the hypothesis of partial mediation. 

 

Table 5.8. Indirect Effect of SI on IN through AT 

 
 

5.4.3. Attitude Mediation Between Performance 
Expectancy and Intention 

The mediation path from Performance Expectancy to 

Intention to Use AI through Attitude is shown in Table 5.9. 

The mediation analysis examined whether Attitude (AT) 

mediates the relationship between Performance Expectancy 

(PE) and Intention (IN). In the first regression model, PE 

significantly predicted Attitude (β = 0.2528, SE = 0.0407, t = 

6.213, 95% CI [0.1728, 0.3327], p < .001), indicating that 

individuals who perceive greater usefulness or benefits from 

the system tend to have more favorable attitudes toward its 

use.  

The model, which controlled for demographic variables 

(age, gender, salary, education, and job experience), 

accounted for 13.86% of the variance in Attitude (R² = 0.1386) 

and was statistically significant (F = 15.510). In the second 

regression model, Intention was regressed on both PE and 

Attitude while controlling for the same covariates.  

Both PE (β = 0.2073, SE = 0.0440, t = 4.710, 95% CI 

[0.1209, 0.2936], p < .001) and Attitude (β = 0.1816, SE = 

0.0415, t = 4.373, 95% CI [0.0998, 0.2634], p < .001) 

remained significant predictors of Intention. The inclusion of 

Attitude in the model slightly reduced the direct effect of PE 

on Intention, indicating a partial mediation. This model 

explained 17.19% of the variance in Intention (R² = 0.1719) 

and was statistically significant (F = 17.973). 

Table 5.9. Mediating Role of AT Between PE and IN 

 



 

39 

To formally test the mediating effect, a bootstrapping 

procedure with 5,000 samples was used. The results 

confirmed a statistically significant indirect effect of PE on 

Intention via Attitude shown in Table 5.10 (indirect effect = 

0.0459, SE = 0.0123, 95% CI [0.0236, 0.0718]), as the 

confidence interval did not include zero. These findings 

provide robust evidence that Attitude partially mediates the 

relationship between Performance Expectancy and Intention. 

In summary, individuals who perceive higher useful ness of 

the system develop more positive attitudes, which in turn 

enhance their behavioral intention to use it. 

 

Table 5.10. Indirect Effect of PE on IN through AT 

 

5.5. Testing Moderating Effects 

The moderation analysis explores the interaction effects 

between the independent variables (Innovation Preference, 

Social Influence, and Performance Expectancy) with Anxiety 

on Attitude, and the interaction between Attitude and Firm 

Reputation on Intention and is shown by Table 5.11 and 5.12. 

A hierarchical multiple regression was conducted to examine 

the influence of demographic variables (age, gender, salary, 

qualification, job experience), main predictors (IP, SI, PE), 

the moderator (AN), and their interactions on the dependent 

variable (AT). 

The moderation analysis results examine how Anxiety (AN) 

moderates the relationship between Innovation Preference  

(IP), Social Influence (SI), and Performance Expectancy (PE) 

on Intention (IN), while controlling for demographic 

variables: Age, Gender, Salary, Qualification, and Job 

Experience. Starting with the control variables across the 

models, Age consistently shows a significant negative effect 

on Intention across all models (e.g., Model 4: B = -0.220, p < 

0.001), indicating that younger individuals are more likely to 

have higher Intention. Gender also shows a significant 

negative relationship with Intention (e.g., Model 4: B = -0.268, 

p < 0.001), suggesting that one gender group (likely females 

if coded 0/1) reports higher Intention. Other control variables, 

including Salary, Qualification, and Job Experience, are not 

significant predictors in any model, indicating minimal 

influence on Intention.  

Moving to the predictors and interaction effects, Model 1 

serves as the baseline with only control variables, showing a 

low explained variance (R² = 0.014). When the main 

predictors are added in Model 2, the model’s explanatory 

power significantly increases (R² = 0.217, Adjusted R² = 

0.218, Sig. F Change = 0.000***). Specifically, IP (B = 0.362, 

p < 0.001), SI (B = 0.157, p < 0.001), and PE (B = 0.090, p < 

0.001) are all significant positive predictors of Intention, 

indicating that higher innovation preference, stronger social 

influence, and higher performance expectancy lead to greater 

intention. In Model 3, Anxiety (AN) is added as a predictor 

and has a small negative, non-significant effect (B = -0.041, 

p = n.s.), suggesting no direct effect on Intention. However, 

IP (B = 0.357, p < 0.001), SI (B = 0.153, p < 0.001), and PE 

(B = 0.084, p < 0.01) remain significant predictors, indicating 

their robust direct effects even when accounting for anxiety. 

Model 4 includes the interaction (moderation) terms to test 

for moderation by Anxiety. Here, the interaction of IP*AN is 

not significant (B = 0.036, p = n.s.), indicating that Anxiety 

does not moderate the relationship between Innovation 

Preference and Intention. Similarly, the interaction term for 

PE*AN is not significant (B = -0.003, p = n.s.), suggesting no 

moderation effect of Anxiety on the Performance 

Expectancy–Intention relationship. However, the interaction 

term for SI*AN is significant (B = 0.185, p < 0.001), 

indicating that Anxiety does moderate the relationship 

between Social Influence and Intention. Specifically, the 

positive effect of Social Influence on Intention is stronger for 

individuals with higher levels of Anxiety. The overall model 

fit improves across steps, with R² increasing from 0.014 

(Model 1) to 0.258 (Model 4). The Adjusted R² in Model 4 is 

0.253, indicating that approximately 25% of the variance in 

Intention is explained by the final model. The F value for 

Model 4 is 14.709, and the Sig. F Change remains significant 

(p < 0.001), confirming the statistical significance of adding 

the interaction terms. 

 

Table 5.11. Moderation of Anxiety 

 



 

40 

For Firm Reputation as a moderator, In the first model, only 

control variables were included —Age, Gender, Salary, 

Qualification, and Job Experience—to account for their 

baseline effects. Among these, Age showed a statistically 

significant negative effect, meaning that as individuals get 

older, their scores on the dependent variable tend to decrease 

slightly. The other control variables, including Gender, Salary, 

Qualification, and Job Experience, did not show significant 

effects at this stage. The explanato ry power of this initial 

model was quite low, accounting for just 3.3% of the variance 

in the dependent variable. 

In Model 2, the main predictor, AT, was introduced into the 

regression. The inclusion of AT had a substantial impact, with 

AT showing a strong and highly significant positive effect on 

the dependent variable. This indicates that individuals with 

higher attitudes tend to score higher on the outcome measure, 

confirming the importance of AT as a predictor. The effect of 

Age remained significant and negative, although slightly 

weaker. The addition of AT also markedly increased the 

explained variance, more than tripling it to 13.2%, 

demonstrating that AT is a key factor influencing the 

dependent variable. Other control variables continued to show 

no significant effects. 

Model 3 extended the analysis by adding the proposed 

moderator, FR, as an additional predictor. FR also showed a 

significant positive effect, meaning that higher FR is 

associated with higher scores on the dependent variable, 

independent of AT. This suggests that FR itself is an important 

contributor to the outcome. Interestingly, the positive effect 

of AT was somewhat reduced in this model, likely because 

some of the variance explained by AT overlaps with FR. In 

addition to the main predictors, Salary became a significant 

positive predictor in this step, indicating that individuals with 

higher salaries tend to have higher outcome scores. The 

explanatory power of the model improved further to 17.6%, 

suggesting that including FR and Salary enhances the 

understanding of what drives the dependent variable. 

Finally, Model 4 introduced the interaction term between 

AT and FR to formally test the moderation hypothesis—

whether the effect of AT on the outcome changes depending 

on the level of FR. The interaction term was statistically 

significant and negative, which means that FR moderates the 

relationship between AT and the dependent variable in a way 

that weakens the positive impact of AT when FR is high. Put 

simply, while AT generally has a positive effect on the 

outcome, this effect diminishes as FR increases. Both AT and 

FR individually continue to have significant positive effects 

on the dependent variable, indicating their independent 

contributions. Salary also remains a significant positive 

predictor, while Age continues to exert a small negative effect. 

The overall explained variance of this final model is 18.3%, 

representing the highest level of variance accounted for 

among the models tested and confirming that including the 

interaction term provides a more complete explanation of the 

data.  

In summary, the analysis reveals that Age negatively 

predicts the outcome, whereas AT and FR both positively 

predict it. The moderation analysis shows a more complex 

relationship where FR reduces the strength of the positive 

association between AT and the outcome. This suggests that 

interventions or interpretations considering the effect of AT 

on the dependent variable should take into account the level 

of FR, as the benefit of a positive attitude may be less 

pronounced when FR is high. This nuanced finding  highlights 

the importance of examining interactions between predictors 

to fully understand their effects on outcomes. 

 

Table 5.12. Moderation of Firm Reputation 

 

5.6. Hypothesis Results 

The analysis reveals significant relationships among key 

factors influencing individuals’ intentions to adopt AI. 

Hypotheses H1, H2, and H3 were supported, indicating that 

innovation preference, social influence, and performance 

expectancy each have a sign ificant effect on AI usage 

intention. This suggests that individuals who value innovation, 

are influenced by social pressures, or expect AI to enhance 

their performance are more likely to develop an intention to 

use AI technologies. Additionally, the mediation hypotheses 

H4a, H4b, and H4c were supported, demonstrating that 

attitude plays a mediating role in the relationships between 

innovation preference, social influence, and performance 

expectancy, and AI usage intention. 

Individuals with a positive attitude toward AI are more 

likely to translate these factors into stronger intentions to 

adopt AI. Regarding the moderation effects of anxiety, 



 

41 

hypotheses H5a, H5b, and H5c explored whether anxiety 

moderates the relationships between innovation preference, 

social influence, and performance expectancy, respectively, 

on attitude. The results show that H5a and H5c were not 

supported, indicating that anxiety does not moderate the 

relationship between innovation preference and attitude, nor 

between performance expectancy and attitude.  

This may be because individuals who prefer innovation are 

more open to new technologies and less influenced by anxiety, 

while beliefs about AI’s performance benefits remain a strong, 

rational factor unaffected by anxiety.  

In contrast, H5b was supported, revealing that anxiety 

moderates the relationship between social influence and 

attitude. Specifically, individuals experiencing anxiety are 

more likely to be influenced by social factors in shaping their 

attitude toward AI. This finding highlights that anxiety 

amplifies the effect of social influence, as anxious individuals 

may seek guidance or reassurance from others when 

considering AI adoption. Finally, hypothesis H6 was 

supported, confirming that firm reputation moderates the 

relationship between attitude and AI usage intention. This 

suggests that a strong and positive firm reputation enhances 

the influence of a user’s positive attitude on their intention to 

use AI. Overall, these results demonstrate that while anxiety 

plays a significant moderating role in the social influence -

attitude link, it does not significantly impact the relationships 

involving innovation preference or performance expectancy. 

The varying influence of anxiety underscores the complex 

interplay between cognitive, emotional, and social factors in 

shaping technology adoption behavior. Table 5.13 shows the 

hypothesis results. 

 

Table 5.13. Hypothesis Results 

 

6. Conclusion 

6.1. Discussion & Conclusion 

The findings of this study highlight the complex 

psychological factors that influence employees' intention to 

use Artificial Intelligence (AI) in the workplace. Performance 

expectancy, social influence, and innovation preference were 

all found to have significant positive effects on employees' 

intention to use AI, reinforcing previous research in 

technology adoption. Specifically, performance expectancy 

emerged as a powerful predictor, suggesting that employees 

are more likely to embrace AI when they percei ve it as a tool 

that will enhance their job performance and efficiency. This 

aligns with the Technology Acceptance Model (TAM), which 

underscores the importance of perceived usefulness in 

technology adoption [39]. 

Social influence also played a critical role in shaping 

employees' attitudes toward AI. Employees who perceive that 

influential figures in their workplace support the use of AI are 

more likely to adopt the technology themselves. This finding 

supports social-cognitive theory, which suggests that 

individuals are influenced by the behaviors and opinions of 

others around them [28]. The positive relationship between 

innovation preference and AI usage intention further 

emphasizes the importance of individual open ness to new 

technologies. Employees who are more willing to embrace 

technological changes are naturally more inclined to accept 

AI in their work environment, supporting the idea that early 

adopters play a crucial role in the diffusion of innovations. 

Another significant finding was the mediating role of attitudes 

between the influencing factors (performance expectancy, 

social influence, and innovation preference) and AI usage 

intention. This suggests that while the perception of AI's 

usefulness and social endorsement are critical, employees’ 

attitudes toward AI ultimately determine whether they will 

adopt the technology. This highlights the need for 

organizations to foster a positive attitude toward AI through 

targeted communication, training, and support me chanisms 

[35]. 

The study also explored the moderating roles of anxiety 

and firm reputation, revealing nuanced insights. Anxiety was 

found to moderate only the relationship between social 

influence and attitude. Specifically, employees experiencing 

higher levels of anxiety were more likely to be influenced by 

social factors, suggesting that anxious individuals may rely 

on others’ opinions for reassurance when forming their views 

about AI. In contrast, anxiety did not moderate the 

relationships between innovation preference or performance 

expectancy and attitude. This could be because individuals 

with a strong preference for innovation tend to approach new 

technologies with excitement and openness, reducing the 

impact of anxiety. For instance, an employee who enjoys 

experimenting with new tools may continue to have a positive 

attitude toward AI despite feeling anxious about uncertainties. 

Similarly, the perceived usefulness of AI—its expected ability 

to improve performance—likely remains a rational, goal-

driven factor that is less susceptible to emotional barriers like 

anxiety. For example, an employee may recognize that AI will 

streamline tasks or provide valuable insights, and this rational 

benefit may outweigh any emotional hesitation they feel. 

Additionally, firm reputation was found to positively 



 

42 

moderate the relationship between attitude and AI usage 

intention. Employees who perceived their organization as 

reputable and trustworthy [37] were more likely to translate a 

positive attitude toward AI into actual intention to use it. This 

underscores the significant role of organizational culture and 

leadership in shaping employees' willingness to embrace AI 

technologies. For instance, if an organization is known for 

ethical practices, data protection, and employee well -being, 

individuals may feel more confident in adopting AI solutions 

endorsed by such a firm. 

Demographic factors (such as age, gender, qualification, 

salary, and job experience) were controlled for in the analysis, 

ensuring that the observed relationships reflect the unique 

effects of the study variables without confounding influences. 

Overall, these findings suggest that while anxiety may not 

universally dampen enthusiasm for AI, it does make 

employees more sensitive to the opinions of others, 

highlighting the need for supportive social environments 

when introducing AI in the workplace. Furthermore, a strong 

firm reputation can strengthen the link between positive 

attitudes and behavioral intentions, emphasizing the 

importance of organizational trust and leadership in 

technology adoption processes. 

6.2. Theoretical Implications 

This research offers significant theoretical insights into the 

adoption of Artificial Intelligence (AI) by applying the 

Unified Theory of Acceptance and Use of Technology 

(UTAUT) as the foundation. The study demonstrates that the 

core constructs of UTAUT—performance expectancy, effort 

expectancy, social influence, and facilitating conditions—are 

critical to understanding AI adoption in a developing country 

context, specifically Pakistan. By adding psychological 

variables such as anxiety and attitude, the research expands 

the UTAUT model, highlighting how emotional and cognitive 

factors influence AI adoption. A notable theoretical 

contribution is the identification of the mediating role of 

attitude in AI adoption. The research shows that not only does 

attitude toward AI influence an individual’s intention to adopt 

AI, but it also serves as a key mediator between UTAUT  

constructs and the adoption process. This indicates that the 

perception of AI, influenced by anxiety or social influences, 

plays a pivotal role in how employees view and accept AI 

technologies. Additionally, the study reveals the significant 

impact of psychological factors like anxiety, which has not 

been adequately explored within the UTAUT framework , 

thereby offering a more nuanced understanding  of AI 

adoption. 

Furthermore, the research highlights the moderating role of 

firm reputation and anxiety in AI adoption. These external 

factors significantly affect how employees perceive and 

respond to AI technologies. This finding suggests that future 

studies should explore how such contextual factors interact 

with UTAUT constructs in different cultural and 

organizational settings. The study also advocates for an 

expanded UTAUT model, incorporating emotional and 

organizational factors, to provide a more comprehensive 

understanding of technology adoption, particularly in 

developing countries like Pakistan. 

6.3. Practical Implications 

This research presents practical insights for organizations 

in Pakistan, and similar developing countries, aiming to adopt 

AI technologies. One of the most critical factors influencing 

AI adoption is performance expectancy. Organizations should 

emphasize how AI can enhance job performance and increase 

productivity. This can be achieved through showcasing AI’s 

practical benefits, conducting pilot projects, and sharing 

success stories that demonstrate AI’s value in a way that 

resonates with employees. 

Social influence is another powerful factor in AI adoption. 

In Pakistan, where organizational structures often follow 

hierarchical norms, leadership support is essential for AI 

acceptance. Senior management must be the first to advocate 

for AI, demonstrating its benefits and addressing concerns 

about job displacement. Peer influence also plays a role, so 

organizations can encourage early adopters or AI advocates to 

influence their colleagues, building broader acceptance. 

Anxiety, identified as a significant barrier to AI adoption, 

needs to be addressed through comprehensive training and 

support programs. Many employees may view AI with 

skepticism, fearing job loss or the complexity of the 

technology. To alleviate these con cerns, organizations should 

focus on providing clear, accessible training and ensuring 

ongoing support for employees. Furthermore, open 

communication about how AI will be integrated into work 

processes can help employees feel more secure and willing to 

adopt the technology. 

The reputation of the organization also significantly 

impacts AI adoption. Organizations with a positive reputation 

for trust, transparency, and ethical practices are more likely to 

experience higher acceptance rates for AI technologies. In 

Pakistan, where organizational trust and loyalty are crucial, 

companies should prioritize building and maintaining a strong 

reputation. This includes clear communication about how AI 

adoption will benefit both the organization and its employees. 

The study shows that demographic factors, such as age, 

education, and prior technological experience, influence AI 

adoption. Younger, more educated employees were found to 

be more receptive to AI. Organizations should consider these 

demographic differences and tailor their AI adoption 

strategies accordingly. For instance, additional support and 

training might be needed for older employees or those with 

less technical experience. 

6.4. Limitations and Future Research 

The primary limitation of this study is that data was 

collected exclusively from employees in Pakistan. This 

geographic and cultural limitation may affect the 

generalizability of the findings to other countries or regions, 

particularly those with different technological landscapes or 

cultural attitudes toward AI. Additionally, the research only 

focused on employees, which means that the perspectives of 

other stakeholders, such as managers, IT specialists, or 

customers, were not included. This limits the sco pe of the 

research in understanding the broader organizational 

dynamics influencing AI adoption. Future studies should aim 

to include a more diverse range of respondents, including 

organizational leadership, technology experts, and end -users, 

to provide a more holistic view of AI adoption. Future 

research can build on this study by exploring additional 

factors that may influence AI adoption. One such area is the 

impact of personality traits on AI usage intention. Personality 

traits, such as openness to experience or technological self-

efficacy, may play a significant role in shaping how 

individuals perceive and use AI. Investigating these 

personality-related factors could provide deeper insights into 

the variability in AI adoption among employees.  



 

43 

Furthermore, expanding the research to include other 

regions or countries with different technological landscapes 

and cultural contexts would provide a more comprehensive 

understanding of the global factors influencing AI adoption. 

Future research could examine the long-term impact of AI 

adoption on employee performance and organizational 

outcomes. This would involve a longitudinal study to assess 

how employees' attitudes and behaviors toward AI evolve 

over time, particularly as AI technologies become more 

integrated into organizational practices. Investigating the 

influence of AI on job satisfaction, productivity, and overall 

employee engagement would be valuable for organizations 

considering AI implementation. This research contributes 

valuable theoretical and practical insights into AI adoption, 

highlighting the importance of psychological, emotional, and 

organizational factors. While the findings are based on a study 

in Pakistan, they offer actionable guidance for organizations 

in similar contexts. Future research could explore additional 

factors like personality traits and the long -term effects of AI 

adoption, further enriching the understanding of AI 

integration in organizational setting. 

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