Behaviour & Information Technology
AI trust and Work Meaningfulness: Mediating Role of AI Job Crafting and
Moderating Role of Leaders’ AI-oriented Change Behavior
Submission ID
Article Type
Keywords
253045522
Research Article
AI trust, work meaningfulness, AI job crafting, le
ader’s AI-oriented change behavior, social cogni
tive theory
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AI trust and Work Meaningfulness: Mediating Role of AI Job
Crafting and Moderating Role of Leaders’ AI-oriented Change
Behavior
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Abstract: Integrating social cognitive theory, this study explores the interplay of cognitive,
behavioral, and environmental factors enabling employees to derive work meaningfulness in AI-
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employee collaboration. It examines how employees' trust in AI influences their work
meaningfulness and identifies the mechanisms and boundary conditions. A three-wave survey was
conducted among 240 high-tech employees from six Chinese technology companies. Results
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indicate that AI trust positively affects work meaningfulness. This relationship is partially mediated
by AI job crafting, suggesting that employees who trust AI are more likely to proactively modify
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their work tasks, relationships, and cognition, thereby enhancing work meaningfulness.
Furthermore, leaders’ AI-oriented change behavior positively moderates this mediation, reinforcing
the role of AI job crafting in the AI trust–work meaningfulness link. This study extends literature
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on trust, job crafting, and leadership behavior and integrates them into a unified framework,
providing novel insights into how employees adapt to AI-driven workplaces. The findings offer
practical guidance for organizations seeking to enhance work meaningfulness by fostering AI trust
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and leadership support.
Keywords: AI trust, work meaningfulness, AI job crafting, leader’s AI-oriented change behavior,
social cognitive theory
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1. Introduction
The widespread application of intelligent technology signifies that workplaces are undergoing
a profound transformation, with organizations increasingly utilizing AI to optimize resource
allocation, enhance productivity, and improve customer experience (Chowdhury et al., 2023). As a
disruptive technology, AI has evolved from being merely an auxiliary tool to becoming a
collaborative partner, redefining not only employees’ work methods and roles but also significantly
shaping their behaviors and cognitions (Li et al., 2019). In this context, the passive, traditional
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planning and control model of job design is no longer effective in motivating high-skilled employees.
Instead, enhancing employee autonomy and encouraging self-management have been proven to be
effective strategies for improving work performance (Tims et al., 2016; Parker et al., 2017). This
trend is particularly evident in technology companies, where employees tend to be younger and
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more highly educated. They exhibit greater independence and innovation awareness, emphasizing
stronger on their subjective work experiences and feelings (Rosso et al., 2010), especially self-
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actualization and the creation of social value in their work (Jia et al., 2024). Therefore, exploring
how to cultivate and sustain work meaningfulness in the era of digital intelligence is considered not
only a means to fulfill the psychological needs of the new generation of employees but also a way
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to enhance organizational performance.
Several studies examining the impact of AI on employee behavior and psychology reveal
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contradictory conclusions. Some studies found that AI may trigger job insecurity, job burnout (Kong
et al., 2023), incivility (He et al., 2024), turnover intention (Kurniawan et al., 2022; Li et al., 2019),
and reduced organizational commitment (Brougham and Haar, 2018). However, other studies have
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suggested that AI adoption has positive implications for employees, particularly high-skilled
workers, as it enhances employee performance, increases work engagement and job satisfaction,
and improves career sustainability (Kong et al., 2023). These conflicting findings may stem from a
lack of consideration of employees’ AI-related perceptions and emotional factors. As AI is highly
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interactive, and its effectiveness heavily depends on users’ abilities and behaviors, employees’
perceptions and emotions toward AI play a critical role in determining the success of AI-employee
collaboration (Glikson and Woolley, 2020; Chowdhury et al., 2022). AI trust refers to employees’
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positive perceptions toward AI, particularly their trust in its potential to influence their roles and
responsibilities. Existing research has identified individual characteristics (Deci and Ryan, 1985;
Spreitzer, 1995) and task characteristics (Hackman and Oldham, 1980) as key antecedents of work
meaningfulness. AI trust, as employees’ positive perception of AI, shares commonalities with both
individual and task characteristics. Therefore, we argue it may serve as a crucial factor influencing
employees’ work meaningfulness.
Therefore, the purpose of this study is to address this element omission by examining the
impact of employees’ AI trust on their work meaningfulness, as well as its underlying mechanisms
and boundary conditions, using social cognitive theory and coping as a framework. According to
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social cognitive theory, employees are cognitively driven and engage in self-regulatory behaviors
to adapt to technological changes, further leading to cognitive transformation. As a typical cognitive
and emotional factor representing employees’ positive perception of the technological environment,
AI trust influences AI-employee collaboration behaviors and further affects employees’ perception
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of their personal value in the workplace (Yu et al., 2023; Kong et al., 2023), therefore shaping their
work meaningfulness. Specifically, when employees have a high level of AI trust, they experience
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a stronger sense of control over their work and are more inclined to collaborate with AI. On the one
hand, the “black-box” nature of AI decision-making logic makes employees’ level of trust in AI
directly determine whether they adopt AI-generated outcomes, thereby determining the success of
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AI collaboration. On the other hand, as AI’s optimal performance requires users to provide a large
amount of task-related private data as input, a lack of AI trust heightens concerns over security,
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thereby impairing AI performance and diminishing employees’ satisfaction with AI collaboration.
Thus, AI trust not only shapes employees’ behaviors but also has profound psychological
implications. Employees with high AI trust actively adjust their work methods, job perceptions, and
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workplace relationships, consequently redefining their work tasks and roles, and moving beyond
traditional work modes. During this collaboration, employees who successfully engage in job
crafting get their competence and confidence enhanced in task execution, reinforcing their sense of
control over technology. This further deepens their sense of uniqueness and mission of human
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intelligence, thereby enhancing their work meaningfulness. Meanwhile, leadership behavior, as an
external environmental factor, fosters a supportive climate and promotes relevant policies,
facilitating employees’ job crafting and, consequently, enhancing their work meaningfulness.
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Considering the above, we propose a conceptual model that illustrate the impact of AI trust on work
meaningfulness and as well as its underlying mechanisms and boundary conditions (see Figure 1).
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AI trust
Work
meaningfulness
AI job crafting
Leader’s AI-oriented
change behavior
Figure 1
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Proposed conceptual model
This study adds several theoretical insights to the existing literature. First, by adopting social
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cognitive theory, it unveils the mechanisms and pathways through which AI trust influences
employees’ work meaningfulness. This study identifies the underlying mechanisms and potential
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causal chains in this relationship and explores the interconnected effects of cognitive factors (AI
trust), environmental factors (leaders’ AI-oriented change behavior), and behavioral factors (AI job
crafting) on employees’ work cognition (work meaningfulness). Second, this study enriches existing
research on trust by revealing the relationship between AI trust and employees’ work
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meaningfulness. This study chooses AI trust as the independent variable in this study, integrating
both job characteristics (AI integrated into work) and individual factors (trust), therefore
comprehensively considers how employees’ cognitive and emotional responses to workplace
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technology usage influence their work meaningfulness. Third, this study responds to the call for
research on AI-induced changes in employees’ behavioral engagement (Liang et al., 2022; Gursoy
and Cai, 2024; Bankins, 2024), particularly in terms of adaptive behavior (Tan et al., 2024), by
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elucidating the mediating mechanism between AI trust and work meaningfulness. The findings
suggest that AI job crafting serves as a mediator in the process where high-skilled employees’ AI
trust enhances work meaningfulness, thereby expanding the pathways through which job crafting
functions as a mediator. Finally, this study responds to existing research calls to consider the
influence of leaders and higher levels of organization or team in the context of AI in organizations
(Chowdhury et al., 2022; Shaikh, 2023; Kong et al., 2023). Therefore, this study clarifies the
boundary conditions of the relationship between AI trust and work meaningfulness by revealing the
moderating role of leaders’ AI-oriented change behavior in the process through which employees’
AI trust fosters AI job crafting, ultimately enhancing work meaningfulness.
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2. Theoretical Foundation and Research Hypotheses
2.1 Social cognitive theory
Social cognitive theory (SCT) emphasizes the triadic reciprocal interaction among individual
cognition, behavior, and environmental factors (Bandura, 1989). This theory posits that individual
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actions are not only determined by internal beliefs, such as expectations and self-efficacy (Bandura,
1989), but also shaped by the surrounding environment like social support (Eisenberger et al., 2001)
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and observed interactions (Otaye-Ebede et al., 2020). SCT is widely used to explain how cognitive
mechanisms, such as trust or confidence, can influence behavioral adaptations to environmental
changes (Boudreaux et al., 2019). It is also widely employed in the interpretation and explanation
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of employees’ reaction and adaption to technology change (Compeau et al., 1999; Venkatesh et.al.,
2003; Lin and Huang, 2008).
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With the widespread application of AI within organizations, its role has shifted from auxiliary
tools to employees' work partners, profoundly changing the current career model and organizational
ecology (Broadbent, 2017). Organizations integrating AI create sociotechnical systems that aim to
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realize a “win-win” situation where employees’ productivity is boosted and technology is employed
to improve efficiency. However, this overly optimistic expectation is not easily realized, as literature
suggests that AI adoption leads to employees experiencing negative feelings such as unfairness
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(Dutta and Mishra, 2024) and insecurity (Wu et al., 2024). Although employees in technology
companies are typically highly skilled and are not at risk of having their jobs replaced by AI, they
are more likely to question the capabilities and performance of AI in assisting their job.
The introduction of social cognitive theory offers an important perspective for understanding
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how employees adapt to AI and enhance their work meaningfulness through cognitive and
behavioral changes. Under the framework of SCT, employees are more likely to collaborate
effectively with AI, and gain a greater sense of control over their tasks, thereby discovering new
sources of work meaning. Furthermore, as SCT emphasizes the impact of cognitive mechanisms on
behavior, we argue that employees with high AI trust are more likely not only try beyond traditional
work methods, but also craft their tasks, relationships and role, thereby further enhancing their work
meaningfulness from the amazing potential of human intelligence. At the same time, leadership
behavior, as an external environmental factor, can encourage employees’ job crafting by creating a
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supportive environment and implementing relevant policies, which in turn contributes to the
enhancement of employees’ work meaningfulness.
2.2 AI trust and work meaningfulness
In the field of human-machine trust, the definition proposed by Lee and See (2004) serves as
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a foundational framework for subsequent research (Hoff and Bashir, 2015). Their definition
approaches trust from an attitudinal perspective, describing it as the belief, under conditions of
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vulnerability and uncertainty, that an agent can reliably achieve desired goals. This definition
emphasizes individuals' dependence on and expectations of technology, establishing the theoretical
basis for automation trust. While automation trust has been extensively studied and applied in
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human-machine interaction research, its explanatory power for the more complex construct of “AI
trust” has gradually shown limitations. AI trust builds upon automation trust by incorporating
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considerations of AI’s autonomy and intelligence, which are particularly salient in dynamic and
complex work scenarios (Glikson and Woolley, 2020). In this study, we adopt the definition of AI
trust proposed by Chowdhury et al. (2022), which describes it as “employees’ positive perceptions
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toward AI, particularly their trust in its potential to influence their roles and responsibilities.” This
definition is particularly suited for examining AI trust in organizational contexts, as it highlights
two critical dimensions. First, AI trust is not limited to recognizing technical functionality but
extends to employees’ confidence in AI’s ability to reshape their roles and responsibilities. Second,
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it incorporates a positive cognitive dimension, reflecting trust grounded in AI’s reliability and
perceived value. In essence, AI trust represents employees’ expectation and acceptance of
technology’s empowering potential. The integrative model of AI trust proposed by Lewis and Marsh
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(2022) further enhances the understanding of its formation mechanisms by arguing that individuals’
judgments of AI trustworthiness are shaped by the quantity and type of available information, which
in turn influence their perceptions of AI’s four key characteristics: capability, predictability, integrity,
and benevolence. This framework complements Chowdhury et al.’s definition by illustrating the
pathways through which employees form positive perceptions of AI. For instance, when employees
perceive AI as capable and predictable, they are more likely to regard it as a reliable collaborator,
reinforcing trust in its application. The integration of Chowdhury et al.’s definition and Lewis and
Marsh’s model provides a comprehensive framework for understanding AI trust. While Chowdhury
et al. emphasize the outcomes of AI trust, Lewis and Marsh elucidate its antecedents and pathways
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of development. Together, they offer a rich implication for examining AI trust’s impact on employee
behaviors (e.g. AI adoption, see Yu et al., 2023; productivity, see Kong et al., 2023), psychological
outcomes (e.g. well-being, see Kong et al., 2023) and organization performance (business
performance, see Chowdhury et al., 2022; customers’ acceptance, see Chi et al., 2023).
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Influenced by globalization and technological transformation, employees in technology
companies exhibit greater independence, confidence, and innovative awareness. They not only
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expect their jobs to fulfill economic needs but also prioritize subjective experiences and feelings,
such as self-actualization and the creation of social value (Rosso et al., 2010). Therefore, fostering
and sustaining work meaningfulness has become a critical driver for enhancing performance in era
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the digital intelligence. Work meaningfulness refers to an individual's subjective evaluation of the
significance of their work, its contribution to long-term personal growth, and its alignment with
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external motivational values (Steger et al., 2012). According to Steger et al. (2012), work
meaningfulness comprises psychological meaningfulness in work, which reflects an individual’s
perception of whether their work is meaningful and valuable; meaning-making through work,
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emphasizing that work serves as a essential source of life meaning by deepening one’s
understanding of self and the surrounding world, thus fostering personal growth; and greater good
motivation, highlighting the broader positive impact of work outcomes on others, communities, and
society (Steger et al., 2012). Studies have explored the sources of work meaningfulness, identifying
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individual characteristics (e.g., intrinsic motivation and self-determination, see Deci and Ryan, 1985;
self-efficacy, see Spreitzer, 1995; proactive personality, see Kim et al., 2022) and task characteristics
(e.g., task variety, task significance, and autonomy, see Hackman and Oldham, 1980) as significant
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antecedents.
AI trust, as a psychological cognition reflecting employees’ confidence in AI’s capability,
reliability, and collaborative potential, directly influences work meaningfulness by enhancing
employees’ work control under the framework of social cognitive theory. Trust has been shown to
influence individuals’ perceived control over the environment (Katyal et al., 2022; Solberg et. al.,
2022). AI trust enables employees to view AI as a reliable partner, alleviating uncertainty and
empowering them to exercise greater control over their responsibilities (Deriu et al., 2024). When
employees feel in control, they develop a higher self-evaluation of competence, autonomy, and
impact, making them more likely to perceive their work as important and aligned with their intrinsic
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values (Faranda, 2001; Jewell and Kidwell, 2005; Morrison et al., 2005), thereby enhancing their
overall sense of work meaningfulness. Therefore, we propose the following hypothesis:
H1: AI trust is positively related to work meaningfulness.
2.3 The mediation effect of AI job crafting
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Job crafting is a strategy individuals use to proactively cope with dynamic organizational
changes, and believes it results from the interaction between the individual and situational factors
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(Petrou et al., 2015). AI job crafting is a domain-specific concept that refers to “the volitional actions
to shape, mold, and redefine one’s job in response to AI” (Li et al., 2024). Research has highlighted
that there are three primary outcomes of job crafting, which are work meaning, work identity, and
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well-being (Wrzesniewski and Dutton, 2001; Slemp and Vella-Brodrick, 2014; Rudolph et al., 2017).
Among these, job crafting has been shown to have a positive impact on work meaningfulness
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(Wrzesniewski and Dutton, 2001, 2010; Lyons, 2006; Zhang and Parker, 2019; Michaelson et al.,
2014).
Employees who engage in AI job crafting actively explore how to incorporate AI into their
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work, which boosts their confidence and competence in performing tasks (Wong et al., 2017).
Research shows that when employees feel more competent at work, they are more likely to perceive
work as meaningful (Hackman and Oldham, 1980; Dutton, 2001; Kubiak, 2022). Through AI job
crafting, employees gain a sense of mastery over technology, leading to a stronger connection with
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their work and, consequently, greater work meaningfulness. Moreover, AI always serves as an
auxiliary tool during AI job crafting, assisting employees in performing tasks more efficiently: it is
ultimately the employees who guide the direction and make decisions. This interaction highlights
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the unique power of human intelligence that technology cannot replicate— the ability to creatively
solve problems, make ethical judgments, and apply emotional and social intelligence (Gliskson and
Woolley, 2020). As employees craft their jobs using AI, they are given an opportunity to reflect on
their value in the workplace, and realize the irreplaceable nature of human capabilities. This
recognition reinforces their sense of calling and uniqueness in their roles and hence enhancing their
work meaningfulness. Therefore, we propose the following hypothesis:
H2: AI job crafting is positively related to work meaningfulness.
The integration of social cognitive theory and job crafting theory provides a comprehensive
framework for understanding how AI trust influences work meaningfulness through AI job crafting.
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SCT supports the cognitive process of AI trust leads to employees’ greater reliability and
engagement with AI. As a result, employees are more likely to explore how to collaborate with AI,
prompting them to engage in AI job crafting accordingly (Khan et al., 2020). According to the job
crafting theory, AI job crafting enables employees to modify their task content, collaboration
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relationship and perception of work. These changes influence employees’ feelings of confidence,
competence, and uniqueness (Zacher, 2016). When employees align their tasks with personal
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interests, goals, and values, it enhances their sense of meaningfulness in their work. Therefore, we
propose the following hypotheses:
H3: AI job crafting plays a mediating role between AI trust and work meaningfulness.
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2.4 The moderation effect of leader’s AI-oriented change behavior
According to social cognitive theory, individuals’ behaviors are influenced by observation and
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environmental factors (Bandura, 1989). In organizations, leaders, who hold a position of power,
become key role models for employees. Their views and behaviors towards AI greatly influence
how employees perceive and interact with AI (Wijayati et. al., 2021; Shaikh et. al., 2023). In
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response to the call for research on how leaders’ AI-related behaviors affect employee outcomes
(Liang et al., 2022; Kong et al., 2023), He et al. introduced the concept of leaders’ AI-oriented
change behavior, which refers to proactive leadership behaviors aimed at enhancing employee
efficiency by replacing outdated methods and technologies with AI-driven solutions. Compared
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with leadership that is subtle to perceive, actual leader behaviors toward AI are more effective in
supporting job crafting in the context of AI collaboration. Leaders who actively demonstrate AIoriented behaviors—such as promoting AI use, modeling AI-related practices, providing training,
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and fostering a culture open to technological innovation—can profoundly influence employees'
cognitive and behavioral responses to AI (Glikson and Woolley, 2020).
AI job crafting involves making changes or challenging existing practices, which may cause
uncertainty or hesitation of employees (Irfan et. al., 2023). In this context, leader modeling plays a
crucial role. When leaders exhibit strong AI-oriented behaviors, they create an atmosphere where
employees feel more confident in their interactions with AI and in making adjustments to their work.
Leaders who bring AI into the workplace and support its adoption provide not only the necessary
resources but also psychological reassurance that their engagement with AI is valued and supported
by the organization. This positive reinforcement increases employees' trust in AI, which in turn
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enhances their likelihood of engaging in AI job crafting. Moreover, AI-oriented change behavior is
characterized by proactivity and a transformative mindset (Ghitulescu, 2013). Leaders who
demonstrate higher levels of AI-oriented behaviors tend to be more open-minded and engage more
actively with employees about forward-thinking expectations and support of AI adoption within the
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organization. They can also allocate organizational resources and rewards to encourage employees’
interaction with AI (Li et. al., 2024). In this supportive environment, employees whose leaders are
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open to AI adoption are more likely to embrace the technology and, consequently, more willing to
engage in AI job crafting. Therefore, we propose the following hypothesis:
H4: Leaders’ AI-oriented change behavior plays a positive moderating role between AI
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trust and AI job crafting.
2.5 The moderated mediation effect
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Based on previous analysis, leaders’ AI-oriented change behavior positively moderates the
effect of AI trust on AI job crafting, and AI job crafting further mediates this effect to work
meaningfulness.
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According to social cognitive theory and job crafting theory, when leaders’ AI-oriented change
behavior is strong, employees are provided with more external support and resources to engage in
AI job crafting (Wrzesniewski et al., 2013; van den Heuvel et al., 2015), which leads to the
enhancement of work meaningfulness. A higher level of leaders’ AI-oriented change behavior
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fosters an organizational climate that encourages AI adoption, increasing employees’ motivation
and sense of security in integrating AI at work. From the perspective of social cognitive, when
employees and their leaders share a similar perception of AI, it reinforces individual performance
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and promotes positive interactions within the organization (van den Heuvel et al., 2015).
Furthermore, research indicates that leadership support is a significant predictor of employees' job
crafting behaviors (Tims et al., 2013), and job crafting is an important source of work
meaningfulness (Wrzesniewski and Dutton, 2001), indirectly suggesting that leaders’ AI-oriented
change behavior also plays a role in promoting work meaningfulness. Conversely, for employees
whose leaders exhibit lower levels of AI-oriented change behavior, the lack of change resources
and support may lead to lower confidence and a sense of unpredictability. This results in a lack of
engagement in AI job crafting, as employees may perceive the required changes as risky and
beyond control. Therefore, leaders’ AI-oriented change behavior plays a crucial role in moderating
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the mediation of AI job crafting in the relationship between AI trust and work meaningfulness.
H5: Leaders’ AI-oriented change behavior positively moderates the mediation of AI job
crafting between AI trust and work meaningfulness.
3. Methodology
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3.1 Sample and procedure
Our sample comprised employees from six technology companies in Shanghai, Jiangsu, and
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Zhejiang provinces in China. These regions and industries were selected due to their advanced levels
of technological development, making AI more likely to be utilized in the workplace. The survey
was conducted through a combination of on-site and online distribution, targeting executives,
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directors, managers, and technical employees in R&D, production, marketing, and human resources
departments. To increase the response rate, we provided a detailed explanation of the study’s
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purpose at the beginning of the questionnaire and assured participants of strict confidentiality of
their personal information and responses.
To mitigate the potential common method variance, a three-wave data collection approach was
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employed, following the recommendations of Podsakoff et al. (2003). In the first wave, participants
were asked to provide data on demographic information, AI trust. Five weeks later, data on leaders’
AI-oriented change behavior were collected. Another five weeks later, data on job crafting and work
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meaningfulness were collected. A total of 297 responses were received. After filtering out
incomplete questionnaires and responses with abnormally short completion times, 240 valid
responses were retained, yielding an effective response rate of 81%.
Among the valid participants, 65.83% were male, and 34.17% were female. Regarding age
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distribution, 26.25% were aged 20–29, 60.83% were aged 30–39, 11.67% were aged 40–49, and
1.25% were aged 50 and above. In terms of current positions, 1.67% were executives, 8.75% were
directors, 28.33% were managers, and the majority, 61.25%, were employees. Concerning
educational background, 3.75% had a high school diploma, 7.92% had a college degree, 19.17%
held a bachelor’s degree, 61.67% had a graduate degree, and 7.50% possessed a postgraduate degree.
Variable
Table 1
Demographics of the sample
Number
Percentage
Gender
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Male
158
65.83
Female
82
34.17
20-29
63
26.25
30-39
146
60.83
40-49
28
11.67
50 and above
3
1.25
Executive
4
1.67
Director
21
8.75
Manager
68
28.33
Employee
147
Age
High school
9
College
19
Bachelor
46
Graduate
148
Postgraduate
18
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Education
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Current Position
7.92
19.17
61.67
7.50
The survey was conducted in Chinese; we translated the original items of each measure into
Chinese using Brislin’s (1970) forward and backward process. Unless otherwise stated, items were
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evaluated on a five-point Likert scale that ranged from 1 (strongly disagree) to 5 (strongly agree).
AI trust. AI trust was measured with an 11-item scale developed by Chowdhury et al. (2022).
A representative sample item is “I have a positive attitude towards the adoption of AI.” The
Cronbach’s α coefficient for this scale was 0.81.
AI job crafting. AI job crafting was measured using the six-item AI crafting scale developed
by Li et al.’s (2024) based on Leana et al.’s (2009) job crafting scale. A representative sample item
is “When working with AI robots, I change the way I do my job on my own to make it easier for
myself.” The Cronbach’s α coefficient for this scale was 0.80.
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Leaders’ AI-oriented change behavior. Leaders’ AI-oriented change behavior was measured
using the six-item AI crafting scale developed by He et al.’s (2024) based on Lin’s (2016) and
Marinova et. al.’s (2015) change-oriented behavior scales. A sample item is “My supervisor often
tries to introduce AI technologies to improve efficiency.” The Cronbach’s α coefficient for this scale
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was 0.72.
Work meaningfulness. Work meaningfulness was measured using the 10-item Work and
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Meaning Inventory developed by Steger et al. (2012). A sample item is “I understand how my work
contributes to my life's meaning.” The Cronbach’s α coefficient for this scale was 0.85.
Control variables. Employees’ gender, age, education and current position were measured as
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control variables in our analyses, since prior research proposed that they could be related to
employees’ work meaningfulness (Wrzesniewski and Dutton, 2001; Steger et. al., 2012; Rosso et.
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al., 2010; Hackman and Oldham, 1976; Tims et. al., 2016).
4. Results
4.1 Common method variance
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A Harman’s single-factor test was conducted to test the common method variance. The single
factor variance was found to be 17.73%, which was less than 40% (Podsakoff et al., 2003),
indicating that common method variance was not a significant concern in this study.
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4.2 Descriptive Statistics, discriminant validity, convergent validity and correlation
This research analyzed the mean, standard deviation, and correlation coefficient of each
variable using AMOS 28, and the results are shown in Table 2. The results showed that AI trust was
significantly positively correlated with AI job crafting (r=0.572, p<0.001) and work meaningfulness
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(r=0.321, p<0.001); between AI job crafting was significantly positively correlated with work
meaningfulness (r=0.456, p<0.001). The correlation analysis results support the research hypotheses
H1 and H2 primarily.
Then the discriminant validity and convergent validity of the scale were tested. According to
Table 2, all variables had AVE greater than 0.5 and CR greater than 0.7, indicating that the scales
in this study had good convergent validity. The square root value of AVE for each variable was
greater than the correlation coefficient between other variables, indicating that the scales in this
study had good discriminant validity. In addition, this study also analyzed the discriminant validity
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by comparing the Goodness-of-fit indices of various measurement models. As shown in Table 3,
the four-factor model has the highest fitting degree (χ2/df=1.53, CFI=0.91; IFI=0.91; TLI=0.90;
RMSEA=0.05), also indicating that the scales had good discriminant validity.
Table 2
Descriptive Statistics, discriminant validity, convergent validity and correlation
Variable
Mean
SD
AVE
CR
AIT
AIJB
LOCB
AIT
4.38
0.42
0.56
0.84
0.75
AIJB
4.19
0.54
0.69
0.87
0.57***
0.83
LOCB
1.95
0.83
0.66
0.85
0.81
0.18**
0.76
WM
4.66
0.65
0.66
0.85
0.32***
0.46***
0.19***
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WM
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0.76
Note: bold numbers are square root of AVE; AIT=AI trust; AIJB=AI job crafting; LOCB= Leaders’
AI-oriented change behavior; WM=working meaningfulness; *p <0.05; **p <0.01; ***p <0.001.
Table 3
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Goodness-of-fit indices of the measurement model
Model
Four-factor model (AIT+AIJB+LOCB+WM)
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Three-factor model (AIT+AIJB+LOCB, WM)
Two-factor model (AIT + AIJB, LOCB, WM)
One-factor model (AIT, AIJB, LOCB, WM)
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4.3 Hypotheses testing
4.3.1 Main effect test
χ2/df
df
CFI
IFI
TLI
RMSEA
1.53
411
0.91
0.91
0.90
0.05
2.56
431
0.71
0.71
0.68
0.08
2.85
433
0.66
0.66
0.63
0.09
3.26
434
0.56
0.58
0.55
0.1
The regression analysis of AI trust on work meaningfulness was conducted in two steps. First,
based on the conclusions from the prior analysis of control variables, gender, age, education, and
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current position were included as control variables in the regression equation to observe their effects
on work meaningfulness. Second, AI trust was added to the regression equation to examine its
impact on work meaningfulness.
As shown in Table 4, AI trust had a significant positive effect on work meaningfulness
(β₁=0.33, p<0.001). Furthermore, compared to Model 1, the inclusion of AI trust in Model 2
increased the explained variance of work meaningfulness by 10%, indicating that AI trust has a
substantial impact on work meaningfulness. Therefore, Hypothesis 1 was supported.
Table 4
Main effect test of AI trust on work meaningfulness
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Work meaningfulness
Variable
M1
M2
AI trust
0.33***
Age
-0.14
-0.11
Education
-0.07
-0.09
Gender
0.15
0.14
Current position
0.01
-0.01
Constant
5.06***
2.89***
R2
0.04
0.14
△R2
0.02
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0.12
29.13***
Similarly, the examination of the relationship between AI job crafting and work
meaningfulness was conducted in two steps. First, based on the conclusions from the prior analysis
of the effects of control variables, we included the control variables into the regression equation to
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observe their impact on work meaningfulness. Second, we added AI job crafting into the regression
equation to examine its effect on work meaningfulness.
As shown in Table 5, AI job crafting had a significant positive impact on work meaningfulness
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(β₂=0.45, p<0.001). Furthermore, compared to Model 1, the inclusion of AI job crafting in Model 2
increased the explained variance of work meaningfulness by 19%, indicating that AI job crafting
has a substantial influence on work meaningfulness. Therefore, Hypothesis 2 was supported.
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Table 5
Main effect test of AI job crafting on work meaningfulness
Variable
Work meaningfulness
M1
AI job crafting
M3
0.45***
Age
-0.14
-0.13
Education
-0.07
-0.06
Gender
0.15
0.07
Current position
0.01
-0.01
Constant
5.06***
2.93***
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R2
0.04
0.23
△R2
0.02
0.21
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1.80
59.31***
4.3.2 Test of the mediation effect
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confidence level of 95%. The goodness-of-fit indices for the relationship between AI trust and work
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meaningfulness mediated by AI job crafting, are presented in Table 6. The incremental fit index
(IFI=0.99), normed fit index (NFI=0.99), comparative fit index (CFI=0.99), and Tucker-Lewis
index (TLI=0.98) all exceeded the threshold of 0.90, and the root mean square error of
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approximation (RMSEA) was 0.07. Therefore, the overall model fit was good.
Table 6
Goodness-of-fit indices of the mediation effect
Index
NFI
IFI
Value
0.99
0.99
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CFI
TLI(NNFI)
RMSEA
0.99
0.98
0.07
Table 7 shows the indirect effect model of AI job crafting as a mediating variable. Based on
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the reported t-values, the standardized regression coefficients were significant at the p<0.01 level.
The bias-corrected 95% confidence intervals of the Bootstrap analysis were (0.58, 0.83) and (0.39,
0.77), which did not include zero, indicating that the mediating effect of AI job crafting was
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significant (p<0.001). Therefore, Hypothesis 3 was supported.
Table 7
Effect
Test of the mediation effect model
95% CI
Estimate
S.E.
AI trust—AI job crafting
0.57
0.07
0.58
0.83
***
AI job crafting—work meaningfulness
0.45
0.07
0.39
0.77
***
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Low limit High limit
p
4.3.3 Test of the moderation effect
This study used the Bootstrap method to test the moderating effect of leaders’ AI-oriented
change behavior. As shown in Table 8, with 5,000 bootstrap samples at a 95% confidence interval
level, the interaction term between AI trust and leaders’ AI-oriented change behavior had a
significant positive moderating effect on AI job crafting (β=0.38, p<0.001). Therefore, Hypothesis
4 was supported.
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Table 8
Test of the moderated mediation effect
Work meaningfulness
Variable
β
SE
t
p
LLCI
ULCI
AI job crafting
1.29
0.24
5.29
0.00
0.81
1.77
AI trust
0.17
0.10
1.63
0.04
0.03
0.37
Leaders’ AI-oriented change behavior
1.83
0.45
4.04
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0.94
2.72
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3.63
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AI job crafting * Leaders’ AI-oriented
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Constant
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114.07
Table 9
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4.22
Bootstrap test of the moderated mediation effect
Index
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95% CI
Low limit
High limit
0.17
0.35
To further examine the moderating effect, this study divides the sample into two groups—high
and low—by adding and subtracting one standard deviation from the mean of leaders’ AI-oriented
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change behavior. A simple slope analysis is then conducted to assess the role of leaders’ AI-oriented
change behavior in the relationship between AI trust and AI job crafting. As shown in Figure 2,
when leaders’ AI-oriented change behavior is high, the positive effect of AI trust on AI job crafting
is significantly stronger (t=2.22, p<0.001); when leaders’ AI-oriented change behavior is low, this
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positive effect is significantly weaker (t=0.86, p<0.001). These results suggest that leaders’ AIoriented change behavior positively moderates the significant positive relationship between AI trust
and AI job crafting. Specifically, as Leaders’ AI-oriented change behavior increases, the positive
effect of AI trust on AI job crafting becomes more pronounced, whereas, when Leaders’ AI-oriented
change behavior is low, the positive relationship between AI trust and AI job crafting is weaker.
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3.5
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Leaders’
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The moderated effect of leaders’ AI-oriented change behavior
4.3.4 Test of the moderated mediation effect
The moderated mediation effect test, also using the Bootstrap method (see Table 8), showed
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that the 95% confidence interval did not include zero, indicating a significant positive moderating
effect of AI trust and leaders’ AI-oriented change behavior on AI job crafting. Specifically, as leaders’
AI-oriented change behavior increased, the positive effect of AI job crafting on work
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meaningfulness was enhanced. Therefore, Hypothesis 5 was supported.
5. Conclusion
By incorporating the social cognitive theory, this research explored how AI trust promotes
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employees’ work meaningfulness. The results confirm that the social cognitive theory applies to the
explanation of how do people adapt to AI similarly to other technologies. Results suggest that AI
trust promotes employees’ work meaningfulness, which was partially mediated by AI job crating.
Moreover, leaders’ AI-oriented change behavior moderates the relationship between AI trust and AI
job crating.
5.1 Theoretical implications
First, this study provides new insights into the mechanisms through which AI trust influences
work meaningfulness based on social cognitive theory. Specifically, this study identifies the
interconnected relationship of cognitive, environmental, and behavioral factors, advancing the
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application of SCT in understanding how high-skilled employees adapt to technological change.
While SCT has been widely applied to explore employees’ responses to technological changes
(Compeau et al., 1999; Venkatesh et al., 2003), research on AI trust in the organizational context
has predominantly relied on frameworks such as social exchange theory, self-determination theory
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and expectancy theory (Xu et al., 2024; Kong et al., 2024). By integrating SCT, this study enriches
the theoretical landscape, shedding light on the interplay between cognitive factors (AI trust),
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environmental factors (leaders’ AI-oriented change behavior), and behavioral factors (AI job
crafting) in shaping employees’ work well-being (work meaningfulness).
Second, this study contributes to the growing body of literature of AI in the workplace by
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expanding its focus beyond adoption and usage to its impact on employees’ cognitive and emotional
states. Although AI has become a common part of work, existing research primarily focuses on
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employees’ AI adoption (Shaikh et al., 2023). However, the use of AI in the workplace not only
influences employee behaviors but also has profound influence on their cognition and emotions
(Glikson and Woolley, 2020). In particular, the ways in which AI-induced cognitive and emotional
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changes affect employees’ psychological states remain underexplored (Georganta and Ulfert, 2024).
This research gap presents a significant opportunity to investigate the dynamic evolution of
employees’ psychological mechanisms in the era of AI-driven organizational behavior. Previous
research shows that individual factors and job-related factors are the main antecedents of the
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formation and enhancement of work meaningfulness (Tims, et al., 2016; Amabile and Pratt, 2016;
Blustein et al., 2023). Notably, technological advancements have introduced new factors affecting
work meaningfulness, creating intersecting influences that encompass both individual and job-
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related factors. For example, Stein et al. (2019) examined the impact of datification on the pursuit
of meaningfulness in work, and Xu et al. (2023) explored how technology characteristics facilitate
employees’ work meaningfulness. Similarly, whether and how AI integration into work influences
work meaningfulness require further investigation (Chang et al., 2024). Based on this, this study
takes AI trust, a concept combining job characteristics (AI integrated into work) and individual
factors (trust), as the independent variable to comprehensively examine how employees’ cognitive
and emotional responses to the use of technology in the workplace influence their work
meaningfulness.
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Additionally, this study clarifies the mediating mechanism of the relationship between AI trust
and work meaningfulness. It is found that AI job crafting serves as a mediator in the process of AI
trust enhancing work meaningfulness. There has been a call for research on the changes in
employees’ behavioral engagement triggered by AI adoption (Liang et al., 2022; Gursoy and Cai,
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2024; Bankins, 2024), particularly in terms of adaptive behavior (Tan et al., 2024). While some
studies have explored job crafting facilitated by AI adoption (Li et al., 2024; Wu, Liang and Wang,
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2024), few have examined the cognitive and emotional changes induced by AI adoption and their
impact on AI job crafting. Building on social cognitive theory, this study focuses on how the
cognitive factor of AI trust promotes proactive behavior, thereby expanding the pathways through
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which job crafting functions as a mediator. AI trust helps foster employees’ sense of control over
technology and recognition of the irreplaceable value of human intelligence, which promotes AI job
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crafting, further leading to the enhancement of work meaningfulness.
Finally, this study explores the boundary conditions of the relationship between AI trust and
work meaningfulness by revealing that leaders’ AI-oriented change behavior moderates the
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relationship between employees’ AI trust and AI job crafting. Previous research strongly advocates
for taking variables from higher level of organization or team into consideration for studies on AI
in organizations (Chowdhury et al., 2022; Shaikh, 2023; Kong et al., 2023). While some studies
have incorporated leadership into their frameworks (Wijayati et al., 2022; Shaikh et al., 2023), the
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visible behaviors of leaders toward AI adoption are likely to have a more significant motivational
impact on employees. Therefore, this study integrates leaders’ AI-oriented change behavior as the
environmental factor within the social cognitive theory framework, demonstrating that such
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behavior fosters a safe, supportive and open environment for change and provides employees with
the necessary resources to navigate technological changes. Leaders’ AI-oriented change behavior
positively moderates the relationship between employees’ AI trust and AI job crafting, ultimately
enhancing work meaningfulness.
5.2 Managerial implications
Based on the findings of this study, organizations can address the challenges of managing high-
skilled employees in the AI era through the following strategies.
First, organizations can guide employees to strengthen their subjective thinking to help them
construct meaningful careers. Organizations can try leverage the high intrinsic motivation, strong
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independent learning ability, and adaptability of high-skilled employees (Lukowski et al. 2021). By
providing targeted onboarding and ongoing training programs, organizations can boost employees’
perception of self-value and guide their work philosophies. These interventions inspire employees
to proactively construct their career paths and enhance their skills, ultimately improving their
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adaptability to uncertain market and technological environments.
Second, organizations should recognize the importance and potential of high-skilled
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employees’ proactivity, encouraging them to engage in job crafting in response to technological
advancements. Organizations can adjust and innovate their management philosophies and
approaches to align with employees’ personalized needs while granting appropriate autonomy.
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Additionally, organizations can optimize resource allocation to providing necessary support and
conditions, creating an environment conducive for employees to craft their jobs in the face of
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technological advancements.
Third, leaders should actively embrace technological advancements and play a model role. By
demonstrating the power of AI technologies to improve work performance, leaders can alleviate
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employees’ concerns about AI adoption, and strengthen their trust and enthusiasm for technology.
Leaders should also take actions to help employees address practical challenges in adopting AI to
enhance their work performance. For example, leaders can ensure substantial technical resources
and training support, regularly communicate with employees to understand their perceptions and
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concerns about AI, and foster an inclusive atmosphere for change so as to encourage employees to
explore innovative job crafting practices that optimize AI integration without fear of failure.
5.3 Limitation and future directions
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Despite the objective and rigorous design and empirical analysis of this study, as well as the
insightful conclusions it has reached, certain limitations remain. First, this study explores the impact
of AI trust on work meaningfulness from the perspective of social cognitive theory. While this
approach aligns with the theoretical framework and antecedents of work meaningfulness, the study
is limited by the scope of data collection and the complexity of the model. It does not thoroughly
examine the mediating roles of various cognitive, environmental, and behavioral factors in the
relationship between AI trust and work meaningfulness. Future research could benefit from testing
a comprehensive model using large-scale data. Case studies can be employed to further describe the
relationship between AI trust and work meaningfulness. Second, this study treats AI trust and AI
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job crafting as unified constructs, examining employees’ overall perceptions and behaviors
regarding these aspects. Future research should differentiate the effects of various dimensions within
these constructs. For instance, study could explore how cognitive AI trust and emotional AI trust
differently influence work meaningfulness (Glikson et al., 2020; Shi et al., 2021). Similarly,
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regarding job crafting, the distinct impacts of task crafting, relational crafting, and cognitive crafting
(Wrzesniewski and Dutton, 2001), or the differential effects of promotion-focused job crafting and
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prevention-focused job crafting (Bindl et al., 2019), on work meaningfulness could be investigated.
Third, this study uses leaders’ AI-oriented change behavior as a contextual variable to measure the
influence of supervisors. Future studies could consider team- or organizational-level factors, such
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as leader-member exchange, technological leadership, or climate for change (Kipfelsberger et al.,
2022), to validate this study’s conclusions. Fourth, industries differ in their focus on AI functions
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and applications. Future research could explore the effects of different kinds of AI (such as agentic
AI and generative AI). and AI adoption in specific sectors. Fifth, as job crafting is a proactive
employee behavior, future research could investigate other proactive behaviors influenced by AI
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trust, such as taking charge or proactive work behavior (Liu, Chen and Li, 2021; Fay et al., 2023),
to provide a more comprehensive understanding of how AI trust impacts employees’ proactive work
behaviors.
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Declaration and Statement
No potential conflict of interest was reported by the authors.
All procedures performed in studies involving human participants were in accordance with the
ethical standards of the institutional and/or national research committee and with the 1964 Helsinki
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declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
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Data can be obtained upon request.
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Tables
Table 1
Variable
Demographics of the sample
Number
Percentage
Male
158
65.83
Female
82
34.17
20-29
63
26.25
30-39
146
60.83
40-49
28
50 and above
3
Gender
Age
1.25
Current Position
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Executive
4
Director
21
Manager
68
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Employee
Education
147
1.67
8.75
28.33
61.25
High school
9
College
19
7.92
Bachelor
46
19.17
Graduate
148
61.67
Postgraduate
18
7.50
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11.67
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3.75
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Table 2
Descriptive Statistics, discriminant validity, convergent validity and correlation
Variable
Mean
SD
AVE
CR
AIT
AIJB
LOCB
AIT
4.38
0.42
0.56
0.84
0.75
AIJB
4.19
0.54
0.69
0.87
0.57***
0.83
LOCB
1.95
0.83
0.66
0.85
0.81
0.18**
0.76
WM
4.66
0.65
0.66
0.85
0.32***
0.46***
0.19***
WM
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0.76
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Note: bold numbers are square root of AVE; AIT=AI trust; AIJB=AI job crafting; LOCB= Leaders’
AI-oriented change behavior; WM=working meaningfulness; *p <0.05; **p <0.01; ***p <0.001.
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Table 3
Goodness-of-fit indices of the measurement model
Model
χ2/df
df
CFI
IFI
TLI
RMSEA
Four-factor model (AIT+AIJB+LOCB+WM)
1.53
411
0.91
0.91
0.90
0.05
Three-factor model (AIT+AIJB+LOCB, WM)
2.56
431
0.71
0.71
0.68
0.08
Two-factor model (AIT + AIJB, LOCB, WM)
2.85
433
0.66
0.66
0.63
0.09
One-factor model (AIT, AIJB, LOCB, WM)
3.26
434
0.56
0.58
0.55
0.1
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32
33
34
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36
37
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40
41
42
43
44
45
46
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51
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58
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Table 4
Main effect test of AI trust on work meaningfulness
Work meaningfulness
Variable
M1
AI trust
M2
0.33***
Age
-0.14
-0.11
Education
-0.07
-0.09
Gender
0.15
Current position
0.01
-0.01
Constant
5.06***
2.89***
R2
0.04
△R2
0.02
e
R
r
F
1.80
w
e
i
v
0.14
0.14
0.12
29.13***
e
e
P
r
o
F
Page 33 of 40
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Table 5
Main effect test of AI job crafting on work meaningfulness
Work meaningfulness
Variable
M1
M3
AI job crafting
0.45***
Age
-0.14
-0.13
Education
-0.07
-0.06
Gender
0.15
Current position
0.01
Constant
5.06***
R2
0.04
△R2
0.02
-0.01
e
R
r
F
1.80
w
e
i
v
0.07
2.93***
0.23
0.21
59.31***
e
e
P
r
o
F
Page 34 of 40
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Table 6
Goodness-of-fit indices of the mediation effect
Index
NFI
IFI
CFI
TLI(NNFI)
RMSEA
Value
0.99
0.99
0.99
0.98
0.07
i
v
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R
r
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e
P
r
o
F
Page 35 of 40
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Table 7
Test of the mediation effect model
95% CI
Effect
Estimate
S.E.
AI trust—AI job crafting
0.57
0.07
0.58
0.83
***
AI job crafting—work meaningfulness
0.45
0.07
0.39
0.77
***
Low limit High limit
w
e
i
v
e
R
r
p
e
e
P
r
o
F
Page 36 of 40
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Table 8
Test of the moderated mediation effect
Work meaningfulness
Variable
β
SE
t
p
LLCI
ULCI
AI job crafting
1.29
0.24
5.29
0.00
0.81
1.77
AI trust
0.17
0.10
1.63
0.04
0.03
0.37
Leaders’ AI-oriented change behavior
1.83
0.45
4.04
0.00
0.94
2.72
0.38
0.10
3.63
0.00
2.08
1.08
1.92
0.06
AI job crafting * Leaders’ AI-oriented
change behavior
Constant
i
v
e
R2
0.53
F
114.07
R
r
w
e
0.17
0.58
0.05
4.22
e
e
P
r
o
F
Page 37 of 40
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57
58
59
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Table 9
Bootstrap test of the moderated mediation effect
Index
Value
SE
AI job crafting
0.26
0.05
95% CI
Low limit
High limit
0.17
0.35
i
v
e
R
r
w
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P
r
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F
Page 38 of 40
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Figures
AI trust
Work
meaningfulness
AI job crafting
Leader’s AI-oriented
change behavior
Figure 1
i
v
e
Proposed conceptual model
R
r
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e
e
P
r
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F
Page 39 of 40
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3.5
High
Leaders’
AI-orien...
3
AI job crafting
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2.5
w
e
Low
Leaders’ AIoriented
change
behavior
2
i
v
1.5
1
e
0.5
0
Low AI Trust
Figure 2
High AI Trust
R
r
Linear (High
Leaders’ AIoriented
change
behavior)
The moderated effect of leaders’ AI-oriented change behavior
e
e
P
r
o
F
Page 40 of 40