The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness

Xuan Liu; Yuci Chen

doi:doi:10.11648/j.ajap.20251403.14

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The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness

Xuan Liu

, Yuci Chen^*

Published in American Journal of Applied Psychology (Volume 14, Issue 3)

Received: 3 June 2025 Accepted: 23 June 2025 Published: 26 June 2025

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Abstract

Amid the wave of digital transformation, the widespread adoption of artificial intelligence (AI) has become an unstoppable trend. An increasing number of organizations are embracing AI to boost efficiency, streamline processes, and enhance decision-making quality. However, while AI helps improve organizational performance, it also exerts a profound influence on employees’ professional attitudes and behaviors. Exploring the impact of artificial intelligence applications on employees' career commitment is crucial. Grounded in the person-environment fit theory, this study aims to investigate how AI usage influences employees' career commitment through the mediating role of job crafting and the moderating role of AI awareness. Based on an empirical analysis of two hundred and two survey responses, the study reveals that AI usage positively correlates with employees' career commitment. Job crafting mediates this relationship, enhancing the positive effect of AI usage on career commitment. Additionally, AI awareness functions as a moderator, negatively adjusting both the impact of AI usage on job crafting and the indirect effect of AI usage on career commitment through job crafting. The research findings not only deepen our understanding of the relationship between AI usage and employee career commitment, but also offer theoretical grounding and practical guidance for organizations seeking to manage employee uncertainty and negative expectations while advancing intelligent transformation.

Published in	American Journal of Applied Psychology (Volume 14, Issue 3)
DOI	10.11648/j.ajap.20251403.14
Page(s)	101-112
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Artificial Intelligence Usage, Career Commitment, Job Crafting, Artificial Intelligence Awareness, Person-environment Fit Theory

1. Introduction

Artificial intelligence (AI), as the core driving force of the Fourth Industrial Revolution, is profoundly transforming the global labor market, reshaping employees' work methods and career paths. Compared to traditional technologies, AI offers higher efficiency, precision, and more human-like intelligence

[1]

. Over the past few years, 80% of large enterprises have integrated artificial intelligence into their core operations

[2]

. From business management and manufacturing to marketing, AI is streamlining processes, boosting efficiency, and fundamentally changing the way employees work

[3, 4]

. Studies have shown that AI usage can enhance employee job satisfaction, organizational commitment, work performance, and innovative behavior

[5, 6]

. However, the widespread use of AI is not without its downsides. Its automation and intelligence capabilities may diminish the need for certain core job skills and even heighten concerns about job security

[7]

. As AI continues to integrate into various industries, the workplace environment is undergoing significant transformation. Employees must adapt to new work models and technological tools, which could also influence their career paths

[8]

. At the same time, their perspective on work is shifting

[9]

. As an important professional attitude

[10]

, career commitment is likely to be impacted in this wave of change.

Career commitment refers to an individual's sense of identity with their profession and their willingness to invest in it over the long term. It is a crucial factor in determining career stability and the quality of career development

[10]

. Low career commitment can lead to a series of negative consequences, such as increased turnover intentions

[11]

, which in turn affect organizational stability. However, Employees with high career commitment are more willing to invest in their work, proactively learn and develop new skills, and enhance their ability to adapt to the professional environment. Their goal is to achieve their career objectives or to remain in their current profession

[12]

. Although existing research has explored the impact of AI applications on job satisfaction, organizational commitment, and job performance

[5, 6, 13]

, there is still a lack of systematic discussion on how AI influences employees' career commitment. In particular, in the context of the growing career disruption caused by AI, it is crucial to clarify its impact mechanisms on career commitment and propose corresponding motivational measures. This is an urgent issue that needs to be addressed in the process of AI-driven transformation.

According to the person-environment fit theory, the degree of alignment between individuals and their work environment influences both vocational adaptability and career success

[14]

. As AI continues to reshape the workplace, it brings both challenges and opportunities. Employees must continually learn new technologies, refine their career goals, and adjust their work strategies to keep pace with evolving job demands

[15]

. In this process, job crafting may play a crucial role in helping employees proactively navigate AI-driven changes and sustain their career commitment. Job crafting is when employees take the initiative to reshape their tasks, work methods, and interactions to better align with their interests, values, and career growth

[16]

. By actively engaging in job crafting, employees gain a clearer sense of their value in career development, reinforcing their professional identity and sense of belonging. This intrinsic motivation, in turn, strengthens their career commitment and lays the groundwork for long-term growth

[17]

. Therefore, this study suggests that job crafting may serve as a key mediating mechanism through which AI usage influences employees' career commitment.

AI awareness, first introduced by Brougham and Haar in 2018, refers to an individual’s perception of the threat posed by AI technology

[9]

. With AI technology becoming more widely adopted, employees are increasingly aware of its impact, and this perception has a profound impact on their career attitudes and behaviors. AI’s automation and intelligence can leave employees feeling sidelined

[18]

, diminishing the value of their skills and their competitiveness in the job market. While they may seek to adapt by adjusting their work strategies. However, if the perceived impact of AI is too overwhelming, it can lead to anxiety and reduce their willingness to proactively reshape their roles

[19]

. Therefore, AI awareness may serve as a key moderating factor in the relationship between AI usage and employees’ career commitment.

In summary, this study, based on the person-environment fit theory, develops a framework exploring the impact mechanism of "AI usage—job crafting—career commitment" while also examining the moderating role of AI awareness. Its potential contributions include:(1) Broadening the Perspective on AI Usage’s Impact on Career Development: While previous studies have primarily focused on AI’s effects on employees’ work attitudes and performance, this study shifts the focus to its impact on career commitment, providing fresh insights into the long-term career implications of AI usage. (2) Uncovering the Mechanism Behind AI’s Influence on Career Commitment: By introducing job crafting, this study explains how employees proactively adjust their work strategies to navigate the uncertainties brought by AI-driven changes. It expands the understanding of job crafting’s role in career adaptation. (3) Introducing AI Awareness as a Moderating Factor: This study explores how AI awareness influences the relationship between AI usage and job crafting, providing theoretical insights into employees' psychological adjustment mechanisms in the AI era. This study provides practical insights for business leaders, offering strategies to help employees enhance career commitment through job crafting. It also highlights the importance of effective talent management policies to minimize AI’s potential disruptions to job stability.

2. Research Hypothesis

2.1. AI usage and Career Commitment

Career commitment refers to an individual's sense of identification with their profession and their attitude toward staying in that profession

[10, 12]

. It not only relates to employee's personal career development but also directly impacts the stability and overall performance of the organization

[20]

. The formation and maintenance of career commitment are influenced by various factors, including personality traits, organizational environment, and job design

[17, 21]

. In the context of the digital era, the widespread application of artificial intelligence technology has become a significant factor affecting job design

[22]

. AI usage refers to the extent to which employees actively use AI technology to achieve their work goals

[23]

, exerting a profound impact on their work behaviors and psychological states. Existing research suggests that AI technology can enhance employees' work engagement, organizational commitment, and workplace well-being while also fostering positive behaviors such as knowledge sharing, digital innovation, and job crafting

[24, 25]

. These positive changes not only improve individual employee performance but also bring higher efficiency and innovation to the organization, helping companies maintain a competitive edge in an increasingly dynamic market

[24]

Specifically, AI usage can enhance employees' career commitment through the following pathways. Firstly, the application of AI technologies, such as smart assistants and automation tools, can reduce repetitive and low-value tasks, allowing employees more time and energy to focus on more challenging and creative work

[26, 27]

. This shift not only increases employees' sense of accomplishment but also enhances their sense of career self-efficacy

[4]

. When employees have confidence in their abilities, they are more likely to develop a positive identification with their profession, which in turn fosters higher career commitment

[28]

. Second, AI usage streamline workflows and optimize resource allocation, enhancing the comfort of the work environment and increasing employee job satisfaction

[13, 29]

. Improved job satisfaction is a crucial prerequisite for strengthening career commitment

[30]

. In addition, AI can provide employees with more precise and valuable work advice and guidance through data analysis and forecasting, sparking their enthusiasm and engagement with their tasks

[31, 32]

. When employees are passionate and fully engaged in their work, they are more likely to develop a strong sense of career commitment

[33]

. Based on the above analysis, this study proposes the following research hypothesis:

H1: AI usage positively influences employees' career commitment.

2.2. The Mediating Role of Job Crafting

Job crafting is a bottom-up approach to work redesign

[34]

, where employees proactively adjust and optimize their tasks, responsibilities, and work relationships based on their skills, interests, and career goals. This process helps employees navigate an evolving organizational environment while supporting their personal development needs

[16]

. By engaging in job crafting, employees not only enhance their work efficiency and job satisfaction but also strengthen their sense of professional identity and belonging, fostering mutual growth between individuals and the organization

[35]

. Tims and Bakker

[36]

, based on the resource perspective and the Job Demands-Resources (JD-R) model, proposed four dimensions of job crafting: increasing structural job resources, increasing social job resources, increasing challenging job demands, and decreasing hindering job demands. Increasing structural job resources involves enhancing professional skills, acquiring new knowledge, and becoming more specialized. Increasing social job resources refers to seeking help and feedback from colleagues or supervisors. Increasing challenging job demands includes being willing to take on additional tasks and seeking more opportunities for growth or challenges. Decreasing hindering job demands involves alleviating work-related stress and emotional exhaustion

[36]

. In the context of AI increasingly permeating the workplace, AI usage has significantly changed the way employees work, thereby driving job crafting

[25]

On one hand, AI, as a new work environment, sets higher standards for jobs, requiring employees to possess stronger professional expertise and skill levels to handle complex and ever-changing tasks

[37]

. Additionally, AI provides employees with abundant control resources, such as smart office software and data analysis tools, enabling them to complete tasks more efficiently

[38]

. This positive balance between high job demands and ample job control resources helps employees better engage in job crafting

[39]

On the other hand, AI usage has greatly enriched the resources available to employees, enabling the automation and intelligent optimization of work processes. Employees can now arrange their tasks according to their preferences and plans, which enhances their sense of job autonomy

[40]

. Job autonomy is a key driver of job crafting

[41]

, as it enables employees to adjust their tasks based on their interests and expertise. Moreover, in order to better utilize AI, employees need to rely on smart learning platforms to acquire new knowledge and skills, enhancing their professional capabilities

[42]

. This process helps them become more specialized, enabling access to more resources and, in turn, fostering greater job crafting behavior.

Finally, whereas employees previously relied primarily on interpersonal interactions for workplace collaboration, the development of AI technology has prompted them to redefine their interactions with AI systems, colleagues, and clients

[43]

. Employees may begin to view AI as a partner in their work rather than just a tool or a threat

[43]

. In a well-functioning human-AI collaboration, AI can quickly process data and perform precise calculations, while employees can leverage their creativity, emotional insight, and decision-making abilities

[27]

. This complementary nature of both enables employees to break through traditional work limitations, tackle more challenging and innovative tasks, and fosters job crafting behavior

[44]

. Based on the above analysis, this study proposes the following research hypothesis:

H2a: AI usage positively influences job crafting.

Existing research suggests that job crafting is an important antecedent variable of career commitment

[45]

. Chinese scholars Chang et al.

[17]

have found that employees who engage in job crafting behaviors tend to exhibit higher levels of career commitment. This relationship can be explained through the "empowerment-adaptation-identification" mechanism: Job crafting grants employees with more autonomy, allowing them to adjust their tasks and enhance their sense of control over their careers

[46]

, thus empowering them. As employees continuously learn AI-related skills in the process of job crafting, they adapt to the rapidly changing work environment

[47]

. This learning process strengthens their professional capabilities, making them more appreciative of their current career opportunities and increasing their adaptability. As job crafting deepens, employees experience greater job satisfaction and develop a stronger sense of identification with their career roles, ultimately reinforcing their career commitment

[30]

. Additionally, employees' use of AI technology helps them handle routine tasks, allowing them to focus on more challenging and meaningful work. This, in turn, enhances their job satisfaction and career stability

[48]

. When employees experience greater autonomy, personal growth, and find more meaning in their work, their level of career commitment is further strengthened

[17]

. Based on this, our study posits that artificial intelligence usage drives employees to reshape their work, which in turn enhances their career commitment. Above all, the following research hypotheses are proposed in this study:

H2b: Job crafting positively influences employees' career commitment.

H2c: Job crafting mediates the relationship between AI usage and career commitment. The stronger the AI usage, the more pronounced the job crafting, and the higher the career commitment.

2.3. Moderating Effect of AI Awareness

AI awareness refers to employees' perception of the potential threat that AI technology may pose to their career development

[9]

. It not only reflects their vigilance toward technological changes but also highlights their uncertainty and anxiety regarding job stability

[7]

. Previous research has mainly focused on the negative impact of AI awareness on employees. Studies have shown that a higher awareness of AI can lead employees to perceive career threats, reduce their sense of organizational support, and decrease their job satisfaction and work engagement

[9]

. In addition, AI awareness is also closely related to job burnout, emotional exhaustion, and organizational deviance

[9]

In the face of the impact of AI technology, employees may feel the instability of their work roles and perceive that their professional skills are gradually being replaced by AI

[49]

. This sense of disruption may influence employees' behavioral choices through cognitive and emotional mechanisms: In terms of cognitive mechanisms, employees may perceive their job roles and professional identities are under threat, believing that AI development will diminish their career competitiveness

[50]

. This, in turn, may reduce their confidence in professional growth and decrease their willingness to proactively adapt to their work

[19]

. In terms of emotional mechanisms, AI awareness can trigger anxiety and burnout. These negative emotions and states deplete employees' psychological resources

[7]

, making them more likely to adopt conservative strategies rather than actively engaging in job crafting

[51]

. Specifically, AI awareness may weaken the positive effect of AI usage on job crafting. This influence manifests in the following ways: First, employees may feel insecure about AI potentially replacing their jobs, leading them to reduce attempts to adjust and optimize tasks in order to avoid increasing job uncertainty

[9]

. Second, when employees strongly perceive the competitive pressure and job replacement risks posed by AI, they may worry that the skills they’ve mastered could become obsolete due to AI’s involvement

[52]

. As a result, they may lack the motivation to participate in relevant training and learning activities, causing them to fall behind in the process of job crafting

[3]

. Finally, when employees have a high level of AI impact awareness, they may underestimate their ability to cope with change, believing they cannot effectively use AI to improve work efficiency or achieve breakthroughs

[53]

. This low sense of self-efficacy further diminishes their motivation and confidence to engage in job crafting

[54]

. In summary, this study proposes the following hypothese:

H3: AI awareness negatively moderates the relationship between AI usage and employees' job crafting.

Person-environment fit theory emphasizes that individual behavior results from the interaction between a person and their environment. It suggests that the degree of alignment between an individual and their work environment is a key predictor of work attitudes and behaviors. When individuals can adapt to environmental changes, they are more likely to develop positive work attitudes and behaviors, whereas a mismatch with the environment may lead to negative outcomes

[14]

. In the AI-driven workplace transformation, AI usage has profoundly altered both the way employees work and the work environment itself, becoming a crucial environmental factor influencing employees' career development

[19]

. When employees have a strong sense of AI awareness, they may feel that their skills are gradually mismatched with the organization's needs, leading to a sense of career insecurity

[55]

. This insecurity can reduce their work engagement and weaken their willingness and motivation to proactively engage in job crafting

[56]

. As a result, they may be more inclined to maintain the status quo rather than proactively adapting to technological changes. Therefore, AI awareness may weaken the positive impact of AI usage on employees' career commitment through job crafting. When AI awareness is high, even if AI technology facilitates job crafting, its positive effects may be offset by anxiety, career insecurity, and negative emotions. As a result, the ultimate influence of AI usage on career commitment may be diminished. In summary, this study proposes the following hypothesis:

H4: AI awareness negatively moderates the indirect effect of AI usage on employees' career commitment through job crafting. The stronger an employee's AI awareness, the weaker the positive impact of AI usage on career commitment via job crafting.

In summary, the theoretical model of this study is presented in Figure 1.

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Figure 1. Theoretical model.

3. Materials and Methods

The Materials and Methods section should provide comprehensive details to enable other researchers to replicate the study and further expand upon the published results. If you have multiple methods, consider using subsections with appropriate headings to enhance clarity and organization.

3.1. Sample and Procedure

This study collected survey data from corporate employees across various industries nationwide using online platforms such as Credamo, similar to Mechanical Turk, ensuring broad participation through electronic questionnaires. Before distributing the questionnaire, employees were informed about the academic purpose of the study, assured of anonymity and confidentiality, and clarified that the survey was solely for research purposes. Only after obtaining their consent were they invited to complete the questionnaire. To reduce common method bias, this study adopted a time-lagged design, collecting data at two separate points with a one-month interval. Responses were tracked and matched using anonymous coding to ensure accuracy and confidentiality. At Time Point 1, participants' demographic information was collected, including gender, age, educational background, years of work experience, and type of employing organization. Subsequently, measurements of AI usage and AI awareness were conducted. At Time Point 2, employees who participated in the Time Point 1 survey were tracked and asked to complete measures of job crafting and career commitment. A total of 251 valid questionnaires were collected for this study. After removing responses that showed obvious patterns, contained missing values, or had contradictory answers, 202 questionnaires were retained, resulting in a final valid response rate of 80.5%.

The sample characteristics are as follows: there are 83 men (41.1%) and 119 women (58.9%). In terms of education level, 3 participants (1.5%) had a high school diploma or below, 33 (16.3%) held a college diploma, 124 (61.4%) had a bachelor's degree, and 42 (20.8%) held a master's degree or higher. Regarding company type, 55 participants (27.2%) worked in government agencies or state-owned enterprises, 108 (53.5%) were employed in private companies, 18 (8.9%) worked in foreign or joint ventures, and 21 (10.4%) were in other types of organizations. As for work experience, 25 participants (12.3%) had less than a year of work experience, 73 (36.1%) had 1 to 5 years, 58 (28.7%) had 6 to 10 years, 28 (13.9%) had 11 to 15 years, 9 (4.5%) had 16 to 20 years, and 9 (4.5%) had more than 20 years of experience.

3.2. Measures

The study employed well-established scales developed by international scholars, all of which used a five-point Likert scale, ranging from 1 ("strongly disagree") to 5 ("strongly agree").

Artificial intelligence usage. This study utilized the scale developed by Tang et al.

[23]

, which consists of three items: “I used artificial intelligence to carry out most of my job functions,” “I spent most of the time working with artificial intelligence,” and “I worked with artificial intelligence in making major work decisions.” In this study, the scale had a Cronbach's α coefficient of 0.871.

Artificial intelligence awareness. This study used the scale developed by Brougham and Haar

[9]

, which includes four items, such as "I think my job could be replaced by AI," "I am personally worried that what l do now in my job will be able to be replaced by AI," "I am personally worried about my future in my organization due to AI replacing employees," and "I am personally worried about my future in my industry due to AI replacing employees." In this study, the scale had a Cronbach’s α of 0.896.

Job crafting. This study used the job crafting scale developed by Tims and Bakker

[36]

, with modifications to accommodate practical considerations, resulting in a total of 17 items. Key items include "I ask others for feedback on my job performance," "I try to develop my capabilities," and so on. In this study, the scale had a Cronbach’s α of 0.702.

Career commitment. This study utilized the career commitment scale developed by Suddaby, Gendron, and Lam

[57]

, consisting of seven items. A key item includes, "My current job is an important part of my career," and so on. In this study, t the scale had a Cronbach’s α of 0.839.

Control variables. Consistent with previous studies by Zhu et al.

[21]

, Son and Kim

[33]

, this study included employees’ gender, age, education level, marital status, tenure, and organization type as control variables.

3.3. Analytical Methods

This study conducted statistical analyses using SPSS 27.0 and Mplus 8.3. Specifically, Mplus 8.3 was used for confirmatory factor analysis and common method bias testing. SPSS 27.0 was employed for Harman’s single-factor analysis, descriptive statistics, correlation analysis, and hierarchical regression analysis. Additionally, the SPSS PROCESS 4.1 macro was used to test the main effects, mediation effects, and moderated mediation effects.

4. Results

4.1. Confirmatory Factor Analysis

This study used Mplus 8.3 for confirmatory factor analysis (CFA) to assess the discriminant validity between the four key variables: AI usage, career commitment, job crafting, and AI awareness. As shown in the results of Table 1, the results indicated that the four-factor model exhibited good fit indices (χ² = 840.222, df = 458, CFI = 0.832, TLI = 0.818, RMSEA = 0.064, SRMR = 0.074) and was significantly superior to other models, demonstrating strong discriminant validity among the variables.

Table 1. Confirmatory factor analysis.

Model	$χ^{2}$	df	$χ^{2} / df$	CFI	TLI	RMSEA	SRMR
Four-factor model: AU; JC; AIA; CC	840.222	458	1.835^***	0.832	0.818	0.064	0.074
Three-factor model 1: AU+JC; AIA; CC	1130.229	461	2.452^***	0.705	0.683	0.085	0.092
Three-factor model 2: AU; JC+AIA; CC	1238.389	461	2.686^***	0.658	0.632	0.091	0.138
Three-factor model 3: AU +AIA; JC; CC	1309.819	461	2.841^***	0.626	0.598	0.095	0.100
Three-factor model 4: AU; JC; AIA+CC	1382.445	461	2.999^***	0.594	0.564	0.099	0.141
Two-factor model: AU; JC+AIA+CC	1404.770	463	3.034^***	0.585	0.556	0.100	0.101
One-factor model: AU+JC+AIA+CC	1741.595	464	3.753^***	0.438	0.399	0.117	0.109
CLF model	701.921	426	1.648^***	0.878	0.858	0.057	0.065

Note: N = 202; AU = artificial intelligence usage, JC = job crafting, AIA = AI awareness, CC = career commitment, “+” represents merging variables into a single factor, CLF = four-factor model + common method factor; *p＜0.05, **p＜0.01, ***p＜0.001.

4.2. Common Method Bias

This study used Harman’s single-factor test to examine common method bias. The results of the unrotated exploratory factor analysis revealed nine factors with eigenvalues greater than one. The first principal component explained 21.619% of the variance, which is below the 40% threshold typically used to assess common method bias. Subsequently, following the method outlined by Podsakoff et al.

[58]

, this study incorporated an unmeasured common method latent factor into the confirmatory factor analysis to construct a five-factor model. This model was then compared with the four-factor model. The results showed that adding the common method factor did not substantially improve model fit, as the changes in fit indices were minimal (ΔCFI = 0.046, ΔTLI = 0.040, ΔRMSEA = 0.007, ΔSRMR = 0.009). Therefore, common method bias does not pose a threat to the validity of the study's findings.

4.3. Descriptive Statistical Analysis

Figure 2 are the means, standard deviations, correlation coefficients, and their significance levels for all variables. The results show that AI usage is significantly positively correlated with job crafting (r = 0.385, p < 0.01) and with career commitment (r = 0.357, p < 0.01). Additionally, job crafting is significantly positively correlated with career commitment (r = 0.566, p < 0.01). Next, the study calculated the variance inflation factor (VIF) for each variable. The results showed that the maximum VIF was 1.206, well below the critical threshold of 10, indicating that there is no severe multicollinearity among the variables.

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Figure 2. Descriptive statistics and correlations among variables.

4.4. Hypothesis Testing

4.4.1. Main Effect and Mediation Effect Testing

The results of the hierarchical regression analysis in this study are shown in Table 2.

Table 2. Hierarchical regression analysis.

Variables	Job Crafting		Career Commitment
Variables	M1	M2	M3	M4
Control Variables
Gender	0.053	0.037	-0.007	-0.034
Age	-0.042	-0.013	0.180	0.201
Marital	0.103	0.052	-0.030	-0.083
Education	0.162	0.063	0.084	0.002
Tenure	0.019	0.002	0.001	-0.009
Enterprise	0.006	0.006	-0.045	-0.048
Independent Variable
AI Usage	0.377^***	0.116^***	0.351^***	0.160
Mediator
Job Crafting				0.508^***
Moderator
AI Awareness		-0.025
Interaction
AI Usage × AI Awareness		-0.053^**
R²	0.185	0.220	0.159	0.370
ΔR²	0.136	0.029	0.118	0.210
F	6.275^***	6.060^***	5.245^***	14.143^***

Note: N = 202; *p＜0.05, **p＜0.01, ***p＜0.001.

Test of Hypothesis 1 (Main Effect): As shown in Model 3, AI usage has a significant positive effect on career commitment (β = 0.351, p < 0.001). Therefore, Hypothesis 1 is supported.

Test of Hypothesis 2 (Mediation Effect): This study tested the mediation effect following the approach outlined by Baron and Kenny

[59]

. As shown in Model 1, AI usage has a significant positive effect on job crafting (β = 0.377, p < 0.001), supporting Hypothesis 2a. As shown in Model 4, when both AI usage and job crafting are included in the regression equation, job crafting positively influences career commitment (β = 0.508, p < 0.001). The positive effect of AI usage on career commitment weakens but is not significant (β = 0.160, p > 0.05). Therefore, job crafting mediates the relationship between AI awareness and career satisfaction, supporting Hypotheses 2b and 2c.

To further validate the significance of the mediation effect, this study followed the approach of Preacher and Hayes and employed the Bootstrap method to test the mediating role of job crafting. Specifically, Model 4 was set in PROCESS, with 5,000 bootstrap samples drawn to calculate the 95% confidence interval. The results show that the total effect of AI usage on career commitment is 0.235, SE = 0.045, with a 95% confidence interval of [0.1460, 0.3234], which does not include 0. The direct effect is 0.107, SE = 0.042, with a 95% confidence interval of [0.0236, 0.1900], which also does not include 0. The indirect effect is 0.128, SE = 0.027, with a 95% confidence interval of [0.0778, 0.1849], which does not include 0. These findings confirm that job crafting mediates the relationship between AI usage and career commitment, providing further support for Hypothesis 2c.

4.4.2. Moderating Effect Testing

Test of Hypothesis 3 (The Moderating Role of AI Awareness): As shown in Model 2 of Table 3, the interaction term between AI usage and AI awareness significantly predicts job crafting (β = -0.053, p < 0.01), indicating the presence of a moderation effect. Further following the approach of Preacher et al., a simple slope analysis was conducted by categorizing AI awareness into high and low levels. An interaction effect plot was then generated to visualize the results. As shown in Figure 3, when AI awareness is low, the regression slope between AI usage and job crafting is stronger. However, when AI awareness is high, the regression slope between AI usage and job crafting is weaker. These results suggest that AI awareness negatively moderates the relationship between AI usage and job crafting. Specifically, the higher the AI awareness, the weaker the positive effect of AI usage on job crafting. Thus, Hypothesis 4 is supported.

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Figure 3. The moderating effect of AI awareness on AI usage and job crafting.

4.4.3. Moderated Mediation Effects Testing

This study further employed the Bootstrap method in the PROCESS program to test the moderated mediation effect. The results show that the index of moderated mediation is -0.061, with a 95% confidence interval of [-0.1088, -0.0235], which does not include 0. This indicates that the moderated mediation effect is significant. As shown in Table 3, when AI awareness is high (M + 1SD), the indirect effect of AI usage on career commitment through job crafting is 0.049, SE = 0.039, with a 95% confidence interval of [-0.0300, 0.1215], which includes 0. However, when AI awareness is low (M – 1SD), the indirect effect of AI usage on career commitment through job crafting is 0.185, SE = 0.036, with a 95% confidence interval of [0.1207, 0.2584], which does not include 0. The difference in the mediation effect of job crafting at high and low levels of AI awareness is significant, with a difference value of -0.136, SE = 0.049, and a 95% confidence interval of [-0.2441, -0.0503], which does not include 0. These results demonstrate that AI awareness negatively moderates the indirect effect of AI usage on career commitment through job crafting. Thus, Hypothesis 3 is supported.

Table 3. Results of moderated mediation tests.

Mediator	Moderator	Indirect Effects	SE	Bias Corrected 95% CI
Job Crafting	Low AI Awareness	0.185	0.036	[0.1207, 0.2584]
	High AI Awareness	0.049	0.039	[-0.0300, 0.1215]
	Slop Difference	-0.136	0.049	[-0.2441, -0.0503]

5. Discussion

This study has important implications for theory and research. Firstly, this study broadens the academic perspective on AI’s affects consequences by examining it through the lens of career development, providing new insights into how AI usage shapes employees’ long-term professional growth. Existing research has largely examined AI’s impact on employees’ work attitudes, such as organizational commitment, job engagement

[7]

, and job satisfaction

[13]

, while also pointing to its downsides, including heavier workloads, job burnout, and higher turnover intentions

[9, 19]

. However, AI is not just a threat—it can also create opportunities that inspire employees to strengthen their career commitment. Drawing on the person-environment fit theory, this study reveals that AI usage strengthens career commitment by encouraging employees to engage in job crafting. This finding provides deeper insights into AI’s role in career development. Moreover, this finding aligns with Brougham and Haar’s

[9]

call for greater focus on employees’ career development in future research, adding depth to the study of career management.

Secondly, this study reinforces AI usage as a crucial environmental factor shaping employees’ career commitment, broadening the current understanding of the factors that influence career commitment. Existing literatures have explored career commitment through the lens of individual factors, such as personality traits and career self-efficacy

[21, 60]

, or organizational influences, including workplace culture and job engagement

[21, 33]

. However, this study highlights AI usage as a contextual factor that enhances employees' career commitment, broadening the scope of existing research. This perspective encourages future scholars to examine how other environmental factors—such as government policies, economic trends, and cultural influences—impact career commitment in the AI era.

Furthermore, this study uncovers the mediating role of job crafting in the relationship between AI usage and employees’ career commitment, offering deeper insights into how job crafting contributes to the formation of career commitment. While prior studies have explored the mechanisms underlying career commitment, research in this area remains limited. For instance, Chen et al.

[61]

found through empirical analysis that career concern enhances career commitment via career exploration and career decision-making self-efficacy. Similarly, Son and Kim

[33]

identified work engagement as a mediating factor in the positive relationship between career growth and career commitment. However, few studies have considered the mediating role of job crafting in this process.

Finally, this study sheds light on the moderating role of AI awareness, clarifying the boundary conditions in the relationship between AI usage and employees' career commitment. As a key factor in employees' perception of AI-related threats, AI awareness plays a pivotal role in shaping their work attitudes and behaviors. Existing research has primarily focused on workplace factors that shape the boundaries of career commitment, such as time, perceived organizational support, and job autonomy

[62]

. This study examines how AI awareness moderates the relationship between AI usage and job crafting, as well as the relationship between job crafting and employees’ career commitment. By doing so, it advances research on the boundary conditions influencing career commitment.

6. Conclusions

This study, drawing on a sample of 202 working professionals and the person-environment fit theory, explores how AI usage shapes employees' career commitment through job crafting and examines the moderating effect of AI awareness on this relationship. The empirical findings indicate that: (1) AI usage is positively correlated with employees’ career commitment; (2) job crafting mediates the relationship between AI usage and career commitment—higher AI usage encourages greater job crafting, which in turn strengthens career commitment; (3) AI awareness negatively moderates both the relationship between AI usage and job crafting and the indirect effect of AI usage on career commitment.

7. Limitations and future research direction

This study has several limitations. First, the sample in this study is drawn from a wide range of companies across the country, lacking in-depth analysis of different industries and job characteristics. Employees in different sectors and roles may vary significantly in their acceptance of AI, approaches to job crafting, and levels of career commitment. Future research could focus on more detailed surveys across different industries and job types, conducting comparative studies to explore how AI usage impacts career commitment in diverse groups. Second, although this study collected data at multiple time points to reduce common method bias, the relatively short time interval between data collections may not have fully captured the causal relationships between the variables. Future research could extend the time interval between data collection to more accurately observe and analyze the dynamic changes between variables. Third, while this study examined the role of job crafting based on the person-environment fit theory, the impact of AI adoption on employees' career commitment is likely more intricate and may also be influenced by factors such as job mobility and occupational anxiety, which warrant further investigation. Finally, this study considered AI awareness as the sole boundary condition. Future research could broaden this perspective by examining other moderating factors, such as psychological capital and organizational support, to develop a deeper understanding of how AI usage influences employees' career commitment.

Abbreviations

AI	Artificial Intelligence
VIF	Variance Inﬂation Factor

Acknowledgments

The authors thank all participants in this study for their precious time.

Author Contributions

Xuan Liu: Conceptualization, Formal Analysis, Project Administration, Software, Supervision, Review & Editing.

Yuci Chen: Data curation, Investigation, Methodology, Resources, Validation, Original Draft Preparation, Review & Editing.

Funding

This work is not supported by any external funding.

Data Availability Statement

The data is available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	HUANG M-H, RUST R T. Artificial intelligence in service [J]. Journal of service research, 2018, 21(2): 155-72. https://doi.org/10.1177/1094670517752459
[2]	BRYNJOLFSSON E, ROCK D, SYVERSON C. The productivity J-curve: How intangibles complement general purpose technologies [J]. American Economic Journal: Macroeconomics, 2021, 13(1): 333-72. https://doi.org/10.1257/mac.20180386
[3]	LI Y, SONG Y, SUN Y, et al. When do employees learn from artificial intelligence? The moderating effects of perceived enjoyment and task-related complexity [J]. Technology in Society, 2024, 77: 102518. https://doi.org/10.1016/j.techsoc.2024.102518
[4]	XU J, TANG X, CHANG E-C, et al. Working with AI: the impact of organizational intelligent service strategy on employees’ perception of career achievement [J]. Humanities and Social Sciences Communications, 2024, 11(1): 1-20. https://doi.org/10.1057/s41599-024-03265-1
[5]	REZVANI M Q, CHOUDHARY N, MANGAL U, et al. Application of artificial intelligence: Organizational commitment and productivity in Indian banking sector [J]. Multidisciplinary Science Journal, 2024, 6. https://doi.org/10.31893/multiscience.2024ss0413
[6]	TONG S, JIA N, LUO X, et al. The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance [J]. Strategic Management Journal, 2021, 42(9): 1600-31. https://doi.org/10.1002/smj.3322
[7]	KONG H, YUAN Y, BARUCH Y, et al. Influences of artificial intelligence (AI) awareness on career competency and job burnout [J]. International Journal of Contemporary Hospitality Management, 2021, 33(2): 717-34. https://doi.org/10.1108/IJCHM-07-2020-0789
[8]	PRESBITERO A, TENG-CALLEJA M. Job attitudes and career behaviors relating to employees' perceived incorporation of artificial intelligence in the workplace: a career self-management perspective [J]. Personnel Review, 2022, 52(4): 1169-87. https://doi.org/10.1108/pr-02-2021-0103
[9]	BROUGHAM D, HAAR J. Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace [J]. Journal of Management & Organization, 2018, 24(2): 239-57. https://doi.org/10.1017/jmo.2016.55
[10]	BLAU G J. The measurement and prediction of career commitment [J]. Journal of occupational Psychology, 1985, 58(4): 277-88. https://doi.org/10.1111/j.2044-8325.1985.tb00201.x
[11]	GAITHER C A. Career commitment: a mediator of the effects of job stress on pharmacists' work-related attitudes [J]. Journal of the American Pharmaceutical Association (1996), 1999, 39(3): 353-61. https://doi.org/10.1016/S1086-5802(16)30437-5
[12]	LONDON M. Toward a theory of career motivation [J]. Academy of management review, 1983, 8(4): 620-30. https://doi.org/10.1111/j.2044-8325.1985.tb00201.x
[13]	ALMOSAWI F, ALDOSERI N, AL-SARTAWI A. The impact of artificial intelligence accounting systems on workforce productivity and job satisfaction [M]. Business Analytical Capabilities and Artificial Intelligence-enabled Analytics: Applications and Challenges in the Digital Era, Volume 2. Springer. 2024: 431-50. https://doi.org/10.1007/978-3-031-57242-5_35
[14]	VAN VIANEN A E. Person–environment fit: A review of its basic tenets [J]. Annual Review of Organizational Psychology and Organizational Behavior, 2018, 5(1): 75-101. https://doi.org/10.1146/annurev-orgpsych-032117-104702
[15]	CHUANG S. Indispensable skills for human employees in the age of robots and AI [J]. European Journal of Training and Development, 2024, 48(1/2): 179-95. https://doi.org/10.1108/EJTD-06-2022-0062
[16]	WRZESNIEWSKI A, DUTTON J E. Crafting a job: Revisioning employees as active crafters of their work [J]. Academy of management review, 2001, 26(2): 179-201. https://doi.org/10.2307/259118
[17]	CHANG P-C, RUI H, WU T. Job autonomy and career commitment: A moderated mediation model of job crafting and sense of calling [J]. Sage Open, 2021, 11(1): 21582440211004167. https://doi.org/10.1177/21582440211004167
[18]	STRICH F, MAYER A-S, FIEDLER M. What do I do in a world of artificial intelligence? Investigating the impact of substitutive decision-making AI systems on employees’ professional role identity [J]. Journal of the Association for Information Systems, 2021, 22(2): 9. https://doi.org/10.17705/1jais.00663.
[19]	LI J J, BONN M A, YE B H. Hotel employee's artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate [J]. Tourism management, 2019, 73: 172-81. https://doi.org/10.1016/j.tourman.2019.02.006
[20]	VAN DER HEIJDEN B I, DAVIES E M, VAN DER LINDEN D, et al. The relationship between career commitment and career success among university staff: The mediating role of employability [J]. European Management Review, 2022, 19(4): 564-80. https://doi.org/org/10.1111/emre.12503
[21]	ZHU D, KIM P B, MILNE S, et al. A meta-analysis of the antecedents of career commitment [J]. Journal of Career Assessment, 2021, 29(3): 502-24. https://doi.org/10.1177/1069072720956983
[22]	YU X, XU S, ASHTON M. Antecedents and outcomes of artificial intelligence adoption and application in the workplace: the socio-technical system theory perspective [J]. Information Technology & People, 2023, 36(1): 454-74. https://doi.org/10.1108/ITP-04-2021-0254
[23]	MAN TANG P, KOOPMAN J, MCCLEAN S T, et al. When conscientious employees meet intelligent machines: An integrative approach inspired by complementarity theory and role theory [J]. Academy of Management journal, 2022, 65(3): 1019-54. https://doi.org/10.5465/amj.2020.1516
[24]	BANKINS S, OCAMPO A C, MARRONE M, et al. A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice [J]. Journal of organizational behavior, 2024, 45(2): 159-82. https://doi.org/10.1002/job.2735
[25]	CHENG B, LIN H, KONG Y. Challenge or hindrance? How and when organizational artificial intelligence adoption influences employee job crafting [J]. Journal of Business Research, 2023, 164: 113987. https://doi.org/10.1016/j.jbusres.2023.113987
[26]	EZIEFULE A O, ADELAKUN B O, OKOYE I N, et al. The role of AI in automating routine accounting tasks: Efficiency gains and workforce implications [J]. European Journal of Accounting, Auditing and Finance Research, 2022, 10(12): 109-34.
[27]	JARRAHI M H. Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making [J]. Business horizons, 2018, 61(4): 577-86. https://doi.org/10.1016/j.bushor.2018.03.007
[28]	NIU H-J. Investigating the effects of self-efficacy on foodservice industry employees’ career commitment [J]. International journal of hospitality management, 2010, 29(4): 743-50. https://doi.org/10.1016/j.ijhm.2010.03.006
[29]	TARAFDAR M, BEATH C M, ROSS J W. Using AI to enhance business operations [J]. MIT Sloan Management Review, 2019, 60(4). https://doi.org/10.7551/mitpress/12588.003.0015.
[30]	CHEN D R, MYRTLE R, LIU C, et al. Job and career influences on the career commitment of health care executives: The mediating effect of job satisfaction [J]. Journal of Health Organization and Management, 2011, 25(6): 693-710. https://doi.org/10.1108/14777261111178565
[31]	ASHRI R. The AI-powered workplace: how artificial intelligence, data, and messaging platforms are defining the future of work [M]. Apress, 2019.
[32]	BAGIS F, YULIANEU A. Enhancing Employee Performance through AI-Enabled HR Analytics: Exploring the Roles of Job Crafting, Perceived Risk, and Employee Engagement [J]. Journal of Digitovation and information system, 2024, 4(1): 81–97-81–97. https://doi.org/10.54433/JDIIS.2024100039
[33]	SON S, KIM D-Y. Organizational career growth and career commitment: Moderated mediation model of work engagement and role modeling [J]. The International Journal of Human Resource Management, 2021, 32(20): 4287-310. https://doi.org/10.1080/09585192.2019.1657165
[34]	TIMS M, BAKKER A B. Job crafting: Towards a new model of individual job redesign [J]. SA Journal of Industrial Psychology, 2010, 36(2): 1-9. https://doi.org/10.4102/sajip.v36i2.841.
[35]	TIMS M, BAKKER A B, DERKS D. Job crafting and job performance: A longitudinal study [J]. European Journal of Work and Organizational Psychology, 2015, 24(6): 914-28. https://doi.org/10.1080/1359432x.2014.969245
[36]	TIMS M, BAKKER A B, DERKS D. Development and validation of the job crafting scale [J]. Journal of vocational behavior, 2012, 80(1): 173-86. https://doi.org/10.1016/j.jvb.2011.05.009
[37]	CHEN N, ZHAO X, WANG L. The effect of job skill demands under artificial intelligence embeddedness on employees’ job performance: A moderated double-edged sword model [J]. Behavioral Sciences, 2024, 14(10): 974. https://doi.org/10.3390/bs14100974
[38]	SIDERSKA J. Robotic Process Automation—a driver of digital transformation? [J]. Engineering Management in Production and Services, 2020, 12(2): 21-31.
[39]	URBANAVICIUTE I, LAZAUSKAITE-ZABIELSKE J. The quality of working life from a person-centred perspective: linking job crafting, work environment types and work engagement [J]. Personnel Review, 2023, 52(8): 1991-2007. https://doi.org/10.1108/PR-04-2021-0243
[40]	WISSKIRCHEN G, BIACABE B T, BORMANN U, et al. Artificial intelligence and robotics and their impact on the workplace [J]. IBA Global Employment Institute, 2017, 11(5): 49-67.
[41]	KIM H, IM J, QU H. Exploring antecedents and consequences of job crafting [J]. International Journal of Hospitality Management, 2018, 75: 18-26. https://doi.org/10.1016/j.ijhm.2018.02.014
[42]	MORANDINI S, FRABONI F, DE ANGELIS M, et al. The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations [J]. Informing Science, 2023, 26: 39-68. https://doi.org/ https://dx.doi.org/10.28945/5078
[43]	CARDON P W, MARSHALL B. Can AI be your teammate or friend? Frequent AI users are more likely to grant humanlike roles to AI [J]. Business and Professional Communication Quarterly, 2024, 87(4): 654-69. https://doi.org/10.1177/23294906241282764
[44]	ZIYING L, XIAOMEI Z, JUAN G. Influence of Man-Machine Cooperation Degree on Employees' Job Crafting in AI Application Scenarios: The Mediating Role of Psychological Empowerment [J]. Chinese Personnel Science, 2023, 50(9): 35.
[45]	KIM E-J, PARK S. Linking work meaningfulness, engagement and job crafting to career commitment [J]. Baltic Journal of Management, 2024, 19(5): 601-21. https://doi.org/10.1108/BJM-02-2024-0077
[46]	DEMEROUTI E. Design your own job through job crafting [J]. European psychologist, 2014. https://doi.org/10.1027/1016-9040/a000188
[47]	BUKARTAITE R, HOOPER D. Automation, artificial intelligence and future skills needs: an Irish perspective [J]. European Journal of Training and Development, 2023, 47(10): 163-85. https://doi.org/10.1108/EJTD-03-2023-0045
[48]	ROŽMAN M, OREŠKI D, TOMINC P. Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA Environment [J]. Sustainability, 2023, 15(6): 5019. https://doi.org/10.3390/su15065019
[49]	JAISWAL A, ARUN C J, VARMA A. Rebooting employees: Upskilling for artificial intelligence in multinational corporations [M]. Artificial Intelligence and International HRM. Routledge. 2023: 114-43.
[50]	MIRBABAIE M, BRüNKER F, MöLLMANN N R, et al. The rise of artificial intelligence–understanding the AI identity threat at the workplace [J]. Electronic Markets, 2022: 1-27. https://doi.org/10.1007/s12525-021-00496-x
[51]	NAM T. Technology usage, expected job sustainability, and perceived job insecurity [J]. Technological Forecasting and Social Change, 2019, 138: 155-65. https://doi.org/10.1016/j.techfore.2018.08.017
[52]	JIA N, LUO X, FANG Z, et al. When and how artificial intelligence augments employee creativity [J]. Academy of Management Journal, 2024, 67(1): 5-32. https://doi.org/10.5465/amj.2022.0426
[53]	CHANG P-C, ZHANG W, CAI Q, et al. Does AI-Driven technostress promote or hinder employees’ artificial intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy [J]. Psychology Research and Behavior Management, 2024: 413-27. https://doi.org/10.2147/PRBM.S441444
[54]	ROŠKOVá E, FARAGOVá L. Job crafting, work engagement, burnout: Mediating role of self-efficacy [J]. Studia Psychologica, 2020, 62(2): 148-63. https://doi.org/10.31577/sp.2020.02.797
[55]	KYAW H H Y. The Effect of AI Awareness, Perceived Job Security, Employee Engagement, and Self-efficacy on Turnover Intention among Thai Employees in Hotel Business [J]. 2023.
[56]	KOO B, CURTIS C, RYAN B. Examining the impact of artificial intelligence on hotel employees through job insecurity perspectives [J]. International Journal of Hospitality Management, 2021, 95: 102763. https://doi.org/10.1016/j.ijhm.2020.102763
[57]	SUDDABY R, GENDRON Y, LAM H. The organizational context of professionalism in accounting [J]. Accounting, organizations and society, 2009, 34(3-4): 409-27. https://doi.org/10.1016/j.aos.2009.01.007
[58]	PODSAKOFF P M, MACKENZIE S B, LEE J-Y, et al. Common method biases in behavioral research: a critical review of the literature and recommended remedies [J]. Journal of applied psychology, 2003, 88(5): 879. https://doi.org/10.1037/0021-9010.88.5.879
[59]	BARON R M, KENNY D A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations [J]. Journal of personality and social psychology, 1986, 51(6): 1173. https://doi.org/10.1037/0022-3514.51.6.1173
[60]	PARK I-J, JUNG H. Relationships among future time perspective, career and organizational commitment, occupational self-efficacy, and turnover intention [J]. Social Behavior and Personality: an international journal, 2015, 43(9): 1547-61.
[61]	CHEN S, XUE Y, CHEN H, et al. Making a commitment to your future: investigating the effect of career exploration and career decision-making self-efficacy on the relationship between career concern and career commitment [J]. Sustainability, 2021, 13(22): 12816.
[62]	HUANG W, ZHANG S, LI H. Effects of person-job fit on occupational commitment among kindergarten teachers: Occupational well-being as mediator and perceived organizational support as moderator [J]. BMC psychology, 2023, 11(1): 402.

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Liu, X., Chen, Y. (2025). The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness. American Journal of Applied Psychology, 14(3), 101-112. https://doi.org/10.11648/j.ajap.20251403.14

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Liu, X.; Chen, Y. The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness. Am. J. Appl. Psychol. 2025, 14(3), 101-112. doi: 10.11648/j.ajap.20251403.14

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Liu X, Chen Y. The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness. Am J Appl Psychol. 2025;14(3):101-112. doi: 10.11648/j.ajap.20251403.14

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@article{10.11648/j.ajap.20251403.14,
  author = {Xuan Liu and Yuci Chen},
  title = {The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness
},
  journal = {American Journal of Applied Psychology},
  volume = {14},
  number = {3},
  pages = {101-112},
  doi = {10.11648/j.ajap.20251403.14},
  url = {https://doi.org/10.11648/j.ajap.20251403.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajap.20251403.14},
  abstract = {Amid the wave of digital transformation, the widespread adoption of artificial intelligence (AI) has become an unstoppable trend. An increasing number of organizations are embracing AI to boost efficiency, streamline processes, and enhance decision-making quality. However, while AI helps improve organizational performance, it also exerts a profound influence on employees’ professional attitudes and behaviors. Exploring the impact of artificial intelligence applications on employees' career commitment is crucial. Grounded in the person-environment fit theory, this study aims to investigate how AI usage influences employees' career commitment through the mediating role of job crafting and the moderating role of AI awareness. Based on an empirical analysis of two hundred and two survey responses, the study reveals that AI usage positively correlates with employees' career commitment. Job crafting mediates this relationship, enhancing the positive effect of AI usage on career commitment. Additionally, AI awareness functions as a moderator, negatively adjusting both the impact of AI usage on job crafting and the indirect effect of AI usage on career commitment through job crafting. The research findings not only deepen our understanding of the relationship between AI usage and employee career commitment, but also offer theoretical grounding and practical guidance for organizations seeking to manage employee uncertainty and negative expectations while advancing intelligent transformation.},
 year = {2025}
}

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TY  - JOUR
T1  - The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness

AU  - Xuan Liu
AU  - Yuci Chen
Y1  - 2025/06/26
PY  - 2025
N1  - https://doi.org/10.11648/j.ajap.20251403.14
DO  - 10.11648/j.ajap.20251403.14
T2  - American Journal of Applied Psychology
JF  - American Journal of Applied Psychology
JO  - American Journal of Applied Psychology
SP  - 101
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PB  - Science Publishing Group
SN  - 2328-5672
UR  - https://doi.org/10.11648/j.ajap.20251403.14
AB  - Amid the wave of digital transformation, the widespread adoption of artificial intelligence (AI) has become an unstoppable trend. An increasing number of organizations are embracing AI to boost efficiency, streamline processes, and enhance decision-making quality. However, while AI helps improve organizational performance, it also exerts a profound influence on employees’ professional attitudes and behaviors. Exploring the impact of artificial intelligence applications on employees' career commitment is crucial. Grounded in the person-environment fit theory, this study aims to investigate how AI usage influences employees' career commitment through the mediating role of job crafting and the moderating role of AI awareness. Based on an empirical analysis of two hundred and two survey responses, the study reveals that AI usage positively correlates with employees' career commitment. Job crafting mediates this relationship, enhancing the positive effect of AI usage on career commitment. Additionally, AI awareness functions as a moderator, negatively adjusting both the impact of AI usage on job crafting and the indirect effect of AI usage on career commitment through job crafting. The research findings not only deepen our understanding of the relationship between AI usage and employee career commitment, but also offer theoretical grounding and practical guidance for organizations seeking to manage employee uncertainty and negative expectations while advancing intelligent transformation.
VL  - 14
IS  - 3
ER  -

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Xuan Liu

College of Economics and Management, Nanjing Tech University, Nanjing, China

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http://orcid.org/0000-0003-3799-2632
Yuci Chen

College of Economics and Management, Nanjing Tech University, Nanjing, China

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http://orcid.org/0009-0006-6283-9030

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Liu, X., Chen, Y. (2025). The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness. American Journal of Applied Psychology, 14(3), 101-112. https://doi.org/10.11648/j.ajap.20251403.14

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Liu, X.; Chen, Y. The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness. Am. J. Appl. Psychol. 2025, 14(3), 101-112. doi: 10.11648/j.ajap.20251403.14

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AMA Style

Liu X, Chen Y. The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness. Am J Appl Psychol. 2025;14(3):101-112. doi: 10.11648/j.ajap.20251403.14

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@article{10.11648/j.ajap.20251403.14,
  author = {Xuan Liu and Yuci Chen},
  title = {The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness
},
  journal = {American Journal of Applied Psychology},
  volume = {14},
  number = {3},
  pages = {101-112},
  doi = {10.11648/j.ajap.20251403.14},
  url = {https://doi.org/10.11648/j.ajap.20251403.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajap.20251403.14},
  abstract = {Amid the wave of digital transformation, the widespread adoption of artificial intelligence (AI) has become an unstoppable trend. An increasing number of organizations are embracing AI to boost efficiency, streamline processes, and enhance decision-making quality. However, while AI helps improve organizational performance, it also exerts a profound influence on employees’ professional attitudes and behaviors. Exploring the impact of artificial intelligence applications on employees' career commitment is crucial. Grounded in the person-environment fit theory, this study aims to investigate how AI usage influences employees' career commitment through the mediating role of job crafting and the moderating role of AI awareness. Based on an empirical analysis of two hundred and two survey responses, the study reveals that AI usage positively correlates with employees' career commitment. Job crafting mediates this relationship, enhancing the positive effect of AI usage on career commitment. Additionally, AI awareness functions as a moderator, negatively adjusting both the impact of AI usage on job crafting and the indirect effect of AI usage on career commitment through job crafting. The research findings not only deepen our understanding of the relationship between AI usage and employee career commitment, but also offer theoretical grounding and practical guidance for organizations seeking to manage employee uncertainty and negative expectations while advancing intelligent transformation.},
 year = {2025}
}

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TY  - JOUR
T1  - The Impact of Artificial Intelligence Usage on Employee Career Commitment: The Moderating Role of Artificial Intelligence Awareness

AU  - Xuan Liu
AU  - Yuci Chen
Y1  - 2025/06/26
PY  - 2025
N1  - https://doi.org/10.11648/j.ajap.20251403.14
DO  - 10.11648/j.ajap.20251403.14
T2  - American Journal of Applied Psychology
JF  - American Journal of Applied Psychology
JO  - American Journal of Applied Psychology
SP  - 101
EP  - 112
PB  - Science Publishing Group
SN  - 2328-5672
UR  - https://doi.org/10.11648/j.ajap.20251403.14
AB  - Amid the wave of digital transformation, the widespread adoption of artificial intelligence (AI) has become an unstoppable trend. An increasing number of organizations are embracing AI to boost efficiency, streamline processes, and enhance decision-making quality. However, while AI helps improve organizational performance, it also exerts a profound influence on employees’ professional attitudes and behaviors. Exploring the impact of artificial intelligence applications on employees' career commitment is crucial. Grounded in the person-environment fit theory, this study aims to investigate how AI usage influences employees' career commitment through the mediating role of job crafting and the moderating role of AI awareness. Based on an empirical analysis of two hundred and two survey responses, the study reveals that AI usage positively correlates with employees' career commitment. Job crafting mediates this relationship, enhancing the positive effect of AI usage on career commitment. Additionally, AI awareness functions as a moderator, negatively adjusting both the impact of AI usage on job crafting and the indirect effect of AI usage on career commitment through job crafting. The research findings not only deepen our understanding of the relationship between AI usage and employee career commitment, but also offer theoretical grounding and practical guidance for organizations seeking to manage employee uncertainty and negative expectations while advancing intelligent transformation.
VL  - 14
IS  - 3
ER  -

Copy | Download