Does the application of artificial intelligence affect task performance in knowledge workers?
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Cite this article:
Sun, H.
(2025). Does the application of artificial intelligence affect task performance in knowledge workers?.
Social Behavior and Personality: An international journal,
53(11),
e15493.
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With the widespread adoption of artificial intelligence (AI) in the business sector, increasingly complex tasks are managed by AI-powered automated systems, which may lead knowledge workers to experience career uncertainty, resulting in negative work states (e.g., slacking or desiring to resign) and pessimism. Hence, this study investigated how knowledge workers’ AI awareness predicted leader–member exchange (LMX) and task performance, with affective commitment as a mediating variable, by surveying 360 knowledge workers in China. The results showed that AI awareness negatively predicted LMX and task performance, while LMX positively predicted affective commitment and task performance. LMX and affective commitment also mediated the relationship between AI awareness and task performance. The study contributes to the emerging discourse on human–AI workplace integration by delineating the psychological mechanisms underlying technological disruption, while providing empirically grounded recommendations for sustaining workforce engagement during digital transformation initiatives.
Article Highlights
- Artificial intelligence awareness among knowledge workers negatively predicted leader–member exchange and task performance.
- Leader–member exchange and affective commitment mediated the relationship between artificial intelligence awareness and task performance.
- The findings underscore the critical role of socioemotional factors in mitigating artificial-intelligence-induced workplace disruptions, suggesting organizations should strengthen leader–subordinate relationships while addressing artificial-intelligence-related anxieties through targeted interventions.
Artificial intelligence (AI) refers to computer systems designed to perform tasks that typically require human intelligence, including learning, reasoning, and decision making (Russell & Norvig, 2022). AI, also referred to as machine intelligence, has become integral to various aspects of modern life, as it enables systematic and programmed processes while reducing reliance on human labor (Russell & Norvig, 2022). Human-like reasoning has played a critical role in advancing technological progress and AI frameworks are now employed across various sectors, including manufacturing, business, healthcare, law, and technology, such as autonomous vehicles that utilize AI to sense and navigate their environment (Russell & Norvig, 2022). My primary aim in the current study was to raise public awareness of artificial intelligence and its impact.
In China, large enterprises are increasingly adopting AI systems to enhance operational efficiency, meet deadlines, and ensure the accuracy of data inputs (Psarommatis et al., 2022). According to Liang et al. (2022) China’s AI ecosystem showcases specialized platforms like Baidu PaddlePaddle (industrial deep learning), Alibaba Enhanced Technology Brain (supply chain), Tencent Youtu (facial recognition), iFlyTek (speech AI), and SenseTime (smart cities). These platforms exemplify China’s sector-specific AI solutions with integrated data processing, particularly enhancing manufacturing, urban management, and consumer services through risk mitigation and time-sensitive optimization. AI applications are particularly prominent in manufacturing and operations, where they facilitate timely production and streamline processes (Bustillo et al., 2021). Additionally, AI plays a vital role in management, with many organizations using it to track employee records and store critical company data for easy retrieval during decision making (Cunningham et al., 2021).
While AI adoption enhances operational efficiency and competitiveness, it simultaneously induces employee anxiety regarding job displacement (Mariani & Borghi, 2021). According to McCartney and McCartney (2020), Frey and Osborne’s (2017) projection of 95% job replacement in hospitality exemplifies AI’s dual impact on employment. Termed AI awareness (Brougham & Haar, 2018), this phenomenon demonstrates significant correlations with job burnout, organizational commitment (Kong et al., 2021), and even counterproductive behaviors like service sabotage (Ma & Ye, 2022). Existing studies have predominantly examined specific outcomes, such as turnover intention (He et al., 2024; Liang et al., 2022), neglecting comprehensive investigations of the impact of AI awareness. Particularly for knowledge workers with specialized expertise, AI sensitivity as a distinct variable remains understudied, creating a literature gap regarding how AI awareness influences supervisor relationships, affective commitment, and task performance.
Knowledge Worker
Drucker (1994) introduced the concept of knowledge workers and defined them as individuals who master and apply symbols and concepts, working primarily with knowledge and information. Knowledge workers, often referred to as k-workers in Asia, are typically characterized by high levels of education and specialized professional skills distinguished by their high demand, short life cycles, and critical importance to the organization (Drucker, 1994). Such competencies include symbolic analysis (Despres & Hiltrop, 1995; Lee & Maurer, 1997; Yang et al., 2006), information analysis, distribution, production capabilities (Banerjee, 2006), and proficiency in utilizing tools or techniques (Barley & Orr, 1997; Kubo & Saka, 2002). In the context of China, Yang et al. (2006) specifically defined Chinese knowledge workers as individuals who possess advanced educational qualifications, professional expertise, and strong problem-solving abilities.
Artificial Intelligence Awareness
The concept of artificial intelligence awareness (Brougham & Haar, 2018) captures employees’ perception of AI’s impact on their career prospects. He et al. (2024) described AI awareness as employees’ recognition of AI as a disruptive force affecting future job opportunities, which emerges after organizational AI adoption. This awareness can drive skill development and operational improvements (He et al., 2024), while also enhancing organizational engagement and employability (Kumar et al., 2023). Employees may perceive AI as either a competency-enhancing opportunity or a job threat (Brougham & Haar, 2018; Ding, 2021). The latter stems from workplace restructuring and role redefinition, triggering proactive adaptation or resistance behaviors (Frey & Osborne, 2017; Kong et al., 2021).
Leader–Member Exchange
Unlike the emerging concept of AI awareness, leader–member exchange has been extensively validated through rigorous experimental studies in psychology (Dansereau et al., 1975). Rooted in social exchange theory (SET), leader–member exchange (LMX) refers to the quality of the relationship between leaders and their subordinates, illustrating how leaders cultivate varying types of exchanges with different followers over time within the same group (Dansereau et al., 1975). The LMX model offers an alternative framework for understanding the superior–subordinate relationship. This model is based on the premise that role development naturally leads to differences in role definitions and the nature of exchanges between leaders and their members (Gerstner & Day, 1997).
LMX theory posits leader–member relationships exist on a negotiation continuum, bifurcated into in-group (high-quality LMX with trust/incentives/autonomy) and out-group (low-quality LMX with contractual compliance only) dynamics (Gerstner & Day, 1997). In-group members receive substantive feedback and role-expansion opportunities, while out-group members are restricted to formal contract terms and routine tasks (Gu et al., 2024).
Affective Commitment
Meyer et al. (1993) defined affective commitment as employees’ psychological attachment to their organization, and it significantly reduces turnover intention through emotional bonds (Chernyak-Hai et al., 2024). Committed employees exhibit organizational enthusiasm and derive work satisfaction (Pulido-Martos et al., 2024), representing a psychological state that reflects both employee–organization emotional ties and internal work perceptions (Torlak et al., 2024).
Task Performance
Task performance encompasses core role responsibilities (e.g., technical tasks) that directly and indirectly support organizational effectiveness (Borman & Motowidlo, 1993). Key situational influencers include human resource practices enhancing job crafting (Dalgıç et al., 2024), respectful engagement boosting work engagement (Basit, 2019), and employee involvement climate (Smith et al., 2018).
The Current Study
Drawing on SET, this study tested the relationships between knowledge workers’ AI awareness, LMX, task performance, and the mediating role of affective commitment.
Figure 1. Theoretical Framework
Note. AI = artificial intelligence.
Artificial Intelligence Awareness and Leader–Member Exchange
In comparison to human employees, AI offers advantages such as a broader range of tasks and extended working hours. Competing with AI can place significant psychological pressure on employees, depleting their psychological resources. According to Tursunbayeva and Renkema (2023), this situation forces employees to allocate more resources toward acquiring new job-related skills. Conservation of resources theory suggests that when individuals perceive their resources are at risk of being lost, they tend to take protective actions to safeguard their existing resources or eliminate potential threats to those resources (Hobfoll, 2002). In line with this perspective, the high stress induced by AI awareness and the substantial depletion of employees’ resources may activate their psychological defense mechanisms to prevent further loss. In such scenarios, employees may perceive their supervisors’ use of AI as an attempt to replace them, which could create tension in their relationship with their supervisors and, as a result, diminish the quality of the LMX between employee and employer. Thus, I proposed the following hypothesis:
Hypothesis 1: There will be a negative relationship between knowledge workers’ artificial intelligence awareness and leader–member exchange.
Artificial Intelligence Awareness and Task Performance
As Chinese companies increasingly integrate AI technologies into workplace operations, employees may experience uncertainty regarding their future career prospects, which could lead to negative psychological states and a sense of pessimism (Liang et al., 2022). AI awareness has the potential to contribute to issues such as depression, job burnout, and emotional exhaustion (He et al., 2024). In this context, the introduction and widespread use of AI has raised concerns about the displacement of human employees (Kwok & Virdi, 2022). When employees become aware of this potential threat, they are likely to experience job insecurity. A previous study has indicated that job burnout and job insecurity can trigger withdrawal intentions and behavior among employees (Jiang & Lavaysse, 2018), which may eventually result in negative emotions or behaviors, such as diminished affective commitment and task performance. Thus, this study proposed the following hypothesis:
Hypothesis 2: There will be a negative relationship between knowledge workers’ artificial intelligence awareness and task performance.
Leader–Member Exchange and Affective Commitment
Affective commitment refers to the positive emotional connection, attachment, and sense of involvement that employees feel toward their organization (Meyer et al., 1993). Previous research has shown that the quality of LMX is positively associated with affective commitment, as high-quality LMX fulfills various socioemotional needs of subordinates (e.g., affiliation, esteem, and emotional reinforcement; Suhendi & Danasasmita, 2024). Subordinates who receive strong support from their supervisors are likely to develop a sense of belonging to and connection with the organization, viewing the supervisor as a representative of the organization (Muliawan et al., 2024). As a result, subordinates are more inclined to remain in a work environment where they feel valued and supported (Schwarz et al., 2025). In other words, employees with high-quality LMX tend to have their emotional needs (e.g., love, respect, and gratitude) met by both their supervisor and the organization, which, in turn, fosters greater affective commitment. Thus, I proposed the following hypothesis:
Hypothesis 3: There will be a significant positive relationship between knowledge workers’ leader–member exchange and affective commitment.
Affective Commitment and Task Performance
Previous studies have suggested that employees with strong emotional attachment to the organization are more likely to be motivated to exert extra effort on behalf of their team in return (Yao et al., 2024). This tendency can be attributed to the positive treatment they have received from leadership (e.g., LMX) or the organization itself. Positive treatment, as a form of social interaction, generates reciprocity expectations, which ultimately drive individuals to perform at their best for the organization (Torlak et al., 2024). Employees with higher levels of affective commitment are expected to demonstrate improved task performance (Fatyandri & Huang, 2023). Thus, I proposed the following hypothesis:
Hypothesis 4: There will be a significant positive relationship between knowledge workers’ affective commitment and task performance.
Artificial Intelligence Awareness, Leader–Member Exchange, Affective Commitment, and Task Performance
The comparative advantages of AI, including its capacity to perform a wide array of tasks and operate continuously without fatigue, create significant competitive pressure for human employees, triggering substantial psychological resource depletion (Tursunbayeva & Renkema, 2023). Drawing on conservation of resources theory (Hobfoll, 2002), this study posited that when employees perceive AI adoption as threatening their job security, they activate psychological defense mechanisms to conserve remaining resources. Crucially, this process may manifest in the LMX domain, so that employees interpret organizational AI implementation as supervisory endorsement of technological replacement, thereby eroding trust in leadership and ultimately degrading LMX quality.
SET posits that LMX leverages its advantages by fostering relationships of mutual exchange between leaders and subordinates (Dansereau et al., 1975). Blau (1964) suggested that the foundation of LMX lies in the principles of SET, proposing that both tangible and intangible forms of reciprocation between supervisors and subordinates enhance the quality of their relationship. As a result, subordinates in these exchange processes may provide valuable services that leaders deem important, such as dedicating themselves to achieving managerial goals or contributing to the organization’s success (Dansereau et al., 1975). This finding highlights the significant role of affective commitment in the social exchange process outlined by LMX.
Meyer and Allen (1991) argued that individuals with stronger affective commitment are more inclined to remain with their organization. Employees with higher levels of affective commitment tend to be more loyal and devoted to their organization, which drives them to demonstrate a greater willingness to contribute, ultimately improving their task performance (Schwarz et al., 2025; Suhendi & Danasasmita, 2024). Hence, I proposed the following hypotheses:
Hypothesis 5: Leader–member exchange will mediate the relationship between artificial intelligence awareness and affective commitment.
Hypothesis 6: Affective commitment will mediate the relationship between leader–member exchange and task performance.
Hypothesis 7: Leader–member exchange and affective commitment will act as sequential mediators of the relationship between artificial intelligence awareness and task performance.
Method
Participants and Procedure
Sekaran and Bougie (2003) asserted that a sample must consist of a sufficient number of appropriate individuals from the target population to accurately estimate population parameters. In this study I used organizational recruitment channels to approach 623 potentially eligible knowledge workers from 10 Chinese companies. Of these, 480 respondents agreed to participate in this research. Hence, the study achieved a 77% response rate among the surveyed population. According to Yang et al. (2006), knowledge workers represent a distinct group characterized by having at least a bachelor’s degree or higher level of education. Thus, I excluded questionnaires completed by 120 respondents with a high school diploma or lower level of education, resulting in a final analytical sample of 360 qualified participants.
This study adopted a 12-month cross-sectional research design that involved a two-stage process: a 6-month email recruitment phase targeting company employees, followed by standardized questionnaire administration to consenting participants for final data collection. To ensure methodological rigor, I explicitly informed participants that the study was completely anonymous and I would not provide them with any monetary compensation. Respondents completed the survey on the Questionnaire Star platform (https://questionstar.com).
Measures
This study used a self-administered questionnaire comprising 26 items, with the first three items assessing respondents’ demographic profiles and the remaining items measuring the four core variables. All items were assessed on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
Artificial Intelligence Awareness
This study measured AI awareness with a four-item scale from Teng et al. (2024). A sample item is “I think my job could be replaced by AI.”
Leader–Member Exchange
This study assessed LMX using the six-item scale developed by Graen and Uhl-Bien (1995). A sample item is “I usually know how satisfied my supervisor is with my work.”
Affective Commitment
I measured affective commitment using a six-item scale from Meyer et al. (1993). A sample item is “I would be very happy to spend the rest of my career with this organization.”
Task Performance
I assessed task performance using the seven-item scale from Koopmans et al. (2014). A sample item is “I managed to plan my work so that it was completed on time.”
Data Analysis
I analyzed data obtained from the self-administered questionnaires using partial least squares structural equation modeling, which is particularly suitable for examining a set of interrelated research questions by modeling the relationships among various constructs (Anderson & Gerbing, 1988). Specifically, I applied SmartPLS 4.0 and SPSS 20.0 software to process the data.
Results
Demographic Profile
The demographic profile of the participants is shown in Table 1. The majority of participants were women, with the rest being men, and all participants were aged between 24 and 58 years (M = 35.3). To ensure the relevance of the sample, educational background served as the primary selection criterion, with a fairly even spread across the two groups of bachelor’s versus master’s or higher degrees.
Table 1. Demographic Characteristics of the Respondents
Composite Reliability and Convergent Validity
The first step in partial least squares structural equation modeling is to evaluate the measurement model. According to Hair et al. (2014), a composite reliability value greater than .70 is acceptable. As shown in Table 2, the composite reliability values for AI, LMX, affective commitment, and task performance all exceeded .70, thereby meeting the criterion for establishing convergent validity.
Table 2. Composite Reliability of Measurement Model
Note. AI = artificial intelligence.
Next, I assessed the model’s convergent validity. According to Hair et al. (2014), for convergent validity to be deemed acceptable, both factor loadings and average variance extracted should be greater than .50. As shown in Table 3, the factor loadings for AI, LMX, affective commitment, and task performance all surpassed .50, and the average variance extracted values also exceeded .50. These findings confirmed that all four variables exhibited acceptable convergent validity.
Table 3. Convergent Validity of Measurement Model
Note. AVE = average variance extracted.
Correlation Analysis
Table 4 shows the Pearson correlation coefficients of the study variables. AI was negatively correlated with LMX, affective commitment, and task performance. LMX was positively correlated with affective commitment and task performance. Affective commitment and task performance also exhibited a positive correlation.
Table 4. Correlation Analysis of Study Variables
Note. AI = artificial intelligence
** p < .01.
Analysis of the Structural Model
R² values are used to assess the quality of each variable in the structural model. If the R² value falls within the range of 0 to 1, it is considered acceptable (Hair et al., 2014). As shown in Figure 2, both affective commitment and task performance, as endogenous variables, showed acceptable values, as did LMX.
Figure 2. Structural Model for Individual Latent Variables
Note. AIA = artificial intelligence awareness; LMX = leader–member exchange; AC = affective commitment; TP = task performance.
Hypothesis Testing
I tested the hypotheses using the bootstrapping technique, which involved repeated random sampling with replacement from the original sample. I then used the standard error derived from this process to assess each hypothesis. Figure 3 illustrates results of this test applied to the model.
Figure 3. Hypothesis Testing of Research Model
Note. AIA = artificial intelligence awareness; LMX = leader–member exchange; AC = affective commitment; TP = task performance.
Table 5 displays the results for the proposed structural relationships among AI awareness, LMX, affective commitment, and task performance. For a two-tailed test at a 5% significance level, the required t value is at least 1.65. The findings indicated that AI awareness was positively related to LMX and task performance. Furthermore, LMX was positively related to affective commitment and task performance. Therefore, Hypotheses 1, 2, 3, and 4 were supported.
Table 5. Hypothesis Testing Outcome
Note. Results are based on 95% confidence intervals. AIA = artificial intelligence awareness; LMX = leader–member exchange; AC = affective commitment; TP = task performance.
I evaluated the extent of the direct path being explained by calculating the variation accounted for, which allowed me to examine the mediating effect of LMX on the relationship between AI awareness and LMX, and the mediating effect of affective commitment on the relationship between LMX and task performance. A variation accounted for value of < .20 indicates no mediation effect, .20–.80 suggests partial mediation, and a > .80 implies full mediation. As shown in Table 6, LMX and affective commitment fully mediated the relationship between AI and task performance. Therefore, Hypotheses 5, 6, and 7 were supported.
Table 6. Mediation Effect Hypothesis Testing Outcome
Note. Results are based on 95% confidence intervals. AIA = artificial intelligence awareness; LMX = leader–member exchange; AC = affective commitment; TP = task performance; VAF = variance accounted for.
Discussion
This study examined the impact of Chinese knowledge workers’ AI awareness through questionnaire data, revealing negative effects on LMX and task performance (Brougham & Haar, 2018; Kong et al., 2021). AI-induced job insecurity eroded leader–subordinate relationships, suggesting organizations should pair AI adoption with training and transparent communication to mitigate workforce anxieties.
My findings align with those of Muliawan et al. (2024) in that LMX positively predicted affective commitment, suggesting high-quality leader–member relationships enhance organizational attachment. However, declining LMX may weaken this commitment, underscoring the need for differentiated management strategies to maintain knowledge workers’ emotional engagement. Furthermore, the study results revealed that LMX and affective commitment acted as sequential mediators of the link between AI awareness and task performance. Therefore, managers should cultivate high-quality LMX relationships, particularly when employees exhibit low AI awareness, to enhance organizational retention and performance. Conversely, elevated AI awareness coupled with LMX deterioration reduces affective commitment, ultimately impairing task performance.
This study synthesized SET and conservation of resources theory to elucidate the AI awareness, LMX, affective commitment, and task performance chain among knowledge workers. The results simultaneously validated these theories’ digital applicability and extended their scope to human–AI collaboration, advancing organizational behavior scholarship. From a practical viewpoint, my results revealed that managing Chinese knowledge workers during AI transformation requires addressing their autonomy–recognition needs through LMX quality enhancement, providing actionable strategies for China’s AI-intensive workplaces.
Limitations and Future Research
This study has several limitations. First, I measured the predictor variable (i.e., AI awareness), the independent variables (i.e., LMX and task performance), and the mediator (i.e., affective commitment) in the same group at the same time. As a result, common method bias cannot be entirely ruled out. To enhance the accuracy of future research, it would be beneficial to collect data on independent and mediating variables at different points in time.
Furthermore, although I gathered the data from a convenience sample of several Chinese companies, the sample size was relatively small. Consequently, the findings of this study may not be generalizable to the broader context of Chinese companies. Future research could aim to include more diverse work groups and consider various cultural contexts.
Conclusion
This study investigated the relationships among AI awareness, LMX, affective commitment, and task performance among knowledge workers in Chinese organizations. The findings revealed negative associations between AI awareness and both LMX and task performance, while LMX demonstrated a positive relationship with affective commitment, which, in turn, positively influenced task performance. Moreover, LMX and affective commitment mediated the indirect effect of AI awareness on task performance. These results underscore the detrimental impact of AI awareness on supervisor–subordinate dynamics and employee outcomes, contributing to the literature on artificial intelligence and SET by highlighting the psychological and behavioral consequences of AI integration in the workplace.
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Figure 1. Theoretical Framework
Note. AI = artificial intelligence.
Table 1. Demographic Characteristics of the Respondents
Table 2. Composite Reliability of Measurement Model
Note. AI = artificial intelligence.
Table 3. Convergent Validity of Measurement Model
Note. AVE = average variance extracted.
Table 4. Correlation Analysis of Study Variables
Note. AI = artificial intelligence
** p < .01.
Figure 2. Structural Model for Individual Latent Variables
Note. AIA = artificial intelligence awareness; LMX = leader–member exchange; AC = affective commitment; TP = task performance.
Figure 3. Hypothesis Testing of Research Model
Note. AIA = artificial intelligence awareness; LMX = leader–member exchange; AC = affective commitment; TP = task performance.
Table 5. Hypothesis Testing Outcome
Note. Results are based on 95% confidence intervals. AIA = artificial intelligence awareness; LMX = leader–member exchange; AC = affective commitment; TP = task performance.
Table 6. Mediation Effect Hypothesis Testing Outcome
Note. Results are based on 95% confidence intervals. AIA = artificial intelligence awareness; LMX = leader–member exchange; AC = affective commitment; TP = task performance; VAF = variance accounted for.
This research was funded by the Jiangsu College Philosophy and Social Science Research Program 2021 (Research on Salary Management in Local Universities under the High-Quality Development Framework; 2021SJA1885).
The data that support the findings of this study are available on request from the author.
Han Sun, Human Resources Department, Yancheng Teachers University, No. 2 South Xiwang Avenue, Yancheng City, Jiangsu Province 224007, People’s Republic of China. Email: [email protected]
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