1. Introduction

Status is crucial in the workplace. Higher-status individuals typically access more resources, information, and development opportunities. They also tend to receive more support from team members. However, the widespread integration of AI is profoundly changing both status hierarchies and competitive dynamics among employees. Individuals differ notably in how they accept and adapt to AI technologies. Some employees quickly master AI tools. This boosts their efficiency and competitiveness. Conversely, others feel psychological insecurity. This insecurity can result from steep learning curves or resistance to these new technologies [1]. AI’s arrival not only redefines the importance of traditional skills. It also, to some extent, blurs perceived capability differences among employees. This, in turn, influences their interactions. Knowledge sharing is a critical way for organizations to navigate the AI revolution. Nevertheless, AI-induced status shifts can make some employees anxious about their standing. This anxiety may lead them to adopt knowledge-hiding strategies. Such behavior hinders organizational learning and innovation. It also fosters inefficient teamwork and contributes to a tense team atmosphere. Indeed, studies show that AI introduction can trigger anxiety and career uncertainty among employees [2]. Therefore, a key challenge for organizations is to help employees adapt to AI. Organizations must also mitigate knowledge barriers that arise from different rates of technology adoption.

Existing research identifies status competition motivation as a key factor in knowledge sharing and hiding. This motivation has two main types: dominance-based and prestige-based [3]. Individuals with dominance-based motivation seek to improve their status by exerting control and authority. They often use information asymmetry as a competitive tactic [4]. This makes them more likely to hide knowledge. In contrast, those with prestige-based motivation tend to earn respect and recognition. They do this by showcasing their abilities and sharing knowledge [5]. Current research has mainly focused on how status competition motivation affects knowledge hiding in traditional team settings.

This study suggests that the political climate within organizational teams significantly moderates the link between status competition motivation and knowledge behaviors. Team political climate refers to members’ subjective views of power dynamics, resource allocation, and political behaviors in the organization [6]. In a highly politicized climate, competition among members often intensifies. This can reduce information sharing and increase knowledge hiding. Conversely, in a harmonious climate, members are usually more open. They are willing to share information to build prestige. This can enhance the team’s overall knowledge and creativity [7]. This research develops a model. The model hypothesizes that team political climate acts as a moderating variable, a boundary condition. It influences how dominance- and prestige-based motivations affect knowledge management behaviors. We predict that in highly politicized organizations, dominance-based motivation will more easily lead to knowledge hiding. In contrast, a harmonious team climate will encourage knowledge sharing driven by prestige-based motivation. This model aims to examine the interplay between team political climate, status motivation, and knowledge management behaviors as AI technologies become more sophisticated. The findings aim to provide theoretical support for organizations. These organizations want to improve their competitiveness and performance by tailoring strategies to different political climates.

2. Theoretical background

2.1. Status competition motivation theory and related research

Status competition motivation theory explains why individuals seek status at work. This theory draws from social comparison theory and organizational behavior research. It views status as a key resource in organizational competition. It also categorizes status-striving motivations into two main types: dominance-based and prestige-based [3]. Individuals with high dominance-based motivation aim to gain and secure status. They do this by controlling resources and creating information imbalances. They often use strategies like knowledge hiding. In contrast, individuals with high prestige-based motivation tend to earn respect. They achieve this by demonstrating competence and sharing knowledge. Their collaborative approach generally improves team performance more effectively. These different competitive strategies significantly affect knowledge flow and collaboration within organizations.

Status competition motivation theory is important for organizational management, especially in two key areas. First, consider resource allocation. Studies show dominance-motivated individuals tend to manipulate resource distribution for personal gain. In contrast, those with high prestige-based motivation enhance team effectiveness by sharing resources [5]. Second, consider team conflict and cooperation. Individuals with high dominance-based motivation are more likely to cause relationship conflicts. Those with prestige-based motivation help achieve goals through collaboration [4].

AI technology is reshaping internal status competition in organizations. This gives traditional status competition motivation theory new relevance. The theory suggests AI’s spread in the workplace means technical skill, especially in AI, is quickly replacing seniority and experience as new competitive capital. Employees with high dominance-based motivation may treat AI skills as private assets to control. Conversely, those with high prestige-based motivation will likely share their tech skills to build reputations and keep their status. The overall effects of these motivations on an organization can be positive or negative. This depends on the specific context and how effective management interventions are. Positively, dominance-based motivation can help protect critical resources in complex situations. This aids the organization in facing external threats. Prestige-based motivation can boost organizational learning and adaptability through knowledge sharing and innovation. Negatively, dominance-based motivation can stifle employee creativity [7]. Prestige-based motivation might lead to a spread of resources. This theory has practical significance. It helps organizations understand AI-driven employee behaviors and their outcomes. It also helps them identify and manage employees with different motivations. This offers valuable guidance for improving organizational effectiveness.

2.2. Impact of AI-induced status changes on employee knowledge behavior and its mediating mechanisms

Research shows status competition motivation critically influences how organizational members share and hide knowledge. This motivation has two main types, i.e., dominance-based or prestige-based [3]. People with strong dominance-based motivation often enhance their status. They show control and authority. They prefer information asymmetry as a competitive strategy [4]. As a result, they are more likely to hide knowledge. This stops others from gaining an advantage. Conversely, people with prestige-based motivation usually earn respect. They show their abilities, share knowledge, and help others [5]. Prestige-based motivation strongly encourages knowledge sharing. This reduces information gaps in the team.

Theoretical Postulates: Consider individuals with high dominance-based motivation. If they see AI causing big status changes, their main goal is still to control resources and keep authority [4]. AI is an emerging technology. It offers new ways to compete for resources and control. Dominance-driven people may believe AI can greatly raise their status. If so, they will likely see AI as a new tool. They can use it to assert control and get resources. They might actively use AI technology to strengthen their dominant position. They may also try to keep their advantage over others using this new technology.

However, some dominance-driven people may see minimal status change from AI. They might believe AI has a small impact on their current position. Their motivation will then likely focus on keeping the current power structure. These people might rely more on old ways of authority and control. They may not actively use AI to change their status.

Hypothesis 1a: The impact of AI-induced status changes on dominance-based competition motivation

Prestige-based motivation focuses on earning respect. People do this by showing competence and adding value [7]. Technology is changing rapidly. AI is an important new technology. It gives prestige-oriented people chances to show their professional skills and worth. If these people believe AI can greatly raise their status, they tend to see AI as a key tool. They can use it to show their abilities and make valuable contributions. Seeing this chance for big status change motivates prestige-oriented people. They will more actively learn and use AI technologies. Being good at AI shows their ability to innovate. It also helps them get more respect and recognition for their work. However, if prestige-oriented people think AI has little effect on their status, their motivation might decrease. They might then look for other ways to show their abilities. They may not see AI as the main way to earn respect.

Hypothesis 1b: The impact of ai-induced status changes on prestige-based competition motivation

People with high prestige-based motivation want to earn respect. They do this by showing their abilities and contributions. Research shows these people tend to share knowledge actively. This creates an open team learning space. It also improves how well the team works together [5]. When teams adopt AI, prestige-oriented people use knowledge sharing to close technology gaps within the team. They also help colleagues learn new technologies. This action helps knowledge exchange. It also makes team collaboration and innovation stronger. For these individuals, knowledge sharing is a key way to gain status. In digital workplaces, employees use this tactic to keep informal influence [8]. Therefore, in an environment with a lot of AI, we expect that people with prestige-based motivation will more actively show their skills and value. They will do this by sharing knowledge. This is especially true if they see AI as a way to improve their status.

Hypothesis 2a: prestige-based motivation influenced knowledge sharing

People with strong dominance-based motivation want to protect their position. So, they are more likely to hide important knowledge. This helps them keep an advantage over others [3]. This tendency is especially strong with AI technology. AI is advancing quickly. It can reduce the technology skill gap between team members. This threatens the status of people with high dominance-based motivation [6]. These people might hide knowledge. They do this to slow down others’ technological progress. They create information gaps. This makes it harder for others to copy or exceed their technical skills. Controlling key knowledge helps them keep their authority in the team. This knowledge hiding limits the team’s ability to adapt to technology. It can also weaken its teamwork and innovation [7]. So, in an environment affected by AI, dominance-driven people will likely hide knowledge more often to strengthen their status.

Hypothesis 2b: Dominance-based motivation influences knowledge hiding

2.3. The moderating role of team political climate

The team’s political climate involves complex power struggles and resource competition. This climate greatly affects how prestige-based motivation works [9]. In a very political climate, internal competition and fights for resources increase. This creates a difficult environment. Prestige-oriented people find it hard to improve their status by cooperating and sharing knowledge. As a result, a high political climate can make prestige-oriented people more careful. They might share less knowledge. They do this to stop others from ignoring or misusing their contributions. This reduces the effect of their prestige-based motivation. The complex competition in such climates can stop prestige-oriented people. They cannot easily gain recognition through normal contributions and showing skills. This is because informal power structures often control how resources are given out. Muiruri’s research shows that in political climates, people often manipulate resources [10]. In these situations, employees use defensive knowledge behaviors. This includes hiding knowledge. In contrast, in a low political climate, team members are usually more open and cooperative. This helps prestige-oriented people easily gain team recognition. They do this by sharing knowledge and showing their abilities. This type of environment creates good interactions for prestige-driven people. It strengthens their motivation to share knowledge. It also allows them to positively show their prestige-based motivation. So, the team’s political climate seems to affect prestige-based motivation in two ways. Highly political climates reduce its positive impact. Less political climates increase it.

Hypothesis 3a: Organizational political climate moderates the impact of AI-induced perceived status changes on prestige-based status motivation

In a highly politicized organizational climate, internal team competitiveness increases significantly, and perceptions of unequal resource distribution are exacerbated. This environment provides stronger impetus for status-protection behaviors among dominance-oriented individuals. A strong political climate, by heightening competitive pressure among team members, can amplify the detrimental effects of dominance-based motivation, leading such individuals to more readily adopt knowledge hiding as a strategic response [4]. In such settings, dominance-oriented individuals may focus more intensely on controlling key resources and information to preserve their status, even if it means sacrificing team collaboration and innovative efficiency. By creating information asymmetries, these individuals aim to hinder competitors’ advancement, thereby reinforcing their own advantageous positions. However, the long-term repercussions of such actions can negatively impact overall team performance.

Hypothesis 3b: Organizational political climate moderates the impact of AI-induced perceived status changes on dominance-based status competition motivation.

In a highly politicized organizational climate, team members often interpret others’ actions as strategic moves to maintain status. This view can weaken the drive of prestige-oriented people. They want to build their reputation by sharing knowledge. In these situations, people may find that active contributions and knowledge sharing do not bring fair rewards. Others might even exploit or suppress them [11]. A political climate increases workplace uncertainty and strategic actions. This makes it harder to gain informal status through contributions and cooperation. This, in turn, lessens individuals’ motivation to show prestige-based competitive behaviors. Employees might usually try to build prestige by sharing knowledge and helping colleagues. However, they may become more cautious or neutral. They are careful about showing their resources or position in a complex political setting. This process could explain a phenomenon. In a very political environment, an AI-induced status threat might not strongly boost prestige-based motivation.

3. Research design

3.1. Sample and procedure

This study used a 2×2 between-subjects experimental design. The factors were perceived AI proficiency (high vs. low) and team political climate (strong vs. harmonious). The study systematically examined how AI skills affect employees’ status perceptions in different organizational settings. Participants read one of four experimental scenarios. A key technical employee narrated each scenario from a first-person view: (1) high perceived AI proficiency/strong political climate; (2) high perceived AI proficiency/harmonious climate; (3) low perceived AI proficiency/strong political climate; and (4) low perceived AI proficiency/harmonious climate. Appendix A provides the full text of these scenarios.

To examine the impact of AI technology perception on workplace behaviors, data were collected online from employees in Chinese companies. These companies varied in size, from fewer than 50 to over 500 employees, and participants were drawn from diverse departments including technology, marketing, human resources, and finance. The Wenjuanxing platform (a popular Chinese online survey tool) was used for data collection across the four scenarios. The sample exhibited broad demographic diversity in terms of gender, education, job level, and organizational size. Specifically, 45% of participants were male and 47.5% were female, with 7.5% selecting “other” or preferring not to disclose. The average age was 29.64 years. Regarding education, 45.83% held a bachelor’s degree and 30% an associate degree (or equivalent diploma), while 11.67% possessed master’s or doctoral degrees. In terms of job roles, 61.67% were general staff, 19.17% were supervisors or managers, and 5.83% were senior managers. The sample included representation from small (<50 employees) to large (>500 employees) organizations.

The research team designed a questionnaire based on the study’s objectives and existing literature, incorporating measures for five key constructs: perceived AI proficiency, political climate, knowledge sharing, knowledge hiding, and status competition motivation.

3.2. Scale sources

Knowledge Hiding: Adapted from Connelly et al. [12], this multi-item scale measures an individual’s tendency to selectively withhold knowledge or information in the workplace. The Cronbach’s α for this scale was 0.89. It ensures the reliability and validity of the scale.

Political Climate: Also adapted from Connelly et al. [12], this scale includes items assessing perceptions of workplace political behaviors. This includes self-serving actions and behind-the-scenes maneuvering. The Cronbach’s α was 0.87, which guarantees measurement consistency and accuracy.

Perceived AI Proficiency: Adapted from Wang et al. [13], this multi-item scale assesses an individual’s familiarity with and ability to apply AI technology. The Cronbach’s α was 0.91. It ensures the reliability and validity of the scale.

Status Motivation: Adapted from Cheng et al. [3], this measure includes two subscales: Dominance Motivation Scale: It assesses the tendency to acquire status through control and authority (e.g., “I enjoy having control over others,” “I tend to use strategies to achieve my goals”). Cronbach’s α was 0.85. Prestige Motivation Scale: It assesses the tendency to earn respect through demonstrations of competence and contributions (e.g., “My peers respect and admire me,” “Others seek my advice”). Cronbach’s α was 0.90.

Knowledge Sharing: Adapted from the Knowledge Sharing Behavior Scale by Bock and Kim [14], this scale assesses an employee’s propensity to proactively share work-related knowledge and experiences within the organization. Example items include: “I proactively share work-related knowledge with colleagues,” “I disseminate useful work experiences to others,” and “Whenever colleagues need it, I always impart what I know without reservation.” Some items (specifically, items 6, 7, and 8 in the original questionnaire) were directly adapted from Bock and Kim, while others were developed to fit the specific research context. Responses were recorded on a 7-point Likert scale. The Cronbach’s α was 0.89.

Note. ***, p<0.01; **, p<0.05; *, p<0.1.

H1a: AI-induced status changes impact dominance-based competition motivation. H1b: AI-induced status changes impact prestige-based competition motivation. H2a: Prestige-based motivation influences knowledge sharing. H2b: Dominance-based motivation influences knowledge hiding. H3a: Organizational political climate moderates the relationship between AI-induced status changes and prestige-based status motivation. H3b: Organizational political climate moderates the relationship between AI-induced status changes and dominance-based status motivation.

In Model 1, regarding the impact of AI-induced perceived status change (AIAC) on status motivations, the analysis revealed that AIAC did not significantly influence dominance-based status competition motivation (DomMot) (B = -0.5451, p > 0.1). This suggests that individuals’ awareness of AI potentially affecting their organizational status does not necessarily trigger dominance-driven strategies to alter that status. Thus, Hypothesis 1a was not supported.

In Model 3, AIAC demonstrated a marginally significant negative predictive effect on prestige-based status competition motivation (PreMot) (B = -0.2409, p < 0.1). This finding indicates that as individuals perceive a greater impact of AI on their status, their reliance on prestige-based strategies to maintain status may decrease. This outcome is contrary to the initial hypothesis (H1b) and suggests that AI’s advancement might temper status-related expectations, potentially dampening the pursuit of prestige (which is often rooted in “knowledge value”) for some. Therefore, Hypothesis 1b was also not supported.

Prestige-based status competition motivation had a marginally significant positive regression coefficient on knowledge sharing behavior (B = 0.2010, p < 0.1). This suggests that when confronted with AI-driven changes, individuals may be more inclined to share AI-related knowledge or skills to enhance their recognition and prestige within the team, thereby subtly increasing their influence. Thus, Hypothesis 2a was supported.

Contrary to expectations, dominance-based status competition motivation had a significant negative impact on knowledge hiding (B = -0.3004, p < 0.05). This indicates that individuals more inclined towards dominant strategies were less likely to hide knowledge. This outcome contradicts the predicted positive relationship in Hypothesis 2b, which was therefore not supported. This surprising finding might suggest that, in the face of AI-induced technological shifts, individuals with dominant tendencies may perceive knowledge dissemination, rather than hoarding, as a more effective means to preserve their status advantage within the organization.

Moderation analysis revealed that the interaction between organizational political climate (TeamPol) and AIAC significantly predicted prestige-based status competition motivation (B = 0.2023, p < 0.05). This indicates that a stronger political climate amplifies the impact of AI-induced perceived status changes on prestige-based motivation. In politically charged organizations, employees might view AI-driven changes as new opportunities to enhance their status through strategic sharing and collaboration within the political landscape. Hypothesis 3a was supported.

The moderating effect of organizational political climate on the relationship between AIAC and dominance-based status competition motivation was marginally significant (B = 0.1851, p < 0.1). This suggests that in teams with a more pronounced political climate, the impact of AIAC on dominance-based motivation tends to become slightly more positive. This might happen because a competitive internal environment in these climates increases the need for control and resource mastery. This makes dominance strategies seem more attractive when responding to AI. Hypothesis 3b was supported.

4. Conclusion

AI integration is greatly changing internal status competition in organizations. This leads to several unexpected behavior changes. This study found something different from common expectations. AI’s spread did not make employees more motivated to seek status through dominance or prestige. Instead, AI encouraged them to change their competitive strategies. Notably, people usually drawn to dominance (resource control) surprisingly shared more knowledge due to AI pressures. At the same time, those motivated by prestige (seeking recognition) more actively shared their technical skills. These changes were especially clear in teams with a strong political climate. This research reveals an important underlying trend. In the AI era, a competence-focused model is increasingly replacing traditional status competition. Technological skill is becoming a key factor in employee influence. This finding gives vital guidance to managers. Organizations should not just rely on old incentive systems. They should focus on creating an open, collaborative technological culture. This method can help employees find new ways to contribute and gain value during the AI transition. Ultimately, an organization’s future competitiveness may depend on its ability. It must turn technological change pressures into a driver for better knowledge flow.

This study adds to our understanding of status competition motivation and knowledge management behaviors. It introduces the idea of perceived status change. Much research sees status systems as stable. This study takes a dynamic view. It shows how AI technology can disrupt existing status hierarchies. This, in turn, can change individual behavior. This provides a new way to understand organizational behavior during technological changes. Additionally, the findings show an unexpected link. Dominance-based motivation and knowledge behaviors in an AI context are connected. Dominance-motivated people hid less knowledge, not more. The study also highlights the complex role of the organizational political climate. Its moderating effects show key conditions. These conditions affect how status motivations turn into knowledge behaviors in AI settings.

This research has several practical implications for organizational management. First, creating a healthy organizational political climate is very important. A harmonious political environment can reduce the negative effects of dominance-based motivation on teamwork. It can also boost the positive effects of prestige-based motivation on behaviors like knowledge sharing. A positive political climate usually improves employee performance and innovation [15]. Second, organizations should offer specific technology support and training. This helps address different AI skill levels and related status views. This can help employees adapt to AI. It can also reduce knowledge barriers caused by skill differences. Finally, organizations can actively encourage and reward knowledge sharing. This is especially true for prestige-motivated individuals. Doing so can strengthen team collaboration and innovation. This helps secure a competitive advantage during technological changes.

This study has limitations, despite its contributions. The sample came from a specific cultural context (China). Future cross-cultural research is needed. This research should test if these findings apply more broadly. Additionally, researchers should further study the long-term dynamic effects of AI adoption. These effects relate to status systems and knowledge management behaviors. Longitudinal studies could offer deeper insights into cause-and-effect relationships over time. Lastly, this study focused on organizational political climate. However, other factors like leadership styles could also affect status competition motivation. For example, Jiang and Wang [13] suggest ethical leadership can reduce employees’ feelings of relative deprivation. It can also lessen knowledge hiding. Similarly, responsible leadership significantly influences employee behavior. It lessens relative deprivation and encourages knowledge sharing [16].

Based on these findings, companies should closely watch for signs of status competition motivation during technological change. They should actively address challenges from the changing status system. First, improving the organization’s political atmosphere is very important. Companies can create an ideal environment for knowledge sharing. They can do this by setting up fair and clear ways to allocate resources. They should also reduce how harmful competition affects team collaboration. Companies should also guide the behavior of dominance-motivated individuals. Clear performance goals and teamwork rewards can reduce the negative impact of their knowledge hiding on team efficiency and innovation. Companies should recognize and reward the knowledge sharing of prestige-motivated individuals. This strengthens their positive role. It allows them to set an example for the team. Companies need to increase support for employees’ technical skills. This helps members who adapt slowly to technology integrate into the AI environment more quickly. Reducing the technology adaptation gap can ease employees’ psychological pressure from perceived status inequality. It can also help reduce the negative impact of knowledge blocking on organizational performance. These practical suggestions align with Hu and Lan’s [17] research. They stressed that organizations should consider employees’ psychological needs. This is important when using digital human resource management to boost innovation and reduce cyberloafing.

This study has some limitations. Currently, organizations do not apply AI technology perfectly. This study’s conclusions apply more to organizations that have already introduced AI or are actively exploring it. Research also shows that different leadership types can reduce employees’ defensive behavior changes [18]. Future research can include organizations with different levels of technological maturity. This can explore differences in how status competition motivation appears. How different cultures understand and value status will also affect if the research conclusions apply universally. This study uses a specific cultural background. Future research can conduct cross-cultural studies. These studies can further show how different cultural backgrounds affect status competition motivation and related knowledge management behaviors. This will make the research conclusions more broadly applicable.