Journal of Organizational Behavior最新文献

{"title":"The A(I) Team: Effects of Human-Likeness and Conformity to Gender Stereotypes on Initial Trust and Willingness to Work With an AI Teammate","authors":"Agata Mirowska,&nbsp;Jbid Arsenyan","doi":"10.1002/job.70009","DOIUrl":"https://doi.org/10.1002/job.70009","url":null,"abstract":"<p>As artificial intelligence (AI) technologies progress, AI agents arise as potential teammates in the workplace. This study explores how the visual representation of the AI agent as well as its conformity to traditional gender stereotypes affects the manifestation of uncanny valley effects in a workplace team context. Using social categorization theory, we conducted three between-subject, randomly assigned experimental studies (N₁ = 239, N₂ = 513, N₃ = 403, N_Total = 1155) and investigated how the AI agents' varying levels of human-likeness (human-like, almost human-like, or cartoon-like) and conformity to traditional gender stereotypes (gender-neutral, male, or female versus warm or competent) in two different workplace contexts (Study 2: traditionally masculine, Study 3: traditionally feminine) affect eeriness reactions, initial trust, and willingness to have the AI agent join the all-human team. In Study 1, we find that a more human-like visual representation is associated with lower eeriness reactions, higher initial trust, and higher willingness to have the AI agent join the team. Once gender and temperament are introduced in Study 2, the warm male AI agent leads to higher eeriness reactions by not conforming to gender stereotypes while the warm female agent elicits the lowest eeriness reactions. When the workplace context is changed into a more traditionally feminine setting in Study 3, the warm male agent elicits the lowest eeriness reaction despite not conforming to gender stereotypes.</p>","PeriodicalId":48450,"journal":{"name":"Journal of Organizational Behavior","volume":"47 8","pages":"1288-1312"},"PeriodicalIF":9.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/job.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does AI at Work Increase Stress? Text Mining Social Media About Human–AI Team Processes and AI Control","authors":"Florian Klonek,&nbsp;Sharon Parker","doi":"10.1002/job.70000","DOIUrl":"https://doi.org/10.1002/job.70000","url":null,"abstract":"<p>With rising use of artificial intelligence (AI) in organizations, alongside increasing mental health issues, we seek to understand how AI use affects human stress. Drawing on the automation–augmentation perspective, we propose that AI control over decision-making thwarts human autonomy and thus contributes to stress. Drawing on models of teamwork and augmentation, we expect that human–AI team processes (i.e., transition, action, and interpersonal processes) help people meet their goals and reduce stress. Finally, we argue that human–AI team processes provide an important social resource, which buffers the stress-enhancing role of AI control. To test our hypotheses, we analyzed over 2700 tweets. Using a trained large language model, validated against human ratings, we indexed key measures. Results confirm that high AI control was associated with increased stress, whereas human–AI team processes were associated with decreased stress. In support of the moderation hypothesis, two human–AI team processes (action and interpersonal) helped further reduce the stress-enhancing effect of AI control. We discuss implications for work design theory and the importance of regulating levels of AI control to protect workers' mental health.</p>","PeriodicalId":48450,"journal":{"name":"Journal of Organizational Behavior","volume":"47 8","pages":"1423-1437"},"PeriodicalIF":9.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/job.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Capital Robotic Integration and Value Creation for Organizations","authors":"Chou-Yu Tsai,&nbsp;Jason D. Marshall,&nbsp;Rory Eckardt,&nbsp;Shelley D. Dionne,&nbsp;Malte F. Jung,&nbsp;YoYo Tsung-Yu Hou,&nbsp;Biying Yang","doi":"10.1002/job.70044","DOIUrl":"https://doi.org/10.1002/job.70044","url":null,"abstract":"<p>Due to rapid advancements in artificial intelligence and machine learning, the research conversation has drifted from viewing robots as replacements for humans (i.e., the substitute view) to a view that considers the possible benefits of human–robot collaboration in the workplace (i.e., the complementary view). However, as the complementary view has been mainly studied at the microlevel of the organization (i.e., individuals, dyads, and teams), we do not yet fully understand the organizational impact of human–robot collaboration. To better understand how human–robot collaboration may impact firm outcomes, we apply the human capital resource emergence literature to develop a multilevel model of human capital robotic integration (HCRI). We specifically emphasize how HCRI influences the emergence of human capital resources through social dynamics in organizations while also considering the moderating effects of task interdependence. Furthermore, we develop theoretical arguments to illustrate how organizations can leverage the HCRI process to create value and gain a competitive advantage from their existing human capital. Finally, with potential positive effects being outlined, we discuss the possible negative consequences that may result from human–robot collaboration.</p>","PeriodicalId":48450,"journal":{"name":"Journal of Organizational Behavior","volume":"47 8","pages":"1459-1475"},"PeriodicalIF":9.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/job.70044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is Our Future Colleague Even Human? Advancing Human–AI Teamwork from an Organizational Perspective","authors":"Anna-Sophie Ulfert,&nbsp;Eleni Georganta,&nbsp;Gudela Grote","doi":"10.1002/job.70125","DOIUrl":"https://doi.org/10.1002/job.70125","url":null,"abstract":"<div>\u0000 \u0000 <p>Artificial intelligence (AI) is increasingly part of collaborative work that was once carried out exclusively by humans. In this special issue, we ask whether our future colleagues will even be human and what this means for today's organizations. The eight contributions examine how design choices such as human-likeness and gendered cues shape perceptions of AI, how AI alters team processes including decision-making, trust, and stress, and how training and organizational integration condition sustainable human–AI collaboration. Together, the contributions suggest that AI reshapes collective work processes, not only individual judgments. Building on this insight and on sociotechnical systems thinking, we develop a framework that distinguishes where AI is positioned in the social system of work and how much agency it exercises. The framework also raises the normative question of how AI's position in organizations should be governed. Together, the contributions and the framework point toward a research agenda that differentiates between human–AI configurations and treats teams rather than dyads as the unit of analysis.</p>\u0000 </div>","PeriodicalId":48450,"journal":{"name":"Journal of Organizational Behavior","volume":"47 8","pages":"1277-1287"},"PeriodicalIF":9.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preparing for Tomorrow's Teamwork: Insights From eSports on How Human Expertise Shapes Training Needs for AI-Integrated Work","authors":"Caitlin M. Lancaster,&nbsp;Christopher Flathmann,&nbsp;Jennifer Hsu,&nbsp;Nathan J. McNeese,&nbsp;Thomas A. O'Neill,&nbsp;Eduardo Salas","doi":"10.1002/job.70036","DOIUrl":"https://doi.org/10.1002/job.70036","url":null,"abstract":"<p>As organizations increasingly adopt human-AI teams (HATs), understanding how to enhance team performance is paramount. A crucially underexplored area for supporting HATs is training, particularly helping human teammates to work with these inorganic counterparts. Indeed, research on training for HATs is limited, often relying on human team training frameworks, failing to consider the humans' expertise-based training needs and how this may affect collaboration with AI. To bridge this gap, we interviewed competitive eSports athletes (<i>N</i> = 22), a group experienced in training with AI, to discuss the gaps in current human–AI training and their desires for future training that better supports humans in AI-integrated work. Using the quantitative ethnography (QE) tool, epistemic network analysis (ENA), we examine these training needs and how they vary based on the participants' task expertise. Our findings indicate that current training methods focus on using AI for taskwork training, with significant expertise differences identified due to diverging perceptions on this taskwork focus as well as tensions related to balancing adaptability with predictability and clashing attitudes toward training with AI. Future training must evolve to deepen understanding and trust between humans and AI, focusing on socio-emotional bonds and role awareness to offer greater benefits for teaming. We conclude with three actionable recommendations for organizational research on training for HATs to expand these findings to broader contexts.</p>","PeriodicalId":48450,"journal":{"name":"Journal of Organizational Behavior","volume":"47 8","pages":"1438-1458"},"PeriodicalIF":9.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/job.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When AI Becomes My Teammate: Unpacking How Employees Perceive and Collaborate With Gendered AI Teammates","authors":"Yidong Tu,&nbsp;Jing Li,&nbsp;Jiahui Chen,&nbsp;Chang-Jun Li,&nbsp;Wei He","doi":"10.1002/job.70038","DOIUrl":"https://doi.org/10.1002/job.70038","url":null,"abstract":"<div>\u0000 \u0000 <p>While artificial intelligence (AI) is increasingly embedded into teams, how employees perceive and collaborate with AI teammates, especially those with gender characteristics, remains an urgent but underexplored question. This research integrates social perception theory and social role theory to investigate how employees socially differentiate AI from human teammates and how these social perceptions consequently shape employee–AI cooperation. Across three studies, including a vignette-based experiment (<i>N</i>1 = 163), a fictional interactive experiment (<i>N</i>2 = 465), and a preregistered field survey (<i>N</i>3 = 155), we found that in general, employees perceived AI teammates as more competent but less warm than human teammates. This competence advantage of AI enhanced employee cooperation with AI, but its perceived lack of warmth hindered cooperation. Moreover, AI teammates designed with female-like characteristics compensated for this warmth deficit (Study 1), but simultaneously diminished AI's competence advantage over humans (Studies 2 and 3). Conversely, AI teammates designed with male-like characteristics amplified AI's competence advantage (Studies 2 and 3) but exacerbated its absence of warmth (Study 1). Finally, these gendered perceptions of AI influenced employee–AI cooperation in paradoxical ways. Overall, our research underscores that incorporating AI into teams, especially ascribing AI gender characteristics, is a <i>mixed blessing</i> for human–AI teamwork.</p>\u0000 </div>","PeriodicalId":48450,"journal":{"name":"Journal of Organizational Behavior","volume":"47 8","pages":"1313-1337"},"PeriodicalIF":9.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Can Teams Benefit From AI Team Members? Exploring the Effect of Generative AI on Decision-Making Processes and Decision Quality in Team–AI Collaboration","authors":"Désirée Zercher,&nbsp;Ekaterina Jussupow,&nbsp;Ivo Benke,&nbsp;Armin Heinzl","doi":"10.1002/job.2898","DOIUrl":"https://doi.org/10.1002/job.2898","url":null,"abstract":"&lt;p&gt;Organizations are integrating generative artificial intelligence (AI) team members to collaborate with humans on increasingly complex decisions (Bankins et al. &lt;span&gt;2024&lt;/span&gt;). AI team members are characterized by their ability to participate in human collaboration processes and their cognitive abilities (McNeese et al. &lt;span&gt;2018&lt;/span&gt;; Seeber et al. &lt;span&gt;2020&lt;/span&gt;). Thus, generative AI systems based on large language models are prime examples of agentic AI team members (Baird and Maruping &lt;span&gt;2021&lt;/span&gt;). They can generate output and provide information, enabling collaboration with humans across a wide range of decision-making tasks (Aydın and Karaarslan &lt;span&gt;2023&lt;/span&gt;). These technological advances are promoting teams collaborating with AI as new organizational decision-making units (Ulfert et al. &lt;span&gt;2024&lt;/span&gt;). Specifically, team–AI collaboration&lt;sup&gt;1&lt;/sup&gt; offers significant potential to benefit human teams with &lt;i&gt;distributed knowledge&lt;/i&gt;, such as multidisciplinary teams in management or medicine, as they are often responsible for making far-reaching and fundamental decisions (Schulz-Hardt and Mojzisch &lt;span&gt;2012&lt;/span&gt;; Sohrab et al. &lt;span&gt;2015&lt;/span&gt;). These teams are expected to make better decisions than each team member alone by achieving synergies through leveraging their distributed knowledge (Lu et al. &lt;span&gt;2012&lt;/span&gt;; Schulz-Hardt and Mojzisch &lt;span&gt;2012&lt;/span&gt;). However, research in the context of hidden profiles has revealed that teams with distributed knowledge often fail to make better decisions due to suboptimal team decision-making processes (Sohrab et al. &lt;span&gt;2015&lt;/span&gt;). The Information Asymmetries Model (IAM) describes hidden profiles as situations where knowledge is distributed asymmetrically, which can lead to suboptimal integration of critical insights during team discussions (Brodbeck et al. &lt;span&gt;2007&lt;/span&gt;). Asymmetric knowledge distribution occurs when individual and collective information points to different decisional alternatives. Thus, teams must counterbalance this asymmetry by effectively integrating distributed information. However, the IAM proposes that teams often fail in this effort due to two key information processing asymmetries at the team level and one at the individual level. The team level asymmetries refer to negotiation focus (reliance on prediscussion preferences) and shared information bias (a tendency to focus discussion on shared rather than unshared information), whereas the individual level asymmetry is social validation (reliance on information that is confirmed by others) (Brodbeck et al. &lt;span&gt;2007&lt;/span&gt;).&lt;/p&gt;&lt;p&gt;However, including a generative AI team member could fundamentally reshape how teams make decisions by introducing new dynamics in how information is processed (Gurkan and Yan &lt;span&gt;2023&lt;/span&gt;). This shift brings both potential benefits and challenges related to the generative AI team member's knowledge and capabilities, which might be deeply interco","PeriodicalId":48450,"journal":{"name":"Journal of Organizational Behavior","volume":"47 8","pages":"1369-1396"},"PeriodicalIF":9.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/job.2898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"You, Me, and the AI: The Role of Third-Party Human Teammates for Trust Formation Toward AI Teammates","authors":"Türkü Erengin,&nbsp;Roman Briker,&nbsp;Simon B. de Jong","doi":"10.1002/job.2857","DOIUrl":"https://doi.org/10.1002/job.2857","url":null,"abstract":"&lt;p&gt;Artificial intelligence (AI)'s role in organizations has transitioned fundamentally during the “fourth industrial revolution” (Schwab &lt;span&gt;2017&lt;/span&gt;). Initially a mere tool for executing orders, AI is now implemented as an active teammate working along humans (Ulfert et al. &lt;span&gt;2024&lt;/span&gt;). In particular, organizations commonly install human-AI teams to obtain high performance and achieve their goals (Kanarik et al. &lt;span&gt;2023&lt;/span&gt;; Seeber et al. &lt;span&gt;2020&lt;/span&gt;).&lt;sup&gt;1&lt;/sup&gt; With increasing AI capabilities, such human-AI teams already serve customers, perform tasks ranging from manufacturing to creative brainstorming, and even produce scientific outputs (Boiko et al. &lt;span&gt;2023&lt;/span&gt;; Evjemo et al. &lt;span&gt;2020&lt;/span&gt;; Ivanov and Webster &lt;span&gt;2019&lt;/span&gt;). As AI's role shifts from tool to teammate, it fundamentally alters employee-AI interactions, with novel forms of AI exhibiting greater autonomy, proactivity, and interdependence compared to traditional AI tools (Chugunova and Sele &lt;span&gt;2022&lt;/span&gt;). Yet, despite increasing adoption of AI in teams, employees often fail to effectively interact with AI (Dwivedi et al. &lt;span&gt;2021&lt;/span&gt;). For successful collaboration with AI, it is critical that employees perceive AI as &lt;i&gt;trustworthy&lt;/i&gt; (i.e., view it as reliable and integer; cf. Whitener et al. &lt;span&gt;1998&lt;/span&gt;) and demonstrate &lt;i&gt;trust behavior&lt;/i&gt; toward it (i.e., make themselves vulnerable to the AI's actions; cf. Mayer, Davis, and Schoorman &lt;span&gt;1995&lt;/span&gt;).&lt;sup&gt;2&lt;/sup&gt;&lt;/p&gt;&lt;p&gt;Forming trust is crucial for shaping interactions with AI, influencing perceptions of AI usefulness and performance, and ultimately efficient usage (Choung, David, and Ross &lt;span&gt;2023&lt;/span&gt;; Lee and See &lt;span&gt;2004&lt;/span&gt;). Consequently, researchers are increasingly interested in understanding the factors that drive trust formation in AI within teams and organizational contexts (Glikson and Woolley &lt;span&gt;2020&lt;/span&gt;; Schelble et al. &lt;span&gt;2024&lt;/span&gt;). Emerging research on trust formation in AI suggests that its antecedents can be grouped into three categories, namely characteristics of (1) the human trustor, (2) the AI trustee, and (3) the context or environment (Hancock et al. &lt;span&gt;2011&lt;/span&gt;; Kaplan et al. &lt;span&gt;2023&lt;/span&gt;). Existing work has primarily concentrated on the first two categories, claiming that “factors specific to each of the human and automation are clearly central in trust development” (Schaefer et al. &lt;span&gt;2016&lt;/span&gt;, p. 385). Regarding human trustors, scholars mainly focused on their propensity to trust (Merritt and Ilgen &lt;span&gt;2008&lt;/span&gt;) and demographics including nationality, gender, and age (Hoff and Bashir &lt;span&gt;2015&lt;/span&gt;; Schaefer et al. &lt;span&gt;2016&lt;/span&gt;). For AI trustees, research has shown that characteristics such as its reliability and anthropomorphism (i.e., human-likeness, Epley, Waytz, and Cacioppo &lt;span&gt;2007&lt;/span&gt;) shape trust in AI (Kaplan et al. &lt;span&gt;2023&lt;/span&gt;), although the influence of antecedents may","PeriodicalId":48450,"journal":{"name":"Journal of Organizational Behavior","volume":"47 8","pages":"1397-1422"},"PeriodicalIF":9.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/job.2857","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating AI Convergence in Human–Artificial Intelligence Teams: A Signaling Theory Approach","authors":"Andria Smith,&nbsp;Hunter Phoenix van Wagoner,&nbsp;Ksenia Keplinger,&nbsp;Can Celebi","doi":"10.1002/job.2856","DOIUrl":"https://doi.org/10.1002/job.2856","url":null,"abstract":"<p>Teams that combine human intelligence with artificial intelligence (AI) have become indispensable for solving complex tasks in various decision-making contexts in modern organizations. However, the factors that contribute to AI convergence, where human team members align their decisions with those of their AI counterparts, still remain unclear. This study integrates signaling theory with self-determination theory to investigate how specific signals—such as signal fit, optional AI advice, and signal set congruence—affect employees' AI convergence in human–AI teams. Based on four experimental studies conducted in facial recognition and hiring contexts with approximately 1100 participants, the findings highlight the significant positive impact of congruent signals from both human and AI team members on AI convergence. Moreover, providing an option for employees to solicit AI advice also enhances AI convergence; when AI signals are chosen by employees rather than forced upon them, participants are more likely to accept AI advice. This research advances knowledge on human–AI teaming by (1) expanding signaling theory into the human–AI team context; (2) developing a deeper understanding of AI convergence and its drivers in human–AI teams; (3) providing actionable insights for designing teams and tasks to optimize decision-making in high-stakes, uncertain environments; and (4) introducing facial recognition as an innovative context for human–AI teaming.</p>","PeriodicalId":48450,"journal":{"name":"Journal of Organizational Behavior","volume":"47 8","pages":"1338-1368"},"PeriodicalIF":9.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/job.2856","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Does Leader Self-Sacrifice Lead to Subordinate Self-Sacrifice? A Conformity Perspective","authors":"Feifan Yang,&nbsp;Mengxi Yang,&nbsp;Yufei Liu,&nbsp;Alexander Newman,&nbsp;Bin Wang","doi":"10.1002/job.70102","DOIUrl":"https://doi.org/10.1002/job.70102","url":null,"abstract":"<div>\u0000 \u0000 <p>Prior studies have typically viewed self-sacrificing managers as “good leaders” who inspire their employees through such positive mechanisms as fostering identification with the leader and cultivating high-quality leader–follower relationships that benefit both parties. Based on theories of conformity, we challenge the prevailing supportive view by arguing that leader self-sacrifice (LSS) can increase subordinate self-sacrifice through a pressure-based mechanism. Specifically, using data from one experiment and two 3-wave field studies, we found that LSS was positively related to subordinate self-sacrificial pressure, which in turn prompted subordinate self-sacrifice. Furthermore, we established that leader autonomy support mitigated these effects: When leaders provided greater autonomy support, their employees experienced lower levels of self-sacrificial pressure induced by LSS. These findings reveal a previously overlooked, pressure-driven mechanism through which LSS influences followers, and thus, even well-intentioned leader behaviors can impose hidden psychological demands on subordinates.</p>\u0000 </div>","PeriodicalId":48450,"journal":{"name":"Journal of Organizational Behavior","volume":"47 7","pages":"1189-1204"},"PeriodicalIF":9.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}