ABSTRACT Digital visibility has become a central organizing condition in digitally mediated work, reshaping how employees are recognized, evaluated, and connected. In this conceptual paper, we theorize digital visibility as a sociomaterial HRM condition that simultaneously enables recognition and generates performative pressures. Anchored in relational cohesion theory, we develop the Digital Visibility–Belonging Paradox concept to explain why the same visibility infrastructures that make employees more observable may also weaken the relational foundations of belonging. We distinguish between authentic belonging and simulated belonging to show how digital visibility can simultaneously support felt recognition and relational connection while encouraging employees to display connection, availability, and engagement through digitally visible cues. We further identify cognitive, emotional, and structural mechanisms through which visibility shapes these divergent trajectories, as well as organizational and system‐level boundary conditions that influence whether visibility supports relational connection or intensifies performative availability. The paper contributes to HRM scholarship by repositioning visibility governance as a relational and humanistic responsibility in digitally mediated work.

In the digital era, organizations are increasingly leveraging artificial intelligence (AI) to optimize their operations and decision-making. However, the opaqueness of AI processes raises concerns over trust, fairness, and autonomy, especially in the gig economy, where AI-driven management is ubiquitous. This study investigates how explainable AI (xAI), through the comparative use of counterfactual versus factual and local versus global explanations, shapes gig workers' acceptance of AI-driven decisions and management relations, drawing on cognitive load theory. Using experimental data from 1107 gig workers, we found that both counterfactual (relative to factual) and local (relative to global) explanations increase the acceptance of AI decisions. However, the combination of local and counterfactual explanations can overwhelm workers, thereby reducing these positive effects. Furthermore, worker acceptance mediated the relationship between xAI explanations and management relations. A follow-up study using a simplified scenario and additional procedural controls confirmed the robustness of these effects. Our findings underscore the value of carefully tailored xAI in fostering equitable, transparent, and constructive organizational practices in digitally mediated work environments.

The micro-corporate social responsibility (CSR) literature has called for research that incorporates multiple observers from different organizational levels to provide a comprehensive understanding of how to shape employees' socially responsible behaviors. Drawing on person-organization fit and organizational legitimacy theories, we propose a model in which top management team (TMT)-employee (in)congruence in attributions of substantive CSR shapes employees' socially responsible behaviors through organizational identification. We tested our model through multiple complementary studies that employed diverse methodologies. Study 1 employed a 2-year longitudinal design and analyzed data from 66 TMTs and 198 employees. Studies 2A and 2B validated the model through one experimental vignette study (n = 242) and one critical incident study (n = 187). Our results highlight that TMT-employee congruence at high levels of attributions of substantive CSR enhances organizational identification and subsequently facilitates employees' socially responsible behaviors; however, this high-level congruence was not superior to the condition in which employees attributed higher substantive CSR than TMTs. Our research contributes to the micro-CSR and broader organizational behavior literatures by providing a nuanced approach to investigating employees' CSR engagement.

This research investigates how inclusion-focused generative AI (GAI), designed with diversity, fairness, and inclusion principles, mitigates disability bias in hiring-particularly under complex, cognitively demanding conditions. Drawing on Construal Level Theory, two experiments with HR professionals (N = 117; 238) compared standard, inclusion-focused, and control conditions. Inclusion-focused GAI significantly reduced bias by emphasizing job-relevant competencies and lowering psychological distance. The study introduces a fairness-oriented AI design that advances accountability and inclusion in HR systems, offering implications for equitable hiring, bias auditing, and the strategic use of ethical AI in talent acquisition.

Grounded in strategic leadership and organizational identification theories, this study investigates the mechanisms through which CEO narcissism influences middle managers' divergent strategic behavior. We propose a moderated mediation model in which CEO narcissism negatively affects middle managers' divergent strategic behavior by diminishing their organizational identification. Furthermore, we examine how performance feedback moderates this mediated relationship. We conducted two experimental studies involving 70 practitioners (Study 1) and 156 middle managers (Study 2) to empirically support the proposed model. The findings reveal that CEO narcissism undermines middle managers' organizational identification, reducing their capacity for divergent strategic behavior. However, the negative relationship between CEO narcissism and organizational identification is weak when an organization's performance feedback is positive and strong when it is negative. This study contributes to the literature on CEO personality and middle managers' strategic behavior by highlighting the complex interplay of leadership traits, organizational dynamics, and feedback mechanisms.

In the gig economy, the role of artificial intelligence (AI) in managing human resource functions such as task allocation and performance management is increasingly significant. However, there is limited understanding of how the reliability of these functions, as experienced by workers, impacts their trust and engagement. Grounded in the transactional model of stress and coping, this study examines the influence of experienced algorithmic reliability on gig workers' trust in their platforms and their subsequent work engagement. We further explore how occupational stigma consciousness moderates this mediated relationship. Through a time-lagged survey of 332 gig workers, our findings indicate that reliable algorithmic management experiences significantly enhance trust and subsequently work engagement. Moreover, this relationship is complicated by occupational stigma consciousness, which can diminish the positive effects of algorithmic reliability on trust and engagement. This study deepens our understanding of technology-mediated work environments, emphasizing the critical role of workers' experiences with AI-driven HRM functions in enhancing engagement and well-being.

This research delves into the relationship between managerial cognitive frameworks, particularly construal levels, working capital management constraints, and new venture performance. We posit that higher construal levels positively influence venture performance, while working capital management constraints have a detrimental effect, reflecting challenges in resource allocation and cash flow. Our study also suggests that these constraints can temper the benefits of high construal levels on performance. Using a mixed-method approach, we gather data from recently IPOed firms (Study 1) and an MBA cohort experiment (Study 2). Our findings elucidate the intricate interplay between cognitive orientation and financial constraints, offering novel insights into new venture operational management. This research enriches venture financing literature and offers valuable insights for decision-makers navigating the entrepreneurial realm.

To assist residents in exploring the optimal household health and medical care consumption decision-making strategies in an intertemporal uncertain environment, a consumption decision model based on a three-dimensional (3D) path planning PSO-GA-ACO was constructed. The results showed significant differences in the average optimal paths for household health and medical care consumption decisions among residents with different consumption preferences during the three different periods of the Covid-19 pandemic. During the Covid-19 period, the gap in household health and medical care consumption narrowed among residents with different consumption preferences, especially between steady and impulsive consumers. When comparing the post-Covid-19 period with the pandemic period, the monthly health and medical care consumption amounts decreased for both conservative and impulsive consumers, while it continued to rise for steady consumers. The 3D path planning PSO-GA-ACO algorithm improved the speed and accuracy of searching for optimal solutions and better described the decision-making behavior of household health and medical consumption compared to the traditional 3D path planning algorithms PSO, GA, and ACO. This study not only provides optimal household health and medical care consumption decision-making strategies for residents facing complex and uncertain environments but also offers scientific evidence for healthcare manufacturers and suppliers to take effective production and supply actions.

Popular business press and academic publications have advocated for stretch goals, particularly to enhance firm performance. The general assumption is that stretch goals can create a more challenging task environment that upsets complacency, inspires motivation, encourages outside-the-box thinking, stimulates search and innovation, and guides efforts and persistence. Surprisingly few systematic empirical studies have been conducted to support stretch goal deployment, such as when and how to use them. This study introduces two reflection strategies - counterfactual reflection (managers confront performance feedback and create possible alternatives) and factual reflection (managers analyse their own decisions and explain performance feedback) - and uses two experimental laboratory studies to test how different reflection strategies contribute to the stretch goal-performance relationship. The results indicated that using stretch goals does not affect firm performance, although theoretically, using stretch goals can create a more challenging task environment and enhance performance. Rather, it is the combination of the type of goal and reflection strategy that affects performance. I suspect that under stretch goals, managers may be unable to implement new ideas as expected, leading to growing performance gaps and perceived continuous failures over time. Consequently, their motivation to search for alternative solutions declines, and they may fall into a spiral of self-constrained thinking. The results demonstrate that under stretch goals, managers use factual reflection strategies to deliberately reflect on performance feedback to achieve higher performance. In contrast, managers who are assigned moderate goals perform better if they use a counterfactual reflection strategy. I suggest that by using a different reflection strategy, managers can further improve performance by encouraging directed search behaviour and avoiding self-constrained thinking spirals. My study provides a richer theoretical and empirical appreciation of the effect of reflection strategy depending on the task environment and goal-setting.

This research investigates the impact of algorithmic management on worker behaviors, focusing on workers' commitment to service quality and referral tendencies. Drawing upon the job demands-resources model, we argue that high levels of algorithmic management could create hindrance demands that impede service quality and demotivate referral behaviors. We propose that high workload, as a challenge demand, buffers the negative effects of algorithmic management on worker outcomes. We find support for our proposed research model in an experiment with a sample of 1362 platform-based food-delivery riders. We also conduct a qualitative study with 21 riders, which provides a more nuanced understanding of how algorithmic management affects workers' attitudes, behaviors, and referral tendencies.

In the evolving landscape of human resource management (HRM), common good HRM (CG-HRM) practices are increasingly recognized for their potential to align organizational goals with societal sustainability. Despite their importance, how CG-HRM influences employee outcomes remain underexplored. Drawing on Conversation of Resources (COR) theory, this research explores the impact of CG-HRM practices, conceptualized as critical organizational-level resources, on employees' perception of meaningfulness of work. This perception significantly affects employee thriving, which in turn fosters innovative behavior. Employing a multi-wave, multisource survey design, we collected data from 45 firms in China, involving 82 executives and 206 employees, to test our conceptual model. The findings from a multilevel path model reveal that organizational CG-HRM practices contribute to employees' perceived meaningfulness of work, which promotes employee thriving and subsequently innovative behavior at the organizational level, while meaningfulness also contributes to thriving and, thus, innovative behavior at the employee level. Our research enriches the COR theory and contributes to the HRM, thriving, and innovation literature, offering insights for future research and practical implications for organizations striving to align their goals with societal and environmental sustainability while nurturing a thriving workforce.

Amid growing interest in integrating sustainability and corporate social responsibility strategy into the design of human resource management (HRM) practices, sustainable HRM practices based on the common good values (SHRM-CGV) have gained prominence in private firms to support organizations in reaching their sustainability goals. However, research on the effect of SHRM-CGV on desired employee behaviors remains scant. In particular, we know very little about whether SHRM-CGV play a similar or different role in entrepreneurial firms compared to established firms. Drawing upon social exchange theory, we address whether and how SHRM-CGV influence employee innovative behavior and whether SHRM-CGV function more or less effectively in entrepreneurial firms compared to established firms. We also examined whether and how SHRM-CGV lead to employee unethical behavior in entrepreneurial firms. Our findings based on two multi-wave field studies support our theoretical hypotheses and significantly contribute to literature and implications for practice.

Existing literature provides limited understanding of how organizational level factors translate into more sustainable employee behaviors. Integrating institutional theory and social exchange theory, this study proposes a cross-level serial mediation model that links firm perceived institutional pressures for sustainability and employee involvement in organizational sustainability behaviors. Specifically, we propose that firm perceived institutional pressures is the main driver for firms to adopt common good human resource management practices, which initiates a micro social exchange mechanism that affects employee perceived organizational support and the subsequent employee efforts toward organizational sustainability. Empirical results based on time-lagged multi-source survey data collected from 96 firms in China support our theoretical model. With its theoretical and practical implications, this study contributes to both organizational sustainability and human resource management literature.