Jatin Pandey

Scaling digital platforms for innovation has become a strategic priority, yet many B2B platforms fail to move beyond initial adoption due to persistent paradoxical tensions such as openness versus control, flexibility versus standardization, and expansion versus protection. Although these tensions are researched, how platform leaders manage them to enable scaling remains underexplored. Addressing this gap, the study examines how paradoxical governance orientations shape B2B platform scaling and how relational and institutional factors condition this process. Using a mixed-method design, the quantitative phase analyzes survey data from 554 respondents across 11 established B2B platform leaders in India and the United States, followed by 43 semi-structured interviews with platform leaders and ecosystem partners. The quantitative results show that paradoxical governance orientations positively influence relational openness in both countries. Relational openness mediates the relationship between governance orientations and platform scaling, with partial mediation in the United States and direct mediation in India. Moderated mediation analysis indicates that legitimacy-granting conditions strengthen this indirect relationship in the United States but remain insignificant in India, a divergence that is further unpacked through qualitative analysis. The study advances platform ecosystem research by conceptualizing paradoxical governance orientations as a means of embedding, rather than resolving, competing platform tensions. It further theorizes relational openness as the mechanism through which such governance orientations translate into scalable platform outcomes. Finally, it reconceptualizes legitimacy-granting conditions as an interactive moderator that provides direction by clarifying governance trajectories and stability by reinforcing trust and predictability among ecosystem actors.

Purpose: This study aims to examine the dual role of personal and organizational resources in reducing employee silence using the Conservation of Resources Theory. Design/methodology/approach: Using survey data collected in 3 waves from 208 full-time employees, mediation and moderation analysis was used to test the hypotheses. Findings: Core self-evaluations (CSE) significantly reduces employee silence, with occupational self-efficacy mediating this relationship. Employees with higher CSE demonstrate higher occupational self-efficacy, enabling them to overcome barriers to expressing their concerns and ideas in the workplace. We also find that perceived organizational support (POS) moderates the occupational self-efficacy–silence relationship. This finding underscores the role of organizational support in strengthening employees' willingness to speak up. Practical implications: Managers and human resource practitioners may design interventions that mitigate employee silence, such as trainings to enhance employee self-efficacy in the work domain and implement policies and culture that foster a supportive climate to enable transparent communication. Originality/value: The study identifies that employee silence is a defensive strategy and depends on the availability of personal resources in the form of CSE and occupational self-efficacy and organizational resources in the form of POS.

Purpose: The integration of AI in HRM practices is increasing and will be inevitable in the coming years. The successful implementation of AI-enabled management in organizations requires stakeholders' trust. This systematic literature review identifies factors influencing trust in AI-enabled HRM systems and their consequences across three stages of AI implementation (ex-ante, in-ante, and post-ante). Design/methodology/approach: This study reviews and synthesizes the past literature on AI in HRM to develop a conceptual model. Findings: This study identifies the factors that enable organizational justice while implementing AI-enabled HRMS. We explain the positive relationship between organizational justice and employees' trust and moderators of this relationship. Further, this study suggests positive and unintended implications (for employees, teams, and organizations) of trust in AI-enabled HRMS. Originality/value: This paper contributes theoretically to the organizational justice framework by expanding it to the domain of AI-enabled HRMS. This paper examines the gap in understanding trust in AI-enabled HRMS across the adoption process of AI in organizations. We also suggest future research directions. The study also outlines practical and societal implications for the responsible implementation of AI-enabled HRMS and identifies directions for future research.

Scholarly interest in gaslighting research has surged across various domains, particularly after it was named the word of the year in 2022. Despite growing research on the topic of gaslighting, the literature on workplace gaslighting remains limited, hindering the establishment of a clear, definitive concept. We review current scholarship on gaslighting through a systematic literature review and analyze definitions across various disciplines. The thematic analysis of definitions was used to delineate the attributes of the gaslighting phenomenon into necessary and contingent. We conceptualize that workplace gaslighting phenomenon comprises of necessary gaslighting behaviors by gaslighters and contingent experience of gaslighting by gaslightees. Gaslighting behaviors have necessary attributes of: (a) trivialization, i.e., minimizing employees’ work and non-work-related concerns and (b) affliction potential, i.e., ability to cause pain, distress, suffering or negative outcome. The experience of gaslighting can escalate to distortion of reality/confusion/self-doubt which is contingent upon perceived power imbalance. Due to the pervasive and covert nature of the construct proving of intention is not always possible, hence intentionality is considered contingent. Based on our findings, we propose a definition of workplace gaslighting and suggest areas for future research.

Oftentimes employees are asked to undertake tasks that are unreasonable or unnecessary, referred to as the illegitimate tasks. Such tasks constitute a distinct and often overlooked stressor that violates employees' expectations about what can legitimately be demanded of them. The current research proposes that such violation of employees' expectations due to illegitimate tasks can turn into impetus for misdirected retaliation towards co‐workers in the form of workplace incivility. Following social cognitive theory, the current study proposes that such misdirected retaliation materializes through the process of moral disengagement by which employees rationalize and justify their mistreatment of co‐workers. Moreover, the current study proposes that an employees' tendency to undertake moral disengagement due to illegitimate tasks further intensifies when perceived support is high rather than low. The current study therefore proposes a moderated mediation model and finds its support through two studies. First, we employed a time‐lagged survey design, collecting data at three time points from hospitality employees from the United States ( N = 222), then a three‐wave panel design with lagged measures across different industries in the United States ( N = 244 ). The theoretical and practical implications along with limitations and future research are discussed.

Purpose: Artificial intelligence (AI) has brought major disruptions in the new generation human resources (HR) ecosystems. The research community as well as chief human resources officers (CHROs) have been taking strong initiatives to examine the use of AI in human resource management (HRM) function, including harmonious human–machine collaboration. The AI-HRM area is under-researched, and this study addresses an important research gap regarding the benefits and challenges of AI applications in HRM. Design/methodology/approach: The study adopts a qualitative research methodology (abductive case research) and collects data from multiple sources in three Indian companies. These organizations span diverse sectors and were at different stages of AI adoption in HRM at the time of the study. The multi-data-sources strategy helps triangulation and establishes credibility of the research. Findings: The findings provide a clear view about the benefits of AI applications in HRM, higher productivity, recruitment efficiency, adaptive learning and high-quality HR decisions. The study also underpins key challenges, including a lack of human touch, employees’ loss of control over jobs and the fear of losing jobs to AI. Originality/value: The research provides a theoretical contribution to the growing AI-HRM literature in the context of the theory of cost economics in the context of recruitment efficiency as well as leveraging adaptive learning from the context of the multi-level organizational learning framework to improve the performance of the HR function. The research also provides significant managerial insights for CHROs recommending that they embrace humanized AI in the HRM function and institutionalize AI ethics.