This study conceptualizes stakeholder relationship management as a firm capability and examines the organizational mechanisms through which it enhances firm performance. Specifically, it proposes that stakeholder relationship management capability (SRMC) improves performance indirectly through knowledge exchange, which subsequently enhances innovativeness, efficiency, and organizational resilience. The study also examines whether stakeholder culture moderates the relationship between SRMC and knowledge exchange. Using survey data from 306 manufacturing firms in Turkey and structural equation modeling, we find support for the proposed mediating relationships but no evidence that stakeholder culture moderates the effect of SRMC on knowledge exchange. The study advances instrumental stakeholder theory by identifying the mechanisms through which stakeholder relationships management drives performance heterogeneity, and provides a capability-based explanation of how stakeholder relationships affect firm performance.
Role congruity theory suggests that men and women are perceived according to their gendered roles and may avoid violating expectations based on those roles. However, little is known about what enables individuals to recognize these gendered expectations, particularly in difficult-to-attain positions such as the CEO role. This study theorizes that men and women recognize expected roles related to risk-taking differently depending on their executive experience and age, leading them to act congruently with those expected roles. Drawing on data on newly appointed CEOs from the S&P 1500, this study contributes to role congruity theory by identifying executive experience and age as pathways through which CEOs recognize and respond to gendered expectations surrounding strategic risk-taking.
The fragmented and project-based nature of the construction industry promotes adversarial relationships, inefficiencies, and undermines sustainability. A framework for construction supply chain collaboration (CSCC) is proposed following a systematic literature review of 74 peer-reviewed engineering/management articles. It distinguishes three tiers of actionable practices: strategic (e.g., long-term partnerships), tactical (e.g., coordination via BIM), and organizational (e.g., workforce training). The framework emphasizes how collaboration, with its associated drivers and barriers, yields both intended and unintended outcomes. This paper offers practical guidance on establishing CSCC. It also provides new research directions, reflecting the role of collaboration in overcoming the industry’s outdated practices.
This mixed-method study explores how employees define and experience quiet quitting. Study 1 analyzes over 44,000 Twitter posts, revealing divergent public sentiments that frame the phenomenon as both a productivity concern and a legitimate act of boundary-setting. Study 2 draws on structured interviews with 17 working adults, identifying quiet quitting as a spectrum ranging from deliberate boundary-setting to emotional disengagement and concealed turnover intention, shaped primarily by structural and relational workplace factors. The findings are interpreted across multiple theoretical frameworks, highlighting the phenomenon’s complexity and its consequences for individuals and teams, with implications for organizations reconsidering assumptions about discretionary effort.
Artificial intelligence (AI) is increasingly viewed as a strategic resource, yet many organizations struggle to convert AI investments into performance gains. Drawing on the resource-based view, this study examines how AI capability (AIC) influences organizational performance through organizational strategy, structure, and environment. Using survey data from 345 e-organizations and partial least squares structural equation modeling (PLS-SEM), the findings show that AIC does not directly improve organizational performance; instead, its effect is fully mediated by these organizational contingencies. The study advances general management theory by demonstrating that AI creates value only when supported by internal organizational alignment. The findings provide practical guidance for managers seeking to translate AI investments into organizational performance.
