All Roads Lead to Rome? A Contingent Configurational Perspective of HRM Systems and Organizational Effectiveness

Meng Xi et al.

Personnel Psychology2025https://doi.org/10.1111/peps.12681article
AJG 4*ABDC A*
Weight
0.58

Abstract

Although fit is a fundamental concept in the linkage between human resource management (HRM) practices and organizational effectiveness in the strategic HRM field, there has been a call for more research to examine configurations that encompass both horizontal fit (i.e., the internal consistency of an organization's HRM practices) and vertical fit (i.e., the alignment of HRM systems with contexts) to achieve optimal desired outcomes. Across two studies featuring multi‐source matched data, we identified multiple distinct types of HRM systems. Furthermore, by adopting a contingent configurational perspective and utilizing fuzzy set qualitative comparative analysis (fsQCA), we found various configurations of HRM systems and multi‐dimensional organizational contexts (including firm ownership, firm size, innovation strategy, environmental uncertainty, local talent supply, and local government interference) that help to achieve high levels of firm performance and/or collective affective commitment. These findings contribute to our understanding of horizontal fit and vertical fit of HRM systems and highlight the importance of aligning HRM systems with multi‐dimensional organizational contexts to drive both organizational and employee‐centric outcomes.

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https://doi.org/https://doi.org/10.1111/peps.12681

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@article{meng2025,
  title        = {{All Roads Lead to Rome? A Contingent Configurational Perspective of HRM Systems and Organizational Effectiveness}},
  author       = {Meng Xi et al.},
  journal      = {Personnel Psychology},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1111/peps.12681},
}

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Evidence weight

0.58

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.58 × 0.4 = 0.23
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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