How do changes in human resource programs lead to innovation: an organizational entrainment perspective on the temporal mechanisms in HRM
Bibliographic record
Abstract
Purpose The current study aims to investigate the temporal mechanisms in HRM systems by focusing on how HRM systems evolve over time and how such changes affect organizational innovation. Design/methodology/approach This paper draws on organizational entrainment theory to examine how pace of change in employee involvement programs (EIPs) influences innovation via data from an eight-year longitudinal survey collected by Statistics Canada. The final sample includes 15,679 workplace–year observations. Findings This research shows that the effects of HRM programs on performance are more than just the mean effect – the pace of change by which changes are implemented in HRM programs matters in the long run. The optimal level of change pace occurs when the EIPs are changing at a pace that entrains (or synchronizes) with organizational rhythm of strategic changes. Results suggest that change pace in EIPs has an inverted-U-shaped relationship with both pace and quality of innovation. The curvilinear effect is more pronounced for organizations with relatively lower mean level of EIPs. Research limitations/implications First, this study captures only key measures of the EIPs and may not be generalizable to other dimensions of the HR systems. Second, the results of this paper should be interpreted at the HR program level or bundles of HR practices – the findings may not be generalizable to lower levels of analysis. Third, as a result of annual measurement, this study cannot capture short-lived minor dynamic HR misfits where workplaces quickly adjust to regain alignment. Fourth, to attain meaningful and consistent measures of strategic HR change, this study only includes surviving workplaces with at least five years of observations. Practical implications This paper provides insights to managers and business leaders on how to implement strategic changes in HRM systems effectively to attain sustained innovation outcomes in the long run. To achieve an optimal level of innovation, organizations need to consider not only what and how many EIPs should be used but also how to strategically change EIPs to meet dynamic internal and external changes. Originality/value The current research introduces organizational entrainment theory to explain and empirically test the conflicting predictions of the universalist and contingency perspectives on the effects of strategic changes in HRM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".