Human resource management in the North American automotive industry
Bibliographic record
Abstract
Purpose This article aims to provide a quantitative review of the range and effects of human resource management (HRM) practices in the North American automotive industry. Design/methodology/approach A total of 14 studies provided data for an employee‐level meta‐analysis of the relationships comprising high performance work systems in the automotive manufacturing sector. As an extension of research in this context, we hypothesized that three clusters of organizational practices (work systems, HR policies, and leadership) would be associated with two clusters of employee‐level psychosocial outcomes (person‐focused, organizational‐focused) which, in turn, would be related to employee performance. Findings It was found that work systems and HR policies related to both person‐focused (comprising individual job satisfaction, health, self‐esteem, and social support) and organization‐focused (comprising organizational commitment and perceptions of organizational justice) outcomes. The leadership cluster had a strong association with the person‐focused outcomes. Organizational – but not personal‐focused outcomes were associated with employee performance comprising employee effectiveness, self‐ratings of performance, turnover, and absenteeism. Research limitations/implications The results from this study provide support for the role of employee‐level psychosocial outcomes as mechanisms between HRM practices and employee performance, supporting an idea that is often discussed but rarely tested in the literature. These results need to tempered by the fact that this meta‐analysis was based on a relatively small number of studies in one industrial sector, thereby limiting the generalizability of the model. Practical implications These data suggest that managing with a high‐involvement orientation is associated with positive consequences for individuals and organizations within the automotive industry. The paper is not espousing the view that technologically‐focused systems are of little value in manufacturing industries, but rather that taking a more humanistic approach to how they are implemented may benefit all parties involved. Originality/value This paper provides an empirical review of HRM practices and outcomes in the automotive manufacturing context. The role of leadership in these systems is highlighted. The results offer guidance to researchers and practitioners interested in researching and managing the human side of automobile manufacturing.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".