Human Resource Management Systems and Attachment Styles: A Multi-Level Conceptual Model
Bibliographic record
Abstract
The purpose of this paper is to develop a multi-level conceptual model outlining the interplay between human resource management and leader-follower adult attachment styles. The paper aims to further elucidate the HR systems-firm performance relationship by theorizing relational mediating mechanisms, namely the interplay between leader and follower attachment styles. We focus specifically on relationship-oriented HR systems, defined as synergized HR practices required to help employees build interpersonal relationships, and offer propositions about their role in activating leaders’ individual-level attachment style and followers’ group-level attachment style. Further, we theorize an indirect relationship between a leader’s contextually-activated attachment style and followers’ group attachment style through LMX exchange, meaning the quality of relationship between leader and followers. Drawing on social contagion theory, we posit the dispersion of group attachment as a collective mechanism. Finally, the role of discretionary work behaviors, including positive behaviors such as organizational citizenship behavior (OCB) and detrimental behaviors such as counter-productive work behaviors (CWB) are considered in predicting followers’ unit-level performance outcomes. Theoretical and practical implications, as well as the avenues for future research, are discussed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".