Always Pulling Back? Examining Face Logic Endorsement Among Newcomers and its Implications
Bibliographic record
Abstract
Face is typically believed to lead individuals to pull back, broadly speaking. We challenge this view. Focusing on newcomers and building on conservation of resources (COR) theory, role theory, and the literature on face and commitment, we suggest that face logic endorsement can, depending on its level, lead newcomers to engage with their role as organizational members and seek to understand the implications of face more broadly. In our examination of these ideas, we introduce the notion of commitment to becoming organizational insiders (CBOI) as a new commitment construct. Specifically, we propose the existence of a U-shaped relationship between face logic endorsement and newcomers’ CBOI, such that low-to-moderate levels of face logic endorsement reduce CBOI, whereas very high levels of face logic endorsement increase CBOI. Moreover, we propose a positive relationship between face logic endorsement and voluntary turnover, which is partly mediated by newcomers’ CBOI. Finally, we suggest that newcomers’ felt accountability moderates the negative relationship between CBOI and turnover as well as the positive indirect relationship between face logic endorsement and turnover, such that these relationships are stronger at higher levels of felt accountability. The results of two studies support our hypotheses. Implications and future research directions 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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".