How do callings relate to job performance? The role of organizational commitment and ideological contract fulfillment
Bibliographic record
Abstract
Do individuals with callings perform better than those without? Why or why not? There are not clear answers to these questions in the literature. Using a social exchange framework, we posit an intervening process between callings and job performance, focusing on the role of organizational commitment and ideological contract fulfillment – the degree to which organizations live up to their ideological promises. Specifically, individuals with callings will be more committed to their organization, and this commitment, in turn, leads to job performance. Further, this relationship of calling to job performance through commitment will be attenuated when employees perceive under-fulfillment of ideological contract. We found support for these hypotheses across three studies that utilized self- or supervisor-rated performance data from a non-profit organization and multiple for-profit organizations. Interestingly, while the relationship between commitment and performance did depend on fulfillment of the ideological psychological contract, contrary to our prediction, the calling-commitment relationship was not attenuated by under-fulfillment of ideological contract. Our findings deepen our understanding of the organizational implications of callings from a social exchange-based perspective. This study further informs practitioners as to hiring and motivating individuals with a calling.
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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 | 0.004 | 0.018 |
| 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 | 0.002 | 0.001 |
| 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".