Imprint-environment Fit and Performance: How Organizational Munificence at the Time of Hire Affects Subsequent Job Performance
Bibliographic record
Abstract
Using a longitudinal study of professionals in two information technology services firms, as well as interview data, this paper illuminates how organizational fortunes influence individual performance over time, examining how the economic situation of an organization leaves a lasting imprint on new employees and how that imprint affects subsequent job performance. The core hypothesis, supported by the results, is that the more similar the initially experienced level of organizational munificence is to the level of munificence in a subsequent period, the higher an individual’s job performance. This relationship between what I call ‘‘imprint–environment fit’’ and performance is contingent on the individual’s career stage when entering the organization and the influence of secondhand imprinting resulting from the social transmission of others’ imprints. A possible implication of the core hypothesis may be a ‘‘curse of extremes,’’ whereby both very high and very low levels of initial munificence are associated with lower average performance during a person’s subsequent tenure. One mechanism underlying these patterns is that employees socialized in different resource environments develop distinct approaches to problem solving and client interactions, which then lead to varying levels of imprint– environment fit in subsequent resource environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".