Growing and aging of entrepreneurial firms: Implications for job rotation and joint reward
Bibliographic record
Abstract
Purpose: The purpose of this paper is to explore whether job rotation strategies and joint reward systems are equally effective in encouraging cross-functional collaboration (CFC) under all organizational contexts, ranging from young and small firms to mature and large ones.Design/methodology/approach: To ensure a wide applicability of findings in this study, the research model and hypotheses were tested with a sample of 232 Canadian firms active in a variety of industrial sectors. A survey instrument that comprised all the questionnaire items corresponding to the examined constructs is the foundation of the data used in this contribution.Findings: This study shows that job rotation and joint rewards are strong and positive drivers of interdepartmental collaboration, which subsequently enhance firm performance. However, this illustration must be considered in the context of the firm shaped by its size and age because these two variables strongly and negatively moderate the relationships between CFC and its two antecedents.Research limitations/implications: The study was limited to Canadian firms only. The manufacturing sector was not differentiated into subsectors, such as technology. Future studies could compare subsectors of manufacturing to see if there is any correlation between types of industries, age, and size.Originality/value: Not all firms will be able to take advantage of the widely accepted values of job rotation and joint reward systems in generating CFC. Firms, to an extent, appear to be confronted with the liability of aging but not with the liability of smallness.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".