Investing in general human capital as a relational strategy: Evidence on flexible arrangements with contract workers
Bibliographic record
Abstract
Abstract Research Summary This article examines a firm's investment in the general skills of contract workers in flexible work arrangements. It theorizes that this investment may prolong a productive firm‐worker collaboration even when workers’ mobility barriers are low. It also proposes that achieving such benefits requires that the firm frames the relational benefits of the investments both to managers and workers. Such a “relational framing” mitigates worker concerns about subsequent productivity demands and manager concerns about worker mobility. Experimental and non‐experimental studies conducted in a multinational cosmetics direct sales company support the theory. Investments in the general skills of workers—even those in flexible work arrangements—can benefit both firms and workers by deepening the firm‐worker relationship while increasing value creation. Managerial Summary Should companies train workers in general skills if the workers can easily leave and transfer productivity gains to competing firms? A common answer to this question is “no,” especially when targeting workers hired under flexible arrangements, such as gig workers and direct sales representatives. This article offers a different perspective. It predicts that these investments signal a company's commitment to nurture workers’ development. In turn, workers reciprocate by prolonging a more productive collaboration. Training thus benefits workers and companies. Using relational terms to frame training programs enables the promotion by managers of training opportunities, and uptake by workers. This framing overcomes managerial concerns about worker exit and worker concerns about subsequent productivity demands. Studies conducted in a multinational cosmetics direct sales company support these arguments.
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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 | 0.007 | 0.025 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".