Offshoring, exit and voice: implications for organizational theory and practice
Bibliographic record
Abstract
Purpose This paper seeks to explore the impact of corporate offshoring moves on the economic and psychological contracts between firms and their employees. Design/methodology/approach The paper draws upon literature from diverse social sciences to explore the phenomena of social contracts and offshoring. Especially deploying the exit‐voice theory of Alfred Hirschman, it is argued here that offshoring decreases the regenerative power promised both by exit and voice in helping organizations recover from decline. Findings Organizational systems and processes designed to deal with the “post‐offshoring worker” only serve to accentuate the sense of alienation felt by workers at the way they are regarded. This scenario poses a serious challenge to researchers and practitioners who need to make sense of these effects and deal with them accordingly. Originality/value This paper highlights, honors and legitimates everyday relations at the workplace on both sides of the offshoring divide, as sites of class struggle, of worker alienation, of intra‐organizational bargaining and, sometimes, of relations of imperialism and cultural dislocation. Understanding the complexity of this context and managing the actions arising out of this are important challenges facing the managers.
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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 | 0.038 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.095 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".