Re‐Evaluating the Offshoring Decision: A Behavioural Approach to the Role of Performance Discrepancy
Bibliographic record
Abstract
Abstract Firms are in a continuous process of critically re‐evaluating their offshoring strategies due to performance discrepancies. While prior research has focused on the implementation of organizational responses to performance shortfalls, we examine the offline search process, a key antecedent of organizational change, during which firms simultaneously explore alternative solutions when facing either a positive or a negative discrepancy between performance and aspirations. We adopt the Behavioural Theory of the Firm (BTOF) to investigate how the search process is affected by the size and nature (as being positive or negative) of the discrepancy as well as how it is moderated by cognitive biases. By examining 441 offshoring initiatives, we study firms' search processes in a novel context that refers either to ‘local’ solutions that are close to the current activity (i.e., expansion in the same host country) or ‘distant’ solutions that are far from the current one (i.e., relocation to a third country or to the home country). Our results provide new insights into organizational search, namely that performance shortfalls lead to distant search unless this choice is moderated by a location‐specific anchor bias relating to the strategic importance of host location, while positive discrepancies trigger local search with decision‐makers more inclined to consider expansion in the current host country.
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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 | 0.013 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".