The impact of offshoring on knowledge‐intensive services: A study of activities in service production processes
Bibliographic record
Abstract
Abstract Research summary: In order to identify the impact of offshoring on knowledge‐intensive services (KIS), the activities related to the transfer and co‐creation of knowledge in the service production process are studied in this paper. The concepts of activity systems and activity structures from practice theory are used to analyze these activities. A qualitative case study of multiple offshored KIS shows that offshoring changes activity systems and structures within the production process. Activities related to the transfer of knowledge are reduced while the co‐creation of knowledge remains evident in the process. As a result, KIS become more modularized and less customized, evidencing changed KIS characteristics. The paper adds to service offshoring and international service management literature and extends practice theory. Managerial summary: This paper studies how knowledge‐intensive services (KIS) are impacted by a geographic relocation of the services across country borders. The relocation, termed offshoring, implies that the service client and provider are geographically separated and need to interact on a distance in order to produce the service. As the service production is dependent on the transfer of existing knowledge and co‐creation of new knowledge by clients and service providers, the geographic distance is challenging KIS production processes. An empirical analysis finds that the geographic distance is changing the way the services are produced. The transfer and co‐creation of knowledge are reduced leading to fewer interactions between clients and service providers, mainly knowledge coproduction remains important. These changes lead to altered KIS production processes and service characteristics.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".