Cultural Sensemaking in Offshore Information Technology Service Suppliers: A Cultural Frame Perspective1
Bibliographic record
Abstract
In today’s global IT outsourcing relationships, individual employees need to operate effectively in culturally diverse environments. Such intercultural interactions can be especially challenging for members of IT service suppliers based in offshore locations. Through an in-depth qualitative case study of one of the largest China-based IT service firms with diverse clients from Japan, the United States, and China, this research elaborates the cultural sensemaking activities of the supplier’s individual employees. Specifically, drawing on the dynamic constructivist view of culture, this study develops the construct of “cultural frames” in the context of global IT outsourcing to characterize the knowledge structures guiding an individual’s collaboration with diverse clients. A portfolio of cultural frames emerges and evolves through the individual’s cultural sensemaking activities, which consist of the iterative enactment, alignment, and retention of cultural frames. In the cultural sensemaking process, the activity of frame bridging, in particular, creates significant value for the outsourcing relationship, and is especially salient among bicultural employees.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".