Constructing an organizational identity with political ideology: The case of Huawei, 1987–2020
Bibliographic record
Abstract
Leveraging archival data, we study how Huawei used Chinese communist political ideology to construct its organizational identity. Covering the time from its founding in 1987 to 2020, we show how Huawei appropriated Fen Dou as a core idea-element of the Chinese communist political ideology to develop its identity as a “national industry revitalizer,” neutralized it as it internationalized and claimed to be an “international corporate citizen,” and then repurposed it as it sought to help advance all of humankind—akin to a “global technology leader.” By mapping the historical evolution of Huawei across different junctures and processual periods, we develop middle-range theory on the role of political ideology in identity construction. We contribute to the literature by introducing political ideology as a resource for identity construction, mapping the process of identity construction with ideology across different contexts, and articulating a resonant theoretical narrative whereby political ideology emerges as a double-edged sword. Our study reveals how political ideology helps create resonance with certain stakeholders, but how the commitment to a particular ideology carries meaningful risks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".