Microdivisionalization as a way toward dynamic capability
Bibliographic record
Abstract
Purpose As entrepreneurial top management teams in multidivisional forms are typically treated in pertinent literature as the default organizational solutions for developing dynamic capabilities, the emerging innovative organizational forms tend to be overlooked, even though they could be a viable means of transforming established enterprises. The present case study examines how Haier's microenterprise and platforms influenced the firm's dynamic capabilities development. Design/methodology/approach The paper presents a qualitative case study of Haier Group Corporation in China. Findings The findings indicate that Haier employed a loosely coupled relationship between its headquarters and the microenterprises, developed quasi market-based exchange relationships and established peer-to-peer learning opportunities and coordination among its microenterprises. Data analyses further revealed that Haier has adopted three-step routines to capture market opportunities and enhance operational efficiency. This research extends the sensing-seizing-reconfiguration model typically recommended in the existing literature. It also demonstrates that organizational configuration is an important aspect of dynamic innovation. In summary, the study results showcase microdivisionalization as a new way for developing dynamic capabilities to better adapt to the ever-changing market environments. Originality/value In summary, our study showcased microdivisionalization as a new way for firms to change the organization structure and business strategies to better adapt to the ever-changing market environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".