Managing Loss of Empowerment in Fast Growing SMEs: Communication and the Chinese Context
Bibliographic record
Abstract
Managers in fast growing SMEs face the developmental challenge of how to control increasing numbers of employees. We examine this by focusing on how the growth rate of small-medium sized enterprises (SMEs) influences employee empowerment within the context of China. We argue that faster growing Chinese SMEs are reluctant to empower their staff compared to slower growing ones. Drawing on the communication literature, we also hypothesize the indirect impact of different types of communication channels (face-to-face vs. computer-mediated) on the relationship between rate of employee growth and employee empowerment. The empirical findings are based on a survey of 114 SMEs in China and confirm a negative impact of growth rate on employee empowerment. The results also provide partial support for technology-mediated communication channels and strong support for face-to-face communication channels as providing boundary conditions to this relationship. The study contributes to the literatures on employee empowerment in changing organizational contexts by highlighting the role of communication channels as change takes place. Practical implications for how we understand organizational challenges faced by SME managers as their ventures grow are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".