The innovation gap is closing: Chinese state-owned enterprises’ mechanisms for developing innovative solutions
Bibliographic record
Abstract
Purpose State-owned enterprises (SOEs) play an immense role throughout the world. Historically lacking in innovativeness, SOEs are now compelled to be more innovative. This study aims to explore the innovative tendencies of SOEs and non-SOEs as they strive to be preferred suppliers to their key accounts. This study also examines the effects of top management involvement (TMI) and customer knowledge utilization on suppliers’ tendencies to provide innovative solutions to key customers. In addition, this study examines the moderating effects of state ownership on these relationships. Design/methodology/approach Following institutional theory and dynamic capabilities logic to guide expectations, this study collected survey responses from 185 managers at SOEs and non-SOEs in Beijing, Shanghai, Guangzhou and Shenzhen in China. Using partial least squares structural equation modeling, this study examined main and moderating effects of variables on innovation. Findings Results indicate that state ownership does not have a significant effect on innovation, suggesting Chinese SOEs may no longer be innovation-disadvantaged vis-à-vis non-SOEs. In addition, both TMI and customer knowledge utilization have positive effects on innovation. The hypothesized magnifying effect of state ownership on TMI’s main effect is not present. Data support our expectation that state ownership amplifies the positive effect of customer knowledge utilization on innovation. Research limitations/implications Our research provides evidence that China’s SOEs are closing the competitive gap in innovation and mechanisms for this occurrence. The relatively small sample from limited geographies necessitates research in more regions of China. Also, research should investigate not just the ownership type of suppliers, but also of buyers. Originality/value This study offers unique insights into factors affecting the innovative tendencies of Chinese SOEs and non-SOEs. Until now, little research has addressed what practices SOEs use to provide more innovative solutions to customers.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".