On the duality of political and economic stakeholder influence on firm innovation performance: <scp>T</scp> heory and evidence from <scp>C</scp> hinese firms
Bibliographic record
Abstract
Research Summary In this study, we propose and test a multi‐stakeholder perspective to address variation in innovation performance across firms. Specifically, we analyze how a focal firm's innovation performance is shaped by its political stakeholders (local and central governments) and economic stakeholders (suppliers, buyers, and competitors). Using a data set consisting of over 26,400 Chinese firms, we first find support for our predictions that a focal firm's innovation performance will be enhanced by both its government connections and the innovativeness of its economic stakeholders. We then analyze whether the interdependent effect of these political and economic stakeholders is more likely to be synergistic versus antagonistic, and find evidence consistent with the antagonistic view. Managerial Summary We show how a firm's innovativeness is influenced strongly by its relationships to external stakeholders. Specifically, we examine the potentially dual‐edged role of political stakeholders (local and central governments) and economic stakeholders (suppliers, buyers, and competitors). Using extensive data on Chinese firms, we find: (a) that the higher the level of government connections, the greater a firm's innovativeness; (b) that firms located in proximity with more innovative economic stakeholders also tend to have higher innovation performance. We also look beyond these independent positive effects to examine the joint effect of these two forms of stakeholder influence, and here we see that more influence is not always better. Specifically, we find that the innovation benefit that typically accrues to firms in proximity to more innovative economic stakeholders is weakened when those firms also have higher‐level government connections.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".