Paternalistic leadership and innovation: the moderating effect of environmental dynamism
Bibliographic record
Abstract
Purpose The purpose of this paper is to focus on how the three elements of paternalistic leadership – authoritarianism, benevolence and moral leadership – affect organizational innovation – both explorative and exploitative innovation – in Chinese enterprises. It also examines the moderating effect of environmental dynamism on the relationship between paternalistic leadership and organizational innovation. Design/methodology/approach Data on 190 superior–subordinate dyads are collected using questionnaire surveys. The supervisors are recruited from the MBA program in a famous university in the city of Hefei, China, who are also asked to distribute subordinate questionnaires to their subordinates. The hierarchical regression analysis is conducted to test the hypotheses by using SPSS 22.0. Findings The analysis of 190 superior–subordinate dyads shows that benevolent and authoritarian leadership is positively related to exploratory innovation, while moral leadership has no significant impact on exploratory innovation. The results also reveal that all three elements of paternalistic leadership is, in general, positively correlated with exploitative innovation. Furthermore, environmental dynamism moderates the relationship between paternalistic leadership and innovation. In a dynamic environment, moral leadership has a stronger positive effect on innovation, but only on exploratory innovation; whereas authoritarian leadership exerts more detrimental effects on both exploratory and exploitative innovation. Originality/value The current work contributes to understanding the relationship between paternalistic leadership and innovation in the Chinese cultural context by examining the effects of the three elements of paternalistic leadership separately and by showing how these effects can be moderated by environmental dynamism.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".