The relationship between knowledge sharing and workplace innovation in a transnational corporation: a behavioral perspective
Bibliographic record
Abstract
There is a paucity of literature addressing the relationship between knowledge sharing and workplace innovation within the context of a knowledge-intensive transnational corporation. This is more so when a behavioural perspective is taken. Thus the key question driving this thesis is: What is the relationship between knowledge sharing and workplace innovation in the context of a transnational corporation, from a behavioural perspective? A survey of 2723 (2695 random + 28 non-random corporate) transnational corporation employees was conducted in seven geographic operating entities (Africa, Asia, Australasia, Canada, Europe, South America, USA) and Corporate (across all geographies). Of these, 853 surveys were completed. Data was analysed using correlation, regression and structural equation modelling. The findings show that the six factors of Subjective Norm, Attitude, Intention, Behaviour, Self-Worth, Perceived Behavioural Control and Knowledge Sharing Activity influence employees’ individual Knowledge Sharing Behaviour. While the factors of Knowledge Absorptive Capability and Organization Citizenship Behaviour influence Knowledge Sharing Behaviour at a team or workgroup level, also directly influence workplace innovation. Overall, Knowledge Sharing Behaviour was shown to be a significant antecedent of Workplace Innovation. This thesis makes four significant contributions to the literature. First, the factors selected appear significantly related to Knowledge Sharing Behaviour. Second, this thesis reveals that Knowledge Sharing Behaviour directly affects Workplace Innovation. Thirdly, an extended model based on the Theory of Planned Behaviour has been supported. Finally, a new scale, Knowledge Sharing Innovation Behaviour, has been developed to support further research into this important area. Practical implications: Given the importance of knowledge sharing as an enabler of workplace innovation in today’s competitive business world, this thesis provides a broader understanding of different dimensions of employees’ Knowledge Sharing Behaviour in relation to Workplace Innovation. These findings suggest that organizational administrators and managers should look into ways of improving the levels of knowledge sharing behaviour in order to facilitate workplace innovation. The composition of work teams, in terms of the behavioural aspects of members, and how their performance is measured is another opportunity for research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| 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".