Dynamic capability: The effect of digital leadership on fostering innovation capability based on market orientation
Bibliographic record
Abstract
Industry 4.0 drives enterprises to transform their capabilities especially in innovation and their capabilities to adapt with dynamic market. The capabilities can be fostered when the leader is oriented towards digital technology and market orientation. The role of digital leadership has gained attention for studies to develop innovation and dynamic capabilities based on market orientation. Studies have been conducted on dynamic capabilities with focus on the strategy, management and economic literature including the understanding of its driving key to success. However, the study on the role of digital leadership on the development of dynamic capability based on innovation capability and market orientation has not been intensively discussed. It is argued that the development of dynamic capability and innovation capability is strongly driven from a combination of digital leadership and market orientation. Data in this study is taken from a survey conducted on 88 Indonesian telecommunication firms as a unit for analyses. The results show that digital leadership had a strong direct and indirect relationship with dynamic capability, however the strong path in developing capability is determined from the development of innovation capability that is driven from digital leadership based on market orientation. The finding reinforces the role of digital leadership as a critical influence on development of dynamic capability. Future studies are suggested to extend the research by exploring the research model to elaborate more on the impact of collaboration, leveraging a larger sample size and better statistical tools. A longitudinal study on the companies that implement the transformation based on dynamic capabilities is also recommended for future studies.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".