Impact of entrepreneurship on technological innovation in the digital age: a knowledge management perspective
Bibliographic record
Abstract
Purpose This study aims to use the knowledge management perspective to examine the mechanism through which entrepreneurship drives firms’ technological innovation in the digital age. The objective is to develop a multi-stage integrated theoretical model to explain how entrepreneurship exerts its influence on firms’ technological innovation with a particular focus on the knowledge management perspective. The findings can be used for the cultivation of entrepreneurship and for the promotion of continuous technological innovation activities. Design/methodology/approach This study uses a case-based qualitative approach to examine the relationship between entrepreneurship and technological innovation. The authors first analyze the case of SANY and then explore the mechanism of how entrepreneurship can promote a firm’s technological innovation from the perspective of knowledge management based on the technology-organization-environment framework. An integrated theoretical model is then developed in this study. Findings Based on a case study, the authors propose that there are three main processes of knowledge management in firms’ technological innovation: knowledge acquisition, knowledge integration and knowledge creation. In the process of knowledge acquisition, the joint effects of innovation spirit, learning spirit, cooperation spirit and global vision drive the construction and its healthy development of firms’ innovation ecosystem. In the process of knowledge integration, the joint effects of innovation spirit, cooperation spirit and learning spirit help complete the integration of knowledge and further the accumulation of firms’ core knowledge resources. In the process of knowledge creation, the joint effects of mission spirit, learning spirit and innovation spirit encourage the top management team to establish long-term goals and innovation philosophy. This philosophy can promote the establishment of a people-oriented incentive mechanism that helps achieve the transformation from the accumulation of core knowledge resources to the research and innovation of core technologies. After these three stages, firms are passively engaged in the “reverse transfer of knowledge” step, which contributes to other firms’ knowledge management cycle. With active knowledge acquisition, integration, creation and passive reverse knowledge transfer, firms can achieve continuous technological innovation. Research limitations/implications This study has important theoretical implications in entrepreneurship research. This study helps advance the understanding of entrepreneurship and literature on the relationship between entrepreneurship and technological innovation in the digital age, which can broaden the application of knowledge management theories. It can also help better understand how to develop healthy firm-led innovation ecosystems to achieve continuous optimization of knowledge and technological innovation in the digital age. Originality/value This study proposes an integrated theoretical model to address the issues of entrepreneurship and firms’ technological innovation in the digital age, and it is also one of few studies that focuses on entrepreneurship and innovation from a knowledge management perspective.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".