SME owners’ roles for mutual reinforcement of innovation and entrepreneurship in dealing with digital technologies: case studies of selected Indonesian SMEs
Bibliographic record
Abstract
This article explores the relationships among innovation, entrepreneurship, and digital technology, specifically how the management of small and medium enterprises (SMEs) utilise innovation and entrepreneurship to deal with digital technologies and/or how the management leverages digital technologies for innovation and entrepreneurship. A qualitative research approach using a case study was employed to examine the phenomena, focusing on deep observations. The research found that SME owners play crucial roles in three areas: (i) demonstrating entrepreneurial ability through knowledge, experience, talent, and innovation; (ii) performing strategic functions by leveraging external sources of innovation, with the help of digital technology; and iii. acting as digitally minded entrepreneurs to enhance manufacturing processes and establish new marketing channels. The mutual reinforcement of innovation and entrepreneurship in dealing with digital technology, is influenced by both user and demand-driven innovation, knowledge and technology-driven innovation, experiential knowledge-driven innovation, articulation of innovation outcomes, digital network-based marketing, and technical guidance on digital transactions in e-commerce and e-business. This research confirmed that there is a mutual reinforcement between the innovation and entrepreneurship of SMEs in adopting digital technology. New insight for digitally minded entrepreneurs is the use of digital technology to boost business efficiency toward global enterprises’ competitiveness and sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".