Knowledge management and SMEs’ digital transformation: A systematic literature review and future research agenda
Bibliographic record
Abstract
This study aims to identify and explain different collaborative approaches, delineate external actors' roles, and examine the interplay between knowledge exploration and exploitation processes for digital transformation. We conducted a search of academic papers using research terms such as “Digital*, Digital transfor*, industry 4.0 (I4.0), industry 5.0, knowledge exploration, knowledge acquisition, ecosystem collaboration*, knowledge networks, and open innovation” in both the Scopus and Web of Science databases. Altogether, 108 papers met the criteria (e.g., ABS 2 & 2+ ranking of journals, only journal papers, and focusing on small- and medium-size enterprises (SMEs) digital transformation) for conducting a systematic literature review in this research. The results indicate that external actors play specific roles in supporting SMEs' digital transformation. We found that customers and suppliers push and encourage SMEs in their digital transformation, while coopetition can elicit greater technological benefits for SMEs with close technological and economic proximity. Intermediaries provide knowledge-brokering services, facilitate innovation processes, and enable technology transfer and capacity-building for SMEs’ digital transformation. Government initiatives, such as favorable policymaking and financial support, are important in promoting and facilitating a collaborative environment for technology development among SMEs. This study’s results present two distinct collaborative mechanisms that SMEs can utilize for digital transformation: (I) core value chain and network actors’ collaborations, which provide linear processes for knowledge exploration and exploitation, and (II) ecosystem and innovation platform-based collaborations, in which SMEs adopt the ambidextrous approach as a nonlinear process for knowledge exploration and exploitation for digital transformation. Certain organizational-level factors (organizational capabilities, micro-foundations, operational capabilities, organization strategies, and culture) are important for SMEs’ knowledge exploitation in digital transformation. The study also presents an integrated framework and offers directions for future research and important insights for practitioners.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".