How does the digital innovation process unfold in practice? A novel third-generation and empirical-based need–solution pairing model
Bibliographic record
Abstract
Purpose There is a lack of empirical-based models derived from practice to explain the digital innovation process. The authors investigate how the digital innovation process unfolds in practice. Design/methodology/approach The authors undertake an exploratory and phenomenological study of 21 Malaysian small and medium enterprises (SMEs) in the information and communication technology (ICT) sector. Findings The findings show that the delineation between digital innovation process and outcome is blurred in practice, due to the process' iterative nature. Under this process, customers' role has changed from being passive receivers of innovative products to active reviewers, testers, influential decision-makers, initiators and co-creators at different review points in the innovation process. Enterprises' role has expanded from being the initiator of the innovation process to being a cogitative actor by seeking and absorbing knowledge from customer reviews into the digital innovation process. Market analysis is often the initiator of the digital innovation process, and the findings shed light on the underlying causative mechanisms of the initiation stage, which are understudied and not well understood in the existing literature. Originality/value The study contributes to academic knowledge by answering scholars' call for developing third-generation practice-based innovation models, which accounts for enterprises' context-specificities and internal and external environments, and for exploring the suitability of the need–solution fit approach for the digital innovation process. Such models have only been conceptually advocated in the literature. The study also informs practitioners on the organizational and operational activities involved in managing and strategizing for the digital innovation process.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".