Digital Innovation & Transformation Opportunities for Researchers & Practitioners – A Structured Literature Review & Proposed Model
Bibliographic record
Abstract
Aim/Purpose Although the latest review on digital innovation was made in 2018 (included articles up to 2017), the purpose of this study is to explore and examine opportunities for research in digital innovation and transformation for both researchers (including graduate students) and practitioners. A conceptual model is proposed. Background Digital innovation is omnipresent today, as it has penetrated deep into the structure and psyche of individuals, communities, organizations, institutions and governments. We find ourselves in a quagmire of opportunities risks and uncertainties, where ubiquitous technological interconnectedness form a new paradigm enabling industry to innovate and grow. All humanity is faced with these disruptive digital pressures. Yet, relatively there is little research done. Unfortunately, a coordinated effort for such a seriously important phenome-non does not exist. Methodology A structured literature review approach was conducted, the results of which were used for a qualitative approach, using nVivo, to extract insights and understanding. Findings This study identifies the extent of research done in the different areas of digital innovation and transformation and puts the results into perspective. Scholarly research is scarce, dispersed and diverse, lacking any direction or cohesion. Research on transformation is more than innovation and in both cases those that study their relationships with human or society are a handful. A conceptual model is proposed by integrating knowledge gained from the literature, the integral theory and the concept of impact assessment. Impact on Society This study shows that the integration of human agency digital innovation research and practice is primary. Researchers and practitioners can use the conceptual model to help them expand and extend their work.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".