DIGITAL INNOVATION MANAGEMENT: FRAMEWORKS, STRATEGIES, AND FUTURE PERSPECTIVES
Bibliographic record
Abstract
Digital innovation management has become crucial in today's rapidly evolving business landscape, marked by technological convergence, accelerated development cycles, and emerging market opportunities. This paper, employing a systematic literature review methodology to critically analyze and synthesize existing literature, offers an in-depth analysis of digital innovation management, examining its unique attributes, frameworks, and best practices. The Digital Innovation Process Model (DIPM) is highlighted, emphasizing the importance of incorporating digital technologies throughout the ideation, development, and commercialization phases to create value and achieve competitive advantage. The challenges and strategies associated with managing digital innovation are explored, such as striking a balance between agility and stability, nurturing collaborative innovation ecosystems, and fostering organizational cultures that promote digital innovation. Additionally, emerging theoretical perspectives like digital innovation logic and platform logic are discussed, shedding light on the converging nature of digital innovation and the role platforms play in value co-creation. In formulating a digital innovation management strategy, the focus is on aligning innovation efforts with business objectives, fostering a supportive organizational culture, and harnessing the power of digital platforms for collaboration. Lastly, potential avenues for future research are outlined, including assessing the impact of digital innovation on performance, investigating the interplay between digital innovation and emerging technologies, and examining the influence of public policy and regulation on the digital innovation landscape. This paper strives to provide a comprehensive understanding of digital innovation management and its implications for organizations, strategists, and researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".