Fueling innovation management research: Future directions and five forward‐looking paths
Bibliographic record
Abstract
Abstract Research about innovation management explores how the future is created—who is creating it (organizations, collaborations, etc.), for what aims (customer satisfaction, market performance, etc.), and with what broader effects (social, environmental, etc.). With this extended essay, we explore the potential futures of innovation management research in three ways. First, we briefly review the history of past research agendas and priorities published in the Journal of Product Innovation Management (JPIM), highlighting three broad topic areas (technological, social/environmental, and organizational) that have emerged over time and their potential disruptive implications for innovation management research. Second, we describe the outcome of a gathering of leading scholars in innovation management tasked with the challenge of identifying critical research paths for our field. This collaboration resulted in five “deep dive” essays into areas ripe for innovation management research in the years ahead: liquid innovation, artificial intelligence in innovation, business model innovation, public value innovation, and responsible innovation. Third, we reflect on this expansive effort and offer a discussion of implications (tensions, challenges, and opportunities) for future innovation management scholarship.
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 | 0.038 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.032 | 0.039 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.012 | 0.015 |
| 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".