Advancing Management Innovation: Synthesizing Processes, Levels of Analysis, and Change Agents
Bibliographic record
Abstract
Despite the mounting evidence that innovation in management can fuel competitive advantage, we still know relatively little about how firms introduce new ways of managing. The goal of this introductory essay—and the Themed Section it introduces—is to advance this knowledge. To this end, we first synthesize the main developments in the field of management innovation and show that the field has branched into four main theoretical perspectives (rational, institutional, international business, and theory development perspectives). We then address the fragmentation issue that emerges from our review by proposing a co-evolutionary framework of management innovation that takes into account the dynamic and multilevel nature of the concept; we thus integrate the generation, diffusion, adoption, and adaptation phases of the management innovation process at the organizational, inter-organizational and macro level. Our integrative framework also addresses the role of human agency (managerial intentionality of internal and external change agents) and makes a distinction between three types of management innovations (new to the world, new to the organization and adapted to its context, and new to the organization without adaptation). Furthermore, we discuss the contributions of the studies included in the Themed Section and identify several avenues for future research that we consider priorities for driving the further development of the field.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".