Building an Integrative Model for Managing Exploratory Innovation
Bibliographic record
Abstract
Abstract Purpose In this paper we develop an integrated model identifying the key factors involved in managing exploratory innovation processes while also maintaining current business models and processes. Methodology/approach We first characterize the problem of innovation as consisting of “the four central problems” organizations face when trying to manage innovation processes (Van de Ven, 1986). We develop an enhanced version of O’Connor’s (2008) Discovery, Incubation and Acceleration (DIA) model by integrating elements of Sanchez’ (2012) theory of architectural isomorphism as well as Markides’ (2008) framework for strategically assessing the benefits of segregation versus integration of innovation processes. We develop and apply our model working with managers in two company contexts to assure the ability of our Integrated Model to identify key organizational and strategic variables that need to be recognized and managed in order to sustain successful exploratory innovation processes. Findings Reviews of our “Enhanced Integrated Model” with managers in the two companies suggest that our model would help them to recognize and manage key issues that were not addressed adequately in their prior efforts at exploratory innovation. Research implications and practical implications Our model building process provides a basic template for other research focused on developing normative management models through case-based research. The specific elements included in our Enhanced Integrated Models should provide managers with a useful model for managing exploratory innovation processes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".