Top Management Involvement in Open Innovation Processes: Learning from a Global Carmaker
Bibliographic record
Abstract
Over the last decades, management scholars have largely investigated several aspects related to Open Innovation Processes – OIPs that concern the beginning, the diffusion and the future developments of the phenomenon, its typologies, its linkages with strategies and business models, its implementation, and even errors to be avoided when managing them. However, there is one aspect that seems to be underexplored and this deals with the involvement of top management.OIPs, like all the other managerial activities, need to be planned and defined ex ante; implemented, launched and managed; analyzed ex post. Thus, a decisional process – pertaining the activities to be carried out, human resources to be involved, criticalities to be avoided and results to be exploited – needs to be managed. Accordingly, the research question posed herein is: How do top managers handle OIPs?In order to respond to the above research question, a case study is presented hereinafter. This case study deals with an OIP launched by Fiat Chrysler Automobiles (FCA), one of the top ten global carmakers, and concerning the car of the future. In particular, through this case study, it is rebuilt and analysed how FCA top managers have handled the whole OIP.By leveraging on the achieved results, the paper speculates on the strong commitment that top managers need to put in practice if they aspire to make OIPs successful.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 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".