Organizational Challenges of Online Customer Co-Creation for Innovation: A Middle-Managers’ Perspective in the Italian Food Context
Bibliographic record
Abstract
In a nutshell, co-creation is described as a way to open up a company to the outside world, helping to generate product innovations and to update brand meanings. Drawing on the online community and co-creation literature, this paper aims at contributing to exploring the main intra-organizational challenges of online customer co-creation for innovation, from the viewpoint of middle managers. The intent is to broaden the existing conceptual understanding of the main internal factors hindering customer co-creation but also of the practices that could be adopted to best manage the related challenges. The study adopts a qualitative approach and is based on the results of eight in-depth, semi-structured interviews with digital and marketing managers, working closely with co-creation initiatives in the Italian food industry, with the aim of exploring intra-organizational challenges perceived by those who are directly involved in co-creation implementation. The results complement existing literature by 1) offering a more longitudinal vision of the implementation process—and the related internal hurdles, 2) identifying the most appropriate coping strategies and 3) formulating some hypothesis that could support an interpretative model of the capabilities needed to start the process and managing it in a strategic perspective.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".