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Record W3133316727 · doi:10.5539/ijms.v13n1p42

Organizational Challenges of Online Customer Co-Creation for Innovation: A Middle-Managers’ Perspective in the Italian Food Context

2021· article· en· W3133316727 on OpenAlexvenueno aff
Carla Rossi

Bibliographic record

VenueInternational Journal of Marketing Studies · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessPerspective (graphical)Process (computing)Co-creationKnowledge managementContext (archaeology)MarketingProduct (mathematics)Qualitative researchMiddle managementOpen innovationSociologyComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.337
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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