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Record W3209148786 · doi:10.1142/s1363919621400077

VALUE APPROPRIATION AND INNOVATION COLLABORATION DYNAMICS: A REVIEW AND RESEARCH AGENDA

2021· review· en· W3209148786 on OpenAlex
Jialei Yang, Pia Hurmelinna‐Laukkanen, Arushi Sharma, Mika Westerlund

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Innovation Management · 2021
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsAppropriationValue (mathematics)Dynamics (music)Knowledge managementEpistemologySociologyComputer scienceEngineering ethicsPublic relationsPolitical scienceEngineeringPedagogy

Abstract

fetched live from OpenAlex

Contemporary innovation management studies on collaboration dynamics and value appropriation lack coherent theoretical articulations and underlying conceptual foundations. It is challenging to manage collaborative value creation without a proper understanding of the dynamic connections between collaboration for and appropriation of innovation. This study conducts a systematic literature review to uncover the dynamic connections between innovation-related value appropriation and collaboration. Topic modelling, a machine-learning-based text analysis method, is applied to a corpus of 270 scholarly articles to uncover relevant elements. Additionally, 77 articles are selected for an in-depth content analysis to examine the elements in a more detailed manner. With these steps, the study contributes to the literature by illustrating and elaborating the role of dynamics of collaboration in value appropriation, and vice versa.

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.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.012
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.097
GPT teacher head0.413
Teacher spread0.315 · 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