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Record W4288757931 · doi:10.1080/13662716.2022.2102462

How spatial proximity facilitates distant search – a social capital perspective on local open innovation

2022· article· en· W4288757931 on OpenAlex

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

VenueIndustry and Innovation · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsUniversité Laval
FundersDeutsche Forschungsgemeinschaft
KeywordsOpenness to experienceOpen innovationPopularitySocial capitalBusinessKnowledge managementProcess (computing)Key (lock)Industrial organizationPerspective (graphical)MarketingComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Distant search has evolved from the open innovation literature as an efficient mechanism to access external knowledge from heterogeneous fields of expertise. Despite its popularity and proven benefits, companies face multiple barriers to benefitting from distant search. In this study, we explore a local open innovation approach in which the spatial distance between solution-seeking firms and problem solvers was deliberately reduced to combine the benefits of distant search with those of spatial proximity. We studied eight local open innovation events and found that spatial proximity supports the implementation of open innovation, overcoming challenges of initiating organisational change towards openness, establishing trusting relationships for knowledge exchange, and successfully applying the external knowledge. By identifying social capital as the key success factor in local open innovation, our study contributes to the theoretical foundations of open innovation by showing how the dimensions of social capital enable key actions in each process phase.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.789
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.066
GPT teacher head0.346
Teacher spread0.280 · 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