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Record W4379881105 · doi:10.1080/26943980.2023.2217500

Key problems of interorganizational collaborations: A multi-level and temporal analysis

2022· article· en· W4379881105 on OpenAlexaff
Émilie Bourdages

Bibliographic record

VenueJournal of Inter-Organizational Relationships · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsKey (lock)Dominance (genetics)Corporate governanceInterpersonal communicationDiversity (politics)NegotiationTourismFeelingManagement scienceComputer sciencePsychologyBusinessSociologySocial psychologyPolitical scienceSocial scienceEconomicsComputer security

Abstract

fetched live from OpenAlex

Researchers analyze interorganizational problems one at a time, at one level of analysis, and a specific point in time. Yet, interorganizational problems certainly appear in groups of interrelated problems that are nested across multiple levels of analysis and evolve over time. Moreover, analyzing problems independently hinders the ability to assess their relative importance. This study establishes key problems of interorganizational collaboration and highlights their timing. A multiple-case study was realized in the tourism industry. Twenty-eight semi-structured interviews, post-interview surveys, and secondary data allowed us to determine eight key interorganizational problems (interpersonal problems, lack of familiarity, cultural differences, roles and responsibilities problems, toxic work climate, inequity feeling, inappropriate governance, and passivity when problems arise). By using a multi-level and temporal analytical framework, our study contributes to the IOR literature in four ways: confirming problem nesting across levels of analysis, demonstrating greater problem diversity, highlighting relational problem dominance, and demonstrating lifecycle problem evolution.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.065
GPT teacher head0.244
Teacher spread0.179 · 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.

Study designObservational
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

Citations3
Published2022
Admission routes1
Has abstractyes

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