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Record W4313511669 · doi:10.3390/jrfm16010035

Paradoxes and Tensions in Interorganizational Relationships: A Systematic Literature Review

2023· article· en· W4313511669 on OpenAlexvenueno aff
Marcos Vinícius Bitencourt Fortes, Lara Agostini, Douglas Wegner, Anna Nosella

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsContext (archaeology)Subject (documents)Variety (cybernetics)Diversity (politics)EpistemologySet (abstract data type)Knowledge managementSystematic reviewSample (material)SociologyManagement scienceComputer sciencePolitical scienceEconomicsArtificial intelligencePhilosophyMEDLINELawGeography

Abstract

fetched live from OpenAlex

This paper examines the literature on paradoxes and tensions in interorganizational relationships (IORs) and identifies how such tensions are managed in interorganizational settings. In a systematic literature review, we analyzed 95 papers published between 1997 and 2021 on the subject of paradoxes in IORs. The sample showed a variety of paradoxes occurring in different interorganizational contexts, such as knowledge sharing and protection, short- and long-term orientation, and exploration and exploitation. The diversity of such paradoxes has led to crescent interest in cooperation. Our main results show that contextual factors and management practices influence the balance between paradoxes. Although the particular context of each IOR may be unique in terms of balancing paradoxical elements, we identified a set of “pre-tension practices” and “post-tension practices” which may help avoid the emergence of tensions or reduce their adverse effects. The findings of our systematic literature review have also enabled us to propose future research avenues concerning managing tensions in IORs, for instance, the link between paradoxes and IOR performance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.224
Teacher spread0.206 · 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 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

Citations20
Published2023
Admission routes1
Has abstractyes

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