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Record W4310375229 · doi:10.5430/ijba.v13n6p39

Critical Success Factors in Strategic Alliances in Iran's Pharmaceutical Industry: A Qualitative Content Analysis Study

2022· article· en· W4310375229 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Business Administration · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCompetitive and Knowledge Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsAllianceBusinessCorporate governanceMarketingStrategic allianceCritical success factorPhase (matter)Industrial organizationPharmaceutical industryCapital (architecture)Selection (genetic algorithm)Duration (music)Computer sciencePolitical science

Abstract

fetched live from OpenAlex

To overcome internal and external obstacles, including political crises, the Iranian pharmaceutical industry forms strategic collaborations. The high failure rate of strategic partnerships emphasizes the need to identify and evaluate success factors at different stages of the alliance lifecycle. Additionally, directed content analysis is used to assess the findings. In the formation phase, the most critical factors are partner selection and choice of the most important governance form, whereas in the operation phase, relational capital, coordination, conflict management, and selecting the appropriate alliance people are crucial. Evaluation of alliance performance should be undertaken throughout the duration of the alliance's existence.

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)0.001
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 score0.087
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
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.246
GPT teacher head0.441
Teacher spread0.195 · 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