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Record W2014084367 · doi:10.5912/jcb645

Market Orientation, Alliance Orientation, and Business Performance in the Biotechnology Industry

2014· article· en· W2014084367 on OpenAlexaffabout
Grant Alexander Wilson, Jason Perepelkin, David Di Zhang, Marc‐Antoine Vachon

Bibliographic record

VenueJournal of Commercial Biotechnology · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAllianceCommercializationMarket orientationBusinessMediationMarketingBiotechnologyBusiness administrationIndustrial organizationBiologySociology

Abstract

fetched live from OpenAlex

The purpose of this study was to test the unexplored relationship between market orientation (MO), alliance orientation (AO), and business performance (PERF) in the medical/healthcare subsector of the Canadian biotechnology industry. The study surveyed Canadian biotechnology executives via mail and web-based questionnaires. It was found that the relationship between MO and PERF was positive and significant and the relationship between AO and PERF was positive and significant. It was also found that the relationship between MO and AO was positive and significant, supporting the existence of a mediation relationship. Specifically, MO’s influence on PERF was found to be fully mediated by AO. This suggests that Canadian medical/healthcare biotechnology companies that were highly market-oriented were also highly alliance-oriented, and highly alliance-oriented companies were top performing companies. This study outlines the apparent sequential relationship between market-oriented behavioural commitments, alliance-oriented activities, and business performance outcomes among Canadian biotechnology companies. Furthermore, it has business development and the commercialization process implications for biotechnology managers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.777
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.240
Teacher spread0.227 · 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 designTheoretical or conceptual
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

Citations42
Published2014
Admission routes2
Has abstractyes

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