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Record W3131615523 · doi:10.5267/j.msl.2021.1.012

The factors affecting inter-organizational relationship success in Vietnam tourism sector

2021· article· en· W3131615523 on OpenAlexvenueno aff
Nguyễn Thị Minh Phương, Mai Ngọc Khương, Phương Ngọc Nguyễn

Bibliographic record

VenueManagement Science Letters · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsTourismBusinessPath analysis (statistics)MarketingAffect (linguistics)Structural equation modelingKnowledge managementPsychologyComputer science

Abstract

fetched live from OpenAlex

This study analyzed the factors influencing the effectiveness of inter-organizational relationship (IOR) through relationship marketing of travel companies with their partners based on data collection with 256 responses who were working for travel companies in Vietnam. The study used a quantitative approach as factor analysis, multiple regressions and path analysis. The results indicated that inter-organizational relationship effectiveness was directly and indirectly affected by coordination, trust, frequency of interaction, commitment, communication, organizational compatibility, and relationship marketing. Surprisingly, formalization did not significantly affect the effectiveness of IOR and relationship marketing. This study is an essential reference for managers to make decisions and to enhance the effectiveness of their collaborative partnerships.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.246
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2021
Admission routes1
Has abstractyes

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