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Record W3108234773 · doi:10.5267/j.msl.2020.10.025

The role of organizational commitment, entrepreneurial orientation, and architectural marketing capabilities on improving marketing performance by using network chain capability of goods in tourism market

2020· article· en· W3108234773 on OpenAlexvenueno aff
Hasyim Hasyim, Sahyar Sahyar, Dina Sarah Syahreza

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessMarketingMarket orientationTourismMarketing managementEntrepreneurial orientationFast-moving consumer goodsChain (unit)Entrepreneurship

Abstract

fetched live from OpenAlex

Entrepreneurial orientation is carried out to improve management's ability to deal with market changes. This study examines a new concept of network chain capability that is capable of mediating the relationship between entrepreneurial orientation and marketing performance of goods in tourism market. The study also verifies the architectural marketing capabilities and organizational commitment to improve network chain capabilities that have an impact on marketing performance of goods in tourism market. In this study, 6 hypotheses are developed and tested with data collected from 185 respondents in the handicraft industry focused on tourism market. Data are analyzed using AMOS 22.0 statistical software which successfully tested 6 hypotheses with significant results. The study proves that network chain capability is declared feasible as a mediating variable. Managerially, network chain capabilities can be practiced as a marketing tool to improve 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 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)0.001
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.220
Teacher spread0.213 · 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
Published2020
Admission routes1
Has abstractyes

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