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Record W4239216332 · doi:10.5539/mas.v12n11p232

A Conceptual Framework for Determinants of E-Exporting (Marketing Applications) Practices and the Business Performance: Empirical Study

2018· article· en· W4239216332 on OpenAlexvenueno aff
Hani Al-Dmour

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsVariation (astronomy)MarketingConceptual frameworkBusinessConceptual modelEmpirical researchIndustrial organizationComputer scienceStatistics

Abstract

fetched live from OpenAlex

This research aims at identifying the determinants of the adoption of e-exporting marketing applications by Jordanian companies and their influence on their exporting performance. For this purpose, a conceptual framework based on the analysis of the literature review and the theoretical adoption models was developed. The required data was gathered through self-administrated questionnaire from 163 exporting industrial companies. The results showed that the extent of e-exporting applications being practiced is considered to be satisfactory (i.e. 62%) and they were varied among exporting companies in terms of their size and experience. The results of factor analysis (FA) indicated that 30 determinants variables could be grouped into three major factors: organizational, environmental and technological and they could explain 76% of the variation of e-exporting applications being implemented and 83% the variation on exporting performance. Furthermore, the results have shown that organizational factor was the most important one determining the extent e-exporting applications being implemented and the environmental factor was the most important one determining the exporting performance. These resultsprovide empirical evidence that the integration approach of the adoption model could produce better explanation of the variation on both the level of e-exporting applications being practised and the business performance. In the final section, research implications and future directions are presented.

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)0.003
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 score0.694
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.043
GPT teacher head0.336
Teacher spread0.293 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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