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Record W4360776650 · doi:10.5267/j.ijdns.2023.3.004

Social media marketing activities and tourists’ purchase intention

2023· article· en· W4360776650 on OpenAlexvenueno aff
Malek Alsoud, Sabri Mahmoud Alfdool, Ali Trawnih, Abdullah Helalat, Lu’ay Al-Mu’ani, Nihaiah Mahrakani

Bibliographic record

VenueInternational Journal of Data and Network Science · 2023
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsSocial mediaSocial media marketingTourismAdvertisingMarketingWord of mouthConstruct (python library)BusinessBrand imagePsychologyDigital marketingPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This study aims to examine the influence of brand image as a mediator between social media marketing activity and tourist intentions to visit tourist sites in Jordan. To test the hypotheses, we utilized the partial least square method and distributed a questionnaire survey to 400 visitors, receiving 289 responses for analysis using SMART-PLS4 software. The study made significant contributions to the literature on social media marketing by developing a research model that links social media usage to brand image and shopping intention, and by using statistical techniques to quantitatively assess the effect of social media marketing activity on the brand image as a mediator. The findings exposed that word of mouth, a construct of social media, significantly positively influenced tourists' purchase intention to visit sites in Jordan. This study sheds light on the importance of social media marketing and its impact on the tourism industry in Jordan, providing useful insights for practitioners and policymakers in the field.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.304
Teacher spread0.266 · 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

Citations25
Published2023
Admission routes1
Has abstractyes

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