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Record W4394912868 · doi:10.5267/j.ijdns.2024.4.005

Short video marketing and consumer engagement: Mediation effect of social sharing

2024· article· en· W4394912868 on OpenAlexvenueno aff
Mahmoud Alghizzawi, Amro Alzghoul, Hasan Alhanatleh, Fandi Omeish, Tariq Abdrabbo, Ibrahim Ezmigna

Bibliographic record

VenueInternational Journal of Data and Network Science · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsMediationBusinessMarketingSocial marketingAdvertisingCustomer engagementSocial mediaSociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This study aims to examine the complex dynamics of short video marketing, social sharing, and consumer engagement specifically within the tourism sector in Jordan. The study used AMOS software for analysis, gathering data from a total of 468 participants, including both internal and external tourists. The results emphasize the significant impact of short video marketing in stimulating social sharing behaviors among travelers, prompting them to share their experiences on various social platforms, thus expanding the reach of tourism-related content. Moreover, the research reveals the mutual relationship between social sharing and consumer engagement, indicating that sharing behaviors play a crucial role in increasing levels of engagement among tourists. The novelty of this research resides in its concentrated investigation of these associations within the unique context of the Jordanian tourism sector. The study's significance lies in its ability to expand comprehension of digital marketing dynamics in the tourism industry, providing practical insights for marketers seeking to improve brand visibility and engagement in digital spaces. This study enhances the current knowledge base by employing a quantitative approach, establishes a basis for future research endeavors, and offers valuable insights for marketers aiming to maximize the effectiveness of digital marketing strategies.

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)0.016
metaresearch head score (Gemma)0.002
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 score0.573
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.100
GPT teacher head0.438
Teacher spread0.338 · 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

Citations35
Published2024
Admission routes1
Has abstractyes

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