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Record W4292959213 · doi:10.5267/j.ijdns.2022.4.018

The effect of social media marketing on customer buying intention on the context of entrepre-neurial firms: Moderating role of customer involvement

2022· article· en· W4292959213 on OpenAlexvenueno aff
Danah Al-Abed, Alaeddin Ahmad, Amani Al-Refai, Mohammad Abuhashesh, Ammar Abdallah, Mohammad Ahmad Sumadi

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsSocial mediaMarketingBusinessSocial media marketingStructural equation modelingCustomer engagementLikert scaleContext (archaeology)AdvertisingPsychologyDigital marketing

Abstract

fetched live from OpenAlex

This study aims to provide comprehensive insights into the social media marketing characteristics affecting customer buying intention. The model was theoretically based on and explored using a quantitative approach. A survey strategy was adopted, and a five-point Likert questionnaire was distributed to Jordanian citizens. A total of 237 responses were received. The hypothesis testing followed structural equation modeling using SPSS software. The findings suggest that all social media marketing subcontracts exhibit a significant positive effect on buying intention, whereby accessibility is the highest. Customer involvement also moderates the relationship between social media marketing and customer buying intention. The proposed model provides new insights into social media marketing drivers affecting buying intentions and engagement with an entrepreneurial brand or product. This study reaffirms that social media marketing can significantly influence the success of Jordanian entrepreneurial firms and understanding how to use this tool effectively can significantly change how businesses operate.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0020.001
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 head0.017
GPT teacher head0.259
Teacher spread0.242 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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