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Record W4226450361 · doi:10.5267/j.ijdns.2022.2.008

The effect of social media marketing, compatibility and perceived ease of use on marketing performance: Evidence from hotel industry

2022· article· en· W4226450361 on OpenAlexvenueno aff
Mohammed T. Nuseir, Ghaleb A. El Refae

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMarketingCompatibility (geochemistry)BusinessSocial mediaUsabilityMarketing researchReturn on marketing investmentDigital marketingViral marketingSocial media marketingAdvertisingEngineeringComputer science

Abstract

fetched live from OpenAlex

This research effort addresses the role of social media marketing activities, the compatibility with latest technology availability and perceived ease of use of social media and technological applications and systems to influence the attitude towards adoption that further enhances the marketing performance. The hotel industry of UAE is required to adopt the social networking sites and online marketing approaches instead of traditional marketing pattern to attract the customers on large-scale worldwide specifically. In addition, Due to Covid19 pandemic era, the need for online applications has increased dramatically to avoid the physical contact. The study contributes to the body of knowledge by explaining the role of social media marketing, its compatibility and ease of use to predict the attitude towards adoption that further influences the marketing performance. The study reported that social media marketing efforts, the compatibility of online applications and perceived ease of use influence the attitude towards the adoption of social networking sites significantly, moreover the marketing performance is influenced by inclined attitude of adoption network related applications.

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)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.002
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 head0.034
GPT teacher head0.289
Teacher spread0.255 · 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

Citations17
Published2022
Admission routes1
Has abstractyes

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