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Record W4293214651 · doi:10.5267/j.ijdns.2022.8.008

The impact of digital content of marketing mix on marketing performance: An experimental study at five-star hotels in Jordan

2022· article· en· W4293214651 on OpenAlexvenueno aff
Mohammad Alkarem Khalayleh, Sulieman Ibraheem Shelash Al-Hawary

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMarketingBusinessContent marketingDigital marketingMarketing mixAdvertisingSocial mediaLoyalty business modelRelationship marketingMarketing managementMarketing strategyService qualityComputer scienceService (business)World Wide Web

Abstract

fetched live from OpenAlex

The study aimed to examine the impact of the marketing mix for digital content on the marketing performance of five-star hotels in Jordan. The dimensions of the marketing mix for digital content were (digital marketing database, social media platforms, digital pricing, and digital advertising), while the dimensions of marketing performance were (customer loyalty, customer satisfaction, and attracting new customers). The study population represented five-star hotel customers in Jordan, where an appropriate sample of (294) customers was used. The data of the study were analyzed using the Structural Equation Modeling (SEM) technique. The study concluded that all dimensions of the marketing mix for digital content had a positive impact on the marketing performance of five-star hotels in Jordan. Accordingly, the study recommended managers in these hotels pay more attention to promoting through digital means by publishing advertisements that include images and videos related to the quality of services available.

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)0.003
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: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.312
Teacher spread0.272 · 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 designNon-randomized trial
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

Citations120
Published2022
Admission routes1
Has abstractyes

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