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Record W4360776722 · doi:10.5267/j.ijdns.2023.3.003

The moderating role of reliability on the relationship between electronic word of mouth and cus-tomer purchase intention in Jordanian real estate enterprises

2023· article· en· W4360776722 on OpenAlexvenueno aff
Jassim Ahmad Al-Gasawneh, Jawad A.AL-Dalaeen- Al-Balqa, Mohammad A Hasan, Ayat Mazin Al. Mahmoud, Ghada Hammad Al-Rawashdeh, Ibrahim Lewis Mukattash, Jumadil Saputra

Bibliographic record

VenueInternational Journal of Data and Network Science · 2023
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingLeverage (statistics)Real estateBusinessReliability (semiconductor)Structural equation modelingContext (archaeology)AdvertisingMarketingValidityPsychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Jordanian real estate enterprises are experiencing difficult market circumstances and an increasingly competitive environment; following these issues, this study examines the leverage of electronic word of mouth (e-WOM) on customer purchase intention in a Jordanian context with specific reference to real estate enterprises, considering the moderating role of reliability. Based on earlier studies, a conceptual model for the study was created. This research includes e-WOM as an independent variable affecting customers' purchase intention as a dependent variable mediated by reliability. The investigation follows descriptive-analytical methods; based on a convenience sampling approach, 300 questionnaires were distributed through Google Forms; nonetheless and 250 responses were accepted. To analyze data and assess hypotheses, a structural Equation Modeling (SEM) using PLS was employed. Results demonstrated a significant effect of e-WOM and Reliability on purchasing intent of customers, and the moderating role of reliability in the relationship between e-WOM and purchase intent was also affirmed. The findings give Jordanian real estate businesses information on ways to use that are most effective e-WOM to persuade buyers to buy.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.094
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.302
Teacher spread0.268 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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