The moderating role of reliability on the relationship between electronic word of mouth and cus-tomer purchase intention in Jordanian real estate enterprises
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".