The Role of Competitive Advantage Between Search Engine Optimization and Shaping the Mental Image of Private Jordanian University Students Using Google
Bibliographic record
Abstract
This study aims to determine the factors that influence the improvement of the mental image of Jordanian private university students who primarily use Google.Building upon the concept of Search Engine Optimization (SEO), we developed and assessed a conceptual framework that includes the influences of On-Page Optimization, Off-Page Optimization, and the functioning of search engines.The theoretical framework of the study draws upon Bedny's perspective of activity, which emphasizes purposeful actions undertaken by individuals in specific contexts.Activity encompasses not only physical actions but also the psychological processes and social interactions associated with them.A sample of 400 respondents was surveyed, revealing a strong relationship between search engine optimization (as an independent variable) and the creation of a positive mental image (as a dependent variable).Competitive advantage served as a mediating variable, with dimensions including scope, site, synergy, and system, particularly among potential students.The results demonstrate that search engine optimization significantly impacts the creation and formation of a positive mental image among students at private Jordanian universities, and it correlates strongly with their self-perceptions.Further research is needed to better understand the role of search engine optimization, artificial intelligence, and big data techniques in attracting students to private universities.This study contributes to the literature on both Search Engine Optimization and Knowledge Graphs by offering a fresh perspective on how these subjects can be effectively utilized in modern marketing.Additionally, it provides insights into the benefits of SEO utilization in the context of Knowledge Graphs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".