How Engaged Customers Can Help the Brand: An Empirical Case Study on a Higher Education Institution (University) in Egypt
Bibliographic record
Abstract
The purpose of this paper is to investigate through an empirical research the relationship between Customer Engagement Behavior (CEB) and some of its alleged outcomes that include Benevolence/Trust, affective commitment and future patronage in a service sector which is Higher Education. Thus, this research investigates customer engagement constructs of conscious attention and emotional participation that lead to various favorable behaviors by the customer, which in turn leads to favorable outcomes of benevolence, affective commitment and future patronage. The researcher starts by a survey of literature which handles various aspects of CEB which include: possible types of these behaviors, factors affecting CEB, outcomes of CEB and CEB as compared to other marketing concepts. Next, the researcher proposes a model where engaged students (CE) will undertake favorable behaviors (CEB) which in turn will lead to favorable outcomes of Benevolence/trust, future patronage/repurchase intention and affective commitment. The researcher tests the model using Partial Least Squares structural equation modeling (PLS-SEM) and empirical data from a survey (self-administered questionnaire) that was distributed among 5 faculties of one private university in Egypt based on a case study approach. The results show that we accept the hypotheses of the model of the model yet the researcher suggests application on other universities to further test the model. Moreover, the researcher invites further research of other favorable outcomes of CEB as loyalty to be considered. The researcher also suggests an investigation of unfavorable CEB as bad word of mouth (WoM) and harmful blogging or unfavorable reviews.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".