The role of engagement intention in mediating the relationship between brand equity and engagement behavior moderated by social media context
Bibliographic record
Abstract
The COVID-19 pandemic has brought about significant transformations in various aspects of life. Shifts in consumer behavior, for instance, can exert influence on global economic operations. This situation indirectly boosts the e-commerce sector while expediting the decline of conventional retail. Matahari Department Store is an example of a retail business adapting to this trend. Matahari, a retail company, recently shifted its focus towards online marketing. On one of its social media platforms, Matahari consistently presents engaging content. Nevertheless, this does not necessarily imply that Matahari enjoys a high level of social media engagement. This study aims to evaluate how brand equity influences engagement behavior, with engagement goals acting as a mediator, while considering social media context as a moderating factor. The research employed a quantitative approach with a causal orientation. The target population for this study comprises the followers of the Matahi Department Store Instagram account. The sampling method employed was purposive, indicating that the sample was selected based on specific criteria. Data collection was conducted through the online distribution of questionnaires using Google Forms. The collected data was analyzed through the utilization of SMART PLS 3.0 software employing the Structural Equation Modeling (SEM) method. As per the research results, brand equity is influenced through engagement intention and exhibits a positive and statistically significant impact on both consumption and donation behavior. Additionally, the connection between intention to be involved and behavior in consumption is affected by media richness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".