The effect of marketing via Instagram on generation Z's preference for gyms and the role of brand image as a moderating variable
Bibliographic record
Abstract
This study investigates the effect of marketing via Instagram on Generation Z’s preference for gyms in Jordan, so it would be helpful for marketers of gyms to be aware of using Instagram to attract Generation Z as their potential customers. Moreover, a sample questionnaire was carried out with 138 respondents, which were mostly female respondents (74%) and male respondents (26%). Additionally, 51% of the total number of respondents were 21-24 years old. Therefore, the data was analyzed by applying various statistical techniques such as Cronbach’s alpha for testing the reliability of the data, and multiple regression using SPSS version 22 for examining the hypotheses. Likewise, the results showed that Instagram and these variables (entertainment, interaction, trendiness, and customization) have an effect on customers' brand choice, but trendiness did not have a significant effect on the consumer's brand choice, but after testing the effect of the brand image, it became clear that it has significant effect, and therefore it was concluded that brand image has an important effect on customer’s brand choice.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".