The effect of Instagram on millennials consumer’s purchase intentions in the fashion industry
Bibliographic record
Abstract
The purpose of the current research is to explore the impact of Instagram pages on consumers’ purchasing intentions among millennials in the fashion industry in Jordan. This study uses a quantitative, cause-effect, and cross-sectional approach. Online surveys were used to collect data from 212 respondents through different social media tools. The collected data was analyzed by SPSS software and Smart PLS to test the research hypothesis. Results show that bloggers’ recommendations significantly affect eWOM and engagement; usefulness information significantly affects eWOM and engagement; while trust insignificantly affects eWOM and engagement; brand familiarity insignificantly affects eWOM and engagement; participation and socialization insignificantly affect eWOM and engagement. Finally, useful information, eWOM, and engagement significantly affect consumers buying intention on Instagram. The study gives new information about the influence of Instagram pages on consumers' intentions. Therefore, this research expands the knowledge about factors that affect customers’ buying intentions. Since the study is a quantitative cross-sectional conducted on fashion industry Instagram users through an online survey in Jordan, which may limit its generalization to other industries and countries, therefore, the study suggests applying similar studies to online users of different ages, industries, and countries. Marketers can use Instagram to contact, promote, advertise, and sell their products by developing strong relationships with their customers through different social media tools. Using social media tools for marketing and selling reduces paperwork, printed advertisement, and transportation, which positively affects corporate social responsibility and reduces the consumption of energy and pollution.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".