Integrating the S-O-R Model to Examine Purchase Intention Based on Instagram Sponsored Advertising
Bibliographic record
Abstract
This study explores how the value of sponsored Instagram advertisements (ads) can enhance consumer ad-related involvement (cognitive and affective) and flow experience, which consequently impacts product purchase intention. To comprehend this issue, we propose a framework that combines the extended Ducoffe’s web advertising value model and the Stimulus-Organism-Response (S-O-R) model. We conceptualize S - stimuli - as perceived advertising value of sponsored Instagram ads; O - organism - as consumer ad-related involvements, comprising cognitive, affective, and flow experiences; and R - response state - as purchase intention. Based on an online survey, data was collected from 337 Malaysian Instagram users. The findings indicate that sponsored Instagram ad-related entertainment, informativeness, credibility, incentives, and celebrity endorsement, are conducive to raising the effectiveness of ad-stimuli, which in turn, enhance consumers’ cognitive and affective ad involvement and flow, to influence purchase behavior. The research offers empirical evidence to support the S-O-R framework and helps to expand the scope of sponsored advertising value research and its effect on consumers’ purchase intention. Furthermore, it benefits marketers and advertisers in promoting effective advertising campaigns using sponsored Instagram advertising. It also provides a platform where marketers can design ads that can help them to reach their marketing goals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".