Building relationships on Instagram: Enhancing customer engagement and visit intentions in restaurant
Bibliographic record
Abstract
The study examines how Instagram interactions impact customer response in the restaurant context using the Stimulus-Organism-Response (SOR) framework. It explores how interactions on Instagram influence customers' intention to visit a restaurant, online engagement (OEG), and customer involvement (CI). The study highlights the significance of Instagram as a platform for effective interaction and relationship-building with customers, emphasizing its role in the hospitality industry. The study focuses on restaurant pages on Instagram, utilizing structural equation modeling to analyze data from a sample of 242 Instagram users in Turkey who are restaurant customers. The findings reveal that social media (SM) interactions are positively and significantly related to OEG, CI, and visit intention (VI). Furthermore, the results suggest that OEG and CI mediate the relationship between social media interactions (SMI) and VI, supporting the hypotheses concerning the indirect relationships between these variables. This research contributes to understanding SM dynamics and provides insights for restaurant marketers to enhance customer engagement and drive business growth.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".