Brand image and customer behavior in container food courts: The role of social media content and generational differences in Indonesia
Bibliographic record
Abstract
This study examines the impact of Social Media Advertising Content (SMAC) and Social Media Sales Promotion Content (SMSPC) on Brand Image (BI) and customer behavior (CB) within the emergent context of container food courts in Indonesia. Focusing on Jakarta and Surabaya, which are cities at the forefront of culinary innovation, this study aims to uncover how digital marketing practices shape consumer perceptions and behaviors in this novel sector. The study employed rigorous methodology, including G*Power for sample size determination and SmartPLS for data analysis, and engaged 292 participants through a carefully designed survey. The findings indicated significant relationships between SMAC and SMSPC on BI, and subsequently on CB, underscoring the critical role of social media content in enhancing BI and shaping CB. Additionally, this study examines the generational differences between Generation Y and Generation Z, offering insights into tailored marketing strategies that cater to their distinct preferences. This study enriches academic discourse on the impact of digital marketing in the food industry and provides practical recommendations for practitioners aiming to leverage social media to enhance BI and foster positive CB in container food courts. The insights gained from this study not only illuminate the dynamics of social media marketing in an Indonesian context but also suggest avenues for future research in an ever-evolving digital landscape.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".