The Influence of Social Commerce Dynamics on Sustainable Hotel Brand Image, Customer Engagement, and Booking Intentions
Bibliographic record
Abstract
Social commerce plays a significant role in various aspects of the hotel industry. By using social commerce platforms such as Facebook, Instagram, and hotel booking websites, hotels can enhance their brand visibility, engage more effectively with guests, and stimulate hotel bookings. Nonetheless, prior research reported a lack of studies in this area, namely social commerce in the context of domestic tourism. Drawing on Stimuli–Organism–Response (SOR) theory as being a well-established framework in social commerce research, this study seeks to examine the impact of social commerce on hotel booking intentions, both directly and indirectly, by considering the mediating effects of both brand image and customer engagement. This research used an online survey questionnaire as a tool for collecting primary data from domestic tourists in Egypt during the period from January to April 2024. The study sample encompassed 315 participants who were recruited using the convenience sampling technique. Using Smart PLS 4.0 software, Structural Equation Modeling was performed to examine the proposed model and hypotheses. The results highlighted the significant impact of social commerce on both hotel brand image and customer engagement, which in turn encouraged booking intentions among potential domestic guests. The results also supported the significant mediating effect of hotel brand image and customer engagement in the relationship between social commerce and hotel booking intentions. This study provides some theoretical contributions to the literature of hospitality management by addressing notable gaps in knowledge. This study also suggests some practical implications for industry executives that support their social commerce techniques and boost booking intentions and behavior among domestic tourists such as creating visually attractive content, sponsoring virtual mega events, actively interacting with social media followers, and incorporating booking functionalities in social media platforms. In so doing, hotels can boost their sustainable marketing practices to target a promising market segment by exploiting Web 2.0 capabilities.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".