Dynamics of social media involvement in building customer engagement and co-creation behavior: the moderating role of brand interactivity
Bibliographic record
Abstract
Purpose Adopting a self-congruence theory (SCT) and service dominant logic (SDL)-informed perspectives; we develop a model that investigates the interface between social media involvement (SMI), self-brand congruence (SBC), customer-brand engagement (CBE), brand co-creation behavior (BCB), brand interactivity and behavioral intentions (BIN) with luxury service hotel–brands. Design/methodology/approach We test a sample of hotel-customers to probe this matter using partial least squares structural equation modeling. Findings The results revealed that SBC and SMI positively impact CBE and BCB and behavioral intentions. The findings also exposed SMI’s and SBC’s indirect effect on customers' BCB and behavioral intentions, mediated through CBE. Finally, the results explored the moderating role of brand interactivity to enhance our model’s explanatory power. Research limitations/implications We focus on SMI, CBE and BCB. This study contributes to the existing marketing and hospitality management research and spawns rich opportunities for further studies. Practical implications The study article assists marketers in comprehending the CBE-based antecedents and consequences and facilitates their increasing CBE, BCB and behavioral intentions. Originality/value While the growing insight into social media, customer engagement and co-creation within the service industries, little remains accredited concerning the link of these and related variables in the luxury hotel-brand context.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".