Bridging Social Responsibility and Sustainability: A Case Study of Modelling Tourist Citizenship Behaviour in Indonesia
Bibliographic record
Abstract
Citizenship behaviour in tourists (CBT), which includes pro-social and voluntary actions such as sustainability advocacy, norm compliance, and cultural respect and appreciation, has the potential to further enhance sustainability in cultural tourism yet remains largely underresearched in this area.While previous studies examined trust, commitment, and identification as relational constructs connected to loyalty, their influence on tourism citizenship behaviour in community-based tourism has hardly been explored.In response to this gap, the current research analyses the influence of a community's reputation and Destination Social Responsibility (DSR) on citizenship behaviour and the formation of trust, identification, and commitment.Penglipuran Village, a UNESCO Sustainable Tourism recognised Village in Bali, Indonesia, was chosen for this study.Quantitative data were collected from 222 respondents and analysed utilising Structural Equation Modelling (SEM).The model fit was exceptional (CMIN/DF = 1.005;CFI = 0.999; RMSEA = 0.005) and of the 7 hypotheses analysed, 5 received empirical support.It was found that community reputation has a positive effect on identification and trust, while DSR has a positive effect on trust but not community identification.Trust was the most significant mediator of the model.It positively affected commitment which enhanced citizenship behaviour.Unexpectedly, identification did not influence commitment, which suggests that identification, contrary to theoretical assumptions, was not a factor of relational loyalty.This research demonstrates the potential of trust to influence pathways of relational loyalty and citizenship behaviour in the context of cultural tourism and relationship marketing theory.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".