CSR among Canadian mass tour operators: good awareness but little action
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide an exploratory case study of mass mainstream tour operators in the Canadian market and evaluate their awareness level of corporate social responsibility (CSR) activities. The research aims to address the structure and ownership of mass Canadian tour operators, how it may influence the adoption of CSR practices, key issues and concerns and awareness level and participation of CSR practices. Although the Canadian outbound leisure mass market is relatively small compared with that of the UK, Canadian travelers are a significant source of tourism to Mexico and the Caribbean islands such as Cuba and the Dominican Republic. Design/methodology/approach Canadian mass tour operators were contacted through interviews and questionnaires to assess the structure and ownership of mass Canadian tour operators, how it may influence the adoption of CSR practices, key issues and concerns and awareness level and participation of CSR practices. Existing responsible tourism practices in the destinations they operate were also gauged. Findings CSR is gaining momentum worldwide as companies begin to realize that their stakeholders are demanding accountability that goes beyond shareholders' interests. Subsequently, reporting levels are increasingly being regulated and corporate strategic initiatives focusing on improving their social and environmental responsibility are on the rise. In the case of tour operators, however, initiatives of this nature are preliminary and there is little implementation of CSR practices. Research limitations/implications The study examines Canadian mass tourism package tour operators and further research is needed to assess all tour operators (inbound and outbound) to determine whether the level of participation in responsible travel is higher or whether size is an implicating factor. As issues such as climate change and responsible tourism have only started to influence consumer demand in the past few years, the study's findings may be changing. Therefore a further follow‐up study would be beneficial in order to determine any barriers to action. Originality/value To date, little research has been done on the tourism industry, and that mainly on hotels. There is a need to understand the structure and contribution of tour operators to the industry and their level of CSR practices and movement towards more responsible tourism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".