Application of corporate Social Responsibility in Stakeholder Management: The Case of Langkawi, Malaysia
Bibliographic record
Abstract
The continued growth of tourism has driven the Malaysian government to focus on various developments and promotional activities to expand the industry.However, these efforts more often than not failed due to the failure of government arms to adopt an inclusive management approach on tourism stakeholders.A destination management organization (DMO) often behaves in manners typical of a government agency.Such approach often brings problems such as communication barriers and bureaucracy between a DMO and its stakeholders.Thus, government arms need to change their management approach by adopting a more stakeholder-friendly approach offered through the corporate social responsibility (CSR) concept.As the support of stakeholders is crucial for a destination's development and sustainability, this paper applies the concept of CSR in destination management by proposing the need for a DMO to adopt an approach similar to CSR if they were to operate more effectively.It uses a case study on Langkawi's DMO in Malaysia called the Langkawi Development Authority (LADA).Using documented evidence and in-depth interviews, the challenges that LADA face in getting stakeholder support are explored.The paper then outlines how, and justifies why a DMO can adopt CSR approach to mitigate the complexity it faces in dealing with its stakeholders.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 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".