The Influence of Digital Marketing on the Financial Performance of Laos’ Hospitality Enterprises
Bibliographic record
Abstract
The hospitality industry in Laos is experiencing remarkable growth, presenting an array of opportunities for businesses to flourish and cater to an increasing number of visitors. However, despite this positive trend, many enterprises are still struggling to effectively implement and leverage digital marketing strategies, which are essential for enhancing their visibility and competitiveness in today’s rapidly evolving digital landscape. This research thoroughly investigates the significant impact of digital marketing on the financial performance of a sample comprising 218 hospitality enterprises in Laos. Employing Smart PLS4 Structural Equation Modeling (SEM), the study analyzes relationships between independent variables—online advertising, social media marketing, content marketing, and mobile marketing—and their effect on the dependent variable, financial performance. Results reveal that online advertising, social media marketing, and content marketing significantly enhance financial performance; however, mobile marketing does not exhibit a notable influence. The findings suggest that low-cost online platforms like Facebook and YouTube are vital revenue sources for these enterprises. To improve financial outcomes, it is recommended that hospitality enterprises prioritize digital marketing strategies and online advertising efforts. Furthermore, it is recommended that government initiatives focus on bolstering digital marketing efforts by enhancing digital security measures, establishing monitoring systems for policy improvement, providing education to remote enterprises, and developing a comprehensive nationwide high-speed internet infrastructure.
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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 | 0.004 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".