The Influence of Food Truck Service Quality Perceptions on Word of Mouth and Customer Loyalty among Malaysian Food Truck Customers
Bibliographic record
Abstract
Malaysia’s food truck industry has experienced remarkable growth, marked by creative culinary concepts and evolving business strategies. However, a noticeable gap exists in academic research exploring this emerging industry’s intricacies. This study aims to address this void, examining the relationship between perceived service quality in the food truck realm and its impact on customer loyalty, along with the potential for word-of-mouth marketing in the Malaysian setting. Service quality is undeniably crucial in today’s business landscape, impacting both service and product-oriented ventures. Food trucks, while primarily food-focused, also encompass service elements, particularly in customer interactions. Aspects like prompt service delivery can markedly shape customer experiences, influencing their decision to return or recommend the business. Though the hotel industry has seen significant research on service quality, smaller ventures such as street food outlets and food trucks are often overlooked. Yet, with the mounting competition in the food truck arena, there’s a pressing demand for these enterprises to enhance customer services for a strategic advantage. In essence, this research endeavors to deepen our understanding of how Malaysian consumers’ perceptions of food truck service quality influence their loyalty and propensity for word-of-mouth endorsements. The study casts light on the intricate ties between service quality perception, word-of-mouth referrals, and customer allegiance. Employing a quantitative method, data was collected from 500 regular food truck visitors in Kuala Lumpur. Initial results highlight a significant link between customers’ perceived service quality and their sustained loyalty. Intriguingly, word-of-mouth recommendations appear to moderate this connection.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".