A Needs Analysis for Curriculum Development: Realigning Business English Pedagogy with 21st-Century Communication Demands in Thailand’s Hospitality Industry
Bibliographic record
Abstract
As Thailand’s hotel industry navigates a post-pandemic recovery shaped by digital transformation, the communicative competence of university graduates has become a critical factor in workforce readiness. This study critically assesses this gap, reframing 21st-century skills as integral components of professional communicative competence. It particularly aims to diagnose specific deficiencies to guide a necessary pedagogical shift from isolated language instruction toward an integrated, interdisciplinary model. Adopting a quantitative needs analysis design, this study surveyed 257 employees in three- to five-star hotels across Thailand. A structured questionnaire, synthesizing global skills frameworks, was used to measure the disparity between current and expected competency levels across eleven domains, analyzed using the Priority Needs Index Modified (PNI Modified). The findings reveal that a critical deficiency lies not merely in “communication” as a standalone skill, but also in a broader set of integrated professional communication competencies. The most urgent needs were identified in ICT literacy (the ability to communicate via digital platforms), creativity (using language for innovative problem-solving), and strategic communication. This suggests that while graduates may possess foundational English skills, they lack the ability to apply them dynamically in technology-rich environments. This study calls for an urgent reform of Business English pedagogy in Thailand. Such an approach would embed language learning within authentic business contexts, fostering the adaptive, tech-savvy communicators required to drive value in a competitive global service economy. This transformation is essential for enhancing graduate employability and ensuring the continued relevance of higher education in meeting real-world industry demands.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".