Differentiating service quality impact between the online and off-line context: an empirical investigation of a corporate travel agency
Bibliographic record
Abstract
Purpose Corporate travel represents a significant source of revenue for the tourism industry. Therefore, the quality of service is essential for maintaining and expanding corporate cliental bases. Despite the importance, the extant literature has yet sufficiently examined corporate travel service quality (SQ) and its impact. To make up for the drawback, this study aims to differentiate the impact of SQ perceptions on customer satisfaction between the online and off-line contexts through an empirical investigation in one of the top five corporate travel agencies in North America. Design/methodology/approach The well-established SERVQUAL measurement is applied in differentiating the impact of SQ dimensions between the online and off-line context. To empirically test the proposed corporate travel agency (CTA) SQ conceptual model, a set of survey data of “Welcome Back Survey” from HRG (a top five CTA in North America) was examined. Findings The study finds that for online services, assurance, responsiveness and empathy affect perceived SQ, whereas for off-line services, assurance, empathy and tangible are the three dimensions of perceived SQ. Research limitations/implications By relying on the existing survey, the off-line context has one less dimension than the online context. Yet as an early effort in differentiating the differences in the impact of SQ between two service contexts, the study offers insightful findings. Practical implications The findings will be helpful for business managers of CTAs to identify the factors that influence SQ in both online booking and off-line booking context. In particular, assurance and empathy are two dimensions that exert a significant impact on customer satisfaction. Originality/value This paper is the first to compare the differences of the SQ of online and off-line corporate travel.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".