Practicing What We Preach in Tourism Education and Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract As teachers of both strategic and operational management in tourism/hospitality programs, we constantly stress the importance of research as a means of providing critical information for destination planning and development. At the same time, in our capacity as managers of academic programs, it is not uncommon for many of us to overlook the use of research in the management of these programs. While we normally undertake market assessments before establishing new programs, and regularly gather operational data on teaching performance, this paper argues that based on the existing literature, tourism education program managers generally fail to take sufficient advantage of several strategic research approaches that are heavily used in other management contexts. Having recognized their own shortcoming in this regard, the present authors have attempted to rectify it by formally integrating a selected number of widely used research approaches into the overall management of their programs. The results, we believe, demonstrate just how valuable the use of formal program research can be in setting innovative directions for program design and development, for enhancing student satisfaction with specific courses, and for evaluating the overall efficiency and effectiveness of well established programs. Part I of the present discussion examines the use and usefulness of one major research technique for tourism education program design and management. The technique in question is Strategic Visioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it