The experience sampling method: examining its use and potential in tourist experience research
Bibliographic record
Abstract
Though a valid and widely used approach in leisure, recreation, and psychology, the experience sampling method (ESM) is rarely used in tourism studies as a way to collect data on immediate conscious experiences during tourist events. This paper examines the use of ESM as it relates to tourist experience research. We begin by introducing ESM before exploring the application of this method to emerging smartphone technology. We then introduce a research approach, which incorporates the use of a digital ESM modified to act as a predominantly qualitative procedure, using voice recording software, to study the experience of educational tourists in Peru. The data gathered using this approach are analysed to examine the application and operational aspects of ESM. We consider the methodological implications of this research method by presenting findings on the length of qualitative discussions, reported mood, qualitative content related to ESM procedures, and post-trip recollection of ESM. The discussion that follows focuses on evidence of participant burden, reactivity, and anthropomorphism related to the use of smartphones as data collection tools. This paper concludes by outlining future research areas, with specific reference to spatial aspects, affect, and smartphone use, which expand the potential of ESM in tourist experience studies.
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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 | 0.223 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".