Going where the Joneses go: understanding how others influence travel decision‐making
Bibliographic record
Abstract
Purpose The purpose of this study is to examine if an evoke set exists for pleasure travelers based on the past and future travel destinations of their peer groups. Design/methodology/approach Researchers distributed a questionnaire to university students enrolled in the same program. The literature review suggests four ways in which peers can influence individuals: not to travel influence, direct influence, indirect influence, and shared goal of future destination influence. Results from the respondents provide data for correlation analysis based on these four types of peer influence. Findings The findings support previous researchers demonstrating a strong influence of peer reference groups on service purchase decisions, specifically tourism destination choice. Given a relatively small sample population, all four types of peer reference found support in the data. Research limitations/implications Limitations are related to sample size and the homogeneity of the sample. Because, the respondents were in the same life stage, their peer groups were similar to all. Consequently, no comparative analysis specifically identifying peers and the degree of proximity at different stages of life was possible. Originality/value Very few studies focus specifically on the nature of peer group influence on service purchase behavior, related specifically with travel destination decision‐making. By recognizing travel patterns of individuals and understanding the influences causing these patterns, tourism marketers and planners have a greater understanding of the mechanisms of peer influence in pleasure travel destination choice.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".