A bibliometric analysis of social media in hospitality and tourism research
Bibliographic record
Abstract
Purpose While the importance of social media will continue to grow, the purpose of this study is to provide a retrospective systematic literature review of the social media research published in major hospitality and tourism journals over a specific time period. Design/methodology/approach The study conducted a bibliometric analysis to review the literature of 439 social media articles published in 51 hospitality and tourism journals over a 15-year time span (2002-2016). Findings Ulrike Gretzel authored the highest fractional citations. The results indicated that social media-related research was mostly published in top-tier journals. The International Journal of Contemporary Hospitality Management was amongst the four leading journals in terms of the percentage of published social media articles. While inter-country social media research collaborations were relatively modest, interestingly, inter-country collaborations have been steadily increasing in the past five years. Another finding indicated that social media research in hospitality and tourism journals has been predominantly quantitative. The results revealed six new areas within the consumer behaviour research theme, namely, eWOM, service recovery, customer satisfaction, brand/destination image and service quality. Finally, it is important to note that four new trends in social media research appeared between 2011 and 2016, namely, big data, netnography, Travel 2.0 and Web 2.0. Research limitations/implications While this study made significant contributions to the social media literature, some limitations do exist. For example, the current research excluded publications from major conferences, books, book chapters and dissertations. Additionally, it is not within the scope of this paper to take into account issues related to self-citations. Practical implications The results obtained from analysis contribute to a comprehensive understanding of social media research progress in hospitality and tourism. For example, evaluating the performance of individual scholars helps educational institutions to compete in the global university ranking system. Additionally, to compete for funding opportunities on the topic of social media, institutions can use citation counts to demonstrate their competitiveness. Furthermore, due to the expected future growth in the number of social media platforms, practitioners need to understand motivating factors and tourists’ needs in different countries, target market segments, age groups and cultures to create highly engaging communities around their brands. Originality/value To the best of the authors’ knowledge, the sample of this study synthesized the largest selection of social media articles published in hospitality and tourism journals. This is the first study to apply the fractional score at the author level, the adjusted appearance score at the university level and the average citation score at the journal and inter-country levels in the analysis. In addition, prevalent research orientations and research trends in social media made significant contributions to existing literature.
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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 | 0.018 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.198 | 0.238 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".