Immersive events: a systematic literature review and future research agenda
Bibliographic record
Abstract
Purpose Innovative immersive technologies and techniques are being applied in the events industry to create new experiences and services for guests. However, academic research in this field is dispersed and lacks coherence. Although event professionals are increasingly turning to immersion for competitive advantage, there is little agreement in the literature on the nature and impacts of immersion or on how to measure these. This paper classifies and analyses the main academic studies to date in this field and presents a conceptual model and future research agenda for its study. Design/methodology/approach A systematic literature review was carried out using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, using sources drawn from Scopus, Web of Science and Google Scholar. Articles were identified through a keyword search. Following this process, 65 articles published in English from 1990 to 2023 were thematically analysed. Findings The study analyses the characteristics of immersive events research, identifies its main themes and research gaps and suggests future directions for this emerging field. Thematic analysis revealed four dominant thematic areas: immersion theory, technology and innovation, event design and attendee behaviour. Originality/value This paper proposes a new conceptual model for research into immersive events from a multidisciplinary perspective, drawing on insights from fields including hospitality and tourism, events, technology, computer science and engineering. Additionally, a future research agenda is proposed for this field, based on the identification of research gaps and the proposal of novel research questions.
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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 | 0.036 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".