What drives metaverse retail environments (non)usage? A behavioral reasoning theory perspective
Bibliographic record
Abstract
The metaverse offers novel opportunities for marketers to captivate users by enhancing their retail experiences in this digital space. Despite extensive discourse on the conceptual aspects of the metaverse, empirical research exploring the factors that motivate or deter user engagement in retail environments remains scarce. This study utilizes Behavioral Reasoning Theory to investigate how consumers' personal values and beliefs, along with their reasons for and against using metaverse retail environments shape their sense of connectedness and intentions to engage with these environments. Using a mixed-methods approach, the study begins with qualitative insights from 100 metaverse users, followed by quantitative analysis of data from 337 participants to empirically validate the proposed framework. The findings reveal escapism, social interaction, playfulness, and immersiveness as primary reasons-for, while inaccuracy, information overload, privacy concerns, and fatigue were identified as reasons-against using the metaverse retail environments. Furthermore, media consumption and digital literacy are shown to moderate these relationships. Collectively, these insights contribute to a foundational understanding of the behavioral intricacies and dynamic interaction patterns within the rapidly evolving metaverse, offering critical implications for managers aiming to optimize user engagement in metaverse retail environments. • Reveals the role of personal values and beliefs in motivators and barriers to engaging with metaverse retail spaces. • Pinpoints escapism, social interaction, playfulness, and immersiveness as primary drivers of the metaverse retail engagement. • Identifies inaccuracy, information overload, privacy concerns and fatigue as key barriers. • Demonstrates how digital literacy and media use shape the link between motivations and intent in metaverse retail engagement. • Offers insights on interaction dynamics, providing strategies for managers to enhance engagement in metaverse retail.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".