Is this real? Cocreation of value through authentic experiential augmented reality: the mediating effect of perceived ethics and customer engagement
Bibliographic record
Abstract
Purpose Rapid advancements in augmented reality (AR) technology have created new opportunities for service providers and customers to cocreate value. Using AR as a platform for generating authentic experiences, the purpose of this study is to explore the impact of authentic experiences on customers' intention to cocreate value while considering the mediating influence of perceived ethics and customer engagement on this relationship. Design/methodology/approach An online survey was used to collect data. Participants were asked to download and try the “IKEA PLACE” AR application. The responses were used as inputs into a structural equation model. Findings The findings reveal that AR generates perceptions of authentic experiences but no direct relationship between authentic experiences and intention to cocreate value was found. On the other hand, the authentic experiences generated through AR increases customer perceptions of ethics and customer engagement, both of which lead to an increased intention to cocreate value. Originality/value The findings from this study highlight the importance of authentic experiences within the cocreation process. The results provide a unique understanding of the relationship between authentic experiences generated through AR technology on the intention to cocreate with the service provider, which is fully mediated by perceived ethics and customer engagement. The findings of this study extend the understanding of the cocreation process and the role of technology within this process.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".