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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Gary, F., Zhou, X., Tang, Y. M., Gu, Y. & Moreira, A. C. (2025). Augmented reality in retail: Technical and emotional factors after experience: E-commerce consumption decision. Journal of Global Information Management. 33 (1)
Exportar Referência (IEEE) 
J. Gary et al.,  "Augmented reality in retail: Technical and emotional factors after experience: E-commerce consumption decision", in Journal of Global Information Management, vol. 33, no. 1, 2025
Exportar BibTeX 
@article{gary2025_1775394447453,
	author = "Gary, F. and Zhou, X. and Tang, Y. M. and Gu, Y. and Moreira, A. C.",
	title = "Augmented reality in retail: Technical and emotional factors after experience: E-commerce consumption decision",
	journal = "Journal of Global Information Management",
	year = "2025",
	volume = "33",
	number = "1",
	doi = "10.4018/JGIM.394246",
	url = "https://www.igi-global.com/gateway/journal/1070"
}
Exportar RIS 
TY  - JOUR
TI  - Augmented reality in retail: Technical and emotional factors after experience: E-commerce consumption decision
T2  - Journal of Global Information Management
VL  - 33
IS  - 1
AU  - Gary, F.
AU  - Zhou, X.
AU  - Tang, Y. M.
AU  - Gu, Y.
AU  - Moreira, A. C.
PY  - 2025
SN  - 1062-7375
DO  - 10.4018/JGIM.394246
UR  - https://www.igi-global.com/gateway/journal/1070
AB  - Retail practice shows that augmented-reality shopping applications with similar technical quality can elicit widely different consumer reactions. This study proposes a dual-pathway Stimulus–Organism–Response model: a technical pathway linking augmented realism, information richness, and personalization to interaction satisfaction, and an emotion-priming pathway where anticipated emotions shape immersion, telepresence, and pleasure without technical appraisal. Both converge at inspiration, the sole System-2 construct converting experience into behavior. Data from quasi-experimental participants were analyzed using PLS-SEM, SHAP-interpreted gradient boosting, and K-Means robustness checks. Information richness showed the strongest technical effect, while anticipated emotions most strongly affected immediate experiences. Inspiration predicted purchase and cross-buying intentions. Machine-learning diagnostics supported the framework and revealed non-linear thresholds in key pathways, clarifying inconsistent AR outcomes and positioning inspiration as the cognitive bridge to purchase.
ER  -