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Gonçalves, R., Lopes da Costa, R., Pereira, L., Dias, Á., Vinhas da Silva, R. & Teixeira, N. (2023). Agile applications of artificial intelligence to apparel industry. International Journal of Agile Systems and Management. 16 (4), 429-457
R. A. Gonçalves et al., "Agile applications of artificial intelligence to apparel industry", in Int. Journal of Agile Systems and Management, vol. 16, no. 4, pp. 429-457, 2023
@article{gonçalves2023_1734881944019, author = "Gonçalves, R. and Lopes da Costa, R. and Pereira, L. and Dias, Á. and Vinhas da Silva, R. and Teixeira, N.", title = "Agile applications of artificial intelligence to apparel industry", journal = "International Journal of Agile Systems and Management", year = "2023", volume = "16", number = "4", doi = "10.1504/IJASM.2023.134012", pages = "429-457", url = "https://www.inderscienceonline.com/doi/10.1504/IJASM.2023.134012" }
TY - JOUR TI - Agile applications of artificial intelligence to apparel industry T2 - International Journal of Agile Systems and Management VL - 16 IS - 4 AU - Gonçalves, R. AU - Lopes da Costa, R. AU - Pereira, L. AU - Dias, Á. AU - Vinhas da Silva, R. AU - Teixeira, N. PY - 2023 SP - 429-457 SN - 1741-9174 DO - 10.1504/IJASM.2023.134012 UR - https://www.inderscienceonline.com/doi/10.1504/IJASM.2023.134012 AB - Artificial intelligence systems are increasing its importance in the field of creating value for companies who seek to gain competitive advantage. This is especially true for the online shopping apparel world, in the case of virtual try-on systems. Following this line of thought the theme consumers acceptance of artificial intelligence virtual try-on systems when shopping online apparel came up as a research problem. In this sense this investigation intendeds to study the acceptance by consumers of the virtual try-on artificial systems when buying apparel online according to specific variables previously defined. To assess this, a quantitative approach was used, based on the structural equations model, the partial least squares technique. This research allowed the creation of a new model based on technology acceptance model by including new variables and revealed that the influence of predictive variables on the dependent variable (ATU) is not the same. ER -