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Fashion shopping on the go: A Dual-stage predictive-analytics SEM-ANN analysis on usage behaviour, experience response and cross-category usage

Author

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  • Ng, Felicity Zi-Xuan
  • Yap, Hui-Yee
  • Tan, Garry Wei-Han
  • Lo, Pei-San
  • Ooi, Keng-Boon

Abstract

With the proliferation of mobile commerce, mobile shopping has become the buzzword in the electronic commerce industry. To examine the predictive factors that affect the usage behaviour, experience response, and cross-category usage in mobile fashion shopping, an integrated research framework, comprising of the Mobile Technology Acceptance Model and individual attributes in terms of lifestyle orientations was proposed. The quantitative data, derived from 500 qualified responses, collected through a survey questionnaire, was validated via a two-stage predictive-analytics SEM-ANN approach to identify the non-compensatory and non-linear relationship. All six of the ANN models showed consistent relationships and rankings with the SEM results. The findings imply that mobile commerce developers and designers should ensure that the functions provided can satisfy the evaluation criteria of users with different lifestyle orientations, whereby the advantages of the mobile commerce platforms should be highlighted in the marketing messages to drive first-time usage, as well as extended usage across different mobile commerce platforms (i.e., mobile sites and mobile applications) and product categories. From the theoretical perspective, the findings revealed the indirect influence of the individual attributes on the usage intention of innovative mobile technology. The research is also the first to adopt non-compensatory neural network analysis to compensate the linear SEM analysis in the study on mobile shopping of fashion products.

Suggested Citation

  • Ng, Felicity Zi-Xuan & Yap, Hui-Yee & Tan, Garry Wei-Han & Lo, Pei-San & Ooi, Keng-Boon, 2022. "Fashion shopping on the go: A Dual-stage predictive-analytics SEM-ANN analysis on usage behaviour, experience response and cross-category usage," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:joreco:v:65:y:2022:i:c:s0969698921004173
    DOI: 10.1016/j.jretconser.2021.102851
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    2. Roy, Sanjit K. & Singh, Gaganpreet & Sadeque, Saalem & Gruner, Richard L., 2024. "Customer experience quality with social robots: Does trust matter?," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    3. Aw, Eugene Cheng-Xi & Tan, Garry Wei-Han & Cham, Tat-Huei & Raman, Ramakrishnan & Ooi, Keng-Boon, 2022. "Alexa, what's on my shopping list? Transforming customer experience with digital voice assistants," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    4. Qingyu Zhang & Salman Khan & Mei Cao & Safeer Ullah Khan, 2023. "Factors Determining Consumer Acceptance of NFC Mobile Payment: An Extended Mobile Technology Acceptance Model," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    5. Nilashi, Mehrbakhsh & Abumalloh, Rabab Ali & Samad, Sarminah & Alrizq, Mesfer & Alyami, Sultan & Alghamdi, Abdullah, 2023. "Analysis of customers' satisfaction with baby products: The moderating role of brand image," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    6. Hauff, Sven & Richter, Nicole Franziska & Sarstedt, Marko & Ringle, Christian M., 2024. "Importance and performance in PLS-SEM and NCA: Introducing the combined importance-performance map analysis (cIPMA)," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    7. Shafqat, Tehmeena & Ishaq, Muhammad Ishtiaq & Ahmed, Anzar, 2023. "Fashion consumption using minimalism: Exploring the relationship of consumer well-being and social connectedness," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).

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