IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i8p6564-d1122069.html
   My bibliography  Save this article

Modeling the Enablers of Consumers’ E-Shopping Behavior: A Multi-Analytic Approach

Author

Listed:
  • Haili Yang

    (Yangtze River Economic Research Center, Chongqing Technology and Business University, Chongqing South Bank, Chongqing 400067, China
    School of Economics, Chongqing Technology and Business University, Chongqing 400067, China)

  • Yueyue Luo

    (Yangtze River Economic Research Center, Chongqing Technology and Business University, Chongqing South Bank, Chongqing 400067, China)

  • Yunhua Qiu

    (Yangtze River Economic Research Center, Chongqing Technology and Business University, Chongqing South Bank, Chongqing 400067, China)

  • Jiantao Zou

    (Yangtze River Economic Research Center, Chongqing Technology and Business University, Chongqing South Bank, Chongqing 400067, China)

  • Mohammad Masukujjaman

    (Graduate School of Business, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Abdullah Mohammed Ibrahim

    (Department of Business Administration, Northern University Bangladesh, Dhaka, Banani C/A, Dhaka 1213, Bangladesh)

Abstract

The evolution of e-commerce amid the positive growth forecast of the e-commerce market has sparked scholarly interest in e-shopping antecedents to better understand customer behavior and ensure sustainable e-shopping services. The purpose of this study is to investigate the relationship between the enablers of customers’ e-shopping intention and e-shopping behavior in the post-pandemic period. Personal innovativeness, service quality, perceived risk, and trust were incorporated into the Unified Theory of Technology Acceptance and Usage (UTAUT) original framework and UTAUT 2 in this study. To explore the relationship among the study variables, data were collected from 420 shoppers via an online survey using a convenience sampling technique. The obtained data were analyzed using a multi-analytic approach, such as structural equation modeling and artificial neural networks (SEM-ANN). The empirical findings showed that trust, habit, and e-shopping intention significantly influence consumers’ e-shopping behavior. Furthermore, the results indicated that personal innovativeness, facilitating conditions, performance expectancy, habit, effort expectancy, perceived risk, price value, hedonic motivation, service quality, and trust were all significantly linked to e-shopping intention. The study revealed that effort expectancy acts as a mediator between service quality and e-shopping behavior. This research provides valuable insights into e-shopping behavior in developing countries during the post-pandemic era. By providing a more comprehensive and accurate understanding of the factors that influence e-shopping behavior, hybrid SEM-ANN analysis can help managers and policymakers arrive at better-informed decisions to promote and encourage e-shopping.

Suggested Citation

  • Haili Yang & Yueyue Luo & Yunhua Qiu & Jiantao Zou & Mohammad Masukujjaman & Abdullah Mohammed Ibrahim, 2023. "Modeling the Enablers of Consumers’ E-Shopping Behavior: A Multi-Analytic Approach," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6564-:d:1122069
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/8/6564/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/8/6564/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aijaz A. Shaikh & Richard Glavee-Geo & Heikki Karjaluoto, 2018. "How Relevant Are Risk Perceptions, Effort, and Performance Expectancy in Mobile Banking Adoption?," International Journal of E-Business Research (IJEBR), IGI Global, vol. 14(2), pages 39-60, April.
    2. Hans van der Heijden & Tibert Verhagen & Marcel Creemers, 2003. "Understanding online purchase intentions: contributions from technology and trust perspectives," European Journal of Information Systems, Taylor & Francis Journals, vol. 12(1), pages 41-48, March.
    3. Yan Shi & Abu Bakkar Siddik & Mohammad Masukujjaman & Guangwen Zheng & Muhammad Hamayun & Abdullah Mohammed Ibrahim, 2022. "The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory," Sustainability, MDPI, vol. 14(11), pages 1-23, May.
    4. Driediger, Fabian & Bhatiasevi, Veera, 2019. "Online grocery shopping in Thailand: Consumer acceptance and usage behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 224-237.
    5. Gansser, Oliver Alexander & Reich, Christina Stefanie, 2021. "A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application," Technology in Society, Elsevier, vol. 65(C).
    6. Lin Zhang & Liwen Chen & Zezhou Wu & Sizhen Zhang & Huanbin Song, 2018. "Investigating Young Consumers’ Purchasing Intention of Green Housing in China," Sustainability, MDPI, vol. 10(4), pages 1-15, April.
    7. Christopher R. Plouffe & John S. Hulland & Mark Vandenbosch, 2001. "Research Report: Richness Versus Parsimony in Modeling Technology Adoption Decisions—Understanding Merchant Adoption of a Smart Card-Based Payment System," Information Systems Research, INFORMS, vol. 12(2), pages 208-222, June.
    8. Brenna Ellison & Brandon McFadden & Bradley J. Rickard & Norbert L. W. Wilson, 2021. "Examining Food Purchase Behavior and Food Values During the COVID‐19 Pandemic," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(1), pages 58-72, March.
    9. Ellen Van Droogenbroeck & Léon Van Hove, 2021. "Adoption and usage of E-grocery shopping: A context-specific UTAUT2 model," ULB Institutional Repository 2013/324918, ULB -- Universite Libre de Bruxelles.
    10. Juan-Pedro Cabrera-Sánchez & Iviane Ramos-de-Luna & Elena Carvajal-Trujillo & Ángel F. Villarejo-Ramos, 2020. "Online Recommendation Systems: Factors Influencing Use in E-Commerce," Sustainability, MDPI, vol. 12(21), pages 1-15, October.
    11. Juan Carlos Martín & Francesca Pagliara & Concepción Román, 2019. "The Research Topics on E-Grocery: Trends and Existing Gaps," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    12. Xiaobei Jiang & Wenlin Yu & Wenjie Li & Jiawen Guo & Xizheng Chen & Hongwei Guo & Wuhong Wang & Tao Chen, 2021. "Factors Affecting the Acceptance and Willingness-to-Pay of End-Users: A Survey Analysis on Automated Vehicles," Sustainability, MDPI, vol. 13(23), pages 1-12, November.
    13. Urvashi Tandon & Ravi Kiran & Ash N. Sah, 2018. "The influence of website functionality, drivers and perceived risk on customer satisfaction in online shopping: an emerging economy case," Information Systems and e-Business Management, Springer, vol. 16(1), pages 57-91, February.
    14. Ahmad Alaiad & Lina Zhou & Gunes Koru, 2014. "An Exploratory Study of Home Healthcare Robots Adoption Applying the UTAUT Model," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 9(4), pages 44-59, October.
    15. Urvashi Tandon, 2021. "Predictors of online shopping in India: an empirical investigation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(1), pages 65-79, March.
    16. Astrachan, Claudia Binz & Patel, Vijay K. & Wanzenried, Gabrielle, 2014. "A comparative study of CB-SEM and PLS-SEM for theory development in family firm research," Journal of Family Business Strategy, Elsevier, vol. 5(1), pages 116-128.
    17. Clemes, Michael D. & Gan, Christopher & Zhang, Junli, 2014. "An empirical analysis of online shopping adoption in Beijing, China," Journal of Retailing and Consumer Services, Elsevier, vol. 21(3), pages 364-375.
    18. Alam, Mohammad Zahedul & Hu, Wang & Kaium, Md Abdul & Hoque, Md Rakibul & Alam, Mirza Mohammad Didarul, 2020. "Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach," Technology in Society, Elsevier, vol. 61(C).
    19. Alalwan, Ali Abdallah & Dwivedi, Yogesh K. & Rana, Nripendra P. & Algharabat, Raed, 2018. "Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk," Journal of Retailing and Consumer Services, Elsevier, vol. 40(C), pages 125-138.
    20. Zhu, Wenlong & Mou, Jian & Benyoucef, Morad, 2019. "Exploring purchase intention in cross-border E-commerce: A three stage model," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 320-330.
    21. Mital, Monika & Chang, Victor & Choudhary, Praveen & Papa, Armando & Pani, Ashis K., 2018. "Adoption of Internet of Things in India: A test of competing models using a structured equation modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 339-346.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan Shi & Abu Bakkar Siddik & Mohammad Masukujjaman & Guangwen Zheng & Muhammad Hamayun & Abdullah Mohammed Ibrahim, 2022. "The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory," Sustainability, MDPI, vol. 14(11), pages 1-23, May.
    2. Ellen Van Droogenbroeck & Léon Van Hove, 2021. "Adoption and usage of E-grocery shopping: A context-specific UTAUT2 model," ULB Institutional Repository 2013/324918, ULB -- Universite Libre de Bruxelles.
    3. Ellen Van Droogenbroeck & Leo Van Hove, 2021. "Adoption and Usage of E-Grocery Shopping: A Context-Specific UTAUT2 Model," Sustainability, MDPI, vol. 13(8), pages 1-27, April.
    4. Arfi, Wissal Ben & Nasr, Imed Ben & Kondrateva, Galina & Hikkerova, Lubica, 2021. "The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    5. Zhou, Min & Huang, Jinlong & Wu, Kexin & Huang, Xin & Kong, Nan & Campy, Kathryn S., 2021. "Characterizing Chinese consumers’ intention to use live e-commerce shopping," Technology in Society, Elsevier, vol. 67(C).
    6. Mengyao Zhang & Hasliza Hassan & Melissa Wendy Migin, 2023. "Exploring the Consumers’ Purchase Intention on Online Community Group Buying Platform during Pandemic," Sustainability, MDPI, vol. 15(3), pages 1-13, January.
    7. Anshu, Kumari & Gaur, Loveleen & Singh, Gurmeet, 2022. "Impact of customer experience on attitude and repurchase intention in online grocery retailing: A moderation mechanism of value Co-creation," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    8. Sonali Singh & Sumeet Singh Jasial & Richa Misra & Ajay Bansal, 2024. "Online Retail Service Quality: What Matters Most for Customer Satisfaction?," FIIB Business Review, , vol. 13(5), pages 600-615, October.
    9. Cabeza-Ramírez, L. Javier & Sánchez-Cañizares, Sandra M. & Santos-Roldán, Luna M. & Fuentes-García, Fernando J., 2022. "Impact of the perceived risk in influencers' product recommendations on their followers' purchase attitudes and intention," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    10. Zhou, Min & Zhao, Lindu & Kong, Nan & Campy, Kathryn S. & Xu, Ge & Zhu, Guiju & Cao, Xianye & Wang, Song, 2020. "Understanding consumers’ behavior to adopt self-service parcel services for last-mile delivery," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    11. Wang, Bin & Shu, Min & Liu, Yunyao & Xie, Fengyuan & Wang, Jin, 2024. "Who do you want to purchase with? The effect of naming strategy on consumer participation in online group purchase," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    12. Jameel, Alaa S. & Harjan, Sinan Abdullah & Ahmad, Abd Rahman, 2023. "Behavioral Intentions to use Artificial Intelligence Among Managers in Small and Medium Enterprises," OSF Preprints w69yh, Center for Open Science.
    13. Jameel, Alaa S. & Harjan, Sinan Abdullah & Ahmad, Abd Rahman, 2023. "Behavioral Intentions to use Artificial Intelligence Among Managers in Small and Medium Enterprises," OSF Preprints w69yh_v1, Center for Open Science.
    14. Nadire Cavus & Yakubu Bala Mohammed & Mohammed Bulama & Muhammad Lamir Isah, 2023. "Examining User Verification Schemes, Safety and Secrecy Issues Affecting M-Banking: Systematic Literature Review," SAGE Open, , vol. 13(1), pages 21582440231, January.
    15. Ashraf Hilal & Concepción Varela-Neira, 2022. "Understanding Consumer Adoption of Mobile Banking: Extending the UTAUT2 Model with Proactive Personality," Sustainability, MDPI, vol. 14(22), pages 1-23, November.
    16. Siraphat Padthar & Phaninee Naruetharadhol & Wutthiya Aekthanate Srisathan & Chavis Ketkaew, 2024. "From Linear to Circular Economy: Embracing Digital Innovations for Sustainable Agri-Food Waste Management among Farmers and Retailers," Resources, MDPI, vol. 13(6), pages 1-29, June.
    17. Khan, Ikram Ullah, 2022. "How does culture influence digital banking? A comparative study based on the unified model," Technology in Society, Elsevier, vol. 68(C).
    18. Dennis Owusu Amofah & Junwu Chai, 2022. "Sustaining Consumer E-Commerce Adoption in Sub-Saharan Africa: Do Trust and Payment Method Matter?," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    19. Terrylina A. Monoarfa & Ujang Sumarwan & Arif I. Suroso & Ririn Wulandari, 2023. "Switch or Stay? Applying a Push–Pull–Mooring Framework to Evaluate Behavior in E-Grocery Shopping," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    20. Xiaoyan Zhang & Qing Yang & Abdullah Al Mamun & Mohammad Masukujjaman & Muhammad Mehedi Masud, 2024. "Acceptance of new agricultural technology among small rural farmers," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6564-:d:1122069. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.