IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i10p5671-d557316.html
   My bibliography  Save this article

AI Technology and Online Purchase Intention: Structural Equation Model Based on Perceived Value

Author

Listed:
  • Jiwang Yin

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Xiaodong Qiu

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

Abstract

(1) Background: AI technology has been deeply applied to online shopping platforms to provide more accurate and personalized services for consumers. It is of great significance to study the different functional experiences of AI for consumers to improve the current application status of AI technology. (2) Method: Based on the “SOR” model, this study divides the AI technology experienced by the consumers of online shopping platforms into three dimensions: accuracy, insight, and interaction experience. The perceived value is taken as the mediating variable from the prospect of perceived utility value and perceived hedonic value. This article uses empirical research methods to analyze the effect of the three dimensions of online shopping AI experience to research the internal influence mechanism of consumers’ purchase intention. (3) Results: 1. The accuracy, insight, and interaction experience of AI marketing technology each have a significant positive impact on consumers’ perceived utility value and hedonic value; 2. Both the perceived utility value and perceived hedonic value obtained by an AI technology experience can promote the formation of consumers’ purchase intention; 3. The perceived hedonic value was better than perceived utility value at promoting the consumers’ purchase intention.

Suggested Citation

  • Jiwang Yin & Xiaodong Qiu, 2021. "AI Technology and Online Purchase Intention: Structural Equation Model Based on Perceived Value," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5671-:d:557316
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/10/5671/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/10/5671/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    2. Sang Joon Ahn & Seong Ho Lee, 2019. "The Effect of Consumers’ Perceived Value on Acceptance of an Internet-Only Bank Service," Sustainability, MDPI, vol. 11(17), pages 1-9, August.
    3. Eroglu, Sevgin A. & Machleit, Karen A. & Davis, Lenita M., 2001. "Atmospheric qualities of online retailing: A conceptual model and implications," Journal of Business Research, Elsevier, vol. 54(2), pages 177-184, November.
    4. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
    5. Overby, Jeffrey W. & Lee, Eun-Ju, 2006. "The effects of utilitarian and hedonic online shopping value on consumer preference and intentions," Journal of Business Research, Elsevier, vol. 59(10-11), pages 1160-1166, October.
    6. Tuck Siong Chung & Michel Wedel & Roland T. Rust, 2016. "Adaptive personalization using social networks," Journal of the Academy of Marketing Science, Springer, vol. 44(1), pages 66-87, January.
    7. Fazal-e-Hasan, Syed Muhammad & Ahmadi, Hormoz & Mortimer, Gary & Grimmer, Martin & Kelly, Louise, 2018. "Examining the role of consumer hope in explaining the impact of perceived brand value on customer–brand relationship outcomes in an online retailing environment," Journal of Retailing and Consumer Services, Elsevier, vol. 41(C), pages 101-111.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wenbo Huang & Yanyan Chen & Shushan Chai, 2021. "Decision-Making Behavior Analysis and Empirical Study under Information Intervention in a Cold Environment," Sustainability, MDPI, vol. 13(20), pages 1-16, October.
    2. Miao Huang & Mohamad Saifudin Mohamad Saleh & Izzal Asnira Zolkepli, 2023. "The Moderating Effect of Green Advertising on the Relationship between Gamification and Sustainable Consumption Behavior: A Case Study of the Ant Forest Social Media App," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    3. Hasan Beyari & Hatem Garamoun, 2022. "The Effect of Artificial Intelligence on End-User Online Purchasing Decisions: Toward an Integrated Conceptual Framework," Sustainability, MDPI, vol. 14(15), pages 1-17, August.
    4. Zhijie Song & Chang Liu & Rui Shi, 2022. "How Do Fresh Live Broadcast Impact Consumers’ Purchase Intention? Based on the SOR Theory," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    5. Jinsoo Hwang & Heather Markham Kim & Kyuhyeon Joo & Muhammad Nawaz & Joonho Moon, 2022. "Travelers’ Perceived Value of Robot Services in the Airline Industry: Focusing on Demographic Characteristics," Sustainability, MDPI, vol. 14(23), pages 1-12, November.
    6. Nazir, Sajjad & Khadim, Sahar & Ali Asadullah, Muhammad & Syed, Nausheen, 2023. "Exploring the influence of artificial intelligence technology on consumer repurchase intention: The mediation and moderation approach," Technology in Society, Elsevier, vol. 72(C).
    7. Noor Irliana Mohd Rahim & Noorminshah A. Iahad & Ahmad Fadhil Yusof & Mohammed A. Al-Sharafi, 2022. "AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
    8. Caroline Kopot & Brenda J. Cude, 2021. "Channel Depth or Consistency? A Study on Establishing a Sustainable Omnichannel Strategy for Fashion Department Store Retailers," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    9. Elizabeth Emperatriz García-Salirrosas & Ángel Acevedo-Duque, 2022. "PERVAINCONSA Scale to Measure the Consumer Behavior of Online Stores of MSMEs Engaged in the Sale of Clothing," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    10. Elizabeth Emperatriz García-Salirrosas & Ángel Acevedo-Duque & Viviana Marin Chaves & Paula Andrea Mejía Henao & Juan Carlos Olaya Molano, 2022. "Purchase Intention and Satisfaction of Online Shop Users in Developing Countries during the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(10), pages 1-14, May.

    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. Lim, Joon Soo & Zhang, Jun, 2022. "Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency," Technology in Society, Elsevier, vol. 69(C).
    2. repec:dgr:rugsom:04f04 is not listed on IDEAS
    3. Shobeiri, Saeed & Laroche, Michel & Mazaheri, Ebrahim, 2013. "Shaping e-retailer’s website personality: The importance of experiential marketing," Journal of Retailing and Consumer Services, Elsevier, vol. 20(1), pages 102-110.
    4. Lin, Tung-Ching & Huang, Shiu-Li & Hsu, Chieh-Ju, 2015. "A dual-factor model of loyalty to IT product – The case of smartphones," International Journal of Information Management, Elsevier, vol. 35(2), pages 215-228.
    5. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    6. D. Veena Parboteeah & Joseph S. Valacich & John D. Wells, 2009. "The Influence of Website Characteristics on a Consumer's Urge to Buy Impulsively," Information Systems Research, INFORMS, vol. 20(1), pages 60-78, March.
    7. Hsu, Chin-Lung & Lin, Judy Chuan-Chuan, 2016. "Effect of perceived value and social influences on mobile app stickiness and in-app purchase intention," Technological Forecasting and Social Change, Elsevier, vol. 108(C), pages 42-53.
    8. Wang, Sijun & Beatty, Sharon E. & Mothersbaugh, David L., 2009. "Congruity's role in website attitude formation," Journal of Business Research, Elsevier, vol. 62(6), pages 609-615, June.
    9. Wen-Yu Tsao, 2019. "Understanding Users’ Preference to Engage in YouTubers," International Journal of Human Resource Studies, Macrothink Institute, vol. 9(1), pages 277-298, December.
    10. Sara Poggesi & Michela Mari & Arash Kamangar & Pinalba Schilleci, 2022. "The Role of Virtual Environment in Online Retailing: State of the Art and Research Challenges," Sustainability, MDPI, vol. 14(7), pages 1-17, March.
    11. Lucrezia Maria Cosmo & Luigi Piper & Arianna Vittorio, 2021. "The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots," Italian Journal of Marketing, Springer, vol. 2021(1), pages 83-102, June.
    12. Ephrem Habtemichael Redda, 2018. "Positive and Negative Antecedents of Consumer Attitude towards Online Shopping," Journal of Economics and Behavioral Studies, AMH International, vol. 10(6), pages 222-228.
    13. Christophe Bezes, 2009. "E-Commerce Website Evaluation: A Critical Review," Working Papers hal-00611008, HAL.
    14. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    15. Amy Wenxuan Ding & Shibo Li & Patrali Chatterjee, 2015. "Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation," Information Systems Research, INFORMS, vol. 26(2), pages 339-359, June.
    16. Uzir, Md Uzir Hossain & Al Halbusi, Hussam & Lim, Rodney & Jerin, Ishraq & Abdul Hamid, Abu Bakar & Ramayah, Thurasamy & Haque, Ahasanul, 2021. "Applied Artificial Intelligence and user satisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19," Technology in Society, Elsevier, vol. 67(C).
    17. Broekhuizen, Thijs L.J. & Jager, Wander, 2004. "A conceptual model of channel choice: measuring online and offline shopping value perceptions," Research Report 04F04, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    18. Masood Ul Hassan & Muhammad Shahid Iqbal & Ume Habibah, 2020. "Self-Service Technology Service Quality: Building Loyalty and Intention Through Technology Trust in Pakistani Service Sector," SAGE Open, , vol. 10(2), pages 21582440209, June.
    19. Richard, Marie-Odile & Habibi, Mohammad Reza, 2016. "Advanced modeling of online consumer behavior: The moderating roles of hedonism and culture," Journal of Business Research, Elsevier, vol. 69(3), pages 1103-1119.
    20. Echchakoui, Saïd, 2015. "Drivers of sales force equity in the service industry," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 140-153.
    21. Rizomyliotis, Ioannis & Kastanakis, Minas N. & Giovanis, Apostolos & Konstantoulaki, Kleopatra & Kostopoulos, Ioannis, 2022. "“How mAy I help you today?” The use of AI chatbots in small family businesses and the moderating role of customer affective commitment," Journal of Business Research, Elsevier, vol. 153(C), pages 329-340.

    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:13:y:2021:i:10:p:5671-:d:557316. 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.