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Exploring the factors affecting customers’ intention to purchase a smart speaker

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  • Ling, Hsiao-Chi
  • Chen, Hong-Ren
  • Ho, Kevin K.W.
  • Hsiao, Kuo-Lun

Abstract

Although the sale of smart speakers is predicted to have a significant growth, the usage of smart speakers is not very popular in some countries and need further investigation. While the Bluetooth earphone and speakers are popular, the sales of smart speakers are challenging. Hence, the research purpose is to explore the determinants of the intention to purchase smart speakers and compare the differences between customers and potential customers. This study proposes ten hypotheses in a research framework according to the diffusion of innovation theory (DOI), task-technology fit (TTF), and perceived values. This study develops an online questionnaire to collect data from popular social networking sites. Finally, 249 valid responses collected from the website users are remained. The ten hypotheses are validated by using smartPLS software. According to the path analyses, five hypotheses were supported in potential customer group while only three hypotheses were supported in customer group. Users' perceived emotion-related value had significant direct influences on all customers' intention to buy smart speakers. Surprisingly, TTF was demonstrated to not significantly influence customers’ intention to purchase. The insights of the findings can be provided to smart speaker developers to design their products and sales strategies.

Suggested Citation

  • Ling, Hsiao-Chi & Chen, Hong-Ren & Ho, Kevin K.W. & Hsiao, Kuo-Lun, 2021. "Exploring the factors affecting customers’ intention to purchase a smart speaker," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:joreco:v:59:y:2021:i:c:s0969698920313394
    DOI: 10.1016/j.jretconser.2020.102331
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Park, Jeongeun & Yang, Donguk & Kim, Ha Young, 2023. "Text mining-based four-step framework for smart speaker product improvement and sales planning," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    2. Zhang, Yaqiong & Wang, Shifu, 2023. "The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    3. Shahzad, Khuram & Zhang, Qingyu & Zafar, Abaid Ullah & Ashfaq, Muhammad & Rehman, Shafique Ur, 2023. "The role of blockchain-enabled traceability, task technology fit, and user self-efficacy in mobile food delivery applications," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    4. Oliveira, Guilherme Gouvea de & Lizarelli, Fabiane Letícia & Teixeira, Jorge Grenha & Mendes, Glauco Henrique de Sousa, 2023. "Curb your enthusiasm: Examining the customer experience with Alexa and its marketing outcomes," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    5. Youssef, Mayada Abd El-Aziz & Eid, Riyad & Agag, Gomaa, 2022. "Cross-national differences in big data analytics adoption in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    6. Ming Xing Wang & Ki Su Kim & Jeoung Kun Kim, 2023. "Investigating the Determinants of IoT Device Continuance Intentions: An Empirical Study of Smart Speakers Through the Lens of Expectation-Confirmation Theory," SAGE Open, , vol. 13(3), pages 21582440231, September.
    7. Hu, Qian & Pan, Zhao, 2023. "Can AI benefit individual resilience? The mediation roles of AI routinization and infusion," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).

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