DOI QR코드

DOI QR Code

Factors Influencing the Use-diffusion of Smart Speakers

스마트 스피커의 사용-확산 관련 영향 요인 -중국소비자를 중심으로

  • Park, Hyun Jung (Dept. of International Business, Chungbuk National University) ;
  • Chen, Qian Qian (Dept. of International Business, Chungbuk National University)
  • 박현정 (충북대학교 국제경영학과) ;
  • 진천천 (충북대학교 국제경영학과)
  • Received : 2019.05.24
  • Accepted : 2019.08.20
  • Published : 2019.08.28

Abstract

This study analyzed the impact of various factors on the use-diffusion of smart speakers. 300 survey responses of Chinese consumers were analyzed using structured models. The results show that both autonomy and adaptability had significant impacts on perceived usefulness and perceived easy of use. Multifunctionality and ability to cooperate affected perceived usefulness, while reactivity did not affected perceived usefulness or perceived easy of use. Anthropomorphism increased perceived enjoyment. Both perceived usefulness and perceived easy of use have been identified to improve the use-diffusion of smart speakers. Perceived enjoyment enhanced the variety of use. We expect these results help understand the factors that need to be considered for the design or marketing communication of smart products.

Keywords

Anthropomorphism;Smartness;Smart product;TAM2;Use-diffusion

References

  1. X. T. Xiao & S. I. Kim. (2018). A Study on the User Experience of Smart Speaker in China-Focused on Tmall Genie and Mi AI Speaker. Journal of Digital Convergence, 16(10), 409-414.
  2. H. J. Lee, C. H. Cho, S. Y. Lee & Y. H. Keel. (2019). A Study on Consumers' Perception of and Use Motivation of Artificial Intelligence(AI) Speaker. Journal of the Korea Contents Association, 19(3), 138-154.
  3. M. J. Kwon & J. M. Kim. (2018). An Analysis of Users Attitudes and Satisfaction toward the Motivation of Artificial Intelligence Speaker -Based on the Theory of Diffusion of Innovations-. Journal of Communication Design, 65, 474-483.
  4. S. A. Rijsdijk & E. J. Hultink. (2009). How today's consumers perceive tomorrow's smart products. Journal of Product Innovation Management, 26(1), 24-42. https://doi.org/10.1111/j.1540-5885.2009.00332.x
  5. Q. Q. Chen & H. J. Park. (2018). Consumer study on the acceptance of VR headsets based on the extended TAM. Journal of Digital Convergence, 16(6), 117-126.
  6. S. D. Cho & K. E. Kim. (2007). A study on the factors influencing the use diffusion of technological products. Korean Journal of Marketing, 22(2), 67-86.
  7. J. H. You & C. Park. (2010). A comprehensive review of technology acceptance model researches. Entrue Journal of Information Technology, 9(2), 31-50.
  8. S. S. Kim, W. J. Jang & G. Y. Gim. (2019). An Exploratory Study on Factors Affecting Intention to Use of AI Speaker. The Journal of Information Technology and Architecture, 16(1), 71-86.
  9. H. J. Park & H. S. Lee. (2014). Product smartness and use-diffusion of smart products: the mediating roles of consumption values. Asian Social Science, 10(3), 54.
  10. J. Bohn, V. Coroama, M. Langheinrich, F. Mattern & M. Rohs. (2004). Living in a world of smart everyday objects-social, economic, and ethical implications. Human and Ecological Risk Assessment, 10(5), 763-785. https://doi.org/10.1080/10807030490513793
  11. N. Epley, A. Waytz & J. T. Cacioppo. (2007). On seeing human: a three-factor theory of anthropomorphism. Psychological Review, 114(4), 864. https://doi.org/10.1037/0033-295X.114.4.864
  12. S. A. Park & S. J. Choi. (2018). A understanding the factors influencing satisfaaction and continued use intention of AI speaker; focusing on the utilitarian and hedonic values. Information Society & Media, 19(3), 159-182.
  13. S. A. Brown & V. Venkatesh. (2005). A model of adoption of technology in the household: A baseline model test and extension incorporating household life cycle. Management Information Systems Quarterly, 29(3), 11.
  14. N. Lee, H. Shin & S. S. Sundar. (2011, March). Utilitarian vs. hedonic robots: role of parasocial tendency and anthropomorphism in shaping user attitudes. In Proceedings of the 6th International Conference on Human-robot Interaction (pp. 183-184). ACM.
  15. P. A. Rauschnabel & A. C. Ahuvia. (2014). You're so lovable: Anthropomorphism and brand love. Journal of Brand Management, 21(5), 372-395. https://doi.org/10.1057/bm.2014.14
  16. V. Venkatesh, J. Y. Thong & X. Xu. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
  17. Y. Sung & J. Kim. (2010). Effects of brand personality on brand trust and brand affect. Psychology & Marketing, 27(7), 639-661. https://doi.org/10.1002/mar.20349
  18. C. Klimmt, T. Hartmann & A. Frey. (2007). Effectance and control as determinants of video game enjoyment. Cyberpsychology & Behavior, 10(6), 845-848. https://doi.org/10.1089/cpb.2007.9942
  19. H. J. Park. (2015).Nscreen service user typology based on use-diffusion and lifestyle. Journal of the Korea Contents Association, 15(2), 444-454.
  20. C. F. Shih & A. Venkatesh. (2004). Beyond adoption: Development and application of a use-diffusion model. Journal of Marketing, 68(1), 59-72. https://doi.org/10.1509/jmkg.68.1.59.24029
  21. K. H. Yim, J. H. Kwon & Z. X. Quan. (2016). The effect of benefits of mobile application use-diffusion and purchase intention in service management. Journal of Digital Convergence, 14(3), 63-69.
  22. D. H. Jo, J. W. Park & H. J. Chun. (2011). The relationships among perceived value, use-diffusion, loyalty of mobile instant messaging service. Journal of Intelligence and Information Systems, 17(4), 193-212.
  23. S. Moussawi. (2016). Investigating personal intelligent agents in everyday life through a behavioral lens. City University of New York.
  24. J. W. Moon & Y. G. Kim. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230. https://doi.org/10.1016/S0378-7206(00)00061-6