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Analyzing the adoption and diffusion of voice-enabled smart-home systems: empirical evidence from Thailand

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Abstract

The market for voice assistants (VAs) and other allied voice-based smart-home products is gradually emerging. The initial growth has been slower than expected; therefore, an in-depth simultaneous intention and diffusion analysis is needed for identifying the relevant factors along with finding out the target consumers. This work uses technology acceptance model as the core for analyzing the adoption intention of the VA-based system and extends it with three additional factors: compatibility, perceived complementarity and privacy concerns. The diffusion analysis for the same is done using the multivariate probit model. Certain characteristics of the VA-based systems like the network effects between the products/services and the importance of protecting personal information are considered in this work, apart from various demographic variables like age, gender, income and education levels. Two separate surveys were conducted for the purpose of data collection from 315 and 1945 participants residing in Thailand for analyzing the adoption and diffusion scenario, respectively. The results show that usefulness, ease of use, compatibility and perceived complementarity have significant positive effects on the purchase intention. In terms of diffusion of the VA market, unlike other Information Communication Technology-based products/services, the results show that the senior consumers are more likely to purchase the VAs and other allied smart-home devices within a given time frame when compared to the younger consumers. Therefore, new strategies should be developed that promote the usage of VAs by the young population for increasing the market demand.

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References

  1. McCue, T.J.: Okay Google: voice search technology and the rise of voice commerce. [Online]. https://www.forbes.com/sites/tjmccue/2018/08/28/okay-google-voice-search-technology-and-the-rise-of-voice-commerce/#3434ba694e29. Accessed 6th Dec 2019

  2. Alsin, A.: How voice technology is disrupting modern computing: and why investors should be listening. [Online]. https://www.forbes.com/sites/aalsin/2018/02/21/how-voice-technology-is-disrupting-modern-computing-and-why-investors-should-be-listening/#503bab166f50. Accessed 6th Dec 2019

  3. Hippold, S.: What’s ahead for AI, smart speakers, and smartphones? [Online]. https://www.gartner.com/smarterwithgartner/whats-ahead-for-ai-smart-speakers-and-smartphones/. Accessed 6th Dec 2019

  4. Moriuchi, E.: Okay, Google!: an empirical study on voice assistants on consumer engagement and loyalty. Psychol. Market. 36(5), 489–501 (2019)

    Article  Google Scholar 

  5. Nass, C.I., Moon, Y.: Machines and mindlessness: social responses to computers. J. Soc. Issues 56(1), 81–103 (2002)

    Article  Google Scholar 

  6. Guzman, A.L.: Voices in and of the machine: source orientation toward mobile virtual assistants. Comput. Hum. Behav. 90, 343–350 (2019)

    Article  Google Scholar 

  7. Park, K., Kwak, C., Lee, J., Ahn, J.H.: The effect of platform characteristics on the adoption of smart speakers: empirical evidence in South Korea. Telemat. Inform. 35, 2118–2132 (2018)

    Article  Google Scholar 

  8. Hoy, M.B.: Alexa, Siri, Cortana, and more: an introduction to voice assistants. Med. Ref. Serv. Q. 37(1), 81–88 (2018)

    Article  Google Scholar 

  9. Kiseleva, J., Williams, K., Jiang, J., Awadallah, A.H., Crook, A.C., Zitouni, I., Anastasakos, T.: Understanding user satisfaction with intelligent assistants. In: Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval (CHIIR ‘16). ACM, New York, pp. 121–130 (2016). https://doi.org/10.1145/2854946.2854961

  10. Kowalczuk, P.: Consumer acceptance of smart speakers: a mixed methods approach. J. Res. Interact. Mark. 12(4), 418–431 (2018)

    Article  Google Scholar 

  11. Han, S., Yang, H.: Understanding adoption of intelligent personal assistants: a parasocial relationship perspective. Ind. Manag. Data Syst. 118(3), 618–636 (2018)

    Article  Google Scholar 

  12. McLean, G., Frimpong, K.O.: Hey Alexa … examine the variables influencing the use of artificial intelligent in-home voice assistants. Comput. Hum. Behav. 99, 28–37 (2019)

    Article  Google Scholar 

  13. Yang, H., Lee, H.: Understanding user behavior of virtual personal assistant devices. IseB 17, 65–87 (2019)

    Article  Google Scholar 

  14. Kim, S.H.: Moderating effects of job relevance and experience on mobile wireless technology acceptance: adoption of a smartphone by individuals. Inf. Manag. 45(6), 387–393 (2008)

    Article  Google Scholar 

  15. Hsiao, K.L., Chen, C.C.: What drives smartwatch purchase intention? Perspectives from hardware, software, design, and value. Telemat. Inform. 35(1), 103–113 (2018)

    Article  Google Scholar 

  16. Pal, D., Funilkul, S., Vanijja, V.: The future of smartwatches: assessing the end-users’ continuous usage using an extended expectation-confirmation model. In: Universal Access in the Information Society, pp. 1–21 (2018). https://doi.org/10.1007/s10209-018-0639-zK

  17. Yu, J., Lee, H., Ha, I., Zo, H.: User acceptance of media tablets: an empirical examination of perceived value. Telemat. Inform. 34(4), 206–223 (2017)

    Article  Google Scholar 

  18. Setirek, A.C., Mardikyan, S.: Understanding the adoption of voice activated personal assistants. E-serv. Mobile Appl. 9(3), 1–21 (2017)

    Article  Google Scholar 

  19. López, G., Quesada, L., Guerrero, L.A.: Alexa vs. Siri vs. Cortana vs. Google assistant: a comparison of speech-based natural user interfaces. In: Proceedings of 2017 International Conference on Applied Human Factors and Ergonomics (AHFE 2017), Los Angeles, USA, pp. 241–250 (2017)

  20. Bogers, T., et.al.: A study of usage and usability of intelligent personal assistants in Denmark. In: Proceedings of International Conference on Information in Contemporary Society (iConference 19), Washington, USA, pp. 79–90 (2019)

  21. Maguire, M.: Development of a heuristic evaluation tool for voice user interfaces. In: Proceedings of International Conference on Human–Computer Interaction (HCII’19), Orlando, USA, pp. 212–225 (2019)

  22. Pal, D., Arpnikanondt, C., Funilkul, S., Varadarajan, V.: User experience with smart voice assistants: the accent perspective. In: Proceedings of 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, pp. 1–6 (2019)

  23. Sears, A., Feng, J., Oscitutu, K., Karat, C.M.: Hands-free, speech-based navigation during dictation: difficulties, consequences, and solutions. Hum. Comput. Interact. 18(3), 229–257 (2003)

    Article  Google Scholar 

  24. Purwins, H., Li, B., Virtanen, T., Schlüter, J., Chang, S., Sainath, T.: Deep Learning For Audio Signal Processing. IEEE J. Select. Top. Signal Process. 13(2), 206–219 (2019)

    Article  Google Scholar 

  25. Alepis, E., Patsakis, C.: Monkey says, monkey does: security and privacy on voice assistants. IEEE Access 5, 17841–17851 (2017)

    Article  Google Scholar 

  26. Yan, C., Zhang, G., Ji, X., Zhang, T., Zhang, T., Xu, W.: The feasibility of injecting inaudible voice commands to voice assistants. IEEE Trans. Dependable Secure Comput. (2020). https://doi.org/10.1109/tdsc.2019.2906165

    Article  Google Scholar 

  27. Zhang, R., Chen, X., Wen, S., Zheng, X., Ding, Y.: Using AI to attack VA: a stealthy spyware against voice assistances in smart phones. IEEE Access 7, 153542–153554 (2019)

    Article  Google Scholar 

  28. Vaidya, T., Sherr, M.: You talk too much: limiting privacy exposure via voice input. In: Proceedings 2019 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA, pp. 84–91 (2019)

  29. Polyakov, E.V., Mazhanov, M.S., Rolich, A.Y., Voskov, L.S., Kachalova, M.V., Polyakov, S.V.: Investigation and development of the intelligent voice assistant for the Internet of Things using machine learning. In: 2018 Moscow Workshop on Electronic and Networking Technologies (MWENT), Moscow, pp. 1–5 (2018)

  30. Petnik, J., Vanus, J.: Design of smart home implementation within IoT with natural language interface. IFAC-PapersOnLine 51(6), 174–179 (2018)

    Article  Google Scholar 

  31. Portet, F., Vacher, M., Golanski, C., Roux, C., Meillon, B.: Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects. Pers. Ubiquit. Comput. 17(1), 127–144 (2013)

    Article  Google Scholar 

  32. Portet, F., et al.: Context-aware voice-based interaction in smart home - VocADom@A4H corpus collection and empirical assessment of its usefulness. In: 2019 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, Fukuoka, Japan, pp. 811–818 (2019)

  33. Bajpai, S., Radha, D.: Smart phone as a controlling device for smart home using speech recognition. In: 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp. 0701–0705 (2019)

  34. Guamán, S., Calvopiña, A., Orta, P., Tapia, P., Yoo, S.G.: Device control system for a smart home using voice commands: a practical case. In: 2018 10th International Conference on Information Management and Engineering (ICIME 2018), ACM, New York, pp. 86–89 (2018). https://doi.org/10.1145/3285957.3285977

  35. Vanus, J., Smolon, M., Martinek, R., Koziorek, J., Zidek, J., Bilik, P.: Testing of the voice communication in smart home care. Hum.Centric Comput. Inf. Sci. 5(15), 1–22 (2015)

    Google Scholar 

  36. Uma, S., Eswari, R., Bhuvanya, R., Kumar, G.S.: IoT based voice/text controlled home appliances. Procedia Comput. Sci. 165, 232–238 (2019)

    Article  Google Scholar 

  37. Yue, C.Z., Ping, S.: Voice activated smart home design and implementation. In: 2017 2nd International Conference on Frontiers of Sensors Technologies (ICFST), Shenzhen, pp. 489–492 (2017)

  38. Jadczyk, T., et al.: Feasibility of a voice-enabled automated platform for medical data collection: cardiocube. Int. J. Med. Inf. 129, 388–393 (2019)

    Article  Google Scholar 

  39. Kinsella, B.: Amazon Alexa has 100 K skills but momentum slows globally. [Online]. https://voicebot.ai/2019/10/01/amazon-alexa-has-100k-skills-but-momentum-slows-globally-here-is-the-breakdown-by-country/. Accessed 9th Dec 2019

  40. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)

    Article  Google Scholar 

  41. Moriuchi, E., Takahashi, I.: An empirical investigation of the factors motivating Japanese repeat consumers to review their shopping experiences. J. Bus. Res. 82, 381–390 (2018)

    Article  Google Scholar 

  42. Pavlou, P.A.: Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. Int. J.Electron. Commer. 7(3), 101–134 (2003)

    Article  Google Scholar 

  43. Roca, J.C., Chiu, C.M., Martinez, F.J.: Understanding e-learning continuance intention: an extension of the technology acceptance model. Int. J. Hum Comput Stud. 64(8), 683–696 (2006)

    Article  Google Scholar 

  44. Yaghoubi, N.M., Bahmani, E.: Factors affecting the adoption of online banking-an integration of technology acceptance model and theory of planned behavior. Int. J. Bus. Manag. 5(9), 159 (2010)

    Article  Google Scholar 

  45. Lee, W., Xiong, L., Hu, C.: The Effect of Facebook users’ arousal and valence on intention to go to the festival: applying an extension of the technology acceptance model. Int. J. Hosp. Manag. 31(3), 819–827 (2012)

    Article  Google Scholar 

  46. Pal, D., Funilkul, S., Vanijja, V., Papasratorn, B.: Analyzing the elderly users’ adoption of smart-home services. IEEE Access 6, 51238–51252 (2018)

    Article  Google Scholar 

  47. Marangunić, N., Granić, A.: Technology acceptance model: a literature review from 1986 to 2013. Univ. Access Inf. Soc. 14(1), 81–95 (2015)

    Article  Google Scholar 

  48. Legris, P., Ingham, J., Collerette, P.: Why do people use information technology? A critical review of the technology acceptance model. Inf. Manag. 40, 191–204 (2003)

    Article  Google Scholar 

  49. Martin, S.S., Lopez, B.C., Ramon, M.A.: Mobile shoppers: types, drivers and impediments. J. Organ. Comput. Electron. Commer. 23(4), 350–371 (2013)

    Article  Google Scholar 

  50. Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2010)

    Google Scholar 

  51. Yang, H., Yu, J., Zo, H., Choi, M.: User acceptance of wearable devices: an extended perspective of perceived value. Telemat. Inform. 33, 256–269 (2016)

    Article  Google Scholar 

  52. Park, E., Kim, S., Kim, Y., Kwon, S.J.: Smart home services as the next mainstream of the ICT industry: determinants of the adoption of smart home services. Univ. Access Inf. Soc. 17(1), 175–190 (2018)

    Article  Google Scholar 

  53. Islam, A.N.: E-learning system use and its outcomes: moderating role of perceived compatibility. Telemat. Inform. 33(1), 48–55 (2016)

    Article  Google Scholar 

  54. Hsu, C.L., Lin, J.C.C.: An empirical examination of consumer adoption of internet of things services: network externalities and concern for information privacy perspectives. Comput. Hum. Behav. 62, 516–527 (2016)

    Article  Google Scholar 

  55. Lin, C.P., Bhattacharjee, A.: Elucidating individual intention to use interactive information technologies: the role of network externalities. Int. J. Electron. Commer. 13, 85–108 (2008)

    Article  Google Scholar 

  56. Lin, C.P., Tsai, Y.H., Wang, Y.J., Chiu, C.K.: Modelling IT relationship quality and its determinants: a potential perspective of network externalities in e-service. Technol. Forecast. Soc. Chang. 78(1), 171–184 (2011)

    Article  Google Scholar 

  57. Smith, H.J., Milberg, S.J., Burke, S.J.: Information privacy: measuring individuals’ concerns about organizational practices. MIS Q. 20(2), 167–196 (1996)

    Article  Google Scholar 

  58. Rauschnabel, P.A., He, J., Ro, Y.K.: Antecedents to the adoption of augmented reality smart glasses: the case of Pokemon Go. Comput. Hum. Behav. 76, 276–286 (2017)

    Article  Google Scholar 

  59. Pal, D., Papasratorn, B., Chutimaskul, W., Funilkul, S.: Embracing the smart-home revolution in Asia by the elderly: an end-user negative perception modeling. IEEE Access 7, 38535–38549 (2019)

    Article  Google Scholar 

  60. Featherman, M.S., Pavlou, P.A.: Predicting e-services adoption: a perceived risk facets perspective. Int. J. Hum Comput Stud. 59, 451–474 (2003)

    Article  Google Scholar 

  61. Edwards, Y.D., Allenby, G.M.: Multivariate analysis of multiple response data. J. Mark. Res. 40(3), 321–334 (2003)

    Article  Google Scholar 

  62. Boztuğ, Y., Hildebrandt, L.: A market basket analysis conducted with a multivariate logit model. In: International Conference on Data and Information Analysis to Knowledge Engineering, pp. 558–565. https://doi.org/10.1007/3-540-31314-1_68

  63. Train, K.E.: Discrete Choice Methods with Simulations, 2nd edn. Cambridge University Press, Cambridge (2009)

    MATH  Google Scholar 

  64. Hong, J., Shin, J., Lee, D.: Strategic management of next-generation connected life: focusing on smart key and car-home connectivity. Technol. Forecast. Soc. Chang. 103, 11–20 (2016)

    Article  Google Scholar 

  65. Shin, J., Jo, M., Lee, J., Lee, D.: Strategic management of cloud computing services: focusing on consumer adoption behavior. IEEE Trans. Eng. Manag. 61(3), 419–427 (2014)

    Article  Google Scholar 

  66. Huber, J., Train, K.: On the similarity of classical and bayesian estimates of individual mean partworths. Mark. Lett. 12(3), 259–269 (2001)

    Article  Google Scholar 

  67. Becker, H., Loder, A., Schmid, B., Axhausen, K.W.: Modelling Car-sharing Membership as a Mobility Tool: a Multivariate Probit Approach with Latent Variables. Travel Behavior and Society 8, 26–36 (2017)

    Article  Google Scholar 

  68. Adamowicz, W., Louviere, J., Williams, M.: Combining revealed and stated preference methods for evaluating environmental amenities. J. Environ. Econ. Manag. 26(3), 271–292 (1994)

    Article  MATH  Google Scholar 

  69. Wardman, M.: A comparison of revealed preference and stated preference models of travel behavior. J. Transp. Econ. Policy 22(1), 71–91 (1988)

    Google Scholar 

  70. Akiva, M.B., Bradley, M., Morikawa, T., Benjamin, J., Novak, T., Oppewal, H., Rao, V.: Combining revealed and stated preferences data. Mark. Lett. 5(4), 335–349 (1994)

    Article  Google Scholar 

  71. Pallant, J.: SPSS Survival Manual: A Step by Step Guide to Data Analysis, 5th edn. Open University Press, New York (2013)

    Google Scholar 

  72. Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C.: Multivariate Data Analysis, 5th edn. Prentice Hall International Inc, New Jersey (1998)

    Google Scholar 

  73. Chin, W.W., Marcolin, B.L., Newsted, P.R.: A partial least squares latent variable modelling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inf. Syst. Res. 14(2), 127–219 (2003)

    Article  Google Scholar 

  74. Kleinbaum, D.G., Kupper, L.L., Muller, K.E.: Applied Regression Analysis and Other Multivariate Methods, 4th edn. Duxbury Press, Boston (2007)

    Google Scholar 

  75. Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981)

    Article  Google Scholar 

  76. Serenko, K., Turel, O., Yol, S.: Moderating roles of user demographics in the American customer satisfaction model within the context of mobile services. In: Proceedings of 12th Americas Conference on Information Systems (AMCIS) (2006)

  77. Kooti, F., Lerman, K., Aiello, L.M., Grbovic, M., Djuric, N., Radosavljevic, V.: Portrait of online shopper: understanding and predicting consumer behavior. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (WSDM ‘16). ACM, New York, pp. 205–214 (2016). https://doi.org/10.1145/2835776.2835831

  78. Yi, Y., Wu, Z., Tung, L.L.: How individual differences influence technology usage behavior? Toward an integrated framework. J. Comput. Inf. Syst. 46(2), 52–63 (2016)

    Google Scholar 

  79. Juvan, E., Dolnicar, S.: The attitude-behavior gap in sustainable tourism. Ann. Tour. Res. 48, 76–95 (2014)

    Article  Google Scholar 

  80. Claudy, M.C., Peterson, M., O’Driscoll, A.: Understanding the attitude-behavior gap for renewable energy systems using behavioral reasoning theory. J. Macromark. 33(4), 273–287 (2013)

    Article  Google Scholar 

  81. Yamoah, F.A., Acquaye, A.: Unravelling the attitude-behavior gap paradox for sustainable food consumption: insight from the UK apple market. J. Clean. Prod. 217, 172–184 (2019)

    Article  Google Scholar 

  82. Chung, S.S., Leung, M.M.Y.: The value-action gap in waste recycling: the case of undergraduates in Hong Kong. Environ. Manag. 40(4), 603–612 (2007)

    Article  Google Scholar 

  83. Koetsier, J.: Research shows Amazon echo owners buy 29% more from Amazon. [Online]. https://www.forbes.com/sites/johnkoetsier/2018/05/30/40k-person-study-buying-echo-increases-amazon-purchases-29-especially-cpg-items/#37909505282e. Accessed on 12th Dec 2019

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Acknowledgements

This work was partially sponsored by the King Mongkut’s University of Technology Thonburi New Researcher Funding.

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  1. School of IT, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Debajyoti Pal, Chonlameth Arpnikanondt & Suree Funilkul

  2. School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK

    Mohammad Abdur Razzaque

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  1. Debajyoti Pal
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This experiment has been approved by the Ethics Committee (IRB) of “King Mongkut’s University of Technology Thonburi.” Further, the data collected from the survey are anonymous.

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Pal, D., Arpnikanondt, C., Funilkul, S. et al. Analyzing the adoption and diffusion of voice-enabled smart-home systems: empirical evidence from Thailand. Univ Access Inf Soc 20, 797–815 (2021). https://doi.org/10.1007/s10209-020-00754-3

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