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Who will be smart home users? An analysis of adoption and diffusion of smart homes

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  • Shin, Jungwoo
  • Park, Yuri
  • Lee, Daeho

Abstract

A smart home is considered a primary service of the Internet of Things (IoT), and global leading companies are launching smart home services/products based on the IoT. However, the spread of smart homes has been slower than expected, and analysis of smart homes from a demand perspective is required. This study suggests implications for promoting the smart home market by analyzing factors affecting adoption and diffusion of smart homes. A technology acceptance model was used to describe the adoption of smart homes and a multivariate probit model was used to describe the diffusion of smart homes. The characteristics of smart homes such as network effects between services/products and the importance of personal information protection were considered in addition to demographic variables. The results of this study show that compatibility, perceived ease of use, and perceived usefulness have significant positive effects on purchase intention. In terms of purchase timing, unlike other information and communication technology (ICT) services/products, older consumers are more likely to purchase smart homes within a given time period than are younger consumers. Therefore, a strategy for promoting smart home purchases by young consumers is required to increase market demand.

Suggested Citation

  • Shin, Jungwoo & Park, Yuri & Lee, Daeho, 2018. "Who will be smart home users? An analysis of adoption and diffusion of smart homes," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 246-253.
    • Handle: RePEc:eee:tefoso:v:134:y:2018:i:c:p:246-253
    DOI: 10.1016/j.techfore.2018.06.029
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    References listed on IDEAS

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    1. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    3. Richter, Laura-Lucia & Pollitt, Michael G., 2018. "Which smart electricity service contracts will consumers accept? The demand for compensation in a platform market," Energy Economics, Elsevier, vol. 72(C), pages 436-450.
    4. Shin, Jungwoo & Park, Yuri & Lee, Daeho, 2016. "Strategic management of over-the-top services: Focusing on Korean consumer adoption behavior," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 329-337.
    5. Hong, Jihoon & Shin, Jungwoo & Lee, Daeho, 2016. "Strategic management of next-generation connected life: Focusing on smart key and car–home connectivity," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 11-20.
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