User Adoption of Intelligent Environments: A Review of Technology Adoption Models, Challenges, and Prospects
Name
User Adoption of Intelligent Environments A Review of Technology Adoption Models Challenges and Prospects.pdf
Description
Published version
Size
1.96 MB
Format
Adobe PDF
Checksum (MD5)
f83760e68dbf4e124f8d020126078a3e
Author(s)
FakhrHosseini, Shabnam
Chan, Kathryn
Lee, Chaiwoo
Jeon, Myounghoon
Son, Heesuk
Rudnik, John
Coughlin, Joseph
Date Issued
February 16, 2024
Journal
International Journal of Human–Computer Interaction
Publisher
Taylor & Francis
Citation
FakhrHosseini, S., Chan, K., Lee, C., Jeon, M., Son, H., Rudnik, J., & Coughlin, J. (2024). User Adoption of Intelligent Environments: A Review of Technology Adoption Models, Challenges, and Prospects. International Journal of Human–Computer Interaction, 40(4), 986–998.
Version
Final published version
Abstract
Recent technological advancements have enabled the development of smarter (more automated) and more intelligent (adaptable) environments. To understand what factors lead users to reject or adopt Intelligent Environments (IEs), we reviewed nine prominent technology adoption theories. We conducted a literature review to investigate the acceptance and adoption of different types of IEs. We found that perceived usefulness, ease of use, perceived control or self-efficacy, affect and enjoyment, and perceived risks are the common factors across the studies explaining the adoption of IEs. However, shortcomings in the design and methods of the reviewed studies present major concerns in the generalizability and application of existing theories to emerging IEs. We identify eight lacunae in the existing literature and propose a new conceptual model for explaining the adoption of IEs. Through this study, we contribute to the formulation of the theoretical background for the successful introduction of IEs and their integration into users’ everyday life.
MIT Department
AgeLab (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/10447318.2022.2118851
