description
Smart home devices, such as smart TVs, smart speakers, smart lights and smart security cameras, are increasingly present in our homes. Unfortunately they also have various privacy risks such as housemates annoyingly being able to see when you come home, governments accessing the data collected on you by your smart devices while you don’t even know it, and a cyber-attacker watching you or listening to you via your smart device.
Many studies have used some kind of measurement to examine how people perceive the privacy and/or security risks of smart home devices (e.g. Al-Husamiyah & Al-Bashayreh, 2021; Fantinato et al., 2018; Kowalczuk, 2018; Sanguinetti et al., 2018; Shuhaiber & Mashal, 2019; Yang et al., 2018). However, these studies all use different measurements, often also focusing on different aspects of the risks, and to our knowledge no study properly developed and tested a measure for the perception of the various ways the one’s privacy can be at stake around smart home devices.
In this study you will develop a multidimensional measure of perceived privacy risk perception of smart home devices, considering multiple parties that are at the root of the risks (housemates, landlords, manufacturers of the devices, governments, cyber-attackers and the technology itself). You will test the validity of the measure by examining its correlation with one or more other variables that one would expect it to correlate to, such as how important people find privacy (Huijts & Haans, 2024) or whether they are taking privacy protective action (Lutz & Newlands, 2021).
Research question
- Can we develop a good measurement for perceived privacy risks of smart IoT?
- What are the dimensions of the perceived privacy risks of smart home IoT?
- Do people that care more about privacy perceive more privacy risks of smart home IoT?
- Do people that score higher on perceived privacy risks of smart home IoT take more protective action?
Research method
The research questions are addressed in a survey study
Data-analysis
The data of this study will be analysed by quantitative data analysis programmes such as SPSS or R.
Literature
- Al-Husamiyah, A., & Al-Bashayreh, M. (2021). A comprehensive acceptance model for smart home services. International Journal of Data and Network Science, 6(1), 45–58. https://doi.org/10.5267/J.IJDNS.2021.10.005
- Fantinato, M., Hung, P. C. K., Jiang, Y., Roa, J., Villarreal, P., Melaisi, M., & Amancio, F. (2018). A preliminary study of Hello Barbie in Brazil and Argentina. Sustainable Cities and Society, 40, 83–90.
- Huijts, N. M. A., & Haans, A. (2024). Values as causes of emotions and acceptability in the digital risk context: an extension of the values scale with privacy. Journal of Risk Research, 1–26. https://doi.org/10.1080/13669877.2024.2423203
- Kowalczuk, P. (2018). Consumer acceptance of smart speakers: a mixed methods approach. Journal of Research in Interactive Marketing, 12(4), 418–431. https://doi.org/10.1108/JRIM-01-2018-0022
- Lutz, C., & Newlands, G. (2021). Privacy and smart speakers: A multi-dimensional approach. The Information Society, 37(3), 147–162. https://doi.org/10.1080/01972243.2021.1897914
- Sanguinetti, A., Karlin, B., & Ford, R. (2018). Understanding the path to smart home adoption: Segmenting and describing consumers across the innovation-decision process. Energy Research and Social Science, 46, 274–283. https://doi.org/10.1016/j.erss.2018.08.002
- Shuhaiber, A., & Mashal, I. (2019). Understanding users’ acceptance of smart homes. Technology in Society. https://doi.org/10.1016/j.techsoc.2019.01.003
- Yang, H., Lee, W., & Lee, H. (2018). IoT Smart Home Adoption: The Importance of Proper Level Automation. Journal of Sensors, 2018. https://doi.org/10.1155/2018/6464036
Information
This project is open to 1 student.
Are you interested in this topic for your thesis? Please contact the theme coordinator Lynn Weiher: l.weiher@utwente.nl