| dc.description.abstract | Despite the recent development of facial recognition technology (FRT) using AI,
its implementation and applications are still controversial in countries. Amid the
COVID-19 pandemic, FRT could be an effective crisis exit strategy for nations.
However, there was a lack of empirical evidence in privacy literature or marketing
research on how authorities, governments, and private sectors should implement
the technology. Using the ground theory of ELM models, privacy calculus to
examine privacy attitude, intention, and behavior, this study provides a theoretical
model that illustrated people's thinking and decision-making process with FRT's
applications. By conducting an online experiment with the random sampling of 603
respondents in the UK and the US, the study showed a structural model thinking
process that led to privacy disclosure behavior. The findings confirmed that
distraction and non-anonymized FRT would increase people's concerns about
government intrusion, which mediately impact willingness to support and
disclosure behavior.
Moreover, moral consideration as moral equity referred that governments should
raise awareness of FRT's importance to society to increase biometric data
disclosure. Finally, a cluster analysis was conducted to classify people into three
groups of "willing-to-share information." This further investigation also suggested
a potential factor as stereotypes of people, which can deviate from the privacy
paradox of people.
This study contributed to privacy and marketing literatures in the context of FRT
government surveillance. | en_US |
