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Effects of distraction and anonymity on privacy trade-offs in facial recognition surveillance

Quevedo, Candace Louise Bergado; Tuan, Do Viet
Master thesis
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2942202.pdf (5.567Mb)
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https://hdl.handle.net/11250/2824224
Utgivelsesdato
2021
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Samlinger
  • Master of Science [1117]
Sammendrag
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.
Beskrivelse
Masteroppgave(MSc) in Master of Science in Strategic Marketing Management - Handelshøyskolen BI, 2021
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Handelshøyskolen BI

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