Research has shown that perceived privacy is associated with individuals’ willingness to share their data. This creates a challenge for enterprises looking to leverage the benefits of personal data. Perceived privacy is contextual and interactive, depending on what to share and with whom. Thus, it would change with time, an evolution of the relationship between the data-exchange parties. But research has also shown that a consumer’s privacy concerns is not absolute, as this often requires a trade-off between the need for privacy and the economic benefits of sharing one’s information.
The key issue here is that to make a decision on such trade-offs, the consumer needs to have the skill/capability to understand the implications of information sharing. And when they lack this competency, they are deemed ‘vulnerable’. This perceived vulnerability would further compound their willingness to share information.
This project aims to address the challenge of measuring the dynamics of an individual’s perceived privacy and vulnerability, and the effectiveness of intervention methods for empowering privacy and reducing vulnerability to enhance consumers’ willingness to share information.
We will embed an application wizard that voluntarily allows the collection of primary information from HAT stakeholders, as the project will be anchored on the HAT database, a rich incubator of a diverse set of applications housing information on various users with varying degrees of privacy concerns and perceived vulnerability.
The modelling approach to be used is the Latent Growth Curve model, which would allow us to investigate the latent aspects of privacy, vulnerability and information-sharing behaviour over time as manifested by their respective observed indicators. This method is particularly appropriate as it could structure for changes in growth of variables over time using longitudinal data supported by HAT.
Theoretical significance: To capture the dynamics of privacy and vulnerability and their behaviour change with the interventions of technology as the HAT.
Empirical implications: This model would potentially enable policy makers and firms to evaluate the effectiveness of their interventional programs to improve individual privacy and reduce vulnerability of personal data. In addition, firms can develop mechanisms to evaluate the changes in consumers’ willingness to share their personal data over time, and identify the key factors for bringing about these changes. This will enable them to design business models to harness the economic values of personal data in a privacy-preserving manner.
Dr Susan Wakenshaw, WMG, University of Warwick
Professor Irene Ng, WMG, University of Warwick
Dr Joshua Ignatius, WMG, University of Warwick
Dr Lalitha Dhamotharan, Universiti Sains Malaysia
Dr Mike Dixon, Ivey Business School, Western University, Canada
Dr Daqiang Chen, Zhejiang Gongshang University China