Table 1 - uploaded by Michael Katell
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Context 1
... were then asked to voice whether or not they consider the exchange of user data for access to free information service (data-for-service) to be "fair." Finally, participants were asked how much, if anything, they would pay to use versions of these services that did not collect information about them for the purposes of tracking, profiling and marketing (see Table 1). ...
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Billions of IoT devices will be deployed in the near future, taking advantage of the faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the blooming of IoT devices, vast quantities of data that may contain private information of users will be generated. The high communication and storage costs, mi...
Citations
... Zhang and Fu (2020) found that men and women manage their privacy similarly, but the link between privacy concerns and privacy protection differs, especially in stressful situations. However, some results reveal a different picture, with men expressing higher concerns for at least some kinds of information (Katell, Mishra, and Scaff 2016) or in interaction with age (Lee et al. 2019). Stark, Stanhaus, and Anthony (2020) demonstrate that women express particular privacy concerns regarding facial recognition software for workplace surveillance, as it facilitates unwanted gaze and resulting sexual harassment by male co-workers and superiors. ...
According to Thelwall's ([2011]. “Privacy and Gender in the Social Web.” In Privacy Online. Perspectives on Privacy and Self-Disclosure in the Social Web, edited by S. Trepte and L. Reinecke, 251–266. Springer) social web gendered privacy model, gender differences in offline privacy risks (i.e. experiences of privacy threat, such as aggressive behaviour or betrayal of a secret) and communication qualities transfer to online contexts, and shape gender differences in online privacy perceptions and behaviours. Using representative data (n = 1,043) from four times of measurement over the course of three years, a structural equation model was set up. I found that people with negative offline privacy experiences at T1 express higher online privacy concerns a year later (T2), and take more actions to protect their online privacy at T3. When adequate privacy protection is established, people disclose more personal information privately (e.g. messenger), but not in public (e.g. status updates) at T4. Females reported more negative offline privacy experiences, offline social support, and offline information disclosure. In contrast to the model’s claims, in an online context, men disclose more personal information both privately and publicly. The results provide evidence for the proposed relations of Thelwall's (2011) model: Offline conditions transfer to online contexts and shape social media users’ privacy perception. However, the findings do not support the idea that women are an especially vulnerable group in online settings.
... Mainly concerns individual privacy awareness, consent, the fair exchange of personal information (further elaboration on this matter, see Reference [19]; Reference [20], and the consequences (mental, emotional, or even physical) for individuals upon the case of the privacy breach and misuse. ...
This paper aims to highlight the developing awareness of concern for digital privacy from business and management viewpoint. The authors compile data privacy literature in management field and visualize the literature into 4 main clusters of concerns. The 4 main cluster of concerns in data privacy discourse on management field are: internet; roles-trust-security; locations; and consumer privacy. This paper contributes on the development of research and discourse in data privacy and management domain. Besides delivering the overviews of the digital privacy concerns in business and management fields, the paper also places suggestions for future researchers.
... Many studies indicate that women tend to be more concerned about online privacy and security issues than men (Adam, 2000;Sieger & Moller, 2012). But when the sites are related to shopping or fashion women-related online behaviors are contradictory with their alleged high privacy concerns (Katell, Mishra, & Scaff, 2016;Nazir et al., 2012). Based on 398 online consumer interviews, Riquelme and Roman found that the influence of both privacy and security on online trust was stronger for younger, more educated, and less extroverted males (Riquelme & Román, 2014). ...
Purpose
In this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users, and ultimately offer a more personalized service for customers who are more likely to respond positively to targeted advertisements.
Design/methodology/approach
We worked with two different data sets to examine whether Twitter users’ gender, inferred from the first name of the account and the profile description, correlates with the privacy setting of the account. We also used a set of features including the inferred gender of Twitter users to develop classifiers that predict user privacy settings.
Findings
We found that the inferred gender of Twitter users correlates with the account’s privacy setting. Specifically, females tend to be more privacy concerned than males. Users whose gender cannot be inferred from their provided first names tend to be more privacy concerned. In addition, our classification performance suggests that inferred gender can be used as an indicator of the user’s privacy preference.
Research limitations
It is known that not all twitter accounts are real user accounts, and social bots tweet as well. A major limitation of our study is the lack of consideration of social bots in the data. In our study, this implies that at least some percentage of the undefined accounts, that is, accounts that had names non-existent in the name dictionary, are social bots. It will be interesting to explore the privacy setting of social bots in the Twitter space.
Practical implications
Companies are investing large amounts of money in business intelligence tools that allow them to know the preferences of their consumers. Due to the large number of consumers around the world, it is very difficult for companies to have direct communication with each customer to anticipate market changes. For this reason, the social network Twitter has gained relevance as one ideal tool for information extraction. On the other hand, users’ privacy preference needs to be considered when companies consider leveraging their publicly available data. This paper suggests that gender recognition of Twitter users, based on Twitter users’ provided first names and their profile descriptions, can be used to infer the users’ privacy preference.
Originality/value
This study explored a new way of inferring Twitter user’s gender, that is, to recognize the user’s gender based on the provided first name and the user’s profile description. The potential of this information for predicting the user’s privacy preference is explored.
... Research shows that users are often concerned about the privacy and security of their data, but some users would still not take any actions for protection (D'Ambrosio et al., 2016). On the other hand, Katell, Mishra, and Scaff (2016) have found that prompting users about permissions on data security and privacy might encourage users to take care of their privacy. Other possibilities to protect data also include biometric security measures when mobile applications are used and, in cases where personal authentications are required (Guerra-Casanova, Sánchez-Ávila, de Santos Sierra, & del Pozo, 2011). ...
The development of smart phones and other smart devices has led to the development of mobile applications, which are in use frequently by the users. It is also anticipated that the number of mobile applications will grow rapidly in the next years. This topic has, therefore, been researched highly in the past years. Mobile applications gather user data and that is why privacy and security in mobile applications is a very important research topic. In this chapter we give an overview of the current research on privacy and security issues of mobile applications.
... Instead, companies are constantly collecting large sets of data about us, saving the data to their own databases and using the data for their own purposes for example for advertising. (Poikola, et al. 2015, Weber 2015, Katell et al. 2016 One way to change the future of personal data ecosystem is to move from organizationcentered model towards human-centered personal data management and towards an ecosystem in which the individual is in the position of being his/her own data controller (Eichelberg et al. 2005, Gnesi et al. 2014, Papadopoulou, et al. 2015. Even if the need for new models is acknowledged (c.f. ...
In this paper, business model is described as a framework of actors, roles and interactions in the ecosystem. The purpose of this paper is to examine what kind of business models there are for platform operators in MyData based service ecosystem, in which individuals are in control over their personal data and can exchange and store the data for their own purposes. We suggest novel business models for platform operators and highlight the role of MyData in value co-creation among the actors as well as illustrate how the ecosystem is shaped from a platform operator's perspective. The method applied is a qualitative case study. We argue that platform operators play a central role in the ecosystem as enablers of value co-creation in the context of MyData as they enable individuals to take control over their data, build trust frameworks, facilitate interaction and offer value-adding services for the network of actors. The paper contributes to the discussion on networked business models and creates new knowledge about human-centered business models for platform operators in personal data management.
The development of smart phones and other smart devices has led to the development of mobile applications, which are in use frequently by the users. It is also anticipated that the number of mobile applications will grow rapidly in the next years. This topic has, therefore, been researched highly in the past years. Mobile applications gather user data and that is why privacy and security in mobile applications is a very important research topic. In this chapter we give an overview of the current research on privacy and security issues of mobile applications.
