Privacy protection, control of information, and privacy-enhancing technologies

Tavani, Herman T.; Moor, James H.

doi:10.1145/572277.572278

Cited by 163 publications

(112 citation statements)

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“…Privacy represents a preferred state in which an individual is protected from intrusion, interference, and information access by unwanted others (Tavani, 2007;Tavani & Moor, 2001). Privacy is both a state of being and a value to be desired (Smith et al, 2011).…”

Section: Privacy Protectionmentioning

confidence: 99%

Integrating the Expanded Task-technology Fit Theory and the Technology Acceptance Model: A Multi-wave Empirical Analysis

Drake¹,

Hall²,

Becton³

et al. 2023

THCI

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Abstract:For human resource (HR) departments, screening job applicants is an integral role in acquiring talent. Many HR departments have begun to turn to social networks to better understand job candidates' character. Using social networks as a screening tool might provide insights not readily available from resumes or initial interviews. However, requiring access to an applicants' social networks and the private activities occurring therein-a practice currently legal in 29 U.S. states (Deschenaux, 2015)-could induce strong moral reactions from the job candidates because of a perceived loss of information privacy. Subsequently, such disclosure requests could induce job candidates to respond in a multitude of ways to protect their privacy. Given that an estimated 2.55 billion individuals will use social media worldwide by 2017 (eMarketer, 2013), the repercussions from requests for access social media environments have potentially far-reaching effects. In this research, we examine how one such disclosure request impacted six information privacy protective responses (IPPRs) (Son & Kim, 2008) based on the job candidates' perceived moral judgment and the perceived moral intensity of the HR disclosure request. These responses occurred when we asked respondents to provide personal login information during a hypothetical interview. By modeling data derived from a sample of 250 participants in PLS-SEM, we found that the five IPPRs (i.e., refusal, negative word of mouth, complaining to friends, complaining to the company, and complaining to third parties) were all significant responses when one judged the request to be immoral and perceived the moral intensity concept of immediate harm. The amount of variance explained by these five IPPRs ranged from 17.7 percent to 38.7 percent, which indicates a solid initial foundation from which future research can expand on this HR issue. Implications for academia and practice are discussed.

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Section: Privacy Protectionmentioning

confidence: 99%

Integrating the Expanded Task-technology Fit Theory and the Technology Acceptance Model: A Multi-wave Empirical Analysis

Drake¹,

Hall²,

Becton³

et al. 2023

THCI

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“…P2, Students as agents, encourages the view that students are collaborators in learning analytics and should be involved in decisions regarding use of their data. This is similar to the recognition of Actors (Information subjects) and Transmission Principles (Nissenbaum, 2010) and management of privacy expressed through choice, consent, and correction (Tavani & Moor, 2001). P3, Student identity and performance are temporal dynamic constructs, and P5, Transparency, speaks to the management aspects of privacy.…”

Section: Future Challengesmentioning

confidence: 67%

“…Contemporary privacy theories proposed by Nissenbaum (2010), Tavani (H. Tavani, 2007;Tavani & Moor, 2001;H.T. Tavani, 2007) and Moor (J. Moor, 2005; J.H.…”

Section: Contemporary Privacy Theories and Application To Learning Anmentioning

confidence: 99%

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Contemporary Privacy Theory Contributions to Learning Analytics

Heath

2014

Learning Analytics

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ABSTRACT:With the continued adoption of learning analytics in higher education institutions, vast volumes of data are generated and "big data" related issues, including privacy, emerge. Privacy is an ill-defined concept and subject to various interpretations and perspectives, including those of philosophers, lawyers, and information systems specialists. This paper provides an overview of privacy and considers the potential contribution contemporary privacy theories can make to learning analytics. Conclusions reflect on the suitability of these theories towards the advancement of learning analytics and future research considers the importance of hearing the student voice in this space.

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“…A number of overviews of the PET landscape have already been published (Blarkom, Borking, & Verhaar, 2003;Burkert, 1997;Camp & Osorio, 2003;Goldberg, 2002;Senicar, Jerman-Blazic, & Klobucar, 2003;Tavani & Moor, 2001). However, most of these studies fail to delineate what sorts of privacy the described technologies enhance, which makes it difficult to differentiate between the various PETs.…”

Section: Introductionmentioning

confidence: 99%