Recent advances in Big Data and Internet of Things enable combinations of previously distinct information sources and the discovery of previously hidden patterns, correlations and other revealing information. Increasingly, aspects of our everyday lives are digitally observed and stored. The resulting data trove contains a wealth of revealing bits of information that, when patched together, may present a startlingly personal, complete picture of each of us – our health, sexual or religious preferences, financial circumstances and the identities of our family and friends, to name a few. On the one hand, the advent of Big Data and Internet of Things offers unprecedented opportunities for data-driven discovery and decision-making. On the other hand, such benefits come with undeniable privacy risks. Indeed, ‘privacy is dead’ shockwaves flood the headlines of newspapers. Even Science published a special issue on the end of privacy, and the editors introduced that issue with a pessimistic view (Enserink & Chin, 2015, p. 491): ‘Privacy as we have known it is ending, and we're only beginning to fathom the consequences.’ In the USA, reports by the White House and the President's Council of Advisors on Science and Technology (PCAST) on big data and privacy call for serious increases in investments for encouraging multi-disciplinary privacy research (White House, 2014a; 2014b). The White House and PCAST reports cite challenges to personal privacy in the digital era as a significant impairment that is undermining societal benefits from large-scale deployments of networking and IT systems (White House, 2014a; 2014b). These reports recommend adoption of a ‘responsible use framework’ that would provide greater focus on the use of data and would hold entities who utilize data accountable for responsible use of the data (White House, 2014a; 2014b). With really big data come really big responsibilities for ensuring appropriate privacy guarantees and ethical use of personal data. We believe that Information Systems (IS) researchers can contribute substantially towards the goal of developing a ‘responsible use framework’ in the Big Data era by encouraging privacy from the point of data acquisition through the point of data use, dissemination, maintenance and disposition. IS scholars are uniquely positioned at the intersection of technology and people. Some IS researchers have already taken actions and formed a scientific community of information privacy researchers. This community's passion and interest regarding information privacy research have been heightened by a number of high-quality literature review articles published by top journals such as MIS Quarterly and Science (e.g. Bélanger & Crossler, 2011; Smith et al., 2011; Acquisti et al., 2015). These literature reviews weave various threads of information privacy research into cohesive frameworks. These articles have developed several roadmaps for guiding future information privacy research, and this editorial, therefore, does not intend to repeat the suggestions made by these review articles. Instead, we seek to highlight some unique opportunities for the IS research community to shape an agenda for the future of information privacy research. In what follows, we first provide an overview of three privacy articles published in this special issue. Then, we discuss several unique opportunities for IS researchers. The articles in this special issue offer an early effort to start addressing some of the challenges IS researchers are facing in studying information privacy in our constantly changing technological environment. Two of the papers study the context of mobile computing with individuals as the primary unit of analysis, while the third paper provides an analysis of information privacy at the organizational level of analysis. All of the papers explore information privacy from different theoretical perspectives, and more importantly, one builds a new theoretical model, while the other two provide extension to current theoretical foundations. Taken together, these papers can offer readers an overview of possible theoretical bases for future information privacy research. In ‘Company Information Privacy Orientation: A Conceptual Framework’, (Greenaway et al., 2015) apply the control theory and the justice theory to develop an organization-level framework that is composed of a company's ethical and legal obligations to its customers, its privacy management strategy and its privacy risk assessment to protect customer information privacy. The conceptual framework of Company Information Privacy Orientation is developed to provide the organizational equivalent of a ‘privacy calculus’ by differentiating the extent to which control and justice influence a company's information privacy behaviours. The authors label these orientations as Privacy Ignorers, Minimisers, Balancers and Differentiators. In ‘Blissfully Ignorant: The Effects of General Privacy Concerns, General Institutional Trust, and Affect in the Privacy Calculus’, (Kehr et al., 2015) extend the privacy calculus model by considering not only the context relevant to the privacy decision-making process of individuals but also the limited cognitive resources and heuristic thinking of individuals when making this decision. More specifically, the authors consider affect and information sensitivity in the decision-making process, contextualizing these variables for the mobile environment. Using participants in two different countries with two different languages, the authors are able to validate their model in different cultures. In addition to results consistent with the traditional privacy calculus, they find that affect and information sensitivity do indeed influence privacy calculus variables. In ‘The Role of Mobile-Computing Self-Efficacy in Consumer Information Disclosure’, (Keith et al., 2015) draw from the social cognitive theory and privacy calculus model to examine mobile users' risk perceptions of disclosing information and their trust beliefs towards mobile app providers via two experimental studies. Regardless of the actual trustworthiness of the mobile app provider, there is a significant direct effect of self-efficacy on users' initial trust beliefs in mobile app providers as well as on their risk perceptions of disclosing information. Results of this work suggest that being skilled in smartphones and mobile apps can lead mobile consumers to place greater levels of trust in mobile app providers and perceive lower levels of privacy risk towards mobile apps. In the privacy policymaking and strategy-making context, the scholarly influence has been seemingly led by the disciplines of computer science, social science and law. The IS community's voice is not strongly heard by policymakers or the general public, which may bring forward the old question about the practical relevance of IS research (Dinev, 2014). Positivist approaches have dominated in the IS field where theory-based work has defined, predicted and explained privacy-related constructs in various nomological models. Such a dominating approach has been reflected in the vast majority of information privacy research in the IS field. As a result, this leads to less design science research that aims to address privacy by design, as well as data-driven approaches such as data mining or ethnographic studies. On the positive side, the theory-based work has helped us build stronger theoretical foundation and methodological rigour. On the negative side, some of us are increasingly raising doubts about whether we can relate what we have found in our research to what practitioners or policymakers truly experience in reality. We urge IS scholars to constantly think about a persuasive ‘so what’ story about our findings and constantly ask how well our scholarship addresses practical information privacy challenges. We need to make sure that our theories, empirics and knowledge transitions are capable of justifying our research investments in the types of scientific knowledge and practical insights we produce. For that to happen, we suggest that future information privacy scholars consider either adopting the design science approach, pursuing rich data-driven inquiry, measuring real information privacy harms, conducting qualitative interpretive research or developing bold theories about relationships between information privacy antecedents and outcomes. We are grateful to Eileen Trauth for the opportunity to edit this special issue and for her helpful guidance. This special issue would not have been possible without the tremendous help of the members of the special issue editorial review board and reviewers: Alessandro Acquisti, Maged Ali, Jeffrey Babb, Louise Barkhuus, Christian Bonnici, Burcu Bulgurcu, Hasan Cavusoglu, Robert E. Crossler, Mary J. Culnan, Tamara Dinev, Christopher Furner, Kai-Lung Hui, Tabitha James, Flavius Kehr, Mark Keith, Patrick Kelley, Bart Knijnenburg, Hanna Krasnova, Airi Lampinen, Gwanhoo Lee, Han Li, Paul Lowry, Xin ‘Robert’ Luo, George Milne, Caroline Miltgen, Milica Milutinovic, Sameer Patil, Chee Wei Phang, Clay Posey, H. Raghav Rao, Tom Roberts, H. Jeff Smith, Janine Spears, Robert St-Louis, Shirish C. Srivastava, Fred Stutzman, Juliana Sutanto, Chuan-Hoo Tan, Weiquan Wang, Pamela Wisniewski and Jie Zhang. Heng Xu is supported by the US National Science Foundation. Any opinion, findings and conclusions or recommendations expressed in this material are those of the authors and editors and do not necessarily reflect the views of the US National Science Foundation.
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Bélanger et al. (2015) studied this question.
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