Flavius Kehr

Background: Research has so far benefited from the use of pedometers in physical activity interventions. However, when public health institutions (eg, insurance companies) implement pedometer-based interventions in practice, people may refrain from participating due to privacy concerns. This might greatly limit the applicability of such interventions. Financial incentives have been successfully used to influence both health behavior and privacy concerns, and may thus have a beneficial effect on the acceptance of pedometer-based interventions. Objective: This paper presents the design and baseline characteristics of a cluster-randomized controlled trial that seeks to examine the effect of financial incentives on the acceptance of and adherence to a pedometer-based physical activity intervention offered by a health insurance company. Methods: More than 18,000 customers of a large Swiss health insurance company were allocated to a financial incentive, a charitable incentive, or a control group and invited to participate in a health prevention program. Participants used a pedometer to track their daily physical activity over the course of 6 months. A Web-based questionnaire was administered at the beginning and at the end of the intervention and additional data was provided by the insurance company. The primary outcome of the study will be the participation rate, secondary outcomes will be adherence to the prevention program, physical activity, and health status of the participants among others. Results: Baseline characteristics indicate that residence of participants, baseline physical activity, and subjective health should be used as covariates in the statistical analysis of the secondary outcomes of the study. Conclusions: This is the first study in western cultures testing the effectiveness of financial incentives with regard to a pedometer-based health intervention offered by a large health insurer to their customers. Given that the incentives prove to be effective, this study provides the basis for powerful health prevention programs of public health institutions that are easy to implement and can reach large numbers of people in need.

Existing research on information privacy has mostly relied on the privacy calculus model, which views privacy-related decision-making as a rational process where individuals weigh the anticipated risks of disclosing personal data against the potential benefits. In this research, we develop an extension to the privacy calculus model, arguing that the situation-specific assessment of risks and benefits is bounded by (1) pre-existing attitudes or dispositions, such as general privacy concerns or general institutional trust, and (2) limited cognitive resources and heuristic thinking. An experimental study, employing two samples from the USA and Switzerland, examined consumer responses to a new smartphone application that collects driving behavior data and provided converging support for these predictions. Specifically, the results revealed that a situation-specific assessment of risks and benefits fully mediates the effect of dispositional factors on information disclosure. In addition, the results showed that privacy assessment is influenced by momentary affective states, indicating that consumers underestimate the risks of information disclosure when confronted with a user interface that elicits positive affect.

Data captured at different points in time provides the basis for longitudinal research. It is unquestioned that several IS phenomena deal with change over time such as post-adoption behavior with respect to IT artifacts. However, cross-sectional research designs are predominantly applied in the IS field up till now. This paper is therefore written not only to motivate the IS community to apply longitudinal research to time-variant IS phenomena but also to discuss common pitfalls. For this purpose, we outline various longitudinal studies and provide four guidelines that should be considered during their planning. In particular, common methodological issues like space and amount of repeated observations or attrition are discussed. Finally, an overview of common longitudinal research questions and corresponding methods of longitudinal analyses is provided.

Investigating cognitive processes that underlie privacy-related decisions, prior research has primarily adopted a "privacy calculus" view, indicating privacy-related decisions to constitute rational anticipations of risks and benefits connected to data disclosure. Referring to psychological limitations and heuristic thinking, however, recent research has discussed notions of bounded rationality in this context. Adopting this view, the current research argues that privacy decisions are guided by thinking styles, i.e. individual preferences to decide in an either rational or intuitive way. Results of a survey indicated that individuals high in rational thinking, as reflected by a high need for cognition, anticipated and weighed risk and benefits more thoroughly. In contrast, individuals relying on experiential thinking (as reflected by a high faith into intuition) overleaped rational considerations and relied on their hunches rather than a privacy calculus when assessing intentions to disclose information. Theoretical and practical implications of these findings are discussed.

This article puts the St. Gallen Multi-Layered Privacy Interaction Framework (SG-PIF) into action. The SG-PIF aims to conceptualize privacy as an outcome of various aspects of life and society. Different levels of interaction between individuals' privacy behavior, understood as a decision-making process on whether to disclose personal information, and their environment are subject of investigation. Those layers include the personal level, organizations, society at large and the government. Thus the SG-PIF includes not only psychological aspects of privacy but also the economic, social, cultural, and legal factors that affect individual decision-making processes. The focus of the framework rests on the interaction of these layers or the reciprocal nature of them and the analysis of the impact on individual privacy behavior (Section 2). We use two case-studies to demonstrate how the SG-PIF can be applied to structure the analysis of privacy-related phenomena. The first case study treats the privacy-intrusive practice of email tracking (Section 3). The second one looks at the more futuristic developments in robotics and the impact on individual privacy (Section 4).

As a potential explanation to measured inconsistencies between stated privacy concerns and actual disclosing behavior, denoted as the "privacy paradox", scholars have proposed a systematic distinction between situational privacy considerations and pre-existing, superordinate factors that shape the decisive situation without being directly connected to the situation itself. Deploying an experimental approach, we explored the dynamics of two types of such pre-existing factors, namely (1) pre-existing attitudes (such as general privacy concerns and general institutional trust) and (2) pre-existing emotional states (such as an individual's current mood) in shaping situation-specific risk and benefit considerations (i.e., a situational privacy calculus). Compared to a negative emotional state, individuals in a positive emotional state were found to perceive lowered situation-specific privacy risks, even if the sources of this state were unrelated to the decisive situation at hand. Moreover, results indicated that pre-existing attitudes may be partially or even fully overridden by situational risk and benefit considerations. Adopting a differentiated view on privacy decision-making, these findings imply that the privacy paradox could be driven by a gap between pre-existing cognitive and affective factors on the one side, and situation-specific considerations and decisions on the other. Implications for researchers and practitioners are discussed.

Early detection and tailored treatment of job strain is important because it negatively affects the health condition of employees, the performance of organizations, and the overall costs of the health care system likewise. Although there exist several self-report instruments for measuring job strain, one major limitation is the low frequency of measurements and, related to it, high-effort and high-costs associated with each wave of data collection. As a result and significant shortcoming, short-term epi-sodes of high job strain with serious negative outcomes cannot be identified reliably. The current research aims therefore to design, implement and evaluate a Job Strain Information System Service (JSISS) that continuously senses the degree of physiological job strain in knowledge workers solely based on mouse interactions. The following questions guide this research endeavour: (1) Which properties of an employee’s motor activity measured by mouse interactions are significantly related to the degree of physiological job strain? (2) Is physiological job strain related to self-reported psychological job strain? This research adopts the Job Demands-Resource model and the stress theory of van Gemmert and van Galen (1997) and proposes a lab experiment to answer the two research questions and thus, to examine the overall utility of the JSISS.