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With cloud and mobile computing, information systems (IS) evolve towards mass-market services. While user involvement is critical for IS success, the IS discipline lacks methods that allow integrating the "voice of the customer" in the case of mass-market services with individual and dispersed users. Conjoint analysis (CA), from marketing research, allows for understanding user preferences and measures user trade-offs for multiple product features simultaneously. While CA has gained popularity in the IS domain, the existing studies have mostly been one-time efforts and no cumulative research patterns have been observed. We argue that CA could have a significant impact on IS research (and practice) if it were fully developed and adopted as a method in IS. From reviewing 70 CA studies published between 1999 and 2019 in the IS field, we find that CA can be leveraged in the initial conceptualization, iterative design and evaluation of IS and their business models. We critically assess the methodological choices along the CA procedure to provide recommendations and guidance on "how" to leverage CA techniques in future IS research. We then synthesize our findings into a "Framework for Conjoint Analysis Studies in IS" that outlines "where" CA can be applied along the IS lifecycle.

Contact tracing apps were considered among the first tools to control the spread of COVID-19 and ease lockdown measures. While these apps can be very effective at stopping transmission and saving lives, the level of adoption remains significantly below the expected critical mass. The public debate as well as academic research about contact tracing apps emphasizes general concerns about privacy (and the associated risks) but often disregards the value-added services, as well as benefits, that can result from a larger user base. To address this gap, the study analyzes goal-congruent features as drivers for user adoption. It uses market research techniques – specifically, conjoint analysis – to study individual and group preferences and gain insights into the prescriptive design. While the results confirm the privacy-preserving design of most European contact tracing apps, they emphasize the role of value-added services in addressing heterogeneous user segments to drive user adoption. The findings thereby are of relevance for designing effective contact tracing apps, but also inform the user-oriented design of apps for health and crisis management that rely on sharing sensitive information.

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The fast-paced business environment and new work arrangements have elevated mental health risks, especially occupational stress and burnouts. Mental health becomes a critical aspect for occupational safety and health. Therefore, employers aim to improve employees' well-being and safety at the workplace through dedicated health initiatives. Internet-of-things (IoT) technology is increasingly being used for such purposes. However, its implementation in the workplace is accompanied by privacy concerns related to sensitive data collection. In this study, we provide a meta-synthesis of existing IoT solutions for supporting employees' mental health in office settings. We classify existing studies into use cases with possible implementation options. We also discuss main challenges emerging from privacy concerns along the IoT data lifecycle. We emphasize the opportunity for the connected workplace to improve occupational health in the future workplace,