UNDERSTANDING USER PERCEPTIONS AND PREFERENCES FOR MASS-MARKET INFORMATION SYSTEMS – LEVERAGING MARKET RESEARCH TECHNIQUES AND EXAMPLES IN PRIVACY-AWARE DESIGN

With cloud and mobile computing, a new category of software products emerges as mass-market information systems (IS) that addresses distributed and heterogeneous end-users. Understanding user requirements and the factors that drive user adoption are crucial for successful design of such systems. IS research has suggested several theories and models to explain user adoption and intentions to use, among them the IS Success Model and the Technology Acceptance Model (TAM). Although these approaches contribute to theoretical understanding of the adoption and use of IS in mass-markets, they are criticized for not being able to drive actionable insights on IS design as they consider the IT artifact as a black-box (i.e., they do not sufficiently address the system internal characteristics). We argue that IS needs to embrace market research techniques to understand and empirically assess user preferences and perceptions in order to integrate the "voice of the customer" in a mass-market scenario. More specifically, conjoint analysis (CA), from market research, can add user preference measurements for designing high-utility IS. CA has gained popularity in IS research, however little guidance is provided for its application in the domain. We aim at supporting the design of mass-market IS by establishing a reliable understanding of consumer’s preferences for multiple factors combing functional, non-functional and economic aspects. The results include a “Framework for Conjoint Analysis Studies in IS” and methodological guidance for applying CA. We apply our findings to the privacy-aware design of mass-market IS and evaluate their implications on user adoption. We contribute to both academia and practice. For academia, we contribute to a more nuanced conceptualization of the IT artifact (i.e., system) through a feature-oriented lens and a preference-based approach. We provide methodological guidelines that support researchers in studying user perceptions and preferences for design variations and extending that to adoption. Moreover, the empirical studies for privacy- aware design contribute to a better understanding of the domain specific applications of CA for IS design and evaluation with a nuanced assessment of user preferences for privacy-preserving features. For practice, we propose guidelines for integrating the voice of the customer for successful IS design. -- Les technologies cloud et mobiles ont fait émerger une nouvelle catégorie de produits informatiques qui s’adressent à des utilisateurs hétérogènes par le biais de systèmes d'information (SI) distribués. Les termes “SI de masse” sont employés pour désigner ces nouveaux systèmes. Une conception réussie de ceux-ci passe par une phase essentielle de compréhension des besoins et des facteurs d'adoption des utilisateurs. Pour ce faire, la recherche en SI suggère plusieurs théories et modèles tels que le “IS Success Model” et le “Technology Acceptance Model”. Bien que ces approches contribuent à la compréhension théorique de l'adoption et de l'utilisation des SI de masse, elles sont critiquées pour ne pas être en mesure de fournir des informations exploitables sur la conception de SI car elles considèrent l'artefact informatique comme une boîte noire. En d’autres termes, ces approches ne traitent pas suffisamment des caractéristiques internes du système. Nous soutenons que la recherche en SI doit adopter des techniques d'étude de marché afin de mieux intégrer les exigences du client (“Voice of Customer”) dans un scénario de marché de masse. Plus précisément, l'analyse conjointe (AC), issue de la recherche sur les consommateurs, peut contribuer au développement de système SI à forte valeur d'usage. Si l’AC a gagné en popularité au sein de la recherche en SI, des recommandations quant à son utilisation dans ce domaine restent rares. Nous entendons soutenir la conception de SI de masse en facilitant une identification fiable des préférences des consommateurs sur de multiples facteurs combinant des aspects fonctionnels, non-fonctionnels et économiques. Les résultats comprennent un “Cadre de référence pour les études d'analyse conjointe en SI” et des recommandations méthodologiques pour l'application de l’AC. Nous avons utilisé ces contributions pour concevoir un SI de masse particulièrement sensible au respect de la vie privée des utilisateurs et nous avons évalué l’impact de nos recherches sur l'adoption de ce système par ses utilisateurs. Ainsi, notre travail contribue tant à la théorie qu’à la pratique des SI. Pour le monde universitaire, nous contribuons en proposant une conceptualisation plus nuancée de l'artefact informatique (c'est-à-dire du système) à travers le prisme des fonctionnalités et par une approche basée sur les préférences utilisateurs. Par ailleurs, les chercheurs peuvent également s'appuyer sur nos directives méthodologiques pour étudier les perceptions et les préférences des utilisateurs pour différentes variations de conception et étendre cela à l'adoption. De plus, nos études empiriques sur la conception d’un SI de masse sensible au respect de la vie privée des utilisateurs contribuent à une meilleure compréhension de l’application des techniques CA dans ce domaine spécifique. Nos études incluent notamment une évaluation nuancée des préférences des utilisateurs sur des fonctionnalités de protection de la vie privée. Pour les praticiens, nous proposons des lignes directrices qui permettent d’intégrer les exigences des clients afin de concevoir un SI réussi

Exploring Information Disclosure in Location-based Services: U.S. vs. German Populations

Location-based services (LBSs) have enabled users to obtain context-specific and personalized services owing to advances in mobile technologies and location analytics. Since location data are classified as personally identifiable, the sharing of locations on LBSs has privacy implications. We employ a privacy calculus lens to study users’ attitudes toward location-information sharing. We explore the role of cultural and institutional environments in users’ disclosure behaviors in two populations: the U.S. and Germany. Our results show similarities between the two samples, despite differences in cultural backgrounds and regulations. Contextualization is a highly valued benefit for LBS users, while monetary rewards are not yet foreseen as potential benefits. Location-information disclosure is not uniform; it varies depending on the sharing parties and the information extent or sensitivity. LBS users have high privacy risk perceptions and low trust in service providers and government regulations to protect their privacy and location-information from misuse

Beyond Panoptic Surveillance: On the Ethical Dilemmas of the Connected Workplace

Technological advances such as the Internet-of-Things, big data, and artificial intelligence have enabled new ways of managerial oversight moving away from panoptic surveillance to what we call “connected surveillance”. The COVID-19 pandemic has accelerated the adoption of connected surveillance which purpose is not only scruitizing employees’ work performance but also health, personal beliefs, and other private matters. With the implementation of connected workplaces, therefore, various ethical dilemmas arise. We highlight four emerging dilemmas, namely: (1) the good of the individual versus the good of the community, (2) ownership versus information disclosure, (3) justice versus mercy, and (4) truth versus loyalty. We discuss those ethical dilemmas for the case of corporate wellness programs which is frequently used as guise to introduce connected surveillance. Following a socio-technical perspective, we discuss ethical responses that focus on people involvement and technology assessment. We also highlight practical responses that can aim at mitigating the dilemmas

Beyond panoptic surveillance: On the ethical dilemmas of the connected workplace

Technological advances such as the Internet-of-Things, big data, and artificial intelligence have enabled new ways of managerial oversight moving away from panoptic surveillance to what we call “connected surveillance”. The COVID-19 pandemic has accelerated the adoption of connected surveillance, which purpose is not only scrutinizing employees’ work performance, but also health, personal beliefs, and other private matters. With the implementation of connected workplaces, therefore, various ethical dilemmas arise. We highlight four emerging dilemmas, namely: (1) the good of the individual versus the good of the community, (2) ownership versus information disclosure, (3) justice versus mercy, and (4) truth versus loyalty. We discuss those ethical dilemmas for the case of corporate wellness programs which is frequently being used as guise to introduce connected surveillance. Following a socio-technical perspective, we discuss ethical responses that focus on people involvement and technology assessment. We highlight practical responses that can aim at mitigating the dilemmas

Leveraging Market Research Techniques in IS – A Review of Conjoint Analysis in IS Research

With the increasing importance of mass-market information systems (IS), understanding individual user preferences for IS design and adoption is essential. However, this has been a challenging task due to the complexity of balancing functional, non-functional, and economic requirements. Conjoint analysis (CA), a marketing research technique, estimates user preferences by measuring tradeoffs between products attributes. Although the number of studies applying CA in IS has increased in the past years, we still lack fundamental discussion on its use in our discipline. We review the existing CA studies in IS with regard to the application areas and methodological choices along the CA procedure. Based on this review, we develop a reference framework for application areas in IS that serves as foundation for future studies. We argue that CA can be leveraged in requirements management, business model design, and systems evaluation. As future research opportunities, we see domain-specific adaptations e.g., user preference models

Leveraging Market Research Techniques in IS: A Review and Framework of Conjoint Analysis Studies in the IS Discipline

With cloud and mobile computing, information systems (IS) have evolved towards mass-market services. While IS success requires user involvement, the IS discipline lacks methods that allow organizations to integrate the “voice of the customer” into mass-market services with individual and dispersed users. Conjoint analysis (CA), from marketing research, provides insights into user preferences and measures user trade-offs for multiple product features simultaneously. While CA has gained popularity in the IS domain, existing studies have mostly been one-time efforts, which has resulted in little knowledge accumulation about CA’s applications in IS. We argue that CA could have a significant impact on IS research (and practice) if this method was further developed and adopted for IS application areas. From reviewing 70 CA studies published between 1999 and 2019 in the IS discipline, we found that CA supports in initially conceptualizing, iteratively designing, and evaluating 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” researchers and practitioners can apply CA along the IS lifecycle

Privacy Vs. Health: The Role of Privacy Trade-offs in the Adoption of Public Health Applications

Innovations in healthcare technology aim to address challenges in health outcomes. COVID-19 contact tracing apps were introduced in various countries as an innovative health solution to stop the spread of the virus. Although aiming for the greater good of the population through maintaining public health, the introduction of these apps was accompanied by controversial debates about their privacy implications leading to low adoption worldwide. In fact, digital contact tracing raised privacy concerns associated to information sharing within the app, which impacts intention to use. However, in the question of health versus privacy several factors come into consideration. We apply a privacy calculus approach to study users’ intention to use COVID-19 apps under privacy trade-offs. Based on representative samples from Germany and Switzerland, we find that individual safety outweighs societal safety benefit, trust is crucial for mitigating risk perceptions, and social norm has a great impact on individual’s intentions to use

A Privacy Impact Assessment Method for Organizations Implementing IoT for Occupational Health and Safety

Internet of Things (IoT) technologies are increasingly being integrated into occupational health and safety (OHS) practices; however, their adoption raises significant privacy concerns. The General Data Protection Regulation (GDPR) has established the requirement for organizations to conduct Privacy Impact Assessments (PIAs) prior to processing personal data, emphasizing the need for privacy safeguards in the workplace. Despite this, the GDPR provisions related to the IoT, particularly in the area of OHS, lack clarity and specificity. This research aims to bridge this gap by proposing a tailored method for conducting PIAs in the OHS context, with a particular focus on addressing the how to aspect of the assessment process. The proposed method integrates insights from domain experts, relevant literature sources, and GDPR regulations, ultimately leading to the development of an online PIA tool

USER-ORIENTED CLOUD SERVICE DESIGN BASED ON MARKET RESEARCH TECHNIQUES

For the IT industry, cloud computing has a disruptive effect, since it fundamentally changes how IT resources are produced, distributed, consumed, and priced. Designing cloud services remains a challenge, as the markets are very dynamic and cloud users are heterogeneous, locally distributed and not within the reach of the organization. This research-in-progress paper suggests the use of market research techniques, namely conjoint analysis, in the requirements elicitation process for cloud services. The contribution is a method component that extends existing requirements engineering methods. It supports cloud service providers in addressing specific questions of cloud service design: to analyse user preferences and the many trade-offs between different functional, non-functional and economic properties, to identify customer segments and develop tailored offerings, to analyse willingness-to-pay for specific features and to simulate market reactions of new designs

The New Normal in the Post-pandemic Workplace? A Meta-Analysis on the Use Cases and Implementation Challenges of Internet-of-Things Technology in Office Settings

While governmental tracing apps received special attention by research and the media during the Covid-19 pandemic, the surge in new work surveillance technologies went almost unnoticed. New organizational infrastructures based on Internet-of-things (IoT) technology have emerged at both, public and private sector organizations, promising a safe return to the workplace but equally threatening the privacy of employees. The goal of this paper is to take a closer look at a technology with ambivalent use by conducting a meta-synthesis of extant IoT studies. We classify the literature into four use cases with their implementation options: physical health monitoring, mental health monitoring, environmental health monitoring, and connected workplace. We also discuss main challenges emerging from privacy concerns along the IoT data lifecycle for occupational health initiatives in the office context. Based on that, we propose normative guidelines to assist employers interested in implementing privacy preserving IoT solu-tions for health and safety at work