We investigate legal concerns in privacy calculus, which are currently not given enough attention in privacy research. Legal aspects can lead to liability issues in various information systems scenarios such as bring your own device (BYOD) in the workplace. To analyze the impact of legal concerns in privacy calculus, we conducted a quantitative study by surveying 542 employees from three countries: United States, Germany, and South Korea. Building on our research model to test our hypothesized relationships, structural equation modeling was employed. Our findings provide recommendations for multinational organizations to mitigate legal concerns in privacy calculus. A comparison of the three countries reveals that employees from the United States and South Korea place greater emphasis on legal concerns compared to German employees. We develop an understanding of employees’ concerns with liability issues, and how these affect their privacy calculus in a BYOD context.

Without an integrated model of how the human brain works and processes information, artificial intelligence (AI) will remain a mysterious black box that can misfire as circumstances change. An integrated study of the three cognitive computing components (AI, cognitive psychology, and neurobiology) is necessary to create explainable AI findings. This paper introduces cognitive computing systems (CCS) as a domain for information systems (IS) research. It reviews the interdisciplinary implications of CCS concepts by developing a new computational method, knowledge similarity transformation (KST), to improve digital-augmented literature analysis in fragmented knowledge areas. Based on the dual CCS and KST contribution, this article outlines strategic implications for organizational value creation opportunities and future research directions from a technological, psychological, and physiological perspective.

Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive process and visualization framework to enable value creating collaboration in inter- and cross-disciplinary teams. Domain experts and data analysts are both empowered to analyze and discuss results and come to well-founded insights and implications. Inspired by a typical auditing problem, we develop and apply a visualization framework to single out unusual data in general subsets for potential further investigation. Our framework is applicable to both unusual data detected manually by domain experts or by algorithms applied by data analysts. Application examples show typical interaction, collaboration, visualization, and decision support.

Clustering for taxonomy-based archetype identification has become an established method in Information Systems (IS) research, aiding strategic decision-making across diverse research and business domains. However, the effectiveness of the approach depends critically on the compatibility of clustering methods and algorithms with the specific data characteristics. This study, based on a comprehensive review of 87 articles employing taxonomy-based clustering in IS research, reveals a notable mismatch between the chosen clustering algorithms and the nature of the data, particularly in the context of archetype development from taxonomy-based data. To address these methodological inconsistencies, we introduce a set of clustering guidelines tailored to the unique requirements of archetype development from taxonomy-based data. These guidelines are informed by a computational study involving seven identified datasets from the taxonomy-building literature, ensuring their practical applicability and scientific relevance. Our guidelines are designed to enhance the robustness and scientific validity of insights and decisions derived from taxonomy-based clustering. By improving the methodological rigor of clustering methods, our research addresses a critical mismatch in current practices and contributes to enhancing the quality of decision-making informed by taxonomy-based analysis in IS research. • Identification of key shortcomings in clustering methods across 87 taxonomy studies. • Appropriate clustering methods show improvement for archetype development. • Tailored clustering guidelines for taxonomy data to support informed decision-making.

Many organizations are pursuing the implementation of chatbots to enable automation of service processes. However, previous research has highlighted the existence of practical setbacks in the implementation of chatbots in corporate environments. To gain practical insights on the issues related to the implementation processes from several perspectives and stages of deployment, we conducted semi-structured interviews with developers and experts of chatbot development. Using qualitative content analysis and based on a review of literature on human computer interaction (HCI), information systems (IS), and chatbots, we present an implementation framework that supports the successful deployment of chatbots and discuss the implementation of chatbots through a user-oriented lens. The proposed framework contains 101 guiding questions to support chatbot implementation in an eight-step process. The questions are structured according to the people, activity, context, and technology (PACT) framework. The adapted PACT framework is evaluated through expert interviews and a focus group discussion (FGD) and is further applied in a case study. The framework can be seen as a bridge between science and practice that serves as a notional structure for practitioners to introduce a chatbot in a structured and user-oriented manner.

Compliance management is a challenging task affected by continuously increasing legal requirements. Compliance with legal requirements can be assured by the incorporation of control activities into business processes. But the maintenance and monitoring of these control activities is a complex, time-consuming and often manual task. However, the timely communication of control exceptions is an important factor for the success of compliance management. The present paper presents an innovative prototypical implementation of an automated compliance monitoring and reporting system. This system is based on established standards and existing technologies. In particular, business processes are notated in BPMN and modeled in XPDL, control activities are linked to risks using COSO, control exceptions are defined using SWRL and access control data is transformed from proprietary models to XACML. The development of the prototype was aligned with common design-science research. The application of the developed prototype and its economic implications are concisely discussed with respect to different business requirements and information needs.