Business & Information Systems Engineering最新文献

Especially older persons are prone to disabilities and chronic diseases. These chronic conditions pose a worldwide challenge, leading to deteriorating health, economic strain, loss of life, and a decline in the quality of life (QoL). Therefore, healthcare institutions seek to enhance their strategies for disease prevention and management to uphold the well-being of the community. This leads to the need to regain independence and improve QoL to properly rehabilitate the patients. Virtual Coaches (VCs) in the form of Embodied Conversational Agents are seen as a relevant digital intervention to support the continuity of care. The paper at hand reports on a Design Science Research project about implementing a VC solution to support older patients' home rehabilitation. The study underpins four pivotal design principles: Adaptivity, Coaching Strategy, Multi-user Interface, and Sustainable Infrastructure. The final artifact was tested with 80 patients which were supported in continuing their inpatient rehabilitation at home by using a VC. The evaluation shows both positive results for usability and acceptance of the intervention for four different use cases and a positive impact on the QoL. Given the comprehensive clinical evaluation, the system represents a safe and appealing solution for ensuring the continuity of medical rehabilitation care and the access to personalized cognitive and motor function treatments.

Artificial intelligence (AI) has significantly advanced healthcare and created unprecedented opportunities to enhance patient-centeredness and empowerment. This progress promotes individualized medicine, where treatment and care are tailored to each patient’s unique needs and characteristics. The Theory of Tailorable Technology Design has considerable potential to contribute to individualized medicine as it focuses on information systems (IS) that users can modify and redesign in the context of use. While the theory accounts for both the designer and user perspectives in the lifecycle of an IS, it does not reflect the inductive learning and autonomy of AI throughout the tailoring process. Therefore, this study posits the conjecture that current knowledge about tailorable technology design does not effectively account for IS that incorporate AI. To investigate this conjecture and challenge the Theory of Tailorable Technology Design, a revelatory design study of an AI-enabled individual IS in the domain of bladder monitoring is conducted. Based on the empirical evidence from the design study, the primary contribution of this work lies in three propositions for the design of tailorable technology, culminating in a Revised Theory of Tailorable Technology Design. As the outcome of the design study, the secondary contribution of this work is concrete design knowledge for AI-enabled individualized bladder monitoring systems that empower patients with neurogenic lower urinary tract dysfunction (NLUTD). Overall, this study highlights the value of AI for patient-centeredness in IS design.

Accounting for individual and situational heterogeneity (i.e., precision) is now an important area of research and treatment in the field of medicine. This essay argues that precision should also be embraced within digital health artifacts, such as by designing digital health apps to tailor recommendations to individual user characteristics, needs, and situations, rather than only providing generic advice. The challenge, however, is that not much guidance is available for embracing precision when designing or researching digital health artifacts. The paper suggests that a shift toward precision in digital health will require embracing heterogeneous treatment effects (HTEs), which are variations in the effectiveness of treatment, such as variations in effects for individuals of different ages. Embracing precision via HTEs is not trivial, however, and will require new approaches to the research and design of digital health artifacts. Thus, this essay seeks to not only define precision digital health, but also to offer suggestions as to where and how machine learning, deep learning, and artificial intelligence can be used to enhance the precision of interventions provisioned via digital health artifacts (e.g., personalized advice from mental health wellbeing apps). The study emphasizes the value of applying emerging causal ML methods and generative AI features within digital health artifacts toward the goal of increasing the effectiveness of digitially provisioned interventions.

At present, businesses can reap impressive benefits from the Internet of Things (IoT). However, with the increasing number of IoT devices and the complexity of the IoT ecosystem, there is a growing concern about security vulnerabilities. This study aims to address the network security investment problem in an IoT environment by developing a game-theoretical model. It examines the impact of IoT service level and customer characteristics on the incentives for both the IoT platform and the manufacturer to invest in security, as well as the platform's profitability. Through analytical analysis, several noteworthy findings are obtained. Firstly, it is found that a higher IoT platform service level corresponds to a higher security responsibility. As a result, the platform needs to carefully consider the costs and benefits associated with security investment and service provision. Additionally, the research demonstrates that both the platform and the manufacturer's efforts to enhance security do not diminish, even when faced with increasing customer losses due to security breaches. This is because the IoT platform indirectly benefits a larger number of consumers who purchase smart devices, thus motivating the platform to prioritize network security efforts. Furthermore, the study reveals the influence of the unit security cost and the amount of highly sensitive customers on the security efforts undertaken by both the IoT platform and the smart device manufacturer. These results have important practical implications for firms operating within an IoT-based supply chain. Specifically, the findings can provide valuable decision-making guidance for enterprises seeking digital transformation and trying to make informed choices regarding platform operations.

In the last years, the increasing scientific and industrial interest in Business Process Management (BPM) approaches and methods on the one side, and Internet-of-Things (IoT) technologies and tools on the other. Are these fields complementary? What are their respective interplays and the research challenges to their realizations? The article presents a Systematic Literature Review (SLR) to gain in-depth insights into the maturity of existing approaches to IoT-aware BPM. The analysis of the retrieved studies, framed along the research questions addressed in the SLR, enables us to systematically evaluate the literature on IoT-aware BPM concerning the phases of the process life cycle covered by the different approaches, the specific topics addressed, the application domains involved, and the possibility to tackle the research challenges. Future research directions are also highlighted.

Process mining—a suite of techniques for extracting insights from event logs of Information Systems (IS)—is increasingly being used by a wide range of organisations to improve operational efficiency. Despite extensive studies of Critical Success Factors (CSFs) in related domains, CSF studies tailored to process mining are limited. Moreover, these studies merely identify factors and do not provide essential details such as a clear conceptual understanding of success factors and their interrelationships. Through a multi-phased approach (applying published process mining case studies, conducting two in-depth case studies and expert interviews), this paper presents an empirically validated process mining CSF model and CSF interrelationships. This validated CSF model identifies ten process mining CSFs, explains how these factors relate to the process mining context and analyses their interrelationships with regard to process mining success. The findings provide a guide for organisations to invest in the right mix of CSFs for value realisation in process mining practice.

Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm that emphasizes the importance of enhancing data systematically and at scale to build effective and efficient AI-based systems. The novel paradigm complements recent model-centric AI, which focuses on improving the performance of AI-based systems based on changes in the model using a fixed set of data. The objective of this article is to introduce practitioners and researchers from the field of Business and Information Systems Engineering (BISE) to data-centric AI. The paper defines relevant terms, provides key characteristics to contrast the paradigm of data-centric AI with the model-centric one, and introduces a framework to illustrate the different dimensions of data-centric AI. In addition, an overview of available tools for data-centric AI is presented and this novel paradigm is differenciated from related concepts. Finally, the paper discusses the longer-term implications of data-centric AI for the BISE community.