Information Systems in the Era of the Internet of Things: A Domain-Specific Modelling Language

Based on the technological developments of the last few years and the associated digitalisation, the Internet of Things is now an essential part of practically every aspect of life. Smart products with their sensors and actuators are not only boundary objects between the physical and digital world, but also tie both worlds more and more together. The capabilities of the products range from simple monitoring tasks by drones in agriculture to autonomous use in mining. However, in order to exploit the full potential of these capabilities, it is not enough to use individual smart products or sensors, it is necessary to develop holistic IoT-based information systems from the ground up. To take such a systemic perspective, it is essential to have a sound conceptual understanding of the domain and a common language. Against this background, the publication presents a domain-specific modelling language for IoT- based information systems

Show me the Money: How to monetize data in data-driven business models?

Increasing digitization and the associated tremendous usage of technology have led to data of unprecedented quantity, variety, and speed, which is generated, processed, and required in almost all areas of industry and life. The value creation and capturing from data presents companies with numerous challenges, as they must create or adapt appropriate structures and processes. As a link between corporate strategy and business processes, business models are a suitable instrument for meeting these challenges. However, few research has been conducted focusing on data-based monetization in the context of data-driven business models so far. Based on a systematic literature review the paper identifies five key components and 23 characteristics of data-driven business models having crucial influence on data-based value creation and value capturing and thus on monetization. The components represent key factors for achieving commercial benefits from data and serve as guidance for exploring and designing suitable data-driven business models

Four Shades of Customer: How Value Flows in Fintech Ecosystems

The financial sector is undergoing a massive transformation, with new technology-driven players challenging established mechanisms and transforming the sector into a fast-moving market. With the gradual transition from a scale economy to a platform-driven network economy, enterprise networks are gaining strategic importance. Despite the growing interest in fintech’s, research has so far lacked a conceptualization of value creation in fintech ecosystems. Therefore, this research paper aims to analyze key players, value creation activities, and value streams based on the analysis of the business models of payment services, personal financial management, robo-advisory, peer-to-peer lending, trading, and cryptocurrency. We present a holistic value network for the fintech ecosystem based on structured literature review and analysis of 171 fintech companies. We were able to show that fintech platform orchestrates multiple market sides and that customers take four distinct roles at the center of the ecosystem when using fintech services

HEALTHCARE IN THE ERA OF DIGITAL TWINS: TOWARDS A DOMAIN-SPECIFIC TAXONOMY

The pursuit of physical and mental integrity is a fundamental instinct that has accompanied humankind since the dawn of time. Based on the digitalization modern medicine nowadays no longer has only access to self-collected information, but to information from almost all areas of the patients’ lives, which makes it possible to create a digital representation of them. This digital twin opens completely new and until now unthinkable possibilities, not only in the field of monitoring and prevention, moreover it is the key to personalized medicine. However, unlike other domains, the healthcare sector lacks a structured approach to exploit this paradigm. Against this background, based on a systematic literature review and the methodological approach of Nickerson et al. (2013), the paper presents the essential dimension as well as characteristics of digital twins healthcare applications as a taxonomy that has been evaluated against 100 healthcare application

Artificial Intelligence for Sign Language Translation – A Design Science Research Study

Although our digitalized society is able to foster social inclusion and integration, there are still numerous communities having unequal opportunities. This is also the case with deaf people. About 750,000 deaf people only in the European Union and over 4 million people in the United States face daily challenges in terms of communication and participation, such as in leisure activities but more importantly in emergencies too. To provide equal environments and allow people with hearing handicaps to communicate in their native language, this paper presents an AI-based sign language translator. We adopted a transformer neural network capable of analyzing over 500 data points from a person’s gestures and face to translate sign language into text. We have designed a machine learning pipeline that enables the translator to evolve, build new datasets, and train sign language recognition models. As proof of concept, we instantiated a sign language interpreter for an emergency call with over 200 phrases. The overall goal is to support people with hearing inabilities by enabling them to participate in economic, social, political, and cultural life

Can Self-Tracking Solutions Help with Understanding Quality of Smart, Connected Products?

Smart connected products (SCPs) have gained significant attention in different domains because they offer numerous benefits and change the form of competition and value creation. While quality is im-portant for SCPs, no holistic quality model is available to target the present quality problems. Further-more, since the concept of an SCP is quite abstract, a bottom-up approach that starts with a concrete example seems suitable. Therefore, this paper identifies the five key components of SCPs and maps them to self-tracking solutions (STS) in order to show that STSs are appropriate examples. We also analyse quality models in corresponding areas to verify the absence of a holistic quality model for the selected example. This contribution identifies the research gap regarding holistic, stakeholder-oriented quality models and helps researchers understand SCPs better

Understanding Value-based Pricing for Industrial Internet of Things

Digital industrial platforms are increasingly implemented as an inter-organizational digital infrastructure to unlock numerous sources of value. Establishing an appropriate pricing strategy is pivotal for platform providers’ success. To profit from these value sources, existing research considers value-based pricing as a superior and customer-oriented pricing strategy, enabling platform organizations to capitalize on the technological platform integration in the manufacturing domain. However, little is known about value-based pricing in the Industrial Internet of Things (IIoT) and how platform organizations understand and establish it. To advance the understanding of value-based pricing at the nexus of IIoT and digital platforms, we report on a qualitative study spanning 19 interviews with digital industrial platform providers. Our preliminary findings incorporate core characteristics, preconditions, outcomes, challenges, and guiding principles experienced by the experts, connecting the fragmented research streams on value-based pricing and digital platforms in IIoT and supporting platform organizations in crafting financially sustainable business models

Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature

The booming adoption of Artificial Intelligence (AI) likewise poses benefits and challenges. In this paper, we particularly focus on the bright side of AI and its promising potential to face our society’s grand challenges. Given this potential, different studies have already conducted valuable work by conceptualizing specific facets of AI and sustainability, including reviews on AI and Information Systems (IS) research or AI and business values. Nonetheless, there is still little holistic knowledge at the intersection of IS, AI, and sustainability. This is problematic because the IS discipline, with its socio-technical nature, has the ability to integrate perspectives beyond the currently dominant technological one as well as can advance both theory and the development of purposeful artifacts. To bridge this gap, we disclose how IS research currently makes use of AI to boost sustainable development. Based on a systematically collected corpus of 95 articles, we examine sustainability goals, data inputs, technologies and algorithms, and evaluation approaches that coin the current state of the art within the IS discipline. This comprehensive overview enables us to make more informed investments (e.g., policy and practice) as well as to discuss blind spots and possible directions for future research

Exploring the Abstraction Levels of Design Principles: The Case of Chatbots

Formulating design principles is the primary mechanism to codify design knowledge which elevates its meaning to a general level and applicability. Although we can observe a great variety of abstraction levels in available design principles, spanning from more situated to more generic levels, there is only limited knowledge about the corresponding (dis-)advantages of using a certain level of abstraction. That is problematic because it hinders researchers in making informed decisions regarding the (intended) level of abstraction and practitioners in being oriented whether the principles are already contextualized or still require effort to apply them within their situation. Against this backdrop, this paper (1) explores different abstraction levels of design principles based on a sample of 69 principles from the chatbot domain, as well as (2) provides a preliminary positioning framework and lessons learned. We aim to complement methodological guidance and strengthen the principles\u27 applicability, ultimately leading to knowledge reuse