The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare

Machine Learning (ML) systems have an enormous potential to improve medical care, but skepticism about their use persists. Their inscrutability is a major concern which can lead to negative attitudes reducing end users trust and resulting in rejection. Consequently, many ML systems in healthcare suffer from a lack of user-centricity. To overcome these challenges, we designed a user-centered, trustworthy ML system by applying design science research. The design includes meta-requirements and design principles instantiated by mockups. The design is grounded on our kernel theory, the Trustworthy Artificial Intelligence principles. In three design cycles, we refined the design through focus group discussions (N1=8), evaluation of existing applications, and an online survey (N2=40). Finally, an effectiveness test was conducted with end users (N3=80) to assess the perceived trustworthiness of our design. The results demonstrated that the end users did indeed perceive our design as more trustworthy

Organizational Readiness Concept for AI: A Quantitative Analysis of a Multi-stage Adoption Process from the Perspective of Data Scientists

Artificial intelligence (AI) is reshaping the business world in ways that enable organizations to create business value and reinvent their business models. Despite the great potential, organizations have difficulties in moving beyond the pilot stage and fully adopting AI applications. To better understand how organizations can implement AI into their core practices, we examine the impact of organizational readiness factors along the adoption process of AI through a quantitative research design. By integrating the organizational readiness factors into the multi-stage adoption process of AI, we unpack the interdependencies between these two literature streams. Due to the multi-faceted nature of organizations, we investigate the differentiating and opposing effects of the organizational readiness factors on the initiation, adoption, and routinization stages of AI

Artificial Intelligence: The Future of Sustainable Agriculture? A Research Agenda

Global warming and the increasing food demand are problems of the current generation and require a change towards sustainable agriculture. In recent years, research in the field of artificial intelligence has made considerable progress. Thus, the use of artificial intelligence in agriculture can be a promising solution to ensure sufficient food supply on a global scale. To investigate the state-of-the-art in the use of artificial intelligence-based systems in agriculture, we provide a structured literature review. We show that research has been done in the field of irrigation and plant growth. In this regard, camera systems often provide images as training/input data for artificial intelligence-based systems. Finally, we provide a research agenda to pave the way for further research on the use of artificial intelligence in sustainable agriculture

Building Sustainable Business Practices: Design Principles for Reusable Artificial Intelligence

Many organizations are struggling to deploy artificial intelligence (AI) solutions due to widely discussed challenges such as lack of financial resources, AI knowledge expertise, and limited data access, but also due to development silos that do not facilitate a holistic approach in solving problems or fostering innovation. We address these challenges by building on design science research and developing a socio-technical artifact, i.e., design principles for reusable AI solutions. We draw on Wenger´s Community of Practice concept to refine and evaluate our artifact via design thinking workshop, focus group discussion, and expert interviews. We contribute to the growing literature on building sustainable business practices and green information systems stream by developing and eval-uating design requirements, and design principles that provide organizations with guidance on how to design AI solutions in a reusable and therefore sustainable manner

Giving Users Control Over How Peers Handle Their Data: A Design Science Study

In today’s interconnected world, Internet users are increasingly concerned about losing control over the data they share with peers, which indicates a need for higher levels of control and notification mechanisms. We address this need by building on design science methodology and developing a socio-technical artifact, i.e., a peer-privacy-friendly online messaging service. We draw on Malhotra et al.’s (2004) Internet Users’ Information Privacy Concerns framework and refine and evaluate our artifact via focus groups, interviews, and a survey among users of online messaging services. Our artifact provides senders with the ability to control how their personal information is processed by peers and allows receivers to be made aware of the sender’s privacy expectations. We contribute to the growing literature on peer privacy concerns by developing and evaluating design requirements, principles, and an instantiation that can mitigate peer privacy concerns that go beyond concerns about organizational data practices

Exploring the Effect of National Culture on Emerging Technologies: A Glimpse into the Future

As organizations become increasingly globalized, understanding the impact of national culture on the successful usage and adoption of emerging technologies is crucial. National culture influences the strategies of organizations, for instance how the employees successfully adopt and use emerging technologies. While the effect of national culture has been widely observed in information systems, it is still challenging to measure the influence of national culture on the usage and adoption of emerging technologies. To contribute to the existing body of knowledge, we conducted a structured literature review on how previous work measured national culture to provide a starting point for further theory development. For instance, our findings emphasize the need to measure culture at an individual level. Finally, we developed a research agenda to provide a starting point for developing theories to measure the influence of national culture on emerging technologies

Acting Egoistically in a Crisis: How Emotions Shape Data Donations

The spread of COVID-19 has affected all of us, be it financially, socially, or even physically. It has caused uncertainty and anxiety, which has put people into a "hot" mental state. Referred to as an empathy gap, people are assumed to make emotion-driven decisions in "hot" states compared to "cold" states, which contrasts with the normative assumption of rational decision-making in privacy research. Based on an experimental survey study among 445 participants, we investigate whether people's mental state interacts with individuals' information disclosure decision-making. We measure our research model in the context of actual health data donation, which constitutes a critical surveillance factor in the COVID-19 crisis. Thereby, we contribute to research by (1) investigating data donation behavior amid a crisis and (2) helping to explain further nuances of privacy decision-making and the importance of trust as a context-dependent driver of data donation

Designing the Organizational Metaverse for Effective Socialization

The metaverse is a virtual world that merges physical, virtual, and augmented reality, enabling collaboration between online users and offering limitless opportunities for connectivity and integration. While the metaverse has gained significant attention in organizations, it presents social challenges as organizations have unprecedented insight and influence over individuals\u27 thoughts and beliefs. Our review is based on a theoretical framework and examines the impact of the environment, collaboration, avatars, and individual behavior on organizational socialization. We develop a conceptual model for the socialization process in the metaverse, contributing to a deep understanding of this emerging field and providing a research agenda for future work

The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare

Machine Learning (ML) systems have an enormous potential to improve medical care, but skepticism about their use persists. Their inscrutability is a major concern which can lead to negative attitudes reducing end users trust and resulting in rejection. Consequently, many ML systems in healthcare suffer from a lack of user-centricity. To overcome these challenges, we designed a user-centered, trustworthy ML system by applying design science research. The design includes meta-requirements and design principles instantiated by mockups. The design is grounded on our kernel theory, the Trustworthy Artificial Intelligence principles. In three design cycles, we refined the design through focus group discussions (N1=8), evaluation of existing applications, and an online survey (N2=40). Finally, an effectiveness test was conducted with end users (N3=80) to assess the perceived trustworthiness of our design. The results demonstrated that the end users did indeed perceive our design as more trustworthy