Exploring the impact of data breaches and system malfunctions on users’ safety and privacy perceptions in the context of autonomous vehicles

Technological advancements allow for increasingly automated driving systems as well as the large-scale availability of fully autonomous vehicles (AVs) in the future. In this research-in-progress paper, we propose a research concept to further investigate the interplay of users’ perceived privacy risks and trust in AV safety associated with data breaches and system failures. Specifically, we aim to analyze whether system malfunctions impact privacy risk perceptions and whether data breaches impact users’ trust in AV safety by considering the trust in the AV manufacturer. Additionally, we offer first insights into preliminary data and explain our future research intentions. A more detailed understanding of the relationship between privacy and safety trust in the context of AVs could help manufacturers to better direct efforts to compensate for or prevent data breaches and system malfunctions potentially leading to increased user acceptance and technology adoption

LET\u27S GET PHYSIC(AI)L – TRANSFORMING AI-REQUIREMENTS OF HEALTHCARE INTO DESIGN PRINCIPLES

As healthcare\u27s digitization advances, artificial intelligence (AI) techniques offer opportunities to improve medical care. In addition to the much-discussed potential in diagnostics, AI-based systems can further support processes in clinics or comparable healthcare facilities, helping to improve medical, organizational, and administrative processes. Nevertheless, apart from single use-cases, AI in healthcare is still not unleashing its full potential. To empower the technology and provide a guideline for developers but also other entities such as medical institutions, we derive and plan to validate design principles guiding the design of AI-based systems specifically operating in clinics and healthcare facilities. In this research in progress study, we conduct the first two phases of the DSR approach by identifying requirements in literature and transforming these into design principles. By doing so, we provide a collection of literature-based design principles that need to be considered when implementing AI-based systems into healthcare contexts

Design of an Information System for Safety-Briefings along Planned Routes

Despite continuous improvements in road and vehicle safety, traffic accidents are still a threat to humans. Road safety even became a target of a sustainable development goal, presented by the United Nations (UN). Traffic-accidents often occur in certain areas. Thus, a briefing before driving a route could support drivers in knowing the dangerous spots and driving more careful and attentive at the dangerous areas. Following a design science research approach, we develop a theory how briefings for traffic related dangers should be designed and a web-application as an instance of the theory

Towards Design Principles for Experimental Simulations in Virtual Reality – Learning from Driving Simulators

Experiments play an important role in Information Systems research. In this area, Virtual Reality (VR) technologies can serve as a tool for enabling and conducting research. e.g., to investigate human behavior in specific situations. A prime example is VR-supported driving simulators that allow researchers in the automotive domain to gather knowledge while reducing cost and complexity compared to field studies with real cars. We argue that the use of carefully designed VR-supported experiments might allow researchers to get deeper insights into human behavior. Thus, we derive design principles for VR Experiments as an artifact from the literature about VR-supported driving simulations that have been accepted as a useful tool for research in their domain

Realism and Experiments: Investigating Virtual Reality Experiments

Experimental research is a fundamental component of scientific inquiry, but the realism of experimental settings may be limited due to a trade-off between internal and external validity. Virtual Reality technology offers a potential solution to this problem by creating highly controlled, yet realistic experimental settings. In this study, we investigate the potential of VR to increase perceived realism in experimental research by identifying and examining the effects of VR experiments on participants\u27 perceived realism. In our experiment, we compare the level of perceived realism between artificial scenarios presented as text vignettes and in VR. Our findings indicate that VR experiments elicite a significantly higher level of perceived realism compared to text-based experiments. Additionally, we use partial least squares structural equation modeling to investigate the identified concepts. We recommend that researchers consider using VR technology to enhance the realism of experimental settings and improve the validity of their findings

How are you Feeling? Inferring Emotions through Movements in the Metaverse

Metaverses are immersive virtual worlds in which people interact as avatars. There is emerging interest in understanding how metaverse users behave and perceive activities and tasks. Our understanding of users’ behavior within metaverses is limited. This study examines the role of emotions in the movement of individuals. We therefore implement a metaverse setting using virtual reality technology and development tools. In our study, we manipulated negative emotions and tracked the movements of our participants. We show how negative emotion influences movements in a metaverse setting. Based on a literature review, we select and calculate movement features to train a support vector machine. As our result, we present a novel way to infer the negative emotions of metaverse users which will help create more engaging and immersive experiences that cater to user’s emotions and behaviors. Our study provides preliminary evidence for the potential utilization of movement data in the metaverse

EXPLORING THREAT-SPECIFIC PRIVACY ASSURANCES IN THE CONTEXT OF CONNECTED VEHICLE APPLICATIONS

Connected vehicles enable a wide range of use cases, often facilitated by smartphone apps and involving extensive processing of driving-related data. Since information about actual driving behavior or even daily routines can be derived from this data, the question of privacy arises. We explore the impact of privacy assurances on driving data sharing concerns. Specifically, we consider two data-intensive cases: usage-based insurance and traffic hazard warning apps. We conducted two experimental comparisons to investigate whether and how privacy-related perceptions about vehicle data sharing can be altered by different types of text-based privacy assurances on fictional app store pages. Our results are largely inconclusive, and we did not find clear evidence that text-based privacy guarantees can significantly alter privacy concerns and download intentions. Our results suggest that general and threat-specific privacy assurance statements likely yield no or only negligible benefits for providers of connected vehicle apps regarding user perceptions

The Colors of Performance – Assessing the Impact of Color-Coding on Worker Behavior in Retail Order Picking

Advanced technologies are introduced in warehouse operations, rendering the interplay between human worker behavior and information systems (IS) a critical issue. We investigate how IS supports manual order picking by studying how visual color-coding information on picking locations provided through personal digital assistants accelerates search and picking tasks. Considering real-world data on a storage system where 20 dissimilar items are stored together at one picking location, we apply a log-logistic accelerated failure time model with N=112,672 picks performed by N=190 workers and find that color-coding accelerates the picking process by up to 17.28%. To increase the internal validity of our field-based examination, we conduct one VR experiment (N=29 participants) providing evidence for an acceleration of 23.74%, and one online experiment (N=178 participants) indicating an acceleration of 24.29%. Based on an innovative method of triangulation, we demonstrate how IS can influence picker behavior and discuss how to better design IT artifacts

Driving Big Data – Integration and Synchronization of Data Sources for Artificial Intelligence Applications with the Example of Truck Driver Work Stress and Strain Analysis

This paper contributes to the issue of big data analysis and data quality with the specific field of time synchronization. As a highly relevant use case, big data analysis of work stress and strain factors for driving professions is outlined. Drivers experience work stress and strain due to trends like traffic congestion, time pressure or worsening work conditions. Although a large professional group with 2.5 million (US) and 3.5 million (EU) truck drivers, scientific analysis of work stress and strain factors is scarce. Driver shortage is growing into a large-scale economic and societal challenge, especially for small businesses. Empirical investigations require big data approaches with sources like physiological and truck, traffic, weather, planning or accident data. For such challenges, accurate data is required, especially regarding time synchronization. Awareness among researchers and practitioners is key and first solution approaches are provided, connecting to many further Machine Learning and big data applications

Replicating IoT-Privacy Studies in Virtual Reality

Understanding users’ interactions with and acceptance of information systems (IS) is a common research goal. However, in the case of the Internet of Things (IoT), user perceptions of connected objects may be difficult to measure using online experiments and surveys. In this regard, the relevant objects and devices can be presented to users in an immersive and cost-effective way by leveraging virtual reality (VR) simulations. As a first step towards a better understanding of VR as a tool in IS research, we intend to use VR to replicate an IoT study regarding privacy concerns