Fraunhofer-Institut für Angewandte Informationstechnik FIT

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Organizing Self-Organizing Systems: A Terminology, Taxonomy, and Reference Model for Entities in Cyber-physical Production Systems

2021 , Berger, Stephan , Häckel, Björn , Häfner, Lukas

Ongoing digitalization accelerates the transformation and integration of physical production and traditional computing systems into smart objects and their interconnectivity, forming the Internet of Things. In manufacturing, the cross-linking of embedded systems creates adaptive and self-organizing Cyber-Physical Production Systems (CPPSs). Owing to ever-increasing cross-linking, rapid technological advances, and multifunctionality, the complexity and structural opacity of CPPSs are rapidly increasing. The development of urgently needed modeling approaches for managing such complexity and structural opacity, however, is impeded by a lack of common understanding of CPPSs. Therefore, in this paper, we contribute to a common understanding of CPPSs by defining and classifying CPPS entities and illustrating their relations. More precisely, we present a terminology, a taxonomy, and a reference model for CPPS entities, created and evaluated using an iterative development process. Thereby, we lay the foundation for future CPPS modeling approaches that make CPPS complexity and structural opacity more manageable.

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A comprehensive model for individuals' acceptance of smart energy technology - A meta-analysis

2020 , Gimpel, Henner , Graf, Vanessa , Graf-Drasch, Valerie

Individuals' use of smart energy technology - i.e., technology that increases energy efficiency or increases the integration of renewable energy sources - holds great potential to solve the energy-related climate problem. However, individuals' current uptake of smart energy technology is low. If policymakers are to successfully address this issue, it is vital that they understand the determinants of individuals' smart energy technology adoption. Hence, this paper provides a comprehensive adoption model for smart energy technology, including data from over 4k individuals in Europe, Asia, and North America involved in various technological contexts and phases of diffusion. A meta-analysis identifies Attitude and Performance Expectancy as the primary determinants of individuals' smart energy technology adoption. Further, results show that Environmental Concern influences all other determinants. Implications for research and policymakers are discussed.

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Sektorenkopplung als ganzheitlicher Ansatz für das Energiesystem. Potentiale und Herausforderungen

2020 , Fridgen, Gilbert , Körner, Marc-Fabian

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Self-Tracking and Gamification: Analyzing the Interplay of Motivations, Usage and Motivation Fulfillment

2019 , Gimpel, Henner , Nüske, Niclas , Rückel, Timon , Urbach, Nils , Entreß-Fürsteneck, Matthias von

The usage of wearable self-tracking devices has emerged as a big trend in lifestyle and personal optimization concerning health, fitness, and well-being. In this context, gamification elements have the potential to contribute to achieving desired user behavior. However, it is not fully understood to which extent the users perceive their self-tracking motivations as being fulfilled through the usage of a wearable self-tracking device, and how gamification affects the interplay of self-tracking motivations, wearable self-tracking device usage, and motivation fulfillment. To address this research gap, we develop a conceptual model and validate it with survey research and structural equation modeling. We find that self-tracking helps users to unexpectedly fulfill motivations without previously striving for them and that significant differences exist between the gamification users and non-users with respect to their motivations by selfentertainment and self-design.

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Determining the Optimal Time to Launch an Emerging Innovation in a Market

2020 , Häckel, Björn , Stirnweiß, Dominic

Investments in emerging technologies and the development of related emerging innovations are necessary to compete in the long term. The market entry timing plays an important role in generating decisive competitive advantages over competitors through investments in emerging innovations. Different market entry strategies offer varied opportunities and risks, which firms must take into account when choosing the optimal time to enter the market. This study develops an optimisation model to make an economically appropriate ex-ante decision in this choice by accounting for several relevant factors and weighing up possible opportunities and risks of the chosen market entry strategy. The evaluation of the simulation results shows that the considered factors influence the optimal market entry to varying ex-tents, and that both early and late market entry can be advantageous for companies.

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Robotic Process Automation

2020 , Hofmann, Peter , Samp, Caroline , Urbach, Nils

Within digital transformation, which is continuously progressing, robotic process automation (RPA) is drawing much corporate attention. While RPA is a popular topic in the corporate world, the academic research lacks a theoretical and synoptic analysis of RPA. Conducting a literature review and tool analysis, we propose in a holistic and structured way four traits that characterize RPA, providing orientation as well as a focus for further research. Software robots automate processes originally performed by human work. Thus, software robots follow a choreography of technological modules and control flow operators while operating within IT ecosystems and using established applications. Ease-of-use and adaptability allow companies to conceive and implement software robots through (agile) projects. Organizational and IT strategy, governance structures, and management systems therefore must address both the direct effects of software robots automating processes and their indirect impacts on firms.

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When does it pay off to integrate sustainability in the business model? - A Game-Theoretic Analysis

2020 , Gimpel, Henner , Graf-Drasch, Valerie , Kammerer, Alexander , Keller, Maximilian , Zheng, Xinyi

Acknowledging sustainability as a challenge of utmost importance, organizations face questions on dealing with different dimensions of sustainability. Respective actions include a fundamental shift in the purpose of business and almost every aspect of how it is conducted, or in short: an integration of sustainability in organizations business model. However, as sustainability is no altruistic end in itself, respective transformation must resonate with organizations economic conditions and their position in the market. But when does it pay off for organizations to integrate sustainability in their business model? Within this research paper we find answers by applying a game-theoretic framework and examining competition strategies for organizations integrating sustainability in their business model. Hereby we consider different market scenarios where symmetric and asymmetric, weak and strong, as well as a varying number of organizations interact. Our results suggest different strategies organizations can apply to gain competitive advantage.

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Driving Sustainably - The Influence of IoT-based Eco-Feedback on Driving Behavior

2020 , Bätz, Alexander , Gimpel, Henner , Heger, Sebastian , Wöhl, Moritz

One starting point to reduce harmful greenhouse gas emissions is driving behavior. Previous studies have already shown that eco-feedback leads to reduced fuel consumption. However, less has been done to investigate how driving behavior is affected by eco-feedback. Yet, understanding driving behavior is important to target personalized recommendations to-wards reduced fuel consumption. In this paper, we investigate a real-world data set from an IoT-based smart vehicle service. We first extract seven distinct factors that characterize driving behavior from data of 5,676 users. Second, we derive initial hypotheses on how eco-feedback may affect these factors. Third, we test these hypotheses with data of another 495 users receiving eco-feedback. Results suggest that eco-feedback, for instance, reduces hard acceleration maneuvers while interestingly speed is not affected. Our contribution extends the understanding of measuring driving behavior using IoT-based data. Furthermore, we contribute to a better understanding of the effect of eco-feedback on driving behavior. One starting point to reduce harmful greenhouse gas emissions is driving behavior. Previous studies have already shown that eco-feedback leads to reduced fuel consumption. However, less has been done to investigate how driving behavior is affected by eco-feedback. Yet, understanding driving behavior is important to target personalized recommendations towards reduced fuel consumption. In this paper, we investigate a real-world data set from an IoT-based smart vehicle service. We first extract seven distinct factors that characterize driving behavior from data of 5,676 users. Second, we derive initial hypotheses on how eco-feedback may affect these factors. Third, we test these hypotheses with data of another 495 users receiving eco-feedback. Results suggest that eco-feedback, for instance, reduces hard acceleration maneuvers while interestingly speed is not affected. Our contribution extends the understanding of measuring driving behavior using IoT-based data. Furthermore, we contribute to a better understanding of the effect of eco-feedback on driving behavior.

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The Power of Related Articles - Improving Fake News Detection on Social Media Platforms

2020 , Gimpel, Henner , Heger, Sebastian , Kasper, Julia , Schäfer, Ricarda

Social media is increasingly used as a platform for news consumption, but it has also become a breeding ground for fake news. This serious threat poses significant challenges to social media providers, society, and science. Several studies have investigated automated approaches to fighting fake news, but little has been done to improve fake news detection on the users' side. A simple but promising approach could be to broaden users' knowledge to improve the perceptual process, which will improve detection behavior. This study evaluates the impact of a digital nudging approach which aims to fight fake news with the help of related articles. 322 participants took part in an online experiment simulating the Facebook Newsfeed. In addition to a control group, three treatment groups were exposed to different combinations of related articles. Results indicate that the presence of controversial related articles has a positive influence on the detection of fake news.

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Modeling IT Availability Risks in Smart Factories. A Stochastic Petri Nets Approach

2020 , Miehle, Daniel , Häckel, Björn , Pfosser, Stefan , Übelhör, Jochen

In the course of the ongoing digitalization of production, industrial production infrastructures have become increasingly intertwined with information and communication technology. There-by, physical production processes depend more and more on the flawless functioning of information networks. Threats, such as attacks and errors, can compromise the components of in-formation networks, and due to the increasing interconnection, can even cause entire smart factories to fail. However, increasing complexity and lack of transparency of information networks in smart factories complicate the detection and analysis of such threats. Following a De-sign Science Research approach, this study aims to develop a methodology to depict and to model information networks in smart factories to enable the identification and analysis of IT availability risks. Based on a modular stochastic Petri net approach, we provide an artifact that enables the simulation and analysis of threats in smart factory information networks. To demonstrate the applicability and feasibility of our approach, we investigate different threat scenarios regarding their impacts on the operational capability of a close-to-reality information network setting. Further, to complement the evaluation from a practical perspective, we integrated the insights from two expert interviews with two global leading companies in the automation and packaging industry. The results indicate that the developed artifact offers a promising approach to better analyze and understand IT availability risks in smart factory information networks.

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