TY - JOUR A1 - Herm, Lukas-Valentin A1 - Janiesch, Christian A1 - Helm, Alexander A1 - Imgrund, Florian A1 - Hofmann, Adrian A1 - Winkelmann, Axel T1 - A framework for implementing robotic process automation projects JF - Information Systems and e-Business Management N2 - Robotic process automation is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes rapidly. In contrast to other process automation technologies, robotic process automation is lightweight and only accesses the presentation layer of IT systems to mimic human behavior. Due to the novelty of robotic process automation and the varying approaches when implementing the technology, there are reports that up to 50% of robotic process automation projects fail. To tackle this issue, we use a design science research approach to develop a framework for the implementation of robotic process automation projects. We analyzed 35 reports on real-life projects to derive a preliminary sequential model. Then, we performed multiple expert interviews and workshops to validate and refine our model. The result is a framework with variable stages that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments as well as for small and medium-sized companies. It is structured by the three phases of initialization, implementation, and scaling. They comprise eleven stages relevant during a project and as a continuous cycle spanning individual projects. Together they structure how to manage knowledge and support processes for the execution of robotic process automation implementation projects. KW - robotic process automation KW - implementation framework KW - project management KW - methodology KW - interview study KW - workshop Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323798 SN - 1617-9846 VL - 21 IS - 1 ER - TY - JOUR A1 - Wanner, Jonas A1 - Herm, Lukas-Valentin A1 - Heinrich, Kai A1 - Janiesch, Christian T1 - The effect of transparency and trust on intelligent system acceptance: evidence from a user-based study JF - Electronic Markets N2 - Contemporary decision support systems are increasingly relying on artificial intelligence technology such as machine learning algorithms to form intelligent systems. These systems have human-like decision capacity for selected applications based on a decision rationale which cannot be looked-up conveniently and constitutes a black box. As a consequence, acceptance by end-users remains somewhat hesitant. While lacking transparency has been said to hinder trust and enforce aversion towards these systems, studies that connect user trust to transparency and subsequently acceptance are scarce. In response, our research is concerned with the development of a theoretical model that explains end-user acceptance of intelligent systems. We utilize the unified theory of acceptance and use in information technology as well as explanation theory and related theories on initial trust and user trust in information systems. The proposed model is tested in an industrial maintenance workplace scenario using maintenance experts as participants to represent the user group. Results show that acceptance is performance-driven at first sight. However, transparency plays an important indirect role in regulating trust and the perception of performance. KW - user acceptance KW - intelligent system KW - artificial intelligence KW - trust KW - system transparency Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323829 SN - 1019-6781 VL - 32 IS - 4 ER - TY - JOUR A1 - Herm, Lukas-Valentin A1 - Steinbach, Theresa A1 - Wanner, Jonas A1 - Janiesch, Christian T1 - A nascent design theory for explainable intelligent systems JF - Electronic Markets N2 - Due to computational advances in the past decades, so-called intelligent systems can learn from increasingly complex data, analyze situations, and support users in their decision-making to address them. However, in practice, the complexity of these intelligent systems renders the user hardly able to comprehend the inherent decision logic of the underlying machine learning model. As a result, the adoption of this technology, especially for high-stake scenarios, is hampered. In this context, explainable artificial intelligence offers numerous starting points for making the inherent logic explainable to people. While research manifests the necessity for incorporating explainable artificial intelligence into intelligent systems, there is still a lack of knowledge about how to socio-technically design these systems to address acceptance barriers among different user groups. In response, we have derived and evaluated a nascent design theory for explainable intelligent systems based on a structured literature review, two qualitative expert studies, a real-world use case application, and quantitative research. Our design theory includes design requirements, design principles, and design features covering the topics of global explainability, local explainability, personalized interface design, as well as psychological/emotional factors. KW - artificial intelligence KW - explainable artificial intelligence KW - XAI KW - design science research KW - design theory KW - intelligent systems Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323809 SN - 1019-6781 VL - 32 IS - 4 ER - TY - JOUR A1 - Janiesch, Christian A1 - Zschech, Patrick A1 - Heinrich, Kai T1 - Machine learning and deep learning JF - Electronic Markets N2 - Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization. KW - analytical model building KW - machine learning KW - deep learning KW - artificial intelligence KW - artificial neural networks Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-270155 SN - 1422-8890 VL - 31 IS - 3 ER - TY - RPRT A1 - Imgrund, Florian A1 - Janiesch, Christian A1 - Fischer, Marcus A1 - Winkelmann, Axel T1 - Success Factors for Process Modeling Projects: An Empirical Analysis N2 - Business process modeling is one of the most crucial activities of BPM and enables companies to realize various benefits in terms of communication, coordination, and distribution of organizational knowledge. While numerous techniques support process modeling, companies frequently face challenges when adopting BPM to their organization. Existing techniques are often modified or replaced by self-developed approaches so that companies cannot fully exploit the benefits of standardization. To explore the current state of the art in process modeling as well as emerging challenges and potential success factors, we conducted a large-scale quantitative study. We received feedback from 314 respondents who completed the survey between July 2 and September 6, 2017. Thus, our study provides in-depth insights into the status quo of process modeling and allows us to provide three major contributions. Our study suggests that the success of process modeling projects depends on four major factors, which we extracted using exploratory factor analysis. We found employee education, management involvement, usability of project results, and the companies’ degree of process orientation to be decisive for the success of a process modeling project. We conclude this report with a summary of results and present potential avenues for future research. We thereby emphasize the need of quantitative and qualitative insights to process modeling in practice is needed to strengthen the quality of process modeling in practice and to be able to react quickly to changing conditions, attitudes, and possible constraints that practitioners face. T3 - Working Paper Series of the Institute of Business Management - 6 KW - Business Process Modeling KW - Business Process Management KW - Success Factors KW - Empirical Analysis Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-179246 ER -