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Sharing Design Knowledge Through Codification in Interdisciplinary DSR Collaborations
@ Hawaii International Conference on System Sciences (HICSS), 06.01.2023
Ernestine Dickhaut, Andreas Janson, Alan Hevner, Jan Marco Leimeister
Konferenzbeitrag
The goals of design science research (DSR) projects are to generate novel and useful artifacts and to produce rigorous and generalizable design knowledge. Often, DSR projects are conducted in collaborative, interdisciplinary project teams. Different disciplinary approaches to codifying design knowledge result in challenging project interactions. To study this situation, we analyze design knowledge codification in interdisciplinary teams over time. We gain insights from a survey of recent DSR papers that have been published in the AIS Senior Scholars’ Basket. We then present a detailed case study of a longitudinal project that brought to light issues of sharing design knowledge across disciplinary borders. Drawing from the survey and case study, we provide actionable guidance on how to effectively codify and share design knowledge to support researchers and practitioners to build useful artifacts and to make interdisciplinary design knowledge contributions reusable and applicable.
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Automotive Manufacturers and Their Stumble from one Supply Crisis to Another: Procurement Departments Could be the Game Changer by Using Data Analytics, but…
@ Hawaii International Conference on System Sciences (HICSS), 06.01.2023
Konferenzbeitrag
With this paper, we examine the use of data analytics for crisis management in automotive procurement departments. Possible business values of data analytics were part of numerous research approaches. Nevertheless, automotive manufacturers are repeatedly confronted with supply chain disruptions. Procurement departments have a central role within supply chains and are predominantly responsible for stable supply processes. Taking into account the potential of data analytics, such crises should be avoided or at least mitigated. Thus, there is the question, why data analytics cannot currently help automotive procurement departments by facing such crises. We therefore evaluate problems and obstacles by implementing and using data analytics in automotive procurement departments. Therefore, we talk to experienced procurement experts for evaluating practical insights. With our findings we provide practical insights and applicable recommendations for action with the goal of helping procurement leaders to better leverage data analytics for meeting current and future crises.
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Collaborative Work Practices for Management Education: Using Collaboration Engineering to Design a Reusable and Scalable Collaborative Learning Instructional Design
@ Hawaii International Conference on System Sciences (HICSS), 06.01.2023
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Pandemics like COVID-19 highlight the needs and pitfalls of inclusive and equitable education in a digital society. IT-based instructional designs are needed to increase learners’ expertise, and to develop higher-order thinking skills. Instructional designs for collaborative learning (CL) seem to be a promising solution. However, they are mostly suitable for face-to-face and not for distance teaching. The core problem that impedes their reusability and scalability is a ‘collaboration problem’ for which collaboration engineering (CE) provides guidance. Therefore, we deploy a design science research study and contribute to CL and CE literature. We develop requirements and provide the design of an IT-based collaborative work practice fostering CL. We provide empirical evidence with an online experiment in a large-scale lecture with undergraduate business information students. This reveals that groups of learners who followed our CL experience achieve higher levels of expertise than those who followed a traditional ad hoc CL experience.
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Transferring Well-Performing Collaborative Work Practices with Parameterized Templates and Guidebooks: Empowering Subject Matter Experts for an Adaptation to Slightly Different Contexts
@ Hawaii International Conference on System Sciences (HICSS), 06.01.2023
Konferenzbeitrag
Collaborative work practices (CWPs) package facilitation expertise and have the potential to increase team productivity up to 90%. Collaboration engineers develop CWPs and deploy them to practitioners that execute them. These CWPs, however, are typically customized to conditions of a specific use case. This creates the challenge that changing use case conditions or even small variations across contexts, hinder well-performing CWPs of being applied more often to create a long-term value. Practitioners fail to adapt existing CWPs due to missing collaboration expertise and adaptation guidelines. To address this challenge in collaboration engineering literature, we introduce a) the Subject Matter Expert role; b) the ‘CWP Adaptation Approach’ that formalizes the transfer of CWPs to different contexts with parameterized Templates and Guidebooks. To show a first proof-of-concept, we further inductively generalize from an exemplarily use case with a well-performing CWP in the educational domain.
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Organizations claim to host what is called a metaverse – an extended version of our real world. First rudimental realizations of such metaverses can be found throughout the internet, e.g., Epic Game’s Fortnite. At the same time, research and practice struggle to specify what a metaverse truly is and how we can characterize it. With our work, we analyze the proximity of the realization of a holistic metaverse platform and present the results of a qualitative interview study (n=30). The goal of our work is to use the expertise of practitioners to discuss different examples that claim to represent a metaverse, e.g., Second Life and Decentraland. To achieve this goal, we develop a typology we call the Metagon and use it to evaluate existing metaverse platforms. We contribute to theory by clarifying the meaning of metaverse platforms. Practitioners are guided by a demonstration of metaverse characteristics.
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Persuasive system design (PSD) is an umbrella term for designs in information systems (IS) that can influence people’s attitude, behavior, or decision making for better or for worse. On the one hand, PSD can improve users’ engagement and motivation to change their attitude, behavior, or decision making in a favorable way, which can help them achieve a desired outcome and, thus, improve their wellbeing. On the other hand, PSD misuse can lead to unethical and undesirable outcomes, such as disclosing unnecessary information or agreeing to terms that do not favor users, which, in turn, can negatively impact their wellbeing. These powerful persuasive designs can involve concepts such as gamification, gamblification, and digital nudging, which all have become prominent in recent years and have been implemented successfully across different sectors, such as education, e-health, e-governance, e-finance, and digital privacy contexts. However, such persuasive influence on individuals raises ethical questions as PSD can impair users’ autonomy or persuade them towards a third party’s goals and, hence, lead to unethical decision-making processes and outcomes. In human-computer interaction, recent advances in artificial intelligence have made this topic particularly significant. These novel technologies allow one to influence the decisions that users make, to gather data, and to profile and persuade users into unethical outcomes. These unethical outcomes can lead to psychological and emotional damage to users. To understand the role that ethics play in persuasive system design, we conducted an exhaustive systematic literature analysis and 20 interviews to overview ethical considerations for persuasive system design. Furthermore, we derive potential propositions for more ethical PSD and shed light on potential research gaps.
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IT support is under growing pressure to ensure efficient, flexible, and scalable use of digital technologies (Kumbakara, 2008). As a result, technical support staff is affected by monotonous work and work overload (Schmidt et al., 2022). Our research aims to augment the precarious workplace of support agents with artificial intelligence (AI). To incorporate an employee-centered perspective a priori and ensure positive impacts, we propose a framework for combining work design theory (e.g. Demerouti et al., 2001) and design science theory (Peffers et al., 2007, Niehaves & Ortbach 2016). The advances in AI promise to leverage large potential in optimizing and enhancing service processes and workplaces (Huang & Rust, 2018, de Keyser, 2019). Introducing AI into service processes, imply effects on work characteristics (Larivière et al., 2017). By combining human and artificial intelligence we propose hybrid intelligence (Dellermann et al., 2019) as a suitable solution for mitigating the persistent issues of support workers and the possible negative impacts of AI. To a great extent, IS research emphasizes the implied impacts of AI use in workplaces (Verma & Singh 2022, Wang et al., 2020). As such, work design models are widely used to empirically evaluate the impacts of AI design (Sturm & Peters, 2020), but are rarely utilized to substantiate the design of AI-augmented work systems. Only Poser et al. (2022) and Zschech et al. (2021) recently applied such models. The goal of this paper is to overcome the lack of work design in design science research (DSR) for AI-based systems and to steer the development into desired socio-technological configurations. The here presented work is expected to answer : How can work design theory inform the design of AI-augmented workplaces? RQ1 How should a hybrid intelligence system be designed to augment IT support agents’ workplaces by incorporating work design theory? RQ2 To systematically design the integration of AI, we make use of the DSR paradigm (Peffers et al., 2007). We first interview support agents and utilize the organizing move theory of Pentland (1992) and the technical support work theory of Das (2003) to ensure relevance. Based on a review of the IS literature on work design theories, we then derive a preliminary theoretical framework (Paul & Benito, 2018) RQ1. The framework represents a kernel theory for the development of meta design requirements. Contributing to the second research question, we design and subsequently evaluate the augmentation based on work-related outcomes RQ2.
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Although conversational agents (CA) are increasingly used for providing purchase recommendations, important design questions remain. Across two experiments we examine with a novel fluency mechanism how recommendation modality (speech vs. text) shapes recommendation evaluation (persuasiveness and risk), the intention to follow the recommendation, and how modality interacts with the style of recommendation explanation (verbal vs. numerical). Findings provide robust evidence that text-based CAs outperform speech-based CAs in terms of processing fluency and consumer responses. They show that numerical explanations increase processing fluency and purchase intention of both recommendation modalities. The results underline the importance of processing fluency for the decision to follow a recommendation and highlight that processing fluency can be actively shaped through design decisions in terms of implementing the right modality and aligning it with the optimal explanation style. For practice, we offer actionable implications on how to make effective sales agents out of CAs.
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The advantages offered by natural language processing (NLP) and machine learning enable students to receive automated feedback on their argumentation skills, independent of educator, time, and location. Although there is a growing amount of literature on formative argumentation feedback, empirical evidence on the effects of adaptive feedback mechanisms and novel NLP approaches to enhance argumentative writing remains scarce. To help fill this gap, the aim of the present study is to investigate whether automated feedback and social comparison nudging enable students to internalize and improve logical argumentation writing abilities in an undergraduate business course. We conducted a mixed-methods study to investigate the impact of argumentative writing on 71 students in a field experiment. Students in treatment group 1 completed their assignment while receiving automated feedback, whereas students in treatment group 2 completed the same assignment while receiving automated feedback with a social comparison nudge that indicated how other students performed on the same assignment. Students in the control group received generalized feedback based on rules of syntax. We found that participants who received automated argumentation feedback with a social comparison nudge wrote more convincing texts with higher-quality argumentation compared to the two benchmark groups (p < 0.05). The measured self-efficacy, perceived ease of use, and qualitative data provide valuable insights that help explain this effect. The results suggest that embedding automated feedback in combination with social comparison nudges enables students to increase their argumentative writing skills by triggering psychological processes. Receiving only automated feedback in the form of in-text argumentative highlighting without any further guidance appears not to significantly influence students’ writing abilities when compared to syntactic feedback.
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Text-based conversational agents (CAs) are widely deployed across a number of daily tasks, including information retrieval. However, most existing agents follow a default design that disregards user needs and preferences, ultimately leading to a lack of usage and an unsatisfying user experience. To better understand how CAs can be designed in order to lead to effective system use, we deduced relevant design requirements from both literature and 13 user interviews. We built and tested a question-answering, text-based CA for an information retrieval task in an education scenario. Results from our experimental test with 41 students indicate that following a user-centered design has a significant positive effect on enjoyment and trust in a CA as opposed to deploying a default CA. If not designed with the user in mind, CAs are not necessarily more beneficial than traditional question-answering systems. Beyond practical implications for effective CA design, this paper points towards key challenges and potential research avenues when deploying social cues for CAs.
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Ausbildung
Jan Marco Leimeister studierte Wirtschaftswissenschaften mit Schwerpunkt Wirtschaftsinformatik an der Universität Hohenheim, wo er auch im Bereich Wirtschaftsinformatik zum Thema systematische Entwicklung, Einführung und Betrieb Virtueller Communities mit Auszeichnung promovierte. Von 2004 bis 2008 war er an der Technischen Universität München, Institut für Informatik, Lehrstuhl für Wirtschaftsinformatik als wissenschaftlicher Assistent tätig. Er habilitierte sich im Jahr 2008 mit einer Arbeit zur hybriden Wertschöpfung in der Gesundheitswirtschaft. Im Jahre 2008 wurde Leimeister an die Universität Kassel berufen, 2012 an die Universität St. Gallen. Er führt beide Tätigkeiten in Teilzeit fort. Leimeister hilft Unternehmen und Organisationen als Coach, Trainer, Berater, Beirat und Referent beim Management von technischem, strategischem und organisatorischem Wandel und ist Mitglied in diversen Beiräten auf nationaler und internationaler Ebene.
Lehraktivitäten
- Business Engineering, Digital Business und Digital Transformation (Master)
- Service Design, Service Innovation
- Collaboration Engineering
- Forschungs-Praxis Ventures (FPV)
- Information Management
- Doktoratskurse
Projekte
- Competence Center Crowdsourcing Compentence Center Crowd
- Competence Center Agile Transformation
- Various research projects on service design, serivce innovation, learning service design and management, digital business, digital transformation
Berufserfahrung
Seit 1998 freiberufliche berufspraktische Erfahrungen im Bereich Anwendungsentwicklung, IT & Strategie, IT Innovationen, Projektmanagement, Change Management, Produkt- und Dienstleistungsentwicklung.
Mehrjährige Erfahrung in diversen Verwaltungsrats- und Beiratsfunktionen in Internetagenturen, Online Marketing Anbietern, IT & Consulting Unternehmen.
Beratungsprojekte u.a. mit BBraun, BMW, Bosch, Daimler, Deutsche Telekom, O2 Telefonica, SAP, Siemens, Volkswagen etc sowie diversen Mittelständlern.
Awards
- AIS Award for Innovation in Teaching 2016.
- Emerald Citation of Excelence 2013
- TUM Research Excellence Award 2010,
- Hessischer Hochschulpreis für Exzellenz in der Lehre 2009,
- Various Best Paper Awards
Die Wirtschaftswoche sieht Jan Marco Leimeister in ihrem 2019er Forschungsleistungsranking auf Platz 4 der forschungsstärksten BWL Professoren auf Basis der Publikationsleistung der letzten 5 Jahre sowie auf Platz 8 im Ranking der Lebenswerke, gemessen an der Publikationsleistung seit Karrierebeginn (von 2.824 untersuchten BWL Professoren).
Das Handelsblatt sah Jan Marco Leimeister in seinem 2014er Forschungsleistungsrankig auf Platz 13 aller BWL-Professoren auf Basis der Publikationsleistung der letzten 5 Jahre (von über 2600 untersuchten Forschern aus D/A/CH).
Im 2012er Forschungsleistungsranking des Handelsblatt war Leimeister der forschungsstärkste Wirtschaftsinformatiker unter 40 Jahren und auf Rang 31 der forschungsstärksten BWL Professoren auf Basis der Publikationsleistung der letzten 5 Jahre (von über 3000 untersuchten Forschern aus D/A/CH).
Beim 2009er Handelsblattranking wurde Leimeister auf Rang 40 aller Forscher bzw. unter den forschungsstärksten fünf Wirtschaftsinformatikern auf Basis der Publikationsleistung der letzten 5 Jahre (von über 2100 untersuchten Forschern aus D/A/CH) eingestuft.
Mitgliedschaften
- vhb - Verband der Hochschullehrer für Betriebswirtschaft
- GI - Gesellschaft für Informatik
- AIS - Association for Information Systems
- ACM - Association for Computing Machinery
- Academy of Management (AoM)
Weitere Informationen
Praxistätigkeiten:
Als Berater, Coach, Beirat, Redner und Moderator hilft Jan Marco Leimeister Organisationen bei der Innovationsentwicklung und dem Management von organisatorischem, technischem und strategischem Wandel. Besonderes Augenmerk liegt auf der Förderung von Start Ups. Schwerpunkte seiner Arbeit umfassen die Anwendung neuer wissenschaftlich fundierter Methoden, Technologien und Konzepte
Diverse freiberufliche beratende, gutachterliche und Beiratstätigkeiten. Langjährige berufspraktische Erfahrungen in Beiräten und Verwaltungsrat im Bereich Digital Business, Management von Informatik-Ressorts, Consulting-Unternehmen und Internetagenturen.
Ausbildung:
- Habilitation an der Technischen Universität München, Thema "Hybride Wertschöpfung in der Gesundheitswirtschaft", Tätigkeit am Institut für Informatik.
- Promotion an der Universität Hohenheim, Fachgebiet Wirtschaftsinformatik, Thema "Pilotierung virtueller Communities im Gesundheitsbereich".
- Studium der Wirtschaftswissenschaften (insb. Wirtschaftsinformatik, Internationales Management und Marketing) an der Universität Hohenheim.