Abstract: Firms have increasingly adopted service agents on their websites to provide services to website users. Despite the increasing penetration of different types of service agents, little knowledge exists on how website users respond to the mere exposure to service agents and, most important, on how they respond to artificial intelligence (AI)-based as compared to human-based service agents. Drawing on cue utilization theory, this study shows for the first time that the mere exposure to AI-based service agents (as compared to an exposure to human-based service agents) can prompt website users to disclose more personal information to the website provider. This is because website users employ information about available service agents as a cue to make inferences about the website provider. In specific, the perceived intrusiveness of the website provider is lower when website users have been exposed to an AI-based versus a human-based service agent. The additional findings about the boundary conditions of these effects can be used to derive practical implications.

Short CV: Dominik Siemon is an Associate Professor in the Department of Software Engineering in the School of Engineering Science at LUT University, Finland. In addition, he holds the title of Docent for Intelligent Information Systems at LUT University. He received his PhD (Dr. rer. pol.) in Business Information Systems from the Braunschweig University of Technology in Germany. His research interests include human-AI collaboration and interaction, conversational agents, intelligent systems, collaboration technology, creativity, and design science. His research has been presented at international conferences such as the International Conference on Information Systems and has been published in journals at the intersection of information systems and human-computer interaction, such as Information Systems Frontiers, Behaviour & Information Technology, Human-Computer Interaction and the Communications of the Association for Information Systems.

https://www.lut.fi/en/profiles/dominik-siemon

Abstract: We examine how a company’s digital upper echelons—the members of the top management team with IT-related responsibilities as their primary role—impact the success of digital platform strategies. Focusing on the S&P1500 companies, we show that an initial negative effect of digital platform strategies on a company’s growth opportunities becomes positive after a lag of six years. We find that this positive effect only holds for companies that implement the digital platform organically (“build strategy”) and not for companies that acquire a digital platform (“buy strategy”). Second, we examine the moderating effect of the maturity of digital upper echelon positions. We analyze whether emergent digital upper echelon positions, such as chief digital officers (CDOs) or established digital upper echelon positions, such as chief information and chief technology officers (CIOs, CTOs), have a greater influence on a digital platform strategy’s success. While we do not find a clear positive moderating effect for either position, a post-hoc test reveals that the presence of a CDO has a positive moderating effect for the build strategy but a partially negative effect for the buy strategy. Our findings contribute to understanding why some companies are successful with digital platform strategies while others are not.

Short CV: Maximilian Schreieck is an Assistant Professor for Information Systems, in particular Digital Services and Platforms, at the Department of Information Systems, Production and Logistics Management at the University of Innsbruck. He holds a Ph.D. in Information Systems from the Technical University of Munich (TUM). From 2020 to 2021 he was a postdoctoral researcher at the Chair for Information Systems at TUM and from 2021 to 2022 he was visiting scholar and DFG Walter Benjamin Fellow at The Wharton School, University of Pennsylvania. Maximilian’s research interests include digital platform ecosystems, platform governance, digital transformation of established companies, and digital platforms for social causes. His research has been published in the Journal of the AIS, European Journal of Information Systems, Journal of Information Technology, Business & Information Systems Engineering, Electronic Markets, IEEE Transactions on Engineering Management, Information Technology for Development, and conference proceedings such as ICIS, AOM, ECIS, HICSS, AMCIS, and PACIS.

https://www.uibk.ac.at/wipl/team/team/schreieck.html.de

Abstract: Pandemics, global warming, and war, whether physical or digital, are grand challenges of our age. Based on a more and more globalized and interconnected world, those challenges’ severity and frequency are increasing (see Global Risk Report by the World Economic Forum). Therefore, managing crises and making organizations resilient become vital capabilities of the top management team (TMT). As the TMT approaches those challenges, the Chief Information Officer (CIO) often emerges as the actor who holds the keys to making the firm more resilient. For example, CIOs triggered the virtualization of workspaces and the provision of hardware such as smartphones and laptops, which later became the essential factors helping the Chief Executive Officer transition the operational mode from physical to digital during COVID-19. However, despite the rising importance, we know little about how TMTs, in general, and the CIO, in particular, make firms more resilient in responding to the grand challenges of our age. Our study builds on the theory of crisis management and organizational resilience to explain how CIOs built proactive resilience before the crisis and utilized this during crisis management as reactive resilience to steer the outcome trajectory of the crisis. The process model illustrates how capacities, authority, and the mitigation of fragilities before a crisis impact the cognitive and behavioral response, which determines if firms suffer adverse effects, can retain the status quo, or even thrive in the advent of an emerging crisis.

Short CV: Andreas Hein is a postdoctoral researcher and head of the research group on digital platforms & e-government at the Krcmar Lab, Technical University of Munich (TUM), Germany. He holds a Ph.D. from TUM in Information Systems and has three years of experience as a Senior Strategy Consultant at IBM. His work has appeared in the Journal of Strategic Information Systems, European Journal of Information Systems, Electronic Markets, Business & Information Systems Engineering, and Information Systems Frontiers, as well as refereed conference proceedings such as the International Conference on Information Systems.

Link: https://scholar.google.com/citations?user=Nx1wVKgAAAAJ&hl=en

Prof. Dr. Kai Spohrer, Associate Professor of Information Systems, Frankfurt School of Finance & Management: “And No One Gets the Short End of the Stick: A Blockchain-Based Approach to Solving the Two-Sided Opportunism Problem in Inter-Organizational Information Sharing”

The threat of opportunistic behavior is an enduring problem for inter-organizational information sharing. The two sides of opportunism in inter-organizational information sharing – information poaching by the information recipient and information manipulation by the information provider – cause high transaction costs and sometimes preclude information sharing relationships altogether. Existing organizational and technological countermeasures against the two-sided threat of opportunism can either not reliably preclude information poaching and manipulation or address only one of them at a time. We develop four design principles for a blockchain-based system that permits information sharing based on sensitive data in inter-organizational business transactions without revealing the actual data. Thus, our solution simultaneously precludes both information poaching and information manipulation, enabling a novel class of information sharing. We instantiate our design principles in a prototype within a multi-firm research consortium for wear-based leasing contracts for machine tools. An evaluation shows that organizations are willing to engage in more inter-organizational information sharing and draw on more sensitive data when using the proposed prototype compared to traditional inter-organizational information systems. Our study contributes to research on opportunism in inter-organizational information sharing and confidentiality in blockchain networks.

Kai Spohrer is Associate Professor of Information Systems with the Frankfurt School of Finance & Management. In his research, he applies qualitative and quantitative methods as well as design science to understand and shape the development and use of information systems. He is particularly interested in questions related to collaboration and coordination in information systems development, IT healthcare, and blockchain systems.

Digital transformations force organizations to develop and maintain innovative digital products and services. Thus, effectively managing these activities and supporting them with appropriate technology at various levels is crucial for organizational success. To support these goals, Kai Spohrer conducts research at several levels, ranging from psychological explanations of biased behavior in AI system users over team-level analyses of software development methods to investigations into the interactions of software architecture and organizational structure in development organizations. His research has appeared and is forthcoming in Information Systems Research, Journal of Management Information Systems, IEEE Transactions of Software Engineering, and others.

Prior to joining Frankfurt School, Kai Spohrer was Assistant Professor at the Business School of the University of Mannheim where he completed his habilitation on digital innovation in healthcare and the software industry, focusing particularly on agile development, design, and use of information systems. He received his Ph.D. from the University of Mannheim for his dissertation on collaborative quality assurance and team cognition in software development teams. Over the years, he has spent time as a student and visiting researcher at institutions such as the University of Arkansas, Washington State University, and Universidad Politécnica de Madrid. He is a founding member and the director of content and media of the AIS special interest group “Advances in Sourcing”.

Kai Spohrer | LinkedIn

Prof. Dr. Kai Spohrer | Frankfurt School (frankfurt-school.de)

Abstract: Although conversational agents (CAs) are increasingly used for providing purchase recommendations, important design questions remain. Our research project seeks to examine whether processing fluency is a novel mechanism to explain how recommendation modality (speech vs. text) shapes recommendation evaluations, the intention to follow the recommendation, and how modality interacts with the style of recommendation presentation. Our main goal is to identify under which conditions a specific modality (speech-based or text-based CAs) is superior in terms of processing fluency and consumer responses. Our first research findings provide robust evidence that text-based CAs outperform speech-based CAs in terms of processing fluency and consumer responses. Moreover, the findings 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 recommendation presentation. For example, numerical explanations increase processing fluency and purchase intention of both recommendation modalities. For practice, we offer actionable implications on how to make effective sales agents out of CAs.

Melanie Schwede () is a Ph.D. student and research associate at the chair of Marketing and Innovation Management at the University of Goettingen, Germany. Her research focuses on artificial intelligence-based assistants (e.g., voice assistants, chatbots and service robots) in the service frontline.

https://www.uni-goettingen.de/de/melanie+schwede%2c+m.sc./631796.html

Prof. Dr. Simon Trang, Assistant Professor, University of Göttingen: “The technology-behavior compensation effect: How beneficial and actively used technologies can counteract their societal goals”

In search of solutions to societal health challenges, policymakers are increasingly turning to innovative apps as complements to existing non-technological interventions. Such apps developed for supporting users’ health are designed with the premise of being seen as beneficial and actively used. However, based on risk homeostasis theory, we theorize that these two classical design goals for effective information systems (IS) unintentionally foster risk compensation and lead to the neglection of other non-technical preventive health behaviors. Evidence from a multi-wave study regarding COVID-19 contact tracing apps confirms the existence of what we call the technology-risk compensation effect: those individuals who perceived the app to be highly beneficial or actively use it in return reduce other preventive health behaviors such as social distancing after app adoption. This technology-risk compensation effect implicates a hitherto overlooked tension between two established IS design goals and the successful exploitation of technology to support users’ health. We expand the established perspectives on dark sides of IS use by revealing a previously neglected type of unintended consequences and draw attention to the implications of this finding for research well beyond the health context.

Prof. Dr. Simon Trang holds the Chair for Information Security and Compliance (‘Juniorprofessur’) at the University of Göttingen. The focus of his work concerns the analysis and effective design of information security measures in companies and for a resilient society. He approaches the topic from a socio-technical perspective, in which information systems and information security are not understood as ends in themselves, but always in the interplay of technology, processes and actors. This includes the study of user behavior and psychology, technical, legal as well as strategic aspects. Prof. Trang and his team have received various grants from renowned public institutions such as the BMBF, the BMWK, the BMG and the Volkswagen Foundation. His work has been published in journals such as the Journal of the Association for Information Systems, the European Journal of Information Systems, and Information Systems Frontiers, among others.

Simon Trang | LinkedIn

Prof. Dr. Simon Trang – Georg-August-Universität Göttingen (uni-goettingen.de)

Prof. Dr. Jana-Rebecca Rehse, Universität Mannheim: „User Behavior Mining: Applying Process Mining to Analyze Human Behavior Change“

User behavior mining (UBM) refers to the application of process mining and machine learning techniques to user interaction logs, i.e., high-resolution event logs that record low-level, manual activities performed by a user during the execution of a task in a software system. By analyzing these event logs, UBM can generate detailed insights into how users of a software interact with its user interface. Those insights can be used for technical purposes, such as the mitigation of software usability issues or the automation of process execution. In addition, UBM is a valuable tool for researchers who want to understand or predict a specific aspects of user behavior. It provides a data-driven, non-intrusive method to obtain a holistic view on the behavior of software users over a longer period of time. Researchers can hence use UBM to gather and analyze empirical data, which can be used to test, support, refute, or develop behavioral theories.

In this talk, I present UBM as an analytical method for examining user behavior in software systems. In the first part, I conceptualize it by means of the four-part UBM framework, which elaborates (1) how UBM data can be captured, (2) which technologies can be applied to analyze it, (3) which objectives UBM can accomplish, and (4) how theories can guide the analytical process. In the second part, I show how we applied UBM to analyze behavior changes in a mobile health (mHealth) scenario. Based on social cognitive theory (SCT), we apply process discovery and sequential rule mining to investigate how people interact with mHealth apps and how these interactions affect their physical activity. We find that by applying UBM, we can empirically substantiate implicit assumptions of SCT and derive recommendations for the optimal design of mHealth apps.

Dr. Jana-Rebecca Rehse is Junior Professor for Management Analytics at the University of Mannheim. Her research focusses on data-driven business process management by means of process mining and machine learning, particularly on methods for process analysis, process assistance and process automation for (business) value. Her research results, funded by the DFG and the BMBF, have been published in more than 40 conference and journal papers so far. From 2015 to 2020, Dr. Rehse was a researcher and project lead at the Institute for Information Systems (IWI) at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken. In 2019, she obtained her PhD from Saarland university with a thesis titled “Leveraging Artificial Intelligence for Business Process Management”. Dr. Rehse holds a bachelor’s and master’s degree in business informatics from Saarland University. In 2014, she spent six months as a visiting scholar at Stevens Institute of Technology in Hoboken, NJ. USA.

Jana Rehse | LinkedIn

Rehse | Universität Mannheim (uni-mannheim.de)

Konstantin Bauman, Ph.D., Ass. Prof. Fox School of Business@Temple University: “Intelligence Augmentation for Higher-Quality Peer Feedback: Supporting Students by Recommending Features of Written Feedback that Should be Improved”

The value of peer feedback is growing for large-scale courses in universities, as well as for MOOCs, where it is used as a method to provide students with formative feedback and support them in their learning journey. However, students usually have little to no experience in providing feedback and especially in formulating ideas in a way that would help feedback recipients understand all the raised critical issues and improve their submission accordingly.  In his research talk, Konstantin Bauman will present his work in collaboration with Roman Rietsche, Matthias Söllner and Jan Marco Leimeister from the Institute of Information Management (HSG) and the University of Kassel. In their work they developed an intelligence augmentation approach which was implemented and proposed to support students in the process of creating peer feedback with personalized recommendations of the most critical text features that need improvement. They designed a novel framework that provides such recommendations aiming to maximize the feedback quality as perceived by the feedback recipient. Furthermore, they also designed a novel Feature Utility Saturation Model (FUSM) that works as part of the framework. To test how the proposed method works in practice, they conduct a field experiment with 490 students taking three different courses at a public university in Europe. The results show that the personalized recommendation approach works well as it outperforms baselines and helps students create longer feedback and of significantly higher quality, as evaluated by feedback recipients.

Dr. Konstantin Bauman joined the Fox School on a tenure track appointment within the Department of Management Information Systems on January 1, 2018. He arrives at Fox from the Stern School of Business at New York University, where he served as a postdoctoral research fellow. Bauman’s research interests lie in the areas of technical information systems, with focus on the fields of quantitative modeling and data science. In particular, he works on developing novel machine learning methods for predicting customer preferences, and designing novel approaches to recommender systems that provide personalized advice to customers. Before joining NYU, Bauman worked as the head of a machine-learning group within the research department of Yandex, where he dealt with large-scale machine learning and data science problems on a daily basis. He also served as a software engineer at Yandex and the Russian Academy of Foreign Trade.

Bauman received his PhD in Mathematics (Geometry and Topology) from Russia’s Moscow State University, where he also earned a Master of Science degree in Mathematics. He also obtained a Master of Science degree in Machine Learning from a joint program between the Moscow Institute of Physics and Technology and the Yandex School of Data Analysis in Russia.

https://www.linkedin.com/in/konstantinbauman/

https://community.mis.temple.edu/kbauman/

Dominik Gutt,  Assistant Professor at Rotterdam School of Management: The Rise of Robo-Reviews – The Effects of Chatbot-Mediated Review Elicitation on Online Reviews

Chatbots have been introduced as a new way to receive product reviews. However, little is known how the chatbot-mediated solicitation influences review characteristics such as the valence or the helpfulness. We conduct four studies to narrow this literature gap. Our results show that (1) chatbot-mediated review elicitation leads to a positivity bias in ratings, particularly strong for low-quality products; (2) chatbot-mediated reviews negatively impact a range of review dimensions (e.g., helpfulness) mainly due to decreased review length, (3) the positivity bias varies depending on the level of anthropomorphism in the chatbot and the nature of the survey deployer (seller vs. platform), and can be explained by audience tuning due to social presence. Our research is among the first to uncover a link between chatbot use, review valence, and other valuable review

Prof. Dr. Ali Sunyaev, Karlsruher Instituts für Technologie (KIT): Design Trade-Offs, Viability, and Management of Applications on Distributed Ledger Technology

While design decisions determine the quality and viability of applications in general, in Distributed Ledger Technology (DLT), the decision for a suitable DLT design (e.g., Ethereum, IOTA) is of particular relevance because the retroactive change of the underlying DLT design is currently very hard and often even impossible. Extant research has revealed several trade-offs between DLT characteristics (e.g., availability vs. consistency). Such trade-offs inhibit a one-size-fits-all DLT design that suites the requirements of applications on DLT. For an appropriate assessment of the suitability of DLT designs for a particular application on DLT, researchers and developers require a systematic and holistic overview of the functioning of different DLT designs, including their configuration of DLT characteristics as well as dependencies and resulting trade-offs between DLT characteristics. In this talk I will first summarize and reflect the design trade-offs, viability, and management of applications on DLT. Second, I will present the configuration and management tool for applications on DLT we currently work on.

CV: https://www.aifb.kit.edu/web/Ali_Sunyaev