Measuring Ecosystem Complexity - Decision-Making Based on Complementarity Graphs

2023 | HICSS | Citations: 0

Authors: Schmidt, Rainer; Alt, Rainer; Zimmermann, Alfed

Abstract: Platforms feature increasingly complex architectures with regard to interconnect ... Expand

Abstract: Platforms feature increasingly complex architectures with regard to interconnecting with other digital platforms as well as with a variety of devices and services. This development also impacts the structure of digital platform ecosystems and forces providers of these services, devices, and services to incorporate this complexity in their decision-making. To contribute to the existing body of knowledge on measuring ecosystem complexity, the present research proposes two key artefacts based on ecosystem intelligence: On the one hand, complementarity graphs represent ecosystems with an ecosystem's functional modules as vertices and complementarities as edges. The nodes carry information about the category membership of the module. On the other hand, a process is suggested that can collect important information for ecosystem intelligence using proxies and web scraping. Our approach allows replacing data, which today is largely unavailable due to competitive reasons. We demonstrated the use of the artefacts in category-oriented complementarity maps that aggregate the information from complementarity graphs and support decision-making. They show which combination of module categories creates strong and weak complementarities. The paper evaluates complementarity maps and the data collection process by creating category-oriented complementarity graphs on the Alexa skill ecosystem and concludes with a call to pursue more research based on functional ecosystem intelligence. Collapse

Topics: digital ecosystem digital dashboard missing data relational database decision making

Methods: data modeling design science

Investigation of Material Supply Strategies to Increase Resilience in Matrix Production Systems

2023 | HICSS | Citations: 0

Authors: Schmidtke, N.; Rettmann, A.; Mohr, J.; Behrendt, F.

Abstract: In order to design a resilient production system, individual system elements ha ... Expand

Abstract: In order to design a resilient production system, individual system elements have to be flexible and adapt towards changing requirements. In contrast to the prevailing paradigm that complexity in production systems is reduced by standardisation or cycle production, complexity in resilient production systems offers great potential in terms of adaptiveness, robustness and anticipation. Within production control, matrix production is seen as a resilient and versatile system. Flexible routing between workstations makes it possible to compensate for failures more quickly in the event of a malfunction, flexible logistics and control processes allow the workstations to be controlled and used in a job-specific manner. In this paper, challenges and operating principles of material supply strategies are investigated that have the biggest influence on the design of resilient processes in matrix production. Using a simulation model and scenarios from the automotive sector, the potentials, requirements and parameters for describing resilience are specified. Collapse

Topics: workstation logistics management manufacturing information system database system

Methods: simulation qualitative interview agent based simulation experiment reference modelling

The Role of AI-Based Artifacts’ Voice Capabilities for Agency Attribution

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Schmitt, Anuschka; Zierau, Naim; Janson, Andreas; Leimeister, Jan Marco

Abstract: The pervasiveness and increasing sophistication of artificial intelligence (AI) ... Expand

Abstract: The pervasiveness and increasing sophistication of artificial intelligence (AI)-based artifacts within private, organizational, and social realms are changing how humans interact with machines. Theorizing about the way that humans perceive AI-based artifacts is, for example, crucial to understanding why and to what extent humans deem these artifacts to be competent for decision-making but has traditionally taken a modality-agnostic view. In this paper, we theorize about a particular case of interaction, namely that of voice-based interaction with AI-based artifacts. We argue that the capabilities and perceived naturalness of such artifacts, fueled by continuous advances in natural language processing, induce users to deem an artifact as able to act autonomously in a goal-oriented manner. We show that there is a positive direct relationship between the voice capabilities of an artifact and users' agency attribution, ultimately obscuring the artifact's true nature and competencies. This relationship is further moderated by the artifact's actual agency, uncertainty, and user characteristics.Anuschka Schmitt is a research associate and PhD candidate at the Institute of Information Management at the University of St.Gallen (HSG), Switzerland and a visiting research fellow at Harvard University's School of Engineering and Applied Sciences. Anuschka is passionate about the societal impact of AI systems, focusing on the role that conversational AI has on human perception and behavior. Her research mainly engages in user trust and decision-making in knowledge work and educational domains. Naim Zierau is a postdoctoral researcher at the University of St.Gallen. His research focuses on how AI-based information systems and their design affect user experience and business outcomes. He is also concerned with the organizational implications of deploying AI technologies.Andreas Janson is a postdoctoral researcher at the Institute of Information Management (IWI-HSG) at the University of St.Gallen, Switzerland. His dissertation investigates how to design digital learning processes. His research interests focus on issues relating to the design of digital services and platform ecosystems. His research has been published in leading information systems and management journals such as the . His work covers digital business, digital transformation, digital service management, crowdsourcing, digital work, digital learning services and collaboration engineering. Jan Marco has been internationally recognized for outstanding research, teaching, and education. Since 2009, he has repeatedly ranked among the top 1% of the most productive researchers and professors in business administration in the German-speaking area. He serves on the AIS leadership council, is co-editor-in-chief of the Journal of Information Technology and serves on the editorial board of Journal of Management Information Systems and Information Systems Research. He is also an entrepreneur and serves as a senior advisor, board member, and keynote speaker for national and international organizations. Collapse

Topics: artificial intelligence decision making human computer interaction usability privacy

Methods: conceptual modelling natural language processing large language model theory development synthesis

Challenges and Good Practices in Conversational AI-Driven Service Automation

2023 | International Conference on Information Systems | Citations: 0

Authors: Schmitt, Kevin; Zierau, Naim; Blohm, Ivo

Abstract: Conversational AI offers novel opportunities for companies to automate customer ... Expand

Abstract: Conversational AI offers novel opportunities for companies to automate customer interactions. However, many companies grapple with effectively implementing conversational AI. Utilizing an engaged, consortium-based research approach, we examine the unique challenges faced by six companies in the insurance and banking sector while implementing conversational AI solutions and identify best practices to address these challenges. Finally, drawing upon the lessons learned, we offer guidance for developing conversational AI capabilities and fostering conversational AI success stories. Collapse

Topics: artificial intelligence customer service organization learning business process automation enterprise architecture

Methods: qualitative interview chatbot action research field study qualitative coding

Investigating German Higher Education Institutions' Transfer Activities: New Measurements Based on Web Mining

2023 | International Conference on Information Systems | Citations: 0

Authors: Schmitt, Michelle; Schröder, Christian; Beck, Günter W.; Werner, Arndt

Abstract: In recent years, higher education institutions (HEI) have expanded their involv ... Expand

Abstract: In recent years, higher education institutions (HEI) have expanded their involvement in diverse transfer activities (TA), extending beyond traditional teaching and research roles. These TA are often heterogeneous and informal, which makes measuring their full scope and effects challenging. In this article, we propose a new and straightforward to implement approach for mastering this task. In a first step, we theoretically derive three different dimensions of transfer, namely the transfer of knowledge, technology and personnel. For each of these categories, we develop an artificial intelligence (AI) optimized keyword list. Finally, we use these lists and apply web mining techniques and natural language processing (NLP) to measure TA from German HEI. To this end, we analyze a total of 299,229 texts from 376 German HEI websites. Our study shows that our proposed approach represents an effective and valuable tool for measuring TA from HEI and provides a foundation for further research. Collapse

Topics: website knowledge sharing artificial intelligence entrepreneurship open source

Methods: web mining natural language processing computational algorithm descriptive statistic literature study

When machines trade on corporate disclosures: Using text analytics for investment strategies

2023 | Decision Support Systems | Citations: 0

Authors: Schmitz, Hans Christian; Lutz, Bernhard; Wolff, Dominik; Neumann, Dirk

Abstract: Can you make profits by trading on corporate disclosures using machine learning? ... Expand

Abstract: Can you make profits by trading on corporate disclosures using machine learning? In this study, we aim to obtain a conservative estimate of profitability, while accounting for the combination of several important realworld aspects. Specifically, we consider the holistic research problem that combines model predictions based on the textual content of corporate disclosures and trading strategies while accounting for transaction costs, order clearance periods, post-publication returns, and liquidity filtering. Furthermore, we aim to understand how the resulting profits are influenced by different model and trading strategy parameters. Based on 354,992 form 8-K filings and 10,204 ad hoc announcements, we find that the proposed trading strategies yield up to 7.81 % and 9.34 % out-of-sample annualized return. In addition, our results suggest that machine learning models should be provided with additional features about prior disclosures, while being trained on the ternary prediction problem that allows for predictions of neutral market reactions. We complement our results with several sensitivity analyses that show how profitability is influenced by transaction costs, different ensemble sizes, return neutrality thresholds, and liquidity filtering. Ultimately, we provide useful insights for practitioners by describing how the machine learning models arrive at decisions in terms of Shapley Additive Explanations. Collapse

Topics: shapley additive explanation accounting telephone electronic mail

Methods: machine learning sensitivity analysis term frequency–inverse document frequency GloVe random forest classification

Theories: transaction cost economics game theory

IMPACT OF DATA COLLECTION ON ML MODELS: ANALYZING DIFFERENCES OF BIASES BETWEEN LOW- VS. HIGH-SKILLED ANNOTATORS

2023 | International Conference on Business Informatics | Citations: 0

Authors: Schneider, Johannes; Eisenhardt, Daniel; Utama, Christian; Meske, Christian

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Topics: Twitter crowdsourcing

Methods: machine learning Kolmogorov-Smirnov test natural language processing experiment parametric test

Theories: information theory

How individuals can shape AI through data - An AI literacy and morality perspective

2023 | European Conference On Information Systems | Citations: 0

Authors: Schneider, Johannes; Meske, Christian; Bikic, Antonio

Abstract: ... Expand

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Topics: artificial intelligence data quality IT productivity privacy Python

Methods: case study descriptive statistic artificial neural network supervised learning logistic regression

Theories: ethical theory

Reuse, Reduce, Support: Design Principles for Green Data Mining

2023 | Business & Information Systems Engineering | Citations: 1

Authors: Schneider, Johannes; Seidel, Stefan; Basalla, Marcus; vom Brocke, Jan

Abstract: This paper reports on a design science research (DSR) study that develops design ... Expand

Abstract: This paper reports on a design science research (DSR) study that develops design principles for “green” – more environmentally sustainable – data mining processes. Grounded in the Cross Industry Standard Process for Data Mining (CRISP-DM) and on a review of relevant literature on data mining methods, Green IT, and Green IS, the study identifies eight design principles that fall into the three categories of reuse, reduce, and support. The paper develops an evaluation strategy and provides empirical evidence for the principles’ utility. It suggests that the results can inform the development of a more general approach towards Green Data Science and provide a suitable lens to study sustainable computing. Collapse

Topics: green IS environmental sustainability data quality computer hardware database system

Methods: machine learning design principle Cross-Industry Standard Process for Data Mining computational algorithm machine learning process

Implementing Smart Contracts: The case of NFT-rental with “pay-per-like”

2023 | International Conference on Business Informatics | Citations: 0

Authors: Schneider, Johannes; Sopi, Alfred; Brocke, Jan vom

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Topics: smart contract blockchain online advertising click fraud mobile application

Methods: design process literature study design science design artifact