Data-driven Student Advisory and Potential Direct Discrimination: A Literature Review on Machine Learning for Predicting Students' Academic Success - Schömer, Daniel; Laumer, Sven; Treischl, Edgar; Weigert, Jonas; Wilbers, Karl; Wolbring, Tobias (Americas Conference on Information Systems, 2021)
The use of machine learning found its way into the field of educational research and address the necessity of data-driven student advisory. Notwithstanding the plethora of current research that predicts and quantifies aspects of students' academic success in various situations during a student's course of study, most studies focus on the technical aspects and overlook potential of direct discrimination. In addressing this gap, this study systematically reviews and identifies scholarly papers that use sensitive attributes to predict students' academic success, which are not allowed to be considered in the decision-making process due to legal restrictions. The review shows that in 95 studies, over 52 percent use at least one discriminating attribute defined by the International Covenant on Civil and Political Rights. This sheer volume highlights the need for practitioners and scholars to explore causal mechanisms to prevent potential discrimination or seek alternative solutions without using protected individual characteristics. <p
Keywords: Diskriminierung

Give a Little, Take a Little? A Factorial Survey Experiment on Students’ Willingness to Use an AI-based Advisory System and to Share Data - Treischl, Edgar; Laumer, Sven; Schömer, Daniel; Weigert, Jonas; Wilbers, Karl; Wolbring, Tobias (Sozialwissenschaftliche Datenerhebung im digitalen Zeitalter, 2021)
Given its ability to handle a large amount of data, artificial intelligence (AI) has the potential to improve data-driven decisions under various situations. The present research identifies the necessary conditions for the implementation of an AI-based advisory system (AS) in higher education. Using a factorial survey design, we examine experimentally varied features of an AI-based AS to explore students’ willingness to use it and students’ willingness to share their data as a core challenge for successful implementation. Theoretically, we focus on the perceived costs and benefits to explain students’ intention, but we also highlight the role of trust and privacy concerns in regard to collecting data for the AS. In terms of benefits, information about the predictive power of the AS significantly increases students’ intention to use the tool and to share data and thus offers an incentive for students to share data. Moreover, a disproportionately long survey duration and survey topics that seem unrelated to the AS reduce students’ willingness to share data. With respect to trust and privacy concerns, our results indicate that providing transparent information about the AS has no effect on students’ willingness to share data, while aspects regarding who has access to the AS results and a long period of data storage reduce students’ intentions to share data. Based on these findings, we advise universities to communicate students’ expected benefits from a system to implement the AS, but we also recommend seriously considering students’ privacy concerns. Who has access to the data and the results of the AS should be transparent, as well as for what reason and how long. Otherwise, a substantial and probably selective part of the student body may not use the tool or share data due to privacy concerns. <

How can artificial intelligence enhance car manufacturing? A Delphi study-based identification and assessment of general use cases - Demlehner, Quirin; Schömer, Daniel; Laumer, Sven (International Journal of Information Management, 2021)
The latest boom of artificial intelligence (AI) has left the information management community in strong need of structure-providing, high-level overview works. Such works are supposed to allow both researchers and practitioners to keep track of that steep development across the technology's numerous possible application domains. So it is among other things that AI is said to incorporate enormous potential for reducing the operational costs of car manufacturers all over the globe. Nevertheless, many of them are still struggling with adopting it at large scale just because of a lack of knowledge on if and where to apply it. This study is therefore designed to find out which general use cases exist for AI within the context of car manufacturing and which ones might be the most promising ones to pursue at this early stage. We conducted a Delphi study with 39 experts in 25 different globally scattered organizations over one and a half years. As a result, we were able to identify 20 different high-level use cases for AI along the entire car manufacturing process. Our panelists have completely ranked and assessed those 20 use cases within two different dimensions, i.e., their estimated business value and their realizability. Besides being the first study to provide such an overview at one glance and to give such quantitative insights on that steeply emerging topic, four use cases from that list have never been discussed in connection with car manufacturing within the scientific literature until now and can therefore be considered as completely new in that regar
Keywords: Artificial intelligence; Use cases; Car manufacturing; Automotive industry; Automobile production; Production strategy

If you go for AI, be aware of the psychological hurdles around it—Practical and theoretical insights on the industrial application of artificial intelligence - Demlehner, Quirin; Schömer, Daniel; Laumer, Sven (Digital Business Models in Industrial Ecosystems - Lessons Learned from Industry 4.0 Across Europe, 2021)

Navigating AI innovation ecosystems in manufacturing: Shaping factors and their implications - Scepanski, Erik; Schömer, Daniel; Zillner, Sonja; Laumer, Sven (Australasian Conference on Information Systems, ACIS 2023, 2023)
Manufacturers often encounter challenges when implementing artificial intelligence (AI) in their manufacturing operations. Similar challenges with other digital transformation technologies have resulted in the emergence of innovation ecosystems. In this paper, we aim to demonstrate the emergence of AI innovation ecosystems and highlight the factors that influence their structure in manufacturing. To achieve this, we conducted a qualitative study of ten manufacturing case studies, analyzing different value propositions, activities, actors, and modules in AI ecosystems in the manufacturing sector. We first visualize the AI innovation ecosystems to showcase their structure and then discuss factors such as trustworthiness, scalability, simulation, and cloud that impact the ecosystem structure. Our study provides practitioners with a better understanding of the structure of AI ecosystems and their influencing factors. For researchers, we introduce influencing factors as a new part of the ecosystem-as-structure concept, which can lead to new research opportunities.
CRIS-Team Scopus Importer:2024-12-16
Keywords: Artificial Intelligence; Innovation Ecosystems; Manufacturing; Shaping Factors

Survival of the Fittest: A Business Model Perspective to explain Innovation Ecosystem Membership - Scepanski, Erik; Schömer, Daniel; Zillner, Sonja; Laumer, Sven (2023)
The manufacturing industry is one of the biggest beneficiaries of artificial intelligence (AI). However, to reap the value and fulfill all the necessary activities, they realize it involves a multitude of different capabilities and resources. Therefore, firms increasingly establishing innovation ecosystems that animate both, (inter)-organizational cooperation, and intra-ecosystem competition among participants. But beyond resources and capabilities, what are the reasons that independent organizations become members in the first place, and how do they secure their position in a field of dynamic competition and collaboration? This study applies an exploratory multi-case study with ten cases from the manufacturing industry. Based on a business model and role of value network perspective, our findings reveal 16 BM characteristics that add to a competitive advantage and therefore ecosystem membership. Our study contributes to the research on competitive advantage in innovation ecosystems, strategic management, and the growing streams of research on artificial intelligence.<br