Designing AI-based systems to last: Identifying the enablers and inhibitors for the AI usage intentions of automotive blue-collar workers - Demlehner, Quirin (Proceedings of the 29th European Conference on Information Systems (ECIS) 2021, 2021)

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)

Setting the hook – The digital transformation from a manufacturing point of view and what it really means - Demlehner, Quirin; Laumer, Sven (Proceedings of the 25th Americas Conference on Information Systems (AMCIS) 2019, 2019)
The digital transformation is gaining momentum. During February 2019, Google has received twenty-five times as many search requests for "digital transformation" as five years earlier. This inflationary upsurge is accompanied by an ever-increasing blurring of the context the term is supposed to address as well as of the definition itself. In this paper, we tackle that problem from a manufacturing point of view by conducting a systematic literature review. As a result, we present an industry-tailored three-layer definition for the phenomenon. Furthermore, we identify 30 technological concepts that are considered as digital transformation-related in that context. Divided into eleven clusters, we embed them into a framework outlining the digital transformation in the manufacturing environment.<br
Keywords: Digital Transformation; Digitalization; Digitization; Literature Review

Shall we use it or not? Explaining the adoption of artificial intelligence for car manufacturing purposes - Demlehner, Quirin; Laumer, Sven (Proceedings of the 28th European Conference on Information Systems (ECIS) 2020, 2020)
Artificial intelligence (AI) is said to incorporate enormous potential for reducing the operational costs of car manufacturers and their suppliers all over the globe. Nevertheless, recent studies suggest that many of them are still struggling with adopting it at large scale. Therefore, in this article we develop a research model explaining the decision-making of organisations from the automotive industry regarding the question whether to adopt AI for car (part) manufacturing purposes or not. To do so, we contextualise the Technology-Organization-Environment (TOE) framework by taking a multiple case study approach including 39 expert interviews in 25 different firms all over the globe. Based on that, we propose a research model that theorises 16 different context-specific factors that influence that decision. Four of them are already well-known from the TOE framework but are renamed for a better contextual accuracy, whereas twelve can be regarded as new in this contextualised setting. The proposed model addresses research gaps in both the organisational adoption research literature and the literature on the application of AI in the automotive environmen
Keywords: Artificial intelligence; automotive production; organisational adoption; TOE

Why context matters: Explaining the digital transformation of the manufacturing industry and the role of the industry's characteristics in it - Demlehner, Quirin; Laumer, Sven (Pacific Asia Journal of the Association for Information Systems, 2020)