Navigating AI innovation ecosystems in manufacturing: Shaping factors and their implications

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

Survival of the Fittest: A Business Model Perspective to explain Innovation Ecosystem Membership

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

Data-driven Student Advisory and Potential Direct Discrimination: A Literature Review on Machine Learning for Predicting Students\u27 Academic Success

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\u27 academic success in various situations during a student\u27s 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\u27 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