ORCID Profile
0000-0003-2859-5651
Current Organisation
University of Applied Sciences Landshut
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Springer Science and Business Media LLC
Date: 21-07-2023
DOI: 10.1007/S12525-023-00645-4
Abstract: Due to eroding business models, companies are seeking new growth opportunities. Business model innovation (BMI) can enable sustainable competitive advantage and future growth. Yet many companies are struggling to innovate their business models. Recent research reveals that collaboration concerning stakeholder integration and open business model innovation can foster BMI, but research has addressed both topics in isolation. By combining these topics, we establish business model co-innovation (BMCI) as a new perspective. Based on a longitudinal case study of an incumbent retailer and an incumbent electronics supplier, we investigate how both parties contribute to innovating the supplier’s business model. We document a BMCI process model highlighting the direct and indirect business model changes of the retailer and the supplier and we identify BMCI characteristics. We contribute to research with a new perspective on how partners co-innovate a business model.
Publisher: Informa UK Limited
Date: 23-02-2021
Publisher: Springer Science and Business Media LLC
Date: 13-12-2021
DOI: 10.1007/S12599-021-00732-W
Abstract: We currently observe the rapid emergence of startups that use Artificial Intelligence (AI) as part of their business model. While recent research suggests that AI startups employ novel or different business models, one could argue that AI technology has been used in business models for a long time already—questioning the novelty of those business models. Therefore, this study investigates how AI startup business models potentially differ from common IT-related business models. First, a business model taxonomy of AI startups is developed from a s le of 100 AI startups and four archetypal business model patterns are derived: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher. Second, drawing on this descriptive analysis, three distinctive aspects of AI startup business models are discussed: (1) new value propositions through AI capabilities, (2) different roles of data for value creation, and (3) the impact of AI technology on the overall business logic. This study contributes to our fundamental understanding of AI startup business models by identifying their key characteristics, common instantiations, and distinctive aspects. Furthermore, this study proposes promising directions for future entrepreneurship research. For practice, the taxonomy and patterns serve as structured tools to support entrepreneurial action.
Publisher: Elsevier BV
Date: 12-2022
Location: Germany
No related grants have been discovered for Markus Böhm.