Abstract
Research Context:Companies' lifespans are shrinking due to rapid technological disruptions, especially by data & AI. Incumbents aim to understand their disruption risks and ways to innovate through these technologies.Theory:Clayton Christensen's disruptive innovation theory discusses how smaller companies disrupt incumbents using technology. Incumbents must adopt an ambidextrous approach (O’Reilly & Tushman, 2016) for survival, balancing current operations with business model innovations. Although research deciphers tech-driven business model patterns (Amshoff et al., 2015), existing literature hasn't focused on patterns specific to data & AI.Methodology:Using a qualitative approach, this thesis merges interpretivist and constructivist paradigms to explore data-driven innovation logics and propose new business model patterns. The research employs a design science in entrepreneurship approach (Seckler et al., 2021) through case studies (Yin, 2014) and expands on solution business model patterns using data & AI. The methodology comprises:Data Triangulation: Analyzing data from 1090 startups and 341 incumbents, spanning 2019-2023 and various geographies.Investigator Triangulation: Examining data-driven models in agriculture and insurance with domain experts.Methodological Triangulation: Merging database searches with consulting workshops for explorative case studies.Contributions:Primary: Identification of 40 data-driven business model patterns.Secondary: Analysis of how data & AI advancements influence business model pattern evolution.Tertiary: Creating data-driven model pattern tools for innovation workshops.Methodological: Crafting a blueprint to identify emerging business model patterns from technological advancements.