## 4 Results
### Knowledge Framework for Data-Driven Business Model Innovation
Management, infrastructure, and talent requirements impact DDBMI capabilities, new data product categories, and new data product performance. While small and young companies focus on a specific technological area and seek external partnerships, large incumbents with existing technological capabilities strengthen their internal capabilities, such as intra-group synergies and complementarities [151]. The nine topics require the creation of theories and frameworks (third aspect) to effectively carry out DDBMI processes, along with problem solving and pattern recognition. Theories explain phenomena, while frameworks provide a structure for organizing and implementing practical solutions. The evaluation framework for theories in the information systems domain describes theories as tools for explanation and prediction. Theories can make novel contributions by addressing focal phenomena not previously discussed, modifying existing theories to explain new phenomena, or providing new perspectives on existing focal phenomena [164]. General and industry-specific theories and frameworks are essential for explaining and predicting the course of DDBMI. Theoretical understanding in technology forecasting for future use is crucial, as BM theory has a high predictive ability for future digital technology use in businesses [155]. Theories and frameworks are utilized to understand problems, recognize patterns, guide future research with critical insight, and, most importantly, facilitate strategies and solutions development (fourth aspect) for DDBMI. A deeper understanding of underlying principles leads to the development of more strategic and intentional decision-making. Business model patterns (BMPs) are innovative tools that identify recurring patterns in BMs, simplifying the characterization of BMs within BM frameworks [165]. BMPs facilitate problem solving, help identify key BM attributes, and enable knowledge transfer from past experiences to the current context [8; 166; 167]. One example is the work by [PERSON] [158], who utilized unsupervised ML (k-medoids) to analyze 63 sharing-economy BMs, considering 17 attributes and 93 configuration options, and identified six solution patterns for sharing-economy BMI. Different qualitative and quantitative evaluation (fifth aspect) measures and metrics are necessary to assess the effectiveness of implemented strategies and solutions. Examples include outcome-based evaluation, process evaluation, data-driven business analytics, Key Performance Indicators (KPIs), and expert insights. Several studies discuss the essential metrics and KPIs for different aspects of DDBMI [159]. The knowledge framework categorizes nine topics and five aspects, resulting in 45 cells. Each cell corresponds to a specific topic and aspect, capturing variability across topics, aspects, and within individual cells. A framework proposed by [PERSON] et al. (2023) [126] for AI acceptance, adoption, and innovation includes technological antecedents (e.g., digitalization, IoT, and big data) and consequences involving BM, process, and product innovation. This can be applied to DDBMI by interpreting the identified topics as antecedents or consequences. Incumbents wishing to implement a DDBMI must assess their preparedness across such topics, embodying antecedents or consequences while considering their various aspects. When utilizing the table, readers can examine a topic across the five aspects, an aspect across the nine topics, or delve into a specific cell (i.e., one topic and one aspect). Instead of elaborating on all 45 cells, we provide three examples illustrating these various approaches to interpreting the table. Firstly, examining a single topic alongside its five aspects can shed light on conceptual nuances within the topic. Due to its significance across various industries, we have chosen digitalization as an example.