###### Abstract  
This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers' preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation. business model innovation; data-driven technology; incumbent manufacturer; knowledge framework; topic modeling; thematic synthesis Article Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers  
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which data to use, handling analytics, and applying insights for operational changes [10]. The process of DDBMI is complex, as it involves data selling, data platforms, business improvements, and data products, each with its challenges [11]. For instance, a data-selling model presents numerous ethical and privacy concerns. The challenges incumbent manufacturers encounter in DDBMI stem from several factors, including the requirement for diverse resources and capabilities and the complexity within and between organizations [12; 13]. DDBMI challenges incumbent manufacturers because existing systems encompass outdated technology and infrastructure, and past process stability creates irreversible lock-in [14; 15]. Due to these challenges, incumbents are likelier to fail in their endeavors for DDBMI [16]. To address these challenges, incumbent manufacturers need to acknowledge the complexities within their organizations, strategize the essential activities, and foresee the preparedness needed for DDBMI. Preparedness involves management and staff readiness to implement performance improvements, recognize organizational benefits, enact process and behavioral changes, and monitor progress [17]. It encompasses technology exploration, resource assessment, leveraging partnerships, adaptability, reinvention, and strategic change. Such preparations represent a pre-stage of potential DDBMI. They can be described as a process of experimentation and learning that enhances the understanding of change initiatives and guides managers to take proactive steps [18]. Organizational preparedness helps incumbent manufacturers navigate DDBMI. Despite the importance of BMI preparedness, current research predominantly concentrates on preparedness in crises or BMI as a response to crises [19; 20]. The lack of clarity extends to understanding the relevant topics essential for incumbent manufacturers to prepare for DDBMI and the interconnectedness of these topics. Existing studies emphasize the critical role of strategy, leadership, and company culture in SMEs' preparedness for AI and big data-related transformations [21]. To better prepare for disruptions, managers must identify internal and external factors that enable BMI, such as digitalization, customer needs, and future trends [22]. Hence, there is a need for further research on how researchers and practitioners can understand these enablers and assess preparedness for DDBMI. Three research questions are raised:  
1. What are the key topics of DDBMI for incumbent manufacturers that have the potential to support the DDBMI process? 2. What are the key aspects of each topic required to understand their impact on DDBMI? 3. What comprises a knowledge framework that integrates the key topics and their aspects, enabling incumbent manufacturers to enhance their preparedness for DDBMI? We answer these research questions by identifying and integrating nine topics categorized into five aspects relevant to the DDBMI of incumbent manufacturers.