## 4 Results  
### Key Topics for Data-Driven Business Models  
Tables 1 and 2 (see Appendix A) display the topics obtained from two different topic models for \(s_{incumbent\_manufactures}\) and \(s_{incumbents}\). These topic models show the recurring keywords under each topic, which help define and characterize the topic. However, the overlapping and industry-specific presence of topics does not fully describe the general idea of DDBMI. For example, Topic 4 in Table 1 (with the highest topic mean proportion) is a mixture of keywords with mixed themes related to BMs, digital economy, circular economy, sustainability, logistics and supply chain management, integration, and transformation. These co-occurring keywords emphasize the importance of adapting BMs to align with digital, circular, and sustainable principles to drive success and innovation in manufacturing. Thematic synthesis is necessary to separate and categorize these co-occurring keywords in different topics (Tables 1 and 2) for an analysis of topics common to incumbent manufacturers. Through manual selection (thematic synthesis) from the topic modeling outcomes, we identified nine non-overlapping topics relevant to incumbent manufacturers' DDBMI. These selected topics are outlined in Table 1. The \"Refined keywords\" column includes words extracted from the topic modeling outcomes, specifically the top main and FREX keywords highlighted in Tables 1 and 2. We regrouped these keywords based on their similarity and relation to one another to create a generalized and non-overlapping overview of topics. Additionally, we provided a topic description based on the keywords in each group and arranged them according to sample size. First, we will briefly define the selected topics and explain their relevance for DDBMI. Topic 1, digitalization, is defined in the BMI context as leveraging digital technologies to transform BMs, create new value [76], and enhance existing capabilities, thereby creating a \"visionary digital value proposition\" [77]. Incumbents must prioritize digitalization in DDBMI to influence value creation, value capture, value delivery, and sustainability [78]. reconfiguration [91] and driving companies' sustainability-oriented innovation through open innovation [9]. Incumbents adopting digital platforms face challenges in developing internal readiness and necessary capabilities for platform ecosystems and managing hybridity as platforms extend existing BMs without replacing them [92; 93]. Topic 4, the IoT, is defined as a group of infrastructures interconnecting connected objects and allowing their management, data mining, and access to the data they generate [94]. IoT aids incumbent manufacturers by generating real-time data for improved energy efficiency, resource and workflow optimization, predictive maintenance, targeted marketing, and new product and service creation. The manufacturing sector sees potential for BMI through IoT-based virtual/digital applications and IoT-based automation [95]. Topic 5, servitization, integrates services into traditional product-centric BMs, enabling a shift towards service-oriented BMs offering services or integrated product-service systems instead of physical products [96; 97]. Servitization is an archetype of I4.0 BMs that exploits digital technologies [98], creates value through digital service innovations (i.e., business enablers, service enhancement, and digital service offerings) [99], and makes reorganizing production and management practices necessary [100]. In manufacturing, there are three types of service-based BMs: Product-oriented, process-oriented, and outcome-oriented digital servitization [101]. Topic 6, AI, is defined as intelligent computer systems with cognitive capabilities similar to humans, employed to sense the environment, comprehend information, learn from data, and act autonomously [102; 103]. AI capabilities positively impact servitization, moderated by absorptive capacity [104].