Preparedness for Data-Driven Business Model Innovation:A Knowledge Framework for Incumbent Manufacturers

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.</p

Cross-Impact Analysis of Entrepreneurial Failure and Business Model Innovation:Navigating the Impact of Societal Perceptions

Failed entrepreneurs often encounter negative societal perceptions that impede their ability to learn from failure, take risks, and innovate business models. Reducing this stigma appears crucial to support entrepreneurship and foster innovation. However, the precise relationship between stigma reduction and desired outcomes remains uncertain. This study addresses this gap by examining the variables influencing the perception of business failure. Through a systematic literature review and content analysis, we identified 20 variables within the network. A subsequent cross-impact analysis helped delineate these variables as critical, influential, dependent, inert, or neuter. Stigma emerged as the critical variable, exerting significant influence. Culture, bankruptcy laws, social capital, the frequency of business failures, and entrepreneurial attributes played pivotal roles as influential variables. Dependent variables encompassed the rate of entrepreneurship, entrepreneurial intention, and learning from failure. This study underscores the importance of comprehending the interplay between these variables and their impact on entrepreneurial outcomes. Although the influence of societal perceptions on business model innovation proved minimal, failed entrepreneurs displayed resilience in defying stigma and engaging in innovative endeavors. Our findings shed light on the significance of societal perceptions within entrepreneurial ecosystems and the adaptability of entrepreneurs in innovating existing business models. This study lays a foundation for further research into the dynamics of these influences.</p

Assessing the current landscape of AI and sustainability literature:Identifying key trends, addressing gaps and challenges

The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research.</p

Identifying key interactions between process variables of different material categories using mutual information-based network inference method

This paper analyzes production data from injection molding processes to identify key interactions between the process variables from different material categories using the network inference method called "bagging conservative causal core network" (BC3net). This approach is an ensemble method with mutual information that is measured between process variables to select pairs that show significant shared information. We construct networks for different time intervals and aggregate them by calculating the proportion of significant pairs of process variables (weighted edges) for each production process over time. The weighted edges of the aggregated network for each product are used in a machine learning model to optimize the network interval size (interval split) and feature selection, where edge weights are the input features and material categories are the output classification labels. The time intervals are optimized based on the classification accuracy of the machine learning model. Our analysis shows that the aggregated edge features of inferred networks can classify different material categories and identify critical features that represent interdependence in the associated process variables. We further used the "one vs. other" labels for the machine learning models to identify material-specific interactions for each material category. Additionally, we constructed an aggregated network over all samples in which the process variable interactions were steady over time. The resulting network showed modular characteristics where process variables of similar categories were grouped in the same community.publishedVersionPeer reviewe