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

Gegen die Zivilisation: völkischer Dezisionismus und historische Subjektivität in der äußeren Rechten

Durch den positiven Bezug auf die europäische Zivilisation und die kulturalistische Interpretation demokratischer Werte gelingt es der äußeren Rechten, Unterstützer: innen zu mobilisieren, die klassische Elemente rechtsextremer Ideologie ablehnen und negative Einstellungen gegenüber insbesondere islamischer Migration nach Europa vertreten. Dieser Artikel ordnet den "Zivilisationismus" ideengeschichtlich ein und stellt dabei fest, dass der positive Bezug auf die europäische Zivilisation in der äußeren Rechten historisch und in der Gegenwart keineswegs unumstritten ist. Der Artikel untersucht daher alternative Konstruktionen historischer Subjektivität und fragt, wie diese die äußere Rechte in der Gegenwart prägen. Augenmerk liegt dabei auf dem völkischen Abendlanddenken und der Ideologie des politischen Dezisionismus, die sich im Ethnopluralismus der Neuen Rechten widerspiegeln.By positively emphasizing European civilization and employing a culturalist interpretation of democratic values, the far right mobilizes supporters who reject traditional elements of far-right ideology while holding negative views on Islamic migration. This article places the concept of "civilizationism" in an intellectual-historical framework, acknowledging that the positive reference to European civilization remains contested within the far right, historically and presently. It explores alternative constructions of historical subjectivity and their impact on the far right. The focus lies on folkish Occidentalism and the political ideology of decisionism. Both are reflected in ethno-pluralism and the contemporary "New Right.

Leveraging observation timing variability to understand intervention effects in panel studies: an empirical illustration and simulation study

To examine developmental processes, intervention effects, or both, longitudinal studies often aim to include measurement intervals that are equally spaced for all participants. In reality, however, this goal is hardly ever met. Although different approaches have been proposed to deal with this issue, few studies have investigated the potential benefits of individual variation in time intervals. In the present paper, we examine how continuous time dynamic models can be used to study nonexperimental intervention effects in longitudinal studies where measurement intervals vary between and within participants. We empirically illustrate this method by using panel data (N = 2,877) to study the effect of the transition from primary to secondary school on students’ motivation. Results of a simulation study also show that the precision and recovery of the estimate of the effect improves with individual variation in time intervals

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