This study explores how governmental and regional investments in the digital economy foster the emergence of new industries and the region's sustainable competitiveness. In particular, we focus on the creation of the big data comprehensive pilot zone (BDCPZ) in China that serves as a proxy for digital economy advancement initiatives. Employing a quasi-experimental design and a panel dataset from 31 Chinese provinces (2009–2020), this study uses the difference-in-differences approach to evaluate the impact of BDCPZ on the region's competitiveness. Our findings show that enhanced digital economy advancement significantly boosts the creation of new industries, such as the creative industry. This study advances understanding by linking economic development and the business environment and conducting a heterogeneity analysis to identify the most influential characteristics for new industry creation. These characteristics are categories that fall under regional ecosystems—including talent mobility, innovation culture, and financial institutions—and act as mediators of this relationship, offering valuable insights for stakeholders to enhance regional sustainable competitiveness and innovation.Managerial relevance statement: This study offers valuable managerial insights for managers, policymakers, and industrial networks to apply in practice. By understanding the importance of regional talent mobility and strong innovation culture, managers in traditional engineering firms can experiment with novel strategies like rotational mobilizing programs to increase agility and diversify into creative sectors. Policymakers can support these efforts in three ways: by incentivizing digital economy advancements like automation, application of emerging technologies through tax breaks and grants; by establishing cross-sector innovation hubs where traditional industries collaborate with tech startups and creative enterprises; and by focusing on training and reskilling programs. Finally, industrial networks, i.e. consultancy networks drive strategy and collaboration, are encouraged to educate financial institutions on how to assess creative industry projects and facilitate talent mobility by linking traditional engineering firms with SMEs and startups to acquire valuable cross-sector experience, foster innovation, and contribute to the development of new industries.

“Fail fast and learn fast” is a principle commonly advanced to quickly grow and scale startups and SMEs. However, the literature lacks detailed insights into how such learning is organized. The paper aims to investigate how knowledge-intensive SMEs learn from failures through organizational learning processes. To answer this question, we present in-depth case studies of three SMEs that operate in a dynamic context where quick adaption to changes, failures, and learning are natural modes of practice. Our findings present the learning from the failure process, which includes three phases: (1) failure recognition, (2) interactive sensemaking, and (3) organizational adaptation. We condense our insights into a framework disentangling how SMEs succeed and fail and how they can learn from failures through their underlying learning processes. We contribute to prior literature on organizational learning in SMEs by focusing on knowledge-intensive SMEs and practices that enable effective learning from failures.

Corporate Social Innovation (CSI) has been built upon evidence from traditional profit-seeking organizations, mainly Multinational Corporations (MNCs); however, there is limited understanding of how hybrid organizations implement CSI. Hybrid organizations mix profit and non-profit logic and hold the potential to pursue transformative change. This study explores the business strategies that enable hybrid organizations to engage in CSI for transformative impact. Focusing on B Corps, sustainability-oriented hybrids that address profit, people, and planetary issues, we conducted a multiple case study of twenty-one B Corps across Latin America. We describe how B Corps leverage CSI through transactional, iterative, and systemic strategic orientations across six business strategies. Our study provides a three-stage framework for implementing transformational change, offering insights into theory and practice, and addressing the growing demand for businesses to tackle Grand Challenges. Future research directions are also suggested.

This study explores the management of the emergence of artificial intelligence (AI)-enabled product–service systems (PSS) that facilitate a manufacturer's transition toward autonomous solutions. We use a case study involving a leading manufacturer actively developing autonomous and integrated solutions while incorporating AI into product–service systems (PSS) from 2015 to 2022. The study contributes to the digital servitization literature in two ways. First, the study merges AI into product–service systems through a multi-layered path model, which includes layers such as AI-enabled PSS, AI-related PSS technologies, and AI-related PSS ecosystem partnerships. Second, the study identifies managerial practices of strategizing, evangelizing, and routinizing to create AI-enabled PSS, providing guidance for managers planning to merge AI into their product–service systems. The study outlines a research agenda for future AI-enabled PSS. Additionally, it gives managers a framework to understand how to pursue the transition journey.

As industrial sustainability concerns mount, manufacturers engaged in digital servitization grapple with the twin transition of configuring and implementing digital solutions to meet their customers' sustainability objectives. To address this challenge, a socio-technical systems theory-based framework is proposed. Drawing on case studies with three manufacturers undertaking digital servitization and on the sustainability, digital servitization, and socio-technical systems theory research, our framework highlights key processes in navigating the twin transition. It emphasizes the importance of contextual factors in defining a customer twin-transition roadmap, outlines key steps in reconfiguring technical and social subsystems, and stresses the joint optimization processes based on feedback and metrics. This systematic approach guides manufacturers in supporting their customers through the twin transition, emphasizing socio-technical systems and iterative processes for sustained success. Our findings contribute to the growing digital servitization and sustainability research by conceptualizing underlying processes in the twin transition and offering manufacturers practical insights. 

The literature on circular business models (CBMs) has generated barrier categories and taxonomies but provides an incomplete understanding of how firms develop and scale CBMs in practice. We challenge prior literature by relating barriers directly to the CBM dimensions of value creation, value delivery, and value capture. Following this approach, we analyze the Swedish manufacturing industry using qualitative content analysis and arrive at three key contributions. First, there is a distinct set of barriers affecting value creation, value delivery, and value capture. Second, the barriers are underpinned by a unique set of problems, many of which are nondecomposable. Third, most barriers inhibit the scaling of CBMs rather than their initial development. Altogether, this has major implications for understanding the nature of CBMs, productively addressing CBM barriers and problems, and scaling CBMs. We conceptualize these insights into a framework with implications for both the CBM literature and for managers innovating CBMs.

The circular economy can enable small and medium-sized enterprises (SMEs) to address sustainability challenges and facilitate the transition from the linear economy. Understanding ecosystem strategies and their implications for circular business model implementation is vital for a successful circular economy transition. Therefore, we seek to answer the following research question: How do SMEs’ circular ecosystem strategies influence business model implementation to achieve circularity goals? Drawing on a multiple case study of 31 SMEs from the food processing industry, our research provides insights into four ecosystem strategies: visioner, synergizer, explorer, and amplifier. These circular ecosystem strategies are contingent on the ability of SMEs to adopt an appropriate role in the ecosystem (leader or complementor) and on the state of ecosystem evolution (emerging or established). In addition, we elucidate activities dealing with the design, development, and commercialization of the circular business model that relate to the ecosystem strategies identified.

The twin transition, which involves the integration of digital and green transformations, is increasingly recognized as crucial for achieving a sustainable and competitive future. These intertwined transitions aim to decarbonize the economy by leveraging advanced digital technologies. Despite growing policy efforts to advance the twin transitions agenda and move toward a net-zero society by 2050, organizations face significant challenges in aligning digital innovations with sustainability goals. These challenges include the lack of a clear conceptualization, foundational success factors, and a structured series of activities needed to achieve the twin transition. These current shortcomings carry practical implications for implementing the twin transition and speak to the need for further research. Consequently, this study addresses these gaps by identifying the factors influencing the organizational implementation of the twin transition. To this end, we conduct a semi-structured literature review to synthesize current research on twin transitions. We provide a novel definition of twin transitions as “two parallel and mutually reinforcing digital and green transitions that amplify each other, leading to sustainable competitiveness for firms”. Moreover, our analysis delineates a twin transition implementation framework, which includes triggers, organizational practices, foundational success factors, and outcomes for organizations. Our findings indicate that twin transitions are manifested through two key organizational practices: the initial stage of twin transition practices and the practices to achieve maturity in the twin transition. Furthermore, the study contributes to the growing literature at the intersection of digitalization and sustainability, providing numerous suggestions for future research and highlighting the importance of focusing on a firm-centric research agenda.

This study explores the interlink between AI capabilities and circular business models (CBMs) through a literature review. Extant literature reveals that AI can act as efficiency catalyst, empowering firms to implement CBM. However, the journey to harness AI for CBM is fraught with challenges as firms grapple with the lack of sophisticated processes and routines to tap into AI's potential. The fragmented literature leaves a void in understanding the barriers and development pathways for AI capabilities in CBM contexts. Bridging this gap, adopting a capabilities perspective, this review intricately brings together four pivotal capabilities: integrated intelligence capability, process automation and augmentation capability, AI infrastructure and platform capability, and ecosystem orchestration capability as drivers of AI-enabled CBM. These capabilities are vital to navigating the multi-level barriers to utilizing AI for CBM. The key contribution of the study is the synthesis of an AI-enabled CBM framework, which not only summarizes the results but also sets the stage for future explorations in this dynamic field.

This study intends to scrutinize the role of Artificial Intelligence (AI) in Product-Service Innovation (PSI). The literature on AI enabled PSI, other related innovation business models, product-service systems, and servitization has grown significantly since 2018; therefore, there is a need to structure the literature in a systematic manner and add to what has been studied thus far. Product-service innovation is used to represent the relevance of achieving innovation in business models dealing with innovation outcomes including artificial intelligence. This study used bibliographic coupling to analyze 159 articles emerging from the fields of computer sciences, engineering, social sciences, decision sciences, and management. This review depicts structures of the literature comprising five (5) clusters, namely, (1) technology adoption and transformational barriers, which depicts the barriers faced during the adoption of AI-enabled technologies and following transformation; (2) data-driven capabilities and innovation, which highlights the data-based capabilities supported through AI and innovation; (3) digitally enabled business model innovation, which explained how AI-enabled business model innovation occurs; (4) smart design changes and sustainability, which reveals the working of AI in product service environments with different design changes and transformations based on sustainability; and (5) sectorial application, which highlights industry examples. Each cluster is comprehensively analyzed based on its contents, including central themes, models, theories, and methodologies, which help to identify the gaps and support suggestions for future research directions.