This study advances understanding of Artificial Intelligence transformation (AIT) barriers that hinder value realization in public organizations. We conducted a 1.5-year case study with a medium-sized Swedish municipality, involving active participation in daily work, development of an AI strategy, interviews, and analysis of four AIT cases. Using the Process, People, Technology (PPT) framework as inspiration, we developed a new AIT framework tailored to public sector transformation. Findings show that AI creates barriers but also offers opportunities, particularly as a driver of innovation and organizational effectiveness. Overcoming knowledge gaps, resource constraints, and integration challenges is critical for sustainable AIT. Furthermore, the results demonstrate that different forms of AI-related value manifest through observable outcomes, including resource gains, organizational adaptations, and the development of new citizen services. The study provides practical examples of AI implementation and illustrates the complex balance between barriers and opportunities in organizational change. The proposed AIT framework contributes both theoretically and practically, offering insights into how municipalities can navigate AI-driven transformation successfully.

Digital technology diffusion is reshaping innovation across supply chain dynamics and ecosystem partners. Prior work often looks at one firm or uniform settings, missing how technology diffusion need to be orchestrated by ecosystem leader across diverse suppliers and other actors. To address this gap, this study employs an exploratory case study of the intelligent vehicle ecosystem, focusing on an original equipment manufacturer (OEM) and six supply chain partners. Although positioned in the supply chain, these actors influence the wider ecosystem thereby affecting technology diffusion beyond dyadic ties. Drawing on 35 interviews, observations, and secondary data, the research advances the technology diffusion and innovation ecosystem literatures in three ways. First, it highlights the evolving role of focal actor as ecosystem leader, demonstrating their progression from transformational to collaborative and ultimately empowering roles across different phases of digital technology diffusion. Second, it identifies three orchestration mechanisms, namely knowledge orchestration, incentive aligned orchestration, and market driven orchestration, and specifies when each should be deployed in response to partner-specific requirements. Third, it offers a novel perspective on how ecosystem leaders shift value propositions to include both core and peripheral partners. From a practical standpoint, the study offers OEMs actionable guidance to diagnose their diffusion context and select appropriate orchestration mechanism. It also provides policymakers with insights for designing targeted instruments that strengthen digital diffusion and support sustainable industrial growth.

Twin transition, the concurrent development of digitalization practices and circular practices, are believed to be the key to firm performance. However, empirical evidence is currently scarce regarding the effect of twin transition on firm performance and the complementary practices that enable and realize it. This study draws on a survey of 188 Swedish manufacturing firms and employs PLS structural equation modelling to test the hypothesis. The results indicate that firm performance can be improved through digitalization practices but with circular practices as a mediator. Moreover, our results show a significant and positive moderation effect of supply chain orchestration and supply chain business model innovation as complementary mechanisms for enabling and realizing the process. This implies time sequencing between the two twin transition dimensions. These results carry theoretical implications for twin transition research and circular economy literatures. The results also provide novel managerial insights by showing that digital investments must be coupled with early orchestration of supply chain partners and aligned business model innovation to effectively scale circular practices and realize performance benefits.

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.

Although much has been made about heroic entrepreneurs, there is recent interest in less glamorous forms of entrepreneurship. The least glamorous is dirty entrepreneurship. In this study, we used an inductive approach and a sample of entrepreneurs engaged in dirty plastic recycling businesses to develop an intersectionality model of entrepreneurs’ dirty recycling businesses. This inductive study offers new insights into how individuals’ intersectionality pushes them into dirty entrepreneurship, how they approach their businesses, and who they stigmatize. Interestingly, individuals’ dirty place and caste push them into dirty entrepreneurship that collectively cleans the environment despite not intending to do so.

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.

Online retail platforms face significant challenges, including intense competition, the chicken-or-egg dilemma in acquiring consumers and sellers for network effects and establishing sustainable growth models. This article addresses these challenges by focusing on the concept of generativity: a platform’s ability to expand product and ecosystem boundaries, thereby unlocking the potential economies of scale and scope. Drawing on experiences from both small and large online retail platforms, we outline an extended set of strategic actions to foster generativity by engaging consumers (e.g., facilitating consumer creativity), complementors (e.g., creating open marketplaces), and their cross-sided interactions (e.g., extending interaction along consumer journeys). For each strategy, we provide practical, actionable recommendations that platform owners can implement to expand their product offerings and add new functions and services to improve interactions between participants. By emphasizing the importance of integrating the contributions of both consumers and complementors, these strategies foster a dynamic and innovative platform ecosystem, which is crucial for the sustained growth and competitiveness of online retailers.