“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.

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. 

Scaling is both the main goal and the ultimate challenge for industrial small tech firms (STFs). This is particularly true when firms offer complex and innovative digital solutions for industrial applications. We delineate scaling strategies deployed by STFs, uncovering the underlying configuration of activities driving the scaling process. This study reveals a distinctive sequence, where scaling evolves from a common phase of systematic digital solution piloting, into two distinctive strategies called ecosystem scaling (i.e., solution market evaluation, ecosystem development, and horizontal partner-led scaling activities) and servitization scaling (i.e., customer insights mining, service operations development, and vertical customer-led scaling activities).

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.

Manufacturers are facing increased competitive pressure to strengthen their business ecosystems in order to adapt to ongoing twin transitions toward digitalization and sustainability and their associated trends. Yet, managing such ecosystems in complex inter- and intra-organizational settings requires a profound, yet little understood, shift in the role and capabilities of internal organizational functions. This study aims to investigate the composition of ecosystem management capabilities. We employ an in-depth single case study of a leading global solution provider in the automotive and transport industry, based on 51 interviews across the ecosystem management function, various organizational functions, and ecosystem actors. Our analysis, rooted in the dynamic capabilities perspective, highlights three sets of crucial ecosystem management capabilities: ecosystem foresight, ecosystem integration, and ecosystem governance. We further detail the underlying routines and micro-foundational activities enabling these capabilities. By illuminating the key capabilities, routines, and activities of ecosystem management in a dynamic context, this study makes significant contributions to management and strategy research on ecosystems and ecosystem management in rapidly evolving business landscapes.

In the race toward autonomous vehicle solutions, providers engage in disruptive market-shaping processes to overcome barriers to commercialization and trigger market formation. This study investigates how disruptive market-shaping processes unfold by employing a multiple case study of 10 AVS providers. Our findings reveal a disruptive market-shaping process comprising four dimensions: shaping the value logic, nurturing the demand side, forging ecosystem alliances, and catalyzing institutional changes. Furthermore, we analyze the temporal unfolding of these activities to illustrate a comprehensive market-shaping process comprising three phases: market exploration, market preparation, and market experimentation. We make significant contributions to the market-shaping and AVS literature. By showing empirically how disruptive market-shaping processes unfold, we refine existing knowledge on the broader market formation stage, anchoring it to the emergence of new markets. Our framework offers micro-level insights into the sequencing of activities and phases embedded in shaping new markets. We argue that successful disruptive market shaping is contingent on creating an attractive market value proposition. Finally, we identify the foundational activities required to overcome market barriers and advance the commercialization of autonomous solutions. We find that granular systems logic reveals AVS's true value, questioning traditional market logic and paving the way for a sustainable, autonomous future.

Manufacturing companies are introducing innovative ways to facilitate the sustainable transition of their customers’ operations. The emerging literature on digital servitization proposes numerous factors, such as the use of advanced artificial intelligence analytics, orientation toward outcomes, and aligning ecosystem partnerships, which can potentially influence the sustainable performance of industrial customers. However, there is currently a lack of understanding regarding how these factors interact to result in sustainable outcomes. Hence, this study seeks to shed light on these complex relationships by identifying viable smart solution strategy configurations for achieving customer sustainable performance. Drawing on a dataset of 180 Swedish manufacturing firms, this study uses a configurational comparative method—namely, fuzzy-set qualitative comparative analysis—to identify the impact of different configurations of “AI-driven optimization,” “outcome orientation,” “value co-creation,” and “ecosystem orchestration” conditions on the realization of customer sustainable performance. This study has identified five smart solution strategies that empower manufacturers to realize sustainable performance for their customers. Among the five configurational strategies identified, the first configurational strategy appears to be the most prominent, as it is based on an outcome-based approach in which the firm uses its technological expertise and its ecosystem partnerships to take over customer operations. Thus, this study contributes to the ongoing discussion in digital servitization on its enabling role for industrial sustainability practices.

As incumbents strive to collaborate with start-ups in the pursuit of cutting-edge digital solutions, the complexities posed by disparate partners and their innovative endeavours often lead to intricate tensions. Our research underscores the critical role of innovation intermediaries in enabling a successful digital co-creation, yet a deeper understanding of this novel and evolving context is required. Through a comprehensive study of two innovation intermediaries, five incumbent companies, and eleven start-ups, we shed light on how intermediaries can effectively mitigate the hard-to-manage tensions that emerge. Our analysis uncovers three primary tensions: incompatible digital co-creation cultures, divergent digital innovation operations, and misaligned technical capabilities. We further propose a four-phase process for innovation intermediation, including the establishment of digital co-creation foundations, catalysing digital innovation projects, orchestrating the co-creation process, and scaling the resulting outcomes.