Recent decades have witnessed increased number of studies focusing on digitalization and related capabilities. Across disciplines digitalization capability is viewed as a sources of sustained competiveness. Nonetheless, several issues related to conceptualizing digitalization capabilities remain ambivalent. The present study, uses co-citation analysis to clarify concept of digitalization capability and identify three underlining capabilities, namely digital integration capabilities, digital platform capabilities, and digital innovation capabilities, that represents micro-foundation of digitalization capabilities. Further, a capability-based model is developed which includes antecedents and consequences of digitalization capabilities in an integrated conceptual model. Suggestions for future research, theoretical contributions and managerial contributions are also presented.

The emerging literature on outbound open innovation has highlighted innovation processes, which presuppose active outward technology transfer to increase firm profits. To contribute to this discourse, our paper goes beyond the emphasis on core-related technologies and knowledge that currently dominates the technology management literature and develops the novel concept of misfit technology. This concept captures technologies that are not aligned with a focal firm's current knowledge base and/or business model, but which may still be of great value to the firm if alternative commercialization options are considered. By developing a framework that acknowledges (1) Sources of misfit technology, (2) Environmental uncertainty, (3) Organizational slack, (4) Industry appropriability regime and (5) Technological complexity, we theorize on how different modes of commercialization relate to misfit technology commercialization success. The paper is conceptual and is presented with the purpose to spawn further research on this important topic, but simultaneously touches upon the issues of utmost relevance to R&D management practice

There is confusion surrounding the concept of industrial ecosystems (IEs). This research therefore presents a systematic literature review on the subject of industrial ecosystems and outlines several paths for future research. The paper defines the key characteristics of IEs, identifies three perplexity drivers that contribute to the conceptual ambiguity of IEs, proposes a four-tier integrative IE framework that outlines the core components of IE research, and presents a conceptual model that clarifies how synergetic effects emerge, leading to IE transformation. Articles are categorized into four categories (industrial symbiosis, metabolism, architecture, and orchestration) from which ten propositions are delineated. The study encourages researchers to tap into several areas from the view that this is a broad, but still rather unexplored area of research with high relevance for policy.

The circular economy (CE) has the potential to capitalise upon emerging digital technologies, such as big data, artificial intelligence (AI), blockchain and the Internet of things (IoT), amongst others. These digital technologies combined with business model innovation are deemed to provide solutions to myriad problems in the world, including those related to circular economy transformation. Given the societal and practical importance of CE and digitalisation, last decade has witnessed a significant increase in academic publication on these topics. Therefore, this study aims to capture the essence of the scholarly work at the intersection of the CE and digital technologies. A detailed analysis of the literature based on emerging themes was conducted with a focus on illuminating the path of CE implementation. The results reveal that IoT and AI play a key role in the transition towards the CE. A multitude of studies focus on barriers to digitalisation-led CE transition and highlight policy-related issues, the lack of predictability, psychological issues and information vulnerability as some important barriers. In addition, product-service system (PSS) has been acknowledged as an important business model innovation for achieving the digitalisation enabled CE. Through a detailed assessment of the existing literature, a viable systems-based framework for digitalisation enabled CE has been developed which show the literature linkages amongst the emerging research streams and provide novel insights regarding the realisation of CE benefits.

To facilitate this transition, firms operating in the electric vehicle (EV) battery ecosystem must reassess their value creation, capture, and delivery methods. Although EV battery second life presents a promising solution for circularity, many vehicle manufacturers and stakeholders in the battery ecosystem struggle to adapt their organizations internally and externally due to a lack of insights into suitable circular business models. The purpose of this study is to identify viable archetypes of circular business models for EV battery second life and examine their implications on company collaborations within the EV battery ecosystem. Three main archetypes of circular business models are identified (i.e., extending, sharing, and looping business models) and further divided into eight sub-archetypes. These models are elucidated in terms of key business model dimensions, including value proposition, value co-creation, value delivery, and value capture. The paper provides visual representations of the necessary interactions and collaborations among companies in the EV battery ecosystem to effectively implement the proposed business model archetypes. This research contributes to the theory of circular business models in general, with specific relevance to EV battery circularity.

Since the infancy of the Delphi Technique for collecting and aggregating expert insight, this methodological tool has been discussed, adapted and applied in over 2,600 published scholarly papers to date. This paper mines the major citation indexing services to analyze five dimensions of these data: primary contribution (methodological or applied), field and subfield, length (in pages), year, and journal/conference. Interpreted visual analytics of these five dimensions (both individually and in combination) provide researchers, practitioners and editors with clear insights about whether the Delphi technique is still as prominently used, discussed, and written about in the academic literature as it was twenty years ago and the related trends that might inform predictions of its future use. Among these insights, a simple time series of frequencies of Delphi publications by year immediately shows that academic acceptance of Delphi as a research tool is not only well established, but it has been growing in popularity and range of research domains for two decades predicting unprecedented levels of use in the years to come.

As AI and ML technologies are increasingly incorporated into products, there is a need to understand the role of these incorporations in enhancing performance. This study uses new types of methodology related to textual data analysis to explore the question of whether there is a difference between market sentiments—and consequently marketing and business performance—when it comes to communicating either AI or ML. We test and confirm the hypothesis that AI rather than ML attracts more positive sentiments in the marketplace. Additionally, we find that AI is mostly used when the discussion centers on innovativeness, and that discussions concerning collaboration in these technologies attract more positive sentiments. We further contribute methodologically by leveraging textual data available online on the titles of web-page contents and the results of the Vader sentiment analysis to test our hypothesis. We conclude that, to enhance business performance, firms should communicate using AI-related vocabulary especially when the topic is innovativeness and collaboration.

Recent advances in AI algorithms and computational power have led to opportunities for new methods and tools. Particularly when it comes to detecting the current status of inter-industry technologies, the new tools can be of great assistance. This is important because the research focus has been on how firms generate value through managing their business models. However, further attention needs to be given to the external technological opportunities that also contribute to value creation in firms. We applied unsupervised machine learning techniques, particularly DBSCAN, in an attempt to generate a macro-level technological map. Our results show that AI and machine learning tools can indeed be used for these purposes, and DBSCAN is a potential algorithm. Further research is needed to improve the maps and to use the generated data to study related phenomena including entrepreneurship.

Digital healthcare platforms (DHPs) represent a relatively new phenomenon that could provide a valuable complement to physical primary care – for example, by reducing costs, improving access to healthcare, and allowing patient monitoring. However, such platforms are mainly used today by the younger generations, which creates a “digital divide” between the younger and the elderly. This article aims to identify: i) the perceived key barriers that inhibit adoption and usage of DHPs by the elderly, and ii) what DHP providers can do to facilitate increased adoption and usage by the elderly. The article draws on qualitative interviews with elderly and complementary process data from a major Swedish DHP. We find that the elderly perceives two key barriers to initial adoption of DHPs: i) negative attitudes and technology anxiety and ii) one key barrier affecting both adoption and usage – lack of trust. The analysis also identifies multiple development suggestions for DHP improvement to better accommodate the needs of the elderly, including suggestions for application development and tailored education activities. We provide an integrated framework outlining the key barriers perceived and ways to address them. In so doing, we contribute to the literature on mHealth and to the literature on platforms in healthcare.

The purpose of this paper is to provide an economic analysis of the technology development patterns in the European wind power sector. The three classic Schumpeterian steps of technological development, invention, innovation and diffusion, are brought together to assess the relationship between these. Three econometric approaches are used, a negative binomial regression model for inventions approximated by patent counts, different learning curve model specifications that have been derived from a Cobb-Douglas cost function to address innovation, and a panel data fixed effect regression for the diffusion model. We suggest an integrated perspective of the technological development process where possible interaction effects between the different models are tested. The dataset covers the time period 1991–2008 in the eight core wind power countries in Western Europe. We find evidence of national and international knowledge spillovers in the invention model. The technology learning model results indicate that there exists global learning but also that the world market price of steel has been an important determinant of the development of wind power costs. In line with previous research, the diffusion model results indicate that investment costs have been an important determinant of the development of installed wind power capacity. The results also point towards the importance of natural gas prices and feed-in tariffs as vital factors for wind power diffusion.

Artificial intelligence (AI) will have a substantial impact on firms in virtually all industries. Without guidance on how to implement and scale AI, companies will be outcompeted by the next generation of highly innovative and competitive companies that manage to incorporate AI into their operations. Research shows that competition is fierce and that there is a lack of frameworks to implement and scale AI successfully. This study begins to address this gap by providing a systematic review and analysis of different approaches by companies to using AI in their organizations. Based on these experiences, we identify key components of implementing and scaling AI in organizations and propose phases of implementing and scaling AI in firms.

Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company’s entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research. © 2020 The Author(s)

The present study investigates the effect of the interaction between digitalization and servitization on the financial performance of manufacturing companies. We challenge the simple linear assumption between digitalization and financial performance with a sample of 131 manufacturing firms and hypothesize a nonlinear U-shaped interaction effect between digitalization and servitization on financial performance. From low to moderate levels of digitalization, the interaction effect between digitalization and high servitization on company financial performance is negative and significant. From moderate to high levels of digitalization, the interplay between digitalization and high servitization becomes positive and significant, improving companies’ financial performance. The results demonstrate the need for an effective interplay between digitalization and servitization, the digital servitization. Without this interplay, a manufacturing company may face the paradox of digitalization. For managers of manufacturing companies, the study provides insights into the complex relationship between digitalization and financial performance, emphasizing the value of servitization in driving financial performance from digitalization. Thus, the study demonstrates how manufacturing companies can become data-driven by advancing servitization.

External technology commercialization, e.g., by means of technology licensing, has recently gained in importance. Despite imperfections in technology markets, out-licensing constitutes a major technology commercialization channel. Although the identification of licensing opportunities represents a significant managerial challenge, prior research has relatively neglected these activities. Therefore, we develop the concept of ‘technology commercialization intelligence' (TCI), which refers to the observation of a firm's environment with particular focus on identifying technology licensing opportunities. Grounded in a dynamic capabilities perspective, we test five hypotheses regarding organizational antecedents and performance consequences of TCI, drawing on data from a survey of 152 companies. The empirical findings provide strong support for the importance of the TCI concept. The findings deepen our understanding of the discrepancies between successful pioneering firms active in technology licensing and many others being less successful. The results have major implications for technology exploitation in open innovation processes.

Firms are faced with increased dynamism due to rapid technological development, digitalization, and sustainability requirements, creating novel opportunities for ecosystem innovation. This is particularly prevalent in smart city contexts where initiatives concerning, for example, energy efficient buildings and smart energy grids drive new kinds of ecosystem formation. Orchestrating emerging innovation ecosystems can offer a path to sustained competitive advantage for ecosystem leaders. Yet, it calls for the development of new capabilities to sense, seize, and reconfigure digitalization opportunities in a highly dynamic ecosystem environment. Yet, prior research lacks insights into the dynamic capabilities and routines required for ecosystem innovation. Therefore, this study investigates how firms can develop dynamic capabilities to orchestrate ecosystem innovation and, thus, gain from it. Through a multiple case study of smart city initiatives, we offer insights into the specific micro-foundations or sub-routines underlying the ecosystem leader's sensing, seizing, and reconfiguring capabilities, which are necessary to orchestrate ecosystem innovation. We develop a capability-based framework demonstrating three orchestration mechanisms – namely, configuring ecosystem partnerships, value proposition deployment, and governing ecosystem alignment. Our findings carry implications for the literature on innovation ecosystems and dynamic capabilities, as well as for managers.

Rapid digitalization of industries has led to the proliferation of complex industrial digital platforms; however, few industrial platform leaders have successfully established sustainable business models around their offerings. The need for a concrete definition of industrial digital platforms and their business models further complicates our understanding of the issue. In this prospecting review, we critically analyze the existing literature on industrial digital platforms to identify key research themes and research gaps and propose a future research agenda for the industrial digital platform literature from a business model perspective. Drawing on insights from research on industrial platforms, digitalization, digital servitization, and business-to-business (B2B) relationships, our analysis focuses on three key themes in defining the boundaries of industrial digital platforms and the crucial aspects of value creation, value delivery, and value capture on such platforms: (a) co-creative value creation, (b) digitally integrated value delivery, and (c) mutual value capture. The findings of this study and a future research agenda framework provide a roadmap for advancing the understanding of business models for industrial digital platforms. This research aims to contribute to the emerging field of industrial digital platforms and guide future research endeavors in this domain, unlocking the full potential of these platforms for businesses and industries.

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.

Recent data leaks such as those involving Dropbox have apparently made Internet users feel less secure than in the past as they face risks when dealing with their digital personal data. However, consumers have increasingly embraced cloud computing empowered Digital Personal Data Stores (DPDSs). To understand this paradox, this study shifts the unit of analysis of DPDSs acceptance from organizations to individuals/consumers and identifies the drivers of adoption of DPDSs (beyond broadly defined cloud computing services). Moreover, it proposes, develops and tests empirically a comprehensive extended version of the Technology Acceptance Model (TAM) in the context of DPDSs, leveraging perceived privacy risks and trust. Using a panel of UK consumers, we find that perceived trust positively influences both usefulness and ease of use. These constructs, in turn, positively affect attitude towards using DPDSs, which ultimately increases the intention to use DPDSs. Privacy risk does not moderate any of the investigated relationships, thus suggesting that trust is a key underlying mechanism enhancing the acceptance of DPDS. Hence, theoretical and managerial implications are discussed.

This paper analyzes through what enabling mechanisms pilot and demonstration plants (PDPs) reduce supply and demand uncertainties, and thereby contributing to the market formation for novel sustainable technologies. The analysis builds on three case studies within the advanced biofuel development in Europe. For each case, we construct a narrative of the technology development and derive detailed insights into how technology actors use PDPs to drive market formation. We develop a comprehensive analytical framework, which highlights how PDPs contribute to supply uncertainty reduction through three main enabling mechanisms: building credibility for the technology, business ecosystem orchestration, and technology learning. The corresponding enabling mechanisms behind demand uncertainty reduction include technology standardization, constructing the narrative, and the creation of legitimacy for the technology. The paper also unfolds the composite activities of each mechanism, and outlines implications for technology developers, policymakers, as well as for the research community.

As e-commerce has increasingly gained traction in the retail market, many traditional “brick-and-mortar” retailers are innovating their business models and making the transition towards digital business models. While scholars have started to examine the influence of digitalization on various business model elements, they have so far paid little attention to its implications on the external relationships in which firms engage for value creation. Building on a qualitative analysis of seventeen interviews, this study develops a two-stage framework for the transition to digital business models. In Stage 1, retailers collaborate with specialized service providers to implement a digital business model. As firms from the retail ecosystem collaborate with firms from the digital-service ecosystem to create a value proposition for end-customers, a meta-ecosystem emerges. In Stage 2, firms (retailers) seek to differentiate themselves from their competitors in the meta-ecosystem. Physical interactions with the digital service providers, the product suppliers, and the customers are a primary means towards this end. Thus, digitalization does not make physical interactions and close personal ties obsolete. Our study has substantial implications for the academic literature and management practice.

At a time when many mature industries have been fundamentally transformed by disruptive innovations, prominent examples such as Apple and Uber reflect how disruptive innovations often originate at the ecosystem or system level rather than in individual firms. Unfortunately, the academic literature has paid little attention to the role of ecosystem development and evolution in relation to disruptive innovations. To overcome this oversight, our study defines disruptive innovation ecosystems and illustrates the impact that the financial technology (FinTech) ecosystem has had on disrupting the financial services industry. We offer an agenda for future research on disruptive innovations and ecosystems and discuss the evolution of the FinTech ecosystem. Our study shows that disruptive innovation ecosystems are not only in need of but also deserving of further attention.

The Smart city is important for sustainability. Governments engaged in developing urban mobility in the smart city need to invest their limited financial resources wisely to realize sustainability goals. A key area for such sustainability investment is how to implement and invest in emerging technologies for urban mobility solutions. However, current frameworks on how to understand the impact of emerging technologies aligned with long-term sustainability strategies are understudied. This article develops a simulation-based comparison between different cities and autonomous vehicle (AV) adoption scenarios to understand which aspects of cities lead to positive AV implementation outcomes. As urban mobility and cities will become smart, the analysis represents a first attempt to explore the impact of AVs on a large scale across different cities around the world. Archetypes are formed and account for most, if not all, world cities. For three of our archetypes (car-centric giants, prosperous innovation centers, and high-density megacities), promoting AV-shuttle use would deliver the greatest advantage as measured by improvements in the model's KPIs. To develop urban powerhouses, however, micromobility would deliver greater benefits. For highly compact middleweights, a shift from private cars to other non-AV modes of transportation would be the smartest choice.

This conceptual paper provides a decision-making framework that enhances our understanding of how Do-It-Yourself (DIY) laboratory entrepreneurs execute ethical standards by dismissing fraud. Although our theory assumes that most DIY entrepreneurs are by nature ‘ethical’, we discuss how the unique nature of DIY laboratory entrepreneurship provides risks for fraud. Drawing on three ethical theoretical lenses, utilitarianism, deontology and egoism, our paper proposes different potential causes of fraud and motivates further analysis about why DIY laboratory entrepreneurship is an important context for the study of fraud. We contribute to theory and government policy by providing a conceptual framework that explains how entrepreneurial choices lead to three main types of fraud based on the dominant decision pathways. Further research and practical implications are discussed.

Manufacturing firms are increasingly transforming toward digital servitization, characterized by convergence and simultaneous gains from digitalization and servitization. Due to the marked academic and practical relevance of digital servitization, we are witnessing a significant upsurge in studies published on this emerging topic. Thus, the present study undertakes a comprehensive bibliometric analysis to synthesize the prior knowledge on digital servitization and, more importantly, to highlight areas for future research. The findings from the analysis are organized so that important authors and organizations are highlighted through analyses of citation chains and co-authorship networks. The bibliographic coupling analysis of HistCite and VOSviewer reveals the emergence of four dominant thematic areas in the digital servitization literature. These four thematic areas are aligning digitalization and servitization transformations, value co-creation perspectives on digital servitization, conceptualizing the platform strategy for digital servitization, and business model innovation in digital servitization. Finally, based on the analysis of how the literature on digital servitization has evolved over the last two decades and the deeper analysis of thematic analysis, we raise important research questions and provide numerous areas for future research.

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.

This study explores the potential of AI to enable circular business model innovation (CBMI) for industrial manufacturers and the corresponding AI capacities and dynamic capabilities required for their commercialization. Employing an analysis of six leading B2B firms engaged in digital servitization, we conceptualize the perceptive, predictive, and prescriptive capacities of AI, which enhance resource efficiency by automating and augmenting data-driven analysis and decision making. We further identify two innovative classes of AI-enabled CBMs – augmentation (e.g., optimization solutions) and automation (e.g., autonomous solutions) business models – and their main circular value drivers. Finally, our research reveals novel dynamic capabilities underpinning the innovation of AI-enabled business models – value discovery, value realization, and value optimization capabilities – which enable manufacturers to make economic and sustainable values come to life in collaborating with customers and ecosystem partners. This study represents an important step in our understanding of how AI can drive circularity and sustainable innovation in industrial digital servitization. Overall, our study contributes to practice and the academic literature on AI, circular business models, and digital servitization by highlighting the potential of AI to empower CBMs for industrial manufacturers and the underlying processes of this digital transformation.

Despite the key role of actor networks in progressing new sustainable technologies, there is a shortage of conceptual knowledge on how policy can help strengthen collaborative practices in such networks. The objective of this paper is to analyze the roles of such policies – so-called network management – throughout the entire technological development processes. The analysis draws on the public management and sustainability transitions literatures, and discusses how various network characteristics could affect the development of sustainable technologies, including how different categories of network management strategies could be deployed to influence actor collaborations. The paper's main contribution is an analytical framework that addresses the changing roles of network management at the interface between various phases of the technological development process, illustrated with the empirical case of advanced biorefinery technology development in Sweden. Furthermore, the analysis also addresses some challenges that policy makers are likely to encounter when pursuing network management strategies, and identifies a number of negative consequences of ignoring such instruments in the innovation policy mix. The latter include inefficient actor role-taking, the emergence of small, ineffective and competing actor networks in similar technological fields, and a shortage of interpretative knowledge.