Generative artificial intelligence challenges established foundations of innovation legitimacy by decoupling output quality from human effort, intention, and responsibility. When creative outputs no longer provide reliable signals of human agency, legitimacy judgments must be reconstructed through alternative interpretive cues. This study reconceptualizes innovation legitimacy as an inferential process formed under conditions of technological opacity and examines how governance practices shape legitimacy judgments in AI-mediated creativity.Drawing on a survey-based experiment (N = 240), we investigate how configurations of AI involvement, transparency timing, and deepfake-related manipulation cues influence perceived deception, perceived human effort, and perceived authenticity. Legitimacy judgments, operationalized as inferential assessments of deception, effort, and authenticity, are distinguished from their downstream attitudinal and behavioral consequences. The findings show that legitimacy in AI-mediated creativity shifts from output-based evaluation toward governance-dependent sensemaking. Transparency operates as an interpretive mechanism whose effects depend critically on timing, while deepfake cues systematically heighten moral uncertainty and undermine legitimacy.By specifying the microfoundations through which legitimacy breaks down and is partially reconstructed under AI-induced opacity, the study advances innovation theory by reframing legitimacy as a governance-dependent inferential achievement. This reframing differs from existing interpretive and institutional accounts: governance is conceptualized not as a mechanism that generates legitimacy directly through compliance or ceremonial conformity, but as an interpretive infrastructure that shapes the conditions under which audiences can form legitimacy judgments when conventional output-based cues are absent. The findings contribute to debates on AI governance, innovation diffusion, and responsible innovation in contexts characterized by ambiguous agency and uncertainty about algorithmic production processes.
Incubators play a vital role in entrepreneurial ecosystems by providing start-ups with resources such as funding, mentorship, and access to networks. Incubator systems differ from traditional organizations because start-ups remain legally and strategically independent, which limits hierarchical control and makes coordination inherently relational. However, existing research tends to emphasize the effects of individual resources, paying less attention to how incubators orchestrate resources through ongoing interaction with ventures, leaving unresolved why incubators with similar resource bases often produce markedly different outcomes. Thus, drawing on Resource Orchestration Theory, this study examines how incubators structure, bundle, and leverage resources, and how orchestration operates across multiple levels to shape start-up growth and incubator performance. Based on a qualitative multi-case study, we identify a four-stage cyclical orchestration process in which incubators (1) structure a foundational resource base, (2) provide tailored initial resource packages, (3) dynamically reconfigure bundled resources as ventures evolve, and (4) activate start-up feedback. Feedback takes concrete forms such as alumni mentoring, network bridging, investment connections, and reputational endorsements, which replenish the incubator's resource base and enable subsequent rounds of support. We further theorize a multi-level mechanism comprising incubator orchestration, start-up orchestration, and joint orchestration; joint orchestration operates through coordinated resource acquisition, bundling, and leveraging, helping explain outcome heterogeneity beyond resource stock differences.
As an important manifestation of data-driven technological innovation paradigms, digital transformation has become a key pathway through which multinational enterprises reshape their innovation processes, enhance global competitiveness, and achieve sustainable value creation. It not only transforms firms’ modes of knowledge creation, organizational coordination mechanisms, and resource allocation logic, but also creates new opportunities for multinational enterprises to deepen their global innovation network embedding and improve ESG performance. However, existing studies have rarely systematically explored the impact mechanism of digital transformation on ESG performance from the perspective of global innovation network embedding. Grounded in the resource-based theory and social network theory, using panel data of Chinese A-share listed multinational enterprises from 2010 to 2022, the impact and mechanism of digital transformation on ESG performance in multinational enterprises were empirically analyzed by employing the two-way fixed effects model, the mediation effect model and the moderation effect model. Results indicate that, (1) Digital transformation has a significantly positive effect on the ESG performance. (2) The mechanism through which digital transformation enhances ESG performance lies in accelerating the global innovation network embedding of multinational enterprises. (3) Internal control quality strengthens the positive effect of digital transformation on ESG performance, whereas external environmental uncertainty weakens this effect. (4) The digital transformation has a stronger incentive effect on ESG performance for large-scale, manufacturing, and technology and labor-intensive multinational enterprises. The obtained conclusions contribute to fully leveraging the driving effect of digital transformation by multinational enterprises in the global value chain and provide empirical evidence that such firms can enhance their global collaborative innovation capability and sustainable competitive advantage through digital transformation.
This paper aims to conduct a hybrid study focusing on blockchain-driven healthcare service user empowerment and its applications in e-healthcare concepts, using bibliometric, network, thematic, and content analyses. The study considered 778 published articles from 2016 to 2024 and conducted a network analysis of the 49 most-cited articles in the domain of blockchain-driven healthcare service user empowerment. The results underscore the underlying research areas, which are further clustered into five major principles, namely, data ownership and control, data privacy and security, trust and transparency, incentivization and engagement, and accessibility and equity. The results offer unique insights for theoretical and managerial implications and propose a conceptual framework for future study in this sector.
The integration of Industry 4.0 technologies into manufacturing systems has largely emphasized productivity and efficiency, with limited attention to how the inclusion of people with disabilities (PwD) can contribute to and reshape these performance outcomes. Addressing this gap, this study investigates how Industry 4.0 technologies can support workers with sensory and physical disabilities within manufacturing environments. The research employs a qualitative methodology, combining semi-structured interviews with professionals from multinational corporations and specialists in this field as well as analysis of corporate reports to evaluate the effects of technologies such as artificial intelligence (AI), collaborative robots (cobots), augmented reality (AR), and exoskeletons in workers inclusion. Findings reveal that these technologies generate upskilling (e.g., AI-enhanced assistive devices for visual impairments), reskilling (e.g., learning how to do quality inspections with multisensory feedback provided by IoT sensors for hearing impairments), and deskilling (e.g., cobot-assisted task simplification for physical disabilities), depending on the disability type and context. The study introduces the concept of “Inclusion 4.0″, positioning Industry 4.0 as a catalyst for equitable workforce participation aligned with the Industry 5.0 human-centric pillar. Practical implications include actionable insights for policymakers and managers to design inclusive industrial systems, while theoretical contributions highlight a socio-technical framework that prioritizes disability-inclusive technological adoption.
Digital platform technologies are reshaping traditional linear value chains and are increasingly positioned as primary facilitators of circular economy implementation in electronics manufacturing. This study investigates how leading manufacturers, including Apple, Samsung, Xiaomi, and Dell, use digital platforms to support circular strategies through a comparative multi-case analysis of electronics platform archetypes. The analysis identifies two distinct digital platform archetypes. Premium integrated ecosystems, represented by Apple and Samsung, target higher-value consumer segments and emphasise component integration, lifecycle control, and tightly governed proprietary platforms. These models prioritise repair, refurbishment, and lifecycle extension within highly integrated product–service systems. In contrast, accessible ecosystem orchestrators, such as Xiaomi and Dell, promote broader participation across partners and users, focusing on asset management, service modularity, and affordability for price-sensitive consumers and business clients. The findings highlight how platform design influences circular outcomes, with governance structures, business models, and digital infrastructure needing alignment with market positioning and user needs. Overall, the paper contributes to research on digital platforms and circular business models by clarifying how platform archetypes shape pathways toward more circular electronics systems.
This study examines whether and how different types of government support (financial, technological, market) impact the three types of co-creation efforts (product, process, and knowledge co-creation) of SMEs and how these, in turn, impact sustainable innovation outcomes. It also investigates whether digital transformation mediates the positive relationship between the three types of co-creation and sustainable innovation. The conceptual model is tested on an original dataset of 188 SMEs in France, collected in 2025. Findings indicate that product co-creation is positively affected by government technological support, and knowledge co-creation by market support. The mediation effect of digital transformation is confirmed for the relationship between product co-creation and sustainable innovation, while process and knowledge co-creation directly lead to sustainable innovation. The implications of the study for research, policy and practice are discussed.
Achieving disruptive innovation in new green technology domains (NGTDs) is essential for firms seeking sustainable competitive advantage, addressing climate challenges, and complying with increasingly stringent regulations. Building on research on technological relatedness and entry strategies, this study argues that although entering NGTDs enables firms to pursue technological leadership, the disruptiveness of their subsequent green innovations may be constrained by the technological relatedness between their existing knowledge bases and the new green domains. Moreover, this negative effect is shaped by firms’ NGTD entry patterns. Using patent data from 994 Chinese listed firms from 2008 to 2016 and applying a Heckman selection model, we find that technological relatedness reduces the disruptiveness of green innovation. An irregular entry rhythm exacerbates this negative effect, whereas a faster entry pace mitigates it. These findings advance the literature by integrating the concepts of pace and rhythm of entry into studies of green innovation and by providing evidence from an emerging economy context. The results suggest that firms should balance reliance on existing technological trajectories with exploration of new knowledge bases to enhance disruptive outcomes. This study offers actionable insights for firms and policymakers seeking to accelerate green technological transitions and foster more disruptive, high-impact environmental innovation.
Prior research has found that brokers, those who stand between two disconnected parties, may access non-redundant information that is conducive to higher innovation. However, research has also shown that occupying brokering network positions does not systematically translate into higher innovation; that is, structural opportunity does not always lead to advantage. To better understand the conditions under which brokers innovate, we adopt a contingency approach and theorize that the relationship between brokerage and individual innovation is contingent on individuals' perception of their ability to influence those they are connected to – which we label “relational sense of power” – an important aspect of individuals' representation of the situation they are in. Using a social network analysis at a consulting and training organization in The Netherlands, we found support for our theory: employees’ relational sense of power is positively related to innovation in brokering network positions.
This paper examines the boundary between human and machine creativity by analysing 593 tasks across 126 occupations in the cultural and creative industries. Theoretically, we propose an evolutionary conceptualisation of creativity, structured around three rule types corresponding to retention (codified), adoption (tacit), and origination (novel) phases. Empirically, using GPT-4, we generate synthetic annotations of the semantic content of task descriptions in the Australian Skills Classification. We derive indicators of cognitive and behavioural rules within tasks and their carriers (human, AI, or hybrid human-AI) to capture creativity, and indicators of AI autonomy feasibility and efficiency potential to capture GenAI exposure scenarios.The agent-rule matching suggests a specialisation of agents in carrying defined, tacit, or novel rules, as well as two mechanisms. The first is the structured novelty effect, whereby AI autonomy feasibility is higher when defined cognitive rules combine with novel rule creation. The second is the tacit knowledge boundary, whereby tacit rules are negatively associated with AI autonomy feasibility. In our interpretation, these mechanisms reflect an efficacy logic (matching the right agent to each rule type) that differs from an efficiency logic (optimising technically feasible gain potentials). Combining both logics, we derive a simulated classification of tasks into three categories. The mixed category, involving hybrid carrier configurations, predominates (86.3%), alongside limited replacement (2.7%) and AI immune (11.0%) categories.We identify two levels of human-AI complementarity. The first lies in rule types within tasks and engages different forms of creativity (combinatorial, exploratory, transformational). The second concerns efficiency potential when performing tasks.
Firms create and manage coopetitive portfolios (i.e., alliance portfolios including competitors) to foster product innovation, as they provide access to various complementary but also compatible resources and knowledge that are essential for innovation. The presence of competitors in coopetitive portfolios can create specific synergies but also conflicts with other partners that could enhance or limit the occurrence of product innovation. To understand under which combinations of partner types in a coopetitive portfolio product innovation can occur, we use a crisp-set Qualitative Comparative Analysis (csQCA) on a sample of 921 Dutch firms in the period 2010-2016. We show that alliances with competitors are complementary with customer alliances in promoting the occurrence of product innovation. In addition, our analysis reveals that when ties with universities are present in a coopetitive portfolio, suppliers must be present as well to introduce new products. Finally, we highlight that coopetitive portfolio configurations leading to product innovation vary according to the focal firm's size and industry dynamics.
Ongoing environmental issues are increasingly challenging the manufacturing sector because of its environmental footprint. These issues, including carbon emissions, intensive resource consumption, large-scale waste generation, and environmental pollution, have stimulated demand for environmentally responsible supply chain practices. The implementation of these practices can be realized through the application of advanced technology, such as Industry 4.0. This study examines the role of Industry 4.0 technologies towards sustainable supply chain performance and investigates the mediating role played by circular supply chain practices and visibility. Moreover, the moderating role of strategic alliances and resilient supply chains was also analyzed. Data were collected using time-lagged research from senior managers working in manufacturing companies in China. PLS-SEM was used to analyse 371 questionnaires. The findings suggest that Industry 4.0 enhances supply chain visibility and circular supply chain practices and improves sustainable supply chain performance. Further, results also show that supply chain resilience moderates the association between circular supply chain practices, supply chain visibility, and sustainable supply chain performance. However, the moderating role of strategic alliances was found to be insignificant. These findings reveal that manufacturing organizations can achieve sustainable supply chain performance by integrating Industry 4.0 into their operations. These findings contribute significantly to Industry 4.0 and the circular economy in academic, managerial, and policy debates, resulting in sustainable supply chains. Potentially, the findings serve to drive sustainable supply chains in developing countries to better manage the digital transition of the manufacturing industry.
Despite the potential applications of bio-marine technologies, many promising innovations remain confined to the laboratory, missing potential high-value applications and damaging the overall perception of the emerging technology. Prior research has highlighted the positive impact of multi-actor collaborations on technology development, particularly to bridge the gap between the lab and the market. However, we know little about the mechanisms that sustain productive multi-actor collaboration in emerging technology commercialization and why they prove insufficient as actors move from low-to high-value applications. We study the case of an emerging technology – the utilization and application of fucoidan, a bio-marine extract – to explore how collaboration is sustained among actors as they face the increasing complexity of the technology commercialization for high-value applications. By building on patents and publications and analyzing in-depth interviews, we identify three key mechanisms that support effective collaboration: anchoring a shared vision, coordinating across knowledge boundaries, and structuring an operational framework. We show how these mechanisms need to be recalibrated, not simply scaled, when actors transition across problem spaces from low to high-value applications, and why the failure to do so produces a low-value trap for the emerging technology.
Crowdsourcing in science is an increasingly popular complementary method of conducting scientific research. However, organizing a crowdsourcing in science initiative is a challenge for scientists, and many such initiatives fail. Nevertheless, the literature on failures of crowdsourcing in science and their causes is still in its infancy, and the results to date are scattered and fragmented. Drawing on a multilevel perspective, we recognize, characterize and offer a structured understanding of the causes of the failure of crowdsourcing in science. We collected data using focus group interviews with 36 scholars representing various scientific fields and disciplines. The results show that the causes of failure are the initiator's insufficient competences, their negative attitude towards crowdsourcing in science, insufficient organisational support, the compulsory nature of crowdsourcing in science, and the crisis of trust in science. In addition, normative pressure and technology connect the interactions within and between levels. On the basis of these findings, we propose a multilevel conceptual framework that structures the causes of the failure of crowdsourcing in science, and takes into account interactions within and between levels. The results and the framework serve as the basis for offering implications for researchers, managerial staff of institutions of higher education and decision-makers. We show how important crowdsourcing in science is, and that careful management of such projects is workable.
A microfoundational account of organizational innovation capability should explain which individual-level attributes distinguish ideators who are better able to generate, select, and elaborate ideas into investment-worthy business concepts. Yet research remains fragmented because prior studies often examine isolated traits and fail to distinguish among key creative ideation outcomes. Drawing on Trait Activation Theory, we conceptualize venture ideation as a staged, trait-relevant context in which cognitive abilities, personality traits, and creative domain experience are differentially expressed across successive stages. We examine this theorizing using a task-based assessment that separates idea quantity, idea originality, and idea quality. Innovation-exposed experts, including R&D experts, managers, and entrepreneurs (n = 208), generated multiple business ideas for a disruptive technology, selected their most promising idea, and refined it into a product or service concept incorporating core elements of a viable business model. Experienced technology-focused venture capitalists evaluated idea quality by indicating whether these concepts merited investment interest. We quantified semantic distinctiveness using multiple large language models as a semantic proxy for selected-idea originality. Divergent thinking predicted idea quantity, but not investor-evaluated idea quality. In contrast, investment-interest judgments were predicted by ideators’ Openness to Experience, creative domain experience, and convergent thinking ability. Overall, our findings show that early-stage venture ideation is not a unitary creative process. Different attributes help ideators generate many ideas and develop selected ideas into investment-worthy business model concepts, advancing a microfoundational account of organizational innovation capability in technology-based venture creation.
Artificial intelligence (AI) is increasingly shaping how innovation is developed, and new product development (NPD) is an important domain at the centre of this shift, given its data-rich, high-uncertainty contexts. In this paper, we focus on the design and training of AI systems in which human expertise is encoded into machine models and the future scope of human and machine agency is determined. Drawing on organizational learning theory, we argue that training strategies defining who provides labels, which data are used, and how feedback is generated and reused, constitute design decisions that differ in how feedback is recursively structured, where knowledge is located, and how deeply expert judgment is codified. We develop and test three strategies, experiential, nudging, and generative, and instantiate them through computational simulations in the domain of geotechnical services for underwater exploration. We further assess the cross-context portability of the most effective strategy by redeploying the trained model in a second NPD setting. Our results show that strategies that recursively incorporate expert feedback across training cycles produce reliable, transferable models, whereas those that decouple human expertise from the training process converge prematurely on narrow solution spaces. These findings suggest that how firms structure the training of AI systems directly shapes model reliability and transferability, and provide a basis for theorizing how organizational routines, competencies, and relational forms of AI agency may develop as AI systems become embedded in NPD processes.
