As AI becomes increasingly embedded in supply chain operations, understanding how supply chain managers engage with these technologies in a responsible and context-sensitive manner is essential for advancing sustainable supply chain. Existing research tends to treat AI use as a trait-based phenomenon, overlooking the behavioral dynamics the same manager could exhibit across varied situations. This perspective article introduces the concept of the responsible AI use signature (RAI Signature), grounded in Cognitive-Affective Personality System theory (CAPS), to capture the stable yet dynamic patterns of responsible AI behavior demonstrated by the same manager across task, relational, and geographical contexts. A multi-level conceptual framework is developed to explain how RAI signature emerges and how it influences sustainable supply chain outcomes. Drawing on CAPS, this perspective article argues that RAI signature reflects the activation of cognitive-affective units in response to situational cues, moderated by individual, organizational, and technological factors. Three main propositions are developed to advance theory by shifting the focus from static competencies to within-person behavioral variability, offering a new lens for understanding, measuring, and developing managerial capacity in AI-enabled supply chains. This article contributes to research at the intersection of responsible AI, supply chain sustainability, and human-centered digital transformation.
This study examines the relationship between technological innovation and economic growth, distinguishing between two key components of innovation at the country level: value creation through invention and value capture through commercialization. Using panel data on 43 OECD member and partner countries from 2000 to 2020, we proxy value creation using aggregated patent counts and multiple dimensions of patent value. We proxy value capture with aggregated venture capital (VC) investment, which serves as an indicator of countries' capacity to commercialize and scale new technologies. To account for persistence in growth and potential endogeneity, we estimate dynamic panel models in first differences with instrumental variables. Our results show that raw patent counts are not systematically associated with economic growth, while value-adjusted patent measures, especially patents with at least one forward citation and patents protecting radical inventions, exhibit positive associations. Moreover, the growth relevance of inventive activity is systematically greater in countries with stronger VC activity, underscoring the importance of financial and institutional conditions that enable technologies to be commercialized at scale. A descriptive application of this framework to artificial intelligence and sustainable technologies reveals pronounced cross-country differences: countries that lead in patent volume often differ from those that lead in invention quality and commercialization capacity. Overall, our findings highlight the policy relevance of innovation strategies that jointly foster high-quality invention and the conditions needed to translate technological advances into broader economic value.
This study examines the interconnectedness between small and medium enterprises (SMEs), ESG, FinTech, energy innovation, and sustainability using a quantile vector autoregression methodology. We find that ESG and SME indices are net transmitters of return shocks across all market states, while sustainability and FinTech indices are net receivers of shocks. In addition, the innovation index typically transmits shocks in volatile markets but can also act as a net receiver under normal market conditions, driven by factors such as geopolitical tensions. Findings also show that the net dynamic shock transfer mechanism is time-varying and quantile dependent. This finding highlights the necessity for adaptable strategies. By proposing concrete recommendations, this study provides policymakers and investors with the knowledge they need to exploit the synergies between SME, ESG, FinTech, sustainability and innovation.
The Bass diffusion model assumes that both external institutional channels (innovation coefficient p) and internal social learning (imitation coefficient q) drive technology adoption, and meta-analyses report p>0 across hundreds of applications. We document a rare empirical case consistent with near-zero institutional influence. Across externally informed market-ceiling scenarios the estimated innovation coefficient is approximately 0.001 and indistinguishable from zero, while the freely estimated ceiling places the optimum at the p=0 boundary; a conventionally sized coefficient (p=0.03) is strongly disfavored under all scenarios (ΔAIC ≥ 22.4). We ground this anomaly in a high-resolution satellite census of 3201 solar-powered wells in central Tunisia, where the post-revolutionary collapse of groundwater governance creates a quasi-experimental setting in which social diffusion is unusually visible. An Authorization Trap, in which subsidy eligibility requires permits the state refuses to issue, blocks formal technology-transfer pathways, consistent with institutional channels being effectively absent. At the micro level, discrete-time hazard models on the full site-year panel show that accumulated local adoption predicts faster subsequent adoption in pooled comparisons; the association disappears within localities, consistent with spatially structured diffusion but not by itself identifying neighbor-to-neighbor learning. Because the technology draws on a rivalrous resource base, we propose a Competitive Diffusion Framework distinguishing an aggregate-level Information Effect from a hypothesized local-level Congestion Effect, motivated by suggestive but inconclusive evidence as an agenda for future testing. Governance architecture can thus produce qualitatively different diffusion regimes, with implications for technology forecasting where institutional channels cannot be assumed operative.
This paper investigates the potential of Decentralized Autonomous Organizations (DAOs), leveraging blockchain technology, to establish the essential features for effective Circular Economy (CE) business models. Key requirements for these models often include network governance, stakeholder empowerment, and enhanced regulatory transparency for both government and civil society.We specifically explore the feasibility of employing a DAO to realize these features within the distinct context of the electronic waste (e-waste) recycling circular flow. To assess technological viability and operational characteristics, we conducted a proof-of concept (PoC). This DAO was codified using smart contracts and deployed in a private instance of Ethereum blockchain network. The transaction costs (Ethereum “gas fee”) involved in public Ethereum blockchain are also evaluated.The results indicate the technical feasibility of several critical features, including: process automation, transparent traceability, democratic decisions about rules, and the implementation of token-based payments and credits. These capabilities show significant potential for attaining the desired characteristics necessary to foster innovative circular business models, despite the limitations inherent to a specific-case PoC.However, the results also shown that transactions costs of the public Ethereum network can turn economically unviable small-scale recycling business models or low value-added waste other than e-waste.
Immersive retail technologies, including AI-driven personalisation, augmented and virtual reality, biometric and affective analytics, spatial computing, and metaverse environments, are transforming consumer experiences and retail innovation systems. However, they also pose systemic governance challenges related to data protection, algorithmic bias, behavioural manipulation, surveillance, platform concentration, and consumer vulnerability. Existing studies typically portray regulators as external rule-setters, compliance authorities, or providers of regulatory instruments, overlooking the organisational capabilities by which they influence innovation system dynamics. This conceptual paper develops a capability-based framework of regulatory orchestration in immersive retail innovation systems by integrating innovation systems theory, regulatory science, dynamic capabilities, and orchestration research. It identifies five regulatory orchestrator capabilities: sensing and sensemaking, experimentation and adaptation, co-creation and boundary spanning, orchestration and coordination, and reflexive learning and meta-regulation. These capabilities are enacted through regulatory sandboxes, audits, impact assessments, standards, codes of conduct, post-market monitoring, and cross-agency coordination. By linking these capabilities to knowledge development, entrepreneurial experimentation, legitimation, market formation, coordination, directionality, and reflexivity, the framework explains how regulators can reduce uncertainty, address knowledge and power asymmetries, and guide AI and XR retail innovation towards responsible and socially aligned outcomes. It therefore conceptualises regulators as active participants in innovation systems.
Technological and organisational innovations are often viewed as complementary in improving firm performance. Much less is known, however, about whether employees share the gains associated with these complementarities. This paper investigates whether expected complementarities between automation and organisational innovation are reflected in employee wages and examines the role of employees' social skills in shaping these outcomes. Using linked employer–employee data for Estonian manufacturing firms, combining firm-level information on automation investments, organisational innovation and ESCO-based measures of social skills, we estimate wage equations using firm fixed effects and coarsened exact matching, and an event study approach. We find no evidence that the joint adoption of automation and organisational innovation generates broad complementarity effects in terms of wage gains. Instead, wage effects are highly heterogeneous. The strongest evidence of wage gains associated with joint adoption of automation and organisational innovation emerges among higher-educated employees working in occupations that require strong social skills and in firms engaged in persistent automation investments.
To create digital innovations with breakthrough potential, firms increasingly empower actors to self-select on innovation projects. However, a clear understanding of the factors that influence actors' commitment to self-select on such projects is still lacking. This knowledge gap leaves a void in the understanding of project team emergence in digital transformation environments. To address this gap, we theorize that actors' commitment to contribute depends on the combined effect of opportunity/threat frames and actors' construal level. We conducted two experimental studies that support our theorizing. Our findings contribute to theory on digital transformation and breakthrough innovation by shedding light on actor commitment.
In fast-changing, complex environments where uncertainty abounds, collaborative foresight is increasingly advocated as a means of navigating these conditions – but under what circumstances do its promised benefits materialize? We investigate when collaborative foresight – foresight conducted jointly by two or more organizations – delivers its promised benefits by examining how group composition shapes collective cognition and the ability to challenge dominant mental models and beliefs inherited from past experiences. Our systematic literature review shows limited rigorous theory development on how the composition (in terms of homogeneity versus heterogeneity) of the group of individuals representing the organizations involved affects collaborative foresight processes. It also shows that much of the literature treats diversity as a uniform construct. We propose a conceptual framework that highlights how the deliberate configuration of heterogeneity and homogeneity across attributes gives rise to distinct cognitive dynamics, with both enabling and constraining effects on collective cognition. In doing so, it advances the understanding of these relationships and provides a conceptual foundation for systematically reflecting on and identifying context-specific group compositions for collaborative foresight processes. Moreover, the review shows how these cognitive dynamics shape the collaborative foresight process while emphasizing its recursive nature, as the process itself continuously influences and reconfigures the group's cognition. The paper concludes by outlining a research agenda to guide future empirical and theoretical work.
As artificial intelligence (AI) becomes increasingly embedded in workplaces, understanding how its various roles impact employee behavior and performance is essential. Through the lens of role theory, this research examines how different AI roles—specifically, AI as a supervisor, peer, subordinate, or tool—affect employee performance. We employed a mixed-methods approach, combining qualitative insights with quantitative evidence from a field survey and a lab experiment to provide comprehensive evidence on the impact of these AI roles in the workplace. Our findings reveal that AI in supervisory roles, compared to peer, subordinate, or tool roles, reduces employee performance by increasing abdication of responsibility and lowering the sense of empowerment. In contrast, AI functioning as a peer or as a subordinate enhances employee performance by fostering a stronger sense of empowerment. The findings provide valuable insights for managers and AI engineers on how to assign AI roles and structure human-AI communication to optimize AI's benefits while mitigating its potential downsides.
Immersive technologies are increasingly reshaping how organizations design experiences, train employees, interact with customers, and create value in digitally mediated environments. Yet research on immersive technologies in business and management remains fragmented across technologies, contexts, and outcomes. This study provides a bibliometric and content-analytic review of 1323 articles published in business and management and adjacent applied journals (rated AJG 3, 4, and 4*). Combining descriptive statistics, bibliographic coupling, structured content analysis, and temporal evolution analysis, we identify six research fronts: human activities and behaviors in immersive environments, industrial and training applications of immersive technologies, psychological responses and experiential mechanisms, platform-based value creation through immersive technologies, immersive online shopping experiences, and immersive tourism experiences. These fronts form two broader blocks: a foundational block centered on human factors, training, and explanatory mechanisms, and an application block centered on consumer, service, tourism, and platform-based value creation. Temporal analysis suggests that the field has moved from foundational questions of perception, behavior, and interaction toward more application-oriented and platform-oriented research, especially in the post-pandemic period. Building on these findings, we develop a conceptual framework linking immersive-technology features, mechanism bundles, multilevel outcomes, and boundary conditions, and we propose future research directions for advancing theory and practice in immersive-technology scholarship.
Europe excels in scientific output but struggles to translate public research into entrepreneurial ventures. Building on the Knowledge Spillover Theory of Entrepreneurship (KSTE), this paper interprets this gap as a systemic knowledge filter problem arising from misalignment between Innovation Systems (IS) and Entrepreneurial Ecosystems (EE). We develop a conceptual framework defining IS–EE alignment as a multi-dimensional governance condition across structural, functional, and resource domains, arguing that its configuration determines knowledge filter permeability. Using the Triple Helix Twin model, we position regional public actors as orchestrations of this alignment. Empirically, we investigate academic spin-offs through a case study conducted across seven European regions. Analysis identifies recurrent systemic tensions and governance archetypes, showing that limited knowledge commercialization stems less from structural constraints or resource scarcity, and more from functional misalignments. Our findings extend KSTE by reframing the knowledge filter as an emergent property of ecosystem governance rather than a fixed barrier. For policymakers, the study highlights that improving commercialization outcomes requires more than expanding support instruments. It depends on improving the functional integration of governance structures and resource systems to better connect actors, align instruments, and sustain commercialization pathways across ecosystem levels.
