Amid widening regional disparities in China and the concentration of university resources in eastern provinces, the role of research-intensive universities in promoting balanced regional growth remains insufficiently understood. Against the backdrop of China's distinctive institutional arrangements, including the Double First-Class Initiative, local government competition and regionally differentiated development strategies, this study examines how university research capacity influences regional economic growth through spatial knowledge spillovers. Using provincial panel data from China for the period 2005-2019, we employ a Spatial Durbin Model (SDM) to analyze these relationships. The findings show that university research capacity significantly promotes both local and cross-regional economic growth. These effects are conditionally moderated by institutional and capability-related factors: university-industry collaboration enhances spillovers, firms' technological absorptive capacity exhibits a non-linear 'J-curve' pattern, and government innovation support operates primarily through long-term mechanisms. The findings highlight the cumulative and intertemporal nature of university-driven spillovers and advance spatial spillover theory by emphasising non-linear, moderated diffusion in an emerging economy context. Policy implications point to differentiated regional strategies - shifting developed regions from expanding collaboration scale to improving collaboration quality, while prioritising absorptive-capacity building and cross-regional knowledge platforms in less-developed areas.

How do high-tech firms learn from innovation failure? Integrating the behavioral theory of the firm (BTF) with the organizational experience perspective, we propose that prior domain-specific experience serves as an interpretive lens that directs problemistic search toward fundamentally different cross-border M&A responses. When negative innovation performance feedback (NIPF) arises in familiar domains, it motivates exploratory M&A to acquire novel knowledge and overcome bottlenecks; when NIPF arises in unfamiliar domains, it motivates exploitative M&A to leverage existing strengths and stabilize performance. We further argue that translating these domain-contingent motives into actual acquisitions is contingent upon the top management team’s (TMT) role structure. Based on matched survey and archival data on 183 cross-border M&A transactions by Chinese high-tech firms (2010–2023), we find that the positive relationships between NIPF in familiar domains and exploratory M&A, and between NIPF in unfamiliar domains and exploitative M&A, are both amplified by the presence of a dedicated Chief Strategy Officer and by team-based contingency rewards, whereas TMT functional concentration exhibits no significant moderating effect. This study advances BTF by demonstrating how domain-specific experience breaks the assumption of homogeneous search, and extends upper echelons theory by highlighting TMT role structure as a critical bridge between strategic motivation and execution. For managers, our findings offer a contingency roadmap: when innovation fails in familiar territory, explore externally; when it fails in unfamiliar territory, exploit internally, and design TMT roles (e.g., appoint a CSO) and team‑based incentives to support that direction.

In online communities, members with similar objectives congregate to undertake innovation tasks within self-organizing groups. These groups are nested virtually in multilevel networks in the fluid and boundary ambiguous online communities. We examine the impact of a salient feature of online community—member overlap—and its effects on online group product innovation performance. Drawing from a multilevel network perspective of online communities, and integrating both the resource competition and resource generativity views in online communities, we propose that member overlap density at the group level negatively affects group product innovation performance, whereas member overlap density at the community level positively affects group product innovation performance. Additionally, we investigate how community-based network structure, specifically internal bonding and external bridging, moderates the effect of member overlap density at both the group and community levels on group product innovation performance. Results of a pooled panel data comprising 37,072 self-organizing groups in 463 game product creative workshop communities from Steam support our theory. The reliability of our findings has been confirmed by a series of robustness checks, including alternative variable operationalizations, rolling-window estimation, sample restriction, and extended dataset analysis covering the COVID-19 pandemic period. Our findings contribute to the literature on innovation in online communities by incorporating the multilevel network perspective, resource competition, and resource generativity views.

As a new wave of technology revolution and industry transformation accelerates,"context"has emerged at the national strategic level as a critical linkage between technology innovation and market demand.Existing research has primarily adopted the dual framework of"demand pull-technology push",with limited attention paid to the theoretical mechanisms from the perspective of institutional construction.This study introduces a new theoretical construct of the"institutional market",and conceptualizes the context as a series of institutional arrangements jointly established by government and market in a specific domain.Through the integrated design of directed science&technology policies,demand cultivation policies,and industry development regulations,the institutional market promotes the deep-integration of policy chain,innovation chain,and industry chain.Thereby generating a distinctive"contextual force"which differs from the forces of technology-push and demand-pull.Based on this framework,the study draws on three industrial cases—integrated circuit,electric vehicle,and high-speed railway—to analyze the construction pathway comparatively of institutional market under the industry development mode:catching up,leapfrogging,and leading.From an institutional perspective,this study promotes a new theoretical framework for explaining context-driven innovation and reveals the essential nature of context.And the research enriches innovation theory,and provides theoretical guidance for the transformation of government toward an enabling role.It also offers policy implications for the construction of high-level innovation contexts.

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.

Employee-driven digital innovation, particularly in the realm of digital idea generation on the shop floor, is a pivotal yet underexplored field. This study addresses the gap by examining how employees using digital devices and processes influence digital idea generation through the lens of the ability-motivation-opportunity (AMO) framework and recombinant theory of creativity. We conducted regression analysis on a sample of 392 process users from a large glass manufacturing company undergoing digital transformation. The results show that (1) employees' data analytical ability positively predicts their digital idea generation; (2) work process-related lead userness fully mediates the relationship between data analytical ability and digital idea generation; (3) job autonomy strengthens the positive effect of data analytical ability on lead userness; and (4) job autonomy amplifies the indirect effect of data analytical ability on digital idea generation through lead userness. This study unpacks the micro-mechanisms of employee-driven digital idea generation, extends research on lead userness in digital transformation contexts, and provides managerial insights for organizations to stimulate employees' digital innovation potential by enhancing data analytical skills, identifying lead users, and granting appropriate job autonomy.

This study extends the concept of open innovation from the firm level to regional innovation ecosystems by integrating social-ecological system insights and operationalizing a 3D index architecture covering basic innovation capacity, open innovation capacity, and innovation adaptability. Using a projection pursuit model optimized by a real-coded accelerating genetic algorithm (RAGA-PPM), kernel density estimation, and spatial correlation analysis, we evaluate the regional innovation capacity of 31 Chinese provinces from 2011 to 2021 and examine their spatiotemporal evolution. Results indicate that innovation adaptability carries the largest and most stable weight among Level 1 dimensions, indicating that coordination, learning, and risk absorption routines increasingly shape how inputs and openness translate into outcomes. Spatially, Beijing-Tianjin-Hebei, the Bohai Rim, the Yangtze River Delta, and the Pearl River Delta all exhibit strong capacity but heterogeneous integration, with tighter coupling in Shanghai-Jiangsu-Zhejiang-Anhui. Globally and locally, positive spatial correlations follow a pattern of initial decline and subsequent increase; local high-high clusters contract overall, with apparent weakening in the north, stronger integration in the east, and improvements around the southwest. Methodologically, RAGA-PPM improves sensitivity to nonlinear, multimodal structures and yields temporally coherent measures compared with entropy-based baselines. Furthermore, policy translation is specified along three tracks: capability formation for adaptability, orchestration of cross-regional collaboration, and demandside measures to enhance absorption, each with concrete instruments for provincial implementation. The findings of the study advance the integration of open innovation and regional innovation systems as well as provide actionable guidance for differentiated public policies.

In the digital age, the emergence of digital commons has brought about unprecedented transformations in open innovation. Digital communities have provided more space for ordinary people to participate in innovation, thus blurring the boundaries between individual-driven distributed innovation and firm-led open innovation. This poses a challenge to the existing organizational framework of open innovation. Drawing on an in-depth case study of the Linux Foundation, we study how open innovation is organized and governed in digital commons. we integrate commons theory with open innovation research by introducing the concept of digital commons based open innovation(DCOI) and develop a conceptual framework to explain its governance mechanisms. Our findings show that digital commons support open innovation through three interrelated mechanisms, including resource pooling, innovation platform provision, and innovation service support, all of which are coordinated through a polycentric governance structure. Together, these mechanisms reduce search, coordination, and enforcement costs, broaden participation in innovation, and strengthen collaborative value creation. Our study contributes to the literature on open innovation and the commons theory by exploring wide-ranging and large-scale collaboration in open innovation in the digital age.

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.

Ambidextrous innovation, defined as an organization’s capacity to simultaneously pursue exploratory innovation and exploitative innovation, serves as the core driving force for the high-quality development of enterprises. Based on existing studies, this paper explores the mechanisms among ambidextrous innovation, organizational resilience, and the high-quality development of enterprises from the dynamic perspective of the enterprise life cycle. The findings indicate that both exploratory innovation and exploitative innovation positively influence the high-quality development of enterprises. Moreover, at different stages of the enterprise life cycle, the effects of exploratory innovation and exploitative innovation on the high-quality development of enterprises are different. Organizational resilience partially mediates the relationship between the two dimensions of ambidextrous innovation and the high-quality development of enterprises. This study can help enterprises enhance the scientific and effective implementation of ambidextrous innovation activities in the context of coexisting crises, while also providing theoretical guidance and practical implications for their high-quality development.

Breakthroughs in artificial intelligence (AI) have spawned numerous AI companies. Yet, AI's role in facilitating corporate innovation and competence remains understudied. Based on innovation theories, the resource-based view, and the organizational change theory, we develop a holistic framework that integrates organizational AI resources, AI innovation capability, and corporate performance to depict how AI empowers corporate innovation and competence. We propose that corporate AI resources, consisting of data, human, and strategic resources, enhance their corporate performance by improving their AI innovation capability. Furthermore, we propose that a greater extent of human-machine collaboration, the ability of humans to utilize algorithms, and computational power effectively in various contexts improves corporate performance. Finally, we outline the key topics for future research, suggesting areas where further investigation could yield valuable insights into AI's role in corporate innovation. Our article offers actionable insights into companies' AI resource allocation and new capability building for competing in the AI era. Firms should prioritize a balanced approach to manage their AI data resources, human resources, strategic resources, and human-machine collaboration to effectively enhance AI innovation capability and improve corporate performance. Our article answers how AI resources could empower corporate competence and contribute to AI innovation, organizational change theory, and resource-based view.

Complexity and innovation are emerging as fundamental issues for the future of human life and society. To deal with them, different versions of systems thinking have been proposed. However, System-of-Systems (SoS) thinking, based on the biperspectival principle and human reflexivity, has not yet been clearly recognized. The unclear distinction between 'systems' and 'SoS' thinking often results in confusion and misapplication of related concepts within the closed cycle of 'system <-> system-of-systems <-> complex system <-> system', implying a need to address a set of inter-related questions: (1) What is the trajectory of systems thinking embracing complexity and innovation? (2) How does such a trajectory of systems thinking shape the future systems thinking in terms of SoS? and (3) What is SoS thinking? To address these questions, this article aims to explore the future of systems thinking in terms of SoS by embracing complexity and innovation, and delve into the theoretical origins of SoS theory, as well as its scientific connotation and logical framework. Firstly, the trajectory of systems thinking is outlined based on varying degrees of complexity across the dimensions known-unknown whole, and fixed-flexible relationships, respectively. Thereafter, the evolution path toward SoS thinking is analysed. Finally, the scientific connotation and logical framework of SoS thinking including (1) reshaping of boundaries, (2) governance of relationships, and (3) self-reflexivity and innovation, are explained based on the co-existence of paradoxes. The research results shed new light on the development of systems thinking in terms of SoS for complexity and innovation, by expanding the theories that integrate systems and complexity perspectives. Moreover, they provide practical guidance for applying SoS thinking based on the biperspectival principle and human reflexivity, along with a logical framework for engaging complexity and innovation at multiple levels of SoS.

Objective: The article aims to identify factors that influence students’ behavioural intentions to use generative artificial intelligence (GenAI). Research Design & Methods: We proposed a research model based on the theory of planned behaviour, the technology acceptance model and a literature review. Findings: The results show that attitude, perceived usefulness, perceived quality, and perceived support from higher education institutions positively impact students’ behavioural intention to use GenAI. Implications & Recommendations: The findings allowed us to propose two practical implications for academic teachers and managers of higher education institutions. Firstly, we recommend supporting students in terms of their knowledge, skills and conscious use of GenAI. Comprehensive education and other forms of training may be of use here. Secondly, we recommend that educational establishments clearly define their expectations regarding students’ use of GenAI, particularly how and when they can safely use GenAI, not only during their studies. Contribution & Value Added: Our study offers a new multilevel model of students’ behavioural intentions to use generative GenAI. It enables the synthesis of our research results and the organisation of variables influencing students’ behavioural intention to use GenAI, as well as the relations between them. Furthermore, as far as we are aware, we are the first to encompass aspects of the perceived quality and ethics of students using GenAI in our research.

Although many studies have investigated how the diversity of R&D human capital influences innovation performance, the impact of the diversity of functional areas in R&D centres remains poorly understood. Drawing on the absorptive capacity framework, this study examines how R&D area diversity influences innovation quality. As R&D activities are embedded in organisational social networks and knowledge networks, we further investigate how social cohesion, knowledge cohesion, and social-knowledge cohesion moderate the effect of R&D area diversity on innovation quality. We argue that R&D area diversity improves intra-organisational cross-function absorptive capacity, which enhances innovation quality. The positive effect of R&D area diversity increases when social cohesion and knowledge cohesion are high, and the coupling of social-knowledge cohesion is low. An analysis of a large longitudinal dataset of 3,061 firm-year observations covering 486 global pharmaceutical firms provides strong support for our hypotheses. We end with a discussion of the theoretical and practical implications.

Introducing firms' external search and competitive intensity, we investigate digital transformation (DT)'s heterogeneous impacts on different types (novelty-centred/efficiency-centred) of business model designs (BMD). Utilizing a unique dataset of 253 Chinese firms under DT, we find that the 'DT-BMD' relationship becomes complicated when considering firms' external search and competitive intensity. Specifically, when facing more intense external competition, DT's positive effect on novelty-centred BMD will be weakened, while its positive effect on efficiency-centred BMD will be strengthened. Furthermore, the positive impact of DT on both types of BMDs is reinforced when firms conduct more external searches.