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This paper aims to investigate the antecedents of recombinant innovation from the perspective of ego–network dynamics, and further disentangle whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under heterogeneous knowledge base.

This paper uses 1,801 patent data in China’s biotechnology field as a sample and adopts fixed effects regression model to examine the effects of ego–network dynamics on recombinant innovation and further uses the Wald tests to discern which ego–network dynamic is more conducive to recombinant innovation under heterogeneous knowledge base.

The empirical results indicate that ego–network dynamics have a positive impact on recombinant innovation. Specifically, for firms with high knowledge breadth and high knowledge depth as well as high knowledge breadth and low knowledge depth, ego–network stability is more conducive to recombinant innovation. By contrast, for firms with low knowledge breadth and high knowledge depth, recombinant innovation benefits more from ego–network expansion. As for firms with low knowledge breadth and low knowledge depth, both ego–network stability and ego–network expansion can promote recombinant innovation, while the effects are not significant.

This research may enlighten managers to choose suitable ego–network dynamics strategies for recombinant innovation based on their knowledge base.

This research not only contributes to the literature on recombinant innovation by revealing the impact of different ego–network dynamics on recombinant innovation but also contributes to network dynamics theory by exploring whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under a heterogeneous knowledge base.

This study aims to specify whether heterogeneous reference-point-based aspirations are related to the cooperation levels of R&D alliance portfolios in a positive or negative (or nonlinear) way, and to unveil how cooperative behaviors evolve in recurrent project cooperation.

This study establishes a network containing a cooperation subnetwork and a project subnetwork based on patent data in the “deep learning” field to investigate how cooperative behaviors evolve in R&D alliance portfolios. A model of evolutionary games on complex networks is constructed to gain insight into the dynamic evolution of DMs’ strategies.

First, the heterogeneous aspirations of DMs can improve the cooperation level in R&D alliance portfolios. Second, compared to prudent DMs, aggressive DMs are more likely to choose the cooperation strategy, implying that an appropriate aspiration level nurtures cooperative R&D endeavors with partners. Third, the effects of effort complementarity, knowledge reorganization capabilities and cooperation supervision on cooperation are contingent on the distribution of DMs’ aspiration types.

Policymakers should identify aspiration types of DMs when screening partners. They can encourage partners to focus more on historical payoffs and establish relatively higher aspiration levels to improve the cooperation level. Developing highly detailed contracts becomes crucial when cooperating with firms that possess extensive knowledge reorganization capabilities.

This work contributes a theoretical framework for investigating cooperation in R&D alliance portfolios through the lens of evolutionary games on complex networks, thus revealing the effects of heterogeneous reference-point-based aspirations of DMs on R&D cooperation.

This study aims to verify the respective and interactive effects of subnational formal and informal institutions (i.e. legal effectiveness and social trust) on foreign firm performance, and further identify the contingent factor (i.e. institutional experience) that moderates these relationships.

Drawing on the institutional-based view, this study develops several hypotheses that are tested using a comprehensive dataset from four main data sources. The authors’ unit of analysis is foreign firms operating in China. The authors ran ordinary least squares (OLS) regression model to investigate the effects. A series of robustness tests and endogeneity tests were performed.

The results show that both legal effectiveness and social trust at subnational level positively affect foreign firm performance respectively. Legal effectiveness and social trust at subnational level have complementary effect in promoting the performance of foreign firms. Foreign firm's institutional experience in target region of emerging economies host country strengthens the positive impact of subnational legal effectiveness on performance, but weakens the positive impact of subnational social trust on performance.

It is important to fully understand the impact of heterogeneous institutional environments of subnational regions in emerging economies on foreign firm performance, which would help foreign firm make a more suitable secondary choice decision of investment destinations at the subnational regional level.

First, drawing on institutional-based view, the authors incorporate the subnational formal and informal institutional factors to investigate their impacts on foreign firm performance by switching the attention from national level to subnational level in emerging economy host countries. Second, this research furthers existing studies by bridging a missing link between both subnational formal and informal institutional environments and foreign firms' outcomes. Third, the authors prove that the model of subnational formal and informal institutions in influencing foreign firms' performance is contingent on their institutional experience in target subnational region of emerging economy host country.

This paper aims to explore how digital technologies can transform the museum into an “interaction platform” able to play a key role in the value co-creation processes of the tourism destination.

The paper applies the “co-creation through interactions” perspective by Ramaswamy and Ozcan. Empirically, the paper is based on the methodology of single case study identified in MArTA, the well-known National Archeological Museum of Taranto (South Italy). Data collection has been implemented through interviews with key informants and secondary data related to online interviews, press release and reports.

Findings provide empirical evidence about the contribution that a digitalization strategy can create a “museum as a platform” in which the interactions between the museum, its stakeholders and other co-creation elements (interfaces, artifacts and processes) bring benefits in terms of tourism experiences and sustainable development of the destination.

This research highlights the cultural changes and the actions that museum management has to implement to properly benefit from digitalization and to transform the museum into a reference point for reflection and innovation.

Elements of originality can be found in (1) the exploration of the wide spectrum of benefits and innovations that digital technologies can offer to the museum-mediated interactions and (2) the contribution to the understanding of the museum as a digitalized “interaction platform” capable of supporting the processes of co-creation of value in the complex network of actors and objects of a tourism destination.

As the scale of mega transportation infrastructure projects (MTIs) continues to expand, the complexity of engineering construction sharply increases and decision-making sustainability faces severe challenges. Decision-making for mega transportation infrastructure projects unveils the knowledge-intensive characteristic, requiring collaborative decisions by cross-domain decision-makers. However, the exploration of heterogeneous knowledge fusion-driven decision-making problems is limited. This study aims to improve the deficiencies of existing decision-making by constructing a knowledge fusion-driven multi-attribute group decision model under fuzzy context to improve the sustainability of MTIs decision-making.

This study utilizes intuitionistic fuzzy information to handle uncertain information; calculates decision-makers and indicators weights by hesitation, fuzziness and intuitionistic fuzzy entropy; applies the intuitionistic fuzzy weighted averaging (IFWA) operator to fuse knowledge and uses consensus to measure the level of knowledge fusion. Finally, a calculation example is given to verify the rationality and effectiveness of the model.

This research finally constructs a two-level decision model driven by knowledge fusion, which alleviates the uncertainty and fuzziness of decision knowledge, promotes knowledge fusion among cross-domain decision-makers and can be effectively applied in practical applications.

This study provides an effective decision-making model for mega transportation infrastructure projects and guides policymakers.

This study investigates how federated learning (FL) and human–robot collaboration (HRC) can be used to manage diverse industrial environments effectively. We aim to demonstrate how these technologies not only improve cooperation between humans and robots but also significantly enhance productivity and innovation within industrial settings. Our research proposes a new framework that integrates these advancements, paving the way for smarter and more efficient factories.

This paper looks into the difficulties of handling diverse industrial setups and explores how combining FL and HRC in the mark of Industry 5.0 paradigm could help. A literature review is conducted to explore the theoretical insights, methods and applications of these technologies that justify our proposal. Based on this, a conceptual framework is proposed that integrates these technologies to manage heterogeneous industrial environments.

The findings drawn from the literature review performed, demonstrate that personalized FL can empower robots to evolve into intelligent collaborators capable of seamlessly aligning their actions and responses with the intricacies of factory environments and the preferences of human workers. This enhanced adaptability results in more efficient, harmonious and context-sensitive collaborations, ultimately enhancing productivity and adaptability in industrial operations.

This research underscores the innovative potential of personalized FL in reshaping the HRC landscape for manage heterogeneous industrial environments, marking a transformative shift from traditional automation to intelligent collaboration. It lays the foundation for a future where human–robot interactions are not only more efficient but also more harmonious and contextually aware, offering significant value to the industrial sector.

Previous research has analyzed the consequence of network stability; however, little is known about how partner type diversity influence network stability in R&D network. Based on knowledge-based view and social network theory, the purpose of this paper is to unravel the internal mechanisms between partner type diversity and network stability through the mediating role of knowledge recombination in R&D network.

The authors collected an unbalanced panel patent data set from information communication technology industry for the period 1994–2016. Then, the authors tested the different dimensions of partner type variety and its relevance in the R&D network and the mediating role of knowledge recombination through adopting the multiple linear regression.

Results indicate an inverted U-shaped relationship between partner type diversity (variety and relevance) and network stability, whereas knowledge recombination partially mediate these relationships.

From the perspective of R&D networks, this paper explores that there are the under-researched phenomena the antecedent of network stability through nodal attributes (i.e. partner type variety and partner type relevance). Moreover, this paper empirically examined the mediating role of knowledge recombination in the partner type diversity–network stability relationships. The novel perspective allows focal firm to recognize importance of nodal attributes, which are critical to fully excavate the potential capabilities of cooperating partners in R&D network.

This study aims to explore the impact mechanism of the degree of innovation failure on breakthrough innovation in Chinese listed companies, and examines the moderating effect of the company’s own knowledge-based capabilities.

Based on organizational learning theory and using the innovation failure data of invention patents from Chinese A-share listed companies on the main board from 2003 to 2017 as research samples, this study constructs and examines a comprehensive framework and its impact on breakthrough innovation regarding “what kind of innovation failure will promote breakthrough innovation”, focusing on innovation failure, enterprise knowledge base, and breakthrough innovation.

Empirical research has found a U-shaped relationship between innovation failure and breakthrough innovation. In other words, both a low level of failure and an extremely high level of failure can significantly promote breakthrough innovation in enterprises. Furthermore, when the depth of enterprise knowledge is high, it further strengthens the non-linear relationship between innovation failure and breakthrough innovation.

The research results enrich the study of the failure predicament and breakthrough innovation of Chinese technology innovation enterprises, revealing effective paths for Chinese technology innovation enterprises to get rid of the passive situation of innovation failure, and providing theoretical support and practical reference for “breaking new ground and achieving breakthrough innovation”.

Responding to calls for deeper analysis of the conceptual foundations of service infusion in manufacturing, this paper examines the underlying assumptions that: (i) manufacturing firms incorporating services follow a pathway, moving from pure-product to pure-service offerings, and (ii) profits increase linearly with this process. We propose that these assumptions are inconsistent with the premises of behavioural and learning theories.

Machine learning algorithms are applied to test whether a successive process, from a basic to a more advanced offering, creates optimal performance. The data were gathered through two surveys administered to USA manufacturing firms in 2021 and 2023. The first included a training sample comprising 225 firms, whilst the second encompassed a testing sample of 105 firms.

Analysis shows that following the base-intermediate-advanced services pathway is not the best predictor of optimal performance. Developing advanced services and then later adding less complex offerings supports better performance.

Manufacturing firms follow heterogeneous pathways in their service development journey. Non-servitised firms need to carefully consider their contextual conditions when selecting their initial service offering. Starting with a single service offering appears to be a superior strategy over providing multiple services.

The machine learning approach is novel to the field and captures the key conditions for manufacturers to successfully servitise. Insight is derived from the adoption and implementation year datasets for 17 types of services described in previous qualitative studies. The methods proposed can be extended to assess other process-based models in related management fields (e.g., sand cone).