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Network embeddedness has been widely considered in enterprise innovation as an effective means of overcoming resource dilemmas. However, while focussing on acquiring external innovation resources, the existing research often ignores the vital role of internal routine updates. Therefore, this study explores the mechanism by which network embeddedness affects innovation performance of enterprises from the perspective of organisational routine updating.

This paper proposes a theoretical model based on social network theory and organisational routines–immune response theory. A total of 328 pieces of research data on high-tech enterprises in China were collected, and the hypotheses were verified using hierarchical regression analysis.

The results show that the two forms of network embeddedness – structural embeddedness and relational embeddedness, have a positive effect on enterprise innovation performance and a significant positive effect on organisational routine revision and organisational routine creation. Both organisational routine revision and organisational routine creation positively affect enterprise innovation performance and partially mediate the relationship between network embeddedness and enterprise innovation performance.

This conclusion provides a new perspective on the impact of network embeddedness on enterprise innovation performance and expands the related research on organisational routine updating. This study provides a theoretical reference for high-tech enterprises to improve their competitiveness and innovation performance through network embeddedness and organisational routine updating.

The practical demonstrations and research which led to the preparation of this paper involved a combination of stakeholder engagement, policy debate and the practical demonstration and testing of autonomous vehicles. By adhering to a design approach which in centred on participation and human-centred engagement, the advent of autonomous vehicles might avoid many of the problems encountered in relation to conventional transport.

The research explored how a new and potentially disruptive technology might be incorporated in urban settings, through the lens of participation and problem-based design. The research critically reviews key strands in the literature (autonomous vehicles, social research and participatory design), with allusion to current case study experiments.

Although there are numerous examples of autonomous vehicles (AV) research concentrating on technical aspects alone, this paper finds that such an approach appears to be an unusual starting point for the design of innovative technology. That is, AVs would appear to hold the potential to be genuinely disruptive in terms of innovation, yet the way that disruption takes place should surely be guided by design principles and by issues and problems encountered by potential users.

The research carries significant implications for practice in that it advocates locating those socio-contextual issues at the heart of the problem definition and design process and ahead of technical solutions.

What sets this research apart from other studies concerning AVs was that the starting point for investigation was the framing of AVs within contexts and scenarios leading to the emergence of wicked problems. This begins with a research position where the potential uses for AVs are considered in a social context, within which the problems and issues to be solved become the starting point for design at a fundamental level.

This study aims to investigate the most effective approach for governments and enterprises to combat desertification by considering the governance cycle. The focus is on understanding how the government can incentivize enterprises to actively engage in desertification combat efforts.

Both the government and the enterprise are treated as rational entities, making strategic choices for joint participation in combating desertification. Recognizing the dynamic nature of the desertification combat area, differential game models are employed to identify the optimal mode for combating desertification.

The findings underscore the significant influence of the governance cycle duration on the selection of desertification combat modes for government and enterprise. A cooperative mode is best suited to a short governance cycle, while an ecological subsidy mode is optimal for a longer cycle. Enhancing governance technology and shortening the governance cycle are conducive to combating desertification. Reducing taxes alone may not be an effective control strategy; rather, the government can better motivate enterprises by adopting tax rate policies aligned with the chosen governance mode.

This research contributes by elucidating the impact mechanism of the government cycle’s length on the desertification combat process. The results may offer valuable insights for governments in formulating strategies to encourage corporate participation in combating desertification and provide theoretical support for selecting optimal desertification combat modes.

The paper aims to examine the influence of closed and open innovation practices on economic performance. This papert also examines the mediating roles of innovation performance and firm performance. The study uses innovation theory based on knowledge management for theoretical support.

The methodology involves two steps. First, all the variables relevant to the adoption of innovative approaches and performance parameters are identified. Subsequently, primary data are gathered from decision-makers of 200 biotechnological firms and a structural equation modeling analysis is performed.

The study's results showed that the open innovation practice, such as interaction with large research and development (R&D) firms and customers, influences the performance parameters. The findings indicate that closed and open innovation practices positively impact performance measures like innovation, firm and economic performance. The results also indicate the mediating role of firm performance. However, the innovation performance was not found to mediate the relationship.

This examination gives experimental bits of knowledge from any confining influence innovation approaches in India. Analysts and specialists of firms can use the results of the current study to comprehend the effect of various innovation practices on different performance measures.

The purpose of this study is to examine how the childhood trauma experiences of CEOs influence firms’ internationalization.

The research used a difference-in-difference method with constructing a treatment group whose chief executive officer (CEO) experienced the great famine in China between the ages of 7 and 11, and a control group whose CEO was born within three years after 1961.

The study reveals a significant inverse correlation between CEOs’ childhood trauma experiences and firm internationalization. However, this correlation is weaker in the case of state-owned enterprises and firms led by CEOs with overseas work experience.

To the best of the authors’ knowledge, this study is the first to extend the theoretical framework to elucidate firms’ internationalization by introducing childhood trauma theory into the field of international business literature. Second, the authors link the literature on the effect of CEO explicit traits and psychological traits on firm internationalization by exploring how CEOs’ childhood trauma experience shapes their risk aversion, which, in turn, influences firm internationalization. Third, the authors address the call for examining the interplay of CEO life experiences by scrutinizing the moderating effect of CEO overseas work experience on the association between CEOs’ childhood trauma exposure and firm internationalization.

Building a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.

This paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.

Finally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.

In this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.

With the growing climate problem, it has become a consensus to develop low-carbon technologies to reduce emissions. Electric industry is a major carbon-emitting industry, accounting for 35% of global carbon emissions. Universities, as an important patent application sector in China, promote their patent application and transformation to enhance Chinese technological innovation capability. This study aims to analyze low-carbon electricity technology transformation in Chinese universities.

This paper uses IncoPat to collect patent data. The trend of low-carbon electricity technology patent applications in Chinese universities, the status, patent technology distribution, patent transformation status and patent transformation path of valid patent is analyzed.

Low-carbon electricity technology in Chinese universities has been promoted, and the number of patents has shown rapid growth. Invention patents proportion is increasing, and the transformation has become increasingly active. Low-carbon electricity technology in Chinese universities is mainly concentrated in individual cooperative patent classification (CPC) classification numbers, and innovative technologies will be an important development for electric reduction.

This paper innovatively uses valid patents to study the development of low-carbon electricity technology in Chinese universities, and defines low-carbon technology patents by CPC patent classification system. A new attempt focuses on the development status and direction in low-carbon electricity technology in Chinese universities, and highlights the contribution of valid patents to patent value.

This study aims to investigate the use of Artificial Intelligence (AI) applied to vertical farms to evaluate whether disrupting technology supports sustainability and increases strategic business model choices in the agricultural sector. The study responds through empirical analysis to the gap on the subject of AI-driven business models present in the growing sector literature.

The paper analyzes the case of “ZERO”, a company linked to the strategy innovation ecosystem of the Ca’ Foscari University of Venice, Italy. The empirical data were collected through a semi-structured questionnaire, interviews and the analysis of public news on the business model available in the analyzed case study. The research is empirical and uses exploratory, descriptive analysis to interpret the findings. The article focuses on the evaluation of AI impact on the agricultural sector and its potential to create new business models.

The study identified how AI can support the decision-making process leading to an increase in productivity, efficiency, product quality and cost reduction. AI helps increase these parameters through a continuous learning process and local production, and the possible decrease in prices directed toward the goal of zero km food with fresh products. AI is a winning technology to support the key elements of the vertical farm business model. However, it must be coupled with other devices, such as robots, sensors and drones, to collect enough data to enable continuous learning and improvement.

The research supports new research trends in AI applied to agriculture. The major implication is the construction of ecosystems between farms, technology providers, policymakers, universities, research centers and local consumer communities.

The ZERO case study underlines the potential of AI as a destructive technology that, especially in vertical farms, eliminates external conditions by increasing productivity, reducing costs and responding to production needs with adequate consumption of raw materials, boosting both environmental and social sustainability.

The study is original, as the current literature presents few empirical case studies on AI-supporting business models in agriculture. The study also favors valuable strategic implications for the policies to be adopted in favor of new business models in agriculture.

The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.