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The purpose of this study is to propose a path-planning strategy based on the velocity-virtual spring method to realize collision-free tasks in dynamic environments and further improve the effect.

By considering factors such as the relative velocity and direction of dynamic obstacles, the repulsive force of the robot is improved, thereby enhancing the adaptability of the strategy and achieving flexible and effective avoidance against dynamic obstacles. The attraction formula has been designed to allow the robot to have better smooth changes and higher gradients near the target, helping robots better reach the target and follow formations. Moreover, to meet the demands of the various stages during the driving process, the null space behavioral control is used to solve multi-task conflict problems and strengthen formation coordination and control.

Comparison of the planning path and formation effects through simulation and physical experiments, the results of this study show that the algorithm proposed can successfully maintain formation stability and plan smooth and safe paths in static or dynamic environments.

This paper proposes a path-planning strategy based on the velocity-virtual spring method to plan collision-free paths for formation in dynamic environments.

This paper aims to explore the direct effects of open innovation (OI) on firms’ innovative performance, and to examine the moderating effects of knowledge attributes, including knowledge distance, knowledge embeddedness and partner opportunism on this relationship.

Survey data of 247 samples from China were used to test the proposed model through hierarchical regression analysis.

The findings indicate that the dimensions of OI are positively related to innovative performance. The results also reveal that knowledge distance positively moderates the relationship between inbound OI and innovative performance, whereas knowledge embeddedness negatively affects that relationship. Knowledge embeddedness negatively affects the relationship between inbound OI and innovative performance, whereas knowledge distance positively moderates that relationship. Thus, a new finding is proposed that knowledge attributes could align effectively with specific OI type to achieve superior innovation outcomes. In addition, the empirical results suggest that partner opportunism plays a negative moderating role on the relationship between outbound OI and innovative performance.

The proposed view that a firm’s innovation outputs will be superior when its knowledge attributes effectively align with OI enriches studies of the OI context and expands the literature of both the resource-based view and the knowledge-based view. Furthermore, this study provides insights into how OI benefits can be influenced by external contexts from the perspective of partners’ opportunistic behaviour.

In the presence of coronavirus disease 2019 (COVID-19), due to the social distance restriction, consumers' regular consumption behaviors and patterns have been changing fundamentally. Thereafter, an innovative group buying model has emerged and developed explosively with a specific focus on consumer's location, known as community-based group buying (CGB). The purpose of this paper is to investigate the transfer mechanism of user's trust in dyadic contexts of social and commercial role-playing in the CGB program.

This study adopts an empirical research method, with an online and offline questionnaire survey, a total of 382 responses have been obtained. Then, both descriptive analysis and hierarchical regression analysis are conducted to explore the dual roles of group leader and its corresponding effects on consumers' trust (i.e. emotional trust and behavioral trust) and engagement actions (i.e. purchase and share) in the CGB program.

Results indicate that resident's trust and their perception of group leader's friend role can positively enhance their engagement actions in the CGB programs. Meanwhile, for the purpose of profit maximization, the group leader is more willing to play a friend role in transactions no matter whether the role conflict exists.

Research findings provide some managerial insights for CGB platform on the selection and training of group leaders and the incentive mechanism design.

Drawing on the tenets of institutional theory, the purpose of this study is to examine the impact of Confucianism on technology for social good, while also considering the moderating influence of extrinsic informal institutions (foreign culture) and intrinsic formal institutions (property rights).

This study constructs a comprehensive database comprising 9,759 firm-year observations in China by using a sample of Chinese A-share listed firms from 2016 to 2020. Subsequently, the hypotheses are examined and confirmed, with the validity of the results being upheld even after conducting endogenous and robustness tests.

The findings of this study offer robust and consistent evidence supporting the notion that Confucianism positively affects technology for social good through both incentive effect and normative effect. Moreover, this positive influence is particularly prominent in organizations with limited exposure to foreign culture and in nonstate-owned enterprises.

The findings contribute to the literature by fostering a deep understanding of technology for social good and Confucianism research, and further provide a nuanced picture of the role of foreign culture and property rights in the process of technology for social good in China.

Building on the resource- and knowledge-based views, this paper aims to explore how coopetition affects radical innovation and the roles of knowledge structure and external knowledge integration in the relationship between coopetition and radical innovation.

This study proposes a research model to examine the mediating role of external knowledge integration on the coopetition-radical innovation link, where the mediation is moderated by the firm’s knowledge structure (including component knowledge and architectural knowledge). The authors use regression and bootstrapping to test the proposed model with survey data from 241 Chinese technology firms.

This study finds that coopetition positively affects radical innovation and the effect is fully mediated by external knowledge integration. Additionally, component knowledge negatively moderates the coopetition-external knowledge integration link and architectural knowledge positively moderates this relationship. Further, the mediating effect of external knowledge integration is also moderated by component knowledge and architectural knowledge.

Firms should engage in coopetition to promote radical innovation. Further, it is necessary for firms to appropriately manage coopetition according to their internal knowledge structure.

This study explains why scholars have different ideas about the relationship between coopetition and radical innovation by exploring the mediating role of external knowledge integration and the moderating effect of knowledge structure. Firms possess increased possibilities for knowledge leakage and partner opportunism with high levels of component knowledge, which will reduce the positive effect coopetition on external knowledge integration; thus, they are less likely to realize radical innovation. Instead, firms possess increased opportunities for resource sharing with high levels of architectural knowledge, thus improving the positive effect coopetition on external knowledge integration and they are more likely to achieve radical innovation.

In as much as it is contested, the Belt and Road Initiative (BRI) is also unexplored, underdiscussed, and, as a result, misunderstood. Frequently viewed through the lens of international relations and global economy, the diverse dimensions of collaboration, including business and research-industry clusters, that BRI enhances, tend to be excluded from the analysis. In a similar manner, the role of the Arab Peninsula in the grand strategy underpinning BRI and its implementation is rarely discussed. BRI is a forward-oriented initiative, an attempt to reap benefits of developments and circumstances that are only nascent. This bears two potent implications. First, as China attempts to influence the context in which it operates, it is subject to change itself; the Chinese business sector evolution attests to that. Second, some of China’s not so obvious partners of today, including those in the Arab Peninsula, are about to turn into key interlocutors of tomorrow. BRI taps into opportunities thus created. This chapter elaborates on these issues and, against this backdrop, outlines how the remaining chapters included in this volume add to this discussion.

This research attempted to establish the underlying dimensions of supply chain management practices, blockchain technology and supply chain performance in the Indian dairy industry. Additionally, the study proposes a conceptual model that shows the mediating effects of blockchain technology in the relationship between supply chain management practices and supply chain performance.

Structural equation modelling (SEM) is incorporated to examine the proposed model using SPSS and AMOS version 24. The study population includes 119 registered Indian dairy processing units operating in Uttar Pradesh and New Delhi (source: Dairy – India). Individual registered dairy processing unit's top four executives, that is Head of the Dairy Processing Plant, Supply Chain head and Marketing Head, and IT head are chosen as the respondents of the study, which renders the sample size of 476. Judgmental sampling based on the organisation's market position and plant production capacity (i.e. one lakh litre per day) has been set as the benchmark for selecting the dairy processing units. The executives are selected as respondents as they are well-versed in the phenomenon of supply chain management practices, blockchain technology and supply chain performance compared to other staff working in the dairy industry. The data was collected from December 2021 to March 2022 through judgmental sampling. The target sample size was 476, but only 286 questionnaires were received in a completed state and were further used for analysis.

Manufacturing practices, information sharing, distribution management, inventory management and blockchain technology have a significant and positive impact on supply chain performance in the Indian dairy industry. Furthermore, the research demonstrates that blockchain technology partially mediates the relationship between supply chain management practices and supply chain performance in the context of the Indian dairy industry.

This research is focused on the Indian dairy industry operating in only two states, namely New Delhi and Uttar Pradesh. More research is needed to determine whether SCM practices and the prospects for blockchain technology among channel members are universally applicable to merchants in non-dairy products. Similar investigations should be carried out on dairy industry operating in various formats and in numerous geographic locations. Further, case studies can be conducted by future researchers to learn how supply chain management methods are deployed, what precisely these practices entail and what costs and time demands are required by these practices in context of small independent retailers across different germane expanse.

While the available literature on the research area is spread out, the influence of blockchain technology in the Indian dairy industry has not yet been sufficiently analysed. Therefore, the research article focused on exploring underlying dimensions of the constructs of supply chain management practices, blockchain technology adoption and supply chain performance in the context of the Indian dairy industry.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.