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This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

The purpose of this paper is to propose an improved differential evolution algorithm (DEA) suitable for motor’s model identification.

The mutation operation of the standard DEA is improved, and the adaptive coefficient is designed to adjust the optimization process.

The application of motor model identification shows that the proposed improved DEA is more robust, with higher modeling accuracy and efficiency, and is more suitable for motor identification modeling applications. Compared with the ultrasonic motor model established by using particle swarm algorithm, the model established in this paper has higher precision.

This paper explores an improved DEA suitable for motor identification modeling. The algorithm can not only obtain the optimal solution but also effectively reduce the iterative generations and time required in the process of optimization identification.

The purpose of this paper is to solve new pricing issues faced by low-carbon companies in the Yellow River Basin, which is caused by the change of key pricing factors in the mixed appliance background of Big Data and blockchain, such as product quality and carbon-emission reduction CER level (hereafter, CER level).

We choose a low-carbon supply chain with a low-carbon manufacturer and a retailer as our research object. Then, we propose that using the ineffective effect of the CER level and the quality and safety level to reflect the relationships among the CER level, the quality and safety level and the market demand is more suitable in the new environment. Based on these, we revise the demand equation. Afterwards, by using Stackelberg game, four cost-sharing situations and their pricing rules are analyzed.

Results indicated that in the four cost-sharing situations, the change trends and the magnitudes of the best retail prices were not affected by the changes of the inputs of the demand information and the traceability services costs (hereafter, DITS costs), the proportion about retailer's DITS costs undertaken by the manufacturer, the ineffective effect coefficient of the CER level and the quality and safety level and the cost optimization coefficient. However, the cost-sharing situations could affect the change magnitudes of the best revenues.

This paper has two main contributions. First, this paper proposes a demand function that is more suitable for the mixed appliance background of Big Data and blockchain. Secondly, this paper improves the cost-sharing model and finds that demand information sharing and traceability service sharing have different impacts on key pricing factors of low-carbon product. In addition, this research provides a theoretical reference for low-carbon supply chain members to formulate pricing strategies in the new background.

This paper attempts to analyze the relationship between the complementarity degrees of imperfect complementary products and sales strategies and give appropriate sales strategies for a two-stage supply chain.

With respect to two-stage supply chain consisting of two manufacturers who produce imperfect complementary products and one retailer who sells the products, aiming at bundling sales strategy, the authors define complementarity elasticity of products and use it to measure the degree of complementary between two products. Based on Stackelberg game and cooperation, the authors analyze the relationship between the complementarity degrees of imperfect complementary products and appropriate sales strategies.

As the impact of complementarity degree on sales strategy decision-making is better, the authors can pinpoint out which sales decision-making is optimal and which bundling sales strategy is the best for a two-stage supply chain. Considering that the degree of complementarity has a significant impact on the product sales strategy, the authors can point out which sales decision-making is optimal, that is, which bundled sales strategy is the optimal in the secondary supply chain of selling complementary products.

An innovative bundling can expand the sales of existing products and new products. It helps a retailer transcend and defeat competitors by reducing marketing expenses while increasing profits. Proper use of bundling can improve consumers utility and create an overall positive effect for both the enterprises and consumer.

The research can help some retailers to make many appropriate bundling sales strategies.

Effectuation, which articulates the process of entrepreneurial action based on nonpredictive control logic, is receiving extensive scholarly attention. What drives the effectual entrepreneurship is featured with high complexity. However, existing studies ignored the complex driving forces underlying entrepreneurial decision-making. Building on a configurational perspective, the purpose of this study was to examine the combinative effects of environmental uncertainty and entrepreneurs’ means on effectual entrepreneurship.

Drawing on 54 entrepreneurs who are launching new ventures in China, this study adopts a fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate two sets of antecedent conditions and how they form different combinations for a highly effectual entrepreneurship.

Our findings disclose four highly effectual entrepreneurship paths involving novice–specialist effectual entrepreneurship in a highly uncertain environment, socialite–specialist effectual entrepreneurship in a highly uncertain environment, pure-specialist effectual entrepreneurship and resourceful effectual entrepreneurship, and one path of barefoot noneffectual entrepreneurship in a highly uncertain environment, which reveals the complex nature of environmental uncertainty and entrepreneurs’ means in driving entrepreneurs to adopt effectuation.

Our study makes the following contributions. First, by taking a configurational perspective, we are able to obtain an elaborate view of the combined effects of environmental uncertainty and entrepreneurs’ means on effectual entrepreneurship. Second, we expand prior thinking on the relationship between environmental uncertainty and effectuation. Third, our study offers a more delicate understanding of entrepreneurs’ means in driving effectuation by splitting means into three separate factors.

Three types of PbTe samples, e.g. nanoparticles, nanowires and bulk ingots, have been prepared. The investigation of the Seebeck coefficient of PbTe nanoparticles and nanowires clearly shows the sign change in the temperature range of 575~650 K, which is not observed for bulk ingots. Unfortunately, this temperature range is within the proposed operation temperature range for thermoelectric devices using PbTe and hence, such a change will affect their proper performance. The observed sign change of Seebeck coefficient is not simply caused by the composition variation at high temperature. It is mainly attributed to the extrinsic-to-intrinsic-semiconductor transition for PbTe nanocrystals due to the competing factors between quantum size effect increasing band gap and self-purification process decreasing their charge carrier concentration, which shift the transition point of nanocrystals to lower range as compare to that of bulk samples. Thus, such phenomenon should be considered carefully in the design of thermoelectric devices using semiconductor nanocrystals, which have attracted much attention recently.

This study aims to examine whether a firm's demand for high-quality auditors is influenced by multiple large shareholders (MLS). As one type of ownership structure, MLS have gained popularity in China recently and have different types of large shareholders, including large institutional shareholder, large foreign shareholder and large state shareholder. The authors also examine whether different types of MLS have heterogeneous impacts on appointing high-quality auditors.

With a sample of 27,131 firm-year observations from Chinese public companies from 2003 to 2018, the authors use multivariate regressions to examine the effect of MLS on auditor choice. Heckman two-stage analysis, a firm fixed effects model, propensity score matching and difference-in-differences test are used as robustness checks.

This paper finds that the presence and power of MLS increase the likelihood of appointing high-quality auditors. With regard to the types of MLS, large institutional shareholders and foreign shareholders have significant positive effects on appointing high-quality auditors, while the presence of state-owned large shareholders has no effect on auditor choice. Further analyses reveal that the positive effect of MLS on high-quality auditor choice is more pronounced in firms with severe agency problems and information asymmetry. Taken together, these results suggest that MLS play a monitoring role by demanding high-quality auditors.

This paper contributes to the literature on the determinants of auditor choice. While prior studies primarily focus on the impact of concentrated ownership structure, corporate governance and the pressure from stakeholders on auditor choice, this paper complements the literature by providing evidence from the heterogeneous effects of different types MLS. This paper also extends the literature on the consequences of MLS from the perspective of auditor choice.

The purpose of this study is to analyze the current situation of the competitiveness development of China’s marine industrial clusters, reveal the existing problems and challenges, provide theoretical support and practical guidance for improving the competitiveness of China’s marine industrial clusters so as to promote industrial upgrading and high-quality development and help China realize the strategic transformation from a marine power to a marine power.

This report first provides a detailed review of the current development status and existing issues of marine industry clusters in China. Second, it constructs an evaluation index system for the competitiveness development of China’s marine industry clusters and conducts competitiveness analysis and evaluation of typical marine industry clusters in China. Third, it explores the development trends and prospects of typical marine industry clusters in China. Finally, the report proposes countermeasures and suggestions for enhancing the competitiveness of marine industry clusters in China, focusing on resource optimization, cluster structure and cluster efficiency.

(1) Significant competitiveness of marine shipbuilding and ocean engineering equipment clusters: Through technological innovation and policy support, the marine shipbuilding and ocean engineering equipment clusters have shown a marked improvement in competitiveness, advancing toward high-quality development despite facing macroeconomic fluctuations. (2) Continuous improvement in marine energy and offshore wind power clusters: The competitiveness of marine energy and offshore wind power clusters has been continuously enhanced under supportive policies. However, there is still a need to optimize resource allocation and strengthen innovation stability to meet market challenges. (3) Strong growth potential of desalination and comprehensive utilization clusters: The desalination and comprehensive utilization clusters demonstrate robust growth potential in technological innovation and regional collaborative development. Future efforts should focus on the application of environmentally friendly and energy-saving technologies to ensure a balance between economic and ecological benefits.

By strengthening the competitiveness of industrial clusters, China can effectively respond to international competition, accelerate the transformation and upgrading of the marine industry and support its transition from a maritime power to a strong maritime nation. Hence, this study will focus on analyzing the competitiveness development of China’s marine industry clusters, identifying existing problems and challenges and providing theoretical support and practical guidance for enhancing the competitiveness of China’s marine industry clusters.

Based on the theory of habitual domain, the purpose of this paper is to explore inter‐firm networks' mechanisms for coping with environmental change and for assisting firms to adapt to their collaborative networks. Business globalization is driving more and more individual firms to form inter‐firm collaborative networks. These networks need to develop new types of strategic capability in order not only to adopt a robust business process that will achieve high efficiency, but also to develop a network system for responding to external environmental changes.

With insights gained from the case study, the paper develops an analytical framework for deconstructing the network behavioral changes based on the network habitual domain concept and discusses the dual roles of the network‐based habitual domain in network behavioral changes.

This paper demonstrates how a Chinese telecommunications service company and its inter‐firm network became the market leader in one of the provincial capital cities in China within six years.

From an endogenous perspective, this paper contributes some useful concepts that may assist both academia and managers to identify network‐based habitual domains, improve the domains continuously, enhance their response capabilities, and formulate appropriate strategies according to the competition's requirements.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.