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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.

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.

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.