Songbo Liu, Jinkai Cheng, Zhen Wang and Shilong Wei
This study aims to investigate how individual career management (ICM) affects career success in Chinese organizations. Leader emergence was examined through the theoretical lens…
Abstract
Purpose
This study aims to investigate how individual career management (ICM) affects career success in Chinese organizations. Leader emergence was examined through the theoretical lens of implicit leadership theory as a mediating mechanism of this relationship. In addition, leadership self-efficacy and organizational warmth were analyzed jointly as boundary conditions strengthening the relationship between ICM and leader emergence.
Design/methodology/approach
To avoid common method bias, the authors adopted a three-wave data collection with a one-month lagged design. A total of 765 questionnaires were distributed and 424 usable questionnaires were collected. Mplus version 8.3 was used to test the hypothesized relationships.
Findings
Findings indicated that ICM is positively related to subjective career success and objective career success via leader emergence. Moreover, leadership self-efficacy and organizational warmth jointly moderate the relationship between ICM and leader emergence.
Originality/value
Based on implicit leadership theory, this study identifies leader emergence as a critical mechanism explaining the positive impact of ICM on career success in the Chinese context. Lastly, results stress the simultaneous need for leadership self-efficacy and organization warmth, which can promote high-ICM employees to emerge as leaders.
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Hong Zhou, Binwei Gao, Shilong Tang, Bing Li and Shuyu Wang
The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly…
Abstract
Purpose
The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.
Design/methodology/approach
A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.
Findings
1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.
Originality/value
NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.
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Chong Huang, Shilong Zhang and Hongshuo Zhang
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
(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.
Originality/value
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.
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Tamoor Khan, Jiangtao Qiu, Ameen Banjar, Riad Alharbey, Ahmed Omar Alzahrani and Rashid Mehmood
The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO2 emissions, farming areas and the labor force in China.
Abstract
Purpose
The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO2 emissions, farming areas and the labor force in China.
Design/methodology/approach
This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.
Findings
The Johansen test findings have shown that the fruit crop growth, energy use, CO2 emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO2 emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO2 will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO2 emissions to agricultural areas to fruit crops.
Originality/value
This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.