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The construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.

Based on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.

Results suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.

This study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.

The sensitivity and fragility of the construction industry’s economic system make the economic resilience of the construction industry (ERCI) a key concern for stakeholders and decision-makers. This study aims to measure the ERCI, identify the heterogeneity and spatial differences in ERCI, and provide scientific guidance and improvement paths for the industry. It provides a foundation for the implementation of resilience policies in the construction industry of developing countries in the future.

The comprehensive index method, Theil index method, standard deviation ellipse method and geographic detector model are used to investigate the spatial differences, spatiotemporal evolution characteristics and the influencing factors of the ERCI from 2005 to 2020 in China.

The ERCI was “high in the east and low in the west”, and Jiangsu has the highest value with 0.64. The Theil index of ERCI shows a wave downward pattern, with significant spatial heterogeneity. The overall difference in ERCI is mainly caused by regional differences, with the contribution rates being higher by more than 70%. Besides, the difference between different regions is increasing. The ERCI was centered in Henan Province, showing a clustering trend in the “northeast-southwest” direction, with weakened spatial polarization and a shrinking distribution range. The market size, input level of construction industry factors, industrial scale and economic scale are the main factors influencing economic resilience. The interaction between each influencing factor exhibits an enhanced relationship, including non-linear enhancement and dual-factor enhancement, with no weakening or independent relationship.

Exploring the spatial differences and driving factors of the ERCI in China, which can provide crucial insights and references for stakeholders, authorities and decision-makers in similar construction economic growth leading to the economic growth of the national economy context areas and countries.

The construction industry development is the main engine for the national economy growth of most developing countries. This study establishes a comprehensive evaluation index on the resilience measurement and analyzes the spatial effects, regional heterogeneity and driving factors on ERCI in the largest developing country from a dynamic perspective. Moreover, it explores the multi-factor interaction mechanism in the formation process of ERCI, provides a theoretical basis and empirical support for promoting the healthy development of the construction industry economy and optimizes ways to enhance and improve the level of ERCI.

Lean construction (LC) consists of very effective techniques; however, its implementation varies considerably from one industry to another. Although numerous lean initiatives do exist in the construction industry, the research topic related to LC implementation is still unexplored due to the scarcity of validated assessment frameworks. This study aims to provide the first attempt in developing a structural model for successful LC implementation.

This study developed a Lean construction model (LCM) by critically reviewing seven previous LC frameworks from different countries, defining 18 subprinciples grouped into 6 major principles and formulating testable hypotheses. The questionnaire was pre-tested with 12 construction management experts and revised by 4 specialized academics. A pilot study with 20 construction units enhanced content reliability. Data from 307 Moroccan construction companies were collected to develop a measurement model. SPSS V. 26 was used for Exploratory Factor Analysis, followed by confirmatory factor analysis using AMOS version 23. Finally, a structural equation model statistically assessed each construct's contribution to the success of LC implementation.

This work led to the development of an original LCM based on valid and reliable LC constructs, consisting of 18 measurement items grouped into 6 LC principles: Process Transparency, People involvement, Waste elimination, Planning and Continuous improvement, Client Focus and Material/information flow and pull. According to the structural model, LC implementation success is positively influenced by Planning and Scheduling/continuous improvement (β = 0.930), followed by Elimination of waste (β = 0.896). Process transparency ranks third (β = 0.858). The study demonstrates that all these factors are mutually complementary, highlighting a positive relationship between LC implementation success and the holistic application of all LC principles.

To the best of the authors’ knowledge, this study is the first attempt to develop a statistically proven model of LC based on structural equation modelling analysis, which is promising for stimulating construction practitioners and researchers for more empirical studies in different countries to obtain a more accurate reflection of LC implementation. Moreover, the paper proposes recommendations to help policymakers, academics and practitioners anticipate the key success drivers for more successful LC implementation.

This study examines the stock market efficiency in China to offer trading strategy guidance to investors and efficiency evaluation insight to policymakers.

This study examines the stock market efficiency in China with a new combined liquidity trading strategy by blending technical analysis into a liquidity buy-and-hold strategy.

Our results show that the combined strategy generates significant excess returns in the whole sample period, suggesting that the Chinese stock market is not consistent with the weak form efficient hypothesis. In addition, the combined strategy yields more significant risk-adjusted excess returns after the 2004 split-share reform, indicating the stock market efficiency in China does not exhibit a distinct upgrade after the reform. Our further test results reinforce the main conclusions after taking transaction costs, market states, short-selling reform and other issues into consideration.

Our study contributes to the literature in two ways: First, we shed light on the mixed documented results about the market efficiency form in China. Second, we contribute to the mixed relation between the 2004 split-share reform and market efficiency in China.

This study aims to provide a comprehensive analysis of two-sided matching (TSM) research, an interdisciplinary field that integrates both theoretical and practical perspectives. By examining 756 research articles from the Web of Science database, this paper seeks to identify key trends, collaboration patterns and emerging research topics within the TSM domain.

The research utilizes bibliometric analysis combined with a structural topic model to analyze TSM-related articles published between January 1, 2000, and September 30, 2022. The study identifies leading subfields, journals, countries/regions and institutions based on publication volume, total citations and average citations per article. Interaction and collaboration patterns among these entities are examined through co-occurrence and coupling networks. Additionally, five major research topics are identified and explored using topic modeling and co-word networks. This hybrid knowledge mining approach better reveals the inherent structural changes in topic clusters. Topic distribution and network analysis are beneficial in capturing the attention allocation of different entities to knowledge.

The analysis reveals five prominent research topics in TSM: communication resource allocation, stable matching research, computing task assignment, TSM decision-making and market matching mechanism design. These topics represent the main directions of TSM research. The study also uncovers a shift in research focus from theoretical aspects to practical applications. Furthermore, the distribution of knowledge and interaction patterns among key entities align with the identified research trends.

This study offers a novel and detailed overview of TSM research highlighting significant trends and collaboration patterns within the field. By integrating bibliometric methods with structural topic modeling the study provides unique insights into the evolution of TSM research making it a valuable resource for both academic and professional communities.

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

The construction industry has experienced an irreversible digital transformation to smart construction. Many countries have published supporting policies to encourage the development of smart construction. However, there is no universally valid approach. This paper thus aims to evaluate smart construction policies issued by 24 pilot cities in China and identify applicable policy tools and their impact.

This paper collected 33 governmental documents on smart construction through the official websites in China. Different policy tools were classified into supply-side, demand-side and environment-side categories. The supporting policies of smart construction development in pilot cities were quantitatively evaluated by using a policy modeling consistency index (PMC-index) model.

Supply-type and environment-type policy instruments were used more frequently than demand-type policies in 24 pilot cities. Most of the 24 pilot cities had an evaluation of PMC-index over 8, realizing the consistency of smart construction policies. Eight pilot cities had an evaluation of PMC-index of 6–7.99, realizing acceptable consistency. Only Foshan City has an evaluation of PMC-index below 4, which may reflect a poor consistency of policy implementation. The paper proposes consistencies of smart construction policies of 24 pilot cities and valid policy instruments, including the presale of commercial residential buildings, additional bonus points in the tendering process and cooperating with multiple departments when promoting smart construction.

This paper contributes to expanding policy evaluation studies in the smart construction field and provides concrete suggestions for policymakers to formulate more effective and specific policies and strategies for the development of smart construction.

A review of sustainability challenges of flame retardants (FRs) for textiles has been conducted. Specifically, the purpose of this paper is to identify and recommend solutions to sustainability challenges emanating from the raw material, processing technology and performance of the FRs used for textiles.

The approach used in preparing this paper was based on the review of various scholarly databases about the subject matter. The review approach is designed to inform the readers about the sustainability challenges of FRs for textiles. The science of burning and FRs for synthetic and cellulosic fibres were reviewed. Both synthetic and natural biodegradable FRs for textiles has been identified. The obtained literature was then synthesised to get information about sustainable challenges of non-halogenated FRs both synthetic and natural biodegradable. Finally, possible approaches for mitigating the identified challenges have been recommended.

The sustainability challenges of the FRs in terms of raw material, processing, affordability and performance have been identified. Synthetic FRs suffer from sustainability challenges in terms of raw materials, processing and non-renewability. Despite the environmental friendliness and sustainability in terms of being renewability, processability and biodegradability, natural biodegradable FRs have poor performance compared to synthetic ones. Moreover, natural biodegradable FRs depend on geographical condition and lack economic variability data. Potentially, the challenges of FRs can be mitigated through eco-friendly synthesis, chemical modification and sustainable methods of applications. Because of its renewability and environmental friendliness, biodegradable FRs have a potential to becoming sustainable if researched more.

In this review, a collection of literature about sustainability challenges of FRs and the approaches to overcome the challenges has been provided. The collected information was analysed and synthesised to bring understanding of the science of burning, types and application of FRs for textiles and biodegradable FRs. Sustainability challenges have been identified, and mitigation approaches are provided.

This study aims to examine the impact of China's “Manufacturing and Internet Integration Development Pilot Demonstration Project” (MIP) policy on the digital transformation (DT) and labor structure optimization (LSU) of manufacturing enterprises, reveal the relationship between DT and LSU at the micro level and investigate the mechanism between them.

This study employs MIP as a quasi-natural experiment and develops a time-varying difference-in-difference (DID) model based on a sample of 2,445 Chinese A-share listed manufacturing enterprises in the Shanghai and Shenzhen markets from 2013 to 2021.

The implementation of MIP significantly increases DT by 0.4366 and optimizes LSU by 0.0507. By enhancing the two mediated variables of organizational learning inputs (SI) and employees' personal digital cognition (PDC), DT can optimize the LSU of pilot enterprises by 0.035 and 0.034, according to the results of the mechanism analysis. The study also reveals that the impact of MIP on LSU is highly heterogeneous. With effects of 0.0691 and 0.0632, the optimization effect is more pronounced in state-owned firms and firms with low ownership concentration, respectively.

This study demonstrates the dual effects of the MIP pilot on DT and LSU. In addition, this study pioneers research on the significance of optimizing the labor structure through SI and PDC on the basis of DT, which provides an empirical foundation for the Chinese Government to expand the scope of MIP pilots and revise policy content, as well as for manufacturing enterprises to upgrade the labor structure.

Recent studies have documented the significant influence of investment bank relationships on the forecast accuracy of affiliated analysts. However, the literature primarily focuses on investment bank-firm relationships, such as whether the investment bank has underwriter or lending relationships with firms. Little attention has been paid to the impact of investment bank-state relationships. To fill this gap in the literature, this paper investigates whether investment bank-state relationships create an information advantage for affiliated analysts.

We empirically test the information advantage of state-affiliated analysts using a large sample of analyst forecasts from China spanning the period 2008 to 2020. Specifically, we investigate whether state-affiliated analysts issue more accurate and timely forecasts around macro events. To address endogeneity concerns, we examine instances where analysts move between state-affiliated and non-affiliated banks, as well as changes in the controlling structure of investment banks.

We find that state-affiliated analysts demonstrate greater accuracy in forecasts driven by macro events, indicating their information advantage. This effect is more pronounced under conditions of heightened economic policy uncertainty and closer ties between investment banks and the state. After transitioning to a non-state-affiliated investment bank, analysts no longer maintain the ability to make superior macro-event-driven forecasts. Conversely, when the investment bank's affiliation changes from non-state to state, financial analysts gain an information advantage following the establishment of new state affiliation. Additionally, state-affiliated analysts tend to issue more assertive forecasts in response to macro events, which are subsequently echoed by their peers. Furthermore, this information advantage is recognized by investors, enhancing stock price informativeness.

Firstly, we contribute to the literature on the economic implications of state ownership in financial intermediaries. Secondly, we enrich the existing literature on financial analysts by acknowledging the pivotal role of investment bank relationships in shaping analysts' behaviors. Lastly, unlike existing literature that treats forecasts driven by macroeconomic events and those influenced by firm-specific events as a homogeneous category, this study contributes to the literature by innovatively categorizing analyst forecasts into two distinct types.