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The purpose of our study is to investigate the effects of politically-connected boards (PCBs) on over-(under-)investment in labor. We also examine the impacts of the supervisory board (SB)’s optimal tenure on the association between PCBs and over-investment in labor.

We constructed the proxy for PCBs using a dummy variable set to 1 (one) if a firm has politically-connected boards and zero (0) otherwise. For the robustness check, we used the number of politically-connected members on the boards as the proxy for PCBs.

We find that the presence of PCBs reduces over-investment in labor. Consistent with our prediction, we found no significant association between PCBs and under-investment in labor. We also find that the SB with optimal tenure strengthens the negative association between PCBs and over-investment in labor. In our channel analysis, we find that the presence of PCB mitigates over-investment in labor through a higher dividend payout ratio.

Due to the unavailability of data in firms’ annual reports regarding the number of poorly-skilled and highly skilled employees, we were not able to examine the effect of low-skilled and high-skilled employees on over-investment in labor. Also, we were not able to examine over-(under-)investment in labor by drawing a distinction between general (generalist) and firm-specific human capital (specialist) as suggested by Sevcenko, Wu, and Kacperczyk (2022). Generally, it is more difficult for managers to hire highly-skilled employees, specialists in particular, thereby driving the choice of either over- or under-investing in the labor forces. In addition, in the firms’ annual reports, there is no information regarding temporary employees. Therefore, if and when such data become available, this would provide another avenue for future research.

Our study offers several practical implications and insights to stakeholders (e.g. insiders or management, shareholders, investors, analysts and creditors) in the following ways. First, our study highlights significant differences between capital investment and labor investment. For instance, labor investment is considered an expense rather than an asset (Wyatt, 2008) because, although such investment is human capital and is not recognized on the firm’s balance sheet (Boon et al., 2017). In addition, labor investment is characterized by: its flexibility which enables firms to make frequent adjustments (Hamermesh, 1995; Dixit & Pindyck, 2012; Aksin et al., 2015), its non-homogeneity since every employee is unique (Luo et al., 2020), its direct impact on morale and productivity of a firm (Azadegan et al., 2013; Mishina et al., 2004; Tatikonda et al., 2013), and its financial outlay which affects the ongoing cash flows of a firm (Sualihu et al., 2021; Khedmati et al., 2020; Merz & Yashiv, 2007). Second, our findings reveal that the presence of PCBs could help to reduce over-investment in labor. However, if managers of a firm choose to under-invest in labor in order to obtain better profit in the short-term through cost saving, they should be aware of the potential consequences of facing a financial loss when a new business opportunity suddenly arises which requires a larger labor force. Third, our findings help stakeholders to re-focus on the labor investment. This is crucial due to the fact that labor investment is often neglected by those stakeholders because the expenditure of labor investment is not recognized on the firm’s balance sheet as an asset. Instead, it is written off as an expense in the firm’s income statement. Fourth, our findings also provide insightful information to stakeholders, suggesting that an SB with optimal tenure is more committed to a firm, and this factor plays an important role in strengthening the negative association between PCBs and over-investment in labor.

First, our findings provide a valuable understanding of the effects of PCBs on over-(under-)investment in labor. Stakeholders could use information disclosed in the financial statements of a publicly-listed firm to determine the extent of the firm’s investment in labor and PCBs, and compare this information with similar firms in the same industry sector. Second, our findings give a better understanding of the association between investment in labor and political connections , which are human and social capital that could determine the long-term survival and success of a firm. Third, for shareholders, the appointment of board members with political connections is an important strategic decision to build political capital, which is likely to have a long-term impact on the financial performance of a firm; therefore, it requires thoughtful consultation with firm insiders.

Our findings highlight the role of PCBs in reducing over-investment in labor. These findings are significant because both investment in labor and political connections as human and social capital can play an important role in determining the long-term survival and success of a firm.

The design of cantilever pile walls (CPWs) presents several common challenges. These challenges include soil variability, groundwater conditions, complex loading conditions, construction considerations, structural integrity, uncertainties in design parameters and construction and monitoring costs. Accordingly, this paper is to provide a detailed literature review on the design criteria of CPWs, specifically in cohesionless soil. This study aims to present a comprehensive overview of the current state of knowledge in this area.

The paper uses a literature review approach to gather information on the design criteria of CPWs in cohesionless soil. It covers various aspects such as excavation support systems (ESSs), deformation behavior, design criteria, lateral earth pressure calculation theories, load distribution methods and conventional design approaches.

The review identifies and discusses common challenges associated with the design of CPWs in cohesionless soil. It highlights the uncertainties in determining load distribution and the potential for excessive wall deformations. The paper presents various approaches and methodologies proposed by researchers to address these challenges.

The paper contributes to the field of geotechnical engineering by providing a valuable resource for geotechnical engineers and researchers involved in the design and analysis of CPWs in cohesionless soil. It offers insights into the design criteria, challenges and potential solutions specific to CPWs in cohesionless soil, filling a gap in the existing knowledge base. The paper draws attention to the limitations of existing analytical methods that neglect the serviceability limit state and assume rigid plastic soil behavior, highlighting the need for improved design approaches in this context.

This study assesses the current landscape of business continuity management (BCM) research while exploring research trends, structures and delineating potential future directions.

A comprehensive bibliometric analysis was conducted on 360 articles from the Scopus and Web of Science databases using Biblioshiny software. A meta-synthesis was employed to aggregate and synthesize findings from the bibliometric results.

The results demonstrate a notable increase in publication numbers since the onset of the pandemic, reaching a peak in 2022 with a total of 342 articles. A collaborative bond among scholars transcends geographical boundaries and national affiliations. The analytical results propose avenues for future research, addressing crucial areas such as the integration of business continuity management systems (BCMS), the development of BCM frameworks and a comparative analysis of business impact analysis (BIA) frameworks through pertinent theories.

The study contributes theoretical and practical implications, serving as a valuable resource for academics and practitioners seeking to deepen their understanding of BCM’s role in business recovery and preserving organizational continuity in the face of disruptions.

This study pioneers a comprehensive approach by integrating bibliometric analysis and qualitative meta-synthesis, providing a consolidated overview of BCM research. Additionally, it presents future research proposals in this area.

This paper aims to solve the problem of uncertain position and attitude between unstructured terrain robot and grasped target and insufficient control accuracy in extreme environment, a grasping mechanism based on attraction domain relationship is proposed, which can realize autonomous positioning, capturing and grasping of robot under low control accuracy.

The grasping mechanism was designed, taking inspiration from fishing behavior this mechanism introduces attraction domains and flexible-elastic structures through the active and passive ends to achieve automatic positioning and capture. After the capture is completed, the grasping mechanism connects the active end and the passive end, simultaneously relying on the gravity of the target object to achieve locking and release between the robot and the target object. This paper adopts theoretical, simulation and experimental verification methods to conduct theoretical and simulation analysis on the autonomous positioning and grasping process of the mechanism, and produces grasping experimental prototypes with different positions and postures.

The experiment shows that the gripping mechanism designed in this paper can achieve automatic positioning capture and gripping of large deviation situations under low control accuracy, with a displacement deviation of up to 10 mm (about 1/6 diameter of the end of the mechanism) and an angle deviation of up to 3°. The scientific research task in the extremely high altitude environment has finally been successfully accomplished.

Inspired by fishing behavior, this paper proposes a positioning, capturing and grasping mechanism. The attraction area built with permanent magnets, coupled with the flexible connection, enables precise capture under low control, while the grasping mechanism can also rely on gravity to self-lock and release.

The purpose of this paper is to explore the degree of inflation persistence across all US metro areas over the post-pandemic period.

Both the Multivariate Core Trend (MCT) model and a fractional integration model, that is the Multivariate Unobserved-Components Stochastic Volatility Outlier-adjusted (MUCSVO) model are estimated.

The findings provide clear evidence of a significant inflation persistence in ten metro areas and the absence of persistence in the remaining areas, implying that in the former areas, inflation clearly indicates a strong persistent pattern. In other words, in these ten areas, the persistent component dominates the evolution of the trend and stands as a significant driver of inflation.

The findings have important implications for US policymakers to consider implementing more targeted policies to address inflation in specific metro areas to reduce the overall inflation rate, or they may need to consider tailoring fiscal policies to address inflationary pressures in specific metro areas. The findings illustrate the need for targeted policy interventions to address inflationary pressures in specific areas, as well as the importance of understanding the drivers of inflation persistence to develop effective policy responses. The findings also provide insights to firms on how to mitigate the risks of inflation. They may need to diversify their products or supplier base so that they do not rely on areas experiencing persistent inflation.

This paper contributes to the literature by extending the discussion of the impact of the recent pandemic crisis on US regional inflation. The findings have important implications for US policymakers to consider implementing more targeted policies to address inflation in specific metro areas to reduce the overall inflation rate, or they may need to consider tailoring fiscal policies to address inflationary pressures in specific metro areas. The findings illustrate the need for targeted policy interventions to address inflationary pressures in specific areas, as well as the importance of understanding the drivers of inflation persistence to develop effective policy responses. The findings also provide insights to firms on how to mitigate the risks of inflation. They may need to diversify their products or supplier base so that they do not rely on areas experiencing persistent inflation.

Learning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.

A total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.

The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.

This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

The purpose of this paper is to propose a taxonomy of artificial intelligence (AI) literacy to support AI literacy education and research.

This study makes use of the facet analysis technique and draws upon various sources of data and information to develop a taxonomy of AI literacy. The research consists of the following key steps: a comprehensive review of the literature published on AI literacy research, an examination of well-known AI classification schemes and taxonomies, a review of prior research on data/information/digital literacy research and a qualitative and quantitative analysis of 1,031 metadata records on AI literacy publications. The KH Coder 3 software application was used to analyse metadata records from the Scopus multidisciplinary database.

A new taxonomy of AI literacy is proposed with 13 high-level facets and a list of specific subjects for each facet.

The proposed taxonomy may serve as a conceptual AI literacy framework to support the critical understanding, use, application and examination of AI-enhanced tools and technologies in various educational and organizational contexts.

The proposed taxonomy provides a knowledge organization and knowledge mapping structure to support curriculum development and the organization of digital information.

The proposed taxonomy provides a cross-disciplinary perspective of AI literacy. It can be used, adapted, modified or enhanced to accommodate education and learning opportunities and curricula in different domains, disciplines and subject areas.

The proposed AI literacy taxonomy offers a new and original conceptual framework that builds on a variety of different sources of data and integrates literature from various disciplines, including computing, information science, education and literacy research.

The study aims to explore the impact of National Health Insurance Scheme (NHIS) enrolment on farm investments in a developing country setting. We classify farm investments into (1) soil and land investments and (2) hired adult labour.

This study used data on 5,883 farm households from the sixth round of the Ghana Living Standard Surveys (GLSS), which is nationally represented data at the household level. The data also includes a Labour Force Survey module. The sample frame was divided into a primary and secondary sampling unit, with interviews taking place in 1,200 enumeration areas (EAs). The estimation of impacts was carried out using ordinary least squares (OLS) estimations and addressed endogeneity concerns using propensity score matching (PSM) and instrumental variable (IV) estimators.

The study finds a strong positive association between the NHIS enrolment status of farm households and investments in agricultural land and soil health improvement. Precisely, farm households who are enroled in the health insurance system tend to invest about 32% more in soil and land improvement activities and 30% more in hired farm labour than households who are not enroled in NHIS.

The overall evidence from our study suggests that instead of high investments in fertilizer and other input subsidy programmes in Africa, sustainable smallholder agricultural investments can be achieved if concerns and issues of farmers’ health coverage are adequately addressed.

This is one of the first papers that have explored the impact of NHIS in developing countries on farm investments.

A section of in-service PE gas pipeline in Guocun, Beijing, was found to appear gas leaking at the electrofusion (EF) joint. This study is dedicated to reveal the material cause of EF joint failure to help with a more accurate prediction of service life of PE gas pipe and further normalize the construction of PE gas pipeline.

Defect detection was carried out on the leaking EF joint using ultrasonic phased array. The mechanical degradation and structural aging behavior was studied by tension test, FTIR technology, TG test and DSC test. The organic components in the soil surrounding the PE gas pipe failure area were qualitatively identified.

The results showed that the organic surfactants in the soil environment could accelerate the aging behavior of PE material, leading to a deterioration of mechanical properties and a serious reduction in the ability of the PE pipe and EF joint, especially at the welding defect, to resist external force.

A novel study was conducted to investigate the failure cause of the EF joint of in-service PE gas pipe, incorporating the analysis of environmental factors and structural deterioration.

In order to explore a new estimation approach of hyperspectral estimation, this paper aims to establish a hyperspectral estimation model of soil organic matter content with the principal gradient grey information based on the grey information theory.

Firstly, the estimation factors are selected by transforming the spectral data. The eigenvalue matrix of the modelling samples is converted into grey information matrix by using the method of increasing information and taking large, and the principal gradient grey information of modelling samples is calculated by using the method of pro-information interpolation and straight-line interpolation, respectively, and the hyperspectral estimation model of soil organic matter content is established. Then, the positive and inverse grey relational degree are used to identify the principal gradient information quantity of the test samples corresponding to the known patterns, and the cubic polynomial method is used to optimize the principal gradient information quantity for improving estimation accuracy. Finally, the established model is used to estimate the soil organic matter content of Zhangqiu and Jiyang District of Jinan City, Shandong Province.

The results show that the model has the higher estimation accuracy, among the average relative error of 23 test samples is 5.7524%, and the determination coefficient is 0.9002. Compared with the commonly used methods such as multiple linear regression, support vector machine and BP neural network, the hyperspectral estimation accuracy of soil organic matter content is significantly improved. The application example shows that the estimation model proposed in this paper is feasible and effective.

The estimation model in this paper not only fully excavates and utilizes the internal grey information of known samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a new hyperspectral estimation model of soil organic matter content based on the principal gradient grey information and effectively dealing with the randomness and grey uncertainty in spectral estimation.