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Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through supervised learning methods; however, the evaluation of classification results remains a challenge. The previous studies mostly adopted simplex evaluation based on empirical and quantitative assessment strategies. This paper aims to shed new light on the comprehensive evaluation and comparison of diverse classification methods through visualization, clustering and ranking techniques.

An empirical study is conducted using 9 state-of-the-art classification methods on a real-world data set of 653 construction accidents in China for predicting the consequence with respect to 39 carefully featured factors and accident type. The proposed comprehensive evaluation enriches the interpretation of classification results from different perspectives. Furthermore, the critical factors leading to severe construction accidents are identified by analyzing the coefficients of a logistic regression model.

This paper identifies the critical factors that significantly influence the consequence of construction accidents, which include accident type (particularly collapse), improper accident reporting and handling (E21), inadequate supervision engineers (O41), no special safety department (O11), delayed or low-quality drawings (T11), unqualified contractor (C21), schedule pressure (C11), multi-level subcontracting (C22), lacking safety examination (S22), improper operation of mechanical equipment (R11) and improper construction procedure arrangement (T21). The prediction models and findings of critical factors help make safety intervention measures in a targeted way and enhance the experience of safety professionals in the construction industry.

The empirical study using some well-known classification methods for forecasting the consequences of construction accidents provides some evidence for the comprehensive evaluation of multiple classifiers. These techniques can be used jointly with other evaluation approaches for a comprehensive understanding of the classification algorithms. Despite the limitation of specific methods used in the study, the presented methodology can be configured with other classification methods and performance metrics and even applied to other decision-making problems such as clustering.

This study sheds new light on the comprehensive comparison and evaluation of classification results through visualization, clustering and ranking techniques using an empirical study of consequence prediction of construction accidents. The relevance of construction accident type is discussed with the severity of accidents. The critical factors influencing the accident consequence are identified for the sake of taking prevention measures for risk reduction. The proposed method can be applied to other decision-making tasks where the evaluation is involved as an important component.

The purpose of this study is to determine the competency characteristics required for the employment of master’s degree students in educational technology.

A combined qualitative and quantitative method was used to consult multiple experts through a modified Delphi method. Competency characteristics were extracted from Chinese recruitment apps, national recruitment websites and university training programs. Ten senior teacher experts who teach educational technology master’s students were consulted through a questionnaire consultation to validate the proposed competency model. The weights of competency characteristics were determined through a combination of the analytic hierarchy process and entropy method.

The results show that when recruiting educational technology master’s students, more emphasis is placed on operational skills. The majority of companies tend to assess practical abilities rather than theoretical knowledge. Relevant knowledge of educational technology, psychology, computer science and education is considered to be the basic knowledge components of educational technology master’s students, while professional skills are the core skills required for their positions. Therefore, universities need to focus on training, educational technology graduate students in these areas of competence. The study also found that professional qualities (such as physical and mental fitness) and personality traits (interpersonal communication and interaction) receive more attention from companies and are essential competencies for educational technology master’s students.

A competence model for educational technology master’s students is proposed, which includes aspects such as knowledge, personal skills/abilities, professional qualities and personality traits. The competence elements included in this model can serve as reference indicators for universities to cultivate the competence of educational technology master’s students, as well as reference points for recruiting units to help them select talents. This represents a new dimension in research related to the employment of educational technology master’s students. The study enriches the research objects and competence dictionary in the field of competence research.

This paper aims to examine the most commonly used categories of sustainability literature review regarding their purpose, nature, strengths, weaknesses and potential for impact. This paper also discusses the motivation, incremental contribution and framing that occurs by considering the research papers included in this Special Issue.

Given the paucity of clear guidelines for undertaking, writing and publishing literature review studies in sustainability research, this paper describes the main types and processes in conducting a literature review and emerging tools that can help advance the field.

This paper finds a variety of approaches in application with strengths and weaknesses, including the emerging role of software support, artificial intelligence and machine learning. This paper reviews the ethical implications of using emerging tools in the sustainability literature review methodology and their impacts on originality, authenticity and accountability. This paper discusses the seven carefully selected and meticulously reviewed articles in this Special Issue through the lens of these findings by specifically highlighting their purpose, strengths, weaknesses and practical and policy implications.

Through the systemization of ways to conduct meaningful literature reviews, this paper explores the significant relevance of the method in creating a basis of academic understanding and advancing future research that can have significant impacts on the industry. Through the discussion of the articles in this Special Issue, this paper highlights the practical and policy implications and limitations of literature reviews in sustainability research.

This paper highlights the purpose of literature reviews in identifying areas for further research and how the papers included in this Special Issue achieve this goal, i.e. how their findings possess specific positive externalities in summarizing and systematizing sustainability research.

This paper systematizes methods and processes for writing impactful literature reviews in sustainability research, particularly focusing on the use of emerging technology and the opportunities and challenges this may offer in this process.

Corporate social responsibility (CSR) might be among the primary factors ensuring any organization’s survival, and disclosing its related information is very important. This research initially investigates the effect of managers’ behavior characteristics, including overconfidence, myopia and narcissism and corporate political ties on the disclosure of CSR. This study also aims to assess the mediating impact of political connections on the association between managerial personality traits and CSR.

The research sample included 129 listed companies on the Tehran Stock Exchange from 2013 to 2020. Behavioral managerers charecteristics. A multivariate regression method with combined data (firm-year) was used to test the research hypotheses.

The results show that overconfidence and managerial myopia cause the disclosure of CSR to decrease. Managers’ overconfidence and short-term attitudes lead to a decrease in the level of CSR activities of the companies and their disclosure, respectively, 0.021 and 0.025. However, the existence of narcissism in managers and having political ties by companies may lead to an increase in the disclosure of the CSR, respectively, around 0.089 and 0.02. Further findings also indicate that political connections may motivate narcissistic managers to increase CSR disclosure near 0.037. However, the results document no significant impact of political ties on the relationship between managerial overconfidence and myopia with CSR involvement.

According to the findings, the authors recommend to stockholders that employing narcissistic managers and improving political connections might be two effective strategies to enhance the level of CSR engagement. One of the critical limitations of the current paper might be its generalizability. As Iran is an emerging and fossil fuel seller country, its institutional settings may significantly differ from those of developed and industrial nations. Thus, the readers of these nations must consider such an important issue.

For the first time, to the best of the authors’ knowledge, this research has investigated the moderating effect of political ties on the association between management behavioral characteristics and the level of fulfilling CSR by listed companies.

Many developing countries have encountered frequent pollution accidents during their rapid development, while the previously weak environmental insurance systems could seriously undermine the progress of sustainable development. Some developing countries like China has initiated and strengthened environmental pollution liability insurance, so how effective this system would be in resolving enterprises environmental risks need to be further revealed.

This research identifies the possible consequence that compulsory environmental pollution liability insurance pilot (CEPLIP) policy would bring to the risk-taking capacity of heavy-polluting corporations of China by the Differences-in-Differences (DID) approach.

The result supports the implementation of CEPLIP policy in increasing corporate risk-taking capacity. Furthermore, the CEPLIP policy can promote the corporate’s risk-taking capacity by reducing financial distress constraints and enhancing trade credit, supporting its dual role of “fallback effect” as well as “external supervision effect” of environmental insurance. As a result of heterogeneity test, the policy is more pronounced in enterprise samples with mature life cycle stage or lower industrial concentration degree. Similarly, it is more significant in enterprise samples owned better environmental management capabilities or greater strategic deviance.

This paper verifies the effectiveness of the CEPLIP policy by strengthening its supervision mechanism and restraining opportunistic behavior tendency and provides implications for alleviating increasing environmental risk pressure and building more sustainable environmental protection management systems.

The objective is to develop an empirical model estimating the relationship and interaction amongst the factors affecting and enhancing green finance (GF) in developing economies like India.

Around nine growth-accelerating enablers of green financing were found through literature and unstructured interviews and analysed using the total interpretive structural modelling (TISM) method. The hierarchical link between each factor is established using TISM, and further to evaluate the driver-dependent relationship the Matriced’ Impacts Croises Appliquee Aaun Classement (MICMAC) approach is utilised.

The findings demonstrate an interrelationship between growth-accelerating factors, where the political environment and information and communication technology (ICT), have minimal dependency but a strong driving force. Political environment and ICT are found as strategic-level factors lying at the bottom of the model driving towards the dependent variables. The government should focus on enacting effective policies such as the green credit guarantee scheme and carbon credit and establishing a regulatory framework to enhance green financing.

This study examines the literature to generalise the findings and focus on the primary motivators for developing green financing. To increase green financial activity, practitioners must concentrate on aspects with significant driving forces. Furthermore, it makes organisations more profitable, efficient and competitive and promotes long-term growth.

The study is the first in the literature which identifies the growth-accelerating factors of green financing using the TISM and MICMAC-based hierarchical models.

This study aims to investigate the micro mechanism of macro rheological characteristics for composite modified asphalt.Grey relational analysis (GRA) was used to analyze the correlation between macro rheological indexes and micro infrared spectroscopy indexes.

First, a dynamic shear rheometer and a bending beam rheometer were used to obtain the evaluation indexes of high- and low-temperature rheological characteristics for asphalt (virgin, SBS/styrene butadiene rubber [SBR], SBS/rubber and SBR/rubber) respectively, and its variation rules were analyzed. Subsequently, the infrared spectroscopy test was used to obtain the micro rheological characteristics of asphalt, which were qualitatively and quantitatively analyzed, and its variation rules were analyzed. Finally, with the help of GRA, the macro-micro evaluation indexes were correlated, and the improvement efficiency of composite modifiers on asphalt was explored from rheological characteristics.

It was found that the deformation resistance and aging resistance of SBS/rubber composite modified asphalt are relatively good, and the modification effect of composite modifier and virgin asphalt is realized through physical combination, and the rheological characteristics change with the accumulation of functional groups. The correlation between macro rutting factor and micro functional group index is high, and the relationship between macro Burgers model parameters and micro functional group index is also close.

Results reveal the basic principle of inherent-improved synergistic effect for composite modifiers on asphalt and provide a theoretical basis for improving the composite modified asphalt.

This study aims to respond to the latest research calls to precisely revisit the nexus between corporate green innovation (CGI) and financial decisions through deeply investigating the mediating effect of corporate environmental performance measured by the effectiveness of emission reduction.

This study analyzes nonfinancial-listed firms on the Australian Securities Exchange from 2002 to 2019 using multiple regression analysis on a panel data set. Initially, different static panel data approaches were used. To account for the potential endogeneity issue and generate robust outcomes, the authors apply the one-step system generalized method of moment, two-stage least squares and lagged model approaches.

The results provide a clear indication that the practices of green innovation can favorably contribute to the level of environmental performance, which in turn affect the firm’s ability in opening the new financial doors and shape solid capital structure. In this context, the effective environmental performance fully mediates the nexus between CGI and capital structure of a firm. More importantly, the outcomes are robust and coherent across different estimation techniques.

The originality of this study lies in its utilization of mediation analysis to explore the relationship between CGI and a firm's financial structure. This approach distinguishes it from previous research by offering a thorough and nuanced understanding of how green innovation practices influence the financing decisions of a firm.

This study aims to investigate the effect of organizational ambidexterity (OA) and organizational green culture (OGC) on corporate sustainability (CS) while incorporating the mediating role of green innovation (GI) to provide a detailed insight into CS. The study also presents a research framework based on the Organizational Ambidexterity theory and Natural Resource-based view to explain the factors contributing to CS.

Using stratified sampling, the study collected data through survey-based empirical research from 307 textile companies registered with the Securities and Exchange Commission of Pakistan (SECP) or the All-Pakistan Textile Mills Association (APTMA). The collected data were analysed using path analysis, mediation analysis and moderation analysis through smart PLS-SEM version 4.0 to assess the composition and causal association of factors.

The study found a significant relationship between OA and OGC with CS. Furthermore, the study revealed that green innovation partially mediates the relationship between OGC and CS. The proposed research framework can be valuable for promoting and recommending actions to enhance CS.

The study on CS in the textile sector of Pakistan has limitations such as a narrow focus, cross-sectional design and reliance on self-reported data. Future research should explore additional factors, conduct longitudinal research, investigate contextual factors, scrutinize specific green innovation practices and broaden the scope of the study to include SMEs and other textile organizations.

The research framework can help senior executives to foster CS by promoting OGC, OA and GI. Practitioners and academicians can also utilize or further investigate the proposed framework for validation and to foster CS.

This study fills gaps in the existing literature by investigating the mediating effect of GI between OGC and CS. The proposed research framework provides a comprehensive understanding of the factors contributing to CS based on the Organizational Ambidexterity theory and Natural Resource-based view.

Coffee is one of the most consumed beverages in the world and the global coffee industry is worth over $100bn. However, the industry faces significant sustainability challenges. Developing a quality traceability system to select the coffee beans and to ensure their authentication would result in economic advantages, because it allows for fraud to be avoided and increases consumer confidence.

Traceability is one of the key elements of sustainability in the coffee sector. The literature reveals that near-infrared (NIR) approaches have a huge potential for gaining rapid information about the origin and properties of coffee beans, without invasive procedures. This study demonstrates the scalability potential of automated methods of manipulation and image acquisition of coffee beans, from experimental scale to industrial lines.

A solution based on the interaction of a manipulation system, a NIR spectrometer acquisition station integrated with a machine learning infrastructure and a compressed air classifier allows for the automatic separation of coffee beans into different classes of origin.

Apart from traceability, the wide industrialization of this system offers further advantages, including reduced workforce, decreased subjectivity in the evaluation and the acquisition of real-time data for labeling.