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Under the background of coexistence of information overload and information fragmentation, it is of great significance to identify influencing factors and reveal the evolution logic of public opinion for public opinion governance.

Taking 24 hot social events as research cases, firstly, the evolution process of public opinion was divided into initial stage and response stage. Secondly, eight antecedent variables were extracted for qualitative comparative analysis of fuzzy sets. Finally, the configuration path of public opinion evolution results was summarized.

The research showed that compared with the initial stage, the influencing factors in the reaction stage played a key role in the continuous evolution of public opinion. The influencing factors in the initial stage and response stage played an indispensable role in promoting the evolution of public opinion to calm down.

This research can provide reference for regulators to timely grasp the initiative, discourse power and leadership of public opinion development.

Research on the two-stage configuration path of public opinion evolution is helpful to clarify the key factors affecting the evolution trend of online public opinion of hot events.

Various decision opinions comprise the foundation of emergency decision-making. However, decision-makers have difficulty establishing trust relationships within a short time because of decision-making groups being temporary. The paper aims to develop an ambiguity-incorporated opinion formation model that considers ambiguous opinions on relevant risks from a psychological perspective during the consensus reaching process.

Addressing the problem of forming a consensus decision-making opinion in an ambiguous environment and relevant risk opinions, different social network structures were first proposed. Subsequently, psychological factors affecting the decision-makers' perception of ambiguous opinions and tolerance for ambiguity under the multi-risk factors were considered. Accordingly, an ambiguity-incorporated opinion formation model was proposed by considering the ambiguity and relevant opinions on multi-risk factors.

A comparison between the ambiguity-incorporated opinion formation model and the F–J model illustrates the superiority of the proposed model. By applying the two types of network structures in the simulation process, the results indicate that the convergence of opinions will be affected by different decision-making network structures.

The research provides a novel opinion formation model incorporating psychological factors and relevant opinions in the emergency decision-making process and provides decision support for practitioners to quantify the influence of ambiguous opinions. The research allows the practitioners to be aware of the influence of different social network structures on opinion formation and avoid inaccurate opinion formation due to unreasonable grouping in emergency decision-making.

A novel asynchronous network-based model is proposed in this paper for the sentiment analysis of online public opinions. This new model provides a new approach to analyze the evolution characteristics of online public opinion sentiments in complex environment.

Firstly, a new sentiment analysis model is proposed based on the asynchronous network theory. Then the graphical evaluation and review technique is employed and extended to design the model-based sentiment analysis algorithms. Finally, simulations and real-world case studies are given to show the effectiveness of the proposed model.

The dynamics of online public opinion sentiments are determined by both personal preferences to certain topics and the complex interactive influences of environmental factors. The application of appropriate quantitative models can improve the prediction of public opinion sentiment.

The proposed model-based algorithms provide simple but effective ways to explore the complex dynamics of online public opinions. Case studies highlight the role of government agencies in shaping sentiments of public opinions on social topics.

This paper proposes a new asynchronous network model for the dynamic sentiment analysis of online public opinions. It extends the previous static models and provides a new way to extract opinion evolution patterns in complex environment. Applications of the proposed model provide some new insights into the online public opinion management.

This study examines opinion leadership's personal and social characteristics to see which one is more effective in opinion leadership in four different industries: fashion, travel and tourism, wellness and book and literature. The specific subject of this investigation is how largely openness, exhibitionism and competence in interpersonal relationships and status and attitude homophily affect the opinion leadership and the decision-making of opinion leaders' followers.

The proposed model was tested with the questionnaire shared via stories featured on Instagram among followers of four micro-influencers in different industries. For the purpose of testing the offered hypotheses of this study, the partial least squares method was used.

The findings show that openness, exhibitionism and competence in interpersonal relationships have a substantial effect on opinion leadership. It was also evident that status and attitude homophily impact opinion leadership. The model supports the effect of both personal and social characteristics on opinion leadership; however, based on the results, the effect of personal characteristics on opinion leadership is more remarkable, both in a direct relationship and through the mediating role of para-social interaction.

This study is novel in categorizing opinion leaders' attributes in two different extents of personal and social characteristics. The authors defined a model of the effectiveness of each personal and social characteristic on opinion leaders. The model investigates whether the personal or social characteristics have the most effect on opinion leadership, particularly with the mediating role of para-social interaction.

The most common reaction to an acute physical illness is anxiety, which may be followed by depression. In patients with chronic diseases, the prevalence of anxiety disorders and depression is almost twice as high as in other diseases. This study aims to extract prominent components in the design of treatment spaces on reducing hospitalized patients’ depression from both experts and patients/users’ point of views. A final model is also presented based on the findings.

This research used an exploratory mixed method. The effective components were extracted through the administration of two Likert-scale researcher-made questionnaires in two phases. Q factor analysis was conducted to reach the components. A total of 205 patients were admitted to Namazi Hospital in Shiraz, and 20 architecture and psychology experts participated in the survey. Final modeling of the data was done through path analysis.

Six factors were found to be effective by experts in reducing depression in therapeutic spaces: nature-oriented space, targeted social space, diverse space, visual comfort, logical process and safe space. On the part of patients, seven components were deemed to be effective: visual perception, naturalism, functionalism, physical security, logical process, psychological safety and diversity. Also, four main cycles were extracted from the final model with the direct effect of diversity and the other five cycles were mediated by naturalism.

A total of 15 interviews with architects and psychologists, who were available at the time of the study, were conducted in January 2018. The only general question during interviews was “In your opinion, what factors are effective in reducing the level of depression of patients in the design of treatment spaces?” This may have limited the range of factors that could be surveyed in the study. After collecting the effective factors from the aforementioned expert’s points of view, the questionnaire of experts was designed (Appendix). The expert questionnaires were distributed and edited in two stages in January 2019 among 20 architect experts who were available at the time of the study. The one-year interval between designing and administering the questionnaires occurred because of the limitations posed by the COVID-19 pandemic situation. However, the interval did not pose methodological obstacles for the study.

Evidence-based investigation of the effectiveness of proper design components of therapeutic spaces in reducing the symptoms of patients with chronic secondary depression has received little attention in the literature. Using a “conceptual model,” the present study brought the issue into its focus so as to find effective components in the design of treatment spaces that can alleviate depression symptoms in chronically hospitalized patients.

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 aimed to provide a framework that includes the principles of sustainable construction to evaluate their application in the construction of government building projects in various environmental, economic, and social aspects distributed over the project phases throughout its life cycle.

Qualitative methods from literature review and analysis of sustainability assessment tools were used to design the framework. The designed framework included six main categories, comprising 19 indicators that include sustainable building principles to assess application levels in government construction projects. It was used to evaluate applying sustainability practices in Jordanian government construction projects. 133 questionnaires were distributed to a convenience sample of three government institutions concerned with the design, implementation, and management of government buildings in Jordan.

After collecting the quantitative data, the results showed that there is an application of six sustainability principles during the initial planning, analysis, and design stages of Jordanian government construction projects. The results focused on the application levels in social sustainability principles versus environmental and economical, especially in the operating stages during the project life cycle.

This study contributes by providing a tool to evaluate the sustainability of government construction projects and increase the efficiency and effectiveness of these types of buildings in both the short and long term by making them more sustainable. Subsequently, recommendations are made on reorienting government construction projects toward a sustainable building approach.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

The present study investigates the impact of opinion leadership and spirituality from three types of social media influencers (SMIs) on individuals’ intentions to conduct recycling. This research is driven by the opinion leadership theory demonstrated by influencer marketing.

This research applies a between-subject experiment to measure the impact of the proposed model. Each participant was exposed to a different influencer: an attractive influencer (Treatment 1, n = 101), an expert influencer (Treatment 2, n = 94), a religious influencer (Treatment 3, n = 99) and a control condition (Treatment 4, n = 102). An ANOVA post-hoc analysis was conducted to further assess the impact dynamics of each influencer based on different demographics such as age, income and level of education. More than 95% of the samples consist of Muslims.

Findings revealed the different dynamics of the effect of opinion leadership and spirituality on the intention to recycle from utilising different types of influencers. Samples derived from a high socio-economic background and exposed to the religious influencer (Treatment 3) have a relatively higher mean score. In general, younger participants with lower incomes and levels of education have less tendency to conduct recycling.

This research attempts to fill the gap in the impact of influencer marketing on green behaviour adoption with the inclusion of spirituality, which has been largely ignored in this context. It offers insights from the perspective of a developing economy that has one of the largest percentages of social media users in the world and from a country that regards a relationship with God as important.

Effective total quality management (TQM) practices rely on the accurate classification of critical success factors (CSFs). The impact matrix cross-reference multiplication technique for classification (MICMAC) or/and fuzzy MICMAC (FMICMAC) can be used to identify key factors in the complex set. However, TQM includes both “hard” and “soft” factors, limiting application of the traditional MICMAC/FMICMAC method.

Previous literature on TQM was reviewed, CSFs were identified, and factors were sorted into soft and hard categories. The combined fuzzy integration and dual-aspect MICMAC (fuzzy dual-aspect MICMAC approach) was then applied to identify, cluster and prioritize the CSFs of TQM.

A total of 20 factors (10 soft and 10 hard) were identified and isolated to assess the manufacturing- and service-related TQM practices of the Pearl River Delta Region of China. Seven driver factors and one linkage factor emerged as the key CSFs that managers should prioritize.

A major limitation of this study is the dependency of the results on the definitions of linguistic labels. If the linguistic definitions of TQM CSFs do not closely correspond to the expert opinion data, then the analysis results may be inaccurate. Additionally, although expert opinions are utilized in the proposed method for comprehensive assessments, these opinions may influence the final results due to their inherent subjectivity.

A novel fuzzy dual-aspect MICMAC approach was developed to identify and classify CSFs for optimal TQM practices. This approach allows clustering of CSFs so that decision-makers can prioritize factors according to their dependence and driving powers. Practitioners should concentrate on the CSFs with higher driving powers for successful TQM.