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This study aims to examine how participation in the Belt and Road Initiative (BRI) affects province-level regional economic resilience. In the context of dual circulation – the new development paradigm proposed by the Chinese Government – participating in the BRI is an important means of connecting both international and domestic circulations and achieving high economic resilience. The complex causal relationship between participation in the BRI and province-level regional economic resilience is investigated.

Based on the complex system view, this study uses fuzzy set qualitative comparative analysis (fsQCA) to examine the impact on regional economic resilience when provinces participate in the BRI through unimpeded trade, infrastructure connectivity, financial integration and people-to-people bonds under the two conditions of attention allocation and buffering capacity. Qualitative textual analysis is applied to analyse provincial work reports, and relevant statistical data are used to measure the economic resilience from 2013 to 2020.

The authors identified three condition configurations that lead to a high regional economic resilience at province-level and one condition configuration that lead to no high-level regional economic resilience.

In-depth analyses of qualitative materials should be conducted to explain the systematic relationships among the conditions.

This research is of practical significance to the development of the theoretical framework and practices of the BRI in the context of dual circulation.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

The urgency to address climate change and its devastating consequences has never been more pressing. As societies become increasingly aware of the detrimental impact of traditional housing on the planet, there is a growing demand for eco-friendly housing solutions that prioritize energy efficiency, resource conservation and reduced carbon emissions. Therefore, this study aims to investigate the factors that influence customers’ priority toward eco-friendly house purchasing intention.

This study collected 386 data using a quantitative research strategy and purposive sampling method. This study uses a hybrid analysis technique using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approaches to identify the influencing factors.

The PLS-SEM analysis found that attitude toward the eco-friendly house, subjective norms, performance expectancy, environmental knowledge and environmental sensitivity have a positive influence on eco-friendly house purchasing intention. However, perceived behavioral control and willingness to pay were found to have insignificant effect on customers’ intention to purchase eco-friendly houses. The fsQCA results further revealed complex causal relationships between the influencing factors.

This research will not only contribute to academic knowledge but also provide practical guidance to real estate developers, policymakers and individuals looking to make environmentally responsible choices. By understanding the factors that influence consumers’ intentions to purchase eco-friendly houses, we can pave the way for a more sustainable and resilient future.

This study has used a hybrid analysis technique, combining PLS-SEM and fsQCA, to enhance the predictive accuracy of eco-friendly house purchase intentions among individuals residing in densely populated and highly polluted developing countries, such as Bangladesh.

The emergence of cryptocurrencies has tremendously changed the way of financial transactions around the world which has led to form distinct discussions in the field regarding its reliability. This paper aims to evaluate the published literatures on cryptocurrency identifying its growth, citation, prolific authors, journals, countries, active funding agencies, collaboration pattern and emerging research hotspots in the area.

Scientometrics and Altmetrics parameters have been incorporated in the study. Literatures covered from the Scopus database searching within “Article Title, Abstract, Keywords” with keywords “cryptocurrency” OR “digital currency” OR “bitcoin” OR “Ethereum” by limiting the time range of 2013–2022, English language and journal articles only. Total 6,107 documents have been identified. The further analysis and visualisation is performed using MSExcel, VOSviewer, Biblioshiny and Tableau. Another tool, Dimension.ai is used to identify the Altmetric Attention Score.

The findings reveal that the growth of research and citation rate hiked from the year 2017 till now. Elie Bouri is the top contributor, IEEE Access is the most prolific journal, China being the prolific country. Topics like Blockchain, Bitcoin, Ethereum, smart contracts, financial markets are emerging researched hotspots. The reliability of crypto market is still not clear because of its high volatility. The findings of the study will be more useful in the academia, subject specialists, research institutions, funding agencies, publishing agencies in decision-making.

To the best of the authors’ knowledge, there is no such study found considering both Scientometrics and Altmetrics approaches on cryptocurrency research with the selected time bound.

Despite the importance of emotional variables in shaping individuals' consumption behavior, nature-love still needs to be addressed concerning various aspects of sustainable consumption behavior (SCB). Considering the dimensions of nature-love, this study aims to investigate the effect of passion-for-nature, intimacy-with-nature and commitment-to-nature on SCB. Furthermore, this study aims to incorporate the construal levels of psychological distance (PD) as a moderating variable between the tested variables to add a more in-depth understanding.

Data was collected from a sample of 311 individuals from Egypt using the snowball sampling method and the ten-time rule technique. The data was analyzed using partial least squares-structural equation modeling (PLS-SEM).

The findings indicate that passion-for-nature and intimacy-with-nature have a significant positive effect on green purchasing, reusability and recycling. On the other hand, commitment-to-nature has a significant positive effect on both green purchasing and reusability. Additionally, a high PD acts as a moderator between the relationships tested. The findings have been discussed in terms of their theoretical and practical implications.

To the best of the authors’ knowledge, this is the first study to integrate PD as a moderator between the relationships tested. Additionally, this paper is the first empirical research investigating these relationships in developing economies.

Prefabricated construction (PC) can benefit construction industry due to its high efficiency, energy saving, consumption reduction and safety. However, the high capital cost is a critical challenge hindering its development in China. The collaboration of PC stakeholders is conducive to improving cost management efficiency and optimizing resource allocation which has been ignored in previous studies. Therefore, this study aims to explore the collaboration paths of stakeholders in the process of solving critical cost influencing factors (CIFs) of PC to reduce costs.

Firstly, 25 CIFs and five main stakeholders that affect PC capital cost were identified through literature research and expert interviews. Then, questionnaires were used to investigate the relationship between stakeholders and CIFs from the perspectives of three stakeholder attributes of proximity, attitude and power, respectively. Finally, based on the survey data, three stakeholder-CIF networks from three attributes perspective and a comprehensive network were constructed and used for subsequent social network analysis.

(1) Stakeholders mainly show willingness and potential to collaborate on organization and management factors; (2) More stakeholders pay attention to incentive policies and the setting of prefabrication rates and assembly rates, while all stakeholders have the right to facilitate information and resource sharing in the PC supply chain; (3) The comprehensive network shows a core-periphery structure. As core stakeholders, contractor, designer and manufacturer are more likely to actively manage the 14 core CIFs.

This paper innovatively combined stakeholder and cost management in PC, and used two-mode network based on three stakeholder perspectives to reveal the collaboration potential and motivation of stakeholders in PC cost management. The findings not only provide guidance for stakeholders to find potential partners and optimize resource allocation in solving specific cost issues, but also facilitate stakeholders' sustainable collaboration to achieve PC's cost performance.

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.

This study examines the role of the big five personality traits: conscientiousness, openness, extroversion, neuroticism and agreeableness in financial planning.

The research design is a quantitative approach. The study has used structured questionnaires to collect data from 403 business students. The hypotheses were tested through structural equation modeling using AMOS.

The findings revealed that extroversion of personality traits have a significant negative influence on financial planning, neuroticism and conscious personalities have a significant positive effect on financial planning. However, two personality traits, namely openness and agreeableness, have no significant influence on financial planning. The study confirmed that out of five, three personality traits have significant impact on financial planning.

The results suggest that all personality traits do not influence financial planning among students. Financial planning is deemed an essential decision in life. Although some people are very conscious about their future expenditures, others are not much concerned. Based on the findings, this study recommends that policymakers may conduct workshops and arrange seminars and conferences for the promotion of financial planning and individual's financial well-being. The government needs to promote financial education that can directly and indirectly enhance the saving planning capabilities of the people.

The results suggest that not all personality traits facilitate financial planning. Financial planning is deemed as a crucial decision in life. Some students are very conscious about their future expenditures, while others are not much concerned. This study recommends that policymakers conduct workshops and arrange seminars and conferences to promote financial planning and individuals' financial well-being. The government of Pakistan needs to promote financial education that can, directly and indirectly, enhance the savings and planning capabilities of the students.

This research contributes to the personality literature, the theory of planned behavior and the life cycle theory by testing the model based on empirical evidence. The current study is the first to focus on the role of the big five personality traits in financial planning among students in Pakistan, an emerging economy.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

The objective of the study is to systematically review the existing research in the topical domain of farmers’ direct link with supermarkets. The authors present a state-of-the-art structure of the field and provide directions for future research in the domain. The major aim of this study is to synthesize the research field and answer some specific questions, like what do we know about this field and where should we be heading.

A pool of 275 articles published from 2002 to 2022 were retrieved from the Scopus database and analyzed using the R-based Biblioshiny and visualized using VOSviewer. The research design is a mix of quantitative bibliometric technique and qualitative content analysis. Bibliometric method ensures the objectivity while content analysis ensures the scholarly evaluation.

Major findings include production trend, dominant keywords, leading publication outlets and country-wise analysis of the selected articles. The authors found that sub-domains like economic aspects, participation hurdles and the rise of supermarkets are the most researched topics while operational issues, their pragmatic solutions, sustainability and innovation are the emerging sub-fields that need more academic attention.

The major limitation is the use of single data source, i.e. Scopus, and it is quite possible that useful studies that are not covered by Scopus remain excluded.

This study is one of the first attempts to systematically review the previous research on the selected topic. It will help researchers to understand the present status, identify future research directions, and pursue more reasonable and relevant topics of research.