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This paper aims to present a comprehensive assessment of the literature about the research agenda for future studies on risk management in small and medium-sized businesses (SMEs) with a research agenda for bibliometric analysis. The author's goals are to point out inconsistencies and gaps in the literature and to suggest directions for future study.

A total of 147 papers were analyzed in terms of bibliographic information, research design, and findings. These included publication by year, most cited documents, citation by year, publication by authors, publication by subject area, publication by country, publication by affiliation, funding sponsor, network representation of keyword co-occurrence, and cluster visualization of keyword co-occurrence. This study used bibliometric analysis methods, keyword searches, and suitability assessment for bibliometric analysis.

The bibliometric research revealed a number of potential risk categories for small and medium enterprises (SMEs). In addition, an analysis of the risk-related elements aimed to avoid or reduce their adverse effects, the published study highlights how crucial a risk management procedure is for SMEs. Risk factors were also shown to be unavoidable; as a result, everyone needs to practice social responsibility in order to reduce the detrimental effects on the economy.

Future studies are required on risk identification, analysis, plan implementation, and control in the context of small and medium-sized enterprise risk management.

This work represents the first in a bibliometric analysis of risk management in small and medium-sized enterprises (SMEs) for the future research agenda.

This study aims to explore the differences in the tourists’ perceived destination image on travel e-commerce platforms (e.g. Ctrip and Fliggy) and social media platforms (e.g. Xiaohongshu and Weibo).

As an important part of national governance, the online communication of education policies usually attracts the attention of many subjects, including the public and the media. Existing research mainly focuses on analysing communication behaviour of a single subject. However, with the rapid development of social media, policy information communication is often accompanied by the participation of multiple subjects and forms diversified communication behaviours and interaction patterns. The comprehensive identification of multiple subjects and their interactions can accurately depict the communication process and effectively support the efficient communication of policies. Therefore, this paper aims to conduct fine-grained analysis on the multiple subjects in information communication of the education policy.

This paper explored the communication and interaction process of the education policy via multidimensional analysis. Specifically, the authors firstly obtained multi-source communication data to identify key communication subjects. Secondly, the authors mined the communication contents generated by communication subjects to measure the diversified correlations between subjects. Finally, the authors depicted the interaction of subjects in policy information communication.

The experimental results reveal that there are multiple key subjects in the policy information communication, and the communication roles of the subjects change with the communication process, including dominance role, one-way or two-way effect role. This further indicates the need to allocate resources dynamically in the process of policy communication.

Analysing the process of policy communication and identifying the dynamic interaction between communication subjects can provide more a comprehensive and detailed decision-making basis for policy formulation and implementation. In addition, the research ideas and methods presented in this paper expand the perspective of information communication research.

The key purpose of this study is to systematically examine the landscape of education research funded by the National Plan of Educational Research Funding (NPERF) in China. The study aims to: (1) identify the thematic focus areas that reflect the national education agenda, (2) analyze the general funding patterns of education research projects and (3) gain insights into the distinctive nature of the research agenda in China. The study employs a rigorous data-driven approach to offer valuable insights into the dynamic discourses within the field of education research in China, which has received relatively little attention despite its potential significance.

In this study, we utilized word co-occurrence analysis and corpus-based frequency analysis to analyze the research projects funded by the National Plan of Educational Research Funding (NPERF) from 2011 to 2020.

The key characteristics of these projects highlight the focus on higher education research, addressing the interests of specific cohorts of students, teachers and disadvantaged populations. Furthermore, these projects demonstrate a remarkable responsiveness to the policy needs of the country and a robust inclination toward an international comparative framework.

The findings offer valuable insights into the landscape and features of funded education research in China, revealing a strong emphasis on addressing practical needs and enhancing the capacity of the education system in the country.

This paper presents a systematic examination of the topics covered in funded research under the National Plan of Educational Research Funding (NPERF) scheme from 2011 to 2020. It contributes to the advancement of understanding regarding knowledge traditions and practices in the Chinese context. Methodologically, this paper is the first in the literature to be prototyped with a word co-occurrence analysis approach to systematically investigate the funded education research in China. Additionally, it includes the development of a comprehensive corpus list to uncover the key characteristics of the funded projects. The analysis provides unique insights into the priorities and directions of education research supported by the Chinese government, which are of potential interest to international readers.

Street-level bureaucracy (SLB) has been essential to public administration in executing government policies and shaping public service quality. This paper aims to uncover the knowledge gaps and ongoing challenges to inform future analysis on SLB.

This literature review analyzes the publications on SLB between 1971 and 2023 by using various bibliometric methods, including trend analysis, network co-occurrence, and thematic evolution from 994 journal articles extracted from the Scopus database.

Research on SLB has shifted focus from specific issues such as work efficiency in the early 2000s to broader themes like governance methods, policy implementation, social policy, and public service delivery. This change reflects the adaptation of the field to global challenges and policy evolutions. International collaborations have contributed to the evolution, enriching SLB discourse with cross-cultural insights and comparative analyses. The partnerships have led to innovative strategies and models to address the challenges faced by SLB, enhancing public service delivery and policy implementation.

This paper shows the need to integrate the shift in SLB from specific practices to broader administrative themes with the global insights from international collaborations. In underrepresented regions, such as Africa, Latin America, and Southeast Asia, research is suggested to enrich the global understanding of SLB.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

This research aims to conduct a literature review of Lean Six Sigma (LSS) in the Indian context and related research publications and apply bibliometric analysis and the author's visualization to map research trends in this area.

This article conducts a bibliometric analysis of LSS-related research in the Indian context using data gathered from Scopus and Web of Science databases from 2011 to 2022. The review provides information on LSS-related research in the Indian context and evaluates performance based on primary sources, authors, keywords, countries, affiliations, and documents. The analysis employs the Biblioshiny app and Bibliometrix R-tool for data analysis and scientific mapping.

The results of the bibliometric analysis indicate that the LSS culture has widely spread in India. The International Journal of Lean Six Sigma and Production Planning and Control were found to be the most productive sources for publishing LSS-related research articles. Antony J. was identified as the most active author in this field, contributing the most over the years. Among all organizations, NITs have conducted the most comprehensive research on LSS, indicating their significant investment of resources and efforts in studying this methodology and its applications in India. Additionally, the study examined the intellectual, social, and conceptual structures to identify implicit gaps and future research opportunities.

The findings of this study can inform academicians, researchers, practitioners, and policymakers about the state-of-the-art and the specifics of the most prolific studies. This study will facilitate their exploration of emerging research areas in LSS.

To the best of the authors knowledge, this is the first bibliometric analysis of LSS in the Indian context, providing an overview of relevant publications published between 2011 and 2022. This study analyzed 194 articles on LSS in India, which can help researchers and academics identify emerging research areas, suitable collaborators, and relevant journals for future publications.

To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.

This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.

Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.

This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

The study aims to provide concrete service remediation and enhancement for LLM developers such as getting user forgiveness and breaking through perceived bottlenecks. It also aims to improve the efficiency of app users' usage decisions.

This paper takes the user reviews of the app stores in 21 countries and 10 languages as the research data, extracts the potential factors by LDA model, exploratively takes the misalignment between user ratings and textual emotions as user forgiveness and perceived bottleneck and uses the Word2vec-SVM model to analyze the sentiment. Finally, attributions are made based on empathy.

The results show that AI-based LLMs are more likely to cause bias in user ratings and textual content than regular APPs. Functional and economic remedies are effective in awakening empathy and forgiveness, while empathic remedies are effective in reducing perceived bottlenecks. Interestingly, empathetic users are “pickier”. Further social network analysis reveals that problem solving timeliness, software flexibility, model updating and special data (voice and image) analysis capabilities are beneficial in breaking perceived bottlenecks. Besides, heterogeneity analysis show that eastern users are more sensitive to the price factor and are more likely to generate forgiveness through economic remedy, and there is a dual interaction between basic attributes and extra boosts in the East and West.

The “gap” between negative (positive) user reviews and ratings, that is consumer forgiveness and perceived bottlenecks, is identified in unstructured text; the study finds that empathy helps to awaken user forgiveness and understanding, while it is limited to bottleneck breakthroughs; the dataset includes a wide range of countries and regions, findings are tested in a cross-language and cross-cultural perspective, which makes the study more robust, and the heterogeneity of users' cultural backgrounds is also analyzed.

The study aims to shed light on the concepts most addressed in scientific research, which blockchain topics are of most interest, how relevant are these tools for academia, and how relevant are they?

The authors have developed a bibliometric study of scientific publications on blockchain made since 2016. For the analysis, the VOSViewer software version 1.6.19 has been used, which allows a statistical analysis of scientific publications on the subject.

The study manifest the relevance of Initial Coin Offering, growth of research interest in this field and the relevance of blockchain technology in the development of entrepreneurial projects.

This study provides a complete and updated picture of the scientific research on blockchain for the subsequent transfer of knowledge to the business world.