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Predicting the impact of social entrepreneurship is crucial as it can help social entrepreneurs to determine the achievement of their social mission and performance. However, there is a lack of existing social entrepreneurship models to predict social enterprises' social impacts. This paper aims to propose the social impact prediction model for social entrepreneurs using a data analytic approach.

This study implemented an experimental method using three different algorithms: naive Bayes, k-nearest neighbor and J48 decision tree algorithms to develop and test the social impact prediction model.

The accurate result of the developed social impact prediction model is based on the list of identified social impact prediction variables that have been evaluated by social entrepreneurship experts. Based on the three algorithms' implementation of the model, the results showed that naive Bayes is the best performance classifier for social impact prediction accuracy.

Although there are three categories of social entrepreneurship impact, this research only focuses on social impact. There will be a bright future of social entrepreneurship if the research can focus on all three social entrepreneurship categories. Future research in this area could look beyond these three categories of social entrepreneurship, so the prediction of social impact will be broader. The prospective researcher also can look beyond the difference and similarities of economic, social impacts and environmental impacts and study the overall perspective on those impacts.

This paper fulfills the need for the Malaysian social entrepreneurship blueprint to design the social impact in social entrepreneurship. There are none of the prediction models that can be used in predicting social impact in Malaysia. This study also contributes to social entrepreneur researchers, as the new social impact prediction variables found can be used in predicting social impact in social entrepreneurship in the future, which may lead to the significance of the prediction performance.

The purpose of this paper is to review the diversified existing literature on tourism and carbon emissions using bibliometric analysis to churn down the multiple studies under one paper, which not only provides insights into the evolution and progress of the research area but also sets the future research agenda.

The study adopted the Scientometrics review methodology based on the bibliometric analysis. Bibliometric analysis is conducted through the following techniques, namely, citation analysis, thematic mapping, country collaboration, co-citation analysis and co-occurrence of keywords with the help of R-based bibliometrix and visualization of similarities (VOS) viewer open-source software.

The study identified the most prominent authors, studies, journals, affiliations and countries in the field of sustainable tourism, as well as the most co-cited authors and journals, based on a bibliometric analysis of 398 research papers retrieved from the Scopus database during the past three decades (1990–2021). Moreover, some of the relevant themes identified by the authors are energy use and carbon dioxide (CO₂) emission of the tourism sector, economic impacts of tourism and CO₂ emissions and CO₂ emissions and carbon tax.

The outcome of the selected studies is a unique contribution to the field of sustainable tourism as it is one of the first known studies to review tourism and carbon emissions. It provides in-depth bibliometric analysis of articles and identification of the important research trends.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

Policymakers worldwide have proposed a new contract – the ‘social impact bond’ (SIB) – which they claim can allay the underperformance afflicting not-for-profits, by tying the private returns of (social) investors to the success of social programs. We investigate experimentally how SIBs perform in a first-best world, where investors are rational and able to obtain hard information on not-for-profits’ performance. Using a principal-agent multitasking framework, we compare SIBs to inputs-based contracts (IBs) and performance-based contracts (PBs). IBs are based on a piece-rate mechanism, PBs on a non-binding bonus mechanism, and SIBs on a mechanism that, due to the presence of an investor, offers full enforceability. Although SIBs can perfectly enforce good behaviour, they also require the principal (i.e., government) to relinquish control over the agent’s (i.e., not-for-profit’s) payoff to a self-regarding investor, which prevents the principal and agent from being reciprocal. In spite of these drawbacks, in our experiment SIBs outperformed IBs and PBs. We therefore conclude that, at least in our laboratory test-bed, SIBs can allay the underperformance of not-for-profits.

This research systematically reviewed studies on tax compliance based on five determinants consisting of tax services, trust in government, personal norm, social norm and religiosity.

The research used a vote-counting method to synthesize 279 studies consisting of 160 empirical studies and 119 non-empirical studies conducted from 1946 until 2017.

The research has made a relatively robust conclusion related to the impacts of determinant factors on tax compliance. Tax service and trust in government are the most critical factors to increase tax compliance. Personal norm, social norm and religiosity encourage tax compliance, yet the influence is not as strong as expected.

This research suggests that improving tax service and government trust are more effective and relatively easier to implement than developing the taxpayers' positive behaviors.

Several studies conducted to synthesize the impacts of determinant factors on tax compliance were only limited to the empirical research which provided sufficient statistical data. On the other hand, there were many substantial research types discussing tax compliance without involving statistical numbers. The facts have distorted the complete picture of tax compliance. Recently, no synthesis studies have comprehensively combined and compared the empirical with non-empirical research based on the related theories. Thus, the synthesis studies that discuss tax compliance based on non-deterrence approach are still limited.

Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain.

The prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment.

The prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information.

The prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises.

The prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain.

The purpose of this paper is to apply link prediction to community mining and to clarify the role of link prediction in improving the performance of social network analysis.

In this study, the 2009 version of Enron e-mail data set provided by Carnegie Mellon University was selected as the research object first, and bibliometric analysis method and citation analysis method were adopted to compare the differences between various studies. Second, based on the impact of various interpersonal relationships, the link model was adopted to analyze the relationship among people. Finally, the factorization of the matrix was further adopted to obtain the characteristics of the research object, so as to predict the unknown relationship.

The experimental results show that the prediction results obtained by considering multiple relationships are more accurate than those obtained by considering only one relationship.

Due to the limited number of objects in the data set, the link prediction method has not been tested on the large-scale data set, and the validity and correctness of the method need to be further verified with larger data. In addition, the research on algorithm complexity and algorithm optimization, including the storage of sparse matrix, also need to be further studied. At the same time, in the case of extremely sparse data, the accuracy of the link prediction method will decline a lot, and further research and discussion should be carried out on the sparse data.

The focus of this research is on link prediction in social network analysis. The traditional prediction model is based on a certain relationship between the objects to predict and analyze, but in real life, the relationship between people is diverse, and different relationships are interactive. Therefore, in this study, the graph model is used to express different kinds of relations, and the influence between different kinds of relations is considered in the actual prediction process. Finally, experiments on real data sets prove the effectiveness and accuracy of this method. In addition, link prediction, as an important part of social network analysis, is also of great significance for other applications of social network analysis. This study attempts to prove that link prediction is helpful to the improvement of performance analysis of social network by applying link prediction to community mining.

This study adopts a variety of methods, such as link prediction, data mining, literature analysis and citation analysis. The research direction is relatively new, and the experimental results obtained have a certain degree of credibility, which is of certain reference value for the following related research.

Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction.

In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results.

The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task.

The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector.