Search Results (13)

Existing studies on greenwashing have primarily focused on post-incident supervision, with limited attention given to proactive mechanisms. This study aims to develop an early warning evaluation model for greenwashing behavior in building materials enterprises exposed to negative public opinion. The main findings are as follows: (1) Drawing on actor network theory, gray system theory, the analytic network process, and gray fuzzy comprehensive evaluation, this study constructs an early warning evaluation model for greenwashing behavior in building materials enterprises. This model comprises 5 first-level dimensions and 20 s-level indicators, integrating key stakeholders (i.e., government, negative public opinion, media, the public, and enterprise) and is validated through case analysis. (2) Government dimension: Environmental regulation intensity emerges as the most critical indicator. (3) Negative public opinion dimension: Attention is the most critical indicator. (4) Media dimension: Media visibility ranks as the most critical indicator. (5) Public dimension: Public sentiment is the most influential indicator. (6) Enterprise dimension: The environmental performance level is the most critical indicator. This study offers both theoretical and practical foundations for the early warning, monitoring, and governance of enterprise greenwashing, contributing to the advancement of sustainable development and transparent environmental communication in the building materials industry. Full article

With the ongoing reform of higher education, the University Practice Education Community (UPEC) has become a crucial platform for advancing collaborative education and innovating talent cultivation models. However, research remains insufficient on the influencing factors of UPEC’s sustainable development and, in particular, on how these factors interact with one another. From a complex systems perspective, this study conceptualizes UPEC as a dynamic and interconnected system in which multiple factors jointly shape sustainability outcomes. Accordingly, the overall objective is to (i) identify key influencing factors, (ii) model and quantify their interrelationships, and (iii) pinpoint critical factors and interaction pathways that structure UPEC sustainability. Adopting this holistic view, we integrate literature review, expert interviews, questionnaire surveys, and social network analysis (SNA) to systematically identify and analyze twenty influencing factors. SNA, as a systems-oriented analytical tool, enables the mapping of structural relationships and interaction pathways among factors, revealing how these interdependencies collectively form the governance ecosystem of UPEC. The results identify eight key factors—including willingness for multi-stakeholder collaboration, stability of cooperation mechanisms, policy and institutional support, effectiveness of communication and coordination mechanisms, feedback and improvement mechanisms, enthusiasm of industry and enterprise participation, local government support, and influence of public opinion—along with five critical paths linking subsystems through chain effects. Based on this diagnostic evidence, this study further outlines strategy implications to support practice-oriented improvement, while the primary contribution remains the identification of key factors and critical interaction structures underlying UPEC sustainability. Full article

Digital transformation (DT) has become an important engine for the development of the digital economy and an important means of reshaping corporate culture, business processes, management models, and so on. Different social communities at different levels have different needs and understandings of digital transformation. Therefore, this paper proposes to explore the communication themes of digital transformation on social media. This study’s main objective is to uncover underlying thematic structures and core ideas from large amounts of textual data in different social media communities to better understand the significance of the communication themes. This paper also aims to reveal the characteristics of diffusion patterns of DT themes by opinion-themed mining. This study uses text mining and social network analysis methods to mine DT themes, theme structure, and the statistical characteristics of hot words across various online communities. The main findings of this study are as follows. The Huawei forum discusses the technological drivers of the digital economy from a micro level. Sohu News explores business operation strategies at a macro level. The Zhihu forum discusses the elements of digital development at the micro level. Moreover, the hot words’ degree centrality and betweenness centrality across various online communities exhibited a power law distribution. In conclusion, this research paper studies and analyzes DT themes of different social media platforms to discover the opinions and attitudes of various social groups in the digital transformation era and deeply interprets social trends and public opinions in order to provide valuable decision-making theoretical support for managers, enterprises, and governments. Full article

As an intermediary in information dissemination and a guide of public opinion, the media represent an important external supervision force in corporate governance. It is very important to fully understand the supporting role of public media in the modernization of environmental governance in China to improve the quality of ESG information disclosure. Based on the data of companies listed on the Shanghai and Shenzhen 300 Index from 2015 to 2020, this paper finds that media attention has a significant positive impact on ESG information disclosure, that is, high-frequency media attention can promote the quality of ESG information disclosure, while different types of media reports can promote the quality of ESG information disclosure. Considering the characteristics of media emotions, it is found that negative media reports can promote the quality of ESG information disclosure. The shareholder relationship network strengthens the positive influence of media attention on the ESG information disclosure of enterprises through the information advantage of a “weak relationship”. These research conclusions reveal the internal influence of media attention on the quality of the ESG information disclosure of enterprises and the regulatory role of the shareholder relationship network to some extent, which provides the governance perspective on and empirical basis for ESG information disclosure research, and it also provides a decision-making reference for promoting the quality of the ESG information disclosure of listed enterprises in China, enriching the theoretical research and practical exploration of ESG information disclosure. Full article

With the prevalence of the Internet in society, social media has considerably altered the ways in which consumers conduct their daily lives and has gradually become an important channel for online communication and sharing activities. At the same time, whoever can rapidly and accurately disseminate online data among different companies affects their sales and competitiveness; therefore, it is urgent to obtain consumer public opinions online via an online platform. However, problems, such as sparse features and semantic losses in short-text online reviews, exist in the industry; therefore, this article uses several deep learning techniques and related neural network models to analyze Weibo online-review short texts to perform a sentiment analysis. The results show that, compared with the vector representation generated by Word2Vec’s CBOW model, BERT’s word vectors can obtain better sentiment analysis results. Compared with CNN, BiLSTM, and BiGRU models, the improved BiGRU-Att model can effectively improve the accuracy of the sentiment analysis. Therefore, deep learning neural network systems can improve the quality of the sentiment analysis of short-text online reviews, overcome the problems of the presence of too many unfamiliar words and low feature density in short texts, and provide an efficient and convenient computational method for improving the ability to perform sentiment analysis of short-text online reviews. Enterprises can use online data to analyze and immediately grasp the intentions of existing or potential consumers towards the company or product through deep learning methods and develop new services or sales plans that are more closely related to consumers to increase competitiveness. When consumers experience the use of new services or products again, they may provide feedback online. In this situation, companies can use deep learning sentiment analysis models to perform additional analyses, forming a dynamic cycle to ensure the sustainable operation of their enterprises. Full article

Realizing the sustainable innovation growth of enterprises is one of the important research directions of management science. Traditional enterprise growth innovation methods cannot effectively estimate the emotional tendency of online public opinion (PO), and they cannot guide the effective growth of enterprises. For this reason, This paper proposes an enterprise growth innovation technology based on the evolutionary game (EG) model of sustainable development and deep learning (DL). Firstly, by obtaining the game payment matrix between network users and enterprises, combined with the deep neural network model, the PO evolution model of the enterprise growth network was constructed and solved. Then, a convolutional neural network (CNN) model was used to extract sequence features from global information, and a gated recurrent unit (GRU) was used to consider the context. A DL network model based on CNN–GRU was proposed. Finally, by introducing the EG model, a stable strategy was generated through the dynamic adjustment of the whole system, which improved the accuracy of online PO judgment. Through simulation experiments, the enterprise growth innovation method proposed in this paper was compared with the other three methods. The results show that the accuracy, precision, recall, and f1 value of this method are 92.21%, 89.33%, 91.86%, and 91.64%, respectively, which are better than the other three methods. This method is of great significance for promoting enterprise innovation technology and sustainable development of enterprises. Full article

In the context of the rapid development of new media such as network citizen journalism, it is of great theoretical and practical significance to use the online public opinion to supervise sewage discharge enterprises’ emission governance behaviors and improve the social opinion supervision mechanism. This paper considers the dynamic characteristics of the spread process of public opinion and the game process of social supervision on corporate pollution control; constructs a tripartite evolutionary game model of the local government, sewage discharge enterprises, and the public by coupling the susceptible–exposed–infected–removed (SEIR) model and the evolutionary game model; and discusses the influence laws of public opinion spread on the tripartite evolutionary game. The results show that (1) the public with higher influence or authority has a more significant restraint effect to restrain the pollution control behavior of the local government and pollutant companies by using online public opinion supervision. (2) Increasing the probability of transforming a latent person into a supervisor and the topic derivative rate or reducing the probability of a supervisor’s self-healing can increase the peak value of supervisors, expand the scope of social public opinion, and improve the effectiveness of public opinion supervision. (3) The relatively high authenticity of public opinion supervision makes public opinion supervision a substitute for local government supervision, but it has a relatively strong inhibitory effect on the over-standard pollutant discharge behavior of sewage discharge enterprises. These conclusions can provide a reference for improving the social supervision mechanism of pollution control in the era of network citizen journalism. Full article

With the rapid growth of Chinese social network users, the open yet anonymous cyberspace makes the Chinese public more inclined to express their feelings and opinions freely on the Internet, and thus generate opinions that are not conducive to the survival and development of Chinese enterprises, i.e., enterprise negative network public opinion. Based on this, this paper takes a Chinese enterprise’s negative network public opinion as the research object. First, our research identifies the stakeholders involved in the dissemination process of public opinion information. Secondly, we model the decision-making behaviors of stakeholders in different stages to obtain the evolutionarily stable strategy. After that, the simulation experiment is conducted to analyze the key points of enterprise strategy adjustment in different stages of negative network public opinion dissemination. The experimental results show that: (1) In its formation stage, opinion leaders usually do not participate in the event, and thus enterprises need to focus on the active ordinary Internet users; (2) In its development stage, if an enterprise wants to reduce the loss caused by negative events, it needs to make use of online media to give corresponding positive guidance; (3) In its control stage, enterprises should take corresponding legal measures to netizens who make improper remarks on the Internet, increase the risk cost of these netizens group, and cooperate with the government’s control work to guide the negative public opinion to turn in a beneficial direction. Finally, the rationality and effectiveness of the proposed model are verified using a practical case. Full article

Food additives play an important role in the food supply, and it has been a food safety topic of great concern to the public. There has been no systematic research on Chinese consumers’ concerns, attitudes, feelings, or opinions on supervision and media coverage of food additives in the past decade, which is an area worth exploring. This study was carried out to deeply understand consumers’ cognition of food additives and formulate food safety risk communication strategies of food additives in China. Big data of consumers’ online public opinion of China on food additives from 2011 to 2020 was collected and cleaned up using Haina Network Public Opinion Monitoring System version 2.0 (HNPOMS V2.0), followed by data analysis and visual display with the Ansi Food Safety Risk Communication System version 2.0 (AFSRCS V2.0). The results showed that the types of food additives of concern to the public have changed from 2011 to 2020, but the amount of food additives has always been of concern. The type of incident that the public is most concerned about is the illegal addition or abuse of additives. The public’s confidence in food production enterprises has been insufficient, but the functions of market supervision are becoming clearer and clearer, and their expectations are constantly increasing. Consumers’ cognition level increases with the strengthening of publicity and popular science, but the influence of “self-media” on public cognition is increasing day by day, and there is cognitive deviation, making it easy to mislead the public. Consumers’ cognition of food additives is the basis of risk communication. Combined with the research results, this paper puts forward corresponding suggestions on the market and social supervision measures, network media guidance strategy and risk communication strategy of China, respectively. Full article

The illegal wildlife trade is resulting in worldwide biodiversity loss and species’ extinction. It should be exposed so that the problems of conservation caused by it can be highlighted and resolutions can be found. Social media is an effective method of information dissemination, providing a real-time, low-cost, and convenient platform for the public to release opinions on wildlife protection. This paper aims to explore the usage of social media in understanding public opinions toward conservation events, and illegal rhino trade is an example. This paper provides a framework for analyzing rhino protection issues by using Twitter. A total of 83,479 useful tweets and 33,336 pieces of users’ information were finally restored in our database after filtering out irrelevant tweets. With 2422 records of trade cases, this study builds up a rhino trade network based on social media data. The research shows important findings: (1) Tweeting behaviors are somewhat affected by the information of traditional mass media. (2) In general, countries and regions with strong negative sentiment tend to have high volume of rhino trade cases, but not all. (3) Social celebrities’ participation in activities arouses wide public concern, but the influence does not last for more than a month. NGOs, GOs, media, and individual enterprises are dominant in the dissemination of information about rhino trade. This study contributes in the following ways: First, this paper conducts research on public opinions toward wildlife conservation using natural language processing technique. Second, this paper offers advice to governments and conservationist organizations, helping them utilize social media for protecting wildlife. Full article

The going-concern opinions of certified public accountants (CPAs) and auditors are very critical, and due to misjudgments, the failure to discover the possibility of bankruptcy can cause great losses to financial statement users and corporate stakeholders. Traditional statistical models have disadvantages in giving going-concern opinions and are likely to cause misjudgments, which can have significant adverse effects on the sustainable survival and development of enterprises and investors’ judgments. In order to embrace the era of big data, artificial intelligence (AI) and machine learning technologies have been used in recent studies to judge going concern doubts and reduce judgment errors. The Big Four accounting firms (Deloitte, KPMG, PwC, and EY) are paying greater attention to auditing via big data and artificial intelligence (AI). Thus, this study integrates AI and machine learning technologies: in the first stage, important variables are selected by two decision tree algorithms, classification and regression trees (CART), and a chi-squared automatic interaction detector (CHAID); in the second stage, classification models are respectively constructed by extreme gradient boosting (XGB), artificial neural network (ANN), support vector machine (SVM), and C5.0 for comparison, and then, financial and non-financial variables are adopted to construct effective going-concern opinion decision models (which are more accurate in prediction). The subjects of this study are listed companies and OTC (over-the-counter) companies in Taiwan with and without going-concern doubts from 2000 to 2019. According to the empirical results, among the eight models constructed in this study, the prediction accuracy of the CHAID–C5.0 model is the highest (95.65%), followed by the CART–C5.0 model (92.77%). Full article

With the rapid development of “We media” technology, netizens can freely express their opinions regarding enterprise products on a network platform. Consequently, online public opinion about enterprises has become a prominent issue. Negative comments posted by some netizens may trigger negative public opinion, which can have a significant impact on an enterprise’s image. From the perspective of helping enterprises deal with negative public opinion, this paper combines user portrait technology and a random forest algorithm to help enterprises identify high-risk users who have posted negative comments and thus may trigger negative public opinion. In this way, enterprises can monitor the public opinion of high-risk users to prevent negative public opinion events. Firstly, we crawled the information of users participating in discussions of product experience, and we constructed a portrait of enterprise public opinion users. Then, the characteristics of the portraits were quantified into indicators such as the user’s activity, the user’s influence, and the user’s emotional tendency, and the indicators were sorted. According to the order of the indicators, the users were divided into high-risk, moderate-risk, and low-risk categories. Next, a supervised high-risk user identification model for this classification was established, based on a random forest algorithm. In turn, the trained random forest identifier can be used to predict whether the authors of newly published public opinion information are high-risk users. Finally, a back propagation neural network algorithm was used to identify users and compared with the results of model recognition in this paper. The results showed that the average recognition accuracy of the back propagation neural network is only 72.33%, while the average recognition accuracy of the model constructed in this paper is as high as 98.49%, which verifies the feasibility and accuracy of the proposed random forest recognition method. Full article

Certified public accounts’ (CPAs) audit opinions of going concern are the important basis for evaluating whether enterprises can achieve normal operations and sustainable development. This study aims to construct going concern prediction models to help CPAs and auditors to make more effective/correct judgments on going concern opinion decisions by deep learning algorithms, and using the following methods: deep neural networks (DNN), recurrent neural network (RNN), and classification and regression tree (CART). The samples of this study are companies listed on the Taiwan Stock Exchange and the Taipei Exchange, a total of 352 companies, including 88 companies with going concern doubt and 264 normal companies (with no going concern doubt). The data from 2002 to 2019 are taken from the Taiwan Economic Journal (TEJ) Database. According to the empirical results, with the important variables selected by CART and modeling by RNN, the CART-RNN model has the highest going concern prediction accuracy (the accuracy of the test dataset is 95.28%, and the average accuracy is 93.92%). Full article