AI Model for Predicting Legal Judgments to Improve Accuracy and Explainability of Online Privacy Invasion Cases

Since there are growing concerns regarding online privacy, firms may have the risk of being involved in various privacy infringement cases resulting in legal causations. If firms are aware of consequences from possible cases of invasion of online privacy, they can more actively prevent future online privacy infringements. Thus, this study attempts to predict the probability of judgment types caused by various invasions within US judicial cases that are related to online privacy invasions. Since legal judgment results are significantly influenced by societal factors and technological development, this study tries to identify a model that can accurately predict legal judgment with explainability. To archive the study objective, it compares the prediction performance by applying five types of classification algorithms (LDA, NNET, CART, SVM, and random forest) of machine learning. We also examined the relationship between privacy infringement factors and adjudications by applying network text analysis. The results indicate that firms could have a high possibility of both civil and criminal law responsibilities if they distributed malware or spyware, intentionally or non-intentionally, to collect unauthorized data. It addresses the needs of reflecting both quantitative and qualitative approach for establishing automatic legal systems for improving its accuracy based on the socio-technical perspective.

Study on Business Ecosystem Research Trend Using Network Text Analysis

The management environment is moving into a new phase with the changing global circumstances. The business ecosystem as a management strategy has been studied for the last 30 years since the concept was introduced. The purpose of this study was to analyze the research trend in business ecosystem by using network next analysis and to understand the concept, being one that is still being actively studied. Network text analysis is a commonly used method to analyze research trends by forming networks based on bibliographic data of the articles, namely, keywords. For the analysis, we collected the data and keywords from 340 research papers published in global academic journals related to business ecosystem on the basis of the Scopus database. Through keywords extraction and cleansing, we found that the keywords of “innovation”, “sustainability”, and “platform” were mentioned most frequently, and the research topics were correlated to each other. Moreover, we conducted degree centrality and betweenness centrality analysis along with clustering analysis by transforming the two-mode network into a one-mode network. Degree centrality involves analyzing the degree to which one keyword links to other keywords, and betweenness centrality shows the mediating effects of a keyword to other keywords. In the centrality analysis results, “innovation”, “sustainability”, “platform”, and “business model” showed the highest degree centrality, and “sustainability”, “innovation”, “China”, and “platform” had the highest betweenness centrality. Then, we classified the clusters of subtopics into five groups. The current study examined accumulated research and suggested a comprehensive understanding of the research trend in business ecosystem by incorporating a method enabling research trend analysis to secure objectivity. This research is expected to help researchers to review the research trend in business ecosystem and identify expandable topics for further studies.

Review of Research on Digital Supply Chain Management Using Network Text Analysis

Studies that have examined the digital transformation’s association with supply chains have been actively conducted for over 10 years, and studies on digital supply chain management have been increasing. However, there is a lack of consensus on the definitions of or views about the digital supply chain; thus, researchers in the academic or industrial fields propound various concepts of digital supply chains, which results in confusion during the exchange of opinions or transmission of concepts in many cases. The purpose of this study is to identify the research trends from various articles on the digital supply chain that have been published so far, summarize and analyze the theories and concepts pertaining to it, and review future topics of research. Network text analysis was conducted by extracting information from unstructured text data to identify research trends. The results of the analysis showed that many studies have examined the digital supply chain in terms of sustainable management. “Sustainability” was the most influential word in the keyword network, and the digital transformation of supply chains is essential for the sustainable viability of firms in the era of Industry 4.0 and digital transformation. Many studies have focused on technology for big data analysis and the IoT as technologies to fulfill digital supply chains and maintained that COVID-19 has increased risk factors such as global supply chain disruptions, which is why global firms must monitor the supply chain in real time by securing end-to-end visibility and making corporate-level efforts to build a digital supply chain to instantly detect and deal with related risks. The common groups of keywords were related to “supply chain integration”, “resilience”, “digital technologies”, and “digital transformation”.

Research Trends in Accounting Fraud Using Network Analysis

Accounting fraud is a highly unethical management activity with a significant negative influence on stakeholders, which can harm a firm’s long-term sustainability prospects. Given the considerable progress in this field, a comprehensive theoretical organization of the research, along with a trend analysis, are needed. This study employed network text analysis to systematically analyze the research trends in accounting fraud by combining text mining techniques and network analysis. Unlike other studies on research trends that present statistical data by classifying research topics and methodologies, this study formed networks using the trait information of studies, such as “keywords” and “authors”, and conducted analyses such as centrality and cluster analyses. These exercises allowed for the identification of key research areas and groups. The results suggest that the literature on accounting fraud was developed based on six keywords: fraud detection techniques, executive compensation, assessments of fraud risks in audit processes, forensic accounting, corporate governance, and various topics related to top management. Overall, authorship analysis suggests that the key cluster contributors are Carpenter, Jones, Brazel, Zimbelman, Cohen, Cumming, Carcello, Kaplan, and Lennox.

Research on the Perception of Historical District of Eight Great Passes in Qingdao Based on Online Travel Notes

In recent years, the protection mode of historical blocks has changed from simple material protection to comprehensive protection integrating various factors, and more and more attention has been paid to the role of human perception. How to pay attention to the practical experience of consumers while protecting has become the main problem. This paper studies the tourists' perception of historical district of Eight Great Passes in Qingdao by using the method of network text analysis, and carries out word frequency analysis, semantic network analysis and emotional analysis on the online travel notes by Rost CM6, so as to comprehend the current situation of tourists' perception of different dimensions of Eight Great Passes historical and Cultural District in Qingdao, and find out the problems in its development as well as put forward the sustainable development strategies