Ecological degradation is an escalating global threat. Increasingly, people are expressing awareness and priority for concerns about environmental problems surrounding them. Environmental protection issues are highlighted. An appropriate information technology tool, the growing popular social network system (virtual community, VC), facilitates public education and engagement with applications for existent problems effectively. Particularly, the exploration of related involvement behavior of VC member engagement is an interesting topic. Nevertheless, member engagement processes comprise interrelated sub‐processes that reflect an interactive experience within VCs as well as the value co‐creation model. To address the top‐focused ecotourism VCs, this study presents an application of a hybrid expert‐based ISM model and DEMATEL model based on multi‐criteria decision making tools to investigate the complex multidimensional and dynamic nature of member engagement. Our research findings provide insightful managerial implications and suggest that the viral marketing of ecotourism protection is concerned with practitioners and academicians alike.

In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

Storage devices in the computer industry have gradually transformed from the hard disk drive (HDD) to the solid-state drive (SSD), of which the key component is error correction in not-and (NAND) flash memory. While NAND flash memory is under development, it is still limited by the “program and erase” cycle (PE cycle). Therefore, the improvement of quality and the formulation of customer service strategy are topics worthy of discussion at this stage. This study is based on computer company A as the research object and collects more than 8000 items of SSD error data of its customers, which are then calculated with data mining and frequent pattern growth (FP-Growth) of the association rule algorithm to identify the association rule of errors by setting the minimum support degree of 90 and the minimum trust degree of 10 as the threshold. According to the rules, three improvement strategies of production control are suggested: (1) use of the association rule to speed up the judgment of the SSD error condition by customer service personnel, (2) a quality strategy, and (3) a customer service strategy.

Due to the social awareness of risk control, we are witnessing the popularization of the insurance concept and the rapid development of financial insurance. The performance of the insurance industry is highly competitive; thus, in order to develop new and old business from existing clients, information on the renewal of client premiums, purchase of new policies, and new client referrals has become an important research topic in this field. However, based on a review of published literature, few scholars have engaged in relevant research on the above topics by data mining, which motivated the formation of this study, hoping to bridge this gap. We constructed 10 mixed classification prediction models (called Models A–J) using advanced data mining techniques. Moreover, 19 conditional attributes (coded as X1–X19) were selected from the collected insurance client database, plus three different decision attributes (coded as X20–X22): whether to pay the renewal insurance premium, whether to buy a new insurance policy, and whether to introduce new clients. In terms of technical methods, we used two data pretreatment techniques, attribute selection and data discretization, combined with different methods of disassembly in proportion and data cross-validation to conduct data analysis of the collected experimental data set. We also combined and calculated 23 important classification algorithms (or classifiers) in seven different classifications of data mining techniques (i.e., decision tree, Bayes, Function, Lazy, Meta, Mise, and Rule). In terms of the experimental results of insurance data, this study has the following important contributions and findings: (1) finding the best classifier; (2) finding the optimal mixed classification model; (3) determining the best disassembly in proportion; (4) comparing the performance of different disassembly in proportion and data cross-validation methods; (5) determining the important factors influencing the decision attribute “whether to purchase a new insurance policy”, including the time interval to the first purchase, the number of valid policies, the total number of purchased policies, the family salary structure, and gender; and (6) building a knowledge base of decision rules and criteria with the decision tree C4.5 technology, which shall be provided to relevant stakeholders such as insurance dealers and insurance salespeople as a reference for looking for valid clients in the future, and is conducive to the rapid expansion of insurance business. Finally, the important research findings and management implications of this study can serve as a basis for further study of sustainable insurance by academic researchers.