Green attributes transparency presents new opportunities and challenges to advertisers. This study developed a research framework to enhance consumers’ willingness to adopt green cosmetics from several green constructs, such as corporate transparency, corporate social responsibility (CSR), green brand image, green brand trust, and green brand equity, and showed the green field strategy implications. This study commissioned a professional survey company to distribute an online questionnaire of cosmetic using experience to the participants. AMOS statistics software was used to analyze the measurement reliability and validity, and to examine the research hypotheses using structural equation modeling. The contribution of the study aimed to provide a correct standpoint for new concepts of green strategy in accordance with environmental trends, activities, and positive enterprise image to increase the consumers’ willingness to adopt green cosmetics from five constructs.

Cloud computing is the next generation in computing, and the next natural step in the evolution of on-demand information technology services and products. However, only a few studies have addressed the adoption of cloud computing from an organizational perspective, which have not proven whether the research model is the best-fitting model. The purpose of this paper is to construct research competing models (RCMs) and determine the best-fitting model for understanding industrial organization's acceptance of cloud services. This research integrated the technology acceptance model and the principle of model parsimony to develop four cloud service adoption RCMs with enterprise usage intention being used as a proxy for actual behavior, and then compared the RCMs using structural equation modeling (SEM). Data derived from a questionnaire-based survey of 227 firms in Taiwan were tested against the relationships through SEM. Based on the empirical study, the results indicated that, although all four RCMs had a high goodness of fit, in both nested and non-nested structure comparisons, research competing model A (Model A) demonstrated superior performance and was the best-fitting model. This study introduced a model development strategy that can most accurately explain and predict the behavioral intention of organizations to adopt cloud services.

The goal of an exam in cognitive diagnostic assessment is to uncover whether an examinee has mastered certain attributes. Different cognitive diagnosis models (CDMs) have been developed for this purpose. The core of these CDMs is the Q-matrix, which is an item-to-attribute mapping, traditionally designed by domain experts. An expert designed Q-matrix is not without issues. For example, domain experts might neglect some attributes or have different opinions about the inclusion of some entries in the Q-matrix. It is therefore of practical importance to develop an automated method to estimate the Q-matrix. This research proposes a deterministic learning algorithm for estimating the Q-matrix. To obtain a sensible binary Q-matrix, a dichotomizing method is also devised. Results from the simulation study shows that the proposed method for estimating the Q-matrix is useful. The empirical study analyzes the ECPE data. The estimated Q-matrix is compared with the expert-designed one. All analyses in this research are carried out in R.

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In the post-COVID-19 era, with tourism activity beginning to revitalize, the behavioral intention of tourists has emerged as the focus of much research interest. While previous studies have suggested that tourists’ perceived risk affects behavioral intention, it has not been found that perceived risk is influenced by other factors that affect behavioral intention in the post-COVID-19 era. This study constructs a research model to understand how tourists’ perceived risk influences emotional attachment to destinations and tourists’ behavioral intention through crisis communication and NPI. Through face-to-face interviews, this study conducted a survey and collected data from 1047 tourists who visited Dadaocheng’s renowned Chinese herbal street in Taiwan and examined the causal relationships through structural equation modeling. The results indicated that an increase in perceived risk had a positive effect on crisis communication and NPI and affected tourists’ behavioral intentions through emotional attachment to the destination. This study provides an opportunity to establish an essential contribution to post-disaster crisis management, which may serve as a marketing reference for tourism operators in the post-COVID-19 era, as well as to address future pandemic challenges.