This study sheds light on a prominent issue in retailing: how the digital atmosphere can affect the consumer decision-making process in a fashion retail store. Digital devices and services such as digital screens and digital signage are widely employed in fashion retail stores, transforming the way consumers make decisions about purchasing fashion products. This research investigates how the digital atmosphere affects consumers’ purchase behavior patterns based on the attention-interest-desire-search-action-share (AIDSAS) model. The findings show that attention is a key antecedent to interest, desire, and behavioral responses (search, action, and share) triggered by the digital atmosphere. The findings further suggest that attention has significantly positive effects on consumers’ purchasing patterns of utilizing the digital atmosphere in two types of fashion retail stores: sports and luxury stores. However, we find that these positive effects are more pronounced for sports retail stores than luxury retail stores. This research contributes to understanding consumer behavior related to the digital atmosphere of fashion retail stores by applying the AIDSAS model and helps uncover the stepwise relationships between attention to the store atmosphere-interest/desire and the products-behavior response. These findings have practical implications that can be applied in the fashion industry.
Improving predictive models for intensive care unit (ICU) inpatients requires a new strategy that periodically includes the latest clinical data and can be updated to reflect local characteristics. We extracted data from all adult patients admitted to the ICUs of two university hospitals with different characteristics from 2006 to 2020, and a total of 85,146 patients were included in this study. Machine learning algorithms were trained to predict in-hospital mortality. The predictive performance of conventional scoring models and machine learning algorithms was assessed by the area under the receiver operating characteristic curve (AUROC). The conventional scoring models had various predictive powers, with the SAPS III (AUROC 0.773 [0.766–0.779] for hospital S) and APACHE III (AUROC 0.803 [0.795–0.810] for hospital G) showing the highest AUROC among them. The best performing machine learning models achieved an AUROC of 0.977 (0.973–0.980) in hospital S and 0.955 (0.950–0.961) in hospital G. The use of ML models in conjunction with conventional scoring systems can provide more useful information for predicting the prognosis of critically ill patients. In this study, we suggest that the predictive model can be made more robust by training with the individual data of each hospital.
The purpose of this research is to examine the effect of a brand's comments in attenuating (enhancing) the negative (positive) influence on brand trust and purchase intention of other consumers' postings on social media. To develop more precise results for this study, a 3 (brand comment: automated comment, personalized comment and no comment) × 3 (other consumers' postings: positive, negative and mixed) experimental research design was employed. With total usable data of 530, MANOVA analysis examined the hypothesized relationships. Findings from the main test revealed brand comments have no effect on brand trust or purchase intention, while other consumers' postings have a significant effect on consumers' brand trust but not on purchase intention.
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