The low birth rate in Taiwan has led to a severe challenge for many universities to enroll a sufficient number of students. Consequently, a large number of students have been admitted to universities regardless of whether they have an aptitude for academic studies. Early diagnosis of students with a high dropout risk enables interventions to be provided early on, which can help these students to complete their studies, graduate, and enhance their future competitiveness in the workplace. Effective prelearning interventions are necessary, therefore students' learning backgrounds should be thoroughly examined. This study investigated how big data and artificial intelligence can be used to help universities to more precisely understand student backgrounds, according to which corresponding interventions can be provided. For this study, 3552 students from a university in Taiwan were sampled. A statistical learning method and a machine learning method based on deep neural networks were used to predict their probability of dropping out. The results revealed that student academic performance (regarding the dynamics of class ranking percentage), student loan applications, the number of absences from school, and the number of alerted subjects successfully predicted whether or not students would drop out of university with an accuracy rate of 68% when the statistical learning method was employed, and 77% for the deep learning method, in the case of giving first priority to the high sensitivity in predicting dropouts. However, when the specificity metric was preferred, then the two approaches both reached more than 80% accuracy rates. These results may enable the university to provide interventions to students for assisting course selection and enhancing their competencies based on their aptitudes, potentially reducing the dropout rate and facilitating adaptive learning, thereby achieving a win-win situation for both the university and the students. This research offers a feasible direction for using artificial intelligence applications on the basis of a university's institutional research database.

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Marketing channel as the downstream end of a supply chain plays a crucial role in generating cash flow and meeting the demands of customers. The extant body of literature postulates that antecedent-consequence links that customer perceived value and service quality foster customer loyalty, leading to long term profitability. Nowadays, given the proliferation of global hypermarket organizations and their adapting retail positioning strategy, it is worthwhile to re-examine the validity of those postulations. This study conducts an empirical study on the customer perception of quality of product/service toward a multinational chained hypermarket store in Taiwan. The objective is to test the established theory in the domain of SCM by constructing the hypotheses based on the extant literature and allowing a structure equation modeling (SEM) for path analysis. The contingency theory in organization behavior explains the findings that are seemingly contradictory to the well-accepted paradigms. The outcome provides an insight into how hypermarket retailers should make improvement in customer satisfaction to reinforce customer loyalty while executing the 'low-price, low-service' marketing strategy.

The main objective of this study was to explore the current practical use of an AI robo-advisor algorithmic technique. This study utilizes Roger’s innovation diffusion theory as a basis to explore the application of robo-advisors for forecasting in the stock market by using an abductive reasoning approach. We used literature reviews and semi-structured interviews to interview representatives of fund companies to see if they had adopted AI big data forecasting models to invest in stock selection. This study summarizes the big data stock market forecasts of the literature. According to the summary, the accuracy of the prediction models of these scholars ranged from 52% to 97%, with the prediction results of the models varying significantly. Interviews with 21 representatives of these fund companies revealed that the stock market forecast model of big data robo-advisors have not become a reference basis for fund investment candidates, mainly because of the unstable model prediction rate, and the lack of apparent relative advantages and observability, as well as being too complex. Thus, from the view of innovation diffusion, there is a lack of diffusion for the robo-advisor. Knowledge occurs when an individual is exposed to the existence of innovation, and gains some understanding of how it functions. Thereby, when investors become more familiar with neural network-like stock prediction models, this novel AI stock market forecasting model is expected to become another indicator of technical analysis in the future.

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