Chihlee Institute of Technology

About: Chihlee Institute of Technology is a education organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Economic order quantity & Service quality. The organization has 366 authors who have published 582 publications receiving 9192 citations.

The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators

Abstract: The purposes of this study are to examine whether perceived playfulness and perceived flow would mediate the relationships among website quality, customer satisfaction, and purchase intention, as well as to assess the degree of reciprocity between perceived playfulness and perceived flow in an online travel agency context. This study suggested a research framework for testing the relationships among the constructs based on the stimulus-organism-response framework. In addition, this study developed a non-recursive model. After validating the measurement scales, empirical analyses were conducted using structural equation modelling. The findings confirm that website quality affects customers' perceived playfulness and perceived flow, and in turn, would influence their satisfaction and purchase intention. Notably, this study finds that the service quality is more important than information and system quality in influencing customer satisfaction and purchase intention. Furthermore, the study reveals that the relationship between perceived playfulness and perceived flow is reciprocal. Based on the findings, the implications are discussed in the paper and directions for future research are also highlighted.

Handling forecasting problems based on two-factors high-order fuzzy time series

Abstract: In our daily life, people often use forecasting techniques to predict weather, economy, population growth, stock, etc. However, in the real world, an event can be affected by many factors. Therefore, if we consider more factors for prediction, then we can get better forecasting results. In recent years, many researchers used fuzzy time series to handle prediction problems. In this paper, we present a new method to predict temperature and the Taiwan Futures Exchange (TAIFEX), based on the two-factors high-order fuzzy time series. The proposed method constructs two-factors high-order fuzzy logical relationships based on the historical data to increase the forecasting accuracy rate. The proposed method gets a higher forecasting accuracy rate than the existing methods.