An Empirical Study of Customer Satisfaction and Loyalty on Health Websites

Numerous health websites are developing rapidly in China and the competition is fierce between these websites. In order to win the competition, the websites operators need to satisfy their customers to attain more market shares. But few attention has been paid to factors affecting customer satisfaction and loyalty on these websites. As a result, the paper aims to empirically explore the factors affecting customer satisfaction and loyalty on health websites based on perceived service quality (responsiveness, empathy and reliability), perceived risk (financial risk and time risk) and trust, and then to propose some targeted measures. A survey was conducted to collect data by means of questionnaires, and a total of 231 usable responses were gathered. Then the hypothesis model was tested using the Structural Equation Modeling(SEM). Results revealed that responsiveness, empathy, time risk and trust had significant impacts on customer satisfaction, whereas reliability and financial risk showed no effects on customer satisfaction. In addition, customer satisfaction and trust significantly influenced customer loyalty. The implications and limitations were discussed

From the machine mind to the human mind: using machine learning to understand (ir)rationality, bias and polarization in human beings

My dissertation, titled “From the machine mind to the human mind: using machine learning to understand (ir)rationality, bias and polarization in human beings,” investigates ways in which human minds operate and seeks to uncover the causes of biasedness, limited rationality, and polarization of human minds, to eventually devise tools to compensate for such human limitations. Chapter 2 of the thesis focuses on the evaluation of information and decision making under enormous information asymmetry, in the setting of patients evaluating doctors’ medical advice. Patients were found to be poor evaluators who were unable to distinguish good from bad due to their lack of medical expertise, and unable to overcome their own irrationality and bias. I emphasize the ramification of such limited rationality, which might lead to the adoption of suboptimal or bad medical opinions, and propose ways to improve this situation by redesigning some features of the platform, and/or implementing new policies to help good doctors on the platform. Chapter 3 focuses on developing a new metric that reliably measures the ideology of the US elites. This metric was developed based on congressional reports which made it unique and relatively independent from established metrics based on roll call votes, such as DW-NOMINATE. First, I leveraged a neural network-based approach to decompose the speech documents into frames and topics components, with all ideological information funneled into the frames component. Eventually, two different ideology metrics were obtained and validated: an embedding vector and an ideological slant score. Later I showed that our new metrics can predict party switchers and trespassers with high recall. In chapter 4, I applied the newly obtained metric (mainly slant scores) to investigate various aspects of the congress, such as the heterogeneity of ideology among the members, the temporal evolution of partisan division, the bill passing, and the re-election strategy of the senators