Mining E-WoM from Online Customer Reviews: An Investigation of Logistics Service Quality, Satisfaction, and Loyalty

• Main Authors: FAN,YUN-HSIN, 范芸馨
• Other Authors: Tsai,Yao-Te
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/kv9wv2

碩士 === 逢甲大學 === 國際經營與貿易學系 === 107 === Information and Communication Technology (ICT) has become the major trend in the competitive market, more and more users prefer to purchase goods through e-commerce platforms. After the customers shop on the e-commerce platform, the shopping experience would be shared on the e-commerce platform in the form of comments. These reviews contain massive information that is useful to merchants. Logistics is an indispensable part of the entire online shopping process. Poor service of distribution personnel, damage of goods, errors in package delivery, and high freight costs may occur in the process of logistics. The existence of these factors affects the customers satisfaction during online shopping. Under this circumstance, understanding the impact of logistics service quality on customers' post-purchase behavior can help e-commerce companies improving customer loyalty, reducing marketing costs, and strengthening enterprises competitiveness. Based on text mining technique, web crawler is adopted to obtain comment text in this paper, and apply data mining algorithm to obtain the factors affecting customer experience satisfaction included in the comment text. This research can quickly and timely obtain the factors affecting customer experience satisfaction from customer reviews, provide effective information support for business operations, and assist decision makers in making decisions. We found that Amazon customers have higher satisfaction with logistics loyalty, return service, and logistics service quality. In terms of logistics customer service, customer satisfaction is lower, and Amazon's logistics customer service needs to be improved.