Logistics service design for cross-border E-commerce using Kansei engineering with text-mining-based online content analysis

• Published in: Telematics and informatics Vol. 34; no. 4; pp. 284 - 302
• Main Authors: Hsiao, Yu-Hsiang, Chen, Mu-Chen, Liao, Wei-Chien
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-07-2017; Elsevier Science Ltd
• Subjects: Business competition; Content analysis; Cross border transactions; Cross-border logistics service; Customer satisfaction; Customer services; Customers; Data mining; Demand analysis; Design engineering; Electronic commerce; Kansei engineering; Logistics; Partial least squares; Service design; Text mining; Texts; Text mining; Cross-border logistics service; Partial least squares; Kansei engineering; Service design
• ISSN: 0736-5853; 1879-324X
• DOI: 10.1016/j.tele.2016.08.002

•A procedure of integrating text mining into Kansei engineering (KE) is proposed.•Kansei design for cross-border logistics services (CBLS) is studied.•Text mining provides an alternative way to capture customer Kansei toward CBLS.•KE and text mining can play a complementary role in developing CBLS design ideas.
With the rapid development of cross-border e-commerce, the demand for and importance of cross-border logistics service (CBLS) also increase. A satisfactory CBLS can help promote business activities in cross-border e-commerce. Because customers’ logistical needs are increasingly complex and the logistics market is increasingly competitive, a CBLS provider has to be devoted to continually improving and differentiating services to maintain its competitive advantage. Kansei engineering (KE) is an approach to design the elements which satisfy customers’ affective and emotional perceptions into services and products. In this study, the KE approach is applied to derive ideas for the development of CBLS. For this purpose, Partial Least Squares (PLS) is used to analyze the relationships between the feelings of customers and service elements of CBLS. Moreover, this study demonstrates the applications of text mining techniques to analyze the online contents regarding CBLS. Online content mining assists in identifying the service elements and Kansei words for CBLS. Importantly, the relationship between the feelings of customers and service elements of CBLS obtained by online content mining provides complementary results for CBLS design.
Relevance to industry: this study offers an exemplification on applying the integration of Kansei engineering and online content analysis to obtain ideas for the process Kansei design in service industry. Our findings imply that in addition to conventional customer survey, user generated online content analysis should be effective way of catching customer-oriented design elements; they provide complementary effects for Kansei design.