C2C interaction quality identification with SVM based on Kano model

Abstract

Customer-to-customer (C2C) interaction is a basic characteristic of social commerce, which is potentially to generate substantial economic benefits. The C2C interaction quality is employed to describe the impact on consumers' purchasing intention. This paper presents a Kano method-based C2C interaction model to study different factors involved with the communication process. Further, with the analysis of consumers' communication factors, a support vector machine classification system is established and learns to identify the quality of C2C interaction.Our results prove that the proposed methodology is feasible for C2C interaction quality distinguishing and highlights the importance of basic communication properties for supporting the purchase decisions. KEYWORDScustomer-to-customer interaction quality, Kano model, social commerce, support vector machine INTRODUCTIONDuring the past decades, social commerce has rapidly developed as a new area of electronic commerce that employs Internet-based social media to allow participating in selling products and services in online marketplaces and communities. 1,2 According to the report of national Bureau of Statistics of China, at the end of year 2016, the online purchasing amount within the last five years has kept rising ( Figure 1). Further, the study by McKinsey Global Institute shows that about 1/3 of the sale belongs to social commerce with the deployment of social media and social technologies. 3,4 Social commerce, which is unlike traditional e-commerce, relies more on gathering information from friends on social networks.Seeing that human behaviors are significantly affected from friends' reviews, the consumer participation inevitably leverages a more interactive purchasing environment. 5Many investigations stress the significance of customer-to-customer (C2C) interaction in social commerce; the C2C interaction is capable of sharing and inquiring information in a way that clearly facilitate the purchase decision-making for online transaction of products and services. 6,7With current cyber-enabled applications, C2C interaction is detected ongoing both online and offline every single moment, which does not necessarily end in itself. 8,9 Research has been continuously trying to describe the customer behaviors and their effects on product diffusion. 10 A deep understanding, accordingly, of the consumer purchasing determinants on social commerce sites via C2C interactions is the foundation in social commerce research. 11-13 Current initiatives such the Research Priorities of Marketing Science Institute emphasize the importance of peer-to-peer communications as well as their influencing consuming decisions. 14 The practical use of customer engagement for purchasing behaviors identification is, however, still limited primarily because the assessment of C2C interaction quality remains to be one of the greatest challenges. Meanwhile, advanced algorithms such as cloud computing are now commonplace and make the transition to online customer interaction relatively easy to captu...

Modelling reverse e-logistics’ factors

“…In other words, people interact with each other. This interaction process is facilitated by several communication methods and advanced technology (Liu & Luo, 2019). A good example would be C2C processes occurring in Internet auctions.…”