Swift Guanxi Data Analysis and Its Application to E-Commerce Retail Strategies Improvement

Introduction

Originally designed for data exchange, the Internet became a point of interest for retailers and consumers alike. This is because the Internet offers an indispensable opportunity to sell and purchase products online through e-commerce. E-commerce has since rapidly developed into a major retail platform (Sprano & Zakak, 2000). This fast development is linked to the advantages consumers might derive from e-commerce sites. The most important benefit appears to be effective and efficient satisfaction of consumer needs (Miyazaki & Frenandez, 2006). Despite advantages deriving from online shopping sites to consumers, it appears that not all consumers accept e-commerce and use it as their main shopping channel. In search of the factors stimulating consumers’ e-commerce acceptance a number of research projects have been executed. At first, researchers focused on the examination of factors driving initial acceptance and e-commerce sites use. Due to the growing importance of consumer retention, researchers also looked at consumers’ re-purchase intentions as only consumers regularly purchasing from e-commerce sites can ensure those sites’ sustainability.

The aforementioned research streams highlight the importance of trust, which appears to be crucial for successful online transactions. The creation of online trust however appears to be a major challenge for e-retailers due to buyer-seller physical and temporal separation online. Recent research by Ou et al. (2014) however indicates that trust can be facilitated by the so-called ‘swift- guanxi’. Swift guanxi is a form of Chinese oriented concept of ‘guanxi’ and it is defined as a close and pervasive interpersonal buyer-seller relationship online. Ou et al. (2014) claims that swift guanxi can address the above-mentioned limitation of online retailing as it increases perception of buyer-seller interaction and presence online. This is because swift guanxi allows for buyer-seller personalized communication via computer-mediated communication technologies. This in turn, enables the seller to address consumers’ risk perception regarding online transaction and hence establish trust. This current study aims to closely examine and analyze the result of the buyer-seller interaction and specifically the exchange of information online (i.e. swift guanxi data), in order to identify the key consumers’ concerns while making purchase online. Consequently, this study aims to analyze swift guanxi data in order to guide retailers in their e-commerce strategies improvement. By addressing consumers’ concerns sellers can gain consumers’ trust and hence ensure the sustainability of their e-commerce strategies.

The remainder of this chapter is organized as follows. First the literature relating to e-commerce acceptance is reviewed in the next section. Specifically, a range of intention-based models used to assess consumers’ attitudes towards e-commerce, their intention to use online shopping sites and the actual use of those sites is discussed. Next, the importance of assessing not only the intentions to purchase but also products re-purchase intentions is stressed and the most recent research on consumers’ online re-purchase behavior is also reviewed. This is subsequently linked to the discussion of swift guanxi, which is defined as swiftly formed buyer-seller relationships based on interaction and exchange of information online. The outcome of the online interactions and information exchange is argued to be a valuable data source, which this study aims to analyze. In the following section, the research methodology is discussed. Specifically, this study builds on an approach to social media data analysis proposed by Chan et al. (2015a). Data sourced from the result of buyer-seller interactions of online fashion retailer is extracted from Twitter and subsequently analyzed. The results of the analysis are presented and discussed in the penultimate section. The study finishes with a conclusion and recommendations section drawing on the analysis conducted.