E-service quality and e-retailers: Attribute-based multi-dimensional scaling

Highlights

• The perceptual map revealed that consumers can perceive top e-retailers as similar or isolated brands.

• Consumers distinguish top e-retailing brands based on all the seven dimensions of e-service quality.

• Preferential maps indicated that consumers prefer e-retailers that provide better service recovery.

• Web traffic analysis divulged Amazon India and Flipkart as the top two e-retailing brands in India.

• Fulfilment and contact were benchmarked as critical dimensions for managing e-service quality from the top two brands.

Abstract

Digital retail is a technology-driven business. Customers shop with the help of cutting-edge self-service technologies deployed by marketers to enhance customer experience and e-service quality (e-SQ). However, there is a lack of understanding of how customers differentiate between various digital retailers while shopping. We attempt to compare similarity and dissimilarity between top e-retailers based on customer perception grounded in seven dimensions of e-SQ using data from an important emerging market. Multi-Dimensional Scaling (MDS) technique was applied to analyze similarity judgments of the respondents to draw an aggregate perceptual map of the selected e-retailers. Subsequently, discriminant analysis was carried out and the results were used to create combined spatial maps of e-retailers and e-SQ attributes. It was found that consumers can perceive top e-retailers as similar or isolated brands. Our findings suggest that all seven e-SQ attributes can create differentiation among leading e-retailing brands. However, we recommend e-retailers to fortify their service recovery dimensions, as consumers give greater importance to them. Further, we benchmarked fulfilment and contact as critical dimensions for managing e-SQ from the top two e-retailers (Amazon India and Flipkart) and discussed how they are deploying cutting-edge technologies to beef up these dimensions.

Introduction

Online shopping has become a routine for many customers; and the quality delivered through an e-retailer's website plays a vital role in differentiating them from other low-quality sites. It can attract potential customers (Bilkova & Kopackova, 2013), encourage first-time purchases, retain repeat purchases, generate more revenue (Balfagih, Mohamed, & Mahmud, 2012; King, Schilhavy, Chowa, & Chin, 2016), discriminate between the loyal and disloyal groups (Pandey & Chawla, 2016), determine perceptions of attitude toward the presented product (Algharabat, Abdallah, Rana, & Dwivedi, 2017) and facilitate the formation of customer emotions (Hsu & Tsou, 2011). Prior research has confirmed that product offerings do not interfere with customers' perceptions of e-satisfaction (Gelard & Negahdari, 2011). In this case, e-shops selling similar products provided by manufacturers can create differentiation through website quality (Bilkova & Kopackova, 2013). For such differentiation, e-retailers are integrating cutting-edge technologies such as artificial intelligence (AI) (Shankar, 2018), chatbots (Chung, Ko, Joung, & Jin, 2020; Pantano & Pizzi, 2020), machine learning (Xia et al., 2012), big data analytics (Bradlow, Gangwar, Kopalle, & Voleti, 2017; Dekimpe, 2020), recommender systems (Zhao et al., 2015), Internet of Things (IoT) (Caro & Sadr, 2019; Fagerstrøm, Eriksson, & Sigurdsson, 2020; Langley et al., 2020; Ng & Wakenshaw, 2017), 3D simulations (Baek et al., 2015), Image Interactivity Technology (IIT), telepresence (Fiore, Kim, & Lee, 2005), etc. into their websites. Researchers argue that website performance is the key indicator of service quality in the online retail segment (Dickinger & Stangl, 2013) and a strategic tool for business differentiation (Hsu, Hung, & Tang, 2012). Therefore, we adopted a website traffic approach to identify top e-retailers that use cutting-edge technologies to reach their customers.

Rapid globalization of economic activities has generated huge opportunities for retailers in emerging markets (EMs) particularly in BRIC (Brazil, India, China, and Russia) nations (Paul, 2020). Studies indicate that BRIC countries have 42 percent of the world population and represent more than 50 percent of world growth (Paul & Benito, 2018; Reinartz et al., 2011; Wilson et al., 2011). Global retailers are focusing on these emerging markets due to high competitive pressure in mature markets (Diallo, 2012). New consumption patterns by middle-class customers are increasing in these countries leading to substantial demand (Kalia, Kaur, & Singh, 2017). Therefore, researchers have recommended new studies relating to technology usage (Ameen, Willis, & Hussain Shah, 2018) and shopping preferences of customers in retailing and allied sectors (Akhlaq & Ahmed, 2015; Paul, 2017; Paul et al., 2016b), particularly in the context of developing and least developed nations (Elg, Ghauri, & Schaumann, 2015). The present study is targeted at the e-retail sector in an emerging market like India because of its huge digital economy worth approximately $4 trillion. India is the fastest growing online retail market in the world. It is estimated to grow over 1200% to $200 billion by 2026, up from $15 billion in 2016 (Akamai India, 2018). Led by the explosive growth of online retailing giants like Flipkart and Amazon, India has become the second-largest online market worldwide (Guru, Nenavani, Patel, & Bhatt, 2020; IBEF, 2020). The lucrativeness of the Indian online market can be understood with the fact that the US retail behemoth like Walmart has bought an 80% stake in Flipkart (ETtech, 2020; Rajan, 2020).

The most important challenge for an e-retailer is to persuade an existing customer to shop with them instead of their competitor (Bourlier & Gomez, 2016). In this scenario, understanding the reactions of the local customers (Grosso, Castaldo, & Grewal, 2018), store image perceptions (Diallo, 2012), branding and clear positioning via customer and competitor centric approaches can enhance the performance (Ramakrishnan, 2010; Reinartz et al., 2011). Many other studies have also advocated brand uniqueness and differentiation to gain a competitive advantage over competitors and remain attractive to customers (Keller, 2013; Lopez & Leenders, 2019; Paul et al., 2016a). In this context, the current study advances knowledge in two main ways, first, primary research based on an Indian sample was carried out to understand similarity and dissimilarity between top e-retailing brands as per customer perception. Second, due to the absence of face-to-face interactions in this high-technology reliant society, companies are using cutting-edge technologies for customer engagement and delivery of e-services (Moriuchi, Landers, Colton, & Hair, 2020). Therefore, we have discriminated top technology-based e-retailers based on seven dimensions of e-service quality (e-SQ) given by Parasuraman et al. (2005). This is the first original study with an attempt to map top e-retailers grounded in e-SQ theoretical attributes using Multi-Dimensional Scaling (MDS) technique and discriminant analysis in an emerging country context, to the best of our knowledge.

The objective of this study is to address the following research questions: (1) Whether customers perceive top e-retailers that use cutting-edge technologies as similar or dissimilar brands? (2) What are the functions (based on e-SQ dimensions), which significantly discriminate top e-retailers? (3) What is the magnitude of e-SQ dimensions discriminating top e-retailers? (4) What is the proximity and positioning of e-retailers to discriminating functions on the preferential maps (to discuss discriminating functions possessed by top e-retailers for benchmarking)? To answer these questions, we did an extensive literature review on e-SQ, competitive positioning, and cutting-edge technologies used by e-retailers. We identified top e-retailers in India through a web traffic overview. Further, we created a perceptual map through the MDS technique to check similarity-dissimilarity between selected e-retailers and applied attribute-based MDS through discriminant analysis to identify functions (service quality dimensions) that significantly discriminate the e-retailers. Further, the results were juxtaposed on preferential maps to observe the proximity and positioning of e-retailers to discriminating functions. Information in this article will be useful for existing or new e-retailers for re-positioning in an emerging e-commerce market.