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Top1. Introduction
Web has created a novice environment for its users including entertainment, exploration, communication, and learning (Huang, 2003). These features of web have influenced digitalized economy, resulting the increased activities of consumers on internet, be it searching or shopping. However, understanding consumer behavior on web is bit difficult due to transformed role of business and consumers on web environment. This transformation has converted all business houses into online stores and influenced people to become an aware customer. Furthermore, It has been observed that customer experience in offline environment is greatly affected by the time spend in store, contact/interaction with employees, and the way offerings are presented. However, online environment provides customers an opportunity to compare the offerings on the basis of value and profit before making final decision.
Recent literature has cited experience as a vital factor persuading purchase among customers due to its influence on customer’s heart and mind (Klaus, 2013). As online environment facilitates the companies to reach the mass in one go, identification and prioritization of components describing online customer experience is considered as need of the hour (Martin et al., 2015). This need of understanding the experience of consumers on online environment have been emerged as very important topic of study (Straub and Watson 2001; Kaufaris, 2002; Lemon and Verhoef, 2016) due to its direct linkage with customer satisfaction and customer loyalty (Court et al., 2009; Lemon and Verhoef, 2016). Thus, in order to identify all those underlying components which directly or indirectly influences the most important component of customer experience are identified and explained through Pareto analysis. Pareto analysis helps in identifying the most important component or factor for the study by ranking variables according to their occurrence in the literature. In such situation, it has become mandatory for the authors to identify some kind of structural relationship among all the components to make it easier for organizations to clearly distinguish between components, having direct and indirect impact on the outcome components of customer experience in online environment.
This type of structural relationship helps the organizations not only in managing the scarce resources but also in handling the contingent situations in effective and efficient manner. Though, various researches have conceptualized the customer experience through exploratory research (Brakus, Schmitt, and Zarantonello 2009; Verhoef et al. 2009), but very few have empirical tested this concept of customer experience in online environment (Lemon and Verhoef, 2016). Thus, the goal of this investigation identifying, prioritizing, and defining the components of online environment to establish better understanding of the concept of customer experience.
The extensive review of literature has helped the authors in identifying the appropriateness of interpretive structural modeling (ISM) in investigating such type of research problems. In ISM, the judgment group of experts, structure the complex relationship among the variables of study. This technique of ISM has been adopted and confirmed by many researchers to establish interrelationship among different variables (Khan, 2015; Lin and Yeh, 2013; Sharma and Gupta, 1995; Singh et al., 2003; Thakkar et al., 2005; Thakkar et al., 2008). As far as the necessity of study is concerned, numerous studies have explained different constructs of customer experience in online setting (Ghani, 1991; Novak et al., 2003; Bilgihan et al., 2014; Bilgihan et. al., 2016), but, as per author’s knowledge, none has worked simplifying the interrelationship of components of customer experience in online environment. Thus, present study tried to contribute in both, extending the existing knowledge by identifying and defining the components of customer experience in online environment and adding new knowledge by prioritizing those components using statistical tools i.e. Pareto analysis and Interpretive Structural Modeling (ISM)) never applied together.
As far as the structure of paper is concerned, the paper begins with introduction followed by theoretical background and literature review. Then, Pareto analysis and Interpretive Structural Modeling (ISM) have been applied to provide an initial model comprising of the components of customer’s online experience. Further, the findings are strengthened through MICMAC Analysis. Lastly, the papers ended with results and managerial implications.