Dependent Variable : Precursor of Cognitive Dissonance

This paper provides managerial guidance on the factors that might prompt cognitive dissonance using actual e-commerce panel data. This is achieved by translating cognitive and emotional antecedent factors found in previous research into observables through online data and constructing a hierarchical model to control individual heterogeneity and situational differences. INTRODUCTION Under fierce competition, e-commerce firms need to aim at increasing the average customer spending through cross-selling and up-selling. To increase the revenue per customer, many EC sites encourage their customers to purchase more products and/or more expensive goods through the use of a recommendation system. However, such purchases are often accompanied by cognitive dissonance in the mind of customers (Holloway, 1967). When this happens, consumers try to resolve the dissonance mainly by two means (Festinger, 1957). One way is to search for information that justifies their purchase decision (Rosenfeld et al., 1986), and the other is to return the purchased product. If the cognitive dissonance is not resolved, it can lead to the customer’s dissatisfaction toward the product and the store (Montgomery and Barnes, 1993), generating negative word-ofmouth, or even stop shopping at the store altogether (Hunt, 1991). Therefore, finding a customer’s action that might prompt cognitive dissonance is crucial to help companies to address cross-selling and up-selling effectively. Current research into cognitive dissonance offer little guidance to identify a consumer’s action forewarning its occurrence. Most studies are conducted through surveys or experiments in laboratories, providing limited applicability to the field setting of EC. When cross-selling and up-selling, EC sites need to know what kinds of shopping behavior and types of product might lead to the consumer’s action forewarning the occurrence of cognitive dissonance, so that sellers can take preventive measures in advance. In light of these gaps and concerns, we aimed to find the “precursor”, the consumer’s action that might prompt cognitive dissonance, and provide managerial guidance on capturing the presages and taking preventive actions in a novel way. Unlike most consumer behavior studies, we utilized field data, specifically actual access log and purchase data collected by an EC site, in the following manner. First, by observing a post purchase online behavior, we classified customer purchases into two categories: one with the precursor and one without. Second, we translated latent antecedent factors of cognitive dissonance that have been validated in previous studies, such as the customer’s cognition and feelings, into observable shopping behaviors, product attributes, and customer characteristics. We then constructed statistical models to relate these factors to the precursor of cognitive dissonance. This study contributes to the theoretical and practical knowledge of cognitive dissonance in two ways. First, we confirmed the antecedent factors of cognitive dissonance in a field setting. Previous research has relied mainly on survey studies because many of these factors are unobservable. Furthermore, their theory testing was conducted at the aggregate level by controlling individual differences in a laboratory setting. Such an approach permits the inclusion of few moderating variables that might affect the occurrence of cognitive dissonance. In contrast, we used panel data to analyze each customer at multiple points in time, accounting for not only situational differences but also individual differences in the occurrence of cognitive dissonance. We incorporated an idea proposed by Festinger (1957), that the occurrence and frequency of cognitive dissonance differ among customers and purchase occasions, into our model to confirm their validity. As far as we know, this is the first empirical study to address this heterogeneity issue. Second, we obtained insight into observable antecedent factors from access log data, which allows EC sites to take preventive actions so that customers do not experience cognitive dissonance. CONCEPTUAL BACKGROUND Among numerous empirical studies of cognitive dissonance, our research is related to investigation of its antecedent factors. The factors studied in previous research include choice difficulty (e.g., Menasco and Hawkins, 1978); emotional factors, such as anxiety about the act of the purchase itself (e.g., Hunt, 1970; Keng and Liao, 2013); confidence of the rightness of a purchase decision (e.g., Bell, 1967; Keng and Liao, 2013); and cognitive factors, such as needs for product knowledge (e.g., Sweeny et al., 2000). Some research has proposed methods to measure the strength of these antecedent factors in stimulating cognitive dissonance (e.g., Montgomery and Barnes, 1993; Sweeney et al., 2000). MODEL SPECIFICATION We constructed a statistical model that relates the precursor of cognitive dissonance to observable antecedent factors. The dependent variable in our model was a binary indicator of whether a postpurchase action presaging the occurrence of cognitive dissonance is observed or not. The post-purchase action will be explained in detail in the next section. The independent variables were the customer’s shopping behaviors before purchase, attributes of the purchased products, and customer characteristics. First, we applied a standard binary probit model to establish the relationship. Let yisp denote the post-purchase action at p-th purchase in session s by customer i. With a latent variable zisp, yisp = I (zisp > 0) where I(c) is an indicator function that returns 1 if condition c is satisfied and 0 otherwise. Given customer’shopping behaviors and product attributes xisp, zisp is zisp = x'isp β + visp , visp ~N (0, 1) where xisp is a (K + 1) × 1 vector composed of a constant term and K independent variables and β is a (K + 1) × 1 parameter vector. Next, to accommodate that the impact of customer’s shopping behaviors and product attributes on the precursor of cognitive dissonance differs among customers, we relaxes the parameters to be customer-specific as βi. Finally, we tried to identify the factors that cause this customer difference with respect to his/her site usage characteristics. Customer-specific βi is specified by a vector of moderating variables describing his/her site usage, di, as βi = Qdi + ni, ni ~MNV (0, Vβ) where di is a (M + 1) × 1 vector composed of a constant and M variables, and Q is a (K + 1) × (M + 1) parameter matrix (Rossi et al., 2005). 370 / Analyzing Antecedent Factors of Cognitive Dissonance using E-Commerce Data The first model, which assumes that all parameters are the same across customers, is referred to as the “base model. ” The second model, in which customer-specific parameters are estimated, is referred to as the “individual model. ” The third model, in which customer’s site usage characteristics are hierarchically incorporated into their own parameters, is referred to as the “hierarchical model. ” The parameters in these three models were estimated using a Markov Chain Monte Carlo method. The model fits were then compared both in estimation and validation periods.