E-COMMERCE RETAIL CUSTOMERS REPURCHASE FACTORS INFLUENCING IDENTIFICATION

With the fast-paced technological development era and the importance of using the Internet in our daily lives, e-commerce as shopping no longer seems new or unusual. Regardless of type or size, companies are using e-commerce advantage to compete in the market. Each of these companies needs a circle of independent and loyal customers. When the customer is satisfied, he gives positive feedback about the company and makes a repeat purchase. In this way, he attracts new customers to the company and provides an independent income for the company. Certain factors influence the customer's attitude and behavior. It is essential to determine what influences their customers' choices when competing to earn profit in the market; the customer must be satisfied in order for him to want to make a repurchase. Repurchases from customers indicate a loyalty to the company. Customer loyalty can be the result of a company consistently meeting and exceeding its customer expectations. Customer loyalty can have a significant impact on business growth. To assess and identify the factors influencing customer satisfaction, they are identified and offered a conceptual reflection of the current situation and offer a conceptual model of Identification Causes and Effects of Customer Satisfaction Framework (IceCSF) in e-commerce retail

Customer experience management: Expanding our understanding of the drivers and consequences of the customer experience

The present doctoral dissertation aims to analyze thenew business landscape that suggests the importance of customer experience ¿ its drivers and consequences from a dynamic perspective. The drivers of customer experience provide firms with crucial knowledge about the experience expectations and desires of the customers, thereby enabling firms to identify the key determinants which significantly shape customer perceptions toward the experience with the firm. This is very important for firms, since the effort dedicated by firms to improve customer experience is not always equally perceived and/or valued by customers. Likewise, integrating the consequences of customer experience allows firms to translate their investment in customer experience into specific opportunities and enhanced performance outcomes (financial, behavioral, and relational). This is specifically critical, considering that a customer experience perceived as favorable by customers might not have a positive impact on firm outcomes. Customer experience is not static but evolve over time. By taking into account the dynamic nature of customer experience, firms may capture the occurred changes in customers and adjust the factors under their controls immediately, thereby ensuring the alignment between customer experience expectations and firms¿ offerings. In this way, through a dynamic lens, we establish the linkage across what firms do, what customers think, what customers do, and finally what firms get. The thesis is consisted of three studies. Study 1 investigates the impact of firms¿ investments in three key strategic levers (i.e., value, the brand, and the relationship) on the customer experience as well as the direct and moderating role played by social influence. We integrate research in customer relationship management (i.e., customer equity framework) (Rust, Lemon, & Zeithaml, 2004) and customer experience management (Lemon & Verhoef, 2016; Verhoef et al., 2009) and offer a unifying framework to understand the linkages between the three equity drivers (i.e., value equity, brand equity, relationship equity), social influence, the customer experience, and its ultimate impact on profitability. Study 2 focuses on the separate and joint effects of customer experience and lock-in on customer retention. Building barriers to lock customers and improving the customer experience are two key strategies employed by firms to enhance customer retention. Although pursuing the same goal, these strategies work differently: the former relies more on a calculative, cost¿benefit approach to the exchange, while the latter promotes the affective aspects of the relationship. Finally, study 3 investigates how different dimensions of customer experience (recency effect, peak effect, trend effect, and fluctuation effect) and different relationship marketing (RM) actions (i.e., advertising communication, product innovation, and conflict) impact customer relationship expansion from a dynamic perspective, and distinguishes their short-term and long-term effects. Self-determination theory posits that motivation for pursuing activities are consisted of intrinsic (the ones originating from the self and one¿s desire) and extrinsic factors (originating from external demands).<br /

DYNAMIC CONSUMER DECISION MAKING PROCESS IN E-COMMERCE

This dissertation studies the dynamic decision making process in E-commerce. In the first essay, we use eye tracking to investigate how consumers make information acquisition decisions on attribute-by-product matrices in online choice environment such as comparison websites. Hierarchical Hidden Markov Model is used to describe this process. The model consists of three connected hierarchical layers: a lower layer that describes the eye movements, a middle layer that identifies product- and attribute-based information acquisition modes, and an upper layer that flexibly captures switching between these modes over time. Findings of a controlled experiment show that low-level properties of the eye and the visual brain play an important role in dynamic information acquisition. Consumer switch frequently between two acquisition modes, and higher switching frequency increases decision time and reduces easiness of decision making. These results have implications for web design and online retailing, and may open new directions for research and theories of online choice. The second essay investigates how usage experience with different types of decision aids contributes to the evolution of online shopping behavior over time. In the context of online grocery stores, we categorize four types of decision aids that are commonly available, namely, those 1) for nutritional needs, 2) for brand preference, 3) for economic needs, and 4) personalized shopping lists. We construct a Non-homogeneous Hidden Markov Model of category purchase incidence and purchase quantity, in which parameters are allowed to vary over time across hidden states as driven by usage experience with different decision aids. The dataset was collected during the period when the retailer first launched its web business, which makes it particularly suited to study the evolution of online purchase behavior. We estimate the model for the spaghetti sauce and liquid detergent categories. Results indicate that four types of decisions influence evolution of purchase behavior differently. Findings from this study enrich the understanding of how purchase behavior may evolve over time in online stores, and provide valuable insights for online retailers to improvement the design of their store environments

Analytical customer relationship management in retailing supported by data mining techniques

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

DECONSTRUCTING E-COMMERCE PRESENCES - A SYSTEMATIC REVIEW AND RESEARCH AGENDA

This literature review systematically analyses recent studies on the effective design of e-commerce presences in order to provide a state-of-the-art overview on this important topic. To do so, our review focuses on the level of webshop elements (i.e., the building blocks to design webshops), which we cluster in eight categories (e.g., color usage, music usage, rich media usage), derived from previous website quality frameworks (e.g., SITEQUAL, WebQual) and prior reviews. The basis of our comprehensive literature review are 91 articles grouped into the webshop element categories and additionally analyzed along three key study criteria, namely the applied research methods, theories, and key dependent variables. Based on the findings from this bibliographic analysis, we formulate an agenda for future research avenues to guide researchers in further exploring the field of e-commerce presences and to support practitioners in their decision-making on the implementation of webshop elements

Exploring online brand choice at the SKU level : the effects of internet-specific attributes

E-Commerce research shows that existing studies on online consumer choice behavior has focused on comparative studies of channel or store choice (online or offline), or online store choice (different e-tailers). Relatively less effort has been devoted to consumers’ online brand choice behavior within a single e-tailer. The goal of this research is to model online brand choice, including generating loyalty variables, setting up base model, and exploring the effects of Internet-specific attributes, i.e., order delivery, webpage display and order confirmation, on online brand choice at the SKU level. Specifically, this research adopts the Multinomial Logit Model (MNL) as the estimation methods. To minimize the model bias, the refined smoothing constants for loyalty variables (brand loyalty, size loyalty, and SKU loyalty) are generated using the Nonlinear Estimation Algorithm (NEA). The findings suggest that SKU loyalty is a better predictor of online brand choice than brand loyalty and size loyalty. While webpage display has little effect on the brand choice, order delivery has positive effect on the choice. Online order confirmation turns out to be helpful in choice estimation. Moreover, online consumers are not sensitive to net price of the alternatives, but quite sensitive to price promotion. These results have meaningful implications for marketing promotions in the online environment and suggestions for future research

Advances in Mathematical Models in Marketing

This dissertation comprises a series of three essays that relate advances made to both theoretical and empirical issues in marketing. The first essay discusses the issue of endogeneity of market share and price in logit models and provides a theoretical procedure to solve this problem. The inseparability of demand and price make the possibility of drawing definite conclusions about either almost impossible. We employ a recently rediscovered mathematical function called the 'LambertW' to solve this problem of endogeneity and in turn yield logit models more conducive to theoretical study. We also employ this methodology to the problem studied by Basuroy and Nguyen (1998). The second essay deals with the issue of pricing implicit bundling. Implicit bundles are products that are sold separately but provide an enhanced level of satisfaction if purchased together. We develop a model that would account for the possible relationships of the products across the different product lines. We show that accounting for these relationships would decrease the amount of price competition in the market and also allow the Firm to enjoy higher profits. We also account for the endogeneity of price and market share when deriving the optimal solutions. We show that optimal prices first increase as the relationship between the firm's two products become stronger and then decrease as the two products become more exclusive to each other. Finally, we also find that a firm's prices increase as the competitor's contingent valuations increase. The third essay helps improve the efficacy of CRM interventions by analyzing the latent psychological loyalty states of the customer. We use state space models to predict these latent loyalty states using observed data. We then use the predicted values of loyalty to derive the probability of repurchase of the customer. We also identify the types of CRM interventions that play a role in improving the loyalty of the customer to the firm and those interventions that have no effect. We compare our model's predictions to those derived from two other estimation methods. We find that our predictions are better than those computed from the other methods discussed

The cause, development and outcome of word-of-mouth marketing: with particular reference to WOM volume, valence and the modeling of viral marketing

Viral marketing is a form of online word-of-mouth (WOM) communication in which individuals are encouraged to pass on promotional messages through social websites. With the growing popularity of online social websites, viral marketing has increasingly garnered attention of marketers and marketing researchers alike. The two most important WOM attributes highlighted in the extant literature are volume and valence. This thesis looked into the cause, development and outcome of WOM marketing and provided computational models for forecasting the development of WOM volume and valence of viral marketing in social websites. With the data extracted from large-scale web-crawling activities, through a series of computer simulation experiments comparable to social websites, the author developed models to predict WOM volume and valence in viral marketing. The model for predicting WOM volume in viral marketing used theories of network topologies. The model for predicting WOM valence in viral marketing used an artificial neural network model. The author discussed the insights from the findings and suggested viral marketing strategies to optimize the performance of WOM volume and valence in social websites. A key contribution of this thesis is the new approaches of modeling and data collection for WOM volume and valance forecasting in viral marketing