Journal of Consumer and Consumption Research (Tüketici ve Tüketim Araştırmaları Dergisi)

23-72

Authors: Yeliz BAŞ & Remzi ALTUNIŞIK

According to modern consumer behavior perspective, consumer buying behavior is shaped according to a process which is in-line with information processing theory. In marketing literature consumer products are classified according to various criteria. The purpose of this study is to examine whether consumer products can be categorized according to the types of information sought for different product classes during buying decision making process. In the context of study, a model trying to explain consumer information-seeking behavior was proposed in light of consumer behavior literature. In the model, various constructs and variables such as types of information sought, involvement, consumer awareness, perceived risk and shopping styles were included. The proposed model was tested on two product categories namely computers and skin care products. Findings indicate that consumers’ information seeking behavior is strongly influenced by the types of information sought and the shopping styles. On the other hand, involvement, awareness and perceived risks has no influence on consumers’ information search behavior. Furthermore, findings give promise to the idea that consumer products can be grouped according to the types of information sought for different product categories, which may be considered as a new perspective in studying consumer behavior.

105-130

Authors: Özlem ÇATLI & Ahmet ÇATLI

Determination of customer behaviors in advance, is quite important for marketers especially for those who have databases and can contact directly with customers. This process should be difficult as the information gathered from customers is limited. Customers’ buying behaviors are variable. Behaviors of some customers are quite regular while others are irregular and unpredictable. For the subsequent group, individual normas and measurements are required to determine the statistical values in order to create comparison criteria. Herein, purchase timing models come into play. By these models it became possible to predict the time between expected purchases individually for each component. Each model, by using a different variable as a base, helps to make predictions about future buying behaviors. In this study purchase timing models that are related to customers’ past behaviors and involving random effects are discussed respectively, while dynamic purchase timing model is examined in detail.