Segmenting and Profiling Online Shopping Consumers: How Do They Differ in Hedonic Shopping Motivations?


  • Aslıhan Kıymalıoğlu Assistant Professor
  • Çağlar Samsa



The purpose of the study is to cluster online shopping customers on their loyalty levels and profile them with regards to their hedonic shopping motivations. To this end, a self-administered online survey was conducted, and 226 usable answers were obtained. First, cluster analysis was performed to segment online shoppers on their loyalty values. Later, the final three-cluster solution was profiled according to hedonic value motivations using Generalized Ordered Logit (GOLOGIT) Regression. The two-step cluster analysis revealed three clusters of online shoppers (non-loyals, moderate loyals, and true loyals). The GOLOGIT results indicated that an increase in idea shopping, role shopping, and value shopping of consumers in their online shopping experience would increase their loyalty levels (from non-loyal to moderately loyal, and from moderately loyal to true loyal). With regards to social shopping value, an increase in this dimension would be significantly affective only for non-loyal consumers, increasing their odds of being in the second (moderately loyal) or third (true loyal). This finding would help in the differentiation of marketing strategies for each segment, which would advance business competitiveness. Considering the increasing importance of online shopping among consumers, findings of this study is expected to contribute to the literature.

Author Biography

Çağlar Samsa

Çağlar SAMSA is an assistant professor at Kafkas University, Vocational School of Social Sciences.




How to Cite

Kıymalıoğlu, A., & Samsa, Çağlar. (2022). Segmenting and Profiling Online Shopping Consumers: How Do They Differ in Hedonic Shopping Motivations?. International Journal of Contemporary Economics and Administrative Sciences, 12(1), 225–242.