THE EFFECT OF RISK PERCEPTION ON PRE-OWNED CAR PURCHASE DECISION: A LOGISTIC REGRESSION APPROACH

The aim of this research article is to investigate the role of risk perception in consumer pre-owned car purchase decision-making process. Considering the recent growth story of used-car market in India, this research is a continuous effort in developing a practical research model that can identify the influence of various perceived risk dimensions on pre-owned car purchase. After analyzing prior literature an in-depth qualitative study was performed to generated initial 21 survey items spanning across 7 perceived risk dimensions. A questionnaire-based survey was conducted using generated items among 150 respondents. To achieve research objective Binary Logistic Regression has been used as a tool for constructing the research model. Overall mean and standard deviation (sd) has been used as the basis for the Initial dichotomous classification (i.e. “HIGH” or “LOW”) of the dependent variable (i.e. Consumers experiencing high/low overall perceived risk) and all the 7 perceived risk dimensions are considered as independent variables. This study identified two relevant predictor variables with significance (Wald statistic) level less than 0.05 (p < 0.05) and out of that physical risk dimension with high wald statistic value is the most important predictor of overall perceived risk regarding pre-owned car purchase scenario.