Assessing pre-purchase risk attributes towards used-products: Evidence from E-shoppers in the Czech Republic

Abstract

Recent years have witnessed a marked increase in research focusing on risk in online transactions. Partly, these research efforts have been situated within the context of risk in online transactions in general with scant evidence considering the risk in the online used goods market in the Czech Republic. Less is also known about the level of risk associated with the customer to embark on online transactions of second-hand goods. Against this background, this paper is aimed at eliciting the magnitude of risky components aligned with the tendency to connect via online in search of second-hand goods. To achieve this, the research proposes a Non-Hierarchical (K-means) algorithm to glean relevant patterns in the data accrued from the Czech Republic. Hence, a simple random technique geared towards demographic variables was adopted to gather data; with 329 out of 411 respondents eligible for our analysis. Consequently, the research revealed through the K- means clustering algorithm that, consumers or respondents in these regions, namely, Pardubicky, Vysocina, and Kralovehradecky regions in the Czech Republic are more circumspect concerning the zeal to connect via online specifically second-hand goods. The findings from this shed light on a significantly ignored research area for researchers and provide a managerial strategy for online used goods vendors. © 2019 Association for Computing Machinery.