Open Access

Leah Pitzen

• This thesis investigates how consumers perceive the value of products in the context of online shopping, particularly on Amazon, and how this perception influences their ability to detect fake reviews. With the shift from traditional to digital retail, challenges arise in how consumers assess product value without direct interaction. Given the rise of deceptive online practices like fake reviews, understanding human detection of such misinformation is crucial. The study integrates two theoretical perspectives: the human detection of fake reviews and the concept of perceived customer value. While most research focuses on algorithmic detection, this study emphasizes consumer decision-making and review evaluation. It builds on Holbrook’s customer value typology and tests Leroi-Werelds’ 2019 update, which introduces 24 value types (positive and negative) relevant to modern digital consumption. A quantitative approach was employed using a survey that examined participants' responses to seven Amazon products of varying value levels (e.g. laptop, wireless headphones, robot vacuums). The Consumer Value Index was operationalized with 21 value types tailored to product assessment and the data was analyzed using structural equation modeling. The results support that Leroi-Werelds' typology effectively captures perceived customer value. This contributes to better understanding of consumer behavior and can help design more effective e-commerce strategies and safeguards against fake reviews.