Overestimation and Built-in Positive Biases in Customer Satisfaction Evaluation
(A2025-126161)

AUTHORS

Jaebeom Suh, Kansas State University; Ta Suh, University of Houston

ABSTRACT

The current research examines systematic bias in customer satisfaction measurements. While customer satisfaction is widely used by companies to gauge performance and predict loyalty, the authors argue that these evaluations are often positively biased and don't reflect true customer assessments. We identify four psychological processes that contribute to this bias: expectation management (lowering pre-purchase expectations), confirmation bias, ownership effect, and dissonance reduction. These biases occur specifically when evaluators are actual product customers/purchasers. We suggest that companies should adjust (discount) their satisfaction metrics since they're likely overestimated. This research aims to demonstrate this positive bias, explain its psychological underpinnings, and provide theoretical and managerial implications.