Abstract
Prior studies have evidenced that consumers can self-select themselves in submitting online reviews, thus introducing biases in the distribution of online review ratings. This kind of bias is termed self-selection bias. This research aims to explore the specific influences of self-selection bias on consumer satisfaction from a product type perspective. The adopted product classification system combines the search and experience differentiation, as well as the vertical and horizontal differentiation. An agent-based modeling approach is employed to systematically examine the combined effects of different types of self-selection bias and products. Based on experiment analysis, a novel theory is developed arguing that self-selection bias can have nuanced influences on consumer satisfaction with different kinds of products, by affecting the usefulness of online reviews in suggesting product quality information.
Original language | English |
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Publication status | Published - 2020 |
Keywords
- Online review
- Self-selection bias
- Product type
- Consumer satisfaction
- Theory building
- Agent-based modelling