When Customers Imagine Better Outcomes in Negative Online Reviews: Upward Counterfactual Thinking, Firm Recovery Strategies, and Prospective Customer Responses

Customers who have had negative purchasing experiences often engage in upward counterfactual thinking, considering how outcomes could have been better if different actions had been taken. These mental simulations become publicly visible when expressed in negative online reviews, potentially influencing prospective customers’ decisions. Focusing on upward counterfactual expressions (UCEs) in negative reviews, our research employs a machine learning approach to fine-tune a BERT (Bidirectional Encoder Representations from Transformers) model and builds on signal detection theory to explain their effects on prospective customer purchase decisions. Across two field studies involving 21,187 negative reviews of 943 US hotels and 12,889 negative reviews of 300 UK restaurants on Tripadvisor, and an online experiment, we find that UCEs significantly reduce sales performance. This effect occurs because UCEs signal the argument quality of negative reviews, which in turn deters prospective customers from making purchase decisions. Importantly, firm responses moderate this relationship. Providing compensation in response to negative reviews containing UCEs mitigates their detrimental effect on sales and can even generate incremental profits for firms. In contrast, offering explanations amplifies the negative impact of UCEs, while empathetic apologies do not significantly alter the relationship between UCEs and sales. Implications for research and practice are discussed.