The widespread adoption of pricing algorithms in online markets has led to increased price volatility, conflicting with consumers’ preference for price stability. This paper investigates algorithmic dynamic pricing (ADP), the focal form of algorithmic pricing in online markets. Drawing on price fairness and range-frequency theory, five studies (one real-life ADP encounter and four incentive-aligned experimental studies) explore the effects of ADP on consumer trust and price search behavior. Overall, consumers are averse to ADP; they trust ADP retailers less and compare prices more frequently under ADP. However, the negative effect of ADP on trust is nullified if consumers have already experienced ADP as they have likely accepted ADP as a pricing rule (the habituation effect). Moreover, ADP’s effect on price search actually reverses (an asymmetric effect) if consumers are price-advantaged or if ADP is the market norm—a favorable effect from a retailer’s perspective. To manage ADP, retailers should offer cash price-matching guarantees. Consumers trust ADP retailers offering such guarantees more and compare prices less. However, retailers should be cautious when communicating a reactive ADP approach (i.e., the automatic matching of competitor prices). It can stimulate trust but can also nudge consumers to compare competitor prices more frequently.
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