Paper Type
ERF
Abstract
Airline multimarket contact (MMC) can sustain tacit coordination when price cuts are observable and retaliation is credible across routes. Yet in digital markets, pricing is increasingly mediated by algorithmic systems that reshape how competitive actions are generated, observed, and punished. This study asks: when do algorithmic pricing systems make MMC more likely to keep fares high in U.S. airlines? Drawing on the IS perspective of digital systems as information architectures, we theorize algorithmic pricing as a dual-orientation mechanism. Competitor-directed algorithms monitor rivals’ fares and enable rapid responses, strengthening deviation detection and cross-market retaliation. In contrast, consumer-directed algorithms tailor fares across passenger segments, creating less observable price deviations and weakening punishment precision. Using U.S. airline route-quarter data, we propose behavioral proxies for these orientations—rival price reactivity and residual fare dispersion—and examine how they condition the MMC–fare relationship. This study contributes by explaining when algorithmic pricing reinforces or weakens MMC-based mutual forbearance.
Paper Number
1040
Recommended Citation
Yang, Yanxia and NAYEEM, ZANNATUN, "Algorithmic Pricing and softer competitive behaviors in U.S. Airlines: When Do Fares Stay High?" (2026). AMCIS 2026 Proceedings. 1.
https://aisel.aisnet.org/amcis2026/agile/agile/1
Algorithmic Pricing and softer competitive behaviors in U.S. Airlines: When Do Fares Stay High?
Airline multimarket contact (MMC) can sustain tacit coordination when price cuts are observable and retaliation is credible across routes. Yet in digital markets, pricing is increasingly mediated by algorithmic systems that reshape how competitive actions are generated, observed, and punished. This study asks: when do algorithmic pricing systems make MMC more likely to keep fares high in U.S. airlines? Drawing on the IS perspective of digital systems as information architectures, we theorize algorithmic pricing as a dual-orientation mechanism. Competitor-directed algorithms monitor rivals’ fares and enable rapid responses, strengthening deviation detection and cross-market retaliation. In contrast, consumer-directed algorithms tailor fares across passenger segments, creating less observable price deviations and weakening punishment precision. Using U.S. airline route-quarter data, we propose behavioral proxies for these orientations—rival price reactivity and residual fare dispersion—and examine how they condition the MMC–fare relationship. This study contributes by explaining when algorithmic pricing reinforces or weakens MMC-based mutual forbearance.
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