How the U.S. Car Rental Industry Talks About Innovation: A Multi-Source Media Analysis of AI-Enabled Service Transformation

This study examines how organizations in the U.S. car rental industry publicly frame and narrate artificial intelligence (AI)-enabled service innovation and what these narratives reveal about dynamic capabilities in asset-intensive service settings. The objective is to analyze how innovation is communicated, justified, and contested across multiple public arenas, and to develop a theoretically grounded understanding of the relationship between public narrative and organizational capability. The study employs a qualitative thematic analysis of multi-source public media data, including newspaper reports, trade publications, corporate investor communications, social media, and business television transcripts published between 2019 and 2026. Applying Dynamic Capabilities Theory as an interpretive lens, the analysis identifies four higher-order themes: sensing, seizing, transforming, and capability tensions. The results show that firms develop divergent sensing and seizing orientations under shared technological and competitive pressures, with some organizations prioritizing operational efficiency and others foregrounding customer experience. Persistent misalignment between seizing and transforming activities, combined with the erosion of feedback mechanisms under increasing automation, creates conditions of algorithmic insulation in which error-correction capacity is progressively diminished. The paper concludes that public innovation discourse operates as an observable signal of capability enactment, extending Dynamic Capabilities Theory beyond its traditional focus on internal routines. These findings contribute to research on AI-enabled service innovation in consumer-facing industries and highlight interpretive framing, organizational redesign, and feedback architecture as determinants of adaptability in AI-mediated service systems. Practical implications address how industry operators and regulators can use discourse patterns to identify capability development trajectories and governance failures before they materialize as operational crises.

Artificial intelligence (AI); Dynamic capabilities

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