Strategic trust transfer in AI driven self-storage: mitigating the liability of newness for SMEs in Taiwan

Abstract

This study examines how resource-constrained small and medium enterprises (SMEs) in Taiwan’s emerging AI-driven self-storage market mitigate the liability of newness. It conceptualizes trust transfer from tangible service cues to brand trust as an associational cognitive hierarchy, providing a perception-based signaling framework for nascent ventures to cultivate adoption intentions. Grounded in signaling and trust transfer theories, the research identifies six antecedents across entrepreneurial signals and tangible trust cues. Structural equation modeling analyzed survey data collected from 362 Taiwanese consumers utilizing a category-level evaluation protocol. Environmental safety, economic feasibility, and locational convenience drive service trust. While reputation and communication positively influence brand trust, enterprise size shows no statistically significant direct association. A full mediation model confirms that service trust indirectly drives usage intention through brand trust, which acts as the primary direct driver. Rather than attempting to artificially project enterprise scale, SME operators should strategically allocate capital toward AI-specific operational touchpoints (such as smart access and automated safety monitoring). Furthermore, managers must implement transparent communication, data privacy standards, and mandatory human-in-the-loop emergency escalation protocols. This research resolves the human-absent physical touchpoint boundary and reverse cognitive pathway gap by integrating signaling and trust transfer frameworks in an unstaffed physical setting. It demonstrates how technology-enabled SMEs can structure localized service trust into brand-level credibility to drive adoption.