GAI-enabled CRM and sales unit performance: A user-level pathway through perceived quality and technology acceptance, moderated by adoption duration

Authors’ abstract

Generative artificial intelligence (GAI) is being progressively integrated into customer relationship management (CRM) systems, yet empirical evidence linking GAI-enabled CRM to performance outcomes remains limited. This study examines the association between the implementation of GAI-enabled CRM and sales unit performance, with a focus on user-level mechanisms and contextual boundary conditions. A conditional process model is proposed and tested in which perceived quality and technology acceptance and use sequentially mediate the relationship between GAI-enabled CRM implementation and performance, with adoption duration and firm size examined as boundary conditions, the latter on an exploratory basis. Survey data were collected from 127 CRM professionals in France with direct experience with GAI-enabled CRM systems and were analyzed using regression-based conditional process modeling. The central finding is that GAI-enabled CRM is not directly associated with sales unit performance. Instead, the association operates indirectly through perceived quality and subsequent technology acceptance and use, which constitutes the study’s primary empirical contribution. Adoption duration shows a moderation pattern consistent with stronger associations at earlier exposure stages, although this finding reaches only marginal significance under conservative inference and should be interpreted with caution. Evidence that firm size further conditions this process is inconclusive and does not support its treatment as a validated boundary condition. Findings are bounded by the operational scope of the retained GAI-enabled CRM construct after measurement refinement and by the cross-sectional, self-reported nature of the data. Overall, the study clarifies the user-level evaluative pathway through which GAI-enabled CRM is associated with sales unit performance.

Paper

Authors

A. Saadé, O. Sabri

Publication date

2026

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Source license

Creative Commons Attribution 4.0 International

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CC-BY-4.0

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