Abstract
Partner relationship management (PRM) is a set of methods, tools, strategies, and web-based capabilities that a business-to-business (B2B) firm uses to manage its relationships with partners, resellers, and other third parties. Integrating artificial intelligence (AI) into PRM helps automate processes and procedures by eliminating human error and processing data faster and more accurately. Following growing attention from scholars and practitioners to AI-PRM, this study builds on the dynamic capability view (DCV) and absorptive capacity theory to develop a conceptual model to understand the requirements for a B2B firm's adoption of AI-PRM and its impact on business value. Since AI-PRM is still relatively new in scholarly research, there are no specific scales in the existing literature that could be used to capture specific factors and preconditions for its adoption, thus we explore a set of new metrics. We test the conceptual model using structural equation modeling with data from 427 B2B firms. Our results show that firms improve operational performance when an AI-PRM system is reflected in customized partner services and partner engagement, which in turn yields business value.
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