Adoption of AI integrated partner relationship management (AI-PRM) in B2B sales channels: Exploratory study

Sheshadri Chatterjee · Ranjan Chaudhuri · Demetris Vrontis · Selma Kadić‐Maglajlić

50 citations0/0 files storedIndustrial Marketing Management

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Digital Innovation in Industries Customer Service Quality and Loyalty Corporate Governance and Management

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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References

1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error 2003Common method biases in behavioral research: A critical review of the literature and recommended remedies.1990Absorptive Capacity: A New Perspective on Learning and Innovation 1997Dynamic capabilities and strategic management 2015A primer on partial least squares structural equation modeling (PLS-SEM)2001Accounting for common method variance in cross-sectional research designs.2009Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration1 2006Scale Development Research

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