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Samadhiya, Ashutosh, Kumar, Anil, Yadav, Sanjeev, Luthra, Sunil, Jabbour, Charbel Jose Chiappetta and Agrawal, Rajat (2023) Artificial Intelligence-partner relationships management for climate management in B2B firms to achieve sustainable competitiveness. Industrial Marketing Management, 115 (Nov23). pp. 510-525. ISSN 1873-2062
Drawing on the dynamic capability view and resource-based view, this study investigates the relationship among artificial intelligence (AI)-based partner relationship management, firm performance, and sustainable competitiveness concerning the climate management of business-to-business (B2B) firms. Throughout this investigation, the study additionally explored the impact of several other dimensions, including information communication technology capability, firm fit, and technological readiness, as fundamental requirements for the implementation of AI-based partner relationship management. The present study also investigates the relationship between firm partner engagement and firm information processing systems for climate management of B2B firms and their impact on achieving firm performance and sustainable competitiveness. A mixed-methods approach was employed that involves in-depth interviews with senior managers from a diverse set of B2B firms, and the proposed hypothesis model was evaluated by analysing the collected B2B data of 142 responses using partial least squares structural equation modelling. The findings of the study show that information communication technology capability and technological readiness play a significant role in improving the performance of AI-based partner relationship management. Furthermore, the implementation of AI-based partner relationship management in B2B firms would help partner engagement and information processing systems for climate management, leading to sustainable firm competitiveness. The outcomes of this study offer many implications for managers and practitioners in the B2B sector and suggest potential avenues for future research.
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Restricted to Repository staff only until 8 November 2026.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.
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