The rise of human–machine collaboration: Managers' perceptions of leveraging artificial intelligence for enhanced B2B service recovery

Nisreen Ameen, Margherita Pagani, Eleonora Pantano, Jun Hwa Cheah, Shlomo Tarba, Senmao Xia

Research output: Contribution to journalArticlepeer-review

Abstract

This research analyses managers’ perceptions of the multiple types of artificial intelligence (AI) required at each stage of the business-to-business (B2B) service recovery journey for successful human–AI collaboration in this context. Study 1 is an exploratory study that identifies managers’ perceptions of the main stages of a B2B service recovery journey based on human–AI collaboration and the corresponding roles of the human–AI collaboration at each stage. Study 2 provides an empirical examination of the proposed theoretical framework to identify the specific types of intelligence required by AI to enhance performance in each stage of B2B service recovery, based on managers’ perceptions. Our findings show that the prediction stage benefits from collaborations involving processing-speed and visual-spatial AI. The detection stage requires logic-mathematical, social and processing-speed AI. The recovery stage requires logic-mathematical, social, verbal-linguistic and processing-speed AI. The post-recovery stage calls for logic-mathematical, social, verbal-linguistic and processing-speed AI.

Original languageEnglish
JournalBritish Journal of Management
Early online date14 May 2024
DOIs
Publication statusE-pub ahead of print - 14 May 2024

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