The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Generative AI and Intelligent Processing of Customer Oppositions: Prospecting for Research Trends
|
|
Author(s): Ikram Ed-Daakouri (Laremef Laboratory, National School of Business and Management, Sidi Mohamed Ben Abdellah University, Morocco), Mustapha Elhissoufi (Laremef Laboratory, National School of Business and Management, Sidi Mohamed Ben Abdellah University, Morocco)and Lhoussaine Alla (Laremef Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Morocco)
Copyright: 2025
Pages: 28
Source title:
Humans and Generative AI Tools for Collaborative Intelligence
Source Author(s)/Editor(s): Jingyuan Zhao (University of Toronto, Canada), V. Vinoth Kumar (Vellore Institute of Technology, India), Polinpapilinho F. Katina (University of South Carolina Upstate, USA)and Joseph Richards (California State University, Sacramento, USA)
DOI: 10.4018/979-8-3693-8332-2.ch020
Purchase
|
Abstract
Generative Artificial Intelligence is transforming customer objection management by enabling personalized, real-time, and adaptive interactions. This chapter examines the role of generative AI in addressing objections through predictive modeling, leveraging historical data, and sentiment analysis to enhance response relevance and customer satisfaction. Ethical considerations, including transparency, bias reduction, and human oversight, are discussed to ensure responsible AI implementation. A proposed framework integrates AI's adaptability with hybrid human-AI collaboration, highlighting its effectiveness in managing complex or sensitive objections. Comparative case studies reveal generative AI's strengths in scalability and adaptability across industries, while also addressing its limitations in high-context scenarios. The chapter concludes with managerial insights, emphasizing ethical AI practices, industry-specific customizations, and predictive analytics as key to balancing automation with human-centric approaches for trust and effectiveness in customer interactions.
Related Content
|
Bikash Kumar, Rhythm Gaba, Rabi Shaw.
© 2026.
40 pages.
|
|
R. Velmurugan, J. Sudarvel, R. Bhuvaneswari, Ravi Thirumalaisamy.
© 2026.
28 pages.
|
|
J. Vijaya, Soumya Chandrakar, Pragya Shrivastava.
© 2026.
42 pages.
|
|
Yamini Ghanghorkar, Amruta Deshpande.
© 2026.
28 pages.
|
|
B. Bharathi, B. Kalaivani, Kasu Manaswi, Kantabathina Tejaswini.
© 2026.
28 pages.
|
|
Moumita Chowdhury, Aastha Agarwal, Alisha Parveen, Abhishek Mukhopadhyay.
© 2026.
42 pages.
|
|
Utkarsh Trivedi, Yash Vardhan, Piyush Kumar, Ansh Aryan, Parth Batra, Hitesh Mohapatra.
© 2026.
28 pages.
|
|
|