The Rise of Human–Machine Collaboration: Managers’ Perceptions of Leveraging Artificial Intelligence for Enhanced B2B Service Recovery

Author:

Ameen Nisreen1ORCID,Pagani Margherita2,Pantano Eleonora3ORCID,Cheah Jun‐Hwa4ORCID,Tarba Shlomo5ORCID,Xia Senmao6ORCID

Affiliation:

1. School of Business and Management, Royal Holloway University of London Egham Hill Egham TW20 0EQ UK

2. SKEMA Centre for Artificial Intelligence SKEMA Business School – Université Côte d'Azur 5 Quai Marcel Dassault Suresnes 92150 France

3. University of Bristol Business School University of Bristol Bristol BS8 1SD UK

4. Norwich Business School University of East Anglia Norwich NR4 7TJ UK

5. Birmingham Business School University of Birmingham Edgbaston Park Road Birmingham B15 2TY UK

6. Surrey Business School University of Surrey Guildford GU2 7XH UK

Abstract

AbstractThis 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.

Publisher

Wiley

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3