Knowledge management in improving business process: an interpretative framework for successful implementation of AI–CRM–KM system in organizations

PurposeThe purpose of this paper is to identify the critical success factors (CSFs) for AI-integrated CRM system for better knowledge management (KM) in organizations to improve business process.Design/methodology/approachThe factors critical for adoption of AI-integrated CRM system for efficient kn...

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Bibliographic Details
Published in:Business process management journal Vol. 26; no. 6; pp. 1261 - 1281
Main Authors: Chatterjee, Sheshadri, Ghosh, Soumya Kanti, Chaudhuri, Ranjan
Format: Journal Article
Language:English
Published: Bradford Emerald Publishing Limited 16-11-2020
Emerald Group Publishing Limited
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Summary:PurposeThe purpose of this paper is to identify the critical success factors (CSFs) for AI-integrated CRM system for better knowledge management (KM) in organizations to improve business process.Design/methodology/approachThe factors critical for adoption of AI-integrated CRM system for efficient knowledge management are innumerable. The salient factors may be identified by several means. Methods like brainstorming and Delphi have been applied here. Sixteen CSFs have been identified. Then the interrelationship among these 16 factors, levels of their importance and the principal driving factors have been established by interpretative structural modelling (ISM) methodology.FindingsThe results show that out of 16 CSFs, leadership support, adequate fund and support of functional area leads are the most important CSFs for AI–CRM–KM integration.Practical implicationsThe results show that support of top management is essential for successful adoption of AI-integrated CRM system for better knowledge management to improve the business process.Originality/valueThis paper has taken a novel attempt to identify CSFs for AI-integrated CRM adoption for efficient knowledge management system in organizations for improvement of business process and to establish interrelationship among those CSFs with the help of ISM methodology.
ISSN:1463-7154
1758-4116
DOI:10.1108/BPMJ-05-2019-0183