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The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context

Chatterjee, S.
; 
Rana, N.P.
; 
Tamilmani, Kuttimani
; 
Sharma, A.
Publication Date
2021-08
End of Embargo
Supervisor
Keywords
AI-CRM, B2B, RBV, Institutional theory, Organization performance, Competitive advantage
Rights
© 2021 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.
cb
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2021-07-28
Institution
Department
Awarded
Embargo end date
Collections
Management and Law Publications
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Additional title
Abstract
Organizations have cultural-cognitive and regulative as well as normative elements that impact their employees. Organizations, by definition, cannot achieve a pure, stable state and always go through various change processes, both incremental and radical changes. Moving from legacy business-to-business (B2B) relationship management to an artificial intelligence-based customer relationship management (AI-CRM) is a gradual but paradigm change. AI-CRM leverages intelligent systems to automate the B2B relationship activities where the decision can be taken automatically without any human intervention. Relationship management in the B2B segment is considered a strategic activity of an organization. Moving from legacy to AI-CRM to facilitate B2B relationship management activities is an important decision, and proper implementation of AI-CRM is a critical success parameter for an organization. This study combines institutional theory and the resource-based view (RBV) in B2B relationship management to understand how AI-CRM could impact the firm's performance with varied firm size, firm age, and industry type.
URI
http://hdl.handle.net/10454/20022
Version
Accepted manuscript
Citation
Chatterjee S, Rana NP, Tamilmani K et al (2021) The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management. 97: 205-219.
Link to publisher’s version
Link to published version
Link to Version of Record
https://doi.org/10.1016/j.indmarman.2021.07.013
Type
Article
Qualification name
Notes