Generative AI: fuelling e-commerce revenue growth

Title

Generative AI: fuelling e-commerce revenue growth

Creator

Sivathanu, Brijesh; Pillai, Rajasshrie

Description

Generative AI (GenAI) is a novel technology that has transformed businesses across various industries. E-commerce companies are increasingly using cutting-edge technologies, such as GenAI, that could drive revenue growth. This study leverages the lens of social-technical systems and dynamic capabilities theories to examine how e-commerce companies adopt GenAI and its subsequent impact on their revenue generation capabilities. This study investigated the factors influencing the adoption of GenAI in e-commerce companies. This study considers both Social factorstop management support, organizational readiness, competitive pressure, and vendor support) and Technical factors (security concerns, technological readiness, and AI explainability). Further, it investigates how the use of GenAI affects the development of technological capabilities and leads to financial performance, particularly in sales growth, revenue generation, profitability, and cost reduction. To validate the proposed model, we surveyed e-commerce company managers and analyzed the collected data using PLS-SEM. These findings offer valuable insights for e-commerce companies and their managers, enabling them to leverage GenAI for revenue generation. This novel study makes a significant contribution to the academic understanding of GenAI adoption and how it can be adopted for successful revenue generation. The Author(s), under exclusive licence to Springer Nature Limited 2025.

Source

Journal of Revenue and Pricing Management;Volume;25;Issue;1;pp.93-108

Date

01-01-2026

Publisher

Palgrave Macmillan

Subject

Dynamic capabilities; GenAI; PLS-SEM; Socio-technical system

Coverage

Sivathanu B., School of Business and Management, Christ University, Karnataka, Bangalore, India; Pillai R., Chandragupt Institute of Management Patna, Bihar, Patna, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 14766930;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.1057/s41272-025-00554-5

https://www.scopus.com/pages/publications/105012844995?origin=resultslist