Assessing Organizational Users’ Intentions and Behavior to AI Integrated CRM Systems: a Meta-UTAUT Approach

Abstract

This paper tests the meta-analysis based unified theory of acceptance and use of technology (meta-UTAUT) model to predict the behavioral intentions of organizational users and their use behavior to artificial intelligence (AI) integrated customer relationship management (CRM) systems. Data was collected from 315 organizational users in India. The hypotheses draw on the theoretical underpinnings which have been statistically validated. Results show that CRM quality and satisfaction significantly influences an organization’s employees attitudes and intentions to use AI integrated CRM systems. The compatibility of CRM systems has, however, a limited impact on employees attitudes. The findings, which are aligned with the extended UTAUT model, provide useful insights into organizations and decision-makers for designing AI integrated CRM systems.

Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model

“…In operation management, the adoption of AI use was influenced by six factors, including social factors and facilitating conditions (Grover et al, 2020). Chatterjee et al (2021) evaluated the use of AI-integrated customer relation systems in Indian organizations, and their findings validated the models underpinning and other exogenous customer relationship management variables. Chatterjee and Bhattacharjee (2020) assessed the adoption of AI in higher education, where perceived risk, facilitating condition, and effort expectancy impact attitude, influencing the intention to use AI in higher education.…”

“…Cao et al (2021) found no significant association between performance expectancy and intention to use technology, but a positive influence was found on attitude toward AI in decision-making. Chatterjee et al (2021) found that performance expectancy was associated with using AI-enabled systems to enhance performance. Based on the above review, the following hypotheses were developed: H1: Performance expectancy will positively affect the use of AIenabled tools.…”

“…The last decade has witnessed an increase in the use of artificial intelligence (AI)-based tools in organizations across all industries ( Andronie et al, 2021a ; Kovacova and Lăzăroiu, 2021 ; Nica et al, 2022 ). AI is used for different processes, such as forecasting future demands of products ( Kawaguchi, 2021 ), hiring employees ( van Esch et al, 2019 ; Li et al, 2021 ), formulation of marketing strategy, channel management, team management, and performance management ( Davenport et al, 2020 ; Huang and Rust, 2020 ; Seeber et al, 2020a ; Chatterjee et al, 2021 ; Nica et al, 2021 ; Vlačić et al, 2021 ). In social development organizations, employees are dispersed across locations for the intervention and execution of programs.…”

“…The exposure of employees working in the social development sector to AI is also limited ( Beede et al, 2020 ). With the increased use of technology and government initiatives, India can see a wave of transformation and the introduction of AI across the organization in social development ( Bajpai and Wadhwa, 2021 , p.7; Chatterjee et al, 2021 ). Researchers from various fields are studying the adoption and use of AI-enabled products in India ( Sobti, 2019 ; Chatterjee et al, 2021 ; Jain et al, 2022 ).…”

Why Do Retail Customers Adopt Artificial Intelligence (AI) Based Autonomous Decision-Making Systems?

“…product adoption [41], customer relationship management [42], disaster relief operations [43] and retailing [44], among others. Venkatesh, et al [45] first proposed the theory by combining eight information systems adoption theories into a unified framework to investigate the motivations driving individuals to adopt information systems.…”