Application of UTAUT2 on Adopting Artificial Intelligence Powered Lead Management System (AI-LMS) in passenger car sales

Sougato Das and Biplab Datta

Technological Forecasting and Social Change, 2024, vol. 201, issue C

Abstract: This paper investigates the acceptance of the AI-Powered Lead Management System (AI-LMS) in a passenger car dealership. The study was conducted using a questionnaire, and the respondents were the sales advisors of the dealerships. The UTUAT or “Unified Theory of Acceptance and Use of Technology” is used to investigate the impact of “Habit, Hedonic Motivation, Facilitating Conditions, Social Influence, Effort, and Performance Expectancy on the Behavioral Intention” of adopting the AI-LMS. The study also investigated the interrelationships among the constructs. Applying SmartPLS 3.3, we analyzed whether “Effort Expectancy, Habit, and Hedonic Motivation impact the Behavioral Intention” of Adopting AI-LMS. Performance Expectancy was impacted by Social Influence and Effort Expectancy, Habit, and Hedonic Motivation, while Facilitating Conditions impacted Effort Expectancy. Age had a partial impact on Behavioral Intention. 77.69 % of respondents thought that AI-LMS would improve their productivity, 66.53 % believed that AI-LMS would be easy to use, and 45.45 % of respondents agreed to use the AI-LMS once implemented. The paper concludes with theoretical, practical, and managerial implications followed by future research directions and conclusion.

Keywords: Artificial Intelligence; UTAUT2; Passenger car sales; Lead management system; AI-powered lead management system; PLS-SEM (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162524000374
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:201:y:2024:i:c:s0040162524000374

DOI: 10.1016/j.techfore.2024.123241

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().