Abstract Book of the 4th World Conference on Business, Management, and Economics
Year: 2025
[PDF]
Exploring the Acceptance of a Decision-support Chatbot Among Executives: Insights from an Adapted Utaut Model
Sven Kottmann, Prof. Dr. Jürgen Seitz
ABSTRACT:
The increasing integration of artificial intelligence into executive workflows underscores the need to understand the acceptance of AI-powered decision-support tools. This study investigates the factors influencing the acceptance of a decision-support chatbot within a marketing and sales context using an adapted Unified Theory of Acceptance and Use of Technology (UTAUT) model. While chatbots are widely employed for customer interaction, their use in managerial decision-making remains underexplored.
A quantitative survey among marketing and sales executives examined how key UTAUT constructs performance expectancy, effort expectancy, social influence shape behavioral intention. The adapted model accounted for the unique cognitive and organizational demands of executive decision-making, where time pressure, data complexity, and strategic responsibility influence technology perceptions.
The results reveal that performance expectancy is significantly influenced by output quality, source trustworthiness, and perceived time savings, underscoring that executives’ belief in the chatbot’s usefulness depends on the accuracy, credibility, and efficiency of the information it provides. In turn, effort expectancy is shaped by reduced cognitive load and computer self-efficacy, indicating that perceived ease of use arises when the system effectively minimizes mental effort and when users feel confident in their technological capabilities.
These findings extend the UTAUT framework to an executive decision-support context and provide actionable implications for developers and organizations implementing AI-driven decision-support systems. By elucidating the psychological and contextual factors that drive or hinder adoption, the study contributes to both theory refinement and practical guidance for fostering effective human–AI collaboration in executive management.
Keywords: Ai Chatbots; Executive Decision Support; Human–ai Interaction; Marketing and Sales Analytics; Technology Acceptance.