Generative conversational AI agent for managerial practices: The role of IQ dimensions, novelty seeking and ethical concerns

Abstract

This study aims to identify and empirically examine the influence of the main factors related to the content quality of generative conversational AI agents on decision-making efficiency. Additionally, this study explores the ramifications of decision-making efficiency facilitated by generative conversational AI agents in organisational innovation performance. This study proposes a model based on the information quality model as well as other factors, such as novelty seeking and ethical concerns. Data from this study was collected using online questionnaires from a purposive sample size of 228 employees in business organisations. Based on Structural Equation Modelling (SEM) analyses using AMOS, the results support the significant impact of information quality (intrinsic information quality, contextual information quality, representational information quality, and accessibility of information quality) on decision-making efficiency. The results also support the significant impact of novelty seeking and ethical concerns on decision-making efficiency. Decision-making efficiency was also found to have a significant positive impact on innovation performance. This empirical study makes a considerable contribution as it is among the first to expand the current understanding of the effective use of generative conversational AI agents in managerial practices (i.e. decision-making and innovation).