The precursors of AI adoption in business: Towards an efficient decision-making and functional performance

Artificial intelligence (AI) is a highly effective solution for enhancing decision-making efficiency and optimising the functional performance of organisations. However, there have been limited attempts to assess the consequences of implementing AI systems on the quality and efficiency of decision-making. This study proposes and empirically examines an extended model covering all aspects that would shape the successful adoption of AI by decision-makers while investigating how the successful adoption of AI enhances the efficiency of the decision-making process. This study also intends to test the validity of the integrated AI acceptance-avoidance model (IAAAM) proposed by Cao et al. (2021) using the Middle East context (i.e. Saudi Arabia). The extended model of the current study was based on the IAAAM and IS professional distinctiveness (ISPD). Two quantitative studies were conducted to achieve the research objectives. The first study was conducted to validate the IAAAM using a purposive sample of employees (non-adopters of AI applications). The second study tested the proposed model using a purposive sample of employees (actual adopters). The structural equation modelling (SEM) results of the first study (non-adopters) supported the validity of the IAAAM in Saudi Arabia. Factors (performance expectancy (PE), facilitating conditions (FC), personal well-being concern (PWC), perceived threat (PT), and attitudes (ATT)) had a significant impact on either ATT or the intention to use AI. The SEM results of actual adopters supported the impact of PE, EE, FC, PWC, and ATT on either ATT or the adoption of AI (AoAI). As an external factor, the ISPD was the most significant predictor of AoAI. The AoAI was confirmed to strongly predict decision-making efficiency, which, in turn, contributes to functional performance. This study enriches the current understanding of the main factors that contribute to the successful implementation of AI systems, offering an in-depth understanding of both AI adopters and non-adopters. It identifies factors important to non-users to enhance future adoption, whereas current AI users focus on improving decision-making quality with the AI assistance.