Determinants of artificial intelligence adoption: research themes and future directions

Information Technology and Management https://doi.org/10.1007/s10799-024-00435-0 Determinants of artificial intelligence adoption: research themes and future directions Ahmad A. Khanfar1 · Reza Kiani Mavi1 · Mohammad Iranmanesh2 · Denise Gengatharen1 Accepted: 10 August 2024 © The Author(s) 2024 Abstract The adoption of artificial intelligence (AI) systems is on the rise owing to their many benefits. This study conducted a bibliometric analysis to identify (1) how the literature on AI adoption has evolved over the past few years, (2) key themes associated with AI adoption in the literature, and (3) the gaps in the literature. To achieve these objectives, we utilised the Biblioshiny of R-package bibliometric analysis tool to analyse the AI adoption literature. A total of 91 articles were reviewed and analysed in this study. Four major themes were identified: AI, machine learning, the unified theory of acceptance and use of technology (UTAUT) model and the technology acceptance model (TAM). Using a content analysis of the identified themes, the study gained additional insight into the studies on AI adoption. Previous studies have been limited to specific industries and systems, and adoption theories like the UTAUT and TAM have also been utilised to a limited extent. Directions for future studies were provided. Keywords Artificial intelligence · Technology adoption · Adoption models · Keyword analysis · Thematic analysis · Bibliometric analysis 1 Introduction Artificial Intelligence (AI) is a technology that simulates human intelligence and transforms data into useful information that helps problem-solving and decision-making [1]. AI can dramatically transform organisations and revolutionise how businesses perform their various operations [2, 3]. AIpowered systems can be used to optimise decision-making processes, automate routine activities, analyse and process large amounts of data, and predict trends and costs [4, 5]. AI capabilities have made it a desirable tool for companies to adopt, and AI systems have been rapidly adopted in recent years. Investments in AI systems are set to grow and reach $77.6 billion in 2022 [3]. Ramsbotham et al. [6] showed that 19% of organisations globally had adopted AI strategies and had started to implement AI-based systems; 45% of organisations had investigated or were piloting AI systems in their * Ahmad A. Khanfar 1 School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia 2 La Trobe Business School, La Trobe University, Melbourne, VIC, Australia businesses; while 36% of organisations had not developed or adopted any AI strategies. Although AI has significant benefits for companies, and its use has received the attention of practitioners, its implementation remains challenging with high failure rates [6–8]. Accordingly, the drivers and barriers to the adoption of AI systems have received a great deal of attention from researchers [9–11]. Considering the growing body of literature on the adoption of AI, scholars have taken an interest in reviewing and synthesising these studies and offering suggestions for further research. The previous reviews on AI adoption literature are presented in Table 1. Pramod [12] investigated the adoption challenges related to personal, technical, operational and strategic challenges of robotic process automation systems. Review studies have also reviewed the adoption challenges of AI-based systems, although they were limited to specific contexts. For instance, Pradhananga et al. [13] and Regona et al. [14] investigated adoption challenges in the construction industry, while Wang et al. [15] focused on the Chinese smart cities industry. Ghandour [16] reviewed the literature to explore the challenges of adopting AI in the banking industry. Additionally, the review by Yu et al. [17] investigated the antecedents and consequences of AI Vol.:(0123456789) Information Technology and Management Table 1  List of review articles Article Description Pramod [12] This review paper explored the adoption of robotic process automation in various industries, explained its benefits and investigated the adoption challenges faced in the industries Regona et al. [14] This study reviewed the literature on the opportunities and adoption challenges of AI in the construction industry Yu et al. [17] This review article identified the antecedents and outcomes of AI adoption and its applications from the socio-technical theory perspective Ghandour [16] This review study identified and assessed the opportunities and challenges of AI adoption in the banking sector Pradhananga et al. [13] This review study identified the adoption barriers of robotics in the US construction industry Wang et al. [15] This study reviewed the literature and identified the adoption challenges of AI and the Internet of Things (IoT) for smart cities adoption in organisations from the socio-technical system theory perspective. The identified antecedents are related to personnel, organisation, technical and environmental factors. These conventional qualitative reviews are only able to cover a limited number of studies and may encounter challenges to keep pace with the rapidly growing number of publications on AI systems. Furthermore, these reviews may be affected by reviewer’s bias and subjectivity. To address these limitations of previous reviews, this study employs a bibliometric approach to provide a more thematic and structured analysis [12–17]. This study aims to explain the dynamics of AI adoption research using a bibliometric approach. Bibliometric analysis helps identify the emergence of AI adoption literature across all industries, investigates the themes of AI adoption literature and uncovers trends in the research domain [18, 19]. To add depth to the review, this study not only employs bibliometric analysis techniques but also explores and reviews the content of the literature to answer the following questions: (1) how has the AI adoption research domain evolved, (2) what are the key themes of the AI adoption literature, and (3) what are the opportunities for future research in the AI adoption research domain? The study contributes to the literature by (i) identifying the evolution of studies on AI adoption, (ii) outlining trending and emerging topics, (iii) exploring the main theories and factors that have been discussed in the literature, and (iv) providing directions for future studies. The study assists scholars in positioning future research directions by identifying the key pillars of this field, potential research gaps, and the directions to be pursued. Furthermore, managers of the companies may benefit from this study by gaining a deeper understanding of the factors that influence the adoption of AI. The remainder of this paper is set as follows: Sect. 2 proposes the bibliometric approach. Section 3 presents the results of the bibliometric analysis. Section 4 is dedicated to the discussion. The implication (...truncated)