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.
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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]. AI-powered 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 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 [6,7,8]. Accordingly, the drivers and barriers to the adoption of AI systems have received a great deal of attention from researchers [9,10,11]. 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 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 [12,13,14,15,16,17]. 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 implications of the study are discussed in Sect. 5. Section 6 proposes guidelines for future research agendas. Finally, Sect. 7 concludes the paper.
2 Bibliometric approach
To achieve the objectives of this study, a bibliometric analysis was conducted on articles concerning AI adoption in organisations. Bibliometric analysis is a powerful approach that helps to understand the dynamics, trends, and the intellectual structure of a specific research domain [15, 16]. There are numerous bibliometric techniques, including bibliographic coupling, co-citation, co-authorship and co-word analysis. These techniques aid in identifying relationships within research articles. Bibliographic coupling examines connections between research groups based on shared references in documents, authors and journal works. Co-citation analysis explores relationships among co-cited documents, authors and journals. Citation analysis measures similarity between documents, authors and journals, assisting in detecting shifts in research paradigms over time. Co-author analysis unveils co-occurrence relationships among authors, countries and institutions, facilitating the identification of author affiliations, social structures and collaborations. Co-word analysis identifies relationships between keywords and terms extracted from titles, abstracts or the body of documents. Analysing keywords and terms aids in identifying the conceptual structure of the research field [20].
In this study, we have identified the frequency of documents over the years and applied co-word analysis. It is important to specify the period of the reviewed articles because it helps us understand the evolution of studies within the research field, enabling the identification of trending and emerging topics [21]. Analysing the keywords assists in identifying and analysing the conceptual structure of the research field [20].
2.1 Database and search terms
The papers selected for review were extracted from the Scopus database on 12 July 2022. Scopus was selected because it is widely recognised for its comprehensive coverage and reliable content [18, 19]. Scopus is the largest database of peer-reviewed journals with many publications published in premier publishers such as Elsevier, EmeraldInsight, Springer and Taylor and Francis [20, 21].
2.2 Article inclusion and exclusion criteria
The inclusion and exclusion criteria used in this paper were as follows: (1) articles should be in English, (2) articles should be published in journals, (3) the AI adoption in organisations from organisational or individual perspectives are discussed in the articles, and (4) full text of the identified articles should be accessible for further analysis [18, 22].
2.3 Search results
The search results returned 4,775 research articles. The articles were shortlisted by excluding the non-journal articles and those in languages other than English. The titles and abstracts of the remaining 2,051 articles were independently reviewed by two authors, resulting in 158 articles. After reviewing the full text, the two authors excluded 55 articles that did not meet the inclusion criteria and 12 articles due to the unavailability of the full text, resulting in a final dataset of 91 articles. To ensure the validity and objectivity of the methodology, the other two authors reviewed the output of the excludion criteria and confirmed its validity. The authors excluded publications not directly related to AI research areas, such as articles discussing topics related to AI and robotics in different fields, such as healthcare, marketing and agriculture, but not related to AI adoption. Another stream of excluded studies discussed blockchain, IoT and/or 3D printing. Furthermore, we excluded studies about AI adoption that are not from an organisational perspective, such as those discussing AI adoption for consumers (not employees) and students (not educators). The results of the search are shown in Fig. 1.
Article search and selection process
2.4 Analysis
This paper employed Biblioshiny of the bibliometrix R-package to conduct the bibliometric analysis and visualise the results. The bibliometrix R-package is a set of tools for quantitative research in bibliometrics and scientometrics that contains effective statistical algorithms and provides access to high-quality routines and data visualisation tools [20]. The package enables the creation of descriptive analysis of bibliographic data, conceptual structure mapping and network mapping for bibliographic coupling, co-citation, collaboration and co-occurrence analyses [20]. Also, Microsoft Visio was used to reproduce some of the figures generated by the Biblioshiny because the figures were not clear when the authors exported and presented them as images in this paper. Figure 2 illustrates the bibliometric analysis process of this study.
Bibliometric analysis process
3 Bibliometric analysis
The results of the 91 articles analysed show that the papers were published between 1984 and 2022 in 77 journals. The total number of author keywords is 317, and the total number of authors is 299, where almost 8% of articles were single-authored. Table 2 presents the main information about the articles.
3.1 Documents frequency over the years
The reviewed articles were published between 1984 and 2022, and the data show unprecedented growth in AI adoption research in the recent past, with about 86% of articles being published between 2020 and 2022. The number of published articles from 1984 to 2019 is 13 articles. This significant increase in publication frequency indicates a growing trend and attention towards AI adoption in organisations by researchers due to AI capabilities and benefits to organisations [6]. Figure 3 depicts the annual number of papers published from 1984 to 2022.
Number of publications over Time
3.2 Top sources
The reviewed articles were published in 77 journals. Interestingly, although there is significant growth in AI adoption research, the maximum number of articles published in a single journal is three. Three journals published three articles each, whereas nine journals published only two articles each. The rest of the journals published only one article each. This indicates that the AI adoption topic has been covered in various disciplines, such as construction, health and sustainability. Table 3 shows all journals that published either three or two articles.
The papers published in the International Journal of Social Robotics mainly investigated the issues of robotics adoption in the legal industry [23], examined the adoption factors influencing the acceptance of human–robot collaboration in the manufacturing industry [24], and analysed the robotics acceptance at work [25]. The papers published in the Journal of Construction Engineering and Management investigated the determinants of construction robot adoption from the perspective of building contractors [26], identified the challenges of robotics adoption in the US construction industry [13], and identified and examined the factors influencing adoption and integration of construction robotics and automation technology in the US construction industry [27]. The papers published in the Sustainability journal identified the human and organisational issues of robotics in the rail infrastructure environment [28], examined the adoption of robotic process automation technology in the Polish service companies to ensure business processes during the COVID-19 pandemic [29], and analysed the adoption challenges of the Internet of Things and AI for smart cities in China [15].
3.3 Keywords analysis
This paper examines author keywords to perform keyword analysis. In total, the analysis of reviewed articles retrieved 317 author keywords. However, we noticed that many keywords have the same meaning. Therefore, this paper standardised the keywords before performing the author’s keywords analysis [20]. The keywords have been revised by four authors. Each author independently revised the keywords and then agreed on the standardisation process. Subsequently, two authors implemented the standardised keywords, while the other two reviewed the output to ensure the reliability and objectivity of the process. Accordingly, similar keywords such as ‘technology-organisation-environment’, ‘technology-organisation-environment framework’, ‘technology organisation environment framework’ and ‘TOE’ were designated as ‘TOE model’. The keywords that describe the same area of specialisation were standardised to a keyword of general usage; for example, terms such as ‘adoption of AI’, ‘AI adoption’ and ‘artificial intelligence adoption’ were standardised to ‘AI adoption’. Table 4 provides examples of the standardised keywords.
After standardisation, the 317 keywords were reduced to 278. Table 5 shows the top 20 most frequent keywords ‘AI’ was the most frequent keyword and occurred almost three times as often as the second keyword on the list. The second, third, fourth and fifth keywords on the list mainly relate to AI adoption in organisations. The technology–organisation–environment (TOE) framework is a firm-level model that explains the challenges of adoption from the technological, organisational and environmental perspectives [30]. The technology acceptance model (TAM) is an individual-level theory that explains technology adoption and helps understand predictors of human behaviour towards acceptance or rejection of a technology [31].
3.4 Keywords trend analysis
This section provides more insights into the evolution of the author’s keywords used in the reviewed articles. Table 6 presents the most frequent keywords and their evolution throughout the past ten years.
The keyword ‘artificial intelligence’ has increased in frequency significantly in the past three years, with an increase of approximately 50% in 2022. The keywords ‘AI adoption’ and ‘technology adoption’ emerged in 2020. The use of technology adoption models and theories in the context of AI adoption in organisations—the TOE and UTAUT—started to emerge in 2019, while the use of the TAM emerged in 2016 with an increased frequency of 90% since 2019. This may be due to rapid advances in AI systems and the benefits that these systems can provide to organisations [6]. Another possible reason is that adopting AI systems in organisations remains challenging, and the failure rate of AI adoption is high [10].
3.5 Major themes and trends
Data patterns (themes) can be identified, analysed and reported using thematic analysis. To identify the themes, this paper utilised the thematic analysis capabilities of the Biblioshiny tool to identify the evolution and emerging themes of AI adoption research. Thematic analysis helps identify the different themes of a given domain by analysing the keyword networks. Themes can be represented on a particular plot, known as a Strategic or Thematic Map [29, 30]. The thematic analysis involves decomposing the text into smaller units of content and analysing these units descriptively. The process of identifying themes entails reading and reviewing the data carefully [32].
The identified themes are represented according to two measures: centrality and density. Centrality measures the intensity of interaction between a network node and other nodes in the network. In the context of this study, centrality measures the strength of a research topic/theme within the AI adoption domain. A high centrality score indicates that the topic/theme has a strong influence on the AI adoption research domain, highlighting its significance and potential impact. Density measures the degree of interconnectedness among nodes in a network. In a keyword network, density refers to the frequency and strength of relationships between the keywords. In the context of this study, a high-density score indicates that the keywords are strongly related and frequently appear together within an underlying topic/theme within the AI adoption domain. This can provide valuable insights into the themes and topics discussed in the data, helping researchers better understand the relationships and patterns within the network [29, 31].
Accordingly, each theme was characterised by the centrality and density parameters and plotted in a two-dimensional diagram known as the Strategic Diagram. The diagram is divided into four quadrants, each representing a theme type. The themes in each quadrant indicate the concepts of the articles identified in that quadrant. Each theme is represented by a circle or a bubble and is named based on the keyword with the highest occurrence value [29, 30]. The themes located in the top-left quadrant have high density and low centrality. This indicates that these themes have strong relations with others within the same quadrant. The topics represented by these themes are not significant in the broader research domain and do not have a significant influence on other themes from other quadrants. Thus, the themes can be identified as highly specialised and peripheral in nature and are referred to as niche themes. The themes located in the lower-left quadrant have low density and low centrality. This suggests that these themes have weak or unimportant relations and connections to themes within the same quadrant and in other quadrants. Thus, the lower-left quadrant represents themes that are either emerging or declining in the research domain. The themes within this quadrant are called Emerging or Declining Themes. Whether these themes are emerging or declining can be identified by conducting a thematic evolution analysis, which provides insight into how topics and themes change over time in a given research area [29, 30]. The themes located in the lower-right quadrant have low density and high centrality. This indicates that the themes have strong connections to themes in other quadrants but weak or unimportant relationships with themes within the same quadrant. Thus, the lower-right quadrant represents important but general themes that are not highly developed or specialised in the research domain. These themes are referred to as basic themes. Lastly, the top-right quadrant consists of themes with high density and centrality and represents the most important and well-developed themes in the research domain. These themes play a central role in shaping, structuring and defining the research domain and are often the focus of significant scholarly attention, as they have strong relationships and interactions with themes both within and outside of their own quadrant [29, 30]. Further, these themes are important for identifying future research directions and should be considered for further development [33, 34]. The themes in the top-right quadrant are called motor themes [29, 30].
3.6 Evolution of AI adoption research
AI adoption research changed significantly after 2019 (Fig. 1). This section will analyse the themes of AI adoption research before and after 2019. Figure 4a represents the thematic areas of the periods between 1984 and 2019. Figure 4b represents the thematic areas of the research domain between 2020 and 2022.
a Thematic evolution (1984–2019), b Thematic evolution (2020–2022)
The themes identified in Fig. 4a from 1984 to 2019 are robots, the TAM, technology acceptance and user acceptance. The robots and TAM themes have strong connections with all other themes but weak connections among the keywords within the basic themes. Technology acceptance and user acceptance showed the opposite and were identified as niche themes. The basic theme studies focused on identifying the challenges of robotic adoption [32, 35] and other studies utilising the TAM to explore the behavioural intentions and individual factors of adopting smart healthcare systems [36] and adopting teaching assistant robots [37]. Studies in the niche theme were mainly concerned with the factors influencing the acceptance of robots in the work [38, 39].
Figure 4b presents the evolved themes after 2019. The newly emerged themes after 2019 are AI, machine learning, construction robots, the UTAUT, automation and customer relationship management (CRM). The figure also shows that AI, machine learning and the TAM are well-developed and structured areas of the AI adoption research domain after 2019. The TAM became well developed and important in AI adoption research after 2019. The robots theme remains a general theme in the AI adoption research domain. Interestingly, three new themes emerged after 2019 in the AI adoption research domain: the UTAUT, automation and CRM. The technology adoption theme emerged after 2019 as a basic theme with strong connections with other themes. The AI adoption research introduced a specialised theme that mainly focuses on robot adoption in the construction industry.
3.7 Thematic map
This section presents the major research themes of AI adoption research between 1984 and 2022. Figure 5 represents the thematic map of the evolved research areas across the niche, emerging or declining, basic and motor themes. The figure also shows many evolved research areas after 2019, and centrality has increased for the major themes: AI, machine learning, the UTAUT and the TAM. These themes are the well-developed and structured areas of the AI adoption research domain after 2019. Each of these themes focused on specific research areas.
Thematic map (1984–2022)
The theme of AI has the highest centrality and lowest density among the motor themes. This theme focuses on adopting AI in organisations and shows growing attention towards it. The keywords associated with this theme indicate that the studies in this field have applied information systems adoption theories to investigate the adoption barriers, such as the TOE, diffusion of innovation theory and task-technology fit (TTF). The theme of machine learning focuses on using AI and machine learning-based systems. The keywords for this theme indicate that studies in this field have investigated the personal factors that affect technology acceptance in the healthcare sector. The UTAUT is an individual-level theory used to investigate the determinants of users’ intentions to use a technology [40]. The keywords associated with the UTAUT theme consistently indicate that studies in this field have investigated behavioural intentions towards accepting technology in the workplace. The TAM is another individual-level theory used to investigate human behaviour towards accepting or rejecting a technology [31]. The TAM theme has the highest density among major themes and represents studies that utilise the TAM to analyse and investigate AI adoption in organisations, particularly in the context of education and management. Table 7 presents the top frequent keywords of the themes. More insights about the studies of these themes are presented in the discussion section of the paper.
4 Discussion
This study analysed articles investigating determinants of AI adoption in organisations to identify the major research themes and draw directions for future research in this emerging domain. The study reviewed 91 research articles published between 1984 and 2022 and identified the themes of the research domains. Before 2020, the main focus of the research was on the challenges of adopting robot systems [32, 35, 36, 41, 42]. The thematic analysis identified four major research themes that evolved between 2020 and 2022, have high density and centrality and are located on the top-right quadrant of the thematic map. These themes represent well-developed and significant topics in the research domain due to their strong connections to other themes of the AI adoption domain [29, 30]. These major themes are important for identifying future research directions and should be considered for further development [33, 34].
Content analysis is an effective technique for identifying research gaps and future research areas within a specific research domain [43]. Therefore, this section applied content analysis to the identified themes through manual and systematic review, summarising the contents of the reviewed articles within each theme. Furthermore, this section identified AI adoption factors within each theme to provide more depth to the content analysis and offer deeper insights into the AI adoption factors. This helps in analysing and providing a better understanding of the topics that should be considered for further development and in identifying the future directions of AI adoption research within this domain [43]. The four major research themes are AI, machine learning, the UTAUT and the TAM themes.
4.1 Major theme 1: artificial intelligence (AI)
The AI theme presents studies that focused on AI adoption in organisations. The theme consists of 29 research articles that discussed AI adoption in various contexts. Table A in Appendix lists the articles in this theme. The studies aimed to identify drivers and barriers of AI adoption. The studies have been conducted in different industries, including manufacturing, banking, smart cities, and legal, and showed that the barriers and drivers of AI adoption vary widely based on the industry. For instance, Brooks et al. [44] investigated the challenges of AI adoption in the legal sector. The study identified that there is resistance from legal services firms to adopt and use AI-based technologies because of the significant transformation that is required in their business models and practices. The challenges related to organisational culture, structure, availability of skilled resources, and fears about data security hinder adopting AI in legal service providers [44]. Wang and Su [45] investigated the determinants of AI adoption in the manufacturing industry. They found production scenario, production process, manager’s support, data preparation, education on workers, and government-driven and market-driven factors as influential factors in the manufacturing context. Considering the influential factors vary across industries, future studies are required to identify the influencing factors of AI adoption in areas with less research, such as higher education, electrical and electronics, and insurance, where AI adoption has been less studied.
From theoretical perspective, the studies have used various theories to identify influential drivers of AI adoption at firm level and individual level (employees). For instance, Hradecky et al. [46] utilised the technology, organisation, and environment (TOE) theory as the theoretical background and identified the maturity of the technology, technological practices in organisations, perceptions of employees toward AI systems, organisational size, financial resources, AI strategy, data management and privacy, and external factors such as COVID-19 as influencing factors. Simões et al. [47] used TOE, diffusion of innovation, and institutional theory to explain the determinants of managers’ intention to adopt collaborative robots. Pan et al. [48] utilised the TOE theory and the transaction cost theory to explain the companies’ AI adoption. Dabbous et al. [49] built on the theory of reasoned action (TRA) and TAM to investigate the employees’ intentions to use AI systems. Table 8 provides a summary of identified influential factors. Top management support, government regulations, and benefit of AI systems are the most common discussed factors in AI adoption literature. Future studies are recommended to develop a more comprehensive model and assess the influence of less studied factors such as technology anxiety and personal self-image. Furthermore, the interactions between factors were less considered in the literature. Future studies are recommended to identify and assess the moderating factors to identify how the factors are interconnected.
4.2 Major theme 2: machine learning
The machine learning theme includes studies that focused on the acceptance of AI and machine learning-based systems in organisations. This theme contains 8 research articles that discussed AI adoption in organisations in various contexts. Four of the articles examined the drivers of adopting AI or machine learning in the healthcare sector [70,71,72,73]. There has been a growing research interest in adopting AI and machine learning technologies in the medical sector due to their potential benefits in medical diagnostics and clinical decisions, which improve patients’ health and save lives [70]. Table B in Appendix lists the articles of the machine learning theme. The summary of identified influential factors in the machine learning theme is provided in Table 9.
This theme primarily investigates the adoption of ML in the healthcare sector. A stream of articles used the UTAUT to examine the determinants of adoption in the healthcare sector. Utilising the UTAUT is crucial in exploring the drivers of adoption and understanding their influence. The UTAUT provides insight into the influence of individual, social and organisational factors. Future studies are recommended to examine the impact of a wider variety of factors across a broader range of industries on AI adoption. The studies in this theme mostly used structural equation modelling (SEM), multiple linear regression, multilevel analysis, and analytic hierarchy process (AHP) to assess the determinants of machine learning adoption. These approaches are not able to show the interrelation between these factors. Future studies can use methods such as the Decision-Making Trials and Evaluation Laboratory (DEMATEL) method or mind-mapping techniques to identify and examine the interrelationships between factors.
4.3 Major theme 3: the UTAUT
The UTAUT model theme presents studies that utilised adoption and acceptance models, specifically the UTAUT model. This theme contains seven research articles, with four studies having applied the UTAUT model to explain the adoption of AI-based systems. Table C in Appendix lists the articles of the UTAUT theme. The adoption of AI has received significant attention from organisations due to its ability to optimise organisational performance, enhance business processes, and provide substantial benefits to products and services [76]. However, the adoption of AI in organisations also brings challenges related to employees [76], which has drawn the attention of researchers.
The UTAUT is utilised in adoption studies due to its explanatory power, the capability of predicting technology adoption, and its power to overcome the limitations of other theories [61,62,63, 77]. This theme mainly applied the UTAUT theory to investigate AI-based systems in organisations. The UTAUT model enables studies to examine employees’ behavioural intention to use new technology and investigate personal, social, and organisational-related factors. The summary of identified influential factors in the UTAUT theme is provided in Table 10. In this theme, most of the studies investigated the drivers of adopting robotics systems, such as healthcare robot [78], educational robots [79], industrial robotics [24], and collaborative robots [80]. Future studies are recommended to identify personal, social, and organisational factors and extend the UTAUT adoption model. Additionally, the studies reveal that organisational and individual-based factors cannot be separated when discussing determinants of AI adoption in organisations [17, 79]. It is recommended that future studies employ organisational-based adoption theories such as TOE and individual-based adoption theories such as TAM and UTAUT to investigate and examine the impact of organisational-level and individual-level factors on AI adoption in organisations. Furthermore, it should be considered that TAM and UTAUT factors were limited to technology and personal related factors, which have limited power to explain human behaviours. Future studies are recommended to use broader theories such as Bourdieu’s theory that examines the role of social structures and cultural capital in shaping individual behaviour [81].
4.4 Major theme 4: the TAM
The TAM model theme mainly represents the studies that utilised TAM to analyse and investigate AI adoption in organisations. This theme contains 11 research articles. Table D in Appendix lists the TAM model theme articles. The summary of identified influential factors in the TAM theme is provided in Table 11. This theme mainly utilised the TAM model to investigate the user acceptance of AI systems in different industries and sectors namely education [84, 85], healthcare [36], manufacturing [86], construction [87] and government [88]. The studies have used contextual and personal factors to enhance the explanatory power of TAM in the AI context. For instance, Park and Kwon [37] demonstrated that, in addition to PEU and PU, perceived enjoyment and service quality have a significant impact on the intention to use teaching assistant robots. The list of considered contextual and personal factors, such as self-efficacy [89], privacy and security [36], and personal innovativeness [89], is provided in Table 11. Future studies are recommended to further enhance the explanatory power of TAM in the AI context by considering additional contextual factors such as experience, information quality, system quality and trust. The studies in this theme considered individual and organisational-level factors, which demonstrate the relationship between these factors. It is recommended that future studies investigate the drivers of adoption at both organisational and individual levels by integrating organisational-based and individual-based models.
5 Research implications
This study aims to provide a deeper understanding of AI adoption research. This research domain’s major themes and emerging topics were explored using keyword analysis and thematic analysis. The keyword analysis shows that scholars have focused on AI adoption models to investigate the AI adoption factors. The thematic analysis revealed the themes covered in the literature and showed a significant transformation in the literature after the year 2019. The main findings derived from the thematic analysis are the emergence of four major fields in the AI adoption research domain: AI, machine learning, the UTAUT and the TAM themes. The themes reveal significant growth in AI adoption studies investigating the drivers of AI adoption in organisations across various industries and sectors.
We reviewed and analysed articles on the four major research themes to better understand the factors driving AI adoption. Further, the analysis provided deep insights into how the literature employed technology adoption theories in the AI adoption literature. Generally, the studies considered factors from various contexts and categories. These factors can be categorised into personal, social, organisational, technological and environmental factors. Some studies have shown that these factors are interrelated and can affect each other. For instance, Alshare et al. [79] and Yu et al. [17] showed the impact of organisational and social factors on personal factors to accept AI-based systems. Some studies have developed adoption frameworks based on technology adoption theories. However, these studies have not presented a clear understanding of the determinants of employee adoption of AI. Hence, further investigation is needed to identify determinants of employee intention to adopt AI systems. This study identified critical trends of AI adoption in the literature and suggested future directions to enrich the AI adoption literature. The trends are mainly related to AI adoption in organisations in the context of employees, the role of personal factors in AI adoption, the impact of social, technological, internal and external organisational factors on AI adoption, as well as the application of technology adoption theories in the AI adoption literature.
6 Directions for future studies
This study conducted a bibliometric analysis of the AI adoption research domain. The study analysed the evolution of the research domain and identified emerging and trending topics. Consequently, the study proposes several future research directions to cover the gaps in research in the AI adoption domain. Future studies should identify factors influencing AI adoption in less explored industries, such as higher education, electronics and insurance. Further, the adoption factors may influence AI adoption at both organisational and personal levels [79]. It is recommended to investigate further the effects of factors at both organisational and personal levels.
This study revealed the emerging utilisation of technology adoption theories, specifically the TAM and UTAUT. The use of these models has been limited to the context of robotics, AI-driven healthcare, chatbots and expert systems and in limited industries, mainly education, healthcare and manufacturing. This study recommends testing the UTAUT and TAM theories in further contexts and industries. Further, studies have applied qualitative and quantitative approaches to explore and investigate AI adoption factors. As noted, AI adoption factors are interrelated and may interact. The interrelation of these factors has been investigated less. Therefore, future studies can investigate the interrelations between the factors by employing multivariate statistical analysis, such as SEM. Studies can also consider applying MCDM modelling or mind-mapping techniques to identify and examine the interrelationships between factors.
Figure 6 summarises the future research directions for the AI adoption research domain. The proposed future studies were identified based on the themes introduced in the discussion section. However, this figure proposes a multi-phased approach for the identified future studies. In phase I, the proposed studies should focus on identifying and categorising the AI adoption factors. Phase II proposes to utilise the organisational-based and individual-based models to develop adoption (extended) frameworks based on the identified factors in phase I. Phase III proposes studies to examine the influence of the adoption factors by applying quantitative methods to measure the impact of factors on AI adoption based on the developed adoption frameworks in phase II.
Directions for future research
7 Conclusion
This study reviewed the research on AI adoption factors between 1984 and 2022. The bibliometric analysis tool used was Biblioshiny of the bibliometrix R-package to identify and visualise the interrelations and characteristics of the topics. The study analysed the frequency of publications over the years, sources, keyword analytics, keyword trend analysis and thematic analysis to uncover the major research themes and trends. As a result, the study identified four major research themes: machine learning, the UTAUT and the TAM. In addition, the study analysed the reviewed articles by conducting a content analysis to obtain more insights about the reviewed articles. The investigation revealed knowledge gaps in the literature and identified promising areas for future research.
The literature on determinants of AI adoption was limited to specific industries (e.g., agriculture, legal, schools and IT sectors) or specific systems (e.g., robotics). Thus, it is recommended to investigate the driver of AI adoption in industries such as higher education, healthcare, insurance and electrical and electronics industries. The other gap identified is the application of technology acceptance models such as the TAM, UTAUT and TOE. Many studies have identified various individual- and organisational-level factors and applied the adoption models to examine their impact or as a basis to develop adoption frameworks that mainly investigate the effects of those factors on AI adoption. Future studies need to investigate the interrelations between those factors, as they may influence each other.
This review has some limitations that should be considered. Firstly, the inclusion and exclusion of articles were based on subjective judgments. To reduce the errors, two authors independently screened the articles and the disagreements were addressed by consensus among all authors. Second, articles were collected from the Scopus database because it is the largest database. However, future studies can consider other databases such as ProQuest, Web of Science and EBSCO for comparative analysis and to complement the findings.
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Khanfar, A.A., Kiani Mavi, R., Iranmanesh, M. et al. Determinants of artificial intelligence adoption: research themes and future directions. Inf Technol Manag (2024). https://doi.org/10.1007/s10799-024-00435-0
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DOI: https://doi.org/10.1007/s10799-024-00435-0