Artificial intelligence research in organizations: a bibliometric approach

Liu, Peng; Lai, Yangjie; Liu, Dege Article Artificial intelligence research in organizations: a bibliometric approach Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Liu, Peng; Lai, Yangjie; Liu, Dege (2024) : Artificial intelligence research in organizations: a bibliometric approach, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-21, https://doi.org/10.1080/23311975.2024.2408439 This Version is available at: https://hdl.handle.net/10419/326585 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Artificial intelligence research in organizations: a bibliometric approach Peng Liu, Yangjie Lai & Dege Liu To cite this article: Peng Liu, Yangjie Lai & Dege Liu (2024) Artificial intelligence research in organizations: a bibliometric approach, Cogent Business & Management, 11:1, 2408439, DOI: 10.1080/23311975.2024.2408439 To link to this article: https://doi.org/10.1080/23311975.2024.2408439 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group View supplementary material Published online: 27 Sep 2024. Submit your article to this journal Article views: 1765 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 ManageMent | ReseaRch aRticle Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2408439 Artificial intelligence research in organizations: a bibliometric approach Peng liu, Yangjie lai and Dege liu school of Management, guangzhou Higher education Mega Center, guangzhou university, guangzhou, People’s Republic of China ABSTRACT although more and more researchers have paid attention to artificial intelligence research in organizations across different subdivided fields in recent years, there is still a lack of integrative and comprehensive research on ai in organizations. Building upon previous quantitative and qualitative studies in the artificial intelligence literature, this study presents a bibliometric analysis of articles on artificial intelligence in the fields of management, business, and applied psychology up to June 2nd, 2023. the research explores the landscape of artificial intelligence articles, highlighting key intellectual contributions and research constituents such as journals, authors, countries, institutions, and topics. additionally, the study investigates the intellectual structure and overlay visualization of keywords to identify popular topics and trends in recent artificial intelligence research. the findings offer readers a systematic understanding of artificial intelligence development and provide new insights that expand upon existing knowledge in artificial intelligence within management, business, and applied psychology. 1. Introduction since the formal introduction of artificial intelligence (ai) in 1956, there have been significant technological advancements in this field. Milestones such as iBM’s Deep Blue defeating the chess champion in 1997, google’s alphago defeating the go master in 2016, and the launch of Openai’s chatgPt in 2020 all exemplify the remarkable progress in ai technology. Over the past decade, due to the vast potential of ai technology, ai has become integral to organizational operations (alnamrouti etal., 2022). enterprises leverage ai to conduct precise customer portrait analysis, identify consumer behavior patterns, and discern customer needs through technologies like social public opinion analysis and natural language processing (Davenport et al., 2020; Davidsson et al., 2020; Fan et al., 2020; gaspar et al., 2016). ai also aids in candidate screening and evaluation for businesses (Black & van esch, 2020; hamilton & Davison, 2018), and provides credit assessment and risk strategies for enterprises (sood, 2020). More specifically, netflix’s recommendation systems, google’s search engines, iBM’s Watson, and Microsoft’s azure are all typical examples of how ai can be used in the enterprise. While more and more enterprises are using ai in their daily operations, there has been significant growth in ai research within the realms of management, business, and applied psychology (Dwivedi etal., 2021; Martínez-lópez & casillas, 2013; Mikalef & gupta, 2021). several notable reviews have examined the current landscape and advancements in ai research within specific domains. For instance, loureiro et al. (2021) scrutinized 404 articles in business-related fields spanning from 1970 to 2019, revealing that ai research in business can be categorized into four primary areas and 18 topics. arsenyan and Piepenbrink (2024) conducted a review of 6,324 articles in management-related fields from 1990 to © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT Dege Liu [email protected] school of Management, guangzhou university, no. 230 Wai Huan Xi Road, guangzhou Higher education Mega Center, guangzhou 510006, People’s Republic of China supplemental data for this article can be accessed online at https://doi.org/10.1080/23311975.2024.2408439. https://doi.org/10.1080/23311975.2024.2408439 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY Received 25 June 2024 Revised 16 august 2024 accepted 19 september 2024 KEYWORDS artificial intelligence; bibliometric review; VOSviewer; scientific visualization; landscape SUBJECTS Work & Organizational Psychology; artificial intelligence; information technology 2 P. liU etal. 2020, highlighting that prior ai research predominantly focused on 41 distinct topics. lee et al. (2023) undertook a systematic review and analysis of articles on ai published in 31 journals covering information systems, business, management, and operations management, pinpointing 70 research topics. Dhamija and Bag (2020) analyzed 1,854 articles between 2018 and 2019 to uncover six emerging clusters of ai in operations management. Mariani et al. (2023) carried out a comprehensive analysis of 1,448 published ai research articles across marketing, consumer research, and psychology, identifying four thematic clusters of ai across these fields. Plathottam etal. (2023) delved into the research literature on ai and machine learning in manufacturing, outlining the potential benefits and challenges of their application in the manufacturing sector. although this body of literature significantly enriches our understanding of ai research in management, business, and applied psychology, there are several limitations to consider. Firstly, the majority of review articles employ qualitative methods to analyze research across various time periods (e.g. Jan et al., 2023). the analysis of research topics and content is often influenced by the authors’ subjective perspectives and understanding of the field. secondly, current research tends to focus more on specific areas such as manufacturing (li et al., 2017), human resource management (li et al., 2023), marketing (Donthu, Kumar, Pattnaik, et al., 2021), and healthcare (ali etal., 2023), instead of providing a comprehensive overview of organizational issues related to ai. thirdly, these studies often overlook the changing popularity of different topics over time, key contributors (such as authors, countries, and institutions), and the intellectual structure of ai research. lastly, there is a lack of in-depth exploration of author cooperation networks and keyword co-occurrence networks in these articles. therefore, significant questions highlighting the need for integrative and comprehensive research on ai in organizations remain unanswered. this is surprising, given the growing use of ai-based technologies in organizations and the long-standing calls from researchers for such integrative and comprehensive studies (Von Krogh, 2018). in recent years, bibliometrics has gained popularity among researchers in the management and business fields due to its distinct advantages (Donthu, Kumar, Pandey, etal., 2021; Donthu etal., 2020; Khan etal., 2021; Merigó & Yang, 2017). through bibliometric analysis, scholars can visually represent the current research status, knowledge structure, and development context of one or more topics. this method also helps in identifying the most influential articles and journals in the research field, author collaborations, and emerging trends and evolution of research topics (Donthu, Kumar, Pandey, etal., 2021; Verma & gustafsson, 2020). to address the limitations of previous studies and fill the research gap in the ai literature, this study aims to utilize bibliometric quantitative research methods to analyze the comprehensive landscape of ai research in the fields of management, business, and applied psychology, and address specific research questions. 1. What are the publishing and citation trends in ai research? 2. What is the knowledge structure of the fields of management, business, and applied psychology related to ai? 3. in what direction should future research advance the development of ai? this study contributes by presenting the latest trends in publication and citation in ai research, aiding both new and experienced researchers in evaluating productivity and impact. additionally, the analysis of co-citation and keyword co-occurrence networks sheds light on the knowledge structure of the field, facilitating a deeper understanding of its development. lastly, through scientific mapping of keywords, the study outlines the evolution and trends of ai research, offering valuable insights for future research directions. this study continues with the following structure. section 2 will outline the methodology employed. section 3 will then present the key results, such as trends in publications/citations over time, insights into influential authors/institutions/countries, a co-authorship network analysis, co-citation mappings, co-occurrence networks, and overlay visualization of keywords. section 4 will discuss the contributions of the current work and potential limitations. avenues for future research in this domain will also be outlined. section 5 will conclude the study by summarizing the main findings and takeaways. cOgent BUsiness & ManageMent 3 2. Method the Web of science core collection database was searched for peer-reviewed articles on ai topics in the fields of management, business, and applied psychology as of June 2nd, 2023. We selected this database as it contains more than 250 subject categories across the sciences, social sciences, arts, and humanities spanning back to 1990 (and even earlier) and is an influential database accepted over the world.1 initially, a total of 5,864 articles were identified. after an initial screening process, 5,561 articles containing ‘artificial intelligence’ in their titles, abstracts, or keywords were retained for further analysis. these articles were then used for bibliometric analysis. the search strategy employed for this analysis is depicted in Figure 1. in order to enhance the effectiveness of VOSviewer’s (version 1.6.19) bibliometric analysis, keywords were recoded to account for synonyms, singular and plural forms, spelling variations, and symbol discrepancies. initially, 119 words were extracted from a pool of 5,561 articles. subsequently, synonyms for identical topic words were consolidated. For instance, terms like ‘ai’, ‘artificial intelligence’, ‘artificial intelligence (ai)’, ‘artificial-intelligence’, ‘distributed artificial intelligence’, and ‘explainable artificial intelligence’ were all uniformly recoded as ‘ai’. similarly, ‘decision-making’ was standardized as ‘decision making’, while variations like ‘neural network’, ‘neural networks’, ‘neural-network’, and ‘neural-networks’ were all coded as ‘nn’. additionally, overly broad terms such as ‘model’, ‘system’, ‘information’, and ‘networks’ were excluded from the recoding process. to enhance the visual presentation of our results in co-authorship network analysis, co-citation analysis, keyword co-occurrence analysis, and overlay visualization of keyword analysis, we adjusted certain parameters in the ‘analysis tab’ of the ‘action panel’. For instance, when performing co-authorship network analysis, we set the ‘attraction’ and ‘Repulsion’ parameters to 2 and –1, respectively (see supplemental material for specific settings). 3. Results 3.1. Descriptive results the analysis presented in Figure 2 demonstrates a general upward trend in annual publications and citations within the field. While other areas of ai research experienced more progress in the 1990s, ai research in management, business, and applied psychology did not see significant development until that time. the evolution of ai research in these fields can be segmented into distinct stages: slow growth Figure 1. Methodology of research. 4 P. liU etal. in papers and citations from 1989 to 1998, a gradual increase from 1999 to 2009, a slight decline between 2010 and 2014, and a rapid rise in both published papers and citations since 2015. By 2022, there were 1,283 annual publications and 8,662 citations. this trajectory aligns with haenlein and Kaplan’s (2019) characterization of the development of ai research across different time periods, indicating a period of increased productivity in ai research within the realms of management science, business studies, and applied psychology since 2015. the significant increase in publications since 2015 can be attributed to various factors. Firstly, advancements in computing power and algorithms have greatly accelerated progress in ai research and its practical applications. For example, in 2015, google’s alphago, utilizing artificial neural networks and deep learning, defeated a human professional chess player, marking a significant milestone in ai research. secondly, the widespread adoption of ai technology by organizations across different domains has created new challenges for managers, prompting researchers to offer solutions (Bamberger, 2018; Von Krogh, 2018). 3.2. Contributions of research constituents 3.2.1. Most prolific journals, countries, authors, and institutions table 1 displays the top 10 journals based on the number of published articles. expert systems with applications stands out with the highest paper count (tP = 591) and over 20,000 total citations. as of June 2nd, 2023, the journal had already published 57 articles in 2023, further solidifying its strong academic standing in the ai field. Following closely are technological Forecasting and social change (tP = 134), the international Journal of Production Research (tP = 104), and the Journal of Business Research Figure 2. annual trends in publications and citations for ai research from 1989 to 2023 (n = 5561). Table 1. the top 10 journals by number of articles published in this field. Rank Journal tP tC JCR iF(five year) 1expert systems with applications 591 20025 Q1 8.3 2technological Forecasting and social Change 134 2935 Q1 12 3international Journal of Production Research 104 3453 Q1 8.8 4Journal of Business Research 101 2412 Q1 11.5 5european Journal of operational Research 93 3725 Q1 6.4 6annals of operations Research 74 1273 Q1 4.6 7ieee transactions on engineering Management 58 433 Q2 5.8 8electronic Markets 43 988 Q1 7.9 9Psychology & Marketing 39 872 Q1 6.3 10 Journal of Manufacturing systems 37 1892 Q1 11.1 Note: tP = total Publications; tC = total Citations; JCR = Journal Citation Report; iF = impact Factor. cOgent BUsiness & ManageMent 5 (tP = 101). these leading journals in management, business, applied psychology, and operations research and management science reflect the increasing influence of ai research in these fields. table 2 presents data on the top 10 authors with the highest number of published articles. Dwivedi, affiliated with swansea University, holds the highest number of publications (tP = 22) in the past four years. Following closely are Kietzmann from Victoria University (tP = 16) and chatterjee from the indian institute of Management (tP = 14). the leading authors in this area show a predominant interest in operations research and management science, business and economics, psychology, and computer science. table 3 displays the top 10 countries in terms of article production in the field of ai. leading the list is the United states with 1,128 articles, followed closely by china (tP = 1,006) and the United Kingdom (tP = 534). notably, the United states also boasts the highest total number of citations (tc = 30,629), indicating its significant research output and influence in the ai domain. Furthermore, it is noteworthy that a total of 19 countries have published over 100 articles, underscoring the increasing global importance and interest in ai research. table 4 displays the top 10 units based on the number of published articles in the field. leading the list is the hong Kong Polytechnic University from china with 62 publications, followed by the University of economic studies Bucharest from Romania with 55, and swansea University from the UK with 32. notably, despite the national University of singapore having only 29 articles, it garnered a total of 1,740 citations, resulting in an average of 60 citations per article. this high average citation count among the top 10 units indicates the significant impact of the national University of singapore in the field of ai research. 3.2.2. Landmark works table 5 presents the top 15 most-cited articles in the fields of management, business, and applied psychology, which account for 0.26% of the total 5,561 papers. huang and Rust’s (2018) article stand out as the most cited, with a total citation count of 777. this article delves into the potential for ai to replace humans in service jobs, proposing an ai job substitution theory that outlines the evolution of ai intelligence levels from mechanical to empathic tasks. the implications of this shift on human employment have sparked significant scholarly interest. Following closely is Wirtz et al.’s (2018) paper Table 2. top 10 authors by number of published articles in this field. Rank author tP tC H-index institution 1Dwivedi, Yogesh K. 22 879 22 swansea university 2Kietzmann, Jan 16 584 24 university of Victoria 3Chatterjee, sheshadri 14 410 35 indian institute of Management Ranchi 4Van esch, Patrick 13 304 19 Kennesaw state university 5gupta, shivam 12 464 42 neoMa Business sch 6Vrontis, Demetris 12 356 19 university of nicosia 7Malik, ashish 11 159 24 university of newcastle 8Parida, Vinit 11 690 47 Lulea university of technology 9Chaudhuri, Ranjan 10 213 20 Leonard de Vinci Pole univ 10 Haenlein, Michael 10 1261 26 esCP Business school Note: tP = total Publication; tC = total Citation. Table 3. top 10 countries by number of articles published in this field. Rank Country tP Percentage (n/5561) tC CCP tLs 1 usa 1128 20.28% 30629 27.15 7442 2 CHina 1006 18.09% 18285 18.18 4531 3 engLanD 534 9.60% 14157 26.51 4931 4 inDia 360 6.47% 5257 14.60 2713 5 geRManY 359 6.46% 7942 22.12 2690 6 FRanCe 336 6.04% 7405 22.04 3533 7 austRaLia 284 5.11% 7369 25.95 2722 8 itaLY 251 4.51% 3618 14.41 1871 9 sPain 244 4.39% 4398 18.02 982 10 CanaDa 223 4.01% 3442 15.43 1444 Note: tP = total Publications; tC = total Citations; CPP = Citations per Publication; CPP = total Citations / total Publications. 6 P. liU etal. with 655 citations, which explores the opportunities and challenges of service robots compared to frontline service employees, highlighting the ethical and social considerations at various levels. Ranked third in annual citations with 646 mentions, tao etal. (2018) discusses the transition to intelligent manufacturing driven by internet of things (iot), cloud computing, big data, and ai technologies. notably, Yang et al.’s (2021) article, ranked thirteenth, introduces a DBn-based state classification multi-sensor health diagnosis method leveraging deep machine learning for structural health applications, boasting a high citation rate per year of 231. Remarkably, each of the top 15 articles has gained over 400 citations. 3.3. Co-authorship network analysis to gain insights into current collaborations and key researchers in the fields of management, business, and applied psychology, we utilized VOSviewer’s ‘co-authorship’ feature to visualize the collaborative network of researchers. We established a threshold of 5 for articles related to ai research, resulting in a network of 99 researchers (Figure 3). node size in the visualization corresponds to the number of published articles, with larger nodes indicating more co-publications. the connections between nodes signify collaborative ties between authors. notably, out of the 47 clusters identified, 29 consisted of only one author, prompting us to focus on clusters with 5 or more authors for further analysis. table 6 provides details on the clustering relationships among authors, number of publications, average publication year, and research topics covered. cluster 1 (red), led by Dwivedi, comprises 11 authors. the average publication year of articles in this cluster is 2021.64, with an average publication volume of 8.37, indicating high productivity. Research by these authors focus on the implementation of emerging technologies like ai and blockchain in business settings, alongside a focus on consumer behavior and market trends. cluster 2 (green), led by gunasekaran, comprises 7 authors. the average publication year of articles by authors in this cluster is 2020.95, making it the cluster with the older average publication year. in terms of productivity, authors in this cluster publish an average of 6.43 papers, which is lower than other clusters. their research focuses on advocating for an economic growth model of the circular economy, with a particular interest in exploring how ai, big data analytics, and supply chain management technologies can facilitate sustainable business development. cluster 3 (steel blue), led by haenlein, is composed of 6 authors. the average publication year of articles published by authors in this cluster is 2020.74, making it the oldest cluster in terms of average publication year. With an average of 8 papers issued, this cluster demonstrates high productivity. authors in this cluster exhibit a particular focus on privacy concerns and moral and ethical implications related to the application of ai, robotics, and other technologies, distinguishing them from other clusters. cluster 4 (yellow), led by Malik, comprises 5 authors. the average publication year of articles by authors in this cluster is 2022.12, making it the youngest cluster. these authors have been notably active in recent years, with an average publication year of 2022. the average number of articles published by authors in this cluster is 7.2. they excel in bibliometric analysis, addressing not only human resource management and employee experience enhancement within enterprises but also sustainable finance in society. Table 4. top 10 institution s by number of published articles in this field. Rank institution Country tP tC CCP tLs 1Hong Kong Polytech univ China 62 2232 36.00 46 2Bucharest univ econ studies Romania 55 59 1.07 8 3swansea univ Britain. 32 1111 34.72 92 4nanyang technol univ singapore 31 719 23.19 19 5neoma Business sch France 31 812 26.19 71 6natl univ singapore singapore 29 1740 60.00 58 7univ Johannesburg south africa 29 822 28.34 52 8 Mit america 26 829 31.88 10 9swinburne univ technol australia 26 638 24.54 30 10 tsinghua univ China 25 357 14.28 13 Note: tP = total Publications; tC = total Citations; CPP = Citations per Publication; CPP = total Citations / total Publications; tLs = total Link strength. cOgent BUsiness & ManageMent 7 cluster 5 (purple), led by Kietzmann, comprises 5 authors with an average publication year of 2021.15, indicating a relatively mature body of work. this cluster stands out for its high productivity, with an average of 9 papers per author. their research has significantly contributed to the advancement of ai in management, business, and applied psychology. these authors specialize in machine learning, particularly in the realms of ai-driven B2B marketing, the dissemination of true and false information on social media, and delving into the customer experience within marketing. cluster 6 (aqua), represented by Wamba, comprises 5 authors. the average publication year of articles by these authors is 2021.65. With an average of 5.8 papers published per author, this cluster has the Table 5. Landmark ai research in management, business, and applied psychology. Rank Year title author Journal tC C/Y 1 2018 artificial intelligence in service Huang, Ming-Hui; Rust, Roland t. Journal of service Research 777 155.40 2 2018 Brave new world: service robots in the frontline Wirtz, Jochen; Patterson, Paul g; Kunz, Werner H.; et al. Journal of service Management 655 131.00 3 2018 Data-driven smart manufacturing tao, Fei; Qi, Qinglin; Liu, ang; et al. Journal of Manufacturing systems 646 129.20 4 2013 application of decision-making techniques in supplier selection: a systematic review of literature Chai, Junyi; Liu, James n. K; ngai, eric W. t. expert systems with applications 604 60.40 5 2004 Credit rating analysis with support vector machines and neural networks: a market comparative study Huang, Z; Chen, Hc; Hsu, Cj; et al. Decision support systems 595 31.32 6 2007 Yahoo! for amazon: sentiment extraction from small talk on the Web Das, sanjiv R; Chen, Mike Y. Management science 593 37.06 7 2018 smart manufacturing Kusiak, andrew international journal of Production Research 587 117.40 8 2019 siri, siri, in my hand: Who’s the fairest in the land? on the interpretations, illustrations, and implications of artificial intelligence Kaplan, andreas; Haenlein, Michael Business Horizons 574 143.50 9 2019 Building dynamic capabilities for digital transformation: an ongoing process of strategic renewal Warner, Kar s.R; Waeger, Maximilian Long Range Planning 552 138.00 10 2009 a survey of dynamic scheduling in manufacturing systems ouelhadj, Djamila; Petrovic, sanja Journal of scheduling 547 39.07 11 2004a the state of the art of nurse Rostering Burke, ek; De Causmaecker, P; Vanden Berghe, g; et al. Journal of scheduling 539 28.37 12 2017 the Future of Retailing grewal, Dhruv; Roggeveen, anne L; nordfalt, Jens Journal of Retailing 478 79.67 13 2021 Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts Yang, Yutao; Chen, Huiling; Heidari, ali asghar; et al. expert systems with applications 462 231.00 14 2013 Failure diagnosis using deep belief learning based health state classification tamilselvan, Prasanna; Wang, Pingfeng Reliability engineering & system safety 434 43.40 15 2020 How artificial intelligence will change the future of marketing Davenport, thomas; guha, abhijit; grewal, Dhruv; et al. Journal of the academy of Marketing science 420 140.00 tC: total Citation; C/Y: Citation per year. 14 P. liU etal. frequency of keyword occurrence, while the lines between nodes signify co-occurrence relationships rather than causal connections. each cluster exhibits unique characteristics within the network. topic cluster 1 (red) is known as ‘algorithms and applications of Machine learning’. this cluster comprises 25 keywords, with an average publication year of 2016.88, placing it in the older research field among the four clusters. it covers various common algorithms in machine learning, including genetic algorithms, deep learning, support vector machines, and neural networks. additionally, it includes practical applications such as decision support, natural language processing, and prediction. genetic algorithms, neural networks, support vector machines, and decision support are considered older topics within this cluster, while deep learning, natural language processing, and prediction are seen as newer. Deep learning, a concept based on artificial neural networks, has demonstrated superior performance compared to shallow machine learning models and traditional data analysis methods in many scenarios, paving the way for advancements in ai (Janiesch et al., 2021). topic cluster 2 (green) focuses on ‘artificial intelligence and Market services’, encompassing 23 keywords. the cluster explores the utilization of ai technology in various business sectors like robots, social media, e-commerce, and marketing. notably, robots feature prominently in discussions, particularly in the context of hotel and tourism management and broader service industries (shin, 2022). ethical considerations surrounding ai, including ethics, trust, and privacy, are key areas of interest within this cluster. Of these, trust and privacy, are emerging topics. While ai advancements offer substantial advantages to organizations, the rapid expansion of ai presents notable challenges related to data security and privacy (Villegas-ch & garcía-Ortiz, 2023). Future endeavors in ai technology must prioritize safeguarding user privacy and enhancing consumer trust. Despite the increasing implementation of robots in customer service by businesses, concerns persist regarding consumer trust and acceptance (Prakash etal., 2023). governments play a crucial role in addressing data privacy and security concerns in ai applications to ensure that ai services enhance convenience and efficiency in citizens’ lives (Kankanhalli et al., 2019). additionally, this cluster delves into the consumer experience within ai applications, examining aspects like customer satisfaction and user acceptance. Overall, the cluster represents a relatively new area of study, with an average publication age of 2020.89. topic cluster 3 (blue) focuses on ‘Decision making, innovation, and Management’, encompassing 22 keywords with an average publication year of 2020.08. Representative terms include decision making, innovation, performance, and knowledge management, all crucial in business organizations. While knowledge management and decision making are considered more established topics, innovation and performance are seen as more recent. Research by Mikalef and gupta (2021) highlights the benefits ai technology can bring to organizational innovation and performance, with ai capabilities further enhancing organizational innovation. additionally, the cluster explores big data and automation technologies. Big data is seen as a valuable resource for enterprises, driving innovation in ai and enabling efficient business operations through data analysis. automation, particularly in the service sector, is shown to boost productivity and mitigate production risks (Meyer et al., 2020). Cluster topic Links occurrences avg. pub. year 4industry 4.0 technologies(yellow) Big Data analytics 59 99 2021.57 Blockchain 61 119 2021.11 Covid-19 56 79 2021.71 Fintech 47 53 2021.36 Health 56 80 2021.35 industry 4.0 62 168 2021.26 internet 75 136 2020.62 iot 61 126 2020.54 Literature Review 72 79 2021.19 sCM 46 64 2019.75 security 45 45 2021.13 supply Chain 53 73 2020.00 sustainability 56 75 2021.61 sum 12112 2019.49 Note: avg. pub. Year = average Publication Year, ai = artificial intelligence, DL = Deep Learning, Dss = Decision support systems, DX = Digital transformation, es = expert systems, ga = genetic algorithms, iot = internet of things, it = information technology, KM = Knowledge Management, ML = Machine Learning, nLP = natural Language Processing, nn = neural networks, RL = Reinforcement Learning, sVM = support Vector Machines, sCM = supply Chain Management. Table 9. Continued. cOgent BUsiness & ManageMent 15 topic cluster 4 (yellow) is ‘industry 4.0 technologies’. the cluster has a total of 13 keywords, and the average publication year is 2021.02. the cluster contains many terms related to industry 4.0, such as big data analytics, blockchain, iot, therefore, the cluster theme is identified as industry 4.0 technologies. in this cluster, big data analytics, blockchain are relatively ‘newer’ and the iot is relatively ‘older’. in the context of industry 4.0, big data analytics techniques can be applied in many areas of operations and supply chain management, such as supply chain risk investigation (Wu et al., 2017), social and environmental sustainability (Dubey et al., 2019), supply chain and organizational performance (gunasekaran etal., 2017). integration between blockchain and ai can enable multiple parties to share large amounts of data for analysis, learning, and decision making without a central authority or third-party intermediary (charles et al., 2023). the integration of iot and ai plays an important role in the digital development of enterprises, providing many opportunities for technological innovation, sigov et al. (2022) predict that industry 4.0 will continue to adopt cutting-edge technologies, and ai technology will drive scientific and technological innovation and continue to contribute significantly to the development of industry 4.0 in the future. the cluster also incorporates concepts for science and technology development, such as security and sustainability. sustainability is essential for the maintenance of the earth’s ecosystems and an ideal quality of life for humans (caradonna, 2022; glavič & lukman, 2007). Previous industrial revolutions have both directly and indirectly led to major changes in the economy, environment and society, making the sustainability impact of industry 4.0 widely concerned by scholars (ghobakhloo, 2020). Overall, the cluster is relatively ‘newer’, with none of the themes being ‘older’. 3.6. Overlay visualization of keywords over time to track the trajectory and trends of ai technology over time, examining the evolution of keywords through overlay visualization can be insightful. in 2009, there was a notable focus on expert systems and case-based reasoning in scholarly research (e.g. Faez et al., 2009; sivakami & Karthikeyan, 2009). subsequently, in 2012, attention shifted towards decision support systems, genetic algorithms, and fuzzy logic (e.g. shafiei et al., 2012; Vinodh & Vimal, 2012). By 2015, scholars were delving into knowledge management and data mining (e.g. Bole et al., 2015; Yang & Ying, 2015), while 2016 saw a surge in studies related to neural networks and support vector machines (e.g. Becker et al., 2016; guan et al., 2016). in 2017, ai integrated applications and reinforcement learning gained traction in research (e.g. Ferretti et al., 2017; li et al., 2017). these significant keywords are highlighted in purple in Figure 7. Figure 7. temporal mapping of Keywords. 16 P. liU etal. around 2019, the application of ai technology in enterprises became increasingly widespread, encompassing terms like supply chain management, iot, internet, digitalization, robot, and big data. By 2020, with the advancement of scientific and technological innovation, new terms such as e-commerce, tourism, management, performance, markets, marketing, decision making, and emotion signified the broad utilization of ai in management, business science, and applied psychology. Moving into 2021, keywords like trust, security, and privacy indicate a growing concern among researchers and practitioners regarding safeguarding privacy, data security, and other security issues in the face of expanding ai technology and data generation. additionally, sustainability and novel coronavirus pneumonia emerge as focal points for scholars during this period. Finally, in 2022, the appearance of keywords like explainable ai, financial performance, and voice assistants reflects the emergence of interpretable artificial intelligence aimed at enhancing user understanding and utilization of ai technology across various domains such as intelligent assistants and finance to support informed decision-making. 4. Discussion 4.1. Contribution Our research aims to conduct a thorough and systematic review of ai research in the domains of management, business, and applied psychology using bibliometric methods. this analysis seeks to provide insights into the current state of the field’s performance and intellectual structure for both researchers and practitioners. By examining 5,561 articles from the Web of science core collection database, we explore various aspects such as the focus of current ai research, top journals, countries, authors, and institutions, influential studies, author collaboration networks, key authors and journals, keyword co-occurrence, and the contextual development of ai research. through this study, we contribute to four key areas. First of all, this paper provides an in-depth analysis and an overview for researchers to understand the latest research status of ai in the fields of management, business, and applied psychology. it is different from previous quantitative analyses of ai research focusing on a specific field (arsenyan & Piepenbrink, 2024;; Mariani et al., 2023), as this study analyzed 5,561 articles related to ai in the fields of management, business, and applied psychology from the Web of science core collection database, and revealed the overall picture of research on ai and enterprise organizations. specifically, our research: (1) analyzed journals (table 1), authors (table 2), countries (table 3), and institutions (table 4) contribute most to ai research in management, business, and applied psychology, as well as what are the iconic and influential studies (table 5). (2) Discovered the most influential journals in the fields of management, business and applied psychology (table 7 and Figure 4). (3) identified the core and most influential authors of ai research in the fields of management, business, and applied psychology (table 8 and Figure 5). (4) Revealed the knowledge structure of ai research in management, business, and applied psychology (table 9 and Figure 6). second, this study delves into the knowledge structure of ai research within the realms of business, management, and applied psychology, enhancing researchers’ comprehension of the current landscape of ai research. the findings suggest that current ai research can be categorized into four clusters: algorithms and applications of machine learning, ai and market services, decision making, innovation and management, and industry 4.0 technologies. the first cluster encompasses fundamental ai technologies like fuzzy logic, genetic algorithms, machine learning, and neural networks, serving as the foundation for ai implementation across various sectors. the second cluster explores ai’s role in market services, including consumer services, e-commerce, and robotics. the third cluster focuses on decision making, innovation, and management in relation to big data analysis, digital transformation, and information systems. lastly, the industry 4.0 technologies cluster covers technologies pertinent to industry 4.0 and their practical applications, such as blockchain, cloud computing, industrial internet of things, and simulation technologies. in addition, ai, as a rapidly evolving field, has gained significant attention in recent years due to its interdisciplinary nature. through our analysis of the ai knowledge graph, researchers have the opportunity to transcend traditional research boundaries and foster collaboration between diverse areas of study. cOgent BUsiness & ManageMent 17 For instance, by integrating ai, industry 4.0, and deep learning, researchers can explore new avenues of research. Our examination of the knowledge structure of ai in business, management, and applied psychology offers a comprehensive foundation and roadmap for researchers looking to engage in interdisciplinary research. third, by discovering the evolution of different research topics related to ai in the fields of management, business, and applied psychology, we identify several new trends in future ai research and contribute to the future direction of academic development. Our analysis of keyword average years of publication and overlay visualizations shed light on the trajectory and trends of different research topics. We found that expert systems and case-based reasoning studies (e.g. choy & lee, 2003; Ruiz-Mezcua etal., 2011) were the early focus of researchers in management, business, and applied psychology. later, researchers shifted their focus to topics such as genetic algorithms, data mining, neural networks, support vector machines, deep learning, and integrated applications of ai (e.g. Fu et al., 2013; stefanovic, 2015; Zafeiriou & Kalles, 2013; li etal., 2015; Bathla et al., 2019). Researchers then shifted their focus to current topics such as perception, personality, big data analytics, sustainability, privacy, industry 4.0, and explainable ai (e.g. alsubhi et al., 2023; hoffman et al., 2022; hu & Min, 2023; Panța & Popescu, 2023; Rosário & Dias, 2022; Yigitcanlar et al., 2023). this shows that the focus of researchers in the fields of management, business, and applied psychology has gradually shifted to issues such as human cognition and behavioral ability in the application of ai in industry 4.0, as well as sustainable development, which is also the trend of ai research and application. Furthermore, our research highlights a rising emphasis among researchers and practitioners on data security, privacy, and trust, despite the widespread applicability and utility of ai across various industries. 4.2 Limitations and future research like all studies, our research has limitations that must be acknowledged. Firstly, we only collected literature from the Web of science database, potentially missing ai literature not indexed in WOs. therefore, researchers should interpret our findings in the context of our sample. in the future, expanding the search to include more databases like scopus could enhance the scope. secondly, while our study provides valuable insights into the research landscape and evolution of ai in management, business, and applied psychology, our bibliometric approach did not allow for detailed analysis of domain-specific topics within these fields. this highlights the need for future research to delve deeper into these areas. lastly, as our study is exploratory and based on bibliometric methods, future research designs, such as meta-analyses, are necessary to provide more conclusive results. 5. Conclusion ai is increasingly being applied in organizations, making it a current research focus. however, there is a lack of comprehensive bibliometric studies to uncover the current state and future trends of ai research in these fields. this study aims to fill this gap by analyzing ai publications from the Web of science database in management, business, and applied psychology. the visualization tool VOSviewer was used to identify influential journals, authors, and publications, and to analyze author cooperation, co-citation, and keyword co-occurrence networks. these analyses not only illuminate the fundamental topics but also identify the research directions of ai research. Our findings suggest that researchers can focus on human cognition, behavioral ability, data security, privacy, trust, customer service, social media, big data analytics, fintech, health, dynamic capabilities, and sustainable development, which are the emerging trends in ai research and application across various industries. Overall, building on these findings, the study proposes future research agendas, providing scholars with a systematic understanding of the current research landscape and its evolving trends. Note 1. information was retrieved from (accessed June 2, 2023): http://www.isiwebofknowledge.com. 18 P. liU etal. Author contributions Yangjie lai was involved in analysis and interpretation of the data, and the drafting of the paper. Peng liu was involved in the conception and design. Dege liu contributed to critical revision of the drafting of the paper. the authors ensure that all listed authors meet the criteria for authorship as per the icMJe guidelines. all authors agree to be accountable for all aspects of the work, and all authors approved the final manuscript and published version. Disclosure statement the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding this research was supported by the MOe (Ministry of education in china) Project of humanities and social sciences [grant iD: 20YJa630044]. About the authors Peng Liu, PhD, is assistant professor of the school of Management at guangzhou University, guangzhou, china. he received his PhD from the chinese academy of sciences University in Management. his area of research interest is artificial intelligence and marketing. Yangjie Lai, is an undergraduate student of the school of Management at guangzhou University, guangzhou, china. her area of research interest is artificial intelligence in organization. Dege Liu, PhD, the corresponding author, is associate professor of the school of Management at guangzhou University, guangzhou, china. he received his PhD from the sun Yat-sen University in Management. his areas of research interest are leadership, narcissism, envy and being envied, artificial intelligence in organization. ORCID Dege liu http://orcid.org/0000-0001-8997-786X Data availability statement the Data generated during the current study are available from the corresponding author (Dege liu) on reasonable request. References ali, O., abdelbaki, W., shrestha, a., elbasi, e., alryalat, M. a. a., & Dwivedi, Y. K. (2023). a systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. 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