Bibliometric Mapping of Technology Acceptance in Artificial Intelligence, Machine Learning, and Neuronal Networks: A Comparative Analysis of WoS and Scopus (1999–2023)
Abstract
Background: This study presents a systematic bibliometric mapping and analysis on the acceptance of technology in the field of artificial intelligence (AI), machine learning (ML) and neuronal networks (NN), evaluating the evolution and research trends within this interdisciplinary field. Methods: Using data from the Web of Science (WoS) and Scopus databases, we identify important authors, institutions, and geographic distributions, highlighting key research areas and emerging themes. The analysis was performed using VOSviewer (v.1.6.20) and RStudio (v.4.1.3) with the Bibliometrix package. Results: Our analysis reveals a steady increase in scientific output between 1999 and 2023, with a notable acceleration in recent years, indicating a growing interest in how AI technologies are accepted in various domains. The research illuminates the central role of technology and AI acceptance models, as demonstrated by thematic and keyword analyses. The study reveals a pronounced focus on the technological facets of AI acceptance, alongside discernible gaps in research linking energy, climate mitigation, and sustainability. Differences in findings underline the characteristics of the WoS and Scopus databases. Conclusions: The findings argue for a diversified research agenda to overcome these identified gaps, fostering a more comprehensive understanding of technology acceptance in the age of AI. This research charts a course for future explorations within this critical interdisciplinary field.
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Buckley, L., Kaye, S. A., & Pradhan, A. K. (2018). Psychosocial factors associated with intended use of automated vehicles: A simulated driving study. Accident Analysis and Prevention, 115, 202–208. https://doi.org/10.1016/j.aap.2018.03.021
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Foroughi, B., Nhan, P. V., Iranmanesh, M., Ghobakhloo, M., Nilashi, M., & Yadegaridehkordi, E. (2023). Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS. Journal of Retailing and Consumer Services, 70. https://doi.org/10.1016/j.jretconser.2022.103158
Go, H., Kang, M., & Suh, S. C. (2020). Machine learning of robots in tourism and hospitality: Interactive technology acceptance model (iTAM)—Cutting edge. Tourism Review, 75(4), 625–636. https://doi.org/10.1108/TR-02-2019-0062
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Jeng, D. J. F., & Tzeng, G. H. (2012). Social influence on the use of Clinical Decision Support Systems: Revisiting the Unified Theory of Acceptance and Use of Technology by the fuzzy DEMATEL technique. Computers and Industrial Engineering, 62(3), 819–828. Scopus. https://doi.org/10.1016/j.cie.2011.12.016
Jen-Hwa Hu, P., Lin, C., & Chen, H. (2005). User acceptance of Intelligence and Security Informatics technology: A study of COPLINK. Journal of the American Society for Information Science and Technology, 56(3), 235–244. https://doi.org/10.1002/asi.20124
Johnson, M. P., Zheng, K., & Padman, R. (2014). Modeling the longitudinality of user acceptance of technology with an evidence-adaptive clinical decision support system. Decision Support Systems, 57(1), 444–453. Scopus. https://doi.org/10.1016/j.dss.2012.10.049
Kamble, S. S., Gunasekaran, A., Kumar, V., Belhadi, A., & Foropon, C. (2021). A machine learning based approach for predicting blockchain adoption in supply Chain. Technological Forecasting and Social Change, 163. https://doi.org/10.1016/j.techfore.2020.120465
Kim, S., Kim, J., Badu-Baiden, F., Giroux, M., & Choi, Y. (2021). Preference for robot service or human service in hotels? Impacts of the COVID-19 pandemic. International Journal of Hospitality Management, 93. https://doi.org/10.1016/j.ijhm.2020.102795
Korn, O., Bieber, G., Fron, C., & ACM. (2018). Perspectives on Social Robots. From the Historic Background to an Experts’ View on Future Developments (WOS:000473401500037). 186–193. https://doi.org/10.1145/3197768.3197774
Kurilovas, E. (2019). Advanced machine learning approaches to personalise learning: Learning analytics and decision making. Behaviour & Information Technology, 38(4), 410–421. https://doi.org/10.1080/0144929X.2018.1539517
Lin, H., Chi, O. H., & Gursoy, D. (2020). Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services. Journal of Hospitality Marketing & Management, 29(5), 530–549. https://doi.org/10.1080/19368623.2020.1685053
Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36–51. https://doi.org/10.1016/j.ijhm.2019.01.005
Mak, M. L., Al-Shaqsi, S. Z., & Phillips, J. (2020). Prevalence of Machine Learning in Craniofacial Surgery. Journal of Craniofacial Surgery, 31(4), 898. https://doi.org/10.1097/SCS.0000000000006234
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Miranda Veiga, F. J., & Valente de Andrade, A. M. (2021). Critical Success Factors in Accepting Technology in the Classroom. International Journal of Emerging Technologies in Learning, 16(18), 4–22. https://doi.org/10.3991/ijet.v16i18.23159
Mohr, S., & Kuhl, R. (2021). Acceptance of artificial intelligence in German agriculture: An application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22(6), 1816–1844. https://doi.org/10.1007/s11119-021-09814-x
Panagiotopoulos, I., & Dimitrakopoulos, G. (2018). An empirical investigation on consumers’ intentions towards autonomous driving. Transportation Research Part C-Emerging Technologies, 95, 773–784. https://doi.org/10.1016/j.trc.2018.08.013
Park, I., Kim, D., Moon, J., Kim, S., Kang, Y., & Bae, S. (2022). Searching for New Technology Acceptance Model under Social Context: Analyzing the Determinants of Acceptance of Intelligent Information Technology in Digital Transformation and Implications for the Requisites of Digital Sustainability. Sustainability (Switzerland), 14(1). Scopus. https://doi.org/10.3390/su14010579
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Almarzouqi, A., Aburayya, A., & Salloum, S. A. (2022). Prediction of User’s Intention to Use Metaverse System in Medical Education: A Hybrid SEM-ML Learning Approach. IEEE ACCESS, 10, 43421–43434. https://doi.org/10.1109/ACCESS.2022.3169285
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. Scopus. https://doi.org/10.1016/j.joi.2017.08.007
Aw, E. C. X., Leong, L. Y., Hew, J. J., Rana, N. P., Tan, T. M., & Jee, T. W. (2023). Counteracting dark sides of robo-advisors: Justice, privacy and intrusion considerations. International Journal of Bank Marketing. https://doi.org/10.1108/IJBM-10-2022-0439
Buckley, L., Kaye, S. A., & Pradhan, A. K. (2018). Psychosocial factors associated with intended use of automated vehicles: A simulated driving study. Accident Analysis and Prevention, 115, 202–208. https://doi.org/10.1016/j.aap.2018.03.021
Buteau, E., & Lee, J. (2021). Hey Alexa, why do we use voice assistants? The driving factors of voice assistant technology use. Communication Research Reports, 38(5), 336–345. https://doi.org/10.1080/08824096.2021.1980380
Cabeza, L. F., Chàfer, M., & Mata, É. (2020). Comparative Analysis of Web of Science and Scopus on the Energy Efficiency and Climate Impact of Buildings. Energies, 13(2), Article 2. https://doi.org/10.3390/en13020409
Chadegani, A. A., Salehi, H., Yunus, M. M., Farhadi, H., Fooladi, M., Farhadi, M., & Ebrahim, N. A. (2013). A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases. Asian Social Science, 9(5), Article 5. https://doi.org/10.5539/ass.v9n5p18
Chen, C. C., & Tsai, J. L. (2019). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628–638. Scopus. https://doi.org/10.1016/j.future.2017.02.028
Chen, J. S., Le, T. T. Y., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, 49(11), 1512–1531. https://doi.org/10.1108/IJRDM-08-2020-0312
Chocarro, R., Cortinas, M., & Marcos-Matas, G. (2023). Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educational Studies, 49(2), 295–313. https://doi.org/10.1080/03055698.2020.1850426
Choi, S., Jang, Y., & Kim, H. (2023). Influence of Pedagogical Beliefs and Perceived Trust on Teachers’ Acceptance of Educational Artificial Intelligence Tools. International Journal of Human-Computer Interaction, 39(4), 910–922. Scopus. https://doi.org/10.1080/10447318.2022.2049145
de Kervenoael, R., Hasan, R., Schwob, A., & Goh, E. (2020). Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots. TOURISM MANAGEMENT, 78. https://doi.org/10.1016/j.tourman.2019.104042
Du, H., Zhu, G., & Zheng, J. (2021). Why travelers trust and accept self-driving cars: An empirical study. TRAVEL BEHAVIOUR AND SOCIETY, 22, 1–9. https://doi.org/10.1016/j.tbs.2020.06.012
Eiam-o-pas, K., Intalar, N., & Jeenanunta, C. (2022). Factors Affecting Acceptance of Dental Appointment Application among Users in Bangkok and Metropolitan Area. 2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), 1–5. https://doi.org/10.1109/iSAI-NLP56921.2022.9960256
Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: Evidence from a banking application in Turkey. International Journal of Bank Marketing, 39(2), 294–311. https://doi.org/10.1108/IJBM-02-2020-0056
Figa-Talamanca, G., Tanzi, P. M., & D’Urzo, E. (2022). Robo-advisor acceptance: Do gender and generation matter? PloS One, 17(6), e0269454. https://doi.org/10.1371/journal.pone.0269454
Foroughi, B., Nhan, P. V., Iranmanesh, M., Ghobakhloo, M., Nilashi, M., & Yadegaridehkordi, E. (2023). Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS. Journal of Retailing and Consumer Services, 70. https://doi.org/10.1016/j.jretconser.2022.103158
Go, H., Kang, M., & Suh, S. C. (2020). Machine learning of robots in tourism and hospitality: Interactive technology acceptance model (iTAM)—Cutting edge. Tourism Review, 75(4), 625–636. https://doi.org/10.1108/TR-02-2019-0062
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied artificial intelligence and trust-The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105–120. https://doi.org/10.1016/j.techfore.2015.12.014
Huq, S. M., Maskeliunas, R., & Damasevicius, R. (2022). Dialogue agents for artificial intelligence-based conversational systems for cognitively disabled: A systematic review. Disability and rehabilitation-assistive technology. https://doi.org/10.1080/17483107.2022.2146768
Ismatullaev, U. V. U., & Kim, S. H. (2024). Review of the Factors Affecting Acceptance of AI-Infused Systems. Human Factors, 66(1), 126–144. https://doi.org/10.1177/00187208211064707
Jeng, D. J. F., & Tzeng, G. H. (2012). Social influence on the use of Clinical Decision Support Systems: Revisiting the Unified Theory of Acceptance and Use of Technology by the fuzzy DEMATEL technique. Computers and Industrial Engineering, 62(3), 819–828. Scopus. https://doi.org/10.1016/j.cie.2011.12.016
Jen-Hwa Hu, P., Lin, C., & Chen, H. (2005). User acceptance of Intelligence and Security Informatics technology: A study of COPLINK. Journal of the American Society for Information Science and Technology, 56(3), 235–244. https://doi.org/10.1002/asi.20124
Johnson, M. P., Zheng, K., & Padman, R. (2014). Modeling the longitudinality of user acceptance of technology with an evidence-adaptive clinical decision support system. Decision Support Systems, 57(1), 444–453. Scopus. https://doi.org/10.1016/j.dss.2012.10.049
Kamble, S. S., Gunasekaran, A., Kumar, V., Belhadi, A., & Foropon, C. (2021). A machine learning based approach for predicting blockchain adoption in supply Chain. Technological Forecasting and Social Change, 163. https://doi.org/10.1016/j.techfore.2020.120465
Kim, S., Kim, J., Badu-Baiden, F., Giroux, M., & Choi, Y. (2021). Preference for robot service or human service in hotels? Impacts of the COVID-19 pandemic. International Journal of Hospitality Management, 93. https://doi.org/10.1016/j.ijhm.2020.102795
Korn, O., Bieber, G., Fron, C., & ACM. (2018). Perspectives on Social Robots. From the Historic Background to an Experts’ View on Future Developments (WOS:000473401500037). 186–193. https://doi.org/10.1145/3197768.3197774
Kurilovas, E. (2019). Advanced machine learning approaches to personalise learning: Learning analytics and decision making. Behaviour & Information Technology, 38(4), 410–421. https://doi.org/10.1080/0144929X.2018.1539517
Lin, H., Chi, O. H., & Gursoy, D. (2020). Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services. Journal of Hospitality Marketing & Management, 29(5), 530–549. https://doi.org/10.1080/19368623.2020.1685053
Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36–51. https://doi.org/10.1016/j.ijhm.2019.01.005
Mak, M. L., Al-Shaqsi, S. Z., & Phillips, J. (2020). Prevalence of Machine Learning in Craniofacial Surgery. Journal of Craniofacial Surgery, 31(4), 898. https://doi.org/10.1097/SCS.0000000000006234
Malhotra, Y., & Galletta, D. F. (1999). Extending the Technology Acceptance Model to account for social influence: Theoretical bases and empirical validation. Proc Hawaii Int Conf Syst Sci, 5. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032740045&partnerID=40&md5=af0f1a3c24313775ebf39212f6b14ed8
Meyer-Waarden, L., & Cloarec, J. (2022). «Baby, you can drive my car”: Psychological antecedents that drive consumers’ adoption of AI-powered autonomous vehicles. Technovation, 109. https://doi.org/10.1016/j.technovation.2021.102348
Miranda Veiga, F. J., & Valente de Andrade, A. M. (2021). Critical Success Factors in Accepting Technology in the Classroom. International Journal of Emerging Technologies in Learning, 16(18), 4–22. https://doi.org/10.3991/ijet.v16i18.23159
Mohr, S., & Kuhl, R. (2021). Acceptance of artificial intelligence in German agriculture: An application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22(6), 1816–1844. https://doi.org/10.1007/s11119-021-09814-x
Panagiotopoulos, I., & Dimitrakopoulos, G. (2018). An empirical investigation on consumers’ intentions towards autonomous driving. Transportation Research Part C-Emerging Technologies, 95, 773–784. https://doi.org/10.1016/j.trc.2018.08.013
Park, I., Kim, D., Moon, J., Kim, S., Kang, Y., & Bae, S. (2022). Searching for New Technology Acceptance Model under Social Context: Analyzing the Determinants of Acceptance of Intelligent Information Technology in Digital Transformation and Implications for the Requisites of Digital Sustainability. Sustainability (Switzerland), 14(1). Scopus. https://doi.org/10.3390/su14010579
Peleg, M., Shachak, A., Wang, D., & Karnieli, E. (2009). Using multi-perspective methodologies to study users’ interactions with the prototype front end of a guideline-based decision support system for diabetic foot care. International Journal of Medical Informatics, 78(7), 482–493. Scopus. https://doi.org/10.1016/j.ijmedinf.2009.02.008
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