Adoption and use factors of artificial intelligence and big data by citizens

  1. Patricia Sánchez-Holgado 1
  2. Carlos Arcila-Calderón 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Revista:
Comunicación y sociedad = Communication & Society

ISSN: 2386-7876

Año de publicación: 2024

Volumen: 37

Número: 2

Páginas: 227-246

Tipo: Artículo

DOI: 10.15581/003.37.2.227-246 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Comunicación y sociedad = Communication & Society

Resumen

The impact of artificial intelligence on people’s lives is demonstrated today. Previous literature has shown that the use of a specific technology is directly linked to the individuals’ intention to use it. The aim of this paper is to study the factors that determine the adoption and use of artificial intelligence and big data in Spain, using a research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), proposed by Venkatesh et al. (2003). This work addresses the specific gap in the validation of the original theoretical model of UTAUT in two dimensions, with respect to the adoption of artificial intelligence by citizens and with respect to the factors that influence this adoption, evaluating the previous ones and proposing some new ones considering the current context. The methodology used is based on a national survey, and it analyzes the research model using the statistical technique of Partial Least Squares Structural Equation Modelling (PLS-SEM), which details the mediating and moderating relationships between constructs. The results show that Intention to Use has a direct positive influence on the Use of artificial Intelligence and big data, confirming previous literature. Performance Expectancy is the strongest predictor of Intention to Use, and indirectly of the adoption of artificial intelligence and big data applications. Effort Expectancy, in its application to the adoption of AI and big data by citizens, is an indirect determinant mediated by the Intention to Use, but its total effect (direct + indirect) is not significant.

Referencias bibliográficas

  • Abdullah, R. & Fakieh, B. (2020). Health care employees’ perceptions of the use of artificial intelligence applications: Survey study. Journal of Medical Internet Research, 22(5), 1-8. https://www.doi.org/10.2196/17620
  • Al Aufa, B., Renindra, I. S., Putri, J. S. & Nurmansyah, M. I. (2020). An application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model for understanding patient perceptions on using hospital mobile application. Enfermería Clínica, 30, 110-113. https://www.doi.org/10.1016/j.enfcli.2020.06.025
  • Alghatrifi, I. & Khalid, H. (2019). A Systematic Review of UTAUT and UTAUT2 as a Baseline Framework of Information System Research in Adopting New Technology: A case study of IPV6 Adoption. 6th International Conference on Research and Innovation in Information Systems (ICRIIS) (pp. 1-6), Johor Bahru, Malaysia. https://www.doi.org/10.1109/ICRIIS48246.2019.9073292
  • Amaya-Amaya, A., Huerta-Castro, F. & Flores-Rodríguez, C. O. (2020). Big Data, a strategy to avoid school dropouts in HEIs. Revista Iberoamericana de Educación Superior, 11(31), 166-178. https://www.doi.org/10.22201/iisue.20072872e.2020.31.712
  • Andreoli, S., Batista, A., Fucksman, B., Gladko, L. & Martínez, K. (2022). Artificial intelligence and education A framework for the analysis and creation of experiences at the higher level. Buenos Aires: Universidad de Buenos Aires (CITEP). Retrieved from http://citep.rec.uba.ar/publicaciones
  • Androniceanu, A. (2023). The new trends of digital transformation and artificial intelligence in public administration. Administratie si Management Public, 40, 147-155. https://www.doi.org/10.24818/amp/2023.40-09
  • Arcila-Calderón, C., Barbosa-Caro, E. & Cabezuelo-Lorenzo, F. (2016). Big data techniques: large-scale text analysis for scientific and journalistic research. El Profesional de la información, 25(4), 623-631. https://www.doi.org/10.3145/epi.2016.jul.12
  • Arcila-Calderón, C., Calderín-Cruz, M. & Sánchez-Holgado, P. (2019). Adoption of social networks by scientific social science journals. El Profesional de la Información, 28(1), 1699-2407. https://www.doi.org/10.3145/epi.2019.ene.05
  • Benda, N. C., Novak, L. L., Reale, C. & Ancker, J. S. (2022). Trust in AI: why we should be designing for APPROPRIATE reliance. Journal of the American Medical Informatics Association, 29(1), 207-212. https://www.doi.org/10.1093/jamia/ocab238
  • Blut, M., Yee, A., Chong, L., Tsiga, Z., Venkatesh, V. & Tech, V. (2021). Meta-Analysis of the Unified Theory of acceptance and use of technology (UTAUT): challenging its validity and charting a research agenda in the red ocean. Journal of the Association for Information Systems, 23(1), 13-95. https://www.doi.org/10.17705/1jais.00719
  • Bonami, B., Piazentini, L. & Dala-Possa, A. (2020). Education, Big Data and Artificial Intelligence: Mixed methods in digital platforms. Comunicar, 65, 43-52. https://www.doi.org/10.3916/C65-2020-04
  • Brünink, L. (2016). Cross-Functional Big Data Integration: Applying the Utaut Model. Master dissertation, University of Twente. Retrieved from https://bit.ly/3maqS0K
  • Cabrera-Sánchez, J.-P. & Villarejo Ramos, Á. F. (2018a). Factors that affect the adoption of Big Data as a marketing instrument in Spanish companies. Interioridade e Competitividade: Desafios Globais Da Gestâo. XXVIII Jornadas Luso-Espanholas de Gestâo Científica (2018), (pp. 1-13). Retrieved from https://bit.ly/3NRn2Wi
  • Cabrera-Sánchez, J.-P. & Villarejo Ramos, Á. F. (2018b). Extending the UTAUT model to evaluate the factors that affect the adoption of Big Data in Spanish companies. Nuevos Horizontes del marketing y de la distribución comercial, March 2020 (pp. 181-200). Retrieved from https://bit.ly/3x5VVA3
  • Carmines, E. & Zeller, R. (1979). Reliability and validity assessment. Beverly, CA: Sage.
  • Chen, You, W. Clayton, E., Lovett Novak, L., Anders, S. & Malin, B. (2023). Human-Centered Design to Address Biases in Artificial Intelligence. Journal of Medical Internet Research, 25(1), e43251. https://www.doi.org/10.2196/43251
  • Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In Modern methods for business research (pp. 295-336). Mahwah, NJ: Lawrence Erlbaum.
  • Chin, W. W., Marcelin, B. L. & Newsted, P. R. (2003). A partial least squares latent variable modelling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2). https://www.doi.org/10.1287/isre.14.2.189.16018
  • Compeau, D. R. & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118-143. https://www.doi.org/10.1287/isre.6.2.118
  • Deng, S., Liu, Y. & Qi, Y. (2011). An empirical study on determinants of web-based question-answer services adoption. Online Information Review, 35(5), 788-798. https://www.doi.org/10.1108/14684521111176507
  • Dhagarra, D., Goswami, M. & Kumar, G. (2020). Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective. International Journal of Medical Informatics, 141, 104164. https://www.doi.org/10.1016/J.IJMEDINF.2020.104164
  • Escobar-Rodríguez, T. and Carvajal-Trujillo, E. (2014). Online purchasing tickets for low-cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management, 43, 70-88. https://www.doi.org/10.1016/j.tourman.2014.01.017
  • European Commission (2023) Digital future of Europe. Retrieved from https://digital-strategy.ec.europa.eu/es/policies/big-data
  • European Investment Bank (EIB) (2020). Who is prepared for the new digital age? Evidences from the EIB Survey. EIB Economics Department. https://www.doi.org/10.2867/03951
  • European Investment Bank (EIB), (2023). Digitalisation in Europe 2022-2023. Evidence from the EIB Investment Survey. https://www.doi.org/10.2867/745542
  • Fernández-Aller, C. & Serrano Pérez, M. M. (2022). Is Data Protection-friendly Artificial Intelligence Possible? Doxa. Cuadernos de Filosofía del Derecho, 45, 307-336. https://www.doi.org/10.14198/DOXA2022.45.11
  • Flores-Vivar, J. M. & García-Peñalvo, F. J. (2023). Reflections on the ethics, potential, and challenges of artificial intelligence in the framework of quality education (SDG4). Comunicar, 30(74), 35-44. https://www.doi.org/10.3916/C74-2023-03
  • Gerke, S., Minssen, T. & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, 295-336. https://www.doi.org/10.1016/B978-0-12-818438-7.00012-5
  • Hair, J. F., Hult, G. T., Ringle, C. M. & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage.
  • Hajro, N., Hjartar, K., Jenkins, P. & Vieira, B. (2022). Digital Sentiment Survey in Europe. McKinsey. Retrieved from https://bit.ly/4bPqQ79
  • Haug, C. J. & Drazen, J. M. (2023). Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. New England Journal of Medicine, 388(13), 1201-1208. https://www.doi.org/10.1056/NEJMra2302038
  • Hee Lee, D. & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 1-18. https://www.doi.org/10.3390/ijerph18010271
  • Henseler, J., Ringle, C. M. & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20(Jan.), 277-319. https://www.doi.org/10.1108/S1474-7979(2009)0000020014
  • Henseler, J., Ringle, C. M. & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://www.doi.org/10.1007/s11747-014-0403-8
  • Hentzen, J. K., Hoffmann, A., Dolan, R. and Pala, E. (2022). Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research. International Journal of Bank Marketing, 40(6), 1299-1336. https://www.doi.org/10.1108/IJBM-09-2021-0417
  • Incio Flores, F. A., Capuñay Sanchez, D. L., Estela Urbina, R. O., Valles Coral, M. Á., Vergara Medrano, E. E. and Elera Gonzales, D. G. (2021). Artificial intelligence in education: a review of the literature in international scientific journals. Apuntes Universitarios, 12(1), 353-372. https://www.doi.org/10.17162/au.v12i1.974
  • Ingrams, A., Kaufmann, W. & Jacobs, D. (2022). In AI we trust? Citizen perceptions of AI in government decision making. Policy and Internet, 14(2), 390-409. https://www.doi.org/10.1002/poi3.276
  • Jadil, Y., Rana, N. P. & Dwivedi, Y. K. (2021). A meta-analysis of the UTAUT model in the mobile banking literature: The moderating role of sample size and culture. Journal of Business Research, 132, 354-372. https://www.doi.org/10.1016/j.jbusres.2021.04.052
  • Jain, R., Garg, N. & Khera, S. N. (2022). Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model. Frontiers in Psychology, 13(June). https://www.doi.org/10.3389/fpsyg.2022.893691
  • Langer, M., König, C. J., Back, C. & Hemsing, V. (2022). Trust in Artificial Intelligence: Comparing Trust Processes Between Human and Automated Trustees in Light of Unfair Bias. Journal of Business and Psychology, 0123456789. https://www.doi.org/10.1007/s10869-022-09829-9
  • Leinweber, J., Alber, B., Barthel, M., Whillier, A. S., Wittmar, S., Borgetto, B. and Starke, A. (2023). Technology use in speech and language therapy: digital participation succeeds through acceptance and use of technology. Frontiers in Communication, 8. https://www.doi.org/https://www.doi.org/10.3389/fcomm.2023.1176827
  • Lynott, P. P. and McCandless, N. J. (2000). The Impact of Age vs. Life Experiences on the Gender Role Attitudes of Women in Different Cohorts. Journal of Women and Aging, 12(2), 5-21. https://www.doi.org/10.1300/j074v12n01_02
  • Mandala, G. Naidu, B., Dharam, A., Mahalakshmi, H. S., Othman, B. & Almashaqbeh, H. A. (2022). A Critical Review of Applications of Artificial Intelligence (AI) and its Powered Technologies in the Financial Industry. 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, (pp. 2362-2365). https://www.doi.org/10.1109/ICACITE53722.2022.9823776
  • Martins, C., Oliveira, T. & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34, 1-13. https://www.doi.org/10.1016/j.ijinfomgt.2013.06.002
  • Marikyan, D. & Papagiannidis, S. (2023). Unified Theory of Acceptance and Use of Technology: A review. In S. Papagiannidis (Ed.), TheoryHub Book. https://www.doi.org/10.4018/ijskd.2020070105
  • Medaglia, R., Gil-García, J. R. & Pardo, T. A. Artificial Intelligence in Government: Taking Stock and Moving Forward. Social Science Computer Review, 41(1), 123-140. https://www.doi.org/10.1177/08944393211034087
  • Mergel, I., Dickinson, H., Stenvall, J. & Gasco, M. (2023). Implementing AI in the public sector. Public Management Review, 0(0), 1-13. https://www.doi.org/10.1080/14719037.2023.2231950
  • Mhlanga, D. (2020). Industry 4.0 in finance: the impact of artificial intelligence (AI) on digital financial inclusion. International Journal of Financial Studies, 8(3), 1-14. https://www.doi.org/10.3390/ijfs8030045
  • Mohammad-salehi, B., Vaez-Dalili, M. & Heidari Tabrizi, H. (2021). Investigating Factors that Influence EFL Teachers’ Adoption of Web 2.0 Technologies: Evidence from Applying the UTAUT and TPACK. The Electronic Journal for English as a Second Language, 25(1), 1-21. Retrieved from https://bit.ly/3tdvuHA
  • Morris, M. G. & Venkatesh, V. (2000). Age Differences in Technology Adoption Decisions: Implications for a Changing Workforce. Personnel Psychology, 53(2), 375-403. https://www.doi.org/10.1111/j.1744-6570.2000.tb00206.x
  • Niehaves, B. & Plattfaut, R. (2010). The age-divide in private Internet usage: A quantitative study of technology acceptance. 16th Americas Conference on Information Systems, 1-14. Retrieved from https://bit.ly/3PT0OFb
  • Pynoo, B., Devolder, P., Tondeur, J., van Braak, J., Duyck, W. & Duyck, P. (2011). Predicting secondary school teachers’ acceptance and use of a digital learning environment: A cross-sectional study. Computers in Human Behaviour, 27, 568-575. https://www.doi.org/10.1016/j.chb.2010.10.005
  • Ringle, C. M., Wende, S. and Becker, J. M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. Retrieved from https://bit.ly/3teYGOi
  • Salam, R., Sinurat, M., Izzatussolekha, Y., Akhmad and Sacipto, R. (2023). Implementation of Artificial Intelligence in Governance: Potentials and Challenges. Influence: international journal of science review, 5(1), 243-255. Retrieved from https://influence-journal.com/index.php/influence/article/view/122
  • Samoili, S., López Cobo, M., Gómez, E., De Prato, G., Martínez-Plumed, F. & Delipetrev, B. (2020). AI Watch - Defining Artificial Intelligence. Towards an operational definition and taxonomy of artificial intelligence. In Joint Research Centre (European Commission). https://www.doi.org/10.2760/382730
  • Sánchez-Holgado, P. (2022). Data Science as a transversal competence in Secondary Education in Spain. In S. Carrascal Domínguez & N. Camuñas Sánchez-Paulete (Eds.), Docencia y Aprendizaje. Competencias, identidad y formación del profesorado (pp. 419-450). Valencia: Tirant lo Blanc.
  • Sánchez-Holgado, P., Arcila-Calderón, C. & Blanco-Herrero, D. (2022). Knowledge and attitudes of Spanish citizens about big data and artificial intelligence. Revista ICONO 14. Revista Científica De Comunicación y Tecnologías Emergentes, 20(1). https://www.doi.org/10.7195/ri14.v21i1.1908
  • Saura, J. R., Ribeiro-Soriano, D. & Palacios-Marqués, D. (2022). Assessing behavioral data science privacy issues in government artificial intelligence deployment. Government Information Quarterly, 39(4). https://www.doi.org/10.1016/j.giq.2022.101679
  • Sebastian, G., George, A. & Jackson Jr., G. (2023). Persuading Patients Using Rhetoric to Improve Artificial Intelligence Adoption: Experimental Study. Journal of Medical Internet Research, 25(1), e41430. https://www.doi.org/10.2196/41430
  • Singh, L. K., Khanna, M. & Singh, R. (2023). Artificial intelligence based medical decision support system for early and accurate breast cancer prediction. Advances in Engineering Software, 175(Oct. 2022), 103338. https://www.doi.org/10.1016/j.advengsoft.2022.103338
  • Solberg, E., Kaarstad, M., Eitrheim, M. H. R., Bisio, R., Reegård, K. & Bloch, M. (2022). A Conceptual Model of Trust, Perceived Risk, and Reliance on AI Decision Aids. In Group and Organization Management, 47(2). https://www.doi.org/10.1177/10596011221081238
  • Strand Partners (2023). Unlocking the potential of AI in Europe in the Digital Decade. Ed. Amazon Web Services (AWS). Retrieved from https://assets.aboutamazon.com/c0/03/ef4cf5f941ed9dbd3e68f075ac18/unlockingeuropesaipotential-spa-report-es.pdf
  • Tamilmani, K., Rana, N. P., Fosso Wamba, S. & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57(Apr. 2020), 102269. https://www.doi.org/10.1016/j.ijinfomgt.2020.102269
  • Thompson, R. L., Higgins, C. A. & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly: Management Information Systems, 15(1), 125-142. https://www.doi.org/10.2307/249443
  • Tucci, V., Saary, J. & Doyle, T. E. (2022). Factors influencing trust in medical artificial intelligence for healthcare professionals: a narrative review. Journal of Medical Artificial Intelligence, 5(Aug. 2021), 0-2. https://www.doi.org/10.21037/jmai-21-25
  • Vallespín, M., Molinillo, S. & Pérez-Aranda, J. (2016). The influence of age and gender on the determinants of mobile channel adoption for travel planning. In P. Correia, M. Santos, J. A. C. Santos & M. Aguas (Edd.), Tourism & Management Studies International Conference TMS (p. 176). Retrieved from https://bit.ly/3ma8rJN
  • Venkatesh, V. & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://www.doi.org/10.1287/mnsc.46.2.186.11926
  • Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478. https://www.doi.org/10.2307/30036540
  • Venkatesh, V., Thong, J. Y. L. and Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376. https://www.doi.org/10.17705/1jais.00428
  • Venkatesh, V., Thong, J. Y. L. & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of acceptance and use technology. MIS Quarterly, 36(1), 157-172. https://www.doi.org/10.2307/41410412
  • Venkatesh, V. (2022). Adoption and use of AI tools: a research agenda grounded in UTAUT. Annals of Operations Research, 308(1-2), 641-652. https://www.doi.org/10.1007/s10479-020-03918-9
  • Weerakkody, V., Molnar, A. & El-Haddadeh, R. (2014). Indicators for measuring the success of video usage in public services: The case of education. 20th Americas Conference on Information System (pp. 1-8). Retrieved from https://bit.ly/3ti2qhV
  • Williams, M. D., Rana, N. P. & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management, 28(3), 443-488. https://www.doi.org/10.1108/JEIM-09-2014-0088
  • Zhu, G., So, K. K. F. & Hudson, S. (2017). Inside the sharing economy: Understanding consumer motivations behind the adoption of mobile applications. International Journal of Contemporary Hospitality Management, 29(9), 2218-2239. https://www.doi.org/10.1108/IJCHM-09-2016-0496
Fuente de los datos: Dialnet