Optimisation of Administrative Processes Through Artificial Intelligence: Analysis of Adoption and Trust in Peruvian Companies in The Telecommunications Sector
DOI:
https://doi.org/10.4067/s0718-27242026000100015Keywords:
Artificial intelligence adoption, Organisational readiness, AI security, Process innovation, Telecommunications sectorAbstract
In the Peruvian business environment, the adoption of Artificial Intelligence is key to optimising processes. However, its implementation faces challenges due to a lack of resources and cultural barriers. This research aims to analyse the relationship between AI adoption and product innovation, security, organisational preparedness, and perceived ease of use. A non-experimental quantitative design with a structural equation model (SEM) was used, collecting data through structured surveys applied to employees in the telecommunications sector. The results show that AI security has a positive and significant effect on AI adoption (p = 0.000), while the perceived ability of the employee directly influences perceived ease of use (p = 0.003). In turn, AI adoption has a significant impact on product innovation (p = 0.000), process innovation (p = 0.000), and AI-driven marketing (p = 0.000), confirming that technological confidence and skills development drive greater business efficiency and innovation. In conclusion, AI adoption is strengthened when there is rigorous management of AI security and staff capacity building, as it increases utility by promoting innovation. This work seeks to provide evidence from the Peruvian context, focusing on workers' real experiences with AI and how these can guide solutions tailored to the needs of the sector.
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References
Abdul Wahab, M. D., & Radmehr, M. (2024). The impact of AI assimilation on firm performance in small and medium-sized enterprises: A moderated multi-mediation model. Heliyon, 10(8), Article e29580. https://doi.org/10.1016/j.heliyon.2024.e29580
Aguilera, R. V., Ciravegna, L., Cuervo-Cazurra, A., & Gonzalez-Perez, M. A. (2017). Multilatinas and the internationalization of Latin American firms. Journal of World Business, 52(4), 447–460. https://doi.org/10.1016/j.jwb.2017.05.006
Ahmed, S. K. (2024). How to choose a sampling technique and determine sample size for research: A simplified guide for researchers. Oral Oncology Reports, 12, Article 100662. https://doi.org/10.1016/j.oor.2024.100662
Alamer, A. (2022). Exploratory structural equation modeling (ESEM) and bifactor ESEM for construct validation purposes: Guidelines and applied example. Research Methods in Applied Linguistics, 1(1), Article 100005. https://doi.org/10.1016/j.rmal.2022.100005
Arroyabe, M. F., Arranz, C. F. A., Fernandez De Arroyabe, I., & Fernandez de Arroyabe, J. C. (2024). Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment. Technology in Society, 79, Article 102733. https://doi.org/10.1016/j.techsoc.2024.102733
Astrachan, C. B., Patel, V. K., & Wanzenried, G. (2014). A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. Journal of Family Business Strategy, 5(1), 116–128. https://doi.org/10.1016/j.jfbs.2013.12.002
Ayinaddis, S. G. (2025). Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis. Journal of Innovation & Knowledge, 10(3), Article 100682. https://doi.org/10.1016/j.jik.2025.100682
Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, Article 103745. https://doi.org/10.1016/j.jfineco.2023.103745
Baviera, T., & Marín-Pérez, B. (2025). Adopting internal social media: An empirical study based on the Technology Acceptance Model. Journal of Technology Management & Innovation, 20(2), 40–51. https://doi.org/10.4067/S0718-27242025000200040
Borda, A., Geleilate, J. M. G., Newburry, W., & Kundu, S. K. (2017). Firm internationalization, business group diversification and firm performance: The case of Latin American firms. Journal of Business Research, 72, 104–113. https://doi.org/10.1016/j.jbusres.2016.11.006
Cash, P., Isaksson, O., Maier, A., & Summers, J. (2022). Sampling in design research: Eight key considerations. Design Studies, 78, Article 101077. https://doi.org/10.1016/j.destud.2021.101077
Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2023). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 745–783. https://doi.org/10.1007/s10490-023-09871-y
Cooper, R. G. (2025). SMEs' use of AI for new product development: Adoption rates by application and readiness-to-adopt. Industrial Marketing Management, 126, 159–167. https://doi.org/10.1016/j.indmarman.2025.01.016
Correa-Mejía, D. A., García-Benau, M. A., & Correa-García, J. A. (2024). The critical role of corporate governance in sustainable development goals prioritisation: A 5 Ps-based analysis for emerging economies. Heliyon, 10(3), Article e25480. https://doi.org/10.1016/j.heliyon.2024.e25480
Frías-Navarro, D., & Pascual Soler, M. (2012). Exploratory factor analysis practices in consumer behavior and marketing research. Suma Psicológica, 19(1), 47–58.
Glassberg, I., Ilan, Y. B., & Zwilling, M. (2025). The key role of design and transparency in enhancing trust in AI-powered digital agents. Journal of Innovation & Knowledge, 10(5), Article 100770. https://doi.org/10.1016/j.jik.2025.100770
Gliem, J. A., & Gliem, R. R. (2003). Calculating, interpreting, and reporting Cronbach's alpha reliability coefficient for Likert-type scales. Proceedings of the Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education, 82–88.
Haleem, A., Javaid, M., Asim Qadri, M., Pratap Singh, R., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119–132. https://doi.org/10.1016/j.ijin.2022.08.005
Hao, Z., Zhang, X., & Wei, J. (2022). Research on the effect of enterprise financial flexibility on sustainable innovation. Journal of Innovation & Knowledge, 7(2), Article 100184. https://doi.org/10.1016/j.jik.2022.100184
Haq, F. ul, & Suki, N. M. (2025). AI-driven innovation in emerging markets: Extending the technology acceptance model–technology-organization-environment framework in small- and medium-sized enterprises. Data Science and Management. Advance online publication. https://doi.org/10.1016/j.dsm.2025.04.002
Hauff, S., Richter, N. F., Sarstedt, M., & Ringle, C. M. (2024). Importance and performance in PLS-SEM and NCA: Introducing the combined importance-performance map analysis (cIPMA). Journal of Retailing and Consumer Services, 78, Article 103723. https://doi.org/10.1016/j.jretconser.2024.103723
Hendricks, J. W., Smith, A., Peres, S. C., & Sasangohar, F. (2023). Workers' acceptance of digital procedures: An application of the Technology Acceptance Model. IISE Transactions on Occupational Ergonomics and Human Factors, 11(1–2), 59–68. https://doi.org/10.1080/24725838.2023.2240342
Holmström, J., & Carroll, N. (2025). How organizations can innovate with generative AI. Business Horizons, 68(5), 559–573. https://doi.org/10.1016/j.bushor.2024.02.010
Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65, Article 102497. https://doi.org/10.1016/j.ijinfomgt.2022.102497
Huang, C. H. (2021). Using PLS-SEM model to explore the influencing factors of learning satisfaction in blended learning. Education Sciences, 11(5), Article 249. https://doi.org/10.3390/educsci11050249
Ibujés-Villacís, J., & Franco-Crespo, A. (2022). Determinant factors of innovation management in the manufacturing industry of Pichincha, Ecuador. Journal of Technology Management & Innovation, 17(1), 50–70. https://doi.org/10.4067/S0718-27242022000100050
Ismail, N., & Joshy, L. M. (2025). Exploring the role of perceived usefulness and AI anxiety in technology-driven self-directed learning: A paradox mindset perspective. Management and Labour Studies, 50(3), 448–465. https://doi.org/10.1177/0258042X251357415
Jeong, S., Kim, S., & Lee, S. (2025). Effects of perceived ease of use and perceived usefulness of technology acceptance model on intention to continue using generative AI: Focusing on the mediating effect of satisfaction and moderating effect of innovation resistance. In Lecture Notes in Networks and Systems (pp. 99–106). Springer. https://doi.org/10.1007/978-3-031-75599-6_7
Jorzik, P., Klein, S. P., Kanbach, D. K., & Kraus, S. (2024). AI-driven business model innovation: A systematic review and research agenda. Journal of Business Research, 182, Article 114764. https://doi.org/10.1016/j.jbusres.2024.114764
Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, Article 101925. https://doi.org/10.1016/j.tele.2022.101925
Kock, N. (2016). Hypothesis testing with confidence intervals and P values in PLS-SEM. International Journal of e-Collaboration, 12(3), 1–6. https://doi.org/10.4018/IJeC.2016070101
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how? International Journal of Information Management, 77, Article 102783. https://doi.org/10.1016/j.ijinfomgt.2024.102783
Legate, A. E., Hair, J. F., Chretien, J. L., & Risher, J. J. (2023). PLS-SEM: Prediction-oriented solutions for HRD researchers. Human Resource Development Quarterly, 34(1), 91–109. https://doi.org/10.1002/hrdq.21466
Manzo-Martínez, L., Manzo-Martínez, M. A., & Ortiz-Villegas, J. J. (2025). Innovación, adopción tecnológica y desarrollo digital en México: Análisis causal desde el enfoque QCA. Journal of Technology Management & Innovation, 20(3), 7–18. https://doi.org/10.4067/S0718-27242025000300007
Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Fjørtoft, S. O., Torvatn, H. Y., Gupta, M., & Niehaves, B. (2023). Examining how AI capabilities can foster organizational performance in public organizations. Government Information Quarterly, 40(2), Article 101797. https://doi.org/10.1016/j.giq.2022.101797
Mohd Amin, M. R., Asbi, A., Sivakumaran, V. M., Kim, J., & Septiarini, E. (2025). Artificial Intelligence (AI) adoption in marketing strategies: Navigating the present and shaping the future business landscape. Social Sciences & Humanities Open, 12, Article 102048. https://doi.org/10.1016/j.ssaho.2025.102048
Muehlemann, S. (2025). Artificial intelligence adoption and workplace training. Journal of Economic Behavior & Organization, 238, Article 107206. https://doi.org/10.1016/j.jebo.2025.107206
Niyawanont, N., & Niyawanont, N. (2022). Structural equation modelling of digital transformation process of Thailand agriculture & food industry. Journal of Technology Management & Innovation, 17(3), 40–51. https://doi.org/10.4067/S0718-27242022000300040
Olan, F., Ogiemwonyi Arakpogun, E., Suklan, J., Nakpodia, F., Damij, N., & Jayawickrama, U. (2022). Artificial intelligence and knowledge sharing: Contributing factors to organizational performance. Journal of Business Research, 145, 605–615. https://doi.org/10.1016/j.jbusres.2022.03.008
Peretz-Andersson, E., Tabares, S., Mikalef, P., & Parida, V. (2024). Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach. International Journal of Information Management, 77, Article 102781. https://doi.org/10.1016/j.ijinfomgt.2024.102781
Portocarrero-Sierra, L., Morató-Farreras, J., Restrepo-Morales, J. A., & López-Cadavid, D. A. (2025). Driving SME digital transformation in Colombia: Analyzing key factors and Sustainable Development Goals (SDGs) impact. Journal of Technology Management & Innovation, 20(3), 59–71. https://doi.org/10.4067/S0718-27242025000300059
Pramanik, P., Jana, R. K., & Ghosh, I. (2024). AI readiness enablers in developed and developing economies: Findings from the XGBoost regression and explainable AI framework. Technological Forecasting and Social Change, 205, Article 123482. https://doi.org/10.1016/j.techfore.2024.123482
Prasad, K. D. V., & De, T. (2024). Generative AI as a catalyst for HRM practices: Mediating effects of trust. Humanities and Social Sciences Communications, 11(1), 1–16. https://doi.org/10.1057/s41599-024-03842-4
Purnamasari, R., Hasanudin, A. I., Zulfikar, R., & Yazid, H. (2025). Technological infrastructure and financial resource availability in enhancing public services and government performance: The role of digital innovation adoption in Indonesia. Social Sciences & Humanities Open, 11, Article 101621. https://doi.org/10.1016/j.ssaho.2025.101621
Ringle, C. M., Sarstedt, M., Sinkovics, N., & Sinkovics, R. R. (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, Article 109074. https://doi.org/10.1016/j.dib.2023.109074
Roberts, D. L., & Candi, M. (2024). Artificial intelligence and innovation management: Charting the evolving landscape. Technovation, 136, Article 103081. https://doi.org/10.1016/j.technovation.2024.103081
Rodríguez-Espíndola, O., Chowdhury, S., Dey, P. K., Albores, P., & Emrouznejad, A. (2022). Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing. Technological Forecasting and Social Change, 178, Article 121562. https://doi.org/10.1016/j.techfore.2022.121562
Roppelt, J. S., Schuster, A., Greimel, N. S., Kanbach, D. K., & Sen, K. (2025). Towards effective adoption of artificial intelligence in talent acquisition: A mixed method study. International Journal of Information Management, 82, Article 102870. https://doi.org/10.1016/j.ijinfomgt.2025.102870
Shamim, S., Yang, Y., Ul Zia, N., Khan, Z., & Shariq, S. M. (2023). Mechanisms of cognitive trust development in artificial intelligence among front line employees: An empirical examination from a developing economy. Journal of Business Research, 167, Article 114168. https://doi.org/10.1016/j.jbusres.2023.114168
ul Haq, F., Suki, N. M., Zaigham, H., Masood, A., & Rajput, A. (2025). Exploring AI adoption and SME performance in resource-constrained environments: A TOE–RBV perspective with mediation and moderation effects. Journal of Digital Economy. Advance online publication. https://doi.org/10.1016/j.jdec.2025.07.002
Urbani, R., Ferreira, C., & Lam, J. (2024). Managerial framework for evaluating AI chatbot integration: Bridging organizational readiness and technological challenges. Business Horizons, 67(5), 595–606. https://doi.org/10.1016/j.bushor.2024.05.004
Uren, V., & Edwards, J. S. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management, 68, Article 102588. https://doi.org/10.1016/j.ijinfomgt.2022.102588
Valtonen, A., Saunila, M., Ukko, J., Treves, L., & Ritala, P. (2025). AI and employee wellbeing in the workplace: An empirical study. Journal of Business Research, 199, Article 115584. https://doi.org/10.1016/j.jbusres.2025.115584
Wei, J., Zhang, X., & Tamamine, T. (2024). Digital transformation in supply chains: Assessing the spillover effects on midstream firm innovation. Journal of Innovation & Knowledge, 9(2), Article 100483. https://doi.org/10.1016/j.jik.2024.100483
Zerilli, J., Bhatt, U., & Weller, A. (2022). How transparency modulates trust in artificial intelligence. Patterns, 3(4), Article 100455. https://doi.org/10.1016/j.patter.2022.100455
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