Barriers to digital service adoption: a data-driven analysis of customer behavior in an internet service provider call center
DOI:
https://doi.org/10.15675/gepros.3051Keywords:
Internet service provider, Call center, Data transcription, Digital channels, Customer behaviorAbstract
Purpose: This study aims to identify and analyze the key barriers preventing customers of an internet service provider (ISP) from adopting digital service channels. Theoretical framework: Barriers to digital channel adoption continue to drive customer preference for telephone-based human service. Methodology/Approach: A structured four-stage data analysis was conducted, integrating Power BI for data tabulation, telephone call transcriptions, and questionnaires with call center agents. Findings: Results reveal that invoice inquiries are the primary reason for customer calls. Additionally, security concerns, age-related challenges, and a lack of trust in digital platforms were identified by call center agents as major factors preventing customers from adopting digital service options. Research, practical, and social implications: This research contributes to the field by integrating phone call transcription technology with agent-based insights to categorize customer interactions. The methodology provides a deeper understanding of customer behavior, offering valuable guidance for ISPs seeking to optimize digital service adoption and improve operational efficiency. Originality/Value: Beyond identifying the key drivers of call center preference, this study proposes a methodology that jointly integrates corporate data analytics, sentiment analysis of customer interactions, and frontline agents’ perspectives. It also suggests strategic actions that ISPs can implement to enhance customer trust and engagement with digital service channels.
References
Abdulaziz Alhumud, A., & Alsulami, A. (2025). Customer Relationship Management in the Digital Age. IntechOpen. https://doi.org/10.5772/intechopen.1011291
Adedoyin, O. & Soykan, E. (2020). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 31 (2), 863-875. https://doi.org/10.1080/10494820.2020.1813180
Arief, M. & Samsudin, N. A. (2023). Neutral Class Handling for Customer Sentiment Analysis in Binary Classification: A Comparative Study of Supervised Machine Learning Classification Algorithm. Eighth International Conference on Informatics and Computing (ICIC), Manado, Indonesia, 1-8. https://doi.org/10.1109/ICIC60109.2023.10381911
Bouzada, M., and Saliby, E. (2009). Call Center Operations and Customer Behavior. International Journal of Service Industry Management, 20 (2), 112-130.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36 (4), 1165-1188. https://doi.org/10.2307/41703503
Couper, M. P. (2017). New developments in survey data collection. Annual Review of Sociology, 43, 121-145. https://doi.org/10.1146/annurev-soc-060116-053613
Da Silva, G. A. F. R., Baierle, I. C., Gomes, L. de C., Correa, R. G. d. F., & Peres, F. A. P. (2024). A comprehensive roadmap for connecting Industry 4.0 technologies to the basic model of collaborative planning, forecasting, and replenishment (CPFR). Administrative Sciences, 14 (108). https://doi.org/10.3390/admsci14060108
Dias, W. S. (2022). Transcription of Audio Recordings in a Call Centre Environment for Speech Sentiment Analysis. Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, 1-55.
Dybå, T., Dingsøyr, T. (2015). Agile project management: From self-managing teams to large-scale development. IEEE/ACM 37th Internation Conference no Software Engineering, Florence, Italy, 2015, 945-946. https://doi.org/10.1109/ICSE.2015.299
Edwards-Jones, A. (2014). Qualitative data analysis with NVivo. Journal of Education for Teaching, 40 (2), 193-195. https://doi.org/10.1080/02607476.2013.866724
Fan, W., & Yan, Z. (2010). Factors affecting response rates of the web survey: A systematic review. Computers in Human Behavior, 26 (2), 132-139. https://doi.org/10.1016/j.chb.2009.10.015
Friese, S. (2019). Qualitative data analysis with ATLAS.ti. SAGE Publications.
Huang, J., Yu, D., & Gong, Y. (2020). An overview of automatic speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 1026-1040.
Johnson, L., Smith, K., & Brown, T. (2023). Digital Exclusion in the 21st Century: Socioeconomic and Demographic Determinants. Technology and Society Review, 15 (1), 45-67.
Khrais, L.T., & Alghamdi, A.M. (2021). The role of mobile application acceptance in shaping e-customer service. Future Internet, 13 (3), 77. https://doi.org/10.3390/fi13030077
Koole, G. (2021). Capacity Planning in Call Centers: A Mathematical Approach. Operations Research Letters, 49 (5), 342-356.
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of AI chatbot disclosure on customer purchases. Marketing Science, 38 (6), 937-947. https://doi.org/10.1287/mksc.2019.1192
Majeed, M., Chaudhary, A., & Chadha, R. (Eds). (2025). Digital transformation in the customer experience (1st ed.). Apple Academic Press. https://doi.org/10.1201/9781003560449
Mäkinen, S., Mäntylä, M. V., & Kerola, P. (2021). Custom software development in modern organizations: Efficiency and adaptability. Journal of Systems and Software, 176, 110972.
Mitzner, T., Savla, J., Boot, W.R., Sharit, J., Charness, N., Czaja, S.J., Rogers, W.A. (2019). Technology adoption by older adults: Findings from the PRISM trial. Gerontologist, 59 (1), 34-44. https://doi.org/10.1093/geront/gny113
Mohad, F. T., Gomes, L. de C., Tortorella, G. da L., & Lermen, F. H. (2024). Operational excellence in total productive maintenance: Statistical reliability as support for planned maintenance pillar. International Journal of Quality and Reliability Management, 42 (4), 1274-1296. https://doi.org/10.1108/IJQRM-09-2023-0290
Olsen, T. L., & Tomlin, B. (2020). Industry 4.0: Opportunities and challenges for operations management. Manufacturing and Service Operations Management, 22 (1), 113-122. https://doi.org/10.1287/msom.2019.0796
Paules, C. I., Marston, H. D., & Fauci, A. S. (2020). Coronavirus infections—More than just the common cold. JAMA, 323 (8), 707-708. https://jamanetwork.com/journals/jama/fullarticle/2759815
Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Schaefer, J. L., Tardio, P. R., Baierle, I. C., & Nara, E. O. B. (2023). GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises. Administrative Sciences, 13 (2), 56. https://doi.org/10.3390/admsci13020056
Sheridan, T. B. (2016). Human–robot interaction: Status and challenges. Human Factors, 58 (4), 525-532. https://doi.org/10.1177/0018720816644364
Silver, C., & Lewins, A. (2014). Using software in qualitative research: A step-by-step guide. SAGE Publications.
Smith, R., & Brown, D. (2020). The Knowledge Society and Digital Inequality. Journal of Information Science, 46 (2), 98-115.
Srinivasan, U., & Arunasalam, B. (2013). Leveraging big data analytics to reduce healthcare costs. IT Professional, 15 (6), 21-28. https://doi.org/10.1109/MITP.2013.55
Stappen, L., Baird, A., Cambria, E., & Schuller, B. W. (2021). Sentiment analysis and topic recognition in video transcriptions. IEEE Intelligent Systems, 36 (2), 88-95. https://doi.org/10.1109/MIS.2021.3062200
Xiong, W., Wu, L., Alleva, F., Droppo, J., & Huang, X. (2018). The Microsoft 2018 conversational speech recognition system. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5934-5938. https://doi.org/10.1109/ICASSP.2018.8461870
Yu, D., & Deng, L. (2016). Automatic speech recognition: A deep learning approach. Springer.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ana Caroline Bugs de Oliveira, Beniamin Achilles Bondarczuk, Leonardo de Carvalho Gomes, Ismael Cristofer Baierle, Fernanda Araujo Pimentel Peres

This work is licensed under a Creative Commons Attribution 4.0 International License.
O(s) autor(es) do artigo autorizam a publicação do texto na revista e garantem que a contribuição é original e inédita, não estando em processo de avaliação em outra(s) revista(s). As opiniões, ideias e conceitos emitidos nos textos são de inteira responsabilidade do(s) autor(es), não sendo a revista responsável por tais conteúdos.
Os editores da revista reservam o direito de efetuar ajustes textuais e de adequação às normas da publicação, caso necessário.
Os autores mantêm os direitos autorais sobre o trabalho e concedem à revista o direito de primeira publicação, sendo o trabalho simultaneamente licenciado sob a Attribution 4.0 International (CC BY 4.0), o que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.
Os autores têm autorização para firmar contratos adicionais, separadamente, para distribuição não-exclusiva da versão do trabalho publicada nesta revista (ex.: publicar em repositório institucional ou como capítulo de livro), com reconhecimento de autoria e publicação inicial nesta revista.


