Effectiveness of NLP Techniques in Responding to Customer Queries: A Survey Among Indian Consumers

Authors

DOI:

https://doi.org/10.66395/globeis.16

Keywords:

Artificial Intelligence, chatbots, customer satisfaction, customer service, Indian consumers, Natural Language Processing

Abstract

Natural Language Processing (NLP) has become an essential tool for automated and scalable customer service. This study looks at how well NLP techniques resolve customer queries among Indian consumers, a group known for its rich linguistic and cultural diversity. Researchers conducted a quantitative survey involving 151 Indian consumers who had experience using NLP-enabled customer service platforms. They explored three independent variables: linguistic and contextual understanding, efficiency and responsiveness, and cultural relevance and personalization. These were examined in relation to customer satisfaction, the dependent variable. All constructs were measured using validated scales, and the data were analyzed using IBM SPSS Statistics Version 28. Exploratory factor analysis confirmed a three-factor structure that accounted for 90.7% of cumulative variance. Cronbach’s alpha values ranged from .856 to .931, showing strong reliability. Pearson correlation and linear regression analyses showed statistically significant positive relationships between each independent variable and customer satisfaction (R² = .280, F(3, 147) = 19.03, p < .001). All three hypotheses were supported. This study provides evidence on NLP adoption in India’s multilingual market and offers practical insights for businesses creating culturally inclusive NLP systems.

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Author Biographies

  • Dr. Arifa Khan, SRM Institute of Science and Technology

    Dr. Arifa Khan holds a Ph.D. in Management from SRM Institute of Science and Technology, India, and currently works as an AI/ML Computational Science Manager . Her research interests include Artificial Intelligence, Natural Language Processing, Large Language Models, Machine Learning, Information Systems, Responsible AI, Explainable AI, and Digital Transformation. She has published research in artificial intelligence, machine learning, AI ethics, and intelligent information systems, with a particular focus on the intersection of biological intelligence, computational systems, and emerging AI technologies.

  • Reshma Khan, SRM Institute of Science and Technology

    Research Scholar, Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu District, Tamil Nadu 603203, India.

  • Fathima Fiza Syed, University of Bath

    MSc Data Science, University of Bath, United Kingdom.

References

1. Olujimi, P. A., & Ade-Ibijola, A. (2023). NLP techniques for automating responses to customer queries: A systematic review. Discover Artificial Intelligence, 3(1), Article 20. https://doi.org/10.1007/s44163-023-00065-5

2. Khanuja, S., Ruder, S., & Talukdar, P. (2022). Evaluating the diversity, equity, and inclusion of NLP technology: A case study for Indian languages. arXiv. https://arxiv.org/pdf/2205.12676

3. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

4. Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The state and fate of linguistic diversity and inclusion in the NLP world. arXiv. https://arxiv.org/pdf/2004.09095

5. Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713–3744. https://doi.org/10.1007/s11042-022-13428-4

6. Ngai, E. W., Lee, M. C., Luo, M., Chan, P. S., & Liang, T. (2021). An intelligent knowledge-based chatbot for customer service. Electronic Commerce Research and Applications, 50, Article 101098. https://doi.org/10.1016/j.elerap.2021.101098

7. Vashishtha, E., & Kapoor, H. (2023). Enhancing patient experience by automating and transforming free text into actionable consumer insights: A natural language processing (NLP) approach. International Journal of Health Sciences and Research, 13(10), 275–288. https://doi.org/10.52403/ijhsr.20231038

8. Piris, Y., & Gay, A. C. (2021). Customer satisfaction and natural language processing. Journal of Business Research, 124, 264–271. https://doi.org/10.1016/j.jbusres.2020.11.058

9. Patel, N., & Trivedi, S. (2020). Leveraging predictive modelling, machine learning personalization, NLP customer support, and AI chatbots to increase customer loyalty. Empirical Quests for Management Essences, 3(3), 1–24.

10. Malik, N., & Bilal, M. (2024). Natural language processing for analyzing online customer reviews: A survey, taxonomy, and open research challenges. PeerJ Computer Science, 10, Article e2203. https://peerj.com/articles/cs-2203.pdf

11. Packard, G., & Berger, J. (2021). How concrete language shapes customer satisfaction. Journal of Consumer Research, 47(5), 787–806. https://doi.org/10.1093/jcr/ucaa038

12. Mozumder, M. A. S., Nguyen, T. N., Devi, S., Arif, M., Ahmed, M. P., Ahmed, E., Bhuiyan, M., Rahman, M. H., Al Mamun, A., & Uddin, A. (2024). Enhancing customer satisfaction analysis using advanced machine learning techniques in the fintech industry. Journal of Computer Science and Technology Studies, 6(3), 35–41. https://doi.org/10.32996/jcsts.2024.6.3.4

13. Natasia, S. R., Wiranti, Y. T., & Parastika, A. (2022). Acceptance analysis of NUADU as an e-learning platform using the Technology Acceptance Model (TAM) approach. Procedia Computer Science, 197, 512–520. https://doi.org/10.1016/j.procs.2021.12.168

14. Toraman, Y. (2022). User acceptance of metaverse: Insights from technology acceptance model (TAM) and planned behaviour theory (PBT). EMAJ: Emerging Markets Journal, 12(1), 67–75. https://doi.org/10.5195/emaj.2022.258

15. Alharahsheh, H. H., & Pius, A. (2020). A review of key paradigms: Positivism VS interpretivism. Global Academic Journal of Humanities and Social Sciences, 2(3), 39–43.

16. Al-Ababneh, M. M. (2020). Linking ontology, epistemology, and research methodology. Science & Philosophy, 8(1), 75–91. http://eiris.it/ojs/index.php/scienceandphilosophy/article/viewFile/500/732

17. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.

18. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.

19. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

20. Kolasani, S. (2023). Optimizing natural language processing and large language models (LLMs) for efficient customer service and hyper-personalization to enable sustainable growth and revenue. Transactions on Latest Trends in Artificial Intelligence, 4(4), 1–31.

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Published

2026-06-30

Issue

Vol. 2 No. 1 (2026): June 2026

Section

Articles

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Copyright (c) 2026 GlobeIS International Journal of Global Information Systems

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How to Cite

Effectiveness of NLP Techniques in Responding to Customer Queries: A Survey Among Indian Consumers. (2026). GlobeIS International Journal of Global Information Systems, 2(1), 106-118. https://doi.org/10.66395/globeis.16