Human–Technology Interaction Models for Digital Workplace Transformation

Authors

  • M.S.R. Prasad Department of CSE, Koneru Lakshmaiah Education Foundation Green Fields, Guntur, Andhra Pradesh, India Author

DOI:

https://doi.org/10.15680/IJCTECE.2026.0901003

Keywords:

Human–Technology Interaction, Digital Workplace, Technology Acceptance Model, UTAUT, Human-Centered Design, Digital Transformation, HCI, Socio-Technical Systems, Employee Experience, Cognitive Overload, User Experience (UX) Design

Abstract

The advent of the digital workplace has profoundly transformed how employees interact with technology, redefining workspaces, communication patterns, and organizational structures. As digital tools and platforms become integral to daily operations, understanding Human–Technology Interaction (HTI) models is critical for managing successful digital workplace transformation. This paper explores the various theoretical frameworks and models that underpin human–technology interaction in modern digital work environments, aiming to bridge the gap between human factors and technological innovation. The study emphasizes the importance of designing user-centric systems that align with cognitive, behavioral, and emotional responses of employees to promote productivity, engagement, and well-being.

 

Drawing on established models such as the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Activity Theory, and Human-Centered Design principles, the research evaluates how these frameworks apply to current digital workplace settings. It examines how interaction paradigms—ranging from human-computer interaction (HCI) to more immersive experiences like augmented reality (AR) and AI-driven assistants—are reshaping employee roles, workflows, and workplace dynamics. The research also explores socio-technical system theory to understand the interplay between organizational culture, digital infrastructure, and employee adaptability.

 

The study uses a mixed-methods approach combining surveys, interviews, and case studies from digitally mature organizations to assess user satisfaction, perceived ease of use, and the behavioral impact of digital tools. Findings reveal that successful digital transformation depends not only on technological innovation but also on inclusivity, usability, and adaptability of the systems introduced. Resistance to change, digital fatigue, and cognitive overload are identified as critical challenges, highlighting the need for adaptive interaction models that support continuous learning and digital literacy.

 

The paper concludes by proposing a multi-dimensional HTI framework for digital workplaces that integrates technical efficiency, human adaptability, emotional intelligence, and organizational support systems. It offers strategic recommendations for business leaders, IT developers, and HR professionals to foster a digital workplace culture that is resilient, inclusive, and human-centric. As the boundary between humans and machines continues to blur, the future of work hinges on our ability to build meaningful, intuitive, and ethical interactions between people and digital technologies.

References

1. Mahajan, R. A., Shaikh, N. K., Tikhe, A. B., Vyas, R., & Chavan, S. M. (2022). Hybrid Sea Lion Crow Search Algorithm-based stacked autoencoder for drug sensitivity prediction from cancer cell lines. International Journal of Swarm Intelligence Research, 13(1), 21. https://doi.org/10.4018/IJSIR.304723

2. Rathod, S. B., Ponnusamy, S., Mahajan, R. A., & Khan, R. A. H. (n.d.). Echoes of tomorrow: Navigating business realities with AI and digital twins. In Harnessing AI and digital twin technologies in businesses (Chapter 12). https://doi.org/10.4018/979-8-3693-3234-4.ch012

3. Rathod, S. B., Khandizod, A. G., & Mahajan, R. A. (n.d.). Cybersecurity beyond the screen: Tackling online harassment and cyberbullying. In AI tools and applications for women’s safety (Chapter 4). https://doi.org/10.4018/979-8-3693-1435-7.ch004

4. Devan, Karthigayan. "ENHANCING CONCOURSE CI/CD PIPELINES WITH REAL-TIME WEBHOOK TRIGGERS: A SCALABLE SOLUTION FOR GITHUB RESOURCE MANAGEMENT."

5. Devan, K. (2025). Leveraging the AWS cloud platform for CI/CD and infrastructure automation in software development. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5049844

6. evan K, Driving Digital Transformation: LeveragingSite Reliability Engineering and Platform Engineeringfor Scalable and Resilient Systems. Appl. Sci. Eng. J.Adv. Res.. 2025;4(1):21-29.

7. Karthigayan Devan. (2025). Api Key-Driven Automation for Granular Billing Insights: An Sre and Finops Approach to Google Maps Platform Optimization. International Journal of Communication Networks and Information Security (IJCNIS), 17(1), 58–65. Retrieved from https://ijcnis.org/index.php/ijcnis/article/view/7939

8. Rajeshwari, J., Karibasappa, K., Gopalakrishna, M.T. (2016). Three Phase Security System for Vehicles Using Face Recognition on Distributed Systems. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 435. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2757-1_55

9. S. K. Musali, R. Janthakal, and N. Rajasekhar, “Holdout based blending approaches for improved satellite image classification,” Int. J. Electr. Comput. Eng. (IJECE), vol. 14, no. 3, pp. 3127–3136, Jun. 2024, doi: 10.11591/ijece.v14i3.pp3127-3136.

10. Sunitha and R. Janthakal, "Designing and development of a new consumption model from big data to form Data-as-a-Product (DaaP)," 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bengaluru, India, 2017, pp. 633-636, doi: 10.1109/ICIMIA.2017.7975538.

11. P. H. C and R. J, "A Comprehensive IoT Security Framework Empowered by Machine Learning," 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), New Delhi, India, 2024, pp. 1-8, doi: 10.1109/DELCON64804.2024.10866748.

12. P. Bavadiya, P. Upadhyaya, A. C. Bhosle, S. Gupta, and N. Gupta, “AI-driven Data Analytics for Cyber Threat Intelligence and Anomaly Detection,” in 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT), 2025, pp. 677–681. doi: 10.1109/InCACCT65424.2025.11011329.

13. Pathik Bavadiya. (2021). A Framework for Resilient Devops Automation in Multi-Cloud KubernetesEcosystems. Journal of Informatics Education and Research, 1(3), 61–66. https://jier.org/index.php/journal/article/view/3584

14. Bathani, R. (2025). Designing an ML-Driven framework for automatic generation of rollback statements for database commands. Journal of Information Systems Engineering & Management, 10(16s), 106–112. https://doi.org/10.52783/jisem.v10i16s.2574

15. Patel, K. A., Pandey, E. C., Misra, I., & Surve, D. (2025, April). Agentic AI for Cloud Troubleshooting: A Review of Multi Agent System for Automated Cloud Support. In 2025 International Conference on Inventive Computation Technologies (ICICT) (pp. 422-428). IEEE.

16. Dash, P., Javaid, S., & Hussain, M. A. (2025). Empowering Digital Business Innovation: AI, Blockchain, Marketing, and Entrepreneurship for Dynamic Growth. In Perspectives on Digital Transformation in Contemporary Business (pp. 439-464). IGI Global Scientific Publishing.

17. Hussain, M. A., Hussain, A., Rahman, M. A. U., Irfan, M., & Hussain, S. D. (2025). The effect of AI in fostering customer loyalty through efficiency and satisfaction. Advances in Consumer Research, 2, 331-340.

18. Das, A., Shobha, N., Natesh, M., & Tiwary, G. (2024). An Enhanced Hybrid Deep Learning Model to Enhance Network Intrusion Detection Capabilities for Cybersecurity. Journal of Machine and Computing, 4(2), 472.

19. Gowda, S. K., Murthy, S. N., Hiremath, J. S., Subramanya, S. L. B., Hiremath, S. S., & Hiremath, M. S. (2023). Activity recognition based on spatio-temporal features with transfer learning. Int J Artif Intell ISSN, 2252(8938), 2103.

20. Shanthala, K., Chandrakala, B. M., & Shobha, N. (2023, November). Automated Diagnosis of brain tumor classification and segmentation of MRI Images. In 2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM) (pp. 1-7). IEEE.

21. Karthik, S. A., Naga, S. B. V., Satish, G., Shobha, N., Bhargav, H. K., & Chandrakala, B. M. (2025). Ai and iot-infused urban connectivity for smart cities. In Future of Digital Technology and AI in Social Sectors (pp. 367-394). IGI Global.

22. Suman, M., Shobha, N., & Ashoka, S. B. (2026). Biometric Fingerprint Verification with Siamese Neural Network & Transfer Learning.

23. Godi, R. K., P, S. R., N, S., Bhoothpur, B. V., & Das, A. (2025). A highly secure and stable energy aware multi-objective constraints-based hybrid optimization algorithms for effective optimal cluster head selection and routing in wireless sensor networks. Peer-to-Peer Networking and Applications, 18(2), 97.

24. Shobha, N., & Asha, T. (2023). Using of Meteorological Data to Estimate the Multilevel Clustering for Rainfall Forecasting. Research Highlights in Science and Technology Vol. 1, 1, 115-129.

25. Jagadishwari, V., & Shobha, N. (2023, December). Deep learning models for Covid 19 diagnosis. In AIP Conference Proceedings (Vol. 2901, No. 1, p. 060005). AIP Publishing LLC.

26. Shanthala, K., Chandrakala, B. M., & Shobha, N. (2023, November). Automated Diagnosis of brain tumor classification and segmentation of MRI Images. In 2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM) (pp. 1-7). IEEE.

27. Jagadishwari, V., Lakshmi Narayan, N., & Shobha, N. (2023, December). Empirical analysis of machine learning models for detecting credit card fraud. In AIP Conference Proceedings (Vol. 2901, No. 1, p. 060013). AIP Publishing LLC.

28. Jagadishwari, V., & Shobha, N. (2023, January). Comparative study of Deep Learning Models for Covid 19 Diagnosis. In 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-5). IEEE

29. Jagadishwari, V., & Shobha, N. (2022, February). Sentiment analysis of COVID 19 vaccines using Twitter data. In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 1121-1125). IEEE.

30. Shobha, N., & Asha, T. (2019). Mean Squared Error Applied in Back Propagation for Non Linear Rainfall Prediction. Compusoft, 8(9), 3431-3439.

31. Ravi, C. S., Bonam, V. S. M., & chitta, S. (2024, December). Hybrid Machine Learning Approaches for Enhanced Insurance Fraud Detection. In International Conference on Recent Trends in AI Enabled Technologies (pp. 93-104). Cham: Springer Nature Switzerland.

32. Madunuri, R., Chitta, S., Bonam, V. S. M., Vangoor, V. K. R., Yellepeddi, S. M., & Ravi, C. S. (2024, September). IoT-Driven Smart Healthcare Systems for Remote Patient Monitoring and Management. In 2024 Asian Conference on Intelligent Technologies (ACOIT) (pp. 1-7). IEEE.

33. Madunuri, R., Ravi, C. S., Chitta, S., Bonam, V. S. M., Vangoor, V. K. R., & Yellepeddi, S. M. (2024, September). Machine Learning-Based Anomaly Detection for Enhancing Cybersecurity in Financial Institutions. In 2024 Asian Conference on Intelligent Technologies (ACOIT) (pp. 1-8). IEEE.

34. Madunuri, R., Yellepeddi, S. M., Ravi, C. S., Chitta, S., Bonam, V. S. M., & Vangoor, V. K. R. (2024, September). AI-Enhanced Drug Discovery Accelerating the Identification of Potential Therapeutic Compounds. In 2024 Asian Conference on Intelligent Technologies (ACOIT) (pp. 1-8). IEEE.

35. Whig, P., Balantrapu, S. S., Whig, A., Alam, N., Shinde, R. S., & Dutta, P. K. (2024, December). AI-driven energy optimization: integrating smart meters, controllers, and cloud analytics for efficient urban infrastructure management. In 8th IET Smart Cities Symposium (SCS 2024) (Vol. 2024, pp. 238-243). IET.

36. Polamarasetti, S., Kakarala, M. R. K., kumar Prajapati, S., Butani, J. B., & Rongali, S. K. (2025, May). Exploring Advanced API Strategies with MuleSoft for Seamless Salesforce Integration in Multi-Cloud Environments. In 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) (pp. 1-9). IEEE.

37. Polamarasetti, S., Kakarala, M. R. K., Gadam, H., Butani, J. B., Rongali, S. K., & Prajapati, S. K. (2025, May). Enhancing Strategic Business Decisions with AI-Powered Forecasting Models in Salesforce CRMT. In 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) (pp. 1-10). IEEE.

38. Polamarasetti, S., Kakarala, M. R. K., Goyal, M. K., Butani, J. B., Rongali, S. K., & kumar Prajapati, S. (2025, May). Designing Industry-Specific Modular Solutions Using Salesforce OmniStudio for Accelerated Digital Transformation. In 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) (pp. 1-13). IEEE.

39. Yadav, S. S., Gupta, S. K., Yadav, M. S., & Shinde, R. (2026). Development of smart and automated solid waste management systems. In Sustainable Solutions for Environmental Pollution (pp. 295-314). Elsevier.

40. Sivasamy, S., Whig, A., Parisa, S. K., & Shinde, R. (2026). Sustainable and economic waste management. In Sustainable Solutions for Environmental Pollution (pp. 463-485). Elsevier.

41. Israr, M., Alemran, A., Parisa, S. K., & Shinde, R. (2026). Sustainable disposal solutions: challenges and strategies for mitigation. In Sustainable Solutions for Environmental Pollution (pp. 443-462). Elsevier.

42. Sharma, S., Achanta, P. R. D., Gupta, H., Shinde, R., & Sharma, A. (2026). Planning for sustainable waste management. In Sustainable Solutions for Environmental Pollution (pp. 267-294). Elsevier.

43. Mishra, M. V., Sivasamy, S., Whig, A., & Shinde, R. (2026). Waste management and future implications. In Sustainable Solutions for Environmental Pollution (pp. 535-563). Elsevier.

44. Gummadi, V. P. K. (2025). MuleSoft Architectural Paradigms and Sustainability: A Comprehensive Technical Analysis. Journal of Computer Science and Technology Studies, 7(12), 534-540.

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Published

2026-01-02

How to Cite

Human–Technology Interaction Models for Digital Workplace Transformation. (2026). International Journal of Computer Technology and Electronics Communication, 9(Issue 1), 20-25. https://doi.org/10.15680/IJCTECE.2026.0901003

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