Direct Technology Diffusion in Higher Education: Evidence from Doctoral Students

Atul Kumar(1), Jaiprakash Paliwal(2), Mahesh Singh(3), Vishwas Pendse(4), Amandeep Saini(5), Rajesh Gade(6), Rahul Dhaigude(7), Abhay Bora(8), Sagar Nimbalkar(9), Shirish Raibagkar(10),


(1) Vikrant University
(2) Symbiosis Center for Skill Development and Symbiosis International (Deemed University)
(3) SBS Swiss Business School Ras Al Khaimah
(4) Sanjay Ghodawat University
(5) Dr D Y Patil School of Management, Savitribai Phule Pune University
(6) Lotus Business School
(7) Symbiosis Center for Skill Development and Symbiosis International (Deemed University)
(8) Sandip Institute of Technology and Research Centre
(9) Vidya Pratishthan’s Institute of Information Technology
(10) The Institute of Cost Accountants of India
Corresponding Author

Abstract


This study compares direct and indirect technology diffusion in higher education, focusing on plagiarism detection software in Indian doctoral education. Using a quantitative survey of research scholars, the study finds that direct diffusion significantly improves outcomes. Similarity levels decreased substantially under direct diffusion, indicating more accurate application of plagiarism rules. Additionally, direct diffusion reduced the time required and lowered costs considerably. The findings suggest that direct access empowers users, minimizes intermediary-related inefficiencies, and enhances both effectiveness and efficiency. The study highlights cost-effectiveness as a key enabler and recommends that policymakers and institutions promote direct technology diffusion to improve adoption and strengthen academic integrity in higher education.

Keywords


Direct diffusion; Indirect diffusion; Plagiarism; Research scholars; Technology diffusion.

References


Ahmed, H., Daim, T., and Basoglu, N. (2007). Information technology diffusion in higher education. Technology in Society, 29(4), 469–482.

Pinho, C., Franco, M., and Mendes, L. (2021). Application of innovation diffusion theory to the e-learning process: Higher education context. Education and Information Technologies, 26(1), 421–440.

Bouteraa, M., Bin-Nashwan, S. A., Al-Daihani, M., Dirie, K. A., Benlahcene, A., Sadallah, M., and Chekima, B. (2024). Understanding the diffusion of AI-generative (ChatGPT) in higher education: Does students' integrity matter? Computers in Human Behavior Reports, 14, 100402.

Chen, R. (2024). A study applying Rogers’ innovation diffusion theory on the adoption process of new teaching methods in secondary education. Research and Advances in Education, 3(2), 6–10.

Kumar, R., Sachan, A., and Mukherjee, A. (2018). Direct vs indirect e-government adoption: An exploratory study. Digital Policy, Regulation and Governance, 20(2), 149–162.

Raibagkar, S. S. (2021). Researchers do not fall prey to misuse of plagiarism regulations. Publishing Research Quarterly, 37(1), 55–68.

Khaire, R., Dixit, N., Ghuge, A., Bhutada, D., Kasisomayajula, S. R., and Dhaigude, R. (2025). Implementation blues for the special Indian legislation to curb plagiarism in research in higher educational institutions. Iberoamerican Journal of Science Measurement and Communication, 5(2), 1–17.

Feng, J., Yu, B., Tan, W. H., Dai, Z., and Li, Z. (2025). Key factors influencing educational technology adoption in higher education: A systematic review. PLOS Digital Health, 4(4), e0000764.

Buchanan, T., Sainter, P., and Saunders, G. (2013). Factors affecting faculty use of learning technologies: Implications for models of technology adoption. Journal of Computing in Higher Education, 25(1), 1–11.

Qashou, A. (2021). Influencing factors in m-learning adoption in higher education. Education and Information Technologies, 26(2), 1755–1785.

Almaiah, M. A., Alhumaid, K., Aldhuhoori, A., Alnazzawi, N., Aburayya, A., Alfaisal, R., and Shehab, R. (2022). Factors affecting the adoption of digital information technologies in higher education: An empirical study. Electronics, 11(21), 3572.

Kanwal, F., and Rehman, M. (2017). Factors affecting e-learning adoption in developing countries: Empirical evidence from Pakistan’s higher education sector. IEEE Access, 5, 10968–10978.

VanDerSchaaf, H. P., Daim, T. U., and Basoglu, N. A. (2021). Factors influencing student information technology adoption. IEEE Transactions on Engineering Management, 70(2), 631–643.

Yakubu, M. N., and Dasuki, S. I. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492–502.

John, S. P. (2015). The integration of information technology in higher education: A study of faculty’s attitude towards IT adoption in the teaching process. Contaduría y Administración, 60, 230–252.

Menzli, L. J., Smirani, L. K., Boulahia, J. A., and Hadjouni, M. (2022). Investigation of open educational resources adoption in higher education using Rogers’ diffusion of innovation theory. Heliyon, 8(7), e09885.

Kumar, R., Sachan, A., and Mukherjee, A. (2018). Direct vs indirect e-government adoption: An exploratory study. Digital Policy, Regulation and Governance, 20(2), 149–162.

Ahmad, S. (2022). Direct Benefit Transfer: The great technological move of India. Journal of Digital Learning and Distance Education, 1(1), 37–41.

Sabherwal, R., Sharma, D., and Trivedi, N. (2019). Using direct benefit transfers to transfer benefits to women: A perspective from India. Development in Practice, 29(8), 1001–1013.

Ghosh, B., Burman, R. R., Padaria, R., Mahra, G. S., Kumar, P., and Bhowmik, A. (2024). Assessing the operational constraints of direct benefit transfers: An empirical investigation of PM-KISAN scheme of India. Journal of Experimental Agriculture International, 46(9), 481–488.

Parmasivan, C., and Arunkumar, G. (2018). Direct benefit transfer—An innovative approach to financial inclusion in India. Journal of Emerging Technologies and Innovative Research, 5(12), 409–418.

Krejcie, R. V., and Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.

Döringer, S. (2021). ‘The problem-centred expert interview’: Combining qualitative interviewing approaches for investigating implicit expert knowledge. International Journal of Social Research Methodology, 24(3), 265–278.

Quigley, M., and Burke, M. (2013). Low-cost Internet of Things digital technology adoption in SMEs. International Journal of Management Practice, 6(2), 153–164.

Foster, A. D., and Rosenzweig, M. R. (2010). Microeconomics of technology adoption. Annual Review of Economics, 2(1), 395–424.


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