Article Sidebar

Submitted
Aug 31, 2024
Accepted
Oct 13, 2024
Published
Oct 30, 2024
Download
PDF
Statistic
Read Counter : 43 Download : 26

Main Article Content

Abstract

The advent of technology may dramatically alter academic research and performance. This study uses the Unified Theory of Adoption and Use of Technology (UTAUT) and Task-Technology Fit (TTF) theories to examine how technology adoption influence Research Performance conducted with sample size of 1,354 South Indian private institution Assistant Professors, with perception as a moderating factor. The research uses Structural Equation Modelling (SEM) with SmartPLS 4.0 to reveal that Performance Expectancy (PE) greatly influence Behavioral Intention (BI) to adopt technology. Higher Performance Expectancy (PE) leads to a stronger intention to use technology. Effort Expectancy (EE) also boosts BI, emphasizing the role of usability in setting user intentions. Technology adoption depends on Social Influence (SI), along with peer and social norms affect BI. Effective technology adoption requires Facilitating Conditions (FC) and enough resources and infrastructure. Task Characteristics (TC) and Technology Characteristics (TCh) greatly alter Task-Technology Fit (TTF), which enhances research procedures. TTF improves research practices but hurts research performance, demonstrating that improved techniques do not necessarily translate to better performance ratings, highlighting the intricacy of task-technology compatibility and research results.

Keywords

Academicians Research Practices Research Performance UTAUT TTF Moderation

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The author declares that the submitted to Journal of e-Learning and Knowledge Society (Je-LKS) is original and that is has neither been published previously nor is currently being considered for publication elsewhere.
The author agrees that SIe-L (Italian Society of e-Learning) has the right to publish the material sent for inclusion in the journal Je-LKS.
The author agree that articles may be published in digital format (on the Internet or on any digital support and media) and in printed format, including future re-editions, in any language and in any license including proprietary licenses, creative commons license or open access license. SIe-L may also use parts of the work to advertise and promote the publication.
The author declares s/he has all the necessary rights to authorize the editor and SIe-L to publish the work.
The author assures that the publication of the work in no way infringes the rights of third parties, nor violates any penal norms and absolves SIe-L from all damages and costs which may result from publication.

The author declares further s/he has received written permission without limits of time, territory, or language from the rights holders for the free use of all images and parts of works still covered by copyright, without any cost or expenses to SIe-L.

For all the information please check the Ethical Code of Je-LKS, available at http://www.je-lks.org/index.php/ethical-code

How to Cite
Doddanavar, I., Subramanyam, M. A., Dombar, V., Subramanyam, L., B R, L., & H S, C. (2024). Exploring technology adoption measures among academicians and its influence on their research practices and performance. Journal of E-Learning and Knowledge Society, 20(3), 71-82. https://doi.org/10.20368/1971-8829/1136038

References

  1. Aboagye, E., Jensen, I., Bergström, G., Björk Brämberg, E., Pico-Espinosa, O. J., et al. (2021). Investigating the association between publication performance and the work environment of university research academics: a systematic review. Scientometrics, 126(4), 3283-3301. https://doi.org/10.1007/s11192-020-03820-y
  2. Aditia, Ezra and Tela, Nengah and Saleh, Nursyam and Ilona, Desi and Zaitul, Zaitul. (2018). Understanding the Behavioral Intention to Use a University Web-Portal. MATEC Web of Conferences. 248. 05004. https://doi.org/10.1051/matecconf/201824805004.
  3. Agudo-Peregrina, Á. F., Hernández-García, Á., and Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301–314. https://doi.org/10.1016/j.chb.2013.10.035
  4. Agustí López, A. I., Martí Aras, A., Rodríguez Martín, A., and Gabarda Méndez, C. (2023). Personality, Burnout and Digital Competence in university professor, An approach to the current reality. Innoeduca. International Journal of Technology and Educational Innovation, 9(2), 162–176. https://doi.org/10.24310/innoeduca.2023.v9i2.15063
  5. Ahmed M. Mutahar, Osama Isaac, Abhijit Ghosh, and Ahmed Hamoud Al-Shibami. (2018). Perceived Value As A Moderator Variable In Mobile Banking Context: An Extension Of Technology Acceptance Model (Tam). International Journal Of Management And Human Science (IJMHS), 2(1), 1-8. https://Ejournal.Lucp.Net/ Index.Php/Ijmhs/Article/View/825
  6. Ahmet Ayaz, Mustafa Yanartaş. (2020) An analysis on the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS), Computers in Human Behavior Reports, Volume 2, ISSN 2451-9588, https://doi.org/10. 1016/j.chbr.2020.100032.
  7. Aljarboa, Soliman and Miah, Shah. (2020). An Integration of UTAUT and Task-Technology Fit Frameworks for Assessing the Acceptance of Clinical Decision Support Systems in the Context of a Developing Country.
  8. ALKursheh, T. (2024). Higher Tertiary Education Perspectives: Evaluating the Electronic Assessment Techniques of the Blackboard Platform for Fairness and Reliability. Innoeduca. International Journal of Technology and Educational Innovation, 10(1), 144–165. https:// doi. org/ 10.24310/ijtei.101.2024.17813
  9. Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E., and Herrera, F. (2009). h-Index: A review focused in its variants, computation and standardization for different scientific fields. Journal of Informetrics, 3(4), 273–289. https://doi.org/10.1016/j.joi.2009.04.001
  10. Alwadain A, Fati SM, Ali K, Ali RF (2024) From theory to practice: An integrated TTF-UTAUT study on electric vehicle adoption behavior. PLoS ONE 19(3): e0297890. https://doi.org/10.1371/ journal.pone.0297890
  11. Archer, L. 2008. Younger academics’ constructions of ‘authenticity’, ‘success’ and professional identity. Studies in Higher Education 33, no. 4: 385–403
  12. Ashish A. Linge , Tushar Chaudhari , Baldeo B. Kakde , and Mahesh Singh. (2023) Analysis of Factors Affecting Use Behavior towards MobilePayment Apps: A SEM Approach; Human Behavior and Emerging TechnologiesVolume 2023, Article ID 3327994, https://doi.org/10.1155/2023/ 3327994
  13. Atiqah, S. N., Hanafiah, M. H., Ismail, H., and Kandappan, B. (2024). Preparing instructors to transition to online distance learning: a pandemic panacea? Innoeduca. International Journal of Technology and Educational Innovation, 10(1), 5–28. https://doi.org/10.24310/ijtei.101.2024. 16820
  14. Azam, N. A. J., Kabiraj, S., Shaoyuan, W., and Azam, M. I. A. (2023). Adoption of TAM to Measure Consumer’s Attitude Towards Self-Service Technology: Utilizing Cultural Perspectives as a Moderator Variable. Global Business Review, 0(0). https://doi.org/10.1177/09721509221142373
  15. Bazeley, Pat(2010) 'Conceptualising research performance', Studies in Higher Education, 35: 8, 889 —903,
  16. Bourdieu, P. 2001. Homo academicus. Cambridge: Polity Press
  17. Brezavšček, A. (2016). Factors Influencing the Behavioural Intention to Use Statistical Software: The Perspective of the Slovenian Students of Social Sciences. EURASIA Journal of Mathematics, Science and Technology Education, 13(3). https://doi.org/10.12973/eurasia.2017.00652a
  18. Burton-Jones, A., and Hubona, G. S. (2005). Individual differences and usage behavior. ACM SIGMIS Database, 36(2), 58–77. https://doi.org/10.1145/1066149. 1066155
  19. Cantero Téllez, R., Romero Galisteo, R. P., and Rodriguez Bailón, M. (2022). Personal and teaching factors related to university professors’ stress levels in the face of COVID-19. Innoeduca. International Journal of Technology and Educational Innovation, 8(1), 102–110. https://doi.org/10.24310/innoeduca.2022.v8i1.11920
  20. Chan, S., and Lu, M. (2004). Understanding Internet Banking Adoption and Use Behavior. Journal of Global Information Management, 12(3), 21–43. https://doi.org/10.4018/jgim.2004070102
  21. Chao, C.-M. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg. 2019.01652
  22. Cigdem, H., and Oncu, S. (2024). Understanding the Role of Self-Regulated Learning in Academic Success. A Blended Learning Perspective in Vocational Education. Innoeduca. International Journal of Technology and Educational Innovation, 10(1), 45–64. https://doi.org/10.24310 /ijtei.101.2024.17432
  23. Cook, D. A., and Triola, M. M. (2014). What is the role of e-learning? Looking past the hype. Medical education, 48(9), 930-937. https://doi.org/10.1111/ medu. 12484
  24. 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
  25. Dishaw, M. T., and Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information and Management, 36(1), 9-21. https://doi.org/10.1016/S0378-7206(98)00101-3
  26. Dishaw, M. T., and Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information and Management, 36(1), 9-21. https://doi.org/10.1016/S0378-7206(98)00101-3
  27. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Williams, M. D. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
  28. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., and Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719-734. https://doi.org/10.1007/s10796-017-9774-y
  29. Faida, E. W., Supriyanto, S., Haksama, S., Suryaningtyas, W., Astuti, W., Nudji, B., and Hasina, S. N. (2022). The effect of performance expectancy and behavioral intention on the use of electronic medical record (EMR) intertie hospital in Indonesia. International Journal of Health Sciences, 6(S9), 1195– 1205. https://doi.org/10.53730/ijhs.v6nS9.12729
  30. Fishman, J., Lushin, V. and Mandell, D.S. Predicting implementation: comparing validated measures of intention and assessing the role of motivation when designing behavioral interventions. Implement Sci Commun 1, 81 (2020). https://doi.org/10.1186/ s43058-020-00050-4
  31. Fitrianie, S., Horsch, C., Beun, R.J. et al. Factors Affecting User’s Behavioral Intention and Use of a Mobile-Phone-Delivered Cognitive Behavioral Therapy for Insomnia: A Small-Scale UTAUT Analysis. J Med Syst 45, 110 (2021). https://doi. org/10. 1007/s10916-021-01785-w
  32. Front. Public Health 10:905968.doi: 10.3389/fpubh. 2022.905968
  33. George, Rowena and Jonathan, Victoria and Michael, Florianna. (2020). The Relationship between Task Characteristics and Employee Engagement. Trends in Undergraduate Research. 3. 1-6. https://doi.org/10.33736/tur.1837.2020.
  34. González Sodis, J. L., and Leiva Olivencia , J. J. . (2022). Perception of TIC conflicts among secondary school students in a private school. School mediation as a response. Innoeduca. International Journal of Technology and Educational Innovation, 8(2), 58–68. https://doi.org/10.24310/innoeduca.2022.v8i2.12655
  35. Goodhue, D. L., and Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236. 10.2307/249689
  36. Goodhue, D.L.: Development and measurement validity of a task-technology fit instrument for user evaluations of information system. Decis. Sci. 29, 105–138 (1998). https://doi.org/10.1111/j. 1540-5915.1998.tb01346.x
  37. Gruzd, A., Staves, K., and Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340–2350. https://doi.org/10.1016/j.chb.2012.07.004
  38. Hackman, J. R. (1968). Effects of task characteristics on group products. Journal of Experimental Social Psychology, 4(2), 162–187. https://doi.org/10.1016/0022-1031(68)90040-1
  39. Hameed, F., Qayyum, A. and Khan, F.A. A new trend of learning and teaching: Behavioral intention towards mobile learning. J. Comput. Educ. 11, 149–180 (2024). https://doi.org/10.1007/s40692-022-00252-w
  40. Hong, S., Thong, J. Y. L., and Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819–1834. https://doi.org/10.1016/j.dss. 2006.03.009
  41. Hoppmann, J., Anadon, L. D., and Narayanamurti, V. (2020). Why matter matters: How technology characteristics shape the strategic framing of technologies. Research Policy, 49(1), 103882. https://doi.org/10.1016/j.respol.2019.103882
  42. Hoque, Md and Bao, Yukun. (2017). Investigating Factors Affecting Elderly's Intention to Use m-Health Services: An Empirical Study. Telemedicine and e-Health. 24. https://doi.org/10.1037/t40308-000
  43. Ibadan, Nigeria. Interdisciplinary Journal of e-Skills and Lifelong Learning, 14, 95-115
  44. Izuma, K. (2017). The Neural Bases of Social Influence on Valuation and Behavior. Decision Neuroscience, 199–209. https://doi.org/10.1016/b978-0-12-805308-9.00016-6
  45. Javed, Y., Ahmad, S., and Khahro, S. H. (2020). Evaluating the Research Performance of Islamabad-Based Higher Education Institutes. SAGE Open, 10(1), 215824402090208. https://doi.org/10.1177/2158244020902085
  46. Jiménez Sabino, M. J., and Cabero Almenara, J. (2021). The technological, pedagogical and content knowledge of Andalusian university professors on ICT. Analysis from the TPACK model. Innoeduca. International Journal of Technology and Educational Innovation, 7(1), 4–18. https://doi.org/ 10.24310/innoeduca.2021.v7i1.11940
  47. Joseph C. Shih, Sophia Shu-Chuan Chang (2013) Journal of Advanced Management Science, Vol. 1, No. 1
  48. Kaczmarek, L. D., Kashdan, T. B., Drążkowski, D., Enko, J., Kosakowski, M., Szäefer, A., and Bujacz, A. (2015). Behavioral Intention Scale. APA PsycTests.
  49. Kesuma, F., and Syamsuar, D. (2021). Task-Technology Fit (TTF) dan Unified Theory of Acceptance and Use of Technology (UTAUT): Analisis Model Penerimaan Teknologi di Perguruan Tinggi. JUSIFO (Jurnal Sistem Informasi), 7(1), 21-31. https://doi.org/10.19109/ jusifo.v7i1.7870
  50. Ma, Lixia and Jing, Jin. (2023). A study on content validity of TEM4 listening comprehension (2016-2023). Journal of Language Teaching. 3. 69-79. https://doi.org/10.54475/jlt.2023.031.
  51. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191. DOI: 10.1287/isre.2.3.173
  52. Obinna C. Ojiaku; Ebuka Christian Ezenwafor; Aihie Osarenkhoe, (2023). International Journal of Technology Marketing, 2024 Vol.18 No.1, pp.113 - 135. https://doi.org/10.1504/IJTMKT.2024.135674
  53. Oguguo, B., Ezechukwu, R. ., Nannim, F., and Offor, K. (2023). Analysis of teachers in the use of digital resources in online teaching and assessment in COVID times . Innoeduca. International Journal of Technology and Educational Innovation, 9(1), 81–96. https://doi.org/10.24310/innoeduca.2023.v9i1.15419
  54. Omotayo, F. O., and Haliru, A. (2019). Perception of task-technology fit of digital library among undergraduates in selected universities in Nigeria. The Journal of Academic Librarianship, 102097. https://doi.org/10.1016/j.acalib.2019.102097
  55. Onaolapo, S., and Oyewole, O. (2018). Performance expectancy, effort expectancy, and facilitating conditions as factors influencing smart phones use for mobile learning by postgraduate students of the University of
  56. Orlikowski, W.J.: Using technology and constituting structures: a practice lens for studying technology in organizations. Organ. Sci. 11, 404–428 (2000). https://doi.org/10.1287/orsc.11.4. 404.14600
  57. Ozer Sanal, S. (2023). Interactive and social reading intervention in design-based research. Innoeduca. International Journal of Technology and Educational Innovation, 9(1), 112–125. https://doi.org/10.24310/innoeduca.2023.v9i1.15403
  58. Padilla-Hernández, A. L., Gámiz-Sánchez, V. M., and Romero-López, M. A. (2019). Proficiency levels of teachers’ digital competence: ?a review of recent international frameworks. Innoeduca. International Journal of Technology and Educational Innovation, 5(2), 140–150. https://doi. org/10.24310/innoeduca.2019.v5i2.5600
  59. Rahmiati, and Susanto, Perengki. (2021). Use Behavior of E-Money: Empirical Analysis Using The UTAUT Model. https://doi.org/10.2991/aebmr.k.210616.061.
  60. Ramírez Hernández, M., Maldonado Berea, G. A., and Marín-Díaz, V. (2015). Internet use in the field of academic university. Innoeduca. International Journal of Technology and Educational Innovation, 1(2), 69–79. https://doi.org/ 10.20548/innoeduca.2015.v1i2.1058
  61. Roy Setiawan, Anis Eliyana, Tatik Suryani, Tatik Suryani, Elias Gabriel Aristo, Aisha Anwar. (2022), A Study Of Behavioral Intention: The Practices For Mobile Payment Technology Users In Indonesia ; Webology (ISSN: 1735-188X)
  62. Sánchez Vega, E., Cantero-Téllez, R., and Rodríguez-Martínez, M. del C. (2016). Internet addiction in future teachers. A case study with university students. Innoeduca. International Journal of Technology and Educational Innovation, 2(1), 46–53. https://doi.org/10.20548/innoeduca.2016.v2i1. 1063
  63. Sun, H., and Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64(2), 53–78. https://doi.org/10.1016/j.ijhcs.2005.04.013
  64. Talan, T., Doğan, Y., and Kalinkara, Y. (2024). Digital Natives’ Mobile Learning Adoption in terms of UTAUT-2 Model: a Structural Equation Model. Innoeduca. International Journal of Technology and Educational Innovation, 10(1), 100–123. https://doi.org/10.24310/ijtei.101.2024 .17440
  65. Talebian, S., Mohammadi, H. M., and Rezvanfar, A. (2014). Infor-mation and communication technology (ICT) in higher education: advantages, disadvantages, conveniences and limitations of applying e-learning to agricultural students in Iran. Procedia-Social and Behavioral Sciences, 152, 300-305. https://doi.org/10.1016/ j.sbspro.2014.09.199
  66. Utomo, P., Kurniasari, F., and Purnamaningsih, P. (2021). The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application. International Journal of Community Service and Engagement, 2(4), 183-197. https://doi.org/10.47747/ijcse.v2i4.529
  67. Venkatesh, V., and Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
  68. Venkatesh, V., and Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc. 46.2.186.11926
  69. Venkatesh, V., Brown, S. A., Maruping, L. M., and Bala, H. (2008). Predicting Different Conceptualizations of System Use: The Competing Roles of Behavioral Intention, Facilitating Conditions, and Behavioral Expectation. MIS Quarterly, 32(3), 483–502. https://doi.org/10.2307/25148853
  70. Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  71. Viswanath Venkatesh, James Y. L. Thong, Xin Xu (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead, Journal of the Association for Information Systems, Volume 17 Issue 5 pp. 328 – 376
  72. Wahab E, Al-Rahmi WM, Alturki U, Aldraiweesh A and Almutairy S (2022) Integrating the Role of UTAUT and TTF Model to Evaluate Social Media Use for Teaching and Learning in Higher Education.
  73. Wan, Liyong and Xie, Shoumei and Shu, Ai. (2020). Toward an Understanding of University Students’ Continued Intention to Use MOOCs: When UTAUT Model Meets TTF Model. SAGE Open. 10. 215824402094185. https://doi.org/10.1177/2158244020941 858
  74. Xu, Q., Erman, J., Gerber, A., Mao, Z., Pang, J., and Venkataraman, S. (2011). Identifying diverse usage behaviors of smartphone apps. Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference - IMC ’11. https://doi.org/10.1145/2068816.2068847
  75. Zarina Kamarozaman and Fahmi Zaidi Abdul Razak 2021 J. Phys.: Conf. Ser. 1793 012022
  76. Zhang H, He J, Shi X, Hong Q, Bao J, Xue S. Technology Characteristics, Stakeholder Pressure, Social Influence, and Green Innovation: Empirical Evidence from Chinese Express Companies. Sustainability. 2020; 12(7):2891. https://doi.org/10.3390/su12072891
  77. Zigurs, I., and Buckland, B. K. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 22(3), 313-334. 10.2307/249668
  78. Zigurs, I., and Buckland, B. K. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 22(3), 313-334. https://doi.org/10.2307/249668