DOI: https://doi.org/10.14710/agrisocionomics.v8i3.23820

A PERSPECTIVE OF TECHNOLOGY ACCEPTANCE MODEL ON THE AGRICULTURE EXTENSION STAFFS USING CYBER EXTENSION

*Dwiningtyas Padmaningrum orcid scopus  -  Department of Agricultural Extension and Communication, Faculty of Agriculture, Universitas Sebelas Maret, Surakarta, Central Java, Indonesia, Indonesia
La Mani  -  Department of Communication, Universitas Bina Nusantara, Jakarta, Indonesia, Indonesia
Triya Ayu Retnaningtyas  -  Department of Agricultural Extension and Communication, Faculty of Agriculture, Universitas Sebelas Maret, Surakarta, Central Java, Indonesia, Indonesia
Tri Sujatmiko  -  Department of Agricultural Extension and Communication, Faculty of Agriculture, Universitas Sebelas Maret, Surakarta, Central Java, Indonesia, Indonesia
Subejo Subejo  -  Department of Agricultural Extension and Communication, Faculty of Agriculture, Universitas Gadjah Mada, Yogyakarta, Indonesia, Indonesia
Emi Widiyanti  -  Department of Agricultural Extension and Communication, Faculty of Agriculture, Universitas Sebelas Maret, Surakarta, Central Java, Indonesia, Indonesia
Open Access Copyright 2024 Agrisocionomics: Jurnal Sosial Ekonomi Pertanian under http://creativecommons.org/licenses/by-sa/4.0.

Citation Format:
Abstract

Cyber extension web facilitates agriculture extension staffs to leverage this media communication system to strengthen their capability and capacity for improving extension services. Through the Technology Acceptance Model approach, this study examines the use of cyber extension and the affect of self-efficacy, perceived usefulness, perceived ease of use, subjective norms, and attitude toward usage on cyber extension usage by agriculture extension staffs in Central Java Province. This study was conducted from April to May 2023 using a quantitative approach. The population includes agriculture extension staffs within the scope of the Central Java Provincial Agriculture Office. Sampling was done using convenience sampling, with 377 agriculture extension staffs spreading across 28 districts and five cities. Data were analyzed using Partial Least Squares - Structural Equation Modeling (PLS-SEM). The study showed that use of the cyber extension web can be categorized as low-level; meanwhile, self-efficacy, perceived ease of use, subjective norms, and attitudes towards positive use were relatively high. Cyber extension usage was directly affected by perceived ease of use, subjective norms, and attitude toward usage. As a recommendation, interventions are needed to improve and continue to update content variations. It is necessary to develop and optimize the use of cyber extension at the provincial and or district level.

Fulltext View|Download
Keywords: adoption, agricultural, cyber extension, digital technology

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
A PERSPECTIVE OF TECHNOLOGY ACCEPTANCE MODEL ON THE AGRICULTURE EXTENSION STAFFS USING CYBER EXTENSION TRANSFORMATION OF MILLENNIAL FARMERS: BETWEEN EXPECTATIONS AND REALITY DYNAMICS OF COMPETITIVENESS AND MARKET POSITION OF INDONESIAN COCONUT SHELL CHARCOAL EXPORTS IN THE INTERNATIONAL MARKET More recent articles
  1. Abdullah, A., Mustabi, J., A, A., & Amrullah. 2019. Identification Of The Use Of Cyber Extension As The Source Of Information In Adoption Of Feed Technology. Suluh Pembangunan: Journal Of Extension And Development, 1(2): 109–114
  2. Abdullah, S. 2015. Implementation Of Cyber Extension Of Fisheries Product Marketing. International Journal Of Education And Research, 3(6): 101–112
  3. Abebe, H., Shitu, S., & Mose, A. 2021. Understanding Of Covid-19 Vaccine Knowledge, Attitude, Acceptance, And Determinantes Of Covid-19 Vaccineaacceptance Among Adult Population In Ethiopia. Infect Drug Resist, 1(14): 2015–2025. https://doi.org/https://doi.org/10.2147/idr.s312116
  4. Abramson, J., Dawson, M., & Stevens, J. 2015. An Examination Of The Prior Use Of E-Learning Within An Extended Technology Acceptance Model And The Factors That Influence The Behavioral Intention Of Users To Use M-Learning. SAGE Open, 1(9)
  5. Adeyongo, I. L., Sennuga, S. O., Lai-Solarin, W. I., & Ezinne, M. E. 2022. Impact Of Cyber-Extension And Social Media (Whatsapp And Facebook) On Extension Workers In Federal Capital Territory, Abuja, Nigeria. Applied Research Frontiers, 1(1). https://doi.org/https://doi.org/10.36686/ariviyal.arf.2022.01.01.007
  6. Adriyani, F. Y. 2019. Pemanfaatan Cyber Extension Sebagai Media Diseminasi Inovasi Pertanian Oleh Penyuluh Pertanian Di Provinsi Lampung. Suluh Pembangunan : Journal Of Extension And Development, 1(1): 1–7
  7. Aertsens, J., Van, H. G., Verbeke, W., & Mondelaers, K. 2009. Personal Determinants Of Organic Food Consumption: A Review. British Food Journal, 111(10): 1140–1167. https://doi.org/https://doi.org/10.1108/00070700910992961
  8. Ajzen, I. 1991. The Theory Of Planned Behavior. Organizational Behavior And Human Decision Processes, 50(2): 179–211. https://doi.org/https://doi.org/10.1016/0749-5978(91)90020-t
  9. Aktag, I. 2015. Computer Self-Efficacy, Computer Anxiety, Performance And Personal Outcomes Of Turkish Physical Education Teachers”,. Educational Research And Reviews, 10(3): 328–337
  10. Annur, C. M. 2023. Inilah 10 Provinsi Dengan Jumlah Petani Milenial Terbanyak Nasional Pada 2023. Databoks. https://databoks.katadata.co.id/datapublish/2023/12/05/inilah-10-provinsi-dengan-jumlah-petani-milenial-terbanyak-nasional-pada-2023-jawa-timur-teratas
  11. Badan Pusat Statistik (BPS). 2024. Luas Panen, Produksi, Dan Produktivitas Padi Menurut Provinsi, 2021-2023. https://www.bps.go.id/id/statistics-table/2/mtq5ocmy/luas-panen--produksi--dan-produktivitas-padi-menurut-provinsi.html
  12. Bamberg, S., Hunecke, M., & Blöbaum, A. 2007. Social Context, Personal Norms And The Use Of Public Transportation: Two Field Studies. Journal Of Environmental Psychology, 27(3): 190–203. https://doi.org/https://doi.org/10.1016/j.jenvp.2007.04.001
  13. Bandura, A. 1977. Self-Efficacy: Toward A Unifying Theory Of Behavioral Change. Advances In Behaviour Research And Therapy, 1(4): 139–161. https://doi.org/https://doi.org/10.1016/0146-6402(78)90002-4
  14. Bandura, A. 1989. Regulation Of Cognitive Pprocesses Through Perceived Self-Efficacy. Developmental Psychology, 25(5), 729–735. https://doi.org/10.1037/0012-1649.25.5.729
  15. Boonjing, R. 2008. A Study On Characteristics Of Agricultural Extensionists. Chiang Mai University Journal Of Social Science And Humanities, 2(2): 101–115
  16. Cook, B. R., Satizabal, P., & Curnow, J. 2021. Humanising Agricultural Extension: A Review. World Development, 140(105337). https://doi.org/10.1016/j.worlddev.2020.105337
  17. Dahi, M., & Ezziane, Z. 2015. Measuring E-Government Adoption In Abu Dhabi With Technology Acceptance Model (TAM). International Journal Of Electronic Governance, 7(3): 206–231. https://doi.org/https://doi.org/10.1504/ijeg.2015.071564
  18. Davis, D. D. 1989. Perceived Usefulness, Perceived Ease Of Use, And User Acceptance Of Information Technology. MIS Quarterly, 13(3): 319–340
  19. Dixit, R. V, & Prakash, G. 2018. Intentions To Use Social Networking Sites (SNS) Using Technology Acceptance Model (TAM). Paradigm, 22(1): 65–79. https://doi.org/https://doi.org/10.1177/0971890718758201
  20. Egmond, C., & Bruel, R. 2007. Nothing Is As Practical As A Good Theory: Analysis Of Theories And A Tool For Developing Interventions To Influence Energy Behaviour
  21. Gitosaputro, S., & Listiana, I. 2018. Dinamika Penyuluhan Pertanian: Dari Era Kolonial Sampai Dengan Era Digital. CV Anugrah Utama Raharja
  22. Gultom, R., Hasanah, L., Subehi, M., Sulistiyowati, H., Uliyah, Abdurahman, A. A., Surasa, J., Heruwaty, & Martono, H. D. 2021. Statistik SDM Pertanian Dan Kelembagaan Petani 2020
  23. Guntoro, B., Qui, N. H., & Triatmojo, A. 2022a. Challenges And Roles Of Extension Workers On Cyber Extension As Information Media. International Conference On Advance & Scientific Innovation ICASI – Life Sciences Chapter, Kne Life Sciences, 547–555. Https://Doi.Org/10.18502/Kls.V0i0.11843
  24. Guntoro, B., Qui, N. H., & Triatmojo, A. 2022b. Challenges And Roles Of Extension Workers On Cyber Extension As Information Media. 3rd International Conference On Advance & Scientific Innovation. ICASI
  25. Hayati. 2022. Factors Affecting The Use Of Cyber Extension By Gender Based Extensioners In Supporting Artificial Intelegence In Agriculture In NTB (Case Study Of Mataram City). Jurnal Penelitian Pendidikan IPA, 8(6): 3187–3195
  26. Howard, N. L., Marshall, P., & Swatman, P. A. 2010. Reconceptualising Motivation In Adoption And Acceptance Research: Back To Basics. Proceedings Of The 21st Australasian Conference On Information Systems, Brisbane
  27. Indriyani, Y. R., Yurisinthae, E., & Dolorosa, E. 2024. Improving The Performance Of Agricultural Extension Workers Through Increasing Capacity, Willingness, And Opportunity. Agrisocionomics, 8(1): 97–111
  28. Jimenez, I. A. C., García, L. C. C., Marcolin, F., Violante, M. G., & Vezzetti, E. 2021. Validation Of A TAM Extension In Agriculture: Exploring The Determinants Of Acceptance Of An E-Learning Platform. Appl. Science, 11(10), 4672. https://doi.org/10.3390/app11104672
  29. Kabir, K. H., Rahman, S., Hasan, M. M., Chowdhury, A., & Gow, G. 2023. Facebook For Digital Agricultural Extension Services: The Case Of Rooftop Gardeners In Bangladesh. Smart Agricultural Technology, 6(100338)
  30. Kaegi, S. 2015. The Experiences Of India’s Agricultural Extension System In Reaching A Large Number Of Farmers With Rural Advisory Services. In Federal Department Of Foreign Affairs FDFA
  31. Kamal, S. A., Shafiq, M., & Kakria, P. 2020. Investigating Acceptance Of Telemedicine Services Through An Extended Technology Acceptance Model (TAM). Technology In Society, 60(101212). https://doi.org/https://doi.org/10.1016/j.techsoc.2019.101212
  32. Kementerian Pertanian. 2023. Statistik SDM Pertanian Dan Kelembagaan Petani 2023
  33. Kock, N. 2022. Warppls User Manual: Version 8.0. Scriptwarp Systems
  34. Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. 2000. The Technology Acceptance Model And The World Wide Web. Decision Support Systems, 29: 269–282
  35. Leeuwis, C. 2004. Communication For Rural Innovation: Rethinking Agricultural Extension. Blackwell Publishing
  36. Liao, S., Hong, J.-C., Wen, M.-H., Pan, Y.-C., & Yun-Wu. 2018. Applying Technology Acceptance Model (TAM) To Explore Users’ Behavioral Intention To Adopt A Performance Assessment System For E-Book Production. EURASIA Journal Of Mathematics, Science And Technology Education, 14(10): 1601
  37. Manik, J. R., Refiswal, & Salsabila. 2018. Analysis Of Factors Affecting The Performance Of Agricultural Extension Agent In Langkat District. International Conference On Sustainable Agriculture And Natural Resources Management, 2(1): 89–94
  38. Muksin, Juanda, I., Wahyono, N. D., Eliyatiningsih, E., Harlianingtyas, I., & Purwoko, D. 2022. Design Of The Needs Model For The Development Of Young Generation Interests In The Agricultural Sector In Banyuwangi Regency. IOP Conferences Series Earth And Environmental Science, 980: 012055
  39. Nugraheni, D. M. K., Hadisoewono, A., & Noranita, B. 2020. Continuance Intention To Use (CIU) On Technology Acceptance Model (TAM) For M-Payment (Case Study: TIX ID). Icicos 2020 - Proceeding: 4th International Conference On Informatics And Computational Sciences. https://doi.org/https://doi.org/10.1109/icicos51170.2020.9299100
  40. Organisation For Economic Co-Operation And Development (OECD). 2021. Strengthening Women’s Entrepreneurship In Agriculture In ASEAN Countries. Secretary-General Of The OECD
  41. Partini, Wastutiningsih, S. P., Nugroho, N. C., & Fatonah, S. 2024. Tantangan Menjadi Penyuluh Kekinian Di Era Disrupsi. Jurnal Penyuluhan, 20(01): 29–40
  42. Pingali, P. L., & Rosegrant, M. W. 1995. Agricultural Commercialization And Diversification: Processes And Policies. Food Policy, 20(3): 171–185. https://doi.org/https://doi.org/10.1016/0306-9192(95)00012-4
  43. Pinho, J. C., & Soares, A. M. 2011. Examining The Technology Acceptance Model In The Adoption Of Social Networks. Academic Public Administration Studies Archive - APAS, Apas Paper
  44. Ramayah, T., & Jantan, M. 2004. Technology Acceptance: An Individual Perspective Current And Future Research In Malaysia. Review Of Business Research, 2(1): 103–111
  45. Rizkiansyah, M., Ariestyani, A., & Yunus, U. 2023. Cyber Extension Content And Interactivity Online Social Media Agricultural Ministry During Covid 19. E3S Web Of Conferences, 388(04043). https://doi.org/https://doi.org/10.1051/e3sconf/202338804043
  46. Rusliyadi, M., Ardi, Y. W. Y., & Winarno, K. 2023. The Factor Influencing Technology Adoption Process Of Farmers In The Term Of Agricultural Extension Policy Case In Central Java Indonesia. Agrisocionomics, 7(2): 250–260
  47. Shachak, A., Kuziemsky, C., & Petersen, C. 2019. Beyond TAM And UTAUT: Future Directions For HIT Implementation Research. Journal Of Biomedical Informatics, 100(103315). https://doi.org/https://doi.org/10.1016/j.jbi.2019.103315
  48. Sudaryati, E., Agustia, D., & Syahputra, M. ’Illiyun. 2017. The Influence Of Perceived Usefulness, Perceived Ease Of Use, Attitude, Subjectif Norm, And Perceived Behavioral Control To Actual Usage PSAK 45 Revision On 2011 With Intention As Intervening Variable In Unair Financial Department. International Conference Of Organizational Innovation (ICOI 2017): Advances In Intelligent Systems Research, 131: 86–92
  49. Sumardjo, Baga, L. M., & Mulyandari, R. S. H. 2010. Cyber Extension: Peluang Dan Tantangan Dalam Revitalisasi Penyuluhan. IPB Press
  50. Sun, S., Wen, X., Jie, S., Gao, Q., Zhu, Y., & Wen, S. 2022. Drivers Of Farmers’ Intention To Use The Digital Agricultural Management System: Integrating Theory Of Planned Behavior And Behavioral Economics. Front. Psychol, 13(901169). https://doi.org/10.3389/fpsyg.2022.901169
  51. Swanson, B. E., Bentz, R. P., & Sofranko, A. J. 1977. Improving Agricultural Extension – A Reference Manual. The History, Development And The Future Of Agricultural Extension. FAO
  52. Taherdoost, H. 2016. Sampling Methods In Research Methodology; How To Choose A Sampling Technique For Research. International Journal Of Academic Research In Management (IJARM), 5(2): 18–27
  53. Taylor, S., & Todd, P. A. 1955. Understanding Information Technology Usage: A Test Of Competing Models. Information Systems Research, 6(2): 144–176
  54. Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. 1999. Intrinsic And Extrinsic Motivation In Internet Usage. Omega, International Journal Of Management Science, 27: 25–27
  55. Venkatesh, V. 2000. Determinants Of Perceived Ease Of Use : Integrating Control, Intrinsic Motivation, And Emotion Into The Technology Acceptance Model. Information Systems Research, 11(4): 342–365
  56. Wang, C., Ahmad, S. F., Ayassrah, A. Y. A. B. A., Awwad, E. M., Irshad, M., Ali, Y. A., Al-Razgan, M., Khan, Y., & Han, H. 2023. An Empirical Evaluation Of Technology Acceptance Model For Artificial Intelligance In E-Commerce. Heliyon, 9(8). https://doi.org/https://www.sciencedirect.com/science/article/pii/s2405844023055573
  57. Wibowo, A. 2021. Indikator Pertanian 2020 (1st Ed., Vol. 1). Badan Pusat Statistik. https://www.bps.go.id/publication/download.html?nrbvfeve=zdg3yjc1mzy2ytayzgjkymm2zgyzn2ew&xzmn=ahr0chm6ly93d3cuynbzlmdvlmlkl3b1ymxpy2f0aw9ulziwmjevmtavmdgvzdg3yjc1mzy2ytayzgjkymm2zgyzn2ewl2luzglryxrvci1wzxj0yw5pyw4tmjaymc5odg1s&twoadfnoarfeauf=mjaymy0woc0
  58. Wijaya, A. S., & Sarwoprasodjo, S. 2015. Pemanfaatan Cyber Extension Sebagai Media Informasi Oleh Penyuluh Pertanian Di Kabupaten Bogor. Jurnal KMP, 13, 1. Jurnal KMP, 13(1)
  59. Yunus, U., Rizkiansyah, M., & Ariestyani, A. 2023. Cyber Extension As The Sustainable Communication For Farmers. AIP Conference Proceedings, 2594, 1200. https://doi.org/https://doi.org/10.1063/5.0110070

Last update:

No citation recorded.

Last update:

No citation recorded.