Utilizing the Technology Acceptance Model (TAM) to Drive E-Warehouse Adoption in Brebes' Shallot Sector
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Jul 8, 2026
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Abstract
This study explores the factors influencing the adoption of SiJuna, a cold storage-based e-warehouse platform, among shallot farmers in Brebes Regency. Price volatility significantly impacts farmers, leading to economic losses, particularly when prices drop below production costs. SiJuna aims to help farmers by offering cold storage to preserve their harvests during price declines and sell when prices stabilize. Using the Technology Acceptance Model (TAM), data was collected from 90 farmers and analyzed through Structural Equation Modeling (SEM). The results reveal that perceived ease of use and perceived usefulness positively influence farmers' behavioral intention to adopt SiJuna, which in turn significantly affects the actual use of the system. The findings underscore the importance of usability and perceived value in technology adoption, suggesting that a user-friendly design and clear benefits can enhance the adoption of agricultural technologies, ultimately improving post-harvest management and farmers' profitability.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Ade Yanyan Ramdhani, Muhammad Iqbal Faturohman, Lina Fatimah Lishobrina, Faizah, Alon Jala Tirta Segara, Arief Rais Bahtiar, Dimas Fanny Hebrasianto, Muhammad Alim Safa’at, Destyana Chandra Priyambodo, Diva Angelica, Rafli Dhafin Kamil, & Farhan Aryo Pangestu. (2026). Utilizing the Technology Acceptance Model (TAM) to Drive E-Warehouse Adoption in Brebes’ Shallot Sector. Jurnal E-Komtek, 10(1), 275-291. https://doi.org/10.37339/e-komtek.v9i1.2213
References
[1] A. A. Santoso, S. Surorejo, R. D. Kurniawan, and G. Gunawan, “Performance evaluation of single moving average and exponential smoothing in shallot production prediction,” J. Mantik, vol. 8, no. 1, pp. 714–723, 2024, doi: 10.35335/mantik.v8i1.5205.
[2] R. B. Nugraha, “Respon Kualitas Pascapanen Bawang Merah (Allium ascalonicum L.) Terolah Minimal Pada Beberapa Suhu Penyimpanan.,” 2015. [Online]. Available: https://api.semanticscholar.org/CorpusID:74714288
[3] S. H. M, S. Sampathkumar, and J. Pradeep, “Smart Cold Controlled Onion Warehouse Design to Improve Shelf Life,” in 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 2024, pp. 2494–2501. doi: 10.1109/ICACCS60874.2024.10716882.
[4] R. Latpate, M. Bhosale, and S. Kurade, “Cold supply chain inventory models for agricultural products with multi-stage stochastic deterioration rates,” J. Manag. Anal., vol. 10, no. 3, pp. 516–549, Jul. 2023, doi: 10.1080/23270012.2023.2229312.
[5] K. Zhu, K. Kraemer, and S. Xu, “Electronic business adoption by European firms: A cross-country assessment of the facilitators and inhibitors,” Eur. J. Inf. Syst., vol. 12, no. 4, pp. 251–268, 2003, doi: 10.1057/palgrave.ejis.3000475.
[6] P. N. X. Truong, “Adopting digital transformation in small and medium enterprises: An empirical model of Influencing factorsbased on TOE-TAM integrated,” Tạp chí Nghiên cứu Tài chính - Mark., vol. 72, no. 6, pp. 45–57, 2023, doi: 10.52932/jfm.vi72.352.
[7] R. Andy, A. C. Dewi, and M. As’adi, “An Empirical Study to Validate The Technology Acceptance Model (TAM) In Evaluating ‘Desa Digital’ Applications,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1125, no. 1, p. 012055, 2021, doi: 10.1088/1757-899x/1125/1/012055.
[8] A. Alambaigi and I. Ahangari, “Technology Acceptance Model (TAM) As a Predictor Model for Explaining Agricultural Experts Behavior in Acceptance of ICT,” Int. J. Agric. Manag. Dev., vol. 6, no. 2, pp. 235–247, 2016, [Online]. Available: www.ijamad.iaurasht.ac.ir
[9] L. Rosiana, M. Hubeis, and E. R. Cahyadi, “Factors Affecting User Behavior of Technology Application of Management Information System,” Indones. J. Bus. Entrep., vol. 6, no. 3, pp. 239–248, 2020, doi: 10.17358/ijbe.6.3.239.
[10] T. T. Division and S. Lanka, “Does intention lead to behaviour ? A case study,” Sci. Inf., pp. 143–148, 2007.
[11] Q. Dai and K. Cheng, “What Drives the Adoption of Agricultural Green Production Technologies? An Extension of TAM in Agriculture,” Sustain., vol. 14, no. 21, 2022, doi: 10.3390/su142114457.
[12] J. D. Bryan and T. Zuva, “A Review on TAM and TOE Framework Progression and How These Models Integrate,” Adv. Sci. Technol. Eng. Syst. J., vol. 6, no. 3, pp. 137–145, 2021, doi: 10.25046/aj060316.
[13] A. Cimino, I. M. Coniglio, V. Corvello, F. Longo, J. K. Sagawa, and V. Solina, “Exploring small farmers behavioral intention to adopt digital platforms for sustainable and successful agricultural ecosystems,” Technol. Forecast. Soc. Change, vol. 204, no. August 2023, p. 123436, 2024, doi: 10.1016/j.techfore.2024.123436.
[14] M. Bahari, I. Arpaci, O. Der, F. Akkoyun, and A. Ercetin, “Driving Agricultural Transformation: Unraveling Key Factors Shaping IoT Adoption in Smart Farming with Empirical Insights,” Sustainability, vol. 16, no. 5. 2024. doi: 10.3390/su16052129.
[15] A. B. Duche-Pérez, O. A. Gutiérrez-Aguilar, C. Y. Vera-Revilla, S. Chicana-Huanca, and B. G. Andía-Gonzales, “The mediating effect of knowledge acquisition on the application of knowledge in interactions: Perceptions of ease of use, actual use, and behavioral intentions in e-learning platforms,” Edelweiss Appl. Sci. Technol., vol. 8, no. 5, pp. 2055–2069, 2024, doi: 10.55214/25768484.v8i5.1949.
[16] N. Mishra et al., “Technology in farming: Unleashing farmers’ behavioral intention for the adoption of agriculture 5.0,” PLoS One, vol. 19, no. 8, pp. 1–28, 2024, doi: 10.1371/journal.pone.0308883.
[17] A. M. A. Zamil, H. M. U. Javed, and S. Ali, “Internet of things platforms adoption in agriculture: comparative theoretical models,” Int. J. Retail Distrib. Manag., vol. 52, no. 9, pp. 965–981, Jan. 2024, doi: 10.1108/IJRDM-10-2022-0420.
[18] J. Wang, L. Liu, K. Zhao, and Q. Wen, “Farmers’ adoption intentions of water-saving agriculture under the risks of frequent irrigation-induced landslides,” Clim. Risk Manag., vol. 39, p. 100484, 2023, doi: https://doi.org/10.1016/j.crm.2023.100484.
[19] Y.-H. Luh, M.-H. Tsai, and Y.-C. Chang, “Actions speak louder than words: Adoption of sustainable farming practices based on an integrated model,” J. Clean. Prod., vol. 416, p. 137856, 2023, doi: https://doi.org/10.1016/j.jclepro.2023.137856.
[20] B. Abadi, “Impact of attitudes, factual and causal feedback on farmers’ behavioral intention to manage and recycle agricultural plastic waste and debris,” J. Clean. Prod., vol. 424, p. 138773, 2023, doi: https://doi.org/10.1016/j.jclepro.2023.138773.
[21] T. P. Coulibaly, J. Du, and D. Diakité, “Sustainable agricultural practices adoption,” Agric., vol. 67, no. 4, pp. 166–176, 2021, doi: 10.2478/agri-2021-0015.
[2] R. B. Nugraha, “Respon Kualitas Pascapanen Bawang Merah (Allium ascalonicum L.) Terolah Minimal Pada Beberapa Suhu Penyimpanan.,” 2015. [Online]. Available: https://api.semanticscholar.org/CorpusID:74714288
[3] S. H. M, S. Sampathkumar, and J. Pradeep, “Smart Cold Controlled Onion Warehouse Design to Improve Shelf Life,” in 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 2024, pp. 2494–2501. doi: 10.1109/ICACCS60874.2024.10716882.
[4] R. Latpate, M. Bhosale, and S. Kurade, “Cold supply chain inventory models for agricultural products with multi-stage stochastic deterioration rates,” J. Manag. Anal., vol. 10, no. 3, pp. 516–549, Jul. 2023, doi: 10.1080/23270012.2023.2229312.
[5] K. Zhu, K. Kraemer, and S. Xu, “Electronic business adoption by European firms: A cross-country assessment of the facilitators and inhibitors,” Eur. J. Inf. Syst., vol. 12, no. 4, pp. 251–268, 2003, doi: 10.1057/palgrave.ejis.3000475.
[6] P. N. X. Truong, “Adopting digital transformation in small and medium enterprises: An empirical model of Influencing factorsbased on TOE-TAM integrated,” Tạp chí Nghiên cứu Tài chính - Mark., vol. 72, no. 6, pp. 45–57, 2023, doi: 10.52932/jfm.vi72.352.
[7] R. Andy, A. C. Dewi, and M. As’adi, “An Empirical Study to Validate The Technology Acceptance Model (TAM) In Evaluating ‘Desa Digital’ Applications,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1125, no. 1, p. 012055, 2021, doi: 10.1088/1757-899x/1125/1/012055.
[8] A. Alambaigi and I. Ahangari, “Technology Acceptance Model (TAM) As a Predictor Model for Explaining Agricultural Experts Behavior in Acceptance of ICT,” Int. J. Agric. Manag. Dev., vol. 6, no. 2, pp. 235–247, 2016, [Online]. Available: www.ijamad.iaurasht.ac.ir
[9] L. Rosiana, M. Hubeis, and E. R. Cahyadi, “Factors Affecting User Behavior of Technology Application of Management Information System,” Indones. J. Bus. Entrep., vol. 6, no. 3, pp. 239–248, 2020, doi: 10.17358/ijbe.6.3.239.
[10] T. T. Division and S. Lanka, “Does intention lead to behaviour ? A case study,” Sci. Inf., pp. 143–148, 2007.
[11] Q. Dai and K. Cheng, “What Drives the Adoption of Agricultural Green Production Technologies? An Extension of TAM in Agriculture,” Sustain., vol. 14, no. 21, 2022, doi: 10.3390/su142114457.
[12] J. D. Bryan and T. Zuva, “A Review on TAM and TOE Framework Progression and How These Models Integrate,” Adv. Sci. Technol. Eng. Syst. J., vol. 6, no. 3, pp. 137–145, 2021, doi: 10.25046/aj060316.
[13] A. Cimino, I. M. Coniglio, V. Corvello, F. Longo, J. K. Sagawa, and V. Solina, “Exploring small farmers behavioral intention to adopt digital platforms for sustainable and successful agricultural ecosystems,” Technol. Forecast. Soc. Change, vol. 204, no. August 2023, p. 123436, 2024, doi: 10.1016/j.techfore.2024.123436.
[14] M. Bahari, I. Arpaci, O. Der, F. Akkoyun, and A. Ercetin, “Driving Agricultural Transformation: Unraveling Key Factors Shaping IoT Adoption in Smart Farming with Empirical Insights,” Sustainability, vol. 16, no. 5. 2024. doi: 10.3390/su16052129.
[15] A. B. Duche-Pérez, O. A. Gutiérrez-Aguilar, C. Y. Vera-Revilla, S. Chicana-Huanca, and B. G. Andía-Gonzales, “The mediating effect of knowledge acquisition on the application of knowledge in interactions: Perceptions of ease of use, actual use, and behavioral intentions in e-learning platforms,” Edelweiss Appl. Sci. Technol., vol. 8, no. 5, pp. 2055–2069, 2024, doi: 10.55214/25768484.v8i5.1949.
[16] N. Mishra et al., “Technology in farming: Unleashing farmers’ behavioral intention for the adoption of agriculture 5.0,” PLoS One, vol. 19, no. 8, pp. 1–28, 2024, doi: 10.1371/journal.pone.0308883.
[17] A. M. A. Zamil, H. M. U. Javed, and S. Ali, “Internet of things platforms adoption in agriculture: comparative theoretical models,” Int. J. Retail Distrib. Manag., vol. 52, no. 9, pp. 965–981, Jan. 2024, doi: 10.1108/IJRDM-10-2022-0420.
[18] J. Wang, L. Liu, K. Zhao, and Q. Wen, “Farmers’ adoption intentions of water-saving agriculture under the risks of frequent irrigation-induced landslides,” Clim. Risk Manag., vol. 39, p. 100484, 2023, doi: https://doi.org/10.1016/j.crm.2023.100484.
[19] Y.-H. Luh, M.-H. Tsai, and Y.-C. Chang, “Actions speak louder than words: Adoption of sustainable farming practices based on an integrated model,” J. Clean. Prod., vol. 416, p. 137856, 2023, doi: https://doi.org/10.1016/j.jclepro.2023.137856.
[20] B. Abadi, “Impact of attitudes, factual and causal feedback on farmers’ behavioral intention to manage and recycle agricultural plastic waste and debris,” J. Clean. Prod., vol. 424, p. 138773, 2023, doi: https://doi.org/10.1016/j.jclepro.2023.138773.
[21] T. P. Coulibaly, J. Du, and D. Diakité, “Sustainable agricultural practices adoption,” Agric., vol. 67, no. 4, pp. 166–176, 2021, doi: 10.2478/agri-2021-0015.