Research article Special Issues

Determinants of the Internet of Things adoption by millennial farmers

  • Received: 24 October 2022 Revised: 30 January 2023 Accepted: 21 February 2023 Published: 08 March 2023
  • Indonesia is experiencing difficulties in ensuring the sustainability of the agricultural system as the younger generation experiences reluctance to enter the business of agriculture. Smart farming is believed to be a solution to the difficulty of millennials entering the business of agriculture. One of the main elements of smart farming is the Internet of Things (IoT). This study aims to determine the factors that encourage millennial farmers in Central Java to adopt IoT-based innovations using a behavioral reasoning theory (BRT) perspective. Data were collected from 120 millennial farmers in Central Java; we applied BRT, an analytical technique, to examine IoT adoption by millennial farmers. Primary survey data analysis was carried out by applying structural equation modeling techniques. The results showed that millennial farmers accepting the adoption of IoT technology is a factor of relative advantage and social influence. Meanwhile, the reason for rejecting the adoption of IoT technology is technology anxiety. This research provides information on the reasons for accepting and reasons for rejecting the adoption of IoT in agriculture by millennial farmers in Central Java province, which will be helpful for the government in the design of a program to attract millennials to go into business in agriculture.

    Citation: Mohamad Harisudin, Kusnandar, Erlyna W. Riptanti, Nuning Setyowati, Isti Khomah. Determinants of the Internet of Things adoption by millennial farmers[J]. AIMS Agriculture and Food, 2023, 8(2): 329-342. doi: 10.3934/agrfood.2023018

    Related Papers:

  • Indonesia is experiencing difficulties in ensuring the sustainability of the agricultural system as the younger generation experiences reluctance to enter the business of agriculture. Smart farming is believed to be a solution to the difficulty of millennials entering the business of agriculture. One of the main elements of smart farming is the Internet of Things (IoT). This study aims to determine the factors that encourage millennial farmers in Central Java to adopt IoT-based innovations using a behavioral reasoning theory (BRT) perspective. Data were collected from 120 millennial farmers in Central Java; we applied BRT, an analytical technique, to examine IoT adoption by millennial farmers. Primary survey data analysis was carried out by applying structural equation modeling techniques. The results showed that millennial farmers accepting the adoption of IoT technology is a factor of relative advantage and social influence. Meanwhile, the reason for rejecting the adoption of IoT technology is technology anxiety. This research provides information on the reasons for accepting and reasons for rejecting the adoption of IoT in agriculture by millennial farmers in Central Java province, which will be helpful for the government in the design of a program to attract millennials to go into business in agriculture.



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    [1] Haryanto Y, Effendy L, Tri Yunandar D (2022) Characteristics of millenial farmers in rice center area in West Java. J Penyul 18: 25–35. https://doi.org/10.25015/18202236982 doi: 10.25015/18202236982
    [2] Nugroho AD, Waluyati LR, Jamhari J (2018) Efforts of engage youth generation to working on agricultural sector in Yogyakarta Province. JPPUMA J Ilmu Pemerintah dan Sos Polit Univ Medan Area 6: 76–95. https://doi.org/10.31289/jppuma.v6i1.1252 doi: 10.31289/jppuma.v6i1.1252
    [3] Arditia MR, Lubis JF, Saragih B, et al. (2021) Millenial work behaviour and it's impact to office design. IOP Conf Ser Earth Environ Sci 794: 012179. https://doi.org/10.1088/1755-1315/794/1/012179 doi: 10.1088/1755-1315/794/1/012179
    [4] Hamdani C (2020) Factors affecting the performance of millennial farmer farming alumni of entrepreneurship training for young farmers in central Java Province (Faktor yang Mempengaruhi Kinerja Berusaha Tani Petani Milenial Alumni Pelatihan Kewirausahaan Bagi Petani Muda di Provinsi Jawa Tengah). J Agriwidya 1: 61–73.
    [5] Gagliardi G, Cosma AIM, Marasco F (2022) A decision support system for sustainable agriculture: The case study of coconut oil extraction process. Agronomy 12: 177. https://doi.org/10.3390/agronomy12010177 doi: 10.3390/agronomy12010177
    [6] Wolfert S, Ge L, Verdouw C, et al. (2017) Big data in smart farming—A review. Agric Syst 153: 69–80. https://doi.org/10.1016/j.agsy.2017.01.023 doi: 10.1016/j.agsy.2017.01.023
    [7] Dryancour G (2017) Smart agriculture for all farms. CEMA; Eur Agric Mach Ind Assoc 32: 1–23.
    [8] Zhai Z, Martínez JF, Beltran V, et al. (2020) Decision support systems for agriculture 4.0: Survey and challenges. Comput Electron Agric 170: 105256. https://doi.org/10.1016/j.compag.2020.105256 doi: 10.1016/j.compag.2020.105256
    [9] De Clercq M, Vatz A, Biel A (2018) Agriculture 4.0: The Future of Farming Technology.
    [10] Ronaghi MH, Forouharfar A (2020) A contextualized study of the usage of the Internet of things (IoTs) in smart farming in a typical Middle Eastern country within the context of Unified Theory of Acceptance and Use of Technology model (UTAUT). Technol Soc 63: 101415. https://doi.org/10.1016/j.techsoc.2020.101415 doi: 10.1016/j.techsoc.2020.101415
    [11] Mukti GW, Budi Kusumo RA, Qanti SR (2017) Successful behavior of young entrepreneurial farmers graduated from the faculty of agriculture, Padjadjaran University (Perilaku Sukses Petani Muda Wirausaha Lulusan Fakultas Pertanian Universitas Padjadjaran). J Agribisnis Terpadu 10: 221–234. https://doi.org/10.33512/jat.v10i2.5076 doi: 10.33512/jat.v10i2.5076
    [12] Nargotra M, Khurjekar MJ (2020) Green house based on IoT and AI for societal benefit, 2020 International Conference on Emerging Smart Computing and Informatics, 109–112. https://doi.org/10.1109/ESCI48226.2020.9167637
    [13] Zhai Z, Martínez JF, Beltran V, et al. (2020) Decision support systems for agriculture 4.0: Survey and challenges. Comput Electron Agric 170: 105256. https://doi.org/10.1016/j.compag.2020.105256 doi: 10.1016/j.compag.2020.105256
    [14] Liakos KG, Busato P, Moshou D, et al. (2018) Machine learning in agriculture: A review. Sensors (Switzerland) 18: 2674. https://doi.org/10.3390/s18082674 doi: 10.3390/s18082674
    [15] Muangprathub J, Boonnam N, Kajornkasirat S, et al. (2019) IoT and agriculture data analysis for smart farm. Comput Electron Agric 156: 467–474. https://doi.org/10.1016/j.compag.2018.12.011 doi: 10.1016/j.compag.2018.12.011
    [16] Ihsaniyati H, Setyowati N, Pardono (2022) Factors motivating the adoption of geographical indication-based quality standards among Robusta coffee farmers in Indonesia. Int J Bus Soc 23: 207–225. https://doi.org/https://doi.org/10.33736/ijbs.4609.20221 doi: 10.33736/ijbs.4609.20221
    [17] Hsu TC (2005) Research methods and data analysis procedures used by educational researchers. Int J Res Method Educ 28: 109–133. https://doi.org/10.1080/01406720500256194 doi: 10.1080/01406720500256194
    [18] Blakeslee JR (2020) Effects of high-fidelity simulation on the critical thinking skills of baccalaureate nursing students: A causal-comparative research study. Nurse Educ Today 92: 104494. https://doi.org/10.1016/j.nedt.2020.104494 doi: 10.1016/j.nedt.2020.104494
    [19] Nurrizky M, Harisudin M, Barokah U (2023) Influence of experiential marketing to consumer satisfaction and repurchase intentions : "Goreng" Taichan restaurants as a case study. Int J Sustain Dev Plan 18: 247–253. https://doi.org/https://doi.org/10.18280/ijsdp.180126 doi: 10.18280/ijsdp.180126
    [20] Banendro S (2019) Millennial Farmers Boost Central Java's Agricultural Export Value(Petani Milenial Dongkrak Nilai Ekspor Pertanian Jawa Tengah). https://humas.jatengprov.go.id/detail_berita_gubernur?id = 3692
    [21] Kim M (2021) Conceptualization of e-servicescapes in the fitness applications and wearable devices context: Multi-dimensions, consumer satisfaction, and behavioral intention. J Retail Consum Serv 61: 102562. https://doi.org/10.1016/j.jretconser.2021.102562 doi: 10.1016/j.jretconser.2021.102562
    [22] Cahyadinata I, Nusril, Gushevinalti (2020) Descriptive, correlation analysis and analytical hierarchy process of coastal community empowerment of Bengkulu City, Indonesia. Int J Adv Sci Eng Inf Technol 10: 1304–1310. https://doi.org/10.18517/ijaseit.10.3.2659 doi: 10.18517/ijaseit.10.3.2659
    [23] Vrontis D, Chaudhuri R, Chatterjee S (2022) Adoption of digital technologies by SMEs for sustainability and value creation : Moderating role of entrepreneurial orientation. Sustainability 14: 7949. https://doi.org/10.3390/su14137949 doi: 10.3390/su14137949
    [24] Hair JF, Hult GTM, Ringle CM, et al. (2014) A primer on partial least squares structural equation modeling (PLS-SEM). Eur J Tour Res 6: 211–213.
    [25] Elijah O, Rahman TA, Orikumhi I, et al. (2018) An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet Things J 5: 3758–3773. https://doi.org/10.1109/JIOT.2018.2844296 doi: 10.1109/JIOT.2018.2844296
    [26] Coutu A, Mottelet S, Guérin S, et al. (2022) Methane yield optimization using mix response design and bootstrapping: application to solid-state anaerobic co-digestion process of cattle manure and damp grass. Bioresour Technol Reports 17: 100883. https://doi.org/10.1016/j.biteb.2021.100883 doi: 10.1016/j.biteb.2021.100883
    [27] Ammad S, Alaloul WS, Saad S, et al. (2021) Personal Protective Equipment (PPE) usage in construction projects: A systematic review and smart PLS approach. Ain Shams Eng J 12: 3495–3507. https://doi.org/10.1016/j.asej.2021.04.001 doi: 10.1016/j.asej.2021.04.001
    [28] Walter A, Finger R, Huber R, et al. (2017) Smart farming is key to developing sustainable agriculture. Proc Natl Acad Sci U S A 114: 6148–6150. https://doi.org/10.1073/pnas.1707462114 doi: 10.1073/pnas.1707462114
    [29] Sekaran U, Bougie R (2016) Research Methods for Business: A Skill- Building Approach, Wiley & Sons, West Sussex.
    [30] Pillai R, Sivathanu B (2020) Adoption of internet of things (IoT) in the agriculture industry deploying the BRT framework. Benchmarking 27: 1341–1368. https://doi.org/10.1108/BIJ-08-2019-0361 doi: 10.1108/BIJ-08-2019-0361
    [31] Tandon A, Dhir A, Kaur P, et al. (2020) Behavioral reasoning perspectives on organic food purchase. Appetite 154: 104786. https://doi.org/10.1016/j.appet.2020.104786 doi: 10.1016/j.appet.2020.104786
    [32] Rachmawati RR (2020) Smart farming 4.0 to build advanced, independent, and modern Indonesian agriculture. Forum Penelit Agro Ekon 38: 137–154.
    [33] Westaby JD, Probst TM, Lee BC (2010) Leadership decision-making: A behavioral reasoning theory analysis. Leadersh Q 21: 481–495. https://doi.org/10.1016/j.leaqua.2010.03.011 doi: 10.1016/j.leaqua.2010.03.011
    [34] Westaby JD (2005) Comparing attribute importance and reason methods for understanding behavior: An application to internet job searching. Appl Psychol 54: 568–583. https://doi.org/10.1111/j.1464-0597.2005.00231.x doi: 10.1111/j.1464-0597.2005.00231.x
    [35] Akter S, D'Ambra J, Ray P (2011) An evaluation of PLS based complex models: The roles of power analysis, predictive relevance and GoF index, AMCIS 2011 PROCEEDINGS, 1–7.
    [36] Spielhofer R, Thrash T, Hayek UW, et al. (2021) Physiological and behavioral reactions to renewable energy systems in various landscape types. Renew Sustain Energy Rev 135: 21–24. https://doi.org/10.1016/j.rser.2020.110410 doi: 10.1016/j.rser.2020.110410
    [37] Mandhani J, Nayak JK, Parida M (2020) Interrelationships among service quality factors of Metro Rail Transit System: An integrated Bayesian networks and PLS-SEM approach. Transp Res Part A Policy Pract 140: 320–336. https://doi.org/10.1016/j.tra.2020.08.014 doi: 10.1016/j.tra.2020.08.014
    [38] Harisudin M, Adi RK, Qonita RRA (2022) Synergy Grand Strategy Matrix, Swot and Qspm as determinants of Tempeh product development strategy. J Sustain Sci Manag 17: 62–82. https://doi.org/10.46754/jssm.2022.08.004 doi: 10.46754/jssm.2022.08.004
    [39] Ellitan L (2022) Increasing repurchase intention through experiential marketing and customer satisfaction. ULIL ALBAB J Ilm Multidisiplin 1: 3559–3565.
    [40] Pakura S, Rudeloff C (2020) How entrepreneurs build brands and reputation with social media PR: Empirical insights from start-ups in Germany. J Small Bus Entrep 35: 153–180. https://doi.org/10.1080/08276331.2020.1728490 doi: 10.1080/08276331.2020.1728490
    [41] Costanza DP, Ravid DM, Slaughter AJ (2021) A distributional approach to understanding generational differences: What do you mean they vary? J Vocat Behav 127: 103585. https://doi.org/10.1016/j.jvb.2021.103585 doi: 10.1016/j.jvb.2021.103585
    [42] Gupta A, Arora N (2017) Consumer adoption of m-banking: A behavioral reasoning theory perspective. Int J Bank Mark 35: 733–747. https://doi.org/10.1108/IJBM-11-2016-0162 doi: 10.1108/IJBM-11-2016-0162
    [43] Munir R, Beh LS (2019) Measuring and enhancing organisational creative climate, knowledge sharing, and innovative work behavior in startups development. Bottom Line 32: 269–289. https://doi.org/10.1108/BL-03-2019-0076 doi: 10.1108/BL-03-2019-0076
    [44] Lombardi M, Pascale F, Santaniello D (2021) Internet of things: A general overview between architectures, protocols and applications. Inf 12: 87. https://doi.org/10.3390/info12020087 doi: 10.3390/info12020087
    [45] Tanveer A, Zeng S, Irfan M, et al. (2021) Do perceived risk, perception of self-efficacy, and openness to technology matter for solar pv adoption? An application of the extended theory of planned behavior. Energies 14:5008. https://doi.org/10.3390/en14165008 doi: 10.3390/en14165008
    [46] Gursoy D, Maier TA, Chi CG (2008) Generational differences: An examination of work values and generational gaps in the hospitality workforce. Int J Hosp Manag 27: 448–458. https://doi.org/10.1016/j.ijhm.2007.11.002 doi: 10.1016/j.ijhm.2007.11.002
    [47] Westaby JD (2005) Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior. Organ Behav Hum Decis Process 98: 97–120. https://doi.org/10.1016/j.obhdp.2005.07.003 doi: 10.1016/j.obhdp.2005.07.003
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