Technological Acceptance of Cattle Farmers in Mobile Applications for Livestock Digital Marketing
Agung Triatmojo(1*), Nguyen Hoang Qui(2), Yasser Basstawy El Sayed(3), Mujtahidah Anggriani Ummul Muzayyanah(4), Suci Paramitasari Syahlani(5), Budi Guntoro(6)
(1) Department of Livestock Socio-economics, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta, 55281 Faculty of Agricultural, Environmental, and Food Sciences, Free University of Bozen-Bolzano, Bolzano, 39100
(2) Department of Animal Science and Veterinary Medicine, School of Agriculture and Aquaculture, Tra Vinh University, Tra Vinh, 87000
(3) Faculty of Economics and Management, Free University of Bozen-Bolzano, Bolzano, 39100
(4) Department of Livestock Socio-economics, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta, 55281
(5) Department of Livestock Socio-economics, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta, 55281
(6) Department of Livestock Socio-economics, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta, 55281
(*) Corresponding Author
Abstract
The farmers have encountered challenges in conducting livestock trade due to the absence of dealer activity caused by Anthrax and Foot Mouth Disease (FMD) epidemics. In this context, it is crucial to utilize technology in livestock marketing to obtain current market information from distant marketplaces and reduce the risk of contagion. To meet these purposes, a mobile phone application has been developed in order to be used by cattle farmers; after that, market testing has been conducted to gain feedback and determine the segmentation. Thus, the study aimed to examine the differences in the perceived ease of use, perceived usefulness, and social impact amongst farmers who are willing and unwilling to embrace a mobile phone application for digital marketing. A total of 968 cattle farmers were surveyed with stratified random sampling techniques in the Special Region of Yogyakarta. The data obtained were analyzed using mean difference inferential analysis. The result showed that farmers with various categories of age, education, farm revenue, farmers group, farmer experience, cattle ownership, and regions have significantly different (p<0.01) perceived usefulness (PU), perceived ease of use (PE), and social influence (SI) on mobile applications for livestock digital marketing. Furthermore, farmers willing to adopt mobile application have significantly higher (p<0.01) PU, PE, and SI factors. This study recommends mobile app developers evaluate potential user needs and background factors that may influence farmers' interest.
Keywords
Full Text:
10. AgungReferences
Abdelsayed, M. 2017. Technical update: Health Data for Healthy Cows. Australian Holstein Journal. 28–29. Available from: https://search.informit.org/doi/10.3316/informit.900883971122647
Abu, B. M., Y. B. Osei-Asare, and S. Wayo. 2014. Market participation of smallholder maize farmers in the upper west region of Ghana. Afr. J. Agric Res. 9: 2427–2435. doi:10.5897/AJAR2014.8545.
Adeoti, A. 2014. Determinants of Market Participation among Maize Producers in Oyo State, Nigeria. British Journal of Economics, Management and Trade 4: 1115–1127. doi:10.9734/BJEMT/2014/7826.
Adunea, D., A. Bezahagn, L. Azeb, and S. Muhammed. 2019. Beef cattle value chain analysis: Evidence from West Hararghe Zone of Ethiopia. International Journal of Agricultural Science and Food Technology. 5: 077–087. doi:10.17352/2455-815X.000046.
Ajzen, I. 1991. The theory of planned behavior. Organ Behav Hum Decis Process. 50: 179–211. doi:10.1016/0749-5978(91)90020-T.
Ajzen, I., and M. Fishbein. 1975. Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley, Massachusett.
Aku, A., P. Mshenga, V. Afari-Sefa, and J. Ochieng. 2018. Effect of market access provided by farmer organizations on smallholder vegetable farmer’s income in Tanzania. Cogent Food Agric. 4: 1560596. doi:10.1080/23311932.2018.1560596.
Ariansyah, K., E. R. E. Sirait, B. A. Nugroho, and M. Suryanegara. 2021. Drivers of and barriers to e-commerce adoption in Indonesia: Individuals’ perspectives and the implications. Telecomm Policy. 45: 102219. doi:10.1016/j.telpol.2021.102219.
Baaziz, A. and L. Quoniam. 2014. How to Use Big Data Technologies to Optimize Operations in Upstream Petroleum Industry. SSRN Electronic Journal. doi:10.2139/ssrn.3429410.
Bai, Q., H. Chen, J. Zhou, G. Li, D. Zang, Y. Sow, and Q. Shen. 2023. Digital literacy and farmers’ entrepreneurial behavior—Empirical analysis based on CHFS2019 micro data. PLoS One. 18:e0288245. doi:10.1371/journal.pone.0288245.
Bureau Statistic of Yogyakarta. 2023. Number of Livestock by Type and District/City in D.I. Yogyakarta (head), 2014-2016. https://yogyakarta.bps.go.id/indicator/ 24/56/1/jumlah-ternak-menurut-jenisnya-dan-kabupaten-kota-di-d-i-yogyakarta-.html
Davis, F. D. 1986. Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Cambridge, Massachusett.
Davis, F. D. 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. 13: 319. doi:10.2307/249008.
Dewi, N. M. A. K., S. P. Syahlani, and F. T. Haryadi. 2021. The choice of information sources and marketing channel of Bali cattle farmers in Bali Province. Open Agric. 6: 413–425. doi:10.1515/opag-2021-0018.
Enu-Kwesi, F., and M. O. Opoku. 2020. Relevance of the technology acceptance model (TAM) in information management research: a review of selected empirical evidence. Pressacademia. 7: 34–44. doi:10.17261/Pressacademia.2020.1186.
Fabiyi, S. D., J. Ren, Y. Han, Q. Zhu, and D. Barclay. 2022. Mobile Platform for Livestock Monitoring and Inspection. In: 2022 3rd International Informatics and Software Engineering Conference (IISEC). IEEE. p. 1–6.
Girma, Y., and A. Kelil. 2021. Mobile phone and beef cattle marketing: The case of Girar Jarso district of Oromia region, Ethiopia. Cogent Food Agric. 7. doi:10.1080/23311932.2021.1911032.
Graf-Vlachy, L., K. Buhtz, and A. König. 2018. Social influence in technology adoption: taking stock and moving forward. Management Review Quarterly. 68: 37–76. doi:10.1007/s11301-017-0133-3.
Guntoro, B., N. H. Qui, and A. Triatmojo. 2022. Challenges and Roles of Extension Workers on Cyber Extension as Information Media. KnE Life Sciences. doi:10.18502/kls.v0i0.11843.
He, Y., Q. Chen, and S. Kitkuakul. 2018. Regulatory focus and technology acceptance: Perceived ease of use and usefulness as efficacy. Cogent Business & Management. 5: 1459006. doi:10.1080/23311975.2018.1459006.
Hsiao, C.-H., J.-J. Chang, and K.-Y. Tang. 2016. Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics. 33: 342–355. doi:10.1016/j.tele.2015.08.014.
Hubert, M., M. Blut, C. Brock, R. W. Zhang, V. Koch, and R. Riedl. 2019. The influence of acceptance and adoption drivers on smart home usage. Eur J Mark. 53: 1073–1098. doi:10.1108/EJM-12-2016-0794.
Kappes, A., T. Tozooneyi, G. Shakil, A. F. Railey, K. M. McIntyre, D. E. Mayberry, J. Rushton, D. L. Pendell, and T. L. Marsh. 2023. Livestock health and disease economics: a scoping review of selected literature. Front Vet Sci. 10. doi:10.3389/fvets. 2023.1168649.
Kibona, C. A., and Z. Yuejie. 2021. Factors that influence market participation among traditional beef cattle farmers in the Meatu District of Simiyu Region, Tanzania. PLoS One. 16:e0248576. doi:10.1371/journal. pone.0248576.
Ma, W., M. A. Marini, and D. B. Rahut. 2023. Farmers’ organizations and sustainable development: An introduction. Annals of Public and Cooperative Economics. 94: 683–700. doi:10.1111/apce.12449.
Michels, M., P. J. W. von Ahlefeld, J. Möllmann, and O. Musshoff. 2019. Development and Validation of a Technology Acceptance Model for the Usage of Forward Contracts in Agriculture. Journal of the Austrian Society of Agricultural Economics. 28: 11.
Niu, Z., C. Chen, Y. Gao, Y. Wang, Y. Chen, and K. Zhao. 2022. Peer effects, attention allocation and farmers’ adoption of cleaner production technology: Taking green control techniques as an example. J Clean Prod. 339: 130700. doi:10.1016/j.jclepro.2022. 130700.
OIE. 2018. Foot and Mouth Disease (Infection with Foot and Mouth Disease Virus). Paris.
Pascucci, F., E. Savelli, and G. Gistri. 2023. How digital technologies reshape marketing: evidence from a qualitative investigation. Italian Journal of Marketing. doi:10.1007/s43039-023-00063-6.
Peng, L., G. Lu, K. Pang, and Q. Yao. 2021. Optimal farmer’s income from farm products sales on live streaming with random rewards: Case from China’s rural revitalisation strategy. Comput Electron Agric. 189: 106403. doi:10.1016/j.compag. 2021.106403.
Pesci, S., R. E. Galt, J. L. Durant, G. M. Manser, L. Asprooth, and N. Pinzón. 2023. A digital divide in direct market farmers’ online sales and marketing: Early pandemic evidence from California. J Rural Stud. 101: 103038. doi:10.1016/j.jrurstud.2023.103038.
Prihantoro, W. P., A. Satria, and H. Hartoyo. 2018. The Determinant Factors of Behavior in M-Commerce Application Usage for Online Purchasing. Indonesian Journal of Business and Entrepreneurship. doi:10.17358/ijbe. 4.2.118.
Rahimi, B., H. Nadri, H. Lotfnezhad Afshar, and T. Timpka. 2018. A Systematic Review of the Technology Acceptance Model in Health Informatics. Appl Clin Inform. 09: 604–634. doi:10.1055/s-0038-1668091.
Roberts, R., R. Flin, D. Millar, and L. Corradi. 2021. Psychological factors influencing technology adoption: A case study from the oil and gas industry. Technovation. 102: 102219. doi:10.1016/j.technovation.2020.102219.
Rogers, E. M. 2003. Diffusion of Innovations. 5th ed. Free Press, New York.
Rose, D. C., W. J. Sutherland, C. Parker, M. Lobley, M. Winter, C. Morris, S. Twining, C. Ffoulkes, T. Amano, and L. V. Dicks. 2016. Decision support tools for agriculture: Towards effective design and delivery. Agric Syst. 149: 165–174. doi:10.1016/j.agsy.2016.09.009.
Scherer, R., F. Siddiq, and J. Tondeur. 2019. The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Comput Educ. 128: 13–35. doi:10.1016/j.compedu. 2018.09.009.
Shah, S. S., and Z. Asghar. 2023. Dynamics of social influence on consumption choices: A social network representation. Heliyon. 9: e17146. doi:10.1016/j.heliyon.2023. e17146.
Sieng, S., I. W. Patrick, S. W. Walkden‐Brown, and C. Sar. 2022. A cost‐benefit analysis of foot and mouth disease control program for smallholder cattle farmers in Cambodia. Transbound Emerg Dis. 69: 2126–2139. doi:10.1111/tbed.14207.
Syahlani, S. P., T. J. Wankar, and A. Triatmojo. 2023. The influence of objective and subjective knowledge on attitude and willingness to pay veterinary control number-certified livestock food product. Livestock and Animal Research. 21: 136. doi:10.20961/lar.v21i3.73266.
Taherdoost, H. 2018. A review of technology acceptance and adoption models and theories. Procedia Manuf. 22: 960–967. doi:10.1016/j.promfg.2018.03.137.
Talukder, M., S. Alyammahi, A. Quazi, A. Abdullah, and R. Johns. 2019. Users’ Sociocultural Orientation and Smart Systems Acceptance Link: Do Demographics Matter? . Journal of Organizational Computing and Electronic Commerce. 29: 223–247. doi:10.1080/10919392.2019.1611287.
Tolno, E., H. Kobayashi, M. Ichizen, M. Esham, and B. S. Balde. 2015. Economic Analysis of the Role of Farmer Organizations in Enhancing Smallholder Potato Farmers’ Income in Middle Guinea. Journal of Agricultural Science. 7. doi:10.5539/jas.v7n3p123.
Ume, C. 2023. The role of improved market access for small-scale organic farming transition: Implications for food security. J Clean Prod. 387: 135889. doi:10.1016/j.jclepro.2023.135889.
Venkatesh, Morris, Davis, and Davis. 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly. 27: 425. doi:10.2307/30036540.
Willys, N. 2018. Customer Satisfaction, Switching Costs and Customer Loyalty: An Empirical Study on the Mobile Telecommunication Service. American Journal of Industrial and Business Management. 08: 1022–1037. doi:10.4236/ajibm.2018.84070.
Wu, Y., K. Duan, and W. Zhang. 2023. The Impact of Internet Use on Farmers’ Land Transfer under the Framework of Transaction Costs. Land (Basel) 12: 1855. doi:10.3390/land12101855.
DOI: https://doi.org/10.21059/buletinpeternak.v48i2.92075
Article Metrics
Abstract views : 962 | views : 540Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Buletin Peternakan (Bulletin of Animal Science) Indexed by:
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.