Technology Acceptance of a Mobile Application to Manage Dairy Businesses

dc.creatorUsuga-Escobar, Junnier Felipe
dc.creatorPalacio-Baena, Luis Guillermo
dc.creatorBarrios , Dursun
dc.date2022-05-30
dc.date.accessioned2025-10-01T23:48:56Z
dc.descriptionThis study aimed to evaluate the technology acceptance of a mobile application to manage diary businesses and to identify the factors that influence the intention to use and the frequency of use of these technologies in the dairy industry. The Technology Acceptance Model (TAM) was used to conduct the evaluation. A survey was administered to 122 dairy farmers, the TAM was calculated by the partial least squares method, and ordered logistic regression was employed to examine the frequency of use. It was found that perceived usefulness has the strongest influence on intention to use. In addition, bigger business sizes increase perceived usefulness. In turn, milk production volume, dairy farmers’ age, and previous knowledge of mobile applications to manage dairy businesses do not influence perceived usefulness or ease of use. The evidence shows that educational attainment influences ease of use, and milking method influences frequency of use. The information in this study can strengthen the management capabilities of the dairy industry, thus favoring its business performance. This can help said industry to narrow technology gaps and address the challenges that the sector is facing.en-US
dc.descriptionEl objetivo de este estudio fue evaluar la aceptación tecnológica de una aplicación móvil para la gestión de negocios lecheros e identificar los factores que influencian la intención y frecuencia de uso de estas tecnologías en la industria lechera. Para la evaluación se seleccionó un modelo de aceptación tecnológica (TAM). Se aplicó una encuesta a 122 empresarios ganaderos, se calculó el TAM por el enfoque de mínimos cuadrados parciales y, para la frecuencia de uso, se utilizó una regresión logística ordenada. La mayor influencia encontrada sobre la intención de uso se debe a la utilidad percibida. El tamaño del negocio, además, aumentó significativamente la utilidad percibida. Por su parte, el volumen de producción de leche, la edad del empresario ganadero y su conocimiento previo de aplicaciones móviles para la gestión de negocios lecheros no influencian la utilidad o facilidad de uso percibidas. Igualmente se presentó evidencia de la influencia que tiene la educación sobre la facilidad de uso y del tipo de ordeño sobre la frecuencia de uso. La información de este estudio fortalecería las capacidades de gestión en la industria lechera, favoreciendo su desempeño empresarial, lo que permitiría el cierre de brechas tecnológicas y enfrentar los desafíos de mercado que presenta el sector.es-ES
dc.formatapplication/pdf
dc.formatapplication/zip
dc.formattext/xml
dc.formattext/html
dc.identifierhttps://revistas.itm.edu.co/index.php/revista-cea/article/view/2007
dc.identifier10.22430/24223182.2007
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7067
dc.languagespa
dc.publisherInstitución Universitaria ITMes-ES
dc.relationhttps://revistas.itm.edu.co/index.php/revista-cea/article/view/2007/2400
dc.relationhttps://revistas.itm.edu.co/index.php/revista-cea/article/view/2007/2407
dc.relationhttps://revistas.itm.edu.co/index.php/revista-cea/article/view/2007/2408
dc.relationhttps://revistas.itm.edu.co/index.php/revista-cea/article/view/2007/2409
dc.relation/*ref*/Alambaigi, A., Ahangari, I. (2016). Technology Acceptance Model (TAM) As a Predictor Model for Explaining Agricultural Experts Behavior in Acceptance of ICT. International Journal of Agricultural Management and Development, v. 6. n. 2, 235-247. http://ijamad.iaurasht.ac.ir/article_523440.html
dc.relation/*ref*/Aldas, J., Uriel, E. (2017). Análisis multivariante aplicado con R. 2ª ed. Ediciones Paraninfo.
dc.relation/*ref*/Amadasun, K. N., Short, M., Shankar-Priya, R., Crosbie, T. (2021). Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing. Data, v. 6, n. 7, 69. https://doi.org/10.3390/data6070069
dc.relation/*ref*/Barrios, D., Olivera, M. (2013). Análisis de la competitividad del sector lechero: caso aplicado al norte de Antioquia, Colombia. Innovar, v. 23, n. 48, 33-41. https://revistas.unal.edu.co/index.php/innovar/article/view/40487
dc.relation/*ref*/Barrios, D., Restrepo-Escobar, F. J., Cerón-Muñoz, M. (2020a). Desempeño empresarial en la industria lechera. Suma de Negocios, v. 11, n. 25, 180-185. http://doi.org/10.14349/sumneg/2020.V11.N25.A9
dc.relation/*ref*/Barrios, D., Restrepo-Escobar, F. J., Cerón-Muñoz, M. (2020b). Factors associated with the technology adoption in dairy agribusiness. Revista Facultad Nacional de Agronomía Medellín, v. 73, n. 2, 9221-9226. https://doi.org/10.15446/rfnam.v73n2.82169
dc.relation/*ref*/Barrios, D., Restrepo-Escobar, F. J., Cerón-Muñoz, M. F. (2016). Antecedentes sobre gestión tecnológica como estrategia de competitividad en el sector lechero colombiano. Livestock Research for Rural Development, v. 28, n. 7, artículo #125. http://www.lrrd.org/lrrd28/7/barr28125.html
dc.relation/*ref*/Barrios, D., Restrepo-Escobar, F. J., Cerón-Muñoz, M. (2019). Adopción tecnológica en agronegocios lecheros. Livestock Research for Rural Development, v. 31, n. 8, artículo #116. http://www.lrrd.org/lrrd31/8/cero31116.html
dc.relation/*ref*/Begnum, M. E. N., Pettersen, L., Sørum, H. (2019). Identifying Five Archetypes of Interaction Design Professionals and Their Universal Design Expertise. Interacting with Computers, v. 31, n. 4, 372-392. https://doi.org/10.1093/iwc/iwz023
dc.relation/*ref*/Belvedere, V., Grando, A., Bielli, P. (2013). A quantitative investigation of the role of information and communication technologies in the implementation of a product-service system. International Journal of Production Research, v. 51, n. 2, 410-426. https://doi.org/10.1080/00207543.2011.648278
dc.relation/*ref*/Bland, J. M., Altman, D. G. (2000). The odds ratio. BMJ, v. 320, 1468. https://doi.org/10.1136/bmj.320.7247.1468
dc.relation/*ref*/Bonke, V., Fecke, W., Michels, M., Musshoff, O. (2018). Willingness to pay for smartphone apps facilitating sustainable crop protection. Agronomy for Sustainable Development, v. 38, n. 5, Article number: 51. https://doi.org/10.1007/s13593-018-0532-4
dc.relation/*ref*/Calsamiglia, S., Astiz, S., Baucells, J., Castillejos, L. (2018). A stochastic dynamic model of a dairy farm to evaluate the technical and economic performance under different scenarios. Journal of Dairy Science, v. 101, n. 8, 7517-7530. https://doi.org/10.3168/jds.2017-12980
dc.relation/*ref*/Chin, W. W. (1998). The partial least squares approach for structural equation modeling. En G. A. Marcoulides (ed.), Modern Methods for Business Research (pp. 295-336). Psychology Press
dc.relation/*ref*/Cristofaro, M. (2020). E-business evolution: an analysis of mobile applications’ business models. Technology Analysis & Strategic Management, v. 32, n. 1, 88-103. https://doi.org/10.1080/09537325.2019.1634804
dc.relation/*ref*/Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Aceeptance of information technology. MIS Quarterly, v. 13, n.3, 319-340. https://doi.org/10.2307/249008
dc.relation/*ref*/de Oca Munguia, O. M., Llewellyn, R. (2020). The Adopters versus the Technology: Which Matters More when Predicting or Explaining Adoption? Applied Economic Perspectives and Policy, v. 42 n. 1, 80-91. https://doi.org/10.1002/aepp.13007
dc.relation/*ref*/Debauche, O., Mahmoudi, S., Andriamandroso, A. L. H., Manneback, P., Bindelle, J., Lebeau, F. (2019). Cloud services integration for farm animals’ behavior studies based on smartphones as activity sensors. Journal of Ambient Intelligence and Humanized Computing, v. 10, n. 12, 4651-4662. https://doi.org/10.1007/s12652-018-0845-9
dc.relation/*ref*/Edwards, J. P., Dela Rue, B. T., Jago, J. G. (2014). Evaluating rates of technology adoption and milking practices on New Zealand dairy farms. Animal Production Science, v. 55, n. 6, 702-709. https://doi.org/10.1071/AN14065
dc.relation/*ref*/Ferris, M. C., Christensen, A., Wangen, S. R. (2020). Symposium review: Dairy Brain—Informing decisions on dairy farms using data analytics. Journal of Dairy Science, v. 103, n. 4, 3874-3881. https://doi.org/10.3168/jds.2019-17199
dc.relation/*ref*/Flett, R., Alpass, F., Humphries, S., Massey, C., Morriss, S., Long, N. (2004). The technology acceptance model and use of technology in New Zealand dairy farming. Agricultural Systems, v. 80, n, 2, 199-211. https://doi.org/10.1016/j.agsy.2003.08.002
dc.relation/*ref*/Folorunso, O., Ogunseye, S. O. (2008). Applying an Enhanced Technology Acceptance Model to Knowledge Management in Agricultural Extension Services. Data Science Journal, v. 7, 31-45. https://doi.org/10.2481/dsj.7.31
dc.relation/*ref*/Fornell, C., Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, v. 18, n. 1, 39-50. https://doi.org/10.2307/3151312
dc.relation/*ref*/Freeze, R., Raschke, R. L. (2007). An Assessment of Formative and Reflective Constructs in IS Research. ECIS 2007 Proceedings. https://aisel.aisnet.org/ecis2007/171
dc.relation/*ref*/Gbadegeshin, S. A., Oyelere, S. S., Olaleye, S. A., Sanusi, I. T., Ukpabi, D. C., Olawumi, O., Adegbite, A. (2019). Application of information and communication technology for internationalization of Nigerian small- and medium-sized enterprises. The Electronic Journal of Information Systems in Developing Countries, v. 85, n. 1, e12059. https://doi.org/10.1002/isd2.12059
dc.relation/*ref*/Gupta, A., Arora, N. (2017). Understanding determinants and barriers of mobile shopping adoption using behavioral reasoning theory. Journal of Retailing and Consumer Services, v. 36, 1-7. https://doi.org/10.1016/j.jretconser.2016.12.012
dc.relation/*ref*/Hair, J., Hollingsworth, C. L., Randolph, A. B., Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, v. 117, n. 3, 442-458. https://doi.org/10.1108/IMDS-04-2016-0130
dc.relation/*ref*/Hundleby, J. D. (1968). [Review of Psychometric Theory, by J. Nunnally]. American Educational Research Journal, v. 5, n. 3, 431-433. https://doi.org/10.2307/1161962
dc.relation/*ref*/Kabbiri, R., Dora, M., Kumar, V., Elepu, G., Gellynck, X. (2018). Mobile phone adoption in agri-food sector: Are farmers in Sub-Saharan Africa connected? Technological Forecasting and Social Change, v. 131, 253-261. https://doi.org/10.1016/j.techfore.2017.12.010
dc.relation/*ref*/Khanal, A. R., Gillespie, J., MacDonald, J. (2010). Adoption of technology, management practices, and production systems in US milk production. Journal of Dairy Science, v. 93, n. 12, 6012-6022. https://doi.org/10.3168/jds.2010-3425
dc.relation/*ref*/Lai, P. (2017). The literature review of technology adoption models and theories for the novelty technology. Journal of Information Systems and Technology Management, v. 14, n. 1, 21-38. http://dx.doi.org/10.4301/S1807-17752017000100002
dc.relation/*ref*/Lamberti, G., Banet Aluja, T., Sanchez, G. (2017). The Pathmox approach for PLS path modeling: Discovering which constructs differentiate segments. Applied Stochastic Models in Business and Industry, v. 33, n. 6, 674-689. https://doi.org/10.1002/asmb.2270
dc.relation/*ref*/Li, L., Paudel, K. P., Guo, J. (2021). Understanding Chinese farmers’ participation behavior regarding vegetable traceability systems. Food Control, v. 130, 108325. https://doi.org/10.1016/j.foodcont.2021.108325
dc.relation/*ref*/Li, Y., Fu, Z. T., Li, H. (2007). Evaluating factors affecting the adoption of mobile commerce in agriculture: An empirical study. New Zealand Journal of Agricultural Research, v. 50, n. 5, 1213-1218. https://doi.org/10.1080/00288230709510404
dc.relation/*ref*/Martínez Ávila, M., Fierro Moreno, E. (2018). Aplicación de la técnica PLS-SEM en la gestión del conocimiento: un enfoque técnico práctico. RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo, v. 8, n. 16, 130-164. https://doi.org/10.23913/ride.v8i16.336
dc.relation/*ref*/Michels, M., Bonke, V., Musshoff, O. (2019). Understanding the adoption of smartphone apps in dairy herd management. Journal of Dairy Science, v. 102, n. 10, 9422-9434. https://doi.org/10.3168/jds.2019-16489
dc.relation/*ref*/Michels, M., von Hobe, C. F., Weller von Ahlefeld, P. J., Musshoff, O. (2021). The adoption of drones in German agriculture: a structural equation model. Precision Agriculture, v. 22, n. 6, 1728-1748. https://doi.org/10.1007/s11119-021-09809-8
dc.relation/*ref*/Mohr, S., Kühl, R. (2021). Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, v. 22, n. 6, 1816-1844. https://doi.org/10.1007/s11119-021-09814-x
dc.relation/*ref*/Naspetti, S., Mandolesi, S., Buysse, J., Latvala, T., Nicholas, P., Padel, S., Van Loo, E. J., Zanoli, R. (2017). Determinants of the Acceptance of Sustainable Production Strategies among Dairy Farmers: Development and Testing of a Modified Technology Acceptance Model. Sustainability, v. 9, n. 10, 1805. https://doi.org/10.3390/su9101805
dc.relation/*ref*/Pappa, I. C., Iliopoulos, C., Massouras, T. (2018). What determines the acceptance and use of electronic traceability systems in agri-food supply chains? Journal of Rural Studies, 58, 123-135. https://doi.org/10.1016/j.jrurstud.2018.01.001
dc.relation/*ref*/Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., Twining, S., Ffoulkes, C., Amano, T., Dicks, L. V. (2016). Decision support tools for agriculture: Towards effective design and delivery. Agricultural Systems, v. 149, 165-174. https://doi.org/10.1016/j.agsy.2016.09.009
dc.relation/*ref*/Ruiz Cortés, T., Orozco, S., Rodríguez, L. S., Idárraga, J., Olivera, M. (2012). Factores que afectan el recuento de UFC en la leche en tanque en hatos lecheros del norte de Antioquia-Colombia. Revista U.D.C.A Actualidad & Divulgación Científica, v. 15, n. 1, 147-155. https://doi.org/10.31910/rudca.v15.n1.2012.812
dc.relation/*ref*/Samoilenko, S., Osei-Bryson, K. M. (2019). A data analytic benchmarking methodology for discovering common causal structures that describe context-diverse heterogeneous groups. Expert Systems with Applications, v. 117, 330-344. https://doi.org/10.1016/j.eswa.2018.09.054
dc.relation/*ref*/Sanchez, G. (2013). PLS Path Modeling with R. Trowchez Editions. https://www.gastonsanchez.com/PLS_Path_Modeling_with_R.pdf
dc.relation/*ref*/Schaak, H., Mußhoff, O. (2018). Understanding the adoption of grazing practices in German dairy farming. Agricultural Systems, v. 165, 230-239. https://doi.org/10.1016/j.agsy.2018.06.015
dc.relation/*ref*/UPRA. (2020, agosto). Prospectiva 2039 Cadena Láctea. https://www.upra.gov.co/documents/10184/124468/20200831_PPT_ProspectivaGA.VF.pdf/3bf1576d-412c-4a20-854c-1dd86a741542
dc.relation/*ref*/Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research, v. 11, n. 4, 342-365. https://doi.org/https://doi.org/10.1287/isre.11.4.342.11872
dc.relation/*ref*/Venkatesh, V., Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, v. 39, n. 2, 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
dc.relation/*ref*/Venkatesh, V., Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, v. 46, n. 2, 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
dc.relation/*ref*/Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, v. 27, n. 3, 425-478. https://doi.org/10.2307/30036540
dc.relation/*ref*/Verma, P., Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting & Social Change, v. 126, 207-216. https://doi.org/10.1016/j.techfore.2017.08.013
dc.relation/*ref*/Zaremohzzabieh, Z., Samah, B. A., Muhammad, M., Omar, S. Z., Bolong, J., Hassan, M. S., Shaffril, H. A. M. (2015). A Test of the Technology Acceptance Model for Understanding the ICT Adoption Behavior of Rural Young Entrepreneurs. International Journal of Business and Management, v. 10, n. 2, 158-169. http://dx.doi.org/10.5539/ijbm.v10n2p158
dc.relation/*ref*/Zulherman, Z., Nuryana, Z., Pangarso, A., Zain, F. M. (2021). Factor of zoom cloud meetings: Technology adoption in the pandemic of COVID-19. International Journal of Evaluation and Research in Education, v. 10, n. 3, 816-825. https://doi.org/10.11591/ijere.v10i3.21726
dc.rightsDerechos de autor 2022 Instituto Tecnológico Metropolitanoes-ES
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0es-ES
dc.sourceRevista CEA; Vol. 8 No. 17 (2022); e2007en-US
dc.sourceRevista CEA; Vol. 8 Núm. 17 (2022); e2007es-ES
dc.source2422-3182
dc.source2390-0725
dc.subjectagroindustryen-US
dc.subjectmultivariate analysisen-US
dc.subjecttechnological changeen-US
dc.subjectrural developmenten-US
dc.subjectdigital literacyen-US
dc.subjectagroindustriaes-ES
dc.subjectanálisis multivariadoes-ES
dc.subjectcambio tecnológicoes-ES
dc.subjectdesarrollo rurales-ES
dc.subjectalfabetización digitales-ES
dc.titleTechnology Acceptance of a Mobile Application to Manage Dairy Businessesen-US
dc.titleAceptación tecnológica de una aplicación móvil para la gestión de negocios lecheroses-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

Archivos

Bloque original

Mostrando 1 - 4 de 4
Cargando...
Miniatura
Nombre:
revistacea_x_2007_final.pdf
Tamaño:
424.98 KB
Formato:
Adobe Portable Document Format
Cargando...
Miniatura
Nombre:
ojsitm_638170562002.epub
Tamaño:
442.54 KB
Formato:
Electronic publishing
Cargando...
Miniatura
Nombre:
ojsitm_638170562002.xml
Tamaño:
161.06 KB
Formato:
Extensible Markup Language
Cargando...
Miniatura
Nombre:
2409.html
Tamaño:
192.36 KB
Formato:
Hypertext Markup Language