FACTORS AFFECTING ADOPTION OF INTERNET OF THINGS IN SELECTED GREENHOUSE FARMS IN KENYA
DOI:
https://doi.org/10.47604/ijts.1668Keywords:
Adoption of Internet of Things Farm Factors Farmer Perception of Technology Product-Related Factors Environmental FactorsAbstract
Purpose: The current study sought to investigate the factors affecting the adoption of IoT in agriculture with a focus on greenhouse farming in Kenya. In particular, the objectives are to establish the effect of farmer perception of technology, product-related, farm and environmental factors on the adoption of IoT technology in the selected greenhouse farms in Kenya.
Methodology: A descriptive cross-section research design was used. The study targeted 198 greenhouse farm managers who were sampled to 130 greenhouse farm managers by a proportionate (stratified) sampling technique. The unit of analysis was the selected 3 greenhouse farms (Amiran Kenya Ltd farms, Illuminum Greenhouses Kenya farms and East Africa Growers Ltd (EAGA) farms). The unit of observation was the greenhouse farm managers and the greenhouse staff of the respective greenhouse farms. Primary data was collected using self-questionnaires. The collected data were coded and analyzed to generate both descriptive statistics as well as inferential statistics. Quantitative data was presented in Tables and figures while qualitative information was evaluated using content analysis, and the findings were presented thematically.
Findings: The findings indicate that farm factors (β=0.413, p=0.000), farmer perception of technology (β=0.139, p=0.005 respectively), product-related factors (β=0.349, p=0.000 respectively) and environmental factors (β=0.383, p=0.000 respectively) have a positive and significant relationship with the adoption of IoT Technology in the selected greenhouse farms in Kenya.
Unique contributions to theory, policy and practice: Theoretically, the findings form the basis of understanding and validating the factors that inform the adoption of IoT among greenhouse farmers. Policy makers and stakeholders in the greenhouse industry are able to assess the areas that are disadvantaged in terms of IoT and increase the awareness, training and usage of such technology to help the farmers identify the benefits of IoT. This information guides the direction of the agricultural industry and the readiness to embrace new technology, the farmers need to be sensitized on the available IoT devices that can boost their yields and optimize production. There is a need for the authority to intensify the sensitization of the use of IoT technology to ensure optimum application of resources to achieve high crop yields and reduce operational costs this is called precision agriculture. The study recommends the policymakers, that is, the Communications Authority of Kenya (CAK) who is responsible to facilitate and intensify the development and spread of the information and IT to the agricultural sector on the need for technological integration in their operation. The study to that extent recommends (based on the advantages that outweigh the disadvantages of IoT) that the farmers have a positive attitude towards the use of IoT. This forms a basis for them to develop and sustain a competitive advantage against their competitors in the industry.
Downloads
References
AlHogail, A. (2018). Improving IoT technology adoption through improving consumer trust. Technologies, 6(3), 2 - 17. https://doi.org/10.3390/technologies6030064
Alomia-Hinojosa, V., Speelman, E. N., Thapa, A., Wei, H. E., McDonald, A. J., Tittonell, P., & Groot, J. C. (2018). Exploring farmer perceptions of agricultural innovations for maize-legume intensification in the mid-hills' region of Nepal. International journal of agricultural sustainability, 16(1), 74-93.
Amiran Kenya Ltd. (2020). Agribusiness (Small Medium Scale Farmers). https://www.baltoncp.com/amirankenya/agribusiness/
Awa, H. O., Ukoha, O., & Emecheta, B. C. (2016). Using TOE theoretical framework to study the adoption of ERP solution. Cogent Business & Management, 3 (1), 1196571.
Ayim, C., Kassahun, A., Tekinerdogan, B., & Addison, C. (2020). Adoption of ICT innovations in the agriculture sector in Africa: A Systematic Literature Review. arXiv preprint arXiv:2006.13031.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99-120.
Birch, I. (2018). Agricultural productivity in Kenya: barriers and opportunities. https://assets.publishing.service.gov.uk/media/5c70028ee5274a0ecbe9a1c2/483_Agricultural_Productivity_in_Kenya_Barriers_and_Opportunities.pdf
Brous, P., Janssen, M., Schraven, D., Spiegeler, J., & Duzgun, B. C. (2017, April). Factors Influencing Adoption of IoT for Data-driven Decision Making in Asset Management Organizations. In IoTBDS (pp. 70-79).
Chatterjee, D., Grewal, R., & Sambamurthy, V. (2002). Shaping up for e-commerce: institutional enablers of the organizational assimilation of web technologies. MIS quarterly, 65-89.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334.
Das, V., Sharma, S., & Kaushik, A. (2019). Views of Irish Farmers on Smart Farming Technologies: An Observational Study. AgriEngineering, 1(2), 164-187.
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems. Cambridge, MA.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
Debnath, A., Kobra, K. T., Rawshan, P. P., Paramita, M., & Islam, M. N. (2018, August). An Explication of Acceptability of Wearable Devices in Context of Bangladesh: A User Study. In 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud) (pp. 136-140). IEEE.
East Africa Growers Ltd. (2020). Our Farms. http://www.eaga.co.ke/farms.htm
Elmustafa, S. A. A., & Mujtaba, E. Y. (2019). Internet of things in Smart Environment: Concept, Applications, Challenges, and Future Directions. World Scientific News, 134(1), 1-51.
FAO. (2018). E-Agriculture in Action: Drones for Agriculture. Available at: http://www.fao.org/3/i8494en/i8494en.pdf
FAO. (2018). Food and Agriculture Organization of the United Nations Kenya, 2018. Available at: https://illuminumgreenhouses.com/
FAO. (2021). Digital Agriculture Profile "¢ Kenya. https://www.fao.org/3/cb3958en/cb3958en.pdf
Farooq, M. S., Riaz, S., Abid, A., Abid, K., & Naeem, M. A. (2019). A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming. IEEE Access, 7, 156237-156271.
Farooq, M. S., Riaz, S., Abid, A., Umer, T., & Zikria, Y. B. (2020). Role of IoT Technology in Agriculture: A Systematic Literature Review. Electronics, 9(2), 2 -41.
Fisher, K. B., Davis, M., Strauss, M. A., Yahil, A., & Huchra, J. P. (1993). The power spectrum of IRAS galaxies. The Astrophysical Journal, 402, 42-57.
Gill, S. S., Chana, I., & Buyya, R. (2017). IoT based agriculture as a cloud and big data service: the beginning of digital India. Journal of Organizational and End User Computing (JOEUC), 29(4), 1-23.
Gomes, R., & Osman, S. S. (2019). Managing Organizational Adoption of IoT: Revisiting Rogers' Diffusion of Innovation Theory. Retrieved from https://www.diva-portal.org/smash/get/diva2:1374639/FULLTEXT01.pdf
Hsu, C. L., & Lin, J. C. C. (2018). Exploring factors affecting the adoption of internet of things services. Journal of Computer Information Systems, 58(1), 49-57.
Illuminium Greenhouse Kenya. (2020). Greenhouses. https://synnefa.io/greenhouses/
Jayashankar, P., Nilakanta, S., Johnston, W. J., Gill, P., & Burres, R. (2018). IoT adoption in agriculture: the role of trust, perceived value and risk. Journal of Business & Industrial Marketing. 33 (6), pp. 804-821.
Jin, J., Ma, Y., Zhang, Y., & Huang, Q. (2018). Design and implementation of an Agricultural IoT based on LoRa. In MATEC Web of Conferences (Vol. 189, p. 04011). EDP Sciences.
Kanake, J. M. (2016). Internet of things for monitoring environmental conditions in greenhouses: a case of Kiambu County (Doctoral dissertation, Strathmore University).
Keskin, M., & Sekerli, Y. E. (2016). Awareness and adoption of precision agriculture in the Cukurova region of Turkey. Agron. Res, 14(4), 1307-1320.
Kiarie, H. (2020). Determinants of digital technologies adoption among small scale farmers in Kenya: A case of Embu and Kirinyaga Counties. (Thesis, Strathmore University). http://hdl.handle.net/11071/10422
Kinyangi, A. A. (2014). Factors influencing the adoption of agricultural technology among smallholder farmers in Kakamega north sub-county, Kenya. A Research Project for Award Degree of Master of Arts in Project Planning and Management of the University of Nairobi. 87pp.
KIPPRA. (2018). Realizing the "Big Four" Agenda through Energy as an Enabler. Available at https://kippra.or.ke/index.php/publications?task=download.send&id=40&catid=6&m=0
Lai, I. K., Tong, V. W., & Lai, D. C. (2011). Trust factors influencing the adoption of internet-based interorganizational systems. Electronic Commerce Research and Applications, 10(1), 85-93.
Meijer, S. S., Catacutan, D., Ajayi, O. C., Sileshi, G. W., & Nieuwenhuis, M. (2015). The role of knowledge, attitudes and perceptions in the uptake of agricultural and agroforestry innovations among smallholder farmers in sub-Saharan Africa. International Journal of Agricultural Sustainability, 13(1), 40-54.
Mujuka, E., Mburu, J., Ogutu, A., & Ambuko, J. (2020). Returns to investment in postharvest loss reduction technologies among mango farmers in Embu County, Kenya. Food and Energy Security, 9(1), e195.
Netherlands Enterprise Agency. (2019). Digital Farming in Kenya Commissioned by the Netherlands Enterprise Agency. https://www.rvo.nl/sites/default/files/2019/12/Digital-Farming-in-Kenya.pdf
Nikou, S. (2018). Consumers "˜perceptions on Smart Home and Smart Living. Research Papers. 47. Available at: https://aisel.aisnet.org/ecis2018_rp/47
Odhiambo, P. L. (2018). Factors that influence farmers decision to adopt IOT sensing and monitoring greenhouse farming solutions in Kenya: a case study of illuminum greenhouse (Doctoral dissertation, University of Nairobi).
Oliveira, T., & Dhillon, G. (2015). From adoption to routinization of B2B e-Commerce: understanding patterns across Europe. Journal of Global Information Management (JGIM), 23(1), 24-43.
Omoro, P. A. (2014). Assessment of Selected Factors Affecting Performance of Greenhouse Technology in Small Scale Farms in Gusii Highlands, Kenya (Doctoral dissertation, Kisii University).
Park, C., & Park, Y. H. (2013). Validity and reliability of Korean version of health empowerment scale (K-HES) for older adults. Asian Nursing Research, 7(3), 142-148.
Paul Antony, A., Sweeney, D., & Lu, J. (2019). Seeds of Silicon: Internet of Things for Smallholder Agriculture. Available at https://dspace.mit.edu/handle/1721.1/123305
Penrose, E. (1959) The Theory of the Growth of the Firm. Basil Blackwell, Oxford.
Penrose, E., & Penrose, E. T. (2009). The Theory of the Growth of the Firm. Oxford university press.
Ramon-Jeronimo, J. M., Florez-Lopez, R., & Araujo-Pinzon, P. (2019). Resource-based view and SMEs performance exporting through foreign intermediaries: The mediating effect of management controls. Sustainability, 11(12), 3241.
Rogers, E. M. (1995). Diffusion of Innovations: modifications of a model for telecommunications. In Die diffusion von innovationen in der telekommunikation (pp. 25-38). Springer, Berlin, Heidelberg.
Rogers, E. M. (2003). Diffusion of innovations/everett m. rogers. NY: Simon and Schuster, 576.
Rogers, Everett M. (1962). Diffusion of innovations (1st ed.). New York: Free Press of Glencoe. OCLC 254636.
Ryan, B., & Gross, N. C. (1943). The diffusion of hybrid seed corn in two Iowa communities. Rural sociology, 8(1), 15.
Saiz-Rubio, V., & Rovira-Más, F. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy, 10(2), 1 - 21.
Sinja, J., Karugia, J. T., Baltenweck, I., Waithaka, M. M., Miano, M. D., Nyikal, R. A., & Romney, D. (2004). Farmer Perception of Technology and its Impact on Technology Uptake: The Case of Fodder Legume in Central Kenya Highlands (No. 306-2016-4861).
Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation. Lexington books.
Uddin, M. A., Mansour, A., Le Jeune, D., & Aggoune, E. H. M. (2017, November). Agriculture internet of things: AG-IoT. In 2017 27th International Telecommunication Networks and Applications Conference (ITNAC) (pp. 1-6). IEEE.
Van der Spijk, C. A. (2018). Greenhouse technology adoption among small and medium-scale tomato farmers in Kenya. Available at: https://www.3r-kenya.org/wp-content/uploads/2019/02/MSc-Thesis-Christiaan-A-van-der-Spijk.pdf
Van der Spijk, C. A. (2018). Greenhouse technology adoption among small and medium-scale tomato farmers in Kenya (Doctoral dissertation, M. Sc. Thesis, Wageningen University, the Netherlands).
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
World Bank. (2017). ICT in Agriculture: Connecting Smallholders to Knowledge, Networks, and Institutions. Updated Edition. Washington, DC: World Bank.
Yang, H., Lee, W., & Lee, H. (2018). IoT smart home adoption: The importance of proper level automation. Journal of Sensors, 2018 (2), 1 - 11.
Yigezu, Y. A., Mugera, A., El-Shater, T., Aw-Hassan, A., Piggin, C., Haddad, A., ... & Loss, S. (2018). Enhancing adoption of agricultural technologies requiring high initial investment among smallholders. Technological Forecasting and Social Change, 134, 199-206.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Caroline Jepkorir, Dr. Thomas Mose , Dr. Mwalili Tobias
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
