Entrepreneurial and attitudinal determinants for adoption of Climate-smart Agriculture technologies in Uganda

References

• Abegunde, V. O., Sibanda, M., & Obi, A. (2019). Determinants of the adoption of climate-smart agricultural practices by small-scale farming households in King Cetshwayo district Municipality, South Africa. Sustainability, 12(1), 195. https://doi.org/10.3390/su12010195
   Web of Science ®Google Scholar
• Achterbosch, T. J., van Berkum, S., Meijerink, G. W., Asbreuk, H., & Oudendag, D. (2014). Cash crops and food security: Contributions to income, livelihood risk and agricultural innovation. LEI Wageningen UR.
   Google Scholar
• Adapa, S., & Roy, S. K. (2017). Consumers’ post-adoption behaviour towards internet banking: Empirical evidence from Australia. Behaviour & Information Technology, 36(9), 970–25. https://doi.org/10.1080/0144929X.2017.1319498
   Web of Science ®Google Scholar
• Agarwal, R. (2000). Individual acceptance of information technologies. Framing the domains of IT management: Projecting the future through the past. Cincinnati, OH: Pinnaflex Education Resources, 85–104.
   Google Scholar
• Aguirre Cuellar, B., Lobo, J. C., & Elcure, F. M. (2022). Shading in family coffee farms as an environmental incentive promoter for ecosystem services in Tolima, Colombia. International Journal of Agricultural Sustainability, 20(6), 1–13. https://doi.org/10.1080/14735903.2022.2041234
   Web of Science ®Google Scholar
• Ahmad, S., Zulkurnain, N., & Khairushalimi, F. (2016). Assessing the validity and reliability of a measurement model in structural equation modeling (SEM). British Journal of Mathematics & Computer Science, 15(3), 1–8. https://doi.org/10.9734/bjmcs/2016/25183
   Google Scholar
• Ajibade, S., Simon, B., Gulyas, M., & Balint, C. (2023). Sustainable intensification of agriculture as a tool to promote food security: A bibliometric analysis. Frontiers in Sustainable Food Systems, 7, 1101528. https://doi.org/10.3389/fsufs.2023.1101528
   Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Taylor and Francis, 26(9), 1113–1127. https://doi.org/10.1080/08870446.2011.613995
   Google Scholar
• Aleminew, A., Tadesse, T., Merene, Y., Bayu, W., Dessalegn, Y., & Tejada Moral, M. (2020). Effect of integrated technologies on the productivity of maize, sorghum and pearl millet crops for improving resilience capacity to climate change effects in the dry lands of Eastern Amhara, Ethiopia. Cogent Food & Agriculture, 6(1), 1728084. https://doi.org/10.1080/23311932.2020.1728084
   Web of Science ®Google Scholar
• Alemu, A., & Dufera, E. (2017). Climate smart coffee (coffea arabica) production. American Journal of Data Mining and Knowledge Discovery, 2(2), 62–68.
   Google Scholar
• Alemu, T., Tolossa, D., Senbeta, F., & Zeleke, T. (2023). Household determinants of continued adoption of sustainable land management measures in central Ethiopia. Heliyon, 9(3), e13946. https://doi.org/10.1016/j.heliyon.2023.e13946
   PubMed Web of Science ®Google Scholar
• Al-Jabri, I. M., & Sohail, M. S. (2012). Mobile banking adoption: Application of diffusion of innovation theory. Journal of Electronic Commerce Research, 13(4), 379–391.
   Web of Science ®Google Scholar
• Amadu, F. O., McNamara, P. E., & Miller, D. C. (2020). Understanding the adoption of climate-smart agriculture: A farm-level typology with empirical evidence from southern Malawi. World Development, 126, 104692. https://doi.org/10.1016/j.worlddev.2019.104692
   Web of Science ®Google Scholar
• Ampaire, E. L., Jassogne, L., Providence, H., Acosta, M., Twyman, J., Winowiecki, L., & Van Asten, P. (2017). Institutional challenges to climate change adaptation: A case study on policy action gaps in Uganda. Environmental Science & Policy, 75, 81–90. https://doi.org/10.1016/j.envsci.2017.05.013
   Web of Science ®Google Scholar
• Amsalu, A., & De Graaff, J. (2007). Determinants of adoption and continued use of stone terraces for soil and water conservation in an Ethiopian highland watershed. Ecological Economics, 61(2–3), 294–302. https://doi.org/10.1016/j.ecolecon.2006.01.014
   Web of Science ®Google Scholar
• Andati, P., Majiwa, E., Ngigi, M., Mbeche, R., & Ateka, J. (2022). Determinants of adoption of climate smart agricultural technologies among potato farmers in Kenya: Does entrepreneurial orientation play a role? Sustainable Technology and Entrepreneurship, 1(2), 100017. https://doi.org/10.1016/j.stae.2022.100017
   Google Scholar
• Anderson, B. S., Kreiser, P. M., Kuratko, D. F., Hornsby, J. S., & Eshima, Y. (2015a). Eshima Y: Reconceptualizing entrepreneurial orientation. Strategic Management Journal, 36(10), 1579–1596. https://doi.org/10.1002/smj.2298
   Web of Science ®Google Scholar
• Anderson, B. S., Kreiser, P. M., Kuratko, D. F., Hornsby, J. S., & Eshima, Y. (2015b). Reconceptualizing entrepreneurial orientation. Strategic Management Journal, 36(10), 1579–1596. https://doi.org/10.1002/smj.2298
   Web of Science ®Google Scholar
• Anwar, M. (2019). Climate smart agriculture: Adaptation and mitigation strategies to climate change in Pakistan. In R. Shrestha & <. I. I. A. I. M. Bokhtiar (Eds.), Climate smart agriculture: Strategies to respond climate change in South Asia (p. 180). SAARC Agriculture Centre.
   Google Scholar
• Ariani, M., Hervani, A., Setyanto, P. (2018). Climate smart agriculture to increase productivity and reduce greenhouse gas emission–a preliminary study. Proceedings of the IOP Conference Series: Earth and Environmental Science: 2018, Central Java-Indonesia (p. 012024). IOP Publishing.
   Google Scholar
• Asegid, A. (2020). Impact of climate change on production and diversity of coffee (coffea arabica L.) in Ethiopia. International Journal Research Studies Science Engineering Technology, 7(8), 31–38. https://www.ijrsset.org/pdfs/v7-i8/5.pdf
   Google Scholar
• Atsiaya, G. O., Gido, E. O., & Waluse Sibiko, K. (2023). Uptake of climate-smart agricultural practices among smallholder sorghum farmers in Busia County, Kenya. Cogent Food & Agriculture, 9(1), 2204019. https://doi.org/10.1080/23311932.2023.2204019
   Web of Science ®Google Scholar
• Aydinalp, C., & Cresser, M. S. (2008). The effects of global climate change on agriculture. American-Eurasian Journal of Agricultural & Environmental Sciences, 3(5), 672–676.
   Google Scholar
• Bandura, A., & Walters, R. H. (1977). Social learning theory. Englewood cliffs Prentice Hall.
   Google Scholar
• Bellamy, A. S. (2013). Banana production systems: Identification of alternative systems for more sustainable production. Ambio, 42(3), 334–343. https://doi.org/10.1007/s13280-012-0341-y
   PubMed Web of Science ®Google Scholar
• Bhatia, M. S., & Awasthi, A. (2018). Assessing relationship between quality management systems and business performance and its mediators: SEM approach. International Journal of Quality & Reliability Management, 35(8), 1490–1507. https://doi.org/10.1108/IJQRM-05-2017-0091
   Web of Science ®Google Scholar
• Bocquého, G., Jacquet, F., & Reynaud, A. (2014). Expected utility or prospect theory maximisers? Assessing farmers’ risk behaviour from field-experiment data. European Review of Agricultural Economics, 41(1), 135–172. https://doi.org/10.1093/erae/jbt006
   Web of Science ®Google Scholar
• Branca, G., Braimoh, A., Zhao, Y., Ratii, M., & Likoetla, P. (2021). Are there opportunities for climate-smart agriculture? Assessing costs and benefits of sustainability investments and planning policies in Southern Africa. Journal of Cleaner Production, 278, 123847. https://doi.org/10.1016/j.jclepro.2020.123847
   Web of Science ®Google Scholar
• Bro, A. S., Clay, D. C., Ortega, D. L., & Lopez, M. C. (2019). Determinants of adoption of sustainable production practices among smallholder coffee producers in Nicaragua. Environment Development and Sustainability, 21(2), 895–915. https://doi.org/10.1007/s10668-017-0066-y
   Web of Science ®Google Scholar
• Brown, S. E., Miller, D. C., Ordonez, P. J., & Baylis, K. (2018). Evidence for the impacts of agroforestry on agricultural productivity, ecosystem services, and human well-being in high-income countries: A systematic map protocol. Environmental Evidence, 7(1), 24. https://doi.org/10.1186/s13750-018-0136-0
   Web of Science ®Google Scholar
• Bryan, E., Deressa, T. T., Gbetibouo, G. A., & Ringler, C. (2009). Adaptation to climate change in Ethiopia and South Africa: Options and constraints. Environmental Science & Policy, 12(4), 413–426. https://doi.org/10.1016/j.envsci.2008.11.002
   Web of Science ®Google Scholar
• Bunn, C., Lundy, M., Läderach, P., Fernández Kolb, P., Castro-Llanos, F. (2019). Climate-smart coffee in Uganda.
   Google Scholar
• Campbell, B. M., Thornton, P., Zougmoré, R., Van Asten, P., & Lipper, L. (2014). Sustainable intensification: What is its role in climate smart agriculture? Current Opinion in Environmental Sustainability, 8, 39–43. https://doi.org/10.1016/j.cosust.2014.07.002
   Web of Science ®Google Scholar
• Cavane, E., & Donovan, C. (2011). Farmers’ attitude and adoption of improved maize varieties and chemical fertilizers in Mozambique. Journal of International Agricultural and Extension Education, 11(1). https://doi.org/10.5191/jiaee.2011.18301
   Google Scholar
• Chandra, A., Dargusch, P., McNamara, K. E., Caspe, A. M., & Dalabajan, D. (2017). A study of climate-smart farming practices and climate-resiliency field schools in Mindanao, the Philippines. World Development, 98, 214–230. https://doi.org/10.1016/j.worlddev.2017.04.028
   Web of Science ®Google Scholar
• Chatterjee, S., Gupta, S. D., & Upadhyay, P. (2020). Technology adoption and entrepreneurial orientation for rural women: Evidence from India. Technological Forecasting and Social Change, 160, 120236. https://doi.org/10.1016/j.techfore.2020.120236
   Web of Science ®Google Scholar
• Chemeda, B. A., Wakjira, F. S., & Hizikias, E. B. (2022). Tree diversity and biomass carbon stock analysis along altitudinal gradients in coffee-based agroforestry system of Western Ethiopia. Cogent Food & Agriculture, 8(1), 2123767. https://doi.org/10.1080/23311932.2022.2123767
   Web of Science ®Google Scholar
• Chin, W. W., Mills A. M., Steel D. J., & and Schwarz A. (2014). Multi-group invariance testing: An illustrative comparison of PLS permutation and covariance-based SEM invariance analysis. In M. Skipper (Ed.), The multiple facets of partial least squares and related Methods: PLS (pp. 267–284). Springer
   Google Scholar
• Chuang, J.-H., Wang, J.-H., & Liou, Y.-C. (2020). Farmers’ knowledge, attitude, and adoption of smart agriculture technology in Taiwan. International Journal of Environmental Research and Public Health, 17(19), 7236. https://doi.org/10.3390/ijerph17197236
   PubMed Web of Science ®Google Scholar
• CIAT: Climate-Smart Agriculture in Uganda. (2017).
   Google Scholar
• Cinner, J. E., Adger, W. N., Allison, E. H., Barnes, M. L., Brown, K., Cohen, P. J., Gelcich, S., Hicks, C. C., Hughes, T. P., Lau, J., Marshall, N. A., & Morrison, T. H. (2018). Building adaptive capacity to climate change in tropical coastal communities. Nature Climate Change, 8(2), 117–123. https://doi.org/10.1038/s41558-017-0065-x
   Web of Science ®Google Scholar
• Collis, B., & Moonen, J. (2001). Flexible learning in a digital world: Experiences and expectations. Psychology press.
   Google Scholar
• Cooper, J., Giovanni, S.: Farmer premiums for the voluntary adoption of conservation plans, Nota di Lavoro. No 272002, Fondazione Eni Enrico Mattei (FEEM), 2002.
   Google Scholar
• Covin, J. G. S., & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic Management Journal, 10(1), 75–87. https://doi.org/10.1002/smj.4250100107
   Web of Science ®Google Scholar
• Dalaa, M. A., Deffor, E. W., Kofituo, R. K., Asare, R. (2020). Adoption of climate-smart cocoa practices in the adjust and cope climatic impact zones of Ghana.
   Google Scholar
• Dalila, L., Jaafar, H., Aziz, N., & Afthanorhan, A. (2020). The mediating effect of personal values on the relationships between attitudes, subjective norms, perceived behavioral control and intention to use. Management Science Letters, 10(1), 153–162. https://doi.org/10.5267/j.msl.2019.8.007
   Google Scholar
• Daniele, V., Viaggi, D., & Raggi, M. (2017). Integrating spatial econometric information and optimisation models to improve agri-Environmental payment design: A resource allocation model for emilia-Romagna (Italy). Agricultural Economics Review, 18(2), 40–59.
   Google Scholar
• Darragh, H. S., & Emery, S. B. (2018). What can and can’t crowding theories tell us about farmers’‘Environmental’Intentions in post‐agri‐Environment scheme contexts? Sociologia ruralis, 58(2), 370–391. https://doi.org/10.1111/soru.12159
   Web of Science ®Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• Deka, N., & Goswami, K. (2020). Organic cultivation and farm entrepreneurship: A case of small tea growers in rural Assam, India. Agroecology & Sustainable Food Systems, 44(4), 446–466. https://doi.org/10.1080/21683565.2019.1646373
   Web of Science ®Google Scholar
• Deng, J., Sun, P., Zhao, F., Han, X., Yang, G., & Feng, Y. (2016). Analysis of the ecological conservation behavior of farmers in payment for ecosystem service programs in eco-environmentally fragile areas using social psychology models. Science of the Total Environment, 550, 382–390. https://doi.org/10.1016/j.scitotenv.2016.01.152
   PubMed Web of Science ®Google Scholar
• Dercon, S., & Christiaensen, L. (2011). Consumption risk, technology adoption and poverty traps: Evidence from Ethiopia. Journal of Development Economics, 96(2), 159–173. https://doi.org/10.1016/j.jdeveco.2010.08.003
   Web of Science ®Google Scholar
• Deressa, T. T., Hassan, R. M., Ringler, C., Alemu, T., & Yesuf, M. (2009). Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change, 19(2), 248–255. https://doi.org/10.1016/j.gloenvcha.2009.01.002
   Web of Science ®Google Scholar
• Dias, C. S., Rodrigues, R. G., & Ferreira, J. J. (2019). Agricultural entrepreneurship: Going back to the basics. Journal of Rural Studies, 70, 125–138. https://doi.org/10.1016/j.jrurstud.2019.06.001
   Web of Science ®Google Scholar
• Ding, Y., Chai, K.-H., Ng, G. (2012). Continued use of information technology: A synthesis and future directions. In: 2012 International Joint Conference on Service Sciences: 2012 Singapore: IEEE; 206–210.
   Google Scholar
• Diro, S., Tesfaye, A., & Erko, B. (2022a). Determinants of adoption of climate-smart agricultural technologies and practices in the coffee-based farming system of Ethiopia. Agriculture & Food Security, 11(1), 1–14. https://doi.org/10.1186/s40066-022-00385-2
   Google Scholar
• Diro, S., Tesfaye, A., & Erko, B. (2022b). Determinants of adoption of climate-smart agricultural technologies and practices in the coffee-based farming system of Ethiopia. Agriculture & Food Security, 11(1), 42. https://doi.org/10.1186/s40066-022-00385-2
   Google Scholar
• Djufry, F., Wulandari, S., & Villano, R. (2022). Climate smart Agriculture implementation on coffee smallholders in Indonesia and strategy to accelerate. Land, 11(7), 1112. https://doi.org/10.3390/land11071112
   Web of Science ®Google Scholar
• Dung, T. Q., Bonney, L. B., Adhikari, R. P., & Miles, M. P. (2020). Entrepreneurial orientation, knowledge acquisition and collaborative performance in agri-food value-chains in emerging markets. Supply Chain Management: An International Journal, 25(5), 521–533. https://doi.org/10.1108/SCM-09-2019-0327
   Web of Science ®Google Scholar
• Engel, S., & Muller, A. (2016). Payments for environmental services to promote “climate‐smart agriculture”? Potential and challenges. Agricultural Economics, 47(S1), 173–184. https://doi.org/10.1111/agec.12307
   Google Scholar
• Etcheverry, P. E., & Agnew, C. R. (2004). Subjective norms and the prediction of romantic relationship state and fate. Personal Relationships, 11(4), 409–428. https://doi.org/10.1111/j.1475-6811.2004.00090.x
   Web of Science ®Google Scholar
• Faisal Salad, A., Zziwa, S., Talengera, D., Nabatanzi, L., Makumbi, O., Najjuma, V., & Nafula, M. (2021). Adoption intensity of climate smart agricultural practices in arabica coffee production in Bududa District. International Journal of Multidisciplinary Research Updates, 1(1), 16–25. https://doi.org/10.53430/ijmru.2021.1.1.0037
   Google Scholar
• FAO. (2016). A dynamic analysis of resilience in Uganda ESA Working Paper, 16, 36.
   Google Scholar
• FAOSTAT. (2018). Disponível em: http://www.fao.org/faostat/en/#home. Acesso em. 30.
   Google Scholar
• Fleischer, A., Mendelsohn, R., & Dinar, A. (2011). Bundling agricultural technologies to adapt to climate change. Technological Forecasting and Social Change, 78(6), 982–990. https://doi.org/10.1016/j.techfore.2011.02.008
   Web of Science ®Google Scholar
• Foster, A. D., & Rosenzweig, M. R. (2010). Microeconomics of technology adoption. Annual Review of Economics, 2(1), 395–424. https://doi.org/10.1146/annurev.economics.102308.124433
   Web of Science ®Google Scholar
• Fox, G., Mooney, J., Rosati, P., & Lynn, T. (2021). AgriTech innovators: A study of initial adoption and continued use of a mobile digital platform by family-operated farming enterprises. Agriculture, 11(12), 1283. https://doi.org/10.3390/agriculture11121283
   Google Scholar
• Glanz, K., Rimer, B. K., & Viswanath, K. (2015). Health behavior: Theory, research, and practice. John Wiley & Sons.
   Google Scholar
• Groot, A., Bolt, J., Jat, H. S., Jat, M. L., Kumar, M., Agarwal, T., & Blok, V. (2019). Business models of SMEs as a mechanism for scaling climate smart technologies: The case of Punjab, India. Journal of Cleaner Production, 210, 1109–1119. https://doi.org/10.1016/j.jclepro.2018.11.054
   Web of Science ®Google Scholar
• Habjan, K. B., & Pucihar, A. (2017). The importance of business model factors for cloud computing adoption: Role of previous experiences. Organizacija, 50(3), 255–272. https://doi.org/10.1515/orga-2017-0013
   Google Scholar
• Hair, J. F., Jr., Babin, B. J., & Krey, N. (2017). Covariance-based structural equation modeling in the journal of advertising: Review and recommendations. Journal of Advertising, 46(1), 163–177. https://doi.org/10.1080/00913367.2017.1281777
   Web of Science ®Google Scholar
• Hair, J. F. J., William, C., Barry, J. B., & Rolph, E. A. (2010). Multivariate data analysis (Seventh ed.). Pearson Prentice Hall.
   Google Scholar
• Harmon-Jones, E., Mills, J. (2019). An introduction to cognitive dissonance theory and an overview of current perspectives on the theory.
   Google Scholar
• Hilary, R. S., Sseguya, H., Kibwika, P., & Yildiz, F. (2017). Information quality, sharing and usage in farmer organizations: The case of rice value chains in Bugiri and Luwero Districts, Uganda. Cogent Food & Agriculture, 3(1), 1350089. https://doi.org/10.1080/23311932.2017.1350089
   Google Scholar
• Hilton, R. P., Isabella, M., & Charlotte, B. (2014). SPSS Explained. In D., Daniel (Ed.), Routledge (2nd ed., p. 386). Routledge. https://doi.org/10.4324/9781315797298.
   Google Scholar
• Huang, F. L. (2017). Conducting multilevel confirmatory factor analysis using R. [ Unpublished Manuscript].
   Google Scholar
• ICO. (2018). Gender equality in coffee sector. (2018). https://www.ico.org/documents/cy2017-18/icc-122-11e-gender-equality.pdf
   Google Scholar
• Inman, A., Winter, M., Wheeler, R., Vrain, E., Lovett, A., Collins, A., Jones, I., Johnes, P., & Cleasby, W. (2018). An exploration of individual, social and material factors influencing water pollution mitigation behaviours within the farming community. Land Use Policy, 70, 16–26. https://doi.org/10.1016/j.landusepol.2017.09.042
   Web of Science ®Google Scholar
• Jassogne L., Mukasa D., Bukomeko H., Kemigisha E., Kirungi D., Giller O., & and Van Asten P. (2017). The Climate Smart Investment Pathway Approach and the Farmer Segmentation Tool CCAFS Info Note. CCAFS Info Note. International Institute of Tropical Agriculture (IITA). https://cgspace.cgiar.org/handle/10568/80747
   Google Scholar
• Kafyulilo, A., Fisser, P., & Voogt, J. (2016). Factors affecting teachers’ continuation of technology use in teaching. Education and Information Technologies, 21(6), 1535–1554. https://doi.org/10.1007/s10639-015-9398-0
   Web of Science ®Google Scholar
• Kakraliya, S. K., Jat, H. S., Sapkota, T. B., Singh, I., Kakraliya, M., Gora, M. K., Sharma, P. C., & Jat, M. L. (2021). Effect of climate-smart agriculture practices on climate change adaptation, greenhouse gas mitigation and economic efficiency of rice-wheat system in India. Agriculture, 11(12), 1269. https://doi.org/10.3390/agriculture11121269
   Google Scholar
• Kangogo, D., Dentoni, D., & Bijman, J. (2021). Adoption of climate‐smart agriculture among smallholder farmers: Does farmer entrepreneurship matter? Land Use Policy, 109, 109. https://doi.org/10.1016/j.landusepol.2021.105666
   Web of Science ®Google Scholar
• Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213. https://doi.org/10.2307/249751
   Web of Science ®Google Scholar
• Karlsson, N. P., Halila, F., Mattsson, M., & Hoveskog, M. (2017). Success factors for agricultural biogas production in Sweden: A case study of business model innovation. Journal of Cleaner Production, 142, 2925–2934. https://doi.org/10.1016/j.jclepro.2016.10.178
   Web of Science ®Google Scholar
• Kessler, E. H., & Chakrabarti, A. K. (1996). Innovation speed: A conceptual model of context, antecedents, and outcomes. The Academy of Management Review, 21(4), 1143–1191. https://doi.org/10.2307/259167
   Web of Science ®Google Scholar
• Kim, B. K. (2008). Understanding the difference in adoption and continued usage of mobile data services. PACIS 2008 Proceedings Suzhou, China. 271.
   Google Scholar
• Kim, Y., & Crowston, K. (2011). Technology adoption and use theory review for studying scientists’ continued use of cyber‐infrastructure. Proceedings of the American Society for Information Science and Technology, 48(1), 1–10.
   Google Scholar
• Kim, D. R., Kim, B. G., Aiken, M. W., & Park, S. C. (2006). The influence of individual, task, organizational support, and subject norm factors on the adoption of groupware. Academy of Information and Management Sciences Journal, 9(2), 93–110. https://www.proquest.com/scholarly-journals/influence-individual-task-organizational-support/docview/214623140/se-2?accountid=11077
   Google Scholar
• Kiwanuka, A. (2015). Acceptance process: The missing link between UTAUT and diffusion of innovation theory. American Journal of Information Systems, 3(2), 40–44.
   Google Scholar
• Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
   Google Scholar
• Kolady, D. E., Van der Sluis, E., Uddin, M. M., & Deutz, A. P. (2021). Determinants of adoption and adoption intensity of precision agriculture technologies: Evidence from South Dakota. Precision Agriculture, 22(3), 689–710. https://doi.org/10.1007/s11119-020-09750-2
   Web of Science ®Google Scholar
• Kuma, T., Dereje, M., Hirvonen, K., & Minten, B. (2019). Cash crops and food security: Evidence from Ethiopian smallholder coffee producers. The Journal of Development Studies, 55(6), 1267–1284. https://doi.org/10.1080/00220388.2018.1425396
   Web of Science ®Google Scholar
• Kurgat, B. K., Lamanna, C., Kimaro, A., Namoi, N., Manda, L., & Rosenstock, T. S. (2020). Adoption of climate-smart agriculture technologies in Tanzania. Frontiers in Sustainable Food Systems, 4(55). https://doi.org/10.3389/fsufs.2020.00055
   Google Scholar
• Lamanna, C., Steward, P., Kamau, H. N., Nowak, A. C., Rosenstock, T. S. (2020). Data Atlas for Climate-Smart Agriculture in KENYA.
   Google Scholar
• Liao, H.-L., & Lu, H.-P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405–1416. https://doi.org/10.1016/j.compedu.2007.11.006
   Web of Science ®Google Scholar
• Li, W., Clark, B., Taylor, J. A., Kendall, H., Jones, G., Li, Z., Jin, S., Zhao, C., Yang, G., Shuai, C., Cheng, X., Chen, J., Yang, H., & Frewer, L. J. (2020). A hybrid modelling approach to understanding adoption of precision agriculture technologies in Chinese cropping systems. Computers and Electronics in Agriculture, 172, 105305. https://doi.org/10.1016/j.compag.2020.105305
   Web of Science ®Google Scholar
• Lin, B. B. (2009). Coffee (café arabica var. Bourbon) fruit growth and development under varying shade levels in the Soconusco region of Chiapas, Mexico. Journal of Sustainable Agriculture, 33(1), 51–65. https://doi.org/10.1080/10440040802395007
   Web of Science ®Google Scholar
• Lipper, L., Thornton, P., Campbell, B. M., Baedeker, T., Braimoh, A., Bwalya, M., Caron, P., Cattaneo, A., Garrity, D., Henry, K., Hottle, R., Jackson, L., Jarvis, A., Kossam, F., Mann, W., McCarthy, N., Meybeck, A., Neufeldt, H. … Tibu, A. (2014). Climate-smart agriculture for food security. Nature Climate Change, 4(12), 1068–1072. https://doi.org/10.1038/nclimate2437
   Web of Science ®Google Scholar
• Luo, L., Qin, L., Wang, Y., & Wang, Q. (2016). Environmentally-friendly agricultural practices and their acceptance by smallholder farmers in China—A case study in Xinxiang County, Henan Province. Science of the Total Environment, 571, 737–743. https://doi.org/10.1016/j.scitotenv.2016.07.045
   PubMed Web of Science ®Google Scholar
• Mahato, A. (2014). Climate change and its impact on agriculture. International Journal of Scientific and Research Publications, 4(4), 1–6.
   Google Scholar
• Maina, K., Ritho, C., Lukuyu, B. A., & Rao, E. (2020). Socio-economic determinants and impact of adopting climate-smart Brachiaria grass among dairy farmers in Eastern and Western regions of Kenya. Heliyon, 6(6), e04335. https://doi.org/10.1016/j.heliyon.2020.e04335
   PubMed Web of Science ®Google Scholar
• Makate, C., Makate, M., Mango, N., & Siziba, S. (2019). Increasing resilience of smallholder farmers to climate change through multiple adoption of proven climate-smart agriculture innovations. Lessons from Southern Africa. Journal of Environmental Management, 231, 858–868. https://doi.org/10.1016/j.jenvman.2018.10.069
   PubMed Web of Science ®Google Scholar
• Mandal, J., Acharya, S., & Parija, S. C. (2011). Ethics in human research. Tropical Parasitology, 1(1), 2. https://doi.org/10.4103/2229-5070.72105
   PubMedGoogle Scholar
• Marangunic, N., & Ve Granic, A. (2014). Technology Acceptance Model: A Literature Review from 1986 to 2013. Univ Access Inf Soc, 14, 81–95. https://doi.org/10.1007/s10209-014-0348-1
   Web of Science ®Google Scholar
• McLeod, S. (2008). Likert scale Likert scale examples how can you analyze data from a Likert scale? Simply Psychology, 1–3. https://www.simplypsychology.org/likert-scale.html
   Google Scholar
• Mead, M. P., & Irish, L. A. (2021). Intraindividual variability of sleep opportunity attitudes, perceived norms, perceived behavioral control, and intentions. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 55(7), 693–697. https://doi.org/10.1093/abm/kaaa112
   PubMed Web of Science ®Google Scholar
• Medellín-Azuara, J., Howitt, R., & Harou, J. (2012). Predicting farmer responses to water pricing, rationing and subsidies assuming profit maximizing investment in irrigation technology. Agricultural Water Management, 108, 73–82. https://doi.org/10.1016/j.agwat.2011.12.017
   Web of Science ®Google Scholar
• Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management Science, 29(7), 770–791. https://doi.org/10.1287/mnsc.29.7.770
   Web of Science ®Google Scholar
• Miller, N. J., Griffin, T., Bergtold, J., Sharda, A., & Ciampitti, I. (2017). Farmers’ Adoption Path of Precision Agriculture Technology. Advances in Animal Biosciences, 8(2), 708–712. https://doi.org/10.1017/S2040470017000528
   Google Scholar
• Montagnini, F., Francesconi, M., & Rossi, E. (2011). Payments for environmental services in Latin America as a tool for restoration and rural development. Ambio, 40(3), 285–297. https://doi.org/10.1007/s13280-010-0114-4
   PubMed Web of Science ®Google Scholar
• Moons, I., De Pelsmacker, P., Pijnenburg, A., Daems, K., & Van de Velde, L. J. (2022). Growers’ adoption intention of innovations is crucial to establish a sustainable greenhouse horticultural industry: An empirical study in Flanders and the Netherlands. Journal of Cleaner Production, 330, 129752. https://doi.org/10.1016/j.jclepro.2021.129752
   Web of Science ®Google Scholar
• Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192
   Web of Science ®Google Scholar
• Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel Psychology, 53(2), 375–403. https://doi.org/10.1111/j.1744-6570.2000.tb00206.x
   Web of Science ®Google Scholar
• Mulinde, C., Majaliwa, J. G. M., Twinomuhangi, R., Mfitumukiza, D., Komutunga, E., Ampaire, E., Asiimwe, J., Van Asten, P., & Jassogne, L. (2021). Perceived climate risks and adaptation drivers in diverse coffee landscapes of Uganda. NJAS: Wageningen Journal of Life Sciences, 88(1), 31–44. https://doi.org/10.1016/j.njas.2018.12.002
   Google Scholar
• Nakabugo, R., Mukwaya, I. P., & Geoffrey, S. (2019). Adoption of climate smart agricultural technologies and practices in drylands in uganda: Evidence from a microlevel study in Nakasongola district. Agriculture and Ecosystem Resilience in Sub Saharan Africa: Livelihood Pathways Under Changing Climate, 30(3), 541–568. https://doi.org/10.1007/978-3-030-12974-3_24
   Google Scholar
• Ndamani, F., & Watanabe, T. (2016). Determinants of farmers’ adaptation to climate change: A micro level analysis in Ghana. Scientia Agricola, 73(3), 201–208. https://doi.org/10.1590/0103-9016-2015-0163
   Web of Science ®Google Scholar
• Ngeywo, J., Basweti, E., & Shitandi, A. (2015). Influence of Gender, Age, marital status and farm size on coffee production: A case of Kisii County, Kenya. Asian Journal of Agricultural Extension, Economics & Sociology, 5(3), 117–125. https://doi.org/10.9734/ajaees/2015/15702
   Google Scholar
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd. ed.). McGraw-Hill, Inc.
   Google Scholar
• Nyasimi, M., Kimeli, P., Sayula, G., Radeny, M., Kinyangi, J., & Mungai, C. (2017). Adoption and dissemination Pathways for climate-smart Agriculture technologies and practices for climate-resilient livelihoods in Lushoto, Northeast Tanzania. Climate, 5(3), 63. https://doi.org/10.3390/cli5030063
   Web of Science ®Google Scholar
• Oberski, D. (2014). Lavaan. survey: An R package for complex survey analysis of structural equation models. Journal of Statistical Software, 57(1), 1–27. https://doi.org/10.18637/jss.v057.i01
   PubMed Web of Science ®Google Scholar
• Ogola, R. J. O., Ouko, K. O., & Tejada Moral, M. (2021). Synergies and trade-offs of selected climate smart agriculture practices in Irish potato farming, Kenya. Cogent Food & Agriculture, 7(1), 1948257. https://doi.org/10.1080/23311932.2021.1948257
   Web of Science ®Google Scholar
• Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. The Electronic Journal of Information Systems Evaluation, 14(1), 110‑121–pp110‑121.
   Google Scholar
• Oliveira, J. R. D., Santana, W. D. R., Altoé, J. A., Carrion, P. A. N., Baldan, W. G., Lima, A. S., Rosa, L. V. C. A. F., Gomes, R. D. S., Cavalcanti, A. C., Nascimento, A. D. L., & Oliveira, V. D. S. (2021). Integrated Pest Management In Coffee. International Journal of Plant & Soil Science, 2021, 9–16. https://doi.org/10.9734/ijpss/2021/v33i1430496
   Google Scholar
• Opio, E. (2019). Influence of climate change on household food security in Uganda: A case study of Luweero district. Nkumba University.
   Google Scholar
• Oseni, T. O., & Masarirambi, M. T. (2011). Effect of climate change on maize (zea mays) production and food security in Swaziland. American-Eurasian Journal of Agricultural Environment Science, 11(3), 385–391.
   Google Scholar
• Pagliacci, F., Defrancesco, E., Mozzato, D., Bortolini, L., Pezzuolo, A., Pirotti, F., Pisani, E., & Gatto, P. (2020). Drivers of farmers’ adoption and continuation of climate-smart agricultural practices. A study from northeastern Italy. Science of the Total Environment, 710, 136345. https://doi.org/10.1016/j.scitotenv.2019.136345
   PubMed Web of Science ®Google Scholar
• Palombi, L., & Sessa, R. (2013). Climate-Smart Agriculture. In Climate-Smart Agriculture Sourcebook (2nd ed., Vol. 9, pp. 1–56). Food and Agriculture Organization of the United Nations (FAO). https://www.fao.org/docrep/018/i3325e/i3325e.pdf
   Google Scholar
• Parveen, F., Jaafar, N. I., & Ainin, S. (2016). Social media’s impact on organizational performance and entrepreneurial orientation in organizations. Management Decision, 54(9), 2208–2234. https://doi.org/10.1108/MD-08-2015-0336
   Web of Science ®Google Scholar
• Passarelli, M., Bongiorno, G., & Cariola, A. (2021). Technology adoption in agriculture: a new entrepreneurial approach is possible? Agribusiness and Information Management, 13(1), 1–25.
   Google Scholar
• Pathak, H. S., Brown, P., & Best, T. (2019). A systematic literature review of the factors affecting the precision agriculture adoption process. Precision Agriculture, 20(6), 1292–1316. https://doi.org/10.1007/s11119-019-09653-x
   Web of Science ®Google Scholar
• Pickens, J. (2005). Attitudes and perceptions. Organizational Behavior in Health Care, 4(7), 43–76.
   Google Scholar
• Pindado Tapia, E., & Sánchez García, M. (2017). Researching the entrepreneurial behaviour of new and existing ventures in European agriculture. Small Business Economics, 49(2), 421–444. https://doi.org/10.1007/s11187-017-9837-y
   Web of Science ®Google Scholar
• Ping, R. A. (2004). On assuring valid measures for theoretical models using survey data. Journal of Business Research, 57(2), 125–141. https://doi.org/10.1016/S0148-2963(01)00297-1
   Web of Science ®Google Scholar
• Rahn, E., Läderach, P., Baca, M., Cressy, C., Schroth, G., Malin, D., van Rikxoort, H., & Shriver, J. (2014). Climate change adaptation, mitigation and livelihood benefits in coffee production: Where are the synergies? Mitigation and Adaptation Strategies for Global Change, 19(8), 1119–1137. https://doi.org/10.1007/s11027-013-9467-x
   Web of Science ®Google Scholar
• Rajendran, N., Tey, Y. S., Brindal, M., Ahmad Sidique, S. F., Shamsudin, M. N., Radam, A., & 1Abdul Hadi, A. H. I. (2016). Factors influencing the adoption of bundled sustainable agricultural practices: A systematic literature review. International Food Research Journal, 23(5), 2271–2279. https://web.s.ebscohost.com/abstract?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=19854668&AN=119017236&h=g52%2b0ZDXSvM7rGnqZmzNyRt1EoZ2SivaIsBL5hqF6%2f8Yi9jR5q3VQQ9LgNYCx202CC0zhP%2b5AU3QUF0qGtvK6Q%3d%3d&crl=c&resultNs=AdminWebAuth&resultLocal=ErrCrlNotAuth&crlhashurl=login.aspx%3fdirect%3dtrue%26profile%3dehost%26scope%3dsite%26authtype%3dcrawler%26jrnl%3d19854668%26AN%3d119017236
   Web of Science ®Google Scholar
• Ramos de Luna, I., Montoro-Ríos, F., & Liébana-Cabanillas, F. (2016). Determinants of the intention to use NFC technology as a payment system: An acceptance model approach. Information Systems & E-Business Management, 14, 293–314. https://doi.org/10.1007/s10257-015-0284-5
   Web of Science ®Google Scholar
• Rogers, E. (1995). Diffusion of innovations (Fourth Paperback ed.).
   Google Scholar
• Rogers, E. (2003). Rogers’ diffusion of innovations theory.
   Google Scholar
• Samaradiwakara, G., & Gunawardena, C. (2014). Comparison of existing technology acceptance theories and models to suggest a well improved theory/model. International Technical Sciences Journal, 1(1), 21–36.
   Google Scholar
• Sangroya, D., & Nayak, J. K. (2017). Factors influencing buying behaviour of green energy consumer. Journal of Cleaner Production, 151, 393–405. https://doi.org/10.1016/j.jclepro.2017.03.010
   Web of Science ®Google Scholar
• Sardar, A., Kiani, A. K., & Kuslu, Y. (2021). Does adoption of climate-smart agriculture (CSA) practices improve farmers’ crop income? Assessing the determinants and its impacts in Punjab province, Pakistan. Environment Development and Sustainability, 23(7), 10119–10140. https://doi.org/10.1007/s10668-020-01049-6
   Web of Science ®Google Scholar
• Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
   Web of Science ®Google Scholar
• Sebatta, C., Mugisha, J., Bagamba, F., Nuppenau, E. A., Domptail, S. E., Kowalski, B., Hoeher, M., Ijala, A. R., Karungi, J., & Tejada Moral, M. (2019). Pathways to sustainable intensification of the coffee-banana agroecosystems in the mt. Elgon region. Cogent Food & Agriculture, 5(1), 1611051. https://doi.org/10.1080/23311932.2019.1611051
   Web of Science ®Google Scholar
• Sekabira, H., Tepa-Yotto, G. T., Djouaka, R., Clottey, V., Gaitu, C., Tamò, M., Kaweesa, Y., & Ddungu, S. P. (2022). Determinants for deployment of climate-smart integrated pest management practices: A meta-analysis approach. Agriculture, 12(7), 1052. https://doi.org/10.3390/agriculture12071052
   Google Scholar
• Senyolo, M. P., Long, T. B., Blok, V., & Omta, O. (2018). How the characteristics of innovations impact their adoption: An exploration of climate-smart agricultural innovations in South Africa. Journal of Cleaner Production, 172(172), 3825–3840. https://doi.org/10.1016/j.jclepro.2017.06.019
   Google Scholar
• Senyolo, M. P., Long, T. B., Blok, V., Omta, O., & van der Velde, G. (2021). Smallholder adoption of technology: Evidence from the context of climate smart agriculture in South Africa. Journal of Development and Agricultural Economics, 13(2), 156–173. https://doi.org/10.5897/JDAE2020.1191
   Google Scholar
• Serote, B., Mokgehle, S., Du Plooy, C., Mpandeli, S., Nhamo, L., & Senyolo, G. (2021). Factors influencing the adoption of climate-smart irrigation technologies for sustainable crop productivity by smallholder farmers in arid areas of South Africa. Agriculture, 11(12), 1222. https://doi.org/10.3390/agriculture11121222
   Google Scholar
• Shaw, H., Ellis, D. A., & Ziegler, F. V. (2018). The technology integration model (TIM). Predicting the continued use of technology. Computers in Human Behavior, 83, 204–214. https://doi.org/10.1016/j.chb.2018.02.001
   Web of Science ®Google Scholar
• Shekmohammed, S., Mahmud, F., Asaduzzaman, M., Hany, U., Mahbub, M. (2022). The role of agroforestry in ecosystem maintenance and climate change Regulation: A Review.
   Google Scholar
• Son, J.-Y., & Benbasat, I. (2007). Organizational buyers’ adoption and use of B2B electronic marketplaces: Efficiency-and legitimacy-oriented perspectives. Journal of Management Information Systems, 24(1), 55–99. https://doi.org/10.2753/MIS0742-1222240102
   Web of Science ®Google Scholar
• Sun, S., Yang, X., Lin, X., Sassenrath, G. F., & Li, K. (2018). Climate-smart management can further improve winter wheat yield in China. Agricultural Systems, 162, 10–18. https://doi.org/10.1016/j.agsy.2018.01.010
   Web of Science ®Google Scholar
• Suvanto, H., Niemi, J. K., & Lähdesmäki, M. (2020). Entrepreneurial identity and farmers’ protein crop cultivation choices. Journal of Rural Studies, 75, 174–184. https://doi.org/10.1016/j.jrurstud.2020.01.022
   Web of Science ®Google Scholar
• Tageo, V., CBMS, D., Chronaki, C., Lowe, C., Berler, A., & Porcu, F. (2020). Business model canvas for adoption of International patient summary standards in mHealth industry. Journal of Business Models, 8(3), 91–106.
   Google Scholar
• Taherdoost, H. (2018). Taherdoost H: A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960–967. https://doi.org/10.1016/j.promfg.2018.03.137
   Google Scholar
• Tama, R. A. Z., Hoque, M. M., Liu, Y., Alam, M. J., & Yu, M. (2023). An application of partial least squares structural equation modeling (PLS-SEM) to examining farmers’ behavioral attitude and intention towards conservation Agriculture in Bangladesh. Agriculture, 13(2), 503. https://doi.org/10.3390/agriculture13020503
   Google Scholar
• Taylor, M. (2018). Climate-smart agriculture: What is it good for? The Journal of Peasant Studies, 45(1), 89–107. https://doi.org/10.1080/03066150.2017.1312355
   Web of Science ®Google Scholar
• Teklewold, H., Kassie, M., & Shiferaw, B. (2013). Adoption of Multiple Sustainable Agricultural Practices in Rural Ethiopia. Journal of Agricultural Economics, 64(3), 597–623. https://doi.org/10.1111/1477-9552.12011
   Web of Science ®Google Scholar
• Tesfaye, K. Kassie, M. Cairns, J. E. Michael, M. Stirling, C. Tsedeke Abate, B. M. P. Mekuria, M. Hailu, H. Bahadur Rahut, D. Erenstein, O. & Gerard, B. (2017). Potential for scaling up climate smart agricultural practices: examples from sub-Saharan Africa. Climate Change Adaptation in Africa: Fostering Resilience and Capacity to Adapt, 4(2), 185–203. https://doi.org/10.1007/978-3-319-49520-0_12
   Google Scholar
• Tran, H., Simelton E., & Quinn C. (2017). CGSpace. 124. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). https://cgspace.cgiar.org/handle/10568/80703
   Google Scholar
• Tripathi, A., Tripathi, D. K., Chauhan, D. K., Kumar, N., & Singh, G. S. (2016). Paradigms of climate change impacts on some major food sources of the world: A review on current knowledge and future prospects. Agriculture, Ecosystems & Environment, 216, 356–373. https://doi.org/10.1016/j.agee.2015.09.034
   Web of Science ®Google Scholar
• Tsai, T.-H., Chang, H.-T., Ho, Y.-L., & Federici, S. (2016). Perceptions of a specific family communication application among grandparents and grandchildren: An extension of the technology acceptance model. PloS One, 11(6), e0156680. https://doi.org/10.1371/journal.pone.0156680
   PubMed Web of Science ®Google Scholar
• UBOS. (2018a). The 2018 Statistical report. https://www.ubos.org/wp-content/uploads/publications/05_2019STATISTICAL_ABSTRACT_2018.pdf
   Google Scholar
• UBOS. (2019). The 2019 Statistical report
   Google Scholar
• UBOS. (2018b). The 2018 Statistical report . Uganda Bureau of Statistics, 345.
   Google Scholar
• UCC. (2011). District information portal, Luwero district.
   Google Scholar
• UCDA. (2015). Uganda national coffee strategy 2040 plan for 2015/16-2019/20. https://ugandacoffee.go.ug/index.php/file-download/download/public/35.
   Google Scholar
• UCDA. (2019a). Robust Coffee Handbook. https://greeninguganda.com/images/product/coffee/robusta.pdf/
   Google Scholar
• UCDA. (2019b). Robust coffee handbook. https://greeninguganda.com/images/product/coffee/robusta.pdf
   Google Scholar
• Van Kleef, G. A., Gelfand, M. J., & Jetten, J. (2019). The dynamic nature of social norms: New perspectives on norm development, impact, violation, and enforcement. Journal of Experimental Social Psychology, 84, 103814. https://doi.org/10.1016/j.jesp.2019.05.002
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Venkatesh, V., & Thong, J. Y. (2012). Xu X: Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
   Web of Science ®Google Scholar
• Venkateswarlu, B. (2017). Climate Change and Climate Variability. In Valavida, V., & Karaba, N. (Eds.), Agriculture Under Climate Change: Threats, Strategies and Policies (Vol. 1, p. 22).
   Google Scholar
• Victory, G. O., Oyewole, A. L., & Olaitan, A. A. (2022). Climate-smart agricultural practices at Oyo State-Nigeria. South Asian Journal of Social Review (ISSN: 2958-2490), 1(1), 1–7. https://doi.org/10.57044/SAJSR.2022.1.1.2201
   Google Scholar
• Vincent, A., & Balasubramani, N. (2021). Climate-smart agriculture (CSA) and extension advisory service (EAS) stakeholders’ prioritisation: a case study of Anantapur district, Andhra Pradesh, India. Journal of Water and Climate Change, 12(8), 3915–3931. https://doi.org/10.2166/wcc.2021.329
   Web of Science ®Google Scholar
• Vondolia, G. K., Eggert, H., & Stage, J. (2021). The effect of fertilizer subsidies on investment in soil and water conservation and productivity among Ghanaian farmers using mechanized irrigation. Sustainability, 13(15), 8242. https://doi.org/10.3390/su13158242
   Web of Science ®Google Scholar
• Waaswa, A., Oywaya Nkurumwa, A., Mwangi Kibe, A., & Ngeno Kipkemoi, J. (2022). Climate-Smart agriculture and potato production in Kenya: Review of the determinants of practice. Climate and Development, 14(1), 75–90. https://doi.org/10.1080/17565529.2021.1885336
   Web of Science ®Google Scholar
• Wang, O., De Steur, H., Gellynck, X., & Verbeke, W. (2015a). Motives for consumer choice of traditional food and European food in mainland China. Appetite, 87, 143–151. https://doi.org/10.1016/j.appet.2014.12.211
   PubMed Web of Science ®Google Scholar
• Wang, O., De Steur, H., Gellynck, X., & Verbeke, W. (2015b). Motives for consumer choice of traditional food and European food in mainland China. Appetite, 87(87), 143–151. https://doi.org/10.1016/j.appet.2014.12.211
   PubMedGoogle Scholar
• Wesana, J., De Steur, H., Dora, M. K., Mutenyo, E., Muyama, L., & Gellynck, X. (2018). Towards nutrition sensitive agriculture. Actor readiness to reduce food and nutrient losses or wastes along the dairy value chain in Uganda. Journal of Cleaner Production, 182(182), 46–56. https://doi.org/10.1016/j.jclepro.2018.02.021
   Google Scholar
• Wiredu, A. N., Zeller, M., Diagne, A., Diagne, A., & Dogbe, W. (2015). Aliou: What determines adoption of fertilizers among rice-producing households in Northern Ghana? Journal of Sustainable Development, 7(1). https://doi.org/10.5539/jsd.v7n1p167
   Google Scholar
• Workman, M. (2014). New media and the changing face of information technology use: The importance of task pursuit, social influence, and experience. Computers in Human Behavior, 31, 111–117. https://doi.org/10.1016/j.chb.2013.10.008
   Web of Science ®Google Scholar
• Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in human behavior. Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028
   Web of Science ®Google Scholar
• Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129–142. https://doi.org/10.1016/j.chb.2011.08.019
   Web of Science ®Google Scholar
• Zahra, S. A. (2018). Entrepreneurial risk taking in family firms: The wellspring of the regenerative capability. Family Business Review, 31(2), 216–226. https://doi.org/10.1177/0894486518776871
   Web of Science ®Google Scholar
• Zerssa, G., Feyssa, D., Kim, D.-G., & Eichler-Löbermann, B. (2021). Challenges of smallholder farming in Ethiopia and opportunities by adopting climate-smart agriculture. Agriculture, 11(3), 192. https://doi.org/10.3390/agriculture11030192
   Google Scholar
• Zeweld, W., Van Huylenbroeck, G., Tesfay, G., & Speelman, S. (2017). Smallholder farmers’ behavioural intentions towards sustainable agricultural practices. Journal of Environmental Management, 187, 71–81. https://doi.org/10.1016/j.jenvman.2016.11.014
   PubMed Web of Science ®Google Scholar