Psycho-social factors influencing the adoption of sustainable intensification practices by smallholder rice farmers in Northern Ghana

References

• Abushanab, E., & Pearson, J. M. (2007). Internet banking in Jordan: The unified theory of acceptance and use of technology (UTAUT) perspective. Journal of Systems and Information Technology, 9(1), 1–17. https://doi.org/10.1108/13287260710817700
   Google Scholar
• Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour: Attitudes. In Intentions and perceived behavioral control. Prentice Hall, Englewood Cliffs, N.J.
   Google Scholar
• Ali, F., Nair, P. K., & Hussain, K. (2016). An assessment of students’ acceptance and usage of computer supported collaborative classrooms in hospitality and tourism schools. Journal of Hospitality, Leisure, Sport and Tourism Education, 18, 51–60. https://doi.org/10.1016/j.jhlste.2016.03.002
   Web of Science ®Google Scholar
• Alwahaishi, S., & Snásel, V. (2013). Consumers’ acceptance and use of information and communications technology: A UTAUT and flow based theoretical model. Journal of Technology Management & Innovation, 8(2), 9–10. https://doi.org/10.4067/S0718-27242013000200005
   Google Scholar
• Barnes, A. P., Soto, I., Eory, V., Beck, B., Balafoutis, A., Sánchez, B., Vangeyte, J., Fountas, S., van der Wal, T., & Gómez-Barbero, M. (2019). Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy, 80, 163–174. https://doi.org/10.1016/j.landusepol.2018.10.004
   Web of Science ®Google Scholar
• Bervell, B., & Umar, I. N. (2017). Validation of the UTAUT model: Re-considering non-linear relationships of exogenous variables in higher education technology acceptance research. EURASIA Journal of Mathematics Science and Technology Education, 13(10), 6471–6490.
   Web of Science ®Google Scholar
• Bollen, K. A. (1989). A new incremental fit index for general structural equation models. Sociological Methods & Research, 17(3), 303–316. https://doi.org/10.1177/0049124189017003004
   Web of Science ®Google Scholar
• Borges, J. A. R., Foletto, L., & Xavier, V. T. (2015). An interdisciplinary framework to study farmers decisions on adoption of innovation: Insights from expected utility theory and theory of planned behavior. African Journal of Agricultural Research, 10(29), 2814–2825.
   Google Scholar
• Borges, J. A. R., Lansink, A., & Emvalomatis, G. (2019). Adoption of innovation in agriculture: A critical review of economic and psychological models. International Journal of Innovation and Sustainable Development, 13(1), 36–56. https://doi.org/10.1504/IJISD.2019.096705
   Web of Science ®Google Scholar
• Brown, B., Nuberg, I., & Llewellyn, R. (2017). Negative evaluation of conservation agriculture: Perspectives from African smallholder farmers. International Journal of Agricultural Sustainability, 15(4), 467–481. https://doi.org/10.1080/14735903.2017.1336051
   Web of Science ®Google Scholar
• Brown, S.A., & Venkatesh, V. (2005) Model of adoption of technology in the household: A baseline model test and extension incorporating household life cycle. Management Information System Quarterly, 29 (3), 399–426. https://doi.org/10.2307/25148690
   Web of Science ®Google Scholar
• Çelik, H. (2008). What determines Turkish customers’ acceptance of internet banking? International Journal of Bank Marketing, 26(5), 353–370. https://doi.org/10.1108/02652320810894406
   Google Scholar
• Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652
   PubMed Web of Science ®Google Scholar
• Chouinard, H., Paterson, T., Wandschneider, P., & Ohler, A. (2008). Will farmers trade profits for stewardship? Heterogeneous motivations for farm practice selection. Land Economics, 84(1), 66–82. https://doi.org/10.3368/le.84.1.66
   Web of Science ®Google Scholar
• Cohen, R. J., & Swerdlik, M. E. (2005). Psychological testing and assessment: An introduction to tests and measurement (6th ed.). McGraw-Hill.
   Google Scholar
• de Sena Abrahão, R. D. S., Moriguchi, S. N., & Andrade, D. F. (2016). Intention of adoption of mobile payment: An analysis in the light of the unified theory of acceptance and use of technology (UTAUT). RAI Revista de Administração e Inovação, 13(3), 221–230. https://doi.org/10.1016/j.rai.2016.06.003
   Google Scholar
• Dessart, F. J., Barreiro-Hurlé, J., & van Bavel, R. (2019). Behavioural factors affecting the adoption of sustainable farming practices: A policy-oriented review. European Review of Agricultural Economics, 46(3), 417–471. https://doi.org/10.1093/erae/jbz019
   Web of Science ®Google Scholar
• Digvijah, S. N., Birthal, P. S., Kumar, A., & Tripathi, G. (2020). Farmers’ social networks and adoption of modern crop varieties in India. IFPRI Discussion Paper 1918. International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/p15738coll2.133684
   Google Scholar
• Doss, C. R. (2006). Analysing technology adoption using microstudies: Limitations, challenges and opportunities for improvement. Agricultural Economics, 34(3), 207–219. https://doi.org/10.1111/j.1574-0864.2006.00119.x
   Web of Science ®Google Scholar
• Duck-Boung, K., Kwang-Jin, C., Yang-Kyu, L., & Min-Uk, J. (2020). A study on the effects of changes in smart farm introduction conditions on willingness to accept agriculture - application of extended UTAUT model. Korean Journal of Organic Agriculture, 28(2), 119–138. Doi: 10.11625/KJOA.2020.28.2.119
   Google Scholar
• Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
   Web of Science ®Google Scholar
• El Bilali, H. L., Hassen, T. B., Bottalico, F., Berjan, S., & Capone, R. (2021). Acceptance and adoption of technologies in agriculture. AGROFOR, 6(1), 135. https://doi.org/10.7251/AGRENG2101135E
   Google Scholar
• Engotoit, E., Kituyi, G. M., & Moya, M. B. (2016). Influence of performance expectancy on commercial farmers’ intention to use mobile-based communication technologies for agricultural market information dissemination in Uganda. Journal of Systems and Information Technology, 18(4), 346–363. https://doi.org/10.1108/JSIT-06-2016-0037
   Google Scholar
• Eweoya, I., Senanu, R., Okuboyejo, S. R., Odetunmibi, O. A., & Odusote, B. O. (2021). An empirical investigation of acceptance, adoption and the use of E-agriculture in Nigeria. Heliyon, 7(7), e07588. https://doi.org/10.1016/j.heliyon.2021.e07588
   PubMed Web of Science ®Google Scholar
• Foguesatto, C. R., Borges, J. A. R., & Machado, J. A. D. (2019). Farmers’ typologies regarding environmental values and climate change: Evidence from southern Brazil. Journal of Cleaner Production, 232(2019), 400–407. https://doi.org/10.1016/j.jclepro.2019.05.275
   Google Scholar
• Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., Liakos, B., Canavari, M., Wiebensohn, J., & Tisserye, B. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40–50. https://doi.org/10.1016/j.compag.2015.05.011
   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
• Garnett, T., & Godfray, C. (2012). Sustainable intensification in agriculture. Navigating a course through competing food system priorities. Food Climate Research Network and the Oxford Martin Programme on the Future of Food, University of Oxford, 51.
   Google Scholar
• Glover, D., Sumberg, J., & Andersson, J. A. (2016). The adoption problem; Or why we still understand so little about technological change in African agriculture. Outlook on Agriculture, 45(1), 3–6. https://doi.org/10.5367/oa.2016.0235
   Web of Science ®Google Scholar
• Glover, D., Sumberg, J., Ton, G., Andersson, J., & Badstue, L. (2019). Rethinking technological change in smallholder agriculture. Outlook on Agriculture, 48(3), 169–180. https://doi.org/10.1177/0030727019864978
   Web of Science ®Google Scholar
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
   Web of Science ®Google Scholar
• Jaradat, M. I. R. M., & Al-Mashaqba, A. M. (2014). Understanding the adoption and usage of mobile payment services by using TAM3. International Journal of Business Information Systems, 16(3), 271–296. https://doi.org/10.1504/IJBIS.2014.063768
   Google Scholar
• Jin, Y., Lin, Q., & Mao, S. (2022). Tanzanian farmers’ intention to adopt improved maize technology: Analyzing influencing factors using SEM and fsQCA methods. Agriculture, 12(12), 1991. https://doi.org/10.3390/agriculture12121991
   Google Scholar
• Jones-Garcia, E., & Krishna, V. V. (2021). Farmer adoption of sustainable intensification technologies in the maise systems of the Global South: A review. Agronomy for Sustainable. Development, 41(1), 8. https://doi.org/10.1007/s13593-020-00658-9
   Web of Science ®Google Scholar
• Kaplan, K. J. (1972). On the ambivalence-indifference problem in attitude theory and measurement: A suggested modification of the semantic differential technique. Psychological Bulletin, 77(5), 361–372. https://doi.org/10.1037/h0032590
   Google Scholar
• Lee, J. H., & Song, C. H. (2013). Effect of trust and perceived risk on user acceptance of a new technology service. Social Behavior and Personality: An International Journal, 41(4), 587–597. https://doi.org/10.2224/sbp.2013.41.4.587
   Web of Science ®Google Scholar
• Liu, H. B., & Luo, X. J. (2018). Understanding farmers’ perceptions and behaviors towards farmland quality change in Northeast China: A structural equation modeling approach. Sustainability, 10(9), 3345. https://doi.org/10.3390/su10093345
   Web of Science ®Google Scholar
• Loos, J., Abson, D. J., Chappell, M. J., Hanspach, J., Mikulcak, F., Tichit, M., & Fischer, J. (2014). Putting meaning back into “sustainable intensification”. Frontiers in Ecology and the Environment, 12(6), 356–361. https://doi.org/10.1890/130157
   Web of Science ®Google Scholar
• Lotte, W., Katrien, D., & Ken, G. E. (2015). Adoptability of sustainable intensification technologies in dryland smallholder farming systems of West Africa. Research Report No. 64. Patancheru 502 324. International Crops Research Institute for the SemiArid Tropics, 84 pp.
   Google Scholar
• Lwoga, E. T., & Lwoga, N. B. (2017). User acceptance of mobile payment: The effects of user- centric security, system characteristics and gender. The Electronic Journal of Information Systems in Developing Countries, 81(1), 1–24. https://doi.org/10.1002/j.1681-4835.2017.tb00595.x
   Google Scholar
• Majidi, S., & Raymond, J. K. (2022). Psychosocial responses to telehealth for diabetes care. In David C. Klonoff, David Kerr, Elissa R. Weitzman (Eds.), Diabetes digital health and telehealth (pp. 159–169). Elsevier.
   Google Scholar
• Marsh, H. W. (2007). Application of confirmatory factor analysis and structural equation modeling in sport and exercise psychology. In G. Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology (pp. 774–798). John Wiley & Sons, Inc. https://doi.org/10.1002/9781118270011.ch35
   Google Scholar
• Michels, M., Bonke, V., & Mußhoff, O. (2019). Understanding the adoption of crop protection smartphone apps: An application of the unified theory of acceptance and use of technology. (No. 1905). Diskussionsbeitrag.
   Google Scholar
• Mills, J., Gaskell, P., Ingram, J., Dwyer, J., Reed, M., & Short, C. (2017). Engaging farmers in environmental management through a better understanding of behaviour. Agriculture and Human Values, 34(2), 283–299. https://doi.org/10.1007/s10460-016-9705-4
   Web of Science ®Google Scholar
• Momani, A. M. (2020). The unified theory of acceptance and use of technology: A new approach in technology acceptance. International Journal of Sociotechnology and Knowledge Development, 12(3), 79–98. https://doi.org/10.4018/IJSKD.2020070105
   Google Scholar
• Mtebe, J. S., & Raisamo, R. (2014). Investigating students’ behavioural intention to adopt and use mobile learning in higher education in East Africa. International Journal of Education and Development Using Information and Communication Technology (IJEDICT), 10(3), 4–20.
   Google Scholar
• Mulugu, L., Birungi, F., Kyazze, P., Kibwika, E., Kikulwe, A., Omondi, B., & Ajambo, S. (2020). Unravelling technology-acceptancefactors influencing farmer use of banana tissue culture planting materials in Central Uganda. African Journal of Science, Technology, Innovation and Development, 12(4), 453–465. https://doi.org/10.1080/20421338.2019.1634900
   Web of Science ®Google Scholar
• O’Shea, R., O’Donoghue, C., Ryan, M., & Breen, J. (2018, August 30–31). Understanding farmers: From adoption to attitudes [Paper presentation]. 166^th EAAE Seminar Sustainability in the Agri-Food Sector, Galway Galway, Ireland. National University of Ireland.
   Google Scholar
• Okumus, B., Ali, F., Bilgihan, A., & Ozturk, A. B. (2018). Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants. International Journal of Hospitality Management, 72, 67–77. https://doi.org/10.1016/j.ijhm.2018.01.001
   Web of Science ®Google Scholar
• Omar, Q., Yap, C. E., Ho, P. L., & Keling, W. (2022). Predictors of behavioral intention to adopt e-AgriFinance app among the farmers in Sarawak, Malaysia. British Food Journal, 124(1), 239–254. https://www.emerald.com/insight/0007-070X.htm https://doi.org/10.1108/BFJ-04-2021-0449
   Web of Science ®Google Scholar
• Onaolapo, S., & Oyewole, O. (2018). Performance expectancy, effort expectancy, and facilitating conditions as factors influencing smart phones use for mobile learning by postgraduate students of the University of Ibadan, Nigeria. Interdisciplinary Journal of e-Skills and Lifelong Learning, 14, 95–115. https://doi.org/10.28945/4085
   Google Scholar
• Pardamean, B., & Susanto, M. (2012). Assessing user acceptance toward blog technology using the UTAUT model. International Journal of Mathematics and Computers in Simulation, 1(6), 203–216.
   Google Scholar
• Piedmont, R. L. (2014). Inter-item correlations. In A. C. Michalos (Ed.), Encyclopedia of quality of life and well-being research (Vol. 1, pp. 3303–3304). Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_1493
   Google Scholar
• Ren, Z., & Zhong, K. (2022). Driving mechanism of subjective cognition on farmers’ adoption behavior of straw returning technology: Evidence from rice and wheat producing provinces in China. Frontiers in Psychology, 13, 922889. https://doi.org/10.3389/fpsyg.2022.922889
   PubMed Web of Science ®Google Scholar
• Rosário, J., Madureira, L., Marques, C., & Silva, R. (2022). Understanding farmers’ adoption of sustainable agriculture innovations: A systematic literature review. Agronomy, 12(11), 2879. https://doi.org/10.3390/agronomy12112879
   Google Scholar
• Schukat, S., & Heise, H. (2021). Towards an understanding of the behavioral intentions and actual use of smart products among German farmers. Sustainability, 13(12), 6666. https://doi.org/10.3390/su13126666
   Web of Science ®Google Scholar
• Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling (2nd ed.). Lawrence Erlbaum Associates Publishers.
   Google Scholar
• Shafinah, K., Sahari, N., Sulaiman, R., Yusoff, M. S. M., & Ikram, M. M. (2013). Determinants of user behavioral intention (BI) on mobile services: A preliminary view. Procedia Technology, 11, 127–133. https://doi.org/10.1016/j.protcy.2013.12.171
   Google Scholar
• Shanmugam, V., & Marsh, J. E. (2016). Application of structural equation modeling to the social sciences: A brief guide for researchers. Mesure et Évaluation en Éducation, 37(3), 99–123. https://doi.org/10.7202/1036329ar
   Google Scholar
• Stein, C. M., Morris, N. J., & Nock, N. L. (2012). Structural equation modeling. Methods in Molecular Biology (Clifton, N.J.), 850, 495–512. https://doi.org/10.1007/978-1-61779-555-8_27
   PubMedGoogle Scholar
• Struik, P. C., & Kuyper, T. W. (2017). Sustainable intensification in agriculture: the richer shade of green. A review. Agronony for Sustainable Development, 37(39), 1–15. https://doi.org/10.1007/s13593-017-0445-7
   PubMedGoogle Scholar
• Taiwo, A. A., & Downe, L. G. (2013). The theory of user acceptance and use of technology (UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical and Applied Information Technology, 49(1), 48–58.
   Google Scholar
• Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144
   Web of Science ®Google Scholar
• Teo, T. (2009). The impact of subjective norm and facilitating conditions on pre-service teachers’ attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Journal of Educational Computing Research, 40(1), 89–109. https://doi.org/10.2190/EC.40.1.d
   Web of Science ®Google Scholar
• Thomas, T. D., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development Using Information and Communication Technology, 9(3), 71–85.
   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., Brown, S.A., Maruping, L.M., & Bala, H.(2008). Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32 (3), 483–502. Doi: https://www.jstor.org/stable/25148853 https://doi.org/10.2307/25148853
   Web of Science ®Google Scholar
• Violato, C., & Hecker, K. G. (2007). How to use structural equation modeling in medical education research: A brief guide. Teaching and Learning in Medicine, 19(4), 362–371. https://doi.org/10.1080/10401330701542685
   PubMed Web of Science ®Google Scholar
• Weerakkody, V., El-Haddadeh, R., Al-Sobhi, F., Shareef, M. A., & Dwivedi, Y. K. (2013). Examining the influence of intermediaries in facilitating e-government adoption: An empirical investigation. International Journal of Information Management, 33(5), 716–725. https://doi.org/10.1016/j.ijinfomgt.2013.05.001
   Web of Science ®Google Scholar
• Whetten, D. A. (2009). An examination of the interface between context and theory applied to the study of Chinese organisations. Management and Organization Review, 5(1), 29–56. https://doi.org/10.1111/j.1740-8784.2008.00132.x
   Web of Science ®Google Scholar
• Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488. https://doi.org/10.1108/JEIM-09-2014-0088
   Web of Science ®Google Scholar
• Yabi, A. J., & Omolehin, A. R. A. (2009). Structural equation modelling of socio-economic determinants of modern technology adoption for sustainable natural resources management in Adja and Nagot Areas of Benin Republic. Journal of Humanities, Social Science and Creative Arts, 4(1), 65–81.
   Google Scholar
• Yamano, T., Rajendran, S., & Malabayuabas, M. L. (2013, April 8–10). Psychological constructs toward agricultural technology adoption: Evidence from Eastern India [Paper presentation]. 87th Annual Conference, Coventry, UK. Warwick University.
   Google Scholar
• Zeweld, W., Huylenbroeck, G. V., Tesfay, G., & Speelman, S. (2017). Smallholder farmers’ behavioural intentions towards sustainable agricultural practices. Journal of Environmental Management, 187(2017), 71–81. https://doi.org/10.1016/j.jenvman.2016.11.014
   PubMedGoogle Scholar
• Zeweld, W., Huylenbroeck, G. V., Tesfay, G., Azadi, H., & Speelman, S. (2018). Impacts of socio-psychological factors on actual adoption of sustainable land management practices in dryland and water stressed areas. Sustainability, 10(9), 2963. https://doi.org/10.3390/su10092963
   Web of Science ®Google Scholar