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DOI: 10.14254/1795-6889.2024.20-1.4
Submitted: 2023-07-19
Published: 2024-05-27

Factors influencing farmer adoption of climate-smart agriculture technologies: Evidence from Malaysia

Universiti Teknologi MARA Pahang, Raub Campus
Faculty of Human Sciences, Universiti Pendidikan Sultan Idris
Faculty of Administrative Science and Policy Studies, Universiti Teknologi MARA, Negeri Sembilan
Institute for Biodiversity and Sustainable Development, Universiti Teknologi MARA Shah Alam Malaysia
School of Economics and Management, Department of Economic History, Lund University
climate smart agriculture technologies institutions environment sustainability

Abstract

As technology advances, people become increasingly dependent on technological tools to increase their work efficiency and productivity. Farming methods in the agriculture sector are also undergoing a shift from conventional to technology-driven modern agriculture practices, primarily because of their benefits and potential to mitigate the effects of climate change. However, the adoption rate of climate-smart agriculture technologies (CSAT) is considered to be very slow. Thus, this study was conducted to examine the factors that lead farmers to adopt CSAT in their agricultural practices. A sample of 185 farmers was used to investigate the main influencing factors in four contexts. The developed model was analyzed using the partial least squares structural equation modeling method. The results of this study suggest that institutions play a critical role as a contextual factor that leads individuals and societies to engage with CSAT, builds confidence, and convinces farmers to adopt these technologies.

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References

  1. Akouwerabou, D. B., Zanré, P. K., Savadogo, K., & Kaboré, P. J. W. (2022). Promoting farmers’ adoption of climate-smart agricultural technologies in Burkina Faso: The role of coordination along the value chain. International Journal of Agricultural Resources, Governance and Ecology, 18(3), 287–308. https://doi.org/10.1504/IJARGE.2022.124650 DOI: https://doi.org/10.1504/IJARGE.2022.124650
  2. Alkhwaldi, A., & Kamala, M. (2017). Why do users accept innovative technologies? A critical review of models and theories of technology acceptance in the information system literature. Journal of Multidisciplinary Engineering Science and Technology, 4(8), 2458–9403.
  3. AlSaleh, D., & Thakur, R. (2019). Impact of cognition, affect, and social factors on technology adoption. International Journal of Technology Marketing, 13(2), 178–200. https://doi.org/10.1504/IJTMKT.2019.102266 DOI: https://doi.org/10.1504/IJTMKT.2019.102266
  4. An, J., Di, H., Yao, M., & Jin, S. (2022). The role of payment technology innovation in environmental sustainability: Mediation effect from consumers’ awareness to practice. Frontiers in Environmental Science, 10, 881293. https://doi.org/10.3389/fenvs.2022.881293 DOI: https://doi.org/10.3389/fenvs.2022.881293
  5. Andrieu, N., Sogoba, B., Zougmore, R., Howland, F., Samake, O., Bonilla-Findji, O., Lizarazo, M., Nowak, A., Dembele, C., & Corner-Dolloff, C. (2017). Prioritizing investments for climate-smart agriculture: Lessons learned from Mali. Agricultural Systems, 154, 13–24. https://doi.org/10.1016/j.agsy.2017.02.008 DOI: https://doi.org/10.1016/j.agsy.2017.02.008
  6. Antwi-Agyei, P., Abalo, E. M., Dougill, A. J., & Baffour-Ata, F. (2021). Motivations, enablers and barriers to the adoption of climate-smart agricultural practices by smallholder farmers: Evidence from the transitional and savannah agroecological zones of Ghana. Regional Sustainability, 2(4), 375–386. https://doi.org/10.1016/j.regsus.2022.01.005 DOI: https://doi.org/10.1016/j.regsus.2022.01.005
  7. Anuga, S. W., Gordon, C., Boon, E., & Surugu, J. M. (2019). Determinants of Climate Smart Agriculture (CSA) adoption among smallholder food crop farmers in the Techiman Municipality, Ghana. Ghana Journal of Geography, 11(1), 124–139.
  8. Autio, A., Johansson, T., Motaroki, L., Minoia, P., & Pellikka, P. (2021). Constraints for adopting climate-smart agricultural practices among smallholder farmers in Southeast Kenya. Agricultural Systems, 194, 103284. https://doi.org/10.1016/j.agsy.2021.103284 DOI: https://doi.org/10.1016/j.agsy.2021.103284
  9. Azadi, H., Moghaddam, S. M., Burkart, S., Mahmoudi, H., Passel, S.V., Kurban A., & Lopez-Carr, D. (2021). Rethinking resilient agriculture: From Climate-Smart Agriculture to Vulnerable-Smart Agriculture. Journal of Cleaner Production, 319, 128602. https://doi.org/10.1016/j.jclepro.2021.128602 DOI: https://doi.org/10.1016/j.jclepro.2021.128602
  10. Bazzana, D., Foltz, J., & Zhang, Y. (2022). Impact of climate smart agriculture on food security: An agent-based analysis. Food Policy, 111, 102304. https://doi.org/10.1016/j.foodpol.2022.102304 DOI: https://doi.org/10.1016/j.foodpol.2022.102304
  11. Bijker, W. E., Hughes, T. P., & Pinch T. J. (1987). The social construction of technological systems: New directions in the sociology and history of technology. Massachusetts: MIT Press
  12. Benami, E., & Carter, M. R. (2021). Can digital technologies reshape rural microfinance? Implications for savings, credit, & insurance. Applied Economic Perspectives and Policy, 43(4), 1196-1220. https://doi.org/10.1002/aepp.13151 DOI: https://doi.org/10.1002/aepp.13151
  13. Casey, J., Bisaro, A., Valverde, A., Martinez, M., & Rokitzki, M. (2021). Private finance investment opportunities in climate-smart agriculture technologies. Retrieved on May 25, 2023, from https://www.casaprogramme.com/wp-content/uploads/2021/10/Private-finance-investment-opportunities-in-climate-smart-agriculture-technologies.pdf DOI: https://doi.org/10.1079/20220030734
  14. Chiesa V., & Frattini, F. (2011). Commercializing technological innovation: Learning from failures in high-tech markets. Journal of Product Innovation Management, 28(4), 437–454. https://doi.org/10.1111/j.1540-5885.2011.00818.x DOI: https://doi.org/10.1111/j.1540-5885.2011.00818.x
  15. Chung, N., Han, H., & Joun, Y. (2015). Tourists’ intention to visit a destination: The role of augmented reality (AR) application for a heritage site. Computers in Human Behavior, 50, 588–599. https://doi.org/10.1016/j.chb.2015.02.068 DOI: https://doi.org/10.1016/j.chb.2015.02.068
  16. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
  17. Dey, K., & Mishra, P. K. (2022). Mainstreaming blended finance in climate-smart agriculture: Complementarity, modality, and proximity. Journal of Rural Studies, 92, 342–353. https://doi.org/10.1016/j.jrurstud.2022.04.011 DOI: https://doi.org/10.1016/j.jrurstud.2022.04.011
  18. Dwivedi, Y. K., Mustafee, N., Carter, L. D., & Williams, M. D. (2010). A bibliometric comparison of the usage of two theories of IS/IT acceptance (TAM and UTAUT). In AMCIS 2010 Proceedings, 183. https://aisel.aisnet.org/amcis2010/183
  19. Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146 DOI: https://doi.org/10.3758/BF03193146
  20. Fernández, A. H., Camargo, C. D. B., & Nascimento, M. S. L. D. (2019). Technologies and environmental education: A beneficial relationship. Research in Social Sciences and Technology, 4(2), 13–30. DOI: https://doi.org/10.46303/ressat.04.02.2
  21. Fu, Z., Yue, J., Li, D., Zhang, X., Zhang, L., & Gao, Y. (2007). Evaluation of learner adoption intention of e-learning in China: A methodology based on perceived innovative attributes. New Zealand Journal of Agricultural Research, 50(5), 609–615. https://doi.org/10.1080/00288230709510329 DOI: https://doi.org/10.1080/00288230709510329
  22. Fusco, G., Melgiovanni, M., Porrini, D., & Ricciardo, T. M. (2020). How to improve the diffusion of climate-smart agriculture: What the literature tells us. Sustainability, 12(12), 5168. https://doi.org/10.3390/su12125168 DOI: https://doi.org/10.3390/su12125168
  23. Graf-Vlachy, L., Buhtz, K., & König, A. (2018). Social influence in technology adoption: Taking stock and moving forward. Management Review Quarterly, 68, 37–76. https://doi.org/10.1007/s11301-017-0133-3 DOI: https://doi.org/10.1007/s11301-017-0133-3
  24. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), New York: Sage.
  25. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203 DOI: https://doi.org/10.1108/EBR-11-2018-0203
  26. Hasan, M. K., Desiere, S., D’Haese, M., & Kumar, L. (2018). Impact of climate-smart agriculture adoption on the food security of coastal farmers in Bangladesh. Food Security, 10(4), 1073–1088. https://doi.org/10.1007/s12571-018-0824-1 DOI: https://doi.org/10.1007/s12571-018-0824-1
  27. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8 DOI: https://doi.org/10.1007/s11747-014-0403-8
  28. Kabir, K. H., Sarker, S., Uddin, M. N., Leggette, H. R., Schneider, U. A., Darr, D., & Knierim, A. (2022). Furthering climate-smart farming with the introduction of floating agriculture in Bangladeshi wetlands: Successes and limitations of an innovation transfer. Journal of Environmental Management, 323, 116258. https://doi.org/10.1016/j.jenvman.2022.116258 DOI: https://doi.org/10.1016/j.jenvman.2022.116258
  29. Karahoca, A., Karahoca, D., & Aksöz, M. (2018). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47(4), 742-770. https://doi.org/10.1108/K-02-2017-0045 DOI: https://doi.org/10.1108/K-02-2017-0045
  30. Khoza, S., de Beer, L. T., van Niekerk, D. & Nemakonde, L. (2021). A gender-differentiated analysis of climate-smart agriculture adoption by smallholder farmers: Application of the extended technology acceptance model. Gender, Technology and Development, 25(1), 1–21. https://doi.org/10.1080/09718524.2020.1830338 DOI: https://doi.org/10.1080/09718524.2020.1830338
  31. Kim, M., Park, H., Sawng, Y. W., & Park, S. Y. (2019). Bridging the gap in the technology commercialization process: Using a three-stage technology-product-market model. Sustainability, 11(22), 6267. https://doi.org/10.3390/su11226267 DOI: https://doi.org/10.3390/su11226267
  32. Korjonen-Kuusipuro, K., Hujala, M., Pätäri, S., Bergman, J. P., & Olkkonen, L. (2017). The emergence and diffusion of grassroots energy innovations: Building an interdisciplinary approach. Journal of Cleaner Production, 140, 1156-1164. https://doi.org/10.1016/j.jclepro.2016.10.047 DOI: https://doi.org/10.1016/j.jclepro.2016.10.047
  33. Lee, J. (2017). Farmer participation in a climate-smart future: Evidence from the Kenya agricultural carbon market project. Land use policy, 68, 72–79. https://doi.org/10.1016/j.landusepol.2017.07.020 DOI: https://doi.org/10.1016/j.landusepol.2017.07.020
  34. Lin, C. H., Lin, H. M., & Hung, A. L. (2008). Social value orientation and information level in selling prices. Social Behavior and Personality, 36(7), 933–940. https://doi.org/10.2224/sbp.2008.36.7.933 DOI: https://doi.org/10.2224/sbp.2008.36.7.933
  35. Long, T. B., Blok, V., & Poldner, K. (2017). Business models for maximising the diffusion of technological innovations for climate-smart agriculture. International Food and Agribusiness Management Review, 20(1), 5–23. https://doi.org/10.22434/IFAMR2016.0081 DOI: https://doi.org/10.22434/IFAMR2016.0081
  36. Lubensky, D., & Schmidbauer, E. (2020). Free product trials: Disclosing quality and match value. Economic Inquiry, 58(4), 1565–1576. https://doi.org/10.1111/ecin.12858 DOI: https://doi.org/10.1111/ecin.12858
  37. Madigan, R., Louw, T., Dziennus, M., Graindorge, T., Ortega, E., Graindorge, M., & Merat, N. (2016). Acceptance of Automated Road Transport Systems (ARTS): An Adaptation of the UTAUT Model. Transportation Research Procedia, 14, 2217–2226. https://doi.org/10.1016/j.trpro.2016.05.237 DOI: https://doi.org/10.1016/j.trpro.2016.05.237
  38. Makate, C. (2019). Effective scaling of climate smart agriculture innovations in African smallholder agriculture: A review of approaches, policy and institutional strategy needs. Environmental Science and Policy, 96, 37–51. https://doi.org/10.1016/j.envsci.2019.01.014 DOI: https://doi.org/10.1016/j.envsci.2019.01.014
  39. Mashi, S. A., Inkani, A. I., & Oghenejabor, O. D. (2022). Determinants of awareness levels of climate smart agricultural technologies and practices of urban farmers in Kuje, Abuja, Nigeria. Technology in Society, 70, 102030. https://doi.org/10.1016/j.techsoc.2022.102030 DOI: https://doi.org/10.1016/j.techsoc.2022.102030
  40. Mazhar, R., Ghafoor, A., Xuehao, B., & Wei, Z. (2021). Fostering sustainable agriculture: Do institutional factors impact the adoption of multiple climate-smart agricultural practices among new entry organic farmers in Pakistan? Journal of Cleaner Production, 283, 124620. https://doi.org/10.1016/j.jclepro.2020.124620 DOI: https://doi.org/10.1016/j.jclepro.2020.124620
  41. Meshesha, A. T., Birhanu, B. S., & Ayele, M. B. (2022). Effects of perceptions on adoption of climate-smart agriculture innovations: Empirical evidence from the upper Blue Nile Highlands of Ethiopia. International Journal of Climate Change Strategies and Management, 14(3), 293–311. https://doi.org/10.1108/IJCCSM-04-2021-0035 DOI: https://doi.org/10.1108/IJCCSM-04-2021-0035
  42. Morgan, E. H., Severs, M. M., Hanson, K. L., McGuirt, J., Becot, F., Wang, W., Kolodinsky, J., Sitaker, M., Pitts, S. B. J., Ammerman, A., & Seguin, R. A. (2018). Gaining and maintaining a competitive edge: Evidence from CSA members and farmers on local food marketing strategies. Sustainability, 10(7), 2177. https://doi.org/10.3390/su10072177 DOI: https://doi.org/10.3390/su10072177
  43. Neufeldt, H., Negra, C., Hancock, J., Foster, K., Nayak, D., & Singh, P. (2015). Scaling up climate-smart agriculture: lessons learned from South Asia and pathways for success. Retrieved on May 28, 2023, https://apps.worldagroforestry.org/downloads/Publications/PDFS/WP15720.pdf
  44. 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 DOI: https://doi.org/10.3390/cli5030063
  45. Ocker, R. (2010). Promoting Group Creativity in Upstream Requirements Engineering. Human Technology, 6(1), 55–70. https://doi.org/10.17011/ht/urn.20105241907 DOI: https://doi.org/10.17011/ht/urn.20105241907
  46. Oudshoorn, N., & Pinch, T. (2005). How users matter: The co-construction of users and technology. Massachusetts: MIT Press
  47. Ouédraogo, M., Houessionon, P., Zougmoré, R. B., & Partey, S. T. (2019). Uptake of climate-smart agricultural technologies and practices: Actual and potential adoption rates in the climate-smart village site of Mali. Sustainability, 11(17), 4710. https://doi.org/10.3390/su11174710 DOI: https://doi.org/10.3390/su11174710
  48. Rahman, H. A. (2009). Global climate change and its effects on human habitat and environment in Malaysia. Malaysian Journal of Environmental Management, 10(2), 17–32.
  49. Raile, E. D., Young, L. M., Kirinya, J., Bonabana-Wabbi, J., & Raile, A. N. W. (2021). Building public will for climate-smart agriculture in Uganda: Prescriptions for industry and policy. Journal of Agricultural and Food Industrial Organization, 19(1), 39–50. https://doi.org/10.1515/jafio-2021-0012 DOI: https://doi.org/10.1515/jafio-2021-0012
  50. Raj, S., & Garlapati, S. (2020). Extension and advisory services for climate-smart agriculture. In V. Venkatramanan, S. Shah & R. Prasad (Eds.), Global climate change: Resilient and smart agriculture (pp. 273–299), Singapore: Springer. https://doi.org/10.1007/978-981-32-9856-9_13 DOI: https://doi.org/10.1007/978-981-32-9856-9_13
  51. Risselada, H., de Vries, L., & Verstappen, M. (2018). The impact of social influence on the perceived helpfulness of online consumer reviews. European Journal of Marketing, 52(3–4), 619–636. https://doi.org/10.1108/EJM-09-2016-0522 DOI: https://doi.org/10.1108/EJM-09-2016-0522
  52. Sain, G., Loboguerrero, A. M., Corner-Dolloff, C., Lizarazo, M., Nowak, A., Martínez-Barón, D., & Andrieu, N. (2017). Costs and benefits of climate-smart agriculture: The case of the Dry Corridor in Guatemala. Agricultural Systems, 151, 163–173. https://doi.org/10.1016/j.agsy.2016.05.004 DOI: https://doi.org/10.1016/j.agsy.2016.05.004
  53. Sair, S. A., & Danish, R. Q. (2018). Effect of performance expectancy and effort expectancy on the mobile commerce adoption intention through personal innovativeness among Pakistani consumers. Pakistan Journal of Commerce and Social Science, 12(2), 501–520.
  54. Scott, S. D., Plotnikoff, R. C., Karunamuni, N., Bize, R., & Rodgers, W. (2008). Factors influencing the adoption of an innovation: An examination of the uptake of the Canadian Heart Health Kit (HHK). Implementation Science, 3(41), 1–8. https://doi.org/10.1186/1748-5908-3-41 DOI: https://doi.org/10.1186/1748-5908-3-41
  55. 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, 3825–3840. https://doi.org/10.1016/j.jclepro.2017.06.019 DOI: https://doi.org/10.1016/j.jclepro.2017.06.019
  56. Shahbaz, P., ul Haq, S., Abbas, A., Batool, Z., Alotaibi, B. A., & Nayak, R. K. (2022). Adoption of climate smart agricultural practices through women involvement in decision making process: Exploring the role of empowerment and innovativeness. Agriculture, 12(8), 1161. https://doi.org/10.3390/agriculture12081161 DOI: https://doi.org/10.3390/agriculture12081161
  57. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189 DOI: https://doi.org/10.1108/EJM-02-2019-0189
  58. Spector, P. E. (2019). Do not cross me: Optimizing the use of cross-sectional designs. Journal of Business and Psychology, 34, 125–137. https://doi.org/10.1007/s10869-018-09613-8 DOI: https://doi.org/10.1007/s10869-018-09613-8
  59. Statista Search Department. (2022). Contribution of agriculture to the gross domestic product (GDP) of Malaysia from 2015 to 2021. Statista. Retrieved on May 23, 2023, from https://www.statista.com/statistics/952990/malaysia-agriculture-share-of-gdp
  60. Totin, E., Segnon, A. C., Schut, M., Affognon, H., Zougmoré, R. B., Rosenstock, T., & Thornton, P. K. (2018). Institutional perspectives of climate-smart agriculture: A systematic literature review. Sustainability, 10(6), 1990. https://doi.org/10.3390/su10061990 DOI: https://doi.org/10.3390/su10061990
  61. Tsige, M., Synnevåg, G., & Aune, J. B. (2020). Gendered constraints for adopting climate-smart agriculture amongst smallholder Ethiopian women farmers. Scientific African, 7, e00250. https://doi.org/10.1016/j.sciaf.2019.e00250 DOI: https://doi.org/10.1016/j.sciaf.2019.e00250
  62. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). 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 DOI: https://doi.org/10.2307/41410412
  63. Waaswa, A., Nkurumwa, A. O., Kibe, A. M., & Kipkemoi, J. N. (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 DOI: https://doi.org/10.1080/17565529.2021.1885336
  64. Wang, L. Y. K., Lew, S. L., Lau, S. H., & Leow, M. C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6), e01788. https://doi.org/10.1016/j.heliyon.2019.e01788 DOI: https://doi.org/10.1016/j.heliyon.2019.e01788
  65. Wekesa, B. M., Ayuya, O. I., & Lagat, J. K. (2018). Effect of climate-smart agricultural practices on household food security in smallholder production systems: Micro-level evidence from Kenya. Agriculture and Food Security, 7, 80. https://doi.org/10.1186/s40066-018-0230-0 DOI: https://doi.org/10.1186/s40066-018-0230-0
  66. 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, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028 DOI: https://doi.org/10.1016/j.chb.2016.10.028
  67. Wut, T. M., Lee, S. W., & Xu, J. (2022). How do facilitating conditions influence student-to-student interaction within an online learning platform? A new typology of the serial mediation model. Education Sciences, 12(5), 337. https://doi.org/10.3390/educsci12050337 DOI: https://doi.org/10.3390/educsci12050337
  68. Yaseen, A., Bryceson, K., & Mungai, A. N. (2018). Commercialization behaviour in production agriculture: The overlooked role of market orientation. Journal of Agribusiness in Developing and Emerging Economies, 8(3), 579-602. https://doi.org/10.1108/JADEE-07-2017-0072 DOI: https://doi.org/10.1108/JADEE-07-2017-0072
  69. Zhu, D. H., & Chang, Y. P. (2014). Investigating consumer attitude and intention toward free trials of technology-based services. Computers in Human Behavior, 30, 328–334. https://doi.org/10.1016/j.chb.2013.09.008 DOI: https://doi.org/10.1016/j.chb.2013.09.008

How to Cite

Aziz, M. A., Ayob, N. H., Ayob, N. A., Ahmad, Y., & Abdulsomad, K. (2024). Factors influencing farmer adoption of climate-smart agriculture technologies: Evidence from Malaysia. Human Technology, 20(1), 70–92. https://doi.org/10.14254/1795-6889.2024.20-1.4