Farmers’ Behavioral Intention to Adopt Agrovoltaic Systems in Semi-Arid Iran: Findings from Structural Equation Modeling and Fuzzy-Set Qualitative Comparative Analysis

Articles in Press

Document Type : Original Article

Authors
Shirin Zarif Moradian
Mahmoud Daneshvar Kakhki
Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Islamic Republic of Iran.
Abstract
Water scarcity and energy insecurity threaten sustainable agriculture in semi-arid regions such as Iran, underscoring the need for innovations like agrovoltaic (AV) systems. This study was conducted in Fariman County, a water-stressed agricultural region in northeastern Iran, and investigates the determinants of Iranian farmers’ behavioral intention to adopt AV technology based on survey data collected from 215 large-scale irrigated farmers using a face-to-face questionnaire. The study employs a mixed-method approach that integrates Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), the study examines key behavioral and contextual factors influencing farmers’ behavioral intentions to adopt Agrovoltaic systems. SEM results indicate that perceived benefits, ease-of-use considerations, and social influences significantly shape behavioral intentions, explaining a substantial proportion of variance in behavioral intention (R² =0.58), while risk perceptions weaken the impact of perceived benefits. fsQCA identifies three alternative pathways leading to high behavioral intention, all characterized by low perceived risk and strong social influence. The findings emphasize that psychological and social factors are as critical as technical and economic ones. Policy efforts should therefore focus on reducing perceived risks and leveraging social networks to accelerate AV adoption in water-stressed agriculture.
Keywords
Agrivoltaics, Farmer decision-making, Risk perception, Solar-based agricultural innovations, Technology acceptance
Subjects
  1. Abbaspour, K. C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J., & Srinivasan, R. (2007). Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333(2–4), 413–430. https://doi.org/10.1016/j.jhydrol.2006.09.014
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  3. Amaducci, S., Yin, X., & Colauzzi, M. (2018). Agrivoltaic systems to optimise land use for electric energy production. Applied Energy, 220. https://doi.org/10.1016/j.apenergy.2018.03.081
  4. Amghani, M. S., Miladi, H., Savari, M., & Mojtahedi, M. (2025). Factors influencing the agricultural extension model sites in Iran. Scientific Reports, 15(1), 9590. https://doi.org/10.1038/s41598-025-94151-6
  5. Ashraf, S., Nazemi, A., & AghaKouchak, A. (2021). Anthropogenic drought dominates groundwater depletion in Iran. Scientific Reports, 11(1), 9135. https://doi.org/10.1038/s41598-021-88522-y
  6. Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as enabler of sustainable farming: An empirical analysis of farmers’ adoption decision of precision agriculture technology. Decision Support Systems, 54(1), 510–520. https://doi.org/10.1016/j.dss.2012.07.002
  7. Barron-Gafford, G. A., Pavao-Zuckerman, M. A., Minor, R. L., Sutter, L. F., Barnett-Moreno, I., Blackett, D. T., Thompson, M., Dimond, K., Gerlak, A. K., Nabhan, G. P., & Macknick, J. E. (2019). Agrivoltaics provide mutual benefits across the food–energy–water nexus in drylands. Nature Sustainability, 2(9). https://doi.org/10.1038/s41893-019-0364-5
  8. Bhatnagr, P., & Rajesh, A. (2024). Neobanking adoption – An integrated UTAUT-3, perceived risk and recommendation model. South Asian Journal of Marketing, 5(2). https://doi.org/10.1108/sajm-06-2022-0040
  9. Chin, W. W. (1988). The Partial Least Squares approach to Structural Equation Modeling (G. A. Marcoulides, Ed.). Lawrence Erlbaum Associates.
  10. 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
  11. Disciglio, G., Stasi, A., Tarantino, A., & Frabboni, L. (2025). Microclimate Modification, Evapotranspiration, Growth and Essential Oil Yield of Six Medicinal Plants Cultivated Beneath a Dynamic Agrivoltaic System in Southern Italy. Plants, 14(15), 2428. https://doi.org/10.3390/plants14152428
  12. Dwivedi, Y. K., Rana, N. P. , J., 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, 719–734.
  13. El Bilali, H., & Allahyari, M. S. (2018). Transition towards sustainability in agriculture and food systems: Role of information and communication technologies. Information Processing in Agriculture, 5(4), 456–464. https://doi.org/10.1016/j.inpa.2018.06.006
  14. Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
  15. Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192–3198. https://doi.org/10.1016/j.jbusres.2015.12.008
  16. Ghasemi, G., Noorollahi, Y., Alavi, H., Marzband, M., & Shahbazi, M. (2019). Theoretical and technical potential evaluation of solar power generation in Iran. Renewable Energy, 138, 1250–1261. https://doi.org/10.1016/j.renene.2019.02.068
  17. Greckhamer, T., Furnari, S., Fiss, P. C., & Aguilera, R. V. (2018). Studying configurations with qualitative comparative analysis: best practices in strategy and organization research. Strategic Organization, 16(4), 482–495. https://doi.org/10.1177/1476127018786487
  18. Haghshenas Haghighi, M., & Motagh, M. (2024). Uncovering the impacts of depleting aquifers: A remote sensing analysis of land subsidence in Iran. Science Advances, 10(19). https://doi.org/10.1126/sciadv.adk3039
  19. Hair, J. F., & Hult, G. T. M. (2017). A primer on partial least squares structural equation modeling (PLS‑SEM). Sage.
  20. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019a). 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
  21. 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(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  22. Iran Water Resources Management Company. (2018). Prohibited plains of Iran: Status of groundwater resources up to the end of 2018. https://www.danab.ir/wp-content/uploads/2020/09/total97.pdf
  23. Johanson, G. A., & Brooks, G. P. (2010). Initial Scale Development: Sample Size for Pilot Studies. Educational and Psychological Measurement, 70(3), 394–400. https://doi.org/10.1177/0013164409355692
  24. 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. https://doi.org/10.2307/249751
  25. Khalilpour, K. R., & Vassallo, A. (2015). Planning and operation scheduling of hybrid renewable energy systems: A review of the state-of-the-art. Renewable and Sustainable Energy Reviews, 447–459.
  26. Kumar, R., Singh, R., Kumar, K., Khan, S., & Corvello, V. (2023). How Does Perceived Risk and Trust Affect Mobile Banking Adoption? Empirical Evidence from India. Sustainability, 15(5), 4053. https://doi.org/10.3390/su15054053
  27. Leiserowitz, A. (2006). Climate Change Risk Perception and Policy Preferences: The Role of Affect, Imagery, and Values. Climatic Change, 77(1–2), 45–72. https://doi.org/10.1007/s10584-006-9059-9
  28. Mahto, R., Sharma, D., John, R., & Putcha, C. (2021). Agrivoltaics: A Climate-Smart Agriculture Approach for Indian Farmers. Land, 10(11), 1277. https://doi.org/10.3390/land10111277
  29. Maia, A. G., Burney, J. A., Martínez, J. D. M., & Cesano, D. (2021). Improving production and quality of life for smallholder farmers through a climate resilience program: An experience in the Brazilian Sertão. PLoS ONE, 16(5 May). https://doi.org/10.1371/journal.pone.0251531
  30. Mehta, K., Jain, R., & Zörner, W. (2025). Agrivoltaics Around the World: Potential, Technology, Crops and Policies to Address the Energy–Agriculture Nexus for Sustainable and Climate-Resilient Land Use. Energies, 18(24), 6417. https://doi.org/10.3390/en18246417
  31. Momani, A. M. (2020). The Unified Theory of Acceptance and Use of Technology. International Journal of Sociotechnology and Knowledge Development, 12(3), 79–98. https://doi.org/10.4018/IJSKD.2020070105
  32. Namdari, M. (2025). Comparative LCA of diesel and grid electricity for agricultural irrigation in Iran: why diesel outperforms in fossil-reliant grids. PLOS One, 20(7), e0324371. https://doi.org/10.1371/journal.pone.0324371
  33. Pahlavan, A. M., Ghosh, S., & Islam, M. R. (2022). Trust and adoption of solar home systems in Bangladesh: Field evidence from rural households. Energy for Sustainable Development, 146–155.
  34. Pappas, I. O. (2021). User Experience in Digital Banking: A Configurational Approach. Journal of Service Theory and Practice, 31.
  35. Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2016). Explaining online shopping behavior with fsQCA: The role of cognitive and affective perceptions. Journal of Business Research, 69(2), 794–803. https://doi.org/10.1016/j.jbusres.2015.07.010
  36. Pappas, I. O., & Woodside, A. G. (2021). Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing. International Journal of Information Management, 58, 102310. https://doi.org/10.1016/j.ijinfomgt.2021.102310
  37. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
  38. Presser, S., Couper, M. P., Lessler, J. T., Martin, E., Martin, J., Rothgeb, J. M., & Singer, E. (2004). Methods for Testing and Evaluating Survey Questions. Public Opinion Quarterly, 68(1), 109–130. https://doi.org/10.1093/poq/nfh008
  39. Ragin, C. C. (2008). Redesigning Social Inquiry. University of Chicago Press. https://doi.org/10.7208/chicago/9780226702797.001.0001
  40. Ragin, C. C. (2009). Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press.
  41. Razavi Khorasan Meteorological Organization. (2022). Agricultural yearbook of Razavi Khorasan Province (2021–2022). https://www.razavimet.ir
  42. Rezaei, R., & Ghofranfarid, M. (2018). Rural households’ renewable energy usage intention in Iran: Extending the unified theory of acceptance and use of technology. Renewable Energy, 122, 382–391. https://doi.org/10.1016/j.renene.2018.02.011
  43. Rezaei‑Moghaddam, K., & Salehi, S. (2010). Agricultural specialists’ intention toward precision agriculture technologies: integrating innovation characteristics to technology acceptance model. Agricultural Research, 1191–1199.
  44. Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
  45. Saemian, P., Tourian, M. J., AghaKouchak, A., Madani, K., & Sneeuw, N. (2022). How much water did Iran lose over the last two decades? Journal of Hydrology: Regional Studies, 41, 101095. https://doi.org/10.1016/j.ejrh.2022.101095
  46. Toledo, C., & Scognamiglio, A. (2021). Agrivoltaic Systems Design and Assessment: A Critical Review, and a Descriptive Model towards a Sustainable Landscape Vision (Three-Dimensional Agrivoltaic Patterns). Sustainability, 13(12), 6871. https://doi.org/10.3390/su13126871
  47. Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
  48. Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
  49. Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 645–653.
Journal of Agricultural Science and Technology

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Available Online from 14 January 2026