Abstract
Post-harvest food losses of 30-40% cost sub-Saharan Africa approximately USD 200 million annually, yet the psychological mechanisms underlying collaborative food waste reduction in resource-constrained, post-conflict settings remain underexplored. Drawing on the theory of planned behaviour, this study examines how resource-based perceived control (RBPC) shapes collaborative food waste reduction intentions (CFWRI) and practices (PFWRB), and tests for the presence of an intention-behaviour gap. Survey data from 399 stakeholders (269 farmers, 80 NGO representatives, 50 government officers) in Bo District, Sierra Leone, were analysed using partial least squares structural equation modelling (PLS-SEM). Results confirm that RBPC exerts a statistically significant dual-pathway effect: it influences PFWRB both indirectly via CFWRI (indirect effect = 0.392, p < 0.001) and directly (beta = 0.560, p < 0.001), with the direct path magnitude virtually identical to the intention-mediated path (Delta beta = 0.001) a rare finding suggesting that resource constraints override the intention-behaviour sequence. RBPC recorded the lowest mean score (2.39/5.0) among all constructs, indicating severely constrained self-efficacy, technology access, training, and market information. The model explains 57.2% of variance in intentions and 36.8% in behaviour. A three-tier intervention framework is proposed: immediate resource provision (0-12 months), motivation enhancement (1-3 years), and systemic transformation (3-5 years). Findings are discussed in relation to Sierra Leone's Feed Salone agricultural transformation strategy. The study contributes a resource-centric behavioural model for food waste reduction in developing countries, demonstrating that when perceived control is critically low, direct resource pathways may supersede intention-mediated routes to behaviour change.
References
Affognon, H., Mutungi, C., Sanginga, P., & Borgemeister, C. (2015). Unpacking postharvest losses in sub-Saharan Africa: A meta-analysis. World Development, 66, 49-68. https:// doi.org/10.1016/j.worlddev.2014.08.002
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11-39). Springer. https:// doi.org/10.1007/978-3-642-69746-3_2
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665-683. https:// doi.org/10.1111/j.1559-1816.2002.tb00236.x
Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40(4), 471-499. https://doi.org/10.1348 /014466601164939
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-336). Lawrence Erlbaum Associates.
Conteh, A. M. H., Yan, X., & Sankoh, F. P. (2022). Agricultural development in Sierra Leone: Challenges, opportunities, and policy implications. Agricultural Systems, 195, 103298. https://doi.org/10.1016/j.agsy.2021.103298
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269-277. https:// doi.org/10.1509/jmkr.38.2.269.18845
FAO. (2019). The State of Food and Agriculture 2019: Moving forward on food loss and waste reduction. Food and Agriculture Organization. https://doi.org/10.4060/ca6030en
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Graham-Rowe, E., Jessop, D. C., & Sparks, P. (2015). Predicting household food waste reduction using an extended theory of planned behaviour. Resources, Conservation and Recycling, 101, 194-202. https://doi.org/10.1016/j.resconrec.2015.05. 020
Government of Sierra Leone. (2023). Feed Salone Strategy: A blueprint for agricultural transformation in Sierra Leone 2023-2028. Ministry of Agriculture and Food Security. https://feedsalone.gov.sl
Gustavsson, J., Cederberg, C., & Sonesson, U. (2011). Global food losses and food waste: Extent, causes and prevention. FAO Report.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications.
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
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
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277-319. https://doi.org/10.1108/S1474-7979(2009)00000 20014
HLPE. (2014). Food losses and waste in the context of sustainable food systems. High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security.
Joreskog, K. G., & Wold, H. (1982). The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects. In K. G. Joreskog & H. Wold (Eds.), Systems under indirect observation, Part I (pp. 263-270). North-Holland.
Kader, A. A. (2005). Increasing food availability by reducing postharvest losses of fresh produce. Acta Horticulturae, 682, 2169-2176. https: //doi.org/10.17660/ActaHortic.2005.682.296
Lipinski, B., Hanson, C., Lomax, J., Kitinoja, L., Waite, R., & Searchinger, T. (2013). Reducing food loss and waste. World Resources Institute Working Paper.
Orbell, S., & Sheeran, P. (1998). "Inclined abstainers": A problem for predicting health-related behaviour. British Journal of Social Psychology, 37(2), 151-165. https:// doi.org/10.1111/j.2044-8309.1998.tb01167.x
Parfitt, J., Barthel, M., & Macnaughton, S. (2010). Food waste within food supply chains: Quantification and potential for change to 2050. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1554), 3065-3081. https://doi.org/10.1098/rstb.2010.0126
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
Principato, L., Secondi, L., & Pratesi, C. A. (2015). Reducing food waste: An investigation on the behaviour of Italian youths. British Food Journal, 117(2), 731-748. https://doi.org /10.1108/BFJ-10-2013-0314
Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4. SmartPLS. https://www. smartpls.com
Secondi, L., Principato, L., & Laureti, T. (2015). Household food waste behaviour in EU-27 countries: A multilevel analysis. Food Policy, 56, 25-40. https://doi.org/10.1016/j.foodpol.2015.07. 007
Sheeran, P. (2002). Intention-behavior relations: A conceptual and empirical review. European Review of Social Psychology, 12(1), 1-36. https://doi.org/10.1080/14792772143000003
Sheeran, P., & Webb, T. L. (2016). The intention-behavior gap. Social and Personality Psychology Compass, 10(9), 503-518. https://doi.org/10.1111 /spc3.12265
Stancu, V., Haugaard, P., & Lahteenmaki, L. (2016). Determinants of consumer food waste behaviour: Two routes to food waste. Appetite, 96, 7-17. https://doi.org/10.1016/j.appet.2015.08.025
Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132(2), 249-268. https://doi.org/10.1037/0033-2909.132.2.249
World Bank. (2011). Missing food: The case of postharvest grain losses in Sub-Saharan Africa. World Bank Report No. 60371-AFR.
Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Joreskog & H. Wold (Eds.), Systems under indirect observation, Part II (pp. 1-54). North-Holland.
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