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Factors influencing farmed fish traders' intention to use improved fish post-harvest technologies in Kenya: application of technology acceptance model

  • Jimmy Brian Mboya (Kenya Marine and Fisheries Research Institute (KMFRI), Sangoro Aquaculture Research Center) ;
  • Kevin Odhiambo Obiero (Kenya Marine and Fisheries Research Institute (KMFRI), Sangoro Aquaculture Research Center) ;
  • Maureen Jepkorir Cheserek (Department of Human Nutrition, Faculty of Health Sciences, Egerton University) ;
  • Kevin Okoth Ouko (Department of Agricultural Economics and Agribusiness Management, Jaramogi Oginga Odinga University of Science and Technology) ;
  • Erick Ochieng Ogello (Department of Animal and Fisheries Sciences, Maseno University) ;
  • Nicholas Otieno Outa (Department of Animal and Fisheries Sciences, Maseno University) ;
  • Elizabeth Akinyi Nyauchi (Kenya Marine and Fisheries Research Institute (KMFRI), Sangoro Aquaculture Research Center) ;
  • Domitila Ndinda Kyule (Kenya Marine and Fisheries Research Institute (KMFRI), National Aquaculture Research Development and Training Center (NARDTC)) ;
  • Jonathan Mbonge Munguti (Kenya Marine and Fisheries Research Institute (KMFRI), National Aquaculture Research Development and Training Center (NARDTC))
  • Received : 2022.10.18
  • Accepted : 2022.12.08
  • Published : 2023.02.28

Abstract

Improved fish post-harvest technologies (IFPT) have been promoted as more efficient methods of fish processing, preservation, and value addition than the traditional methods prevalent in developing countries. The adoption rates, however, do not appear to be convincing. The purpose of this study was to determine the socio-demographic and psychological factors that influence intention of Kenyan farmed fish traders to use IFPT. The technology acceptance model (TAM) was used to properly explain the impact of TAM constructs such as perceived usefulness (PU), perceived ease of use (PEOU), and attitude (ATT), as well as socio-demographic factors such as gender, age, education level and fish trading experience on traders' intention to use the technologies. A cross-sectional survey was conducted to collect data using a semi-structured questionnaire from 146 traders in Busia, Siaya and Kakamega counties. At a significance level of p = 0.05, a linear regression model was used to examine the socio-demographic and psychological determinants of the traders' behavioral intention to use the improved technologies. The regression analysis revealed that PU (β = 0.443; p = 0.000), PEOU (β = 0.364; p = 0.000) and ATT (β = 0.615; p = 0.000) influence traders' intention to use IFPT, with ATT having the highest influence on intention. However, the traders' socio-demographic characteristics have no effect on their intention to use the technologies, as the coefficients for gender (β = 0.148; p = 0.096), age (β = 0.016; p = 0.882), level of education (β = -0.135; p = 0.141) and fish trading experience (β = 0.017; p = 0.869) are all insignificant. These findings show that the traders intend to use IFPT and will use them when it is in their best economic interests.

Keywords

Acknowledgement

The authors thank the county departments of fisheries in Busia, Kakamega and Siaya counties for helping in co-ordination during data collection for this study. We are also grateful to the fish traders for their cooperation during the interviews.

References

  1. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50:179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  2. Bagheri A, Bondori A, Allahyari MS, Surujlal J. Use of biologic inputs among cereal farmers: application of technology acceptance model. Environ Dev Sustain. 2021;23:5165-81. https://doi.org/10.1007/s10668-020-00808-9
  3. Balami S, Sharma A, Karn R. Significance of nutritional value of fish for human health. Malays J Halal Res. 2019;2:32-4. https://doi.org/10.2478/mjhr-2019-0012
  4. Beavers AS, Lounsbury JW, Richards JK, Huck SW, Skolits GJ, Esquivel SL. Practical considerations for using exploratory factor analysis in educational research. Pract Assess Res Eval. 2013;18:1-13.
  5. Budaev SV. Using principal components and factor analysis in animal behaviour research: caveats and guidelines. Ethology. 2010;116:472-80. https://doi.org/10.1111/j.1439-0310.2010.01758.x
  6. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13:319-40. https://doi.org/10.2307/249008
  7. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Manag Sci. 1989;35:982-1003. https://doi.org/10.1287/mnsc.35.8.982
  8. Eaton P, Frank B, Johnson K, Willoughby S. Comparing exploratory factor models of the brief electricity and magnetism assessment and the conceptual survey of electricity and magnetism. Phys Rev Phys Educ Res. 2019;15:020133.
  9. Eyduran E, Topal M, Sonmez AY. Use of factor scores in multiple regression analysis for estimation of body weight by several body measurements in brown trouts (Salmo trutta fario). Int J Agric Biol. 2010;12:611-5.
  10. Farm Africa. Kenya market-led aquaculture programme (KMAP): a 2019 guide to profitable fish farming. Farm Africa. 2019 [cited 2022 Sep 9]. https://www.farmafrica.org/downloads/2019/kenya-market-led-aquaculture-programme-business-cases-compressed.pdf
  11. Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: an introduction to theory and research. Reading, MA: Addison-Wesley; 1975.
  12. Flett R, Alpass F, Humphries S, Massey C, Morriss S, Long N. The technology acceptance model and use of technology in New Zealand dairy farming. Agric Syst. 2004;80:199-211. https://doi.org/10.1016/j.agsy.2003.08.002
  13. Food and Agriculture Organization of the United Nations [FAO]. The state of world fisheries and aquaculture 2022: towards blue transformation. Rome: FAO; 2022.
  14. Githukia CM, Drexler SS, Obiero KO, Nyawanda BO, Odhiambo JA, Chesoli JW, et al. Gender roles and constraints in the aquaculture value chain in Western Kenya. Afr J Agric Res. 2020;16:732-45.
  15. Hair JF, Black W, Babin B, & Anderson R. Multirative data analysis: a global perspective. Int J Proj Manag. 2010;7:552-9.
  16. Hasan SA, Mohan Muzumdar J, Nayak R, Wu WK. Using the theory of planned behavior to understand factors influencing south Asian consumers' intention to seek pharmacist-provided medication therapy management services. Pharmacy. 2019;7:88.
  17. Kaminski AM, Cole SM, Elizabeth Al Haddad R, Kefi AS, Chilala AD, et al. Fish losses for whom? A gendered assessment of post-harvest losses in the Barotse floodplain fishery, Zambia. Sustainability. 2020;12:10091.
  18. Kawala M, Hyuha TS, William E, Walekwa P, Elepu G, Kalumba SC. Determinants for choice of fish market channels: the case of Busia (Uganda/Kenya) border. J Agric Sci. 2018;10:118-124. https://doi.org/10.5539/jas.v10n8p118
  19. Keerthana PS, Gopan S, Rajabudeen R, Fathima R, Shibu K, Nisha R, et al. Post-harvest losses in the fisheries sector-facts, figures, challenges and strategies. Int J Fish Aquat Stud. 2022;10:101-8. https://doi.org/10.22271/fish.2022.v10.i4b.2691
  20. Keyombe JLA, Bironga HC, Obiero MO, Malala JO, Olilo CO, Aura CM, et al. Monitoring the effectiveness of interventions of solar polyethylene dryer in reducing post-harvest losses in lake Turkana and share the findings. Mombasa: Kenya Marine and Fisheries Research Institute; 2018. Report No.: KMF/RS/2018/C2.1(i).
  21. Khalili Tilami S, Sampels S. Nutritional value of fish: lipids, proteins, vitamins, and minerals. Rev Fish Sci Aquacult. 2018;26:243-53. https://doi.org/10.1080/23308249.2017.1399104
  22. Kumar G, Engle C, Tucker C. Factors driving aquaculture technology adoption. J World Aquacult Soc. 2018;49:447-6.
  23. Kyule DN, Yongo E, Opiyo MA, Obiero K, Munguti JM, Charo-Karisa H. Fish product development and market trials of fish and fish products in Kenya: a case study of Kirinyaga and Meru counties. Livest Res Rural Dev. 2014;26:178-89.
  24. Mishra SP, Sarkar U, Taraphder S, Datta S, Swain DP, Saikhom R, et al. Multivariate statistical data analysis- principal component analysis (PCA) Sidharth. Int J Livest Res. 2017;7:60-78.
  25. Munguti J, Obiero K, Orina P, Mirera D, Kyule D, Mwaluma J, et al. State of aquaculture report in Kenya 2021: towards nutrition sensitive fish food production systems. Nairobi: Techplus Media House; 2021.
  26. Obiero K, Meulenbroek P, Drexler S, Dagne A, Akoll P, Odong R, et al. The contribution of fish to food and nutrition security in Eastern Africa: emerging trends and future outlooks. Sustainability. 2019a;11:1-1636. https://doi.org/10.3390/su11061636
  27. Obiero KO, Waidbacher H, Nyawanda BO, Munguti JM, Manyala JO, Kaunda-Arara B. Predicting uptake of aquaculture technologies among smallholder fish farmers in Kenya. Aquacult Int. 2019b;27:1689-707. https://doi.org/10.1007/s10499-019-00423-0
  28. Opiyo MA, Marijani E, Muendo P, Odede R, Leschen W, Charo-Karisa H. A review of aquaculture production and health management practices of farmed fish in Kenya. Int J Vet Sci Med. 2018;6:141-8. https://doi.org/10.1016/j.ijvsm.2018.07.001
  29. Ouko KO, Mukhebi AW, Obiero KO, Opondo FA. Using technology acceptance model to understand fish farmers' intention to use black soldier fly larvae meal in Nile tilapia production in Kenya. All Life. 2022;15:884-900. https://doi.org/10.1080/26895293.2022.2112765
  30. Pitcher TJ, Lam ME. Fish commoditization and the historical origins of catching fish for profit. Marit Stud. 2015;14:2.
  31. Sampels S. The effects of storage and preservation technologies on the quality of fish products: a review. J Food Process Preserv. 2015;39:1206-15. https://doi.org/10.1111/jfpp.12337
  32. Shrestha N. Factor analysis as a tool for survey analysis. Am J Appl Math Stat. 2021;9:4-11. https://doi.org/10.12691/ajams-9-1-2
  33. Silva AG, Canavari M, Sidali KL. A technology acceptance model of common bean growers' intention to adopt integrated production in the Brazilian central region. Die Bodenkultur J Land Manag Food Environ. 2017;68:131-43. https://doi.org/10.1515/boku-2017-0012
  34. Stepp JR. Ethnobiology [Internet]. UNESCO-EOLOSS. 2010 [cited Year Month Day]. https://www.eolss.net/outlinecomponents/ethnobiology.aspx
  35. Taylor S, Todd PA. Understanding information technology usage: a test of competing models. Inf Syst Res. 1995;6:144-76. https://doi.org/10.1287/isre.6.2.144
  36. Tur JA, Bibiloni MM, Sureda A, Pons A. Dietary sources of omega 3 fatty acids: public health risks and benefits. Br J Nutr. 2012;107:S23-52. https://doi.org/10.1017/S0007114512001456
  37. Ulhaq I, Pham NTA, Le V, Pham HC, Le TC. Factors influencing intention to adopt ICT among intensive shrimp farmers. Aquaculture. 2022;547:737407.
  38. Ullah A, Saqib SE, Kachele H. Determinants of farmers' awareness and adoption of extension recommended wheat varieties in the rainfed areas of Pakistan. Sustainability. 2022;14:3194.
  39. Verma S, Bhattacharyya SS, Kumar S. An extension of the technology acceptance model in the big data analytics system implementation environment. Inf Process Manag. 2018;54:791-806. https://doi.org/10.1016/j.ipm.2018.01.004