Digital Transformation in the Poultry Industry: Assessing Adoption Through the UTAUT Model and Managerial Profiles

Subject Areas : Information Technology in Agriculture

Adeline Sedina ¹ , Atsu Frank Yayra Ihou ² , Ayira Korem ³ , Paul Mansingh J ⁴

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Received: 2025-03-20 Accepted : 2025-06-01 Published : 2025-07-01

Keywords: Digitalization, UTAUT model, Poultry, Togo, structure equation modeling,

Abstract :

his study investigates the factors influencing the adoption and effective use of digital technologies in the poultry industry in Togo’s Kara region. Using the UTAUT framework and a logit model, the study aimed to assess how perceptions of usefulness, ease of use, and managerial characteristics affect technology uptake. A purposive sample of 80 participants involved in breeding, retailing, slaughtering, feed supply, and poultry sales was surveyed. Data analysis was conducted using Smart PLS 4 for structural equation modeling and STATA 17 for logistic regression. Results show strong indicator reliability (outer loadings > 0.7), robust composite reliability (0.865–0.992), and acceptable convergent validity (AVE values > 0.5). Key findings reveal that performance expectancy and effort expectancy significantly influence behavioral intention and actual usage, emphasizing the importance of perceived productivity benefits and ease of use. In contrast, facilitating conditions, digital flexibility, and social influence were not significant. The socio-demographic analysis indicates that younger and male managers are more likely to adopt digital technologies, while marital status and cooperative membership showed no significant effects. These insights provide practical benefits by informing policy recommendations aimed at improving digital infrastructure, enhancing training programs, and designing targeted interventions that encourage broader digital adoption in Togo’s poultry sector.

References:

1. Abdulai, A. R., KC, K. B., & Fraser, E. (2023). What factors influence the likelihood of rural farmer participation in digital agricultural services? experience from smallholder digitalization in Northern Ghana. Outlook on Agriculture, 52(1), 57–66. https://doi.org/10.1177/00307270221144641
2. Abiri, R., Rizan, N., Balasundram, S. K., Shahbazi, A. B., & Abdul-Hamid, H. (2023). Application of digital technologies for ensuring agricultural productivity. Heliyon, 9(12), e22601. doi.org/10.1016/j.heliyon.2023.e22601
3. Ali, J. (2012). Factors affecting the adoption of information and communication technologies (ICTs) for farming decisions. Journal of Agricultural and Food Information, 13(1), 78–96. https://doi.org/10.1080/10496505.2012.636980
4. ANANG, B. T. (2018). Farm Technology Adoption By Smallholder Farmers in Ghana. Review of Agricultural and Applied Economics, 21(2), 41–47. https://doi.org/10.15414/raae.2018.21.02.41-47
5. Ashokkumar, B., & Naik, A. (2021). Transforming Indian Agriculture with Digital Technologies. Asian Journal of Agricultural Extension, Economics & Sociology, 39(6), 76–90. https://doi.org/10.9734/ajaees/2021/v39i630596
6. Banik, T., & Narendra. (2024). Farming in the digital age: Unleashing the power of farming as a service (FaaS). International Journal of Agricultural Extension and Social Development, 7(5), 99–102. https://doi.org/10.33545/26180723.2024.v7.i5b.601
7. Buchdadi, A. D., Rahmawati, A. A., Siregar, M. E. S., Muttaqien, M. R., & Zaki, A. (2024). Analysis of factors affecting behavioral intention to use QRIS in MSMEs: Expansion of technology acceptance model Agung. Journal of Infrastructure, Policy and Development, 8(15), 1–15. https://doi.org/10.1109/ic-ETITE58242.2024.10493415
8. Cheng, R. J. (2024). Unified Theory of Acceptance and Use of Technology ( UTAUT ) Implementation of Islamic Financing with Maqasid Values Theories. International Journal of Academic Research in Business & Social Sciences, 14(9), 2023–2032. https://doi.org/10.6007/IJARBSS/v14-i9/23022
9. Chuchird, R., Sasaki, N., & Abe, I. (2017). Influencing factors of the adoption of agricultural irrigation technologies and the economic returns: A case study in Chaiyaphum Province, Thailand. Sustainability (Switzerland), 9(9). https://doi.org/10.3390/su9091524
10. Dawane, V. T., Kapse, P. S., Kadam, R. P., Jakkawad, S. ., & Deshmukh, P. R. (2025). Analysis of Socio- Economic Determinants of Farmers’ Attitude towards Online Agricultural Marketing. International Journal of Agricultural Science, Research and Technology in Extension and Education Systems, 15(1), 1–6. https://sanad.iau.ir/Journal/ijasrt Figure
11. Degila, J., Sodedji, F. A. K., Avakoudjo, H. G. G., Tahi, S. P. G., Houetohossou, S. C. A., Honfoga, A. C., Tognisse, I. S., & Assogbadjo, A. E. (2023). Digital Agriculture Policies and Strategies for Innovations in the Agri-Food Systems—Cases of Five West African Countries. Sustainability (Switzerland), 15(12). https://doi.org/10.3390/su15129192
12. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
13. Fróna, D., & Szenderák, J. (2024). Digitalization and digital technologies: The obstacles to adaptation among Hungarian farmers. In Equilibrium. Quarterly Journal of Economics and Economic Policy (Vol. 19, Issue 3). https://doi.org/10.24136/eq.3237
14. Gbadebo, A. D. (2024). Digital Transformation for Educational Development in Sub-Saharan Africa. International Journal of Social Science and Religion, 5(3), 397–418. https://doi.org/DOI: https://doi.org/10.53639/ijssr.v5i2.262
15. Geng, W., Liu, L., Zhao, J., Kang, X., & Wang, W. (2024). Digital Technologies Adoption and Economic Benefits in Agriculture: A Mixed-Methods Approach. Sustainability (Switzerland), 16(11). https://doi.org/10.3390/su16114431
16. Greene, W. W. H. (2012). Econometric Analysis (7th ed.). Prentice Hall.
17. Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010) Multivariate Data Analysis. 7th Edition, Pearson, New York.
18. Hill, L., & Kau, P. (1973). Application of Multivariate Probit to a Threshold Model of Grain Dryer Purchasing Decisions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, 55(1), 19-27.
19. Hong., S. (2022). A Study on Digital Transformation of Agricultural Management Using the Unified Technology Adoption Theory(UTAUT); Moderated Effect of Rural Education. The Journal of Next-Generation Convergence Technology Association, 6(8), 1420–1429.
20. Indrayanto, C., Farikhin, & Prahasto, T. (2024). A Combination Model Of Utaut, Hot, And Contextual Variables For Analyzing Farmers’ Behavior Towards Internet Of Things (IoT)-Based Agricultural Technology. 4th International Conference of Science and Informa.
21. James, G. G., Okpako, A. E., & Agwu, C. O. (2023). Tention to use IoT technology on agricultural processes in Nigeria based on modified UTAUT model: perpectives of Nigerians’ farmers. Scientia Africana, 21(3), 199–214. https://doi.org/10.4314/sa.v21i3.16
22. Karidjo, B. Y., Wang, Z., Boubacar, Y., & Wei, C. (2018). Factors influencing farmers’ Adoption of Soil and Water Control Technology (SWCT) in Keita valley, a semi-arid Area of Niger. Sustainability (Switzerland), 10(2). https://doi.org/10.3390/su10020288
23. Khidir, A. A., Oladele, I. O., & Ekpa, D. (2022). The Estimation Model of Determinant of Mobile Phone Apps’ Usage by Smallholder Farmers in North West Nigeria. International Journal of Agricultural Science, Research and Technology in Extension and Education Systems, 12(2), 81–88. https://doi.org/20.1001.1.22517588.2022.12.2.3.4
24. Kirillova, O. V., Sadreeva, A. F., Mukhametshina, F. A., & Samysheva, E. Y. (2021). Priority directions for the development of the agrarian economy in the context of the digitalization of the agro-industrial complex. BIO Web of Conferences, 37. https://doi.org/10.1051/bioconf/20213700084
25. Kolmykova, T. S., Obukhova, A. S., Klykova, S. V., Mashegov, P. N., Zaitsev, A. G., & Popova, O. V. (2021). Features and Benefits of Digital Technologies in Agricultural Enterprises. E3S Web of Conferences, 247, 3–6. https://doi.org/10.1051/e3sconf/202124701018
26. M. N. Ismail. (2024). Investigating Three Digital Transformation Theories TAM, TTF, and UTAUT,. 36th Conference of Open Innovations Association (FRUCT), Lappeenranta, Finland, 538–548. https://doi.org/10.23919/FRUCT64283.2024.10749924.
27. Nugroho, E. P., Wasesa, M., Info, A., Platforms, D., & Planning, P. (2024). Adoption Drivers of Digital Platform for Coal Production Planning : an Extended UTAUT Model Using PLS-SEM Analysis. International Journal of Advances in Data and Information Systems, 5(2), 123–135. https://doi.org/10.59395/ijadis.v5i2.1321
28. Oteng, S. A., Manful, E., & Nkansah, J. O. (2024). Digital Literacy in the Informal Economy of Ghana: Life-long Learning and Extending Working Lives of Older Persons in Post-Covid-19 Era. Journal of Cross-Cultural Gerontology, 49, 375–395. https://doi.org/10.1007/s10823-024-09514-9
29. Oyenuga, M. O., & Omale, S. A. (2024). IS AFRICA JINXED? EXPLORING THE CHALLENGES OF Technology Access and Adoption in Africa. African Journal of Economics and Sustainable Development, 7(4), 142–161. https://doi.org/10.52589/AJESD-ULN1LRNF
30. Pivoto, D., Barham, B., Waquil, P. D., Foguesatto, C. R., Corte, V. F. D., Zhang, D., & Talamini, E. (2019). Factors influencing the adoption of smart farming by Brazilian grain farmers. International Food and Agribusiness Management Review, 22(4), 571–588. https://doi.org/10.22434/IFAMR2018.0086
31. Prahalathan, G., Babu, S. K., & H. D., P. (2021). Digitalization and Automation in Agriculture Industry. IGI Global Scientific, 205–216. https://doi.org/10.4018/978-1-7998-3375-8.ch014
32. Sekabira, H., Tepa-Yotto, G. T., Ahouandjinou, A. R. M., Thunes, K. H., Pittendrigh, B., Kaweesa, Y., & Tamò, M. (2023). Are digital services the right solution for empowering smallholder farmers? A perspective enlightened by COVID-19 experiences to inform smart IPM. Frontiers in Sustainable Food Systems, 7. https://doi.org/10.3389/fsufs.2023.983063
33. Sharma, A., Mohan, A., Johri, A., & Asif, M. (2024). Determinants of fintech adoption in agrarian economy: Study of UTAUT extension model in reference to developing economies. Journal of Open Innovation: Technology, Market, and Complexity, 10(2), 100273. https://doi.org/10.1016/j.joitmc.2024.100273
34. Shi, Y., Siddik, A. B., Masukujjaman, M., Zheng, G., Hamayun, M., & Ibrahim, A. M. (2022). The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory. Sustainability (Switzerland), 14(11). https://doi.org/10.3390/su14116640
35. Sihombing, M. T., Hubeis, M., Magister, P., Manajemen, I., & Manajemen, D. (2024). Analisis Adopsi dan Penggunaan Aplikasi Pertanian Digital oleh Petani Skala Kecil di Kabupaten Tuban dengan Model UTAUT. Manajemen IKM, 19(2), 78–90.
36. Wang, A. W. (2024). East African Journal of Information Technology Social Economic Barriers to Information Communication Technology ( ICT ) Access for Persons with Disabilities in Africa : Literature Review. East African Journal of Information Technology, 7(1), 318–327. https://doi.org/10.37284/eajit.7.1.2235.IEEE
37. Zelisko, N., Raiter, N., Markovych, N., Matskiv, H., & Vasylyna, O. (2024). Improving business processes in the agricultural sector considering economic security, digitalization, risks, and artificial intelligence. Ekonomika APK, 31(3), 10–21. https://doi.org/10.32317/2221-1055.2024030.10
38. Zhang, J., Trautman, D., Liu, Y., Bi, C., Chen, W., Ou, L., & Goebel, R. (2024). Achieving the Rewards of Smart Agriculture. Agronomy, 14(3), 1–10. https://doi.org/10.3390/agronomy14030452

Full-Text:



Received: 20 March 2025

Reviewed: 28 April 2025

 Revised: 06 May 2025

Accepted: 01 June 2025 

 

 

 

 

 

 

Digital Transformation in the Poultry Industry: Assessing Adoption Through the UTAUT Model and Managerial Profiles

 

Adeline Sedina1, Atsu Frank Yayra Ihou2*, Ayira Korem3 and Paul Mansingh J4

 

1Msc Graduate Regional Center of Excellence in Avian Sciences (CERSA), Department of Marketing Socio Economic, University of Lomé, Togo.

2Research Scholar, Department of Agricultural Extension & Economics, VIT School of Agricultural Innovations and Advanced Learning (VAIAL), Vellore Institute of Technology, India,

*Corresponding author: [e-mail]

3Faculty of Economics and Management Sciences (FSEG), University of Lomé, Togo

4Professor, Department of Agricultural Extension & Economics, VIT School of Agricultural Innovations and Advanced Learning (VAIAL), Vellore Institute of Technology, India.

 

 

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Keywords  Digitalization; UTAUT model; Poultry; Togo; Structure Equation Modeling (SEM)

Abstract

              his study investigates the factors influencing the adoption and effective use of digital technologies in the poultry industry in Togo’s Kara region. Using the UTAUT framework and a logit model, the study aimed to assess how perceptions of usefulness, ease of use, and managerial characteristics affect technology uptake. A purposive sample of 80 participants involved in breeding, retailing, slaughtering, feed supply, and poultry sales was surveyed. Data analysis was conducted using Smart PLS 4 for structural equation modeling and STATA 17 for logistic regression. Results show strong indicator reliability (outer loadings > 0.7), robust composite reliability (0.865–0.992), and acceptable convergent validity (AVE values > 0.5). Key findings reveal that performance expectancy and effort expectancy significantly influence behavioral intention and actual usage, emphasizing the importance of perceived productivity benefits and ease of use. In contrast, facilitating conditions, digital flexibility, and social influence were not significant. The socio-demographic analysis indicates that younger and male managers are more likely to adopt digital technologies, while marital status and cooperative membership showed no significant effects. These insights provide practical benefits by informing policy recommendations aimed at improving digital infrastructure, enhancing training programs, and designing targeted interventions that encourage broader digital adoption in Togo’s poultry sector.

 

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