Adoption of IT Tools in Supplier Selection
A Pilot Study of Cattle Farmers in Kamrup (M) District, Assam
DOI:
https://doi.org/10.63635/mrj.v2i2.159Keywords:
IT Tool Adoption, Supplier Selection, Cattle Farmers, Livestock Farming, Digital Literacy, Kamrup (M) District, UTAUT2Abstract
This pilot study aims to investigate the degree to which cattle farmers in the Kamrup (M) District of Assam, North-East India, have accepted and embraced information technology [IT] tools in choosing suppliers and for necessary inputs like livestock, equipment, feed and cattle health-care. Grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, the study examines five constructs, namely Performance Expectancy, Effort Expectancy, Facilitating Conditions, Price Value and Trust, using data from 33 cattle farmers collected through a structured Likert-scale questionnaire. Multiple Spearman correlation tests are also employed along with descriptive analysis. Findings indicate strong positive perceptions of IT in improving supplier decision quality (Performance Expectancy mean: 3.36–3.61) and value recognition (Price Value mean: 3.48–3.64). Significant trust-related barriers like data privacy (mean: 3.73) and information credibility are also found. Facilitating conditions like limited internet access and high IT service costs emerge as the primary adoption barriers. The study contributes a theoretically grounded baseline for understanding digital procurement adoption in rural livestock farming contexts in Northeast India.
Downloads
References
[1] Wilhelm, M.M.; Blome, C.; Bhakoo, V.; Paulraj, A. Sustainability in multi-tier supply chains: Understanding the double agency role of the first-tier supplier. Journal of Operations Management 2016, 41, 42–60. https://doi.org/10.1016/j.jom.2015.11.001 DOI: https://doi.org/10.1016/j.jom.2015.11.001
[2] Verbeke, W. Stakeholder, citizen and consumer interests in farm animal welfare. Animal Welfare 2009, 18(4), 325–333. https://doi.org/10.1017/S0962728600000725 DOI: https://doi.org/10.1017/S0962728600000725
[3] Enticott, G.; Little, R. Playing games with 'good farming': exploring the potential impact of disease control policies on farmers' cattle purchasing practices. Journal of Rural Studies 2022, 92, 371–382. https://doi.org/10.1016/j.jrurstud.2022.04.008 DOI: https://doi.org/10.1016/j.jrurstud.2022.04.008
[4] Rege, J.E.; Marshall, K.; Notenbaert, A.; Ojango, J.M.; Okeyo, A.M. Pro-poor animal improvement and breeding—What can science do? Livestock Science 2011, 136(1), 15–28. https://doi.org/10.1016/j.livsci.2010.09.003 DOI: https://doi.org/10.1016/j.livsci.2010.09.003
[5] Lambin, E.F.; Gibbs, H.K.; Heilmayr, R.; Carlson, K.M.; Fleck, L.C.; Garrett, R.D.; Walker, N.F. The role of supply-chain initiatives in reducing deforestation. Nature Climate Change 2018, 8(2), 109–116. https://doi.org/10.1038/s41558-017-0061-1 DOI: https://doi.org/10.1038/s41558-017-0061-1
[6] Newton, P.; Alves-Pinto, H.N.; Pinto, L.F.G. Certification, forest conservation and cattle: theories and evidence of change in Brazil. Conservation Letters 2015, 8(3), 206–213. https://doi.org/10.1111/conl.12116 DOI: https://doi.org/10.1111/conl.12116
[7] Nepstad, D.; McGrath, D.; Stickler, C.; Alencar, A.; Azevedo, A.; Swette, B.; Hess, L. Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains. Science 2014, 344(6188), 1118–1123. https://doi.org/10.1126/science.1248525 DOI: https://doi.org/10.1126/science.1248525
[8] Krishnan, R.; Rohit, G.; Kumar, P.R. An Integrated Sustainable and Resilient Closed-Loop Supply Chain Network Design: A Case Study of the Indian Mango Pulp Industry. Sustainable and Resilient Supply Chain: Environmental Accounting and Management Focus 2024; Emerald Publishing Limited; pp. 13–47. https://doi.org/10.1108/S1479-359820240000012005 DOI: https://doi.org/10.1108/S1479-359820240000012005
[9] Hoddinott, J.; Headey, D.; Dereje, M. Cows, missing milk markets and nutrition in rural Ethiopia. The Journal of Development Studies 2015, 51(8), 958–975. https://doi.org/10.1080/00220388.2015.1018903 DOI: https://doi.org/10.1080/00220388.2015.1018903
[10] Quisumbing, A.R.; Rubin, D.; Manfre, C.; Waithanji, E.; Van den Bold, M.; Olney, D.; Meinzen-Dick, R. Gender, assets and market-oriented agriculture: learning from high-value crop and livestock projects in Africa and Asia. Agriculture and Human Values 2015, 32(4), 705–725. https://doi.org/10.1007/s10460-015-9587-x DOI: https://doi.org/10.1007/s10460-015-9587-x
[11] Omondi, I.; Galiè, A.; Teufel, N.; Loriba, A.; Kariuki, E.; Baltenweck, I. Women's empowerment and livestock vaccination: evidence from Peste des Petits ruminants vaccination interventions in northern Ghana. Animals 2022, 12(6), 717. https://doi.org/10.3390/ani12060717 DOI: https://doi.org/10.3390/ani12060717
[12] Kanter, D.R.; Bartolini, F.; Kugelberg, S.; Leip, A.; Oenema, O.; Uwizeye, A. Nitrogen pollution policy beyond the farm. Nature Food 2020, 1(1), 27–32. https://doi.org/10.1038/s43016-019-0001-5 DOI: https://doi.org/10.1038/s43016-019-0001-5
[13] Niemi, J.; Bennett, R.; Clark, B.; Frewer, L.; Jones, P.; Rimmler, T.; Tranter, R. A value chain analysis of interventions to control production diseases in the intensive pig production sector. PLoS One 2020, 15(4), e0231338. https://doi.org/10.1371/journal.pone.0231338 DOI: https://doi.org/10.1371/journal.pone.0231338
[14] Braun, A.; Jiggins, J.; Röling, N.; Van Den Berg, H.; Snijders, P. A global survey and review of farmer field school experiences. A Report for the International Livestock Research Institute, Wageningen 2006.
[15] Bush, R.D.; Page, B.; Macdonald, T.; Young, J.R.; Nampanya, S.; Suon, S.; Windsor, P.A. Target feeding for improved smallholder beef production in the Mekong region: lessons from Cambodia and Lao PDR. Animal Production Science 2014, 54(9), 1219–1223. https://doi.org/10.1071/AN14133 DOI: https://doi.org/10.1071/AN14133
[16] Lambrecht, I.; Vanlauwe, B.; Merckx, R.; Maertens, M. Understanding the process of agricultural technology adoption: mineral fertilizer in eastern DR Congo. World Development 2014, 59, 132–146. https://doi.org/10.1016/j.worlddev.2014.01.024 DOI: https://doi.org/10.1016/j.worlddev.2014.01.024
[17] Le Gal, P.Y.; Dugué, P.; Faure, G.; Novak, S. How does research address the design of innovative agricultural production systems at the farm level? A review. Agricultural Systems 2011, 104(9), 714–728. https://doi.org/10.1016/j.agsy.2011.07.007 DOI: https://doi.org/10.1016/j.agsy.2011.07.007
[18] Pender, J.; Nkonya, E.; Jagger, P.; Sserunkuuma, D.; Ssali, H. Strategies to increase agricultural productivity and reduce land degradation: evidence from Uganda. Agricultural Economics 2004, 31(2-3), 181–195. https://doi.org/10.1016/j.agecon.2004.09.006 DOI: https://doi.org/10.1016/j.agecon.2004.09.006
[19] Ugochukwu, A.I.; Phillips, P.W.; Ochieng', B.J. Driving adoption and commercialization of subunit vaccines for bovine tuberculosis and Johne's disease: policy choices and implications for food security. Vaccines 2020, 8(4), 667. https://doi.org/10.3390/vaccines8040667 DOI: https://doi.org/10.3390/vaccines8040667
[20] Schreinemachers, P.; Berger, T. An agent-based simulation model of human–environment interactions in agricultural systems. Environmental Modelling & Software 2011, 26(7), 845–859. https://doi.org/10.1016/j.envsoft.2011.02.004 DOI: https://doi.org/10.1016/j.envsoft.2011.02.004
[21] Deressa, T.T.; Hassan, R.M.; Ringler, C.; Alemu, T.; Yesuf, M. Determinants of farmers' choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change 2009, 19(2), 248–255. https://doi.org/10.1016/j.gloenvcha.2009.01.002 DOI: https://doi.org/10.1016/j.gloenvcha.2009.01.002
[22] Nerlove, M. The dynamics of supply: retrospect and prospect. American Journal of Agricultural Economics 1979, 61(5), 874–888. Available online: http://www.jstor.com/stable/3180340 DOI: https://doi.org/10.2307/3180340
[23] Amuno, M. Exploring traceability challenges amongst small businesses in Tasmanian red meat supply chains: the role and potential impact of information technology. Doctoral Dissertation, University of Tasmania 2020. https://doi.org/10.25959/23238494
[24] Venkatesh, V.; Thong, J.Y.; Xu, X. Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly 2012, 36(1), 157–178. Available online: https://www.jstor.org/stable/41410412 DOI: https://doi.org/10.2307/41410412
[25] Slade, E.L.; Dwivedi, Y.K.; Piercy, N.C.; Williams, M.D. Modeling consumers' adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with innovativeness, risk and trust. Psychology & Marketing 2015, 32(8), 860–873. https://doi.org/10.1002/mar.20823 DOI: https://doi.org/10.1002/mar.20823
[26] Tarhini, A.; Hone, K.; Liu, X.; Tarhini, T. Examining the moderating effect of individual-level cultural values on users' acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments 2017, 25(3), 306–328. https://doi.org/10.1080/10494820.2015.1122635 DOI: https://doi.org/10.1080/10494820.2015.1122635
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Prachurjya Sarmah, Abhijit Sarmah, Prayash Baruah (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright © Author(s) retain the copyright of this article.
