Journal article · 2019
Factors impacting farm management decision making software adoption
International Journal of Sustainable Agricultural Management and Informatics, 5(1), pp. 1–14 · Published
Summary
Strang et al. (2019) applied structural equation modelling to 97 valid survey responses from farm owners, managers and labourers on the Jos Plateau, Nigeria, to test whether confirmation experience, perceived usefulness and satisfaction explain intention to continue using agricultural information system (AIS) software. Confirmation experience significantly influenced satisfaction (path = 0.524) and perceived usefulness (path = 0.383), but no factor significantly influenced continuance intention, which was very high (median 5, mean 4.49 on a 1–5 scale). The sample is small, and the authors recommend replication.
Practical implication. Policy makers should reconsider government technology funding and farmer education about alternative agricultural information system (AIS) products, given that no factor significantly predicted farmers’ intention to continue using AIS.
- The Problem
- Agricultural information systems can improve farm decisions in Nigeria, but little is known about why farmers continue or stop using such software.
- The Methodology
- Quantitative positivist survey grounded in expectation confirmation theory with 97 valid responses from farm owners, managers and labourers on the Jos Plateau, Nigeria, analysed with confirmatory factor analysis and structural equation modelling.
- The Core Finding
- Structural equation modelling of 97 Jos Plateau farm owners, managers and labourers showed that confirmation experience significantly drove satisfaction (path = 0.524) and perceived usefulness (path = 0.383), yet no factor significantly predicted continuance intention, which was uniformly very high.
- The Citation
- Strang, K. D., Bitrus, S. N., & Vajjhala, N. R. (2019). Factors impacting farm management decision making software adoption. International Journal of Sustainable Agricultural Management and Informatics, 5(1), 1–14. https://doi.org/10.1504/IJSAMI.2019.10019819
The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the paper, written from the published abstract and text. The official abstract and citation details are given further down.
Key findings
- Finding 1. The study analyzed 97 valid survey responses from farm owners, managers and labourers in Jos Plateau State, Nigeria, most of whom (over 81%) had more than one year of experience using agricultural information systems.
- Finding 2. All four constructs in the expectation confirmation model were reliable, with Cronbach alpha of 0.703 for perceived usefulness, 0.909 for satisfaction, 0.773 for confirmation and 0.955 for continuance intention.
- Finding 3. The structural equation model of Nigerian farmers' AIS use showed acceptable fit (CFI = 0.959, GFI = 0.891, AGFI = 0.816, RMSEA = 0.083, RMSR = 0.041, NFI = 0.905).
- Finding 4. Confirmation experience was the strongest factor in the Nigerian farm software model, significantly influencing satisfaction (path coefficient = +0.524, p = .001) and perceived usefulness (+0.383, p < .001).
- Finding 5. Only two of five hypotheses were supported: satisfaction did not significantly influence continuance intention (+0.132, p = .203), and perceived usefulness did not significantly influence satisfaction (+0.320, p = .106) or continuance intention (+0.230, p = .886).
- Finding 6. Despite no significant predictors, Nigerian farmers reported very high intention to continue using AIS (median 5, mean 4.49, SD 0.82 on a 1–5 scale), higher than their satisfaction (mean 4.21) or confirmed experience (mean 4.43).
- Finding 7. The authors suggest high power distance in Nigerian national culture, lack of awareness of alternative AIS software, or AIS being more effective than manual methods may explain why farmers intend to keep using AIS regardless of satisfaction.
Source: Strang et al. (2019), International Journal of Sustainable Agricultural Management and Informatics, 5(1), pp. 1–14. DOI: 10.1504/IJSAMI.2019.10019819
The study in detail
What question does this paper answer?
Which factors — confirmation experience, perceived usefulness and satisfaction — explain whether Nigerian farmers intend to continue using agricultural information system (AIS) software for farm management and crop planning?
What did the study find, in detail?
Using structural equation modeling on 97 valid survey responses from farm owners, managers and labourers on the Jos Plateau, Nigeria, the study found that confirmation experience significantly influenced satisfaction (path = 0.524, p < .001) and perceived usefulness (path = 0.383, p < .001). No factor significantly influenced continuance intention, even though farmers reported a very high intention to keep using AIS (median 5, mean 4.49 on a 1–5 scale).
Why does it matter?
The results raise controversial issues about the AIS education given to Nigerian farmers and the effectiveness of government agriculture technology funding. The authors suggest policy makers provide more educational seminars on alternative AIS products, including local vendor demonstrations, and recommend replication in other developing countries in Western Africa and elsewhere.
This study is part of the evidence overview What is holding back Nigerian agriculture and farm technology adoption? Evidence from extension workers and farmer surveys
Methods and data
| Research question | Do confirmation experience, perceived usefulness and satisfaction predict Nigerian farmers' intention to continue using agricultural information systems? |
|---|---|
| Design | Quantitative, positivist survey based on expectation confirmation theory, piloted in Yola, Nigeria, and offered in English, Hausa or Berom |
| Sample | 105 farm owners, farm managers and labourers on farms of 0.5–40 hectares in Jos Plateau State, Nigeria (March 2018), selected by stratified random sampling; 97 valid responses |
| Methods | Cronbach alpha, confirmatory factor analysis (maximum likelihood) and structural equation modeling path analysis |
| Main result | Only H2 (confirmation → satisfaction, 0.524) and H5 (confirmation → perceived usefulness, 0.383) were supported; no factor significantly predicted continuance intention |
| Implication | Government technology funding and farmer education about alternative AIS products should be reconsidered |
| Citation | Strang et al. (2019) · DOI 10.1504/IJSAMI.2019.10019819 |
Definitions and key terms
- Agricultural information system (AIS)
- A system in which agricultural information is generated, transformed, transferred, consolidated, received and fed back so that these processes function synergistically to underpin knowledge utilisation by agricultural producers (Röling, 1988); in this study it includes farm management information systems. — as used in Strang et al. (2019)
- Expectation confirmation theory (ECT)
- A theory explaining how continued use of information technology is sustained, linking confirmation of expectations, perceived usefulness and satisfaction to continuance intention. — as used in Strang et al. (2019)
- Continuance intention
- A user's intention to continue using an existing system rather than discontinue it. — as used in Strang et al. (2019)
Abstract
In this study, we use an unconventional socio-cultural ideology to examine if Western African farmers think agricultural information systems (AISs) improve economic production, at the individual level of analysis. Food production is a necessity for our survival but it has been negatively impacted by unstable financial markets, climate change, political upheavals and health pandemics, especially in Western African-based developing countries. We developed a four factor model based on the information systems expectancy confirmation theory which could determine why Nigerian farmers adopt agricultural information systems. We used a survey to collect data from farmers, we validated the questions using a pilot study, and we developed a structural equation model to quantitatively explain why farmers make the decision to adopt or discontinue the use of AIS electronic software. We found that farmer’s satisfaction was positively influenced by high confirmation experience with AIS. To a lesser extent we found that farmer’s satisfaction was positively impacted by AIS perceived usefulness (PU). Interestingly, we found no evidence that any factor was related to farmer’s behavioural intent for continued use of AIS. The results raise controversial issues concerning AIS effectiveness and government technology funding in Western African countries.
Abstract as published in International Journal of Sustainable Agricultural Management and Informatics.
Keywords: agricultural information system; AIS; farm management; crop planning; decision making software; adoption; perceived use; continuance intention; satisfaction; confirmation experience; expectancy confirmation theory; Western Africa; Nigeria
Limitations and open questions
- The sample is small, so the authors recommend the study be replicated, especially given the controversial findings.
- The RMSEA (0.083) was slightly above the desired 0.08, and the continuance intention factor did not meet the AVE benchmark.
Open questions
- Why is AIS continuance intention high but unexplained by satisfaction and perceived usefulness, and do power distance or unawareness of alternative software account for it?
Open questions from the evidence overview Agriculture, food security and farm information systems.
Practical implications
- Policy makers should, the authors suggest, provide more educational seminars on alternative agricultural information system (AIS) products, including local vendor demonstrations.
- The authors suggest farmers may intend to keep using agricultural information systems (AIS) regardless of satisfaction because of Nigerian national culture’s high power distance, lack of awareness of alternative software, or AIS being more effective than manual methods.
- For researchers applying expectation confirmation theory to farmers, neither satisfaction nor perceived usefulness significantly influenced continuance intention (satisfaction +0.132, p = .203; perceived usefulness +0.230, p = .886).
- Researchers are advised to replicate the study, because the sample is small and the findings are controversial.
Drawn from Strang et al. (2019), International Journal of Sustainable Agricultural Management and Informatics, 5(1), pp. 1–14.
When this research may be relevant
This article may be relevant to literature on farm management and agricultural information systems adoption, continuance intention and expectation confirmation theory, technology acceptance among smallholder farmers in sub-Saharan Africa, and structural equation modelling with small agricultural samples.
Research topics addressed: agricultural information systems; farm management information systems; technology adoption; continuance intention; expectation confirmation theory; perceived usefulness; user satisfaction; structural equation modeling; confirmatory factor analysis; survey; smallholder farmers; Nigeria; Jos Plateau; West Africa; food security
How to cite
- APA 7
- Strang, K. D., Bitrus, S. N., & Vajjhala, N. R. (2019). Factors impacting farm management decision making software adoption. International Journal of Sustainable Agricultural Management and Informatics, 5(1), 1–14. https://doi.org/10.1504/IJSAMI.2019.10019819
- MLA 9
- Strang, Kenneth David, et al. “Factors impacting farm management decision making software adoption.” International Journal of Sustainable Agricultural Management and Informatics, vol. 5, no. 1, 2019, pp. 1–14. https://doi.org/10.1504/IJSAMI.2019.10019819.
- IEEE
- K. D. Strang, S. N. Bitrus, and N. R. Vajjhala, “Factors impacting farm management decision making software adoption,” International Journal of Sustainable Agricultural Management and Informatics, vol. 5, no. 1, pp. 1–14, 2019, doi: 10.1504/IJSAMI.2019.10019819.
BibTeX
@article{strang2019nigerian,
title = {Factors impacting farm management decision making software adoption},
author = {Strang, Kenneth David and Bitrus, Sarah Nankyer and Vajjhala, Narasimha Rao},
journal = {International Journal of Sustainable Agricultural Management and Informatics},
volume = {5},
number = {1},
pages = {1--14},
year = {2019},
publisher = {Inderscience},
doi = {10.1504/IJSAMI.2019.10019819},
url = {https://doi.org/10.1504/IJSAMI.2019.10019819}
}RIS
TY - JOUR TI - Factors impacting farm management decision making software adoption AU - Strang, Kenneth David AU - Bitrus, Sarah Nankyer AU - Vajjhala, Narasimha Rao JO - International Journal of Sustainable Agricultural Management and Informatics PY - 2019 DA - 2019 VL - 5 IS - 1 SP - 1 EP - 14 PB - Inderscience DO - 10.1504/IJSAMI.2019.10019819 UR - https://www.narasimharao.net/research/nigerian-farmers-agricultural-information-systems-continuance/ LA - en ER -
Links and files
- DOI: https://doi.org/10.1504/IJSAMI.2019.10019819
- This page as text: https://www.narasimharao.net/research/nigerian-farmers-agricultural-information-systems-continuance/paper.md
- Permanent address of this page: https://www.narasimharao.net/research/nigerian-farmers-agricultural-information-systems-continuance/
Related work
Related work: the literature this study cites
The AIS model builds on expectation confirmation theory[1] and the perceived-usefulness construct[2], which were developed outside African agriculture. The cited literature attributes part of Africa’s lag in food production to low AIS adoption[3] and describes FMIS moving from record-keeping to complex systems[4].
- [1] Bhattacherjee, A. (2001) ‘Understanding information systems continuance: an expectation-confirmation model’, MIS Quarterly, Vol. 25, No. 3, pp.351–370.
- [2] Davis, F.D. (1989) ‘Perceived usefulness, perceived ease of use, and user acceptance of information technology’, MISQ, Vol. 13, No. 3, pp.319–340,. https://doi.org/10.2307/249008
- [3] Aker, J. C. (2011). Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), 631–647. https://doi.org/10.1111/j.1574-0862.2011.00545.x
- [4] Fountas, S., Carli, G., Sørensen, C., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., & Tisseyre, B. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 11(5), 25–42. https://doi.org/10.1016/j.compag.2015.05.011
From the evidence overview What is holding back Nigerian agriculture and farm technology adoption? Evidence from extension workers and farmer surveys, which cites only works that appear in the studies’ own reference lists. This is a selection, not the full reference list of Strang et al. (2019).
Related research
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In a survey of 105 farm owners, managers and labourers on the Jos Plateau, only marital status, gender and language correlated with FMIS e-adoption; age, education, land size and software experience did not, and a discriminant model using the three significant factors was statistically significant (p = .014).
A critical review of 2015–2020 empirical studies identifies lack of modern-method training, corrupt input dissemination, poor infrastructure, terrorism-driven insecurity, lack of credit and weak strategic planning as the key problems, and finds only weak evidence of significant information-system use in agriculture.
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Extension workers from three local government areas of Adamawa State confirmed themes from the literature and added new ones, showing that rural farmers face significant problems with government support in six areas: farm input quality and dissemination, fair input subsidization, training, market facilitation, corruption and insecurity.
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