Factors impacting farm management decision making software adoption

Summary

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?

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.

Study at a glance

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

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.

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.

Continuance intention

A user's intention to continue using an existing system rather than discontinue it.

Limitations

• 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.

How to cite

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

@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}
}