Factors influencing the behavioural intention for smart farming in Sarawak, Malaysia
Agriculture is an industry that contributes to the economic growth and social progress of many countries worldwide, as well as positive impacts to the environment. However, the agricultural industry also faces many challenges, such as the quality of crops and land available for farming activities, c...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Federal Agricultural Marketing Authority (FAMA)
2022
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/45844/1/Factors%20Influencing%20the%20Behavioural%20Intention%20for%20Smart%20Farming%20in%20Sarawak.pdf http://ir.unimas.my/id/eprint/45844/ https://www.fama.gov.my/volume-9-issue-1-factors-influencing-the-behavioral-intention-for-smart-farming-in-sarawak-malaysia#:~:text=The%20results%20indicated%20that%20performance,strongest%20predictor%20of%20behavioural%20intention. https://doi.org/10.56527/jabm.9.1.4 |
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| Summary: | Agriculture is an industry that contributes to the economic growth and social progress of many countries worldwide, as well as positive impacts to the environment. However, the agricultural industry also faces many challenges, such as the quality of crops and land available for farming activities, climate change, poor economic conditions for farmers, and lack of technology. As the agricultural trend is towards achieving food security, improving nourishment, and advancing sustainable agriculture, Smart Farming harnesses the potential of Industry 4.0 revolution to achieve the goals outlined. The critical consideration would be the intention of farmers to integrate and adopt these smart, connected technologies in their farming activities. This study examined the behavioural intention to use Smart Farming technologies from the perspective of farmers using the Unified Theory of Acceptance and Use of Technology (UTAUT). A cross-sectional study was conducted using quantitative method. Data were derived from farmers in Malaysia via a face-to-face survey in 2021 (n = 381). Partial Least Squares (PLS) regression was applied for model and hypothesis testing. The results indicated that performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) influenced the behavioural intention to adopt SFT. Social influence (SI) was found to be the strongest predictor of behavioural intention. This study contributes to the theoretical understanding of applying UTAUT to examine the behavioural intention to adopt Smart Farming among farmers. In practice, this study also provides implications for the Sarawak government to advance digital inclusion for all communities to achieve high income and advanced status by 2030. |
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