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Predicting Sustainable Farm Performance—Using Hybrid Structural Equation Modelling with an Artificial Neural Network Approach

Author

Listed:
  • Naeem Hayat

    (Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, Pengkalan Chepa, Kota Bharu 16100, Malaysia)

  • Abdullah Al Mamun

    (Faculty of Business and Management, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia)

  • Noorul Azwin Md Nasir

    (Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, Pengkalan Chepa, Kota Bharu 16100, Malaysia)

  • Ganeshsree Selvachandran

    (Faculty of Business and Management, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia)

  • Noorshella Binti Che Nawi

    (Faculty of Business and Management, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia)

  • Quek Shio Gai

    (Faculty of Business and Management, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia)

Abstract

The adoption of innovative technology has always been a complex issue. The agriculture sectors of developing countries are following unsustainable farming policies. The currently adopted intensive farming practices need to replace with conservative agriculture practices (CAPs). However, the adoption of CAPs has remained low since its emergence and reports have suggested that the use of CAPs is scant for sustainable farm performance. This article aims to study three scenarios: Firstly, the influence of personal and CAPs level factors on the intention to adopt CAPs; secondly, the influence intention to adopt CAPs, facilitating conditions and voluntariness of use on the actual use of CAPs; and thirdly, the impact of the actual use of CAPs on sustainable farm performance. This study is based on survey data collected by structured interviews of rice farmers in rural Pakistan, which consists of 336 samples. The final analysis is performed using two methods: (1) a well-established and conventional way of Partial Least Squares Structural Equation Modeling (PLS-SEM) using Smart PLS 3.0, and (2) a frontier technology of computing using an artificial neural network (ANN), which is generated through a deep learning algorithm to achieve maximum possible accuracy. The results reveal that profit orientation and environment attitude as behavioural inclination significantly predicts the intention to adopt CAPs. The perception of effort expectancy can significantly predict the intention to adopt CAPs. Low intention to adopt CAPs caused by the low-level trust on extension, low-performance expectancy, and low social influence for the CAPs. The adoption of CAPs is affected by facilitating conditions, voluntary use of CAPs, and the intention to adopt CAPs. Lastly, the use of CAPs can positively and significantly forecast the perception of sustainable farm performance. Thus, it is concluded that right policies are required to enhance the farmers’ trust on extension and promote social and performance expectation for CAPs. Besides, policy recommendations can be made for sustainable agriculture development in developing and developed countries.

Suggested Citation

  • Naeem Hayat & Abdullah Al Mamun & Noorul Azwin Md Nasir & Ganeshsree Selvachandran & Noorshella Binti Che Nawi & Quek Shio Gai, 2020. "Predicting Sustainable Farm Performance—Using Hybrid Structural Equation Modelling with an Artificial Neural Network Approach," Land, MDPI, vol. 9(9), pages 1-37, August.
    • Handle: RePEc:gam:jlands:v:9:y:2020:i:9:p:289-:d:402707
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    References listed on IDEAS

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    1. Naeem Hayat & Anas A. Salameh & Abdullah Al Mamun & Mohd Helmi Ali & Zafir Khan Mohamed Makhbul, 2022. "Tax Compliance Behavior Among Malaysian Taxpayers: A Dual-stage PLS-SEM and ANN Analysis," SAGE Open, , vol. 12(3), pages 21582440221, September.
    2. Albahri, A.S. & Alnoor, Alhamzah & Zaidan, A.A. & Albahri, O.S. & Hameed, Hamsa & Zaidan, B.B. & Peh, S.S. & Zain, A.B. & Siraj, S.B. & Alamoodi, A.H. & Yass, A.A., 2021. "Based on the multi-assessment model: Towards a new context of combining the artificial neural network and structural equation modelling: A review," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    3. Ali Raza & Guangji Tong & Vasilii Erokhin & Alexey Bobryshev & Lyubov Chaykovskaya & Natalya Malinovskaya, 2023. "Sustaining Performance of Wheat–Rice Farms in Pakistan: The Effects of Financial Literacy and Financial Inclusion," Sustainability, MDPI, vol. 15(9), pages 1-19, April.

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