IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v9y2020i9p289-d402707.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/9/9/289/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/9/9/289/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zulfiqar, Farhad & Thapa, Gopal B., 2017. "Agricultural sustainability assessment at provincial level in Pakistan," Land Use Policy, Elsevier, vol. 68(C), pages 492-502.
    2. Leong, Lai-Ying & Hew, Teck-Soon & Ooi, Keng-Boon & Chong, Alain Yee-Loong, 2020. "Predicting the antecedents of trust in social commerce – A hybrid structural equation modeling with neural network approach," Journal of Business Research, Elsevier, vol. 110(C), pages 24-40.
    3. Shira Bukchin & Dorit Kerret, 2018. "Food for Hope: The Role of Personal Resources in Farmers’ Adoption of Green Technology," Sustainability, MDPI, vol. 10(5), pages 1-11, May.
    4. D’Souza, Alwin & Mishra, Ashok K., 2018. "Adoption and Abandonment of Partial Conservation Technologies in Developing Economies: The Case of South Asia," Land Use Policy, Elsevier, vol. 70(C), pages 212-223.
    5. Brick, Kerri & Visser, Martine, 2015. "Risk preferences, technology adoption and insurance uptake: A framed experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 118(C), pages 383-396.
    6. Ritu Agarwal & Jayesh Prasad, 1998. "A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology," Information Systems Research, INFORMS, vol. 9(2), pages 204-215, June.
    7. Lalani, Baqir & Dorward, Peter & Holloway, Garth & Wauters, Erwin, 2016. "Smallholder farmers' motivations for using Conservation Agriculture and the roles of yield, labour and soil fertility in decision making," Agricultural Systems, Elsevier, vol. 146(C), pages 80-90.
    8. Shmueli, Galit & Ray, Soumya & Velasquez Estrada, Juan Manuel & Chatla, Suneel Babu, 2016. "The elephant in the room: Predictive performance of PLS models," Journal of Business Research, Elsevier, vol. 69(10), pages 4552-4564.
    9. Giovanni Pino & Pierluigi Toma & Cristian Rizzo & Pier Paolo Miglietta & Alessandro M. Peluso & Gianluigi Guido, 2017. "Determinants of Farmers’ Intention to Adopt Water Saving Measures: Evidence from Italy," Sustainability, MDPI, vol. 9(1), pages 1-14, January.
    10. Liébana-Cabanillas, Francisco & Marinkovic, Veljko & Ramos de Luna, Iviane & Kalinic, Zoran, 2018. "Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 117-130.
    11. Buddhini Ranjika Walisinghe & Shyama Ratnasiri & Nicholas Rohde & Ross Guest, 2017. "Does agricultural extension promote technology adoption in Sri Lanka," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 44(12), pages 2173-2186, December.
    12. F. van Winsen & Y. de Mey & L. Lauwers & S. Van Passel & M. Vancauteren & E. Wauters, 2016. "Determinants of risk behaviour: effects of perceived risks and risk attitude on farmer's adoption of risk management strategies," Journal of Risk Research, Taylor & Francis Journals, vol. 19(1), pages 56-78, January.
    13. repec:ucp:bkecon:9780226316529 is not listed on IDEAS
    14. Wamsler, Christine & Brink, Ebba, 2018. "Mindsets for Sustainability: Exploring the Link Between Mindfulness and Sustainable Climate Adaptation," Ecological Economics, Elsevier, vol. 151(C), pages 55-61.
    15. Kamonthip Maichum & Surakiat Parichatnon & Ke-Chung Peng, 2016. "Application of the Extended Theory of Planned Behavior Model to Investigate Purchase Intention of Green Products among Thai Consumers," Sustainability, MDPI, vol. 8(10), pages 1-20, October.
    16. Adnan, Nadia & Nordin, Shahrina Md & bin Abu Bakar, Zulqarnain, 2017. "Understanding and facilitating sustainable agricultural practice: A comprehensive analysis of adoption behaviour among Malaysian paddy farmers," Land Use Policy, Elsevier, vol. 68(C), pages 372-382.
    17. Mariano, Marc Jim & Villano, Renato & Fleming, Euan, 2012. "Factors influencing farmers’ adoption of modern rice technologies and good management practices in the Philippines," Agricultural Systems, Elsevier, vol. 110(C), pages 41-53.
    18. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    19. Ali, Akhter & Hussain, Imtiaz & Rahut, Dil Bahadur & Erenstein, Olaf, 2018. "Laser-land leveling adoption and its impact on water use, crop yields and household income: Empirical evidence from the rice-wheat system of Pakistan Punjab," Food Policy, Elsevier, vol. 77(C), pages 19-32.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Riffat Ara Zannat Tama & Md Mahmudul Hoque & Ying Liu & Mohammad Jahangir Alam & Mark Yu, 2023. "An Application of Partial Least Squares Structural Equation Modeling (PLS-SEM) to Examining Farmers’ Behavioral Attitude and Intention towards Conservation Agriculture in Bangladesh," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
    2. Giuseppe Timpanaro & Gaetano Chinnici & Roberta Selvaggi & Giulio Cascone & Vera Teresa Foti & Alessandro Scuderi, 2023. "Farmer?s adoption of agricultural insurance for Mediterranean crops as an innovative behavior," Economia agro-alimentare, FrancoAngeli Editore, vol. 25(2), pages 155-188.
    3. Tewari, Alok & Mathur, Smriti & Srivastava, Smriti & Gangwar, Divya, 2022. "Examining the role of receptivity to green communication, altruism and openness to change on young consumers’ intention to purchase green apparel: A multi-analytical approach," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    4. Francisco Liébana-Cabanillas & Nidhi Singh & Zoran Kalinic & Elena Carvajal-Trujillo, 2021. "Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: a multi-analytical approach," Information Technology and Management, Springer, vol. 22(2), pages 133-161, June.
    5. Cristopher Siegfried Kopplin, 2021. "Two heads are better than one: matchmaking tools in coworking spaces," Review of Managerial Science, Springer, vol. 15(4), pages 1045-1069, May.
    6. Sara Moussawi & Marios Koufaris & Raquel Benbunan-Fich, 2021. "How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 343-364, June.
    7. Abadi, Bijan & Yadollahi, Arash & Bybordi, Ahmad & Rahmati, Mehdi, 2020. "The discrimination of adopters and non-adopters of conservation agricultural initiatives in northwest Iran: Attitudinal, soil testing, and topographical modules," Land Use Policy, Elsevier, vol. 95(C).
    8. Li, Fuduo & Zhang, Kangjie & Ren, Jing & Yin, Changbin & Zhang, Yang & Nie, Jun, 2021. "Driving mechanism for farmers to adopt improved agricultural systems in China: The case of rice-green manure crops rotation system," Agricultural Systems, Elsevier, vol. 192(C).
    9. Francisco Rejón-Guardia & Juán Sánchez-Fernández & Francisco Muñoz-Leiva, 2011. "Motivational Factors that influence the Acceptance of Microblogging Social Networks: The µBAM Model," FEG Working Paper Series 06/11, Faculty of Economics and Business (University of Granada).
    10. Hiran, Kamal Kant & Dadhich, Manish, 2024. "Predicting the core determinants of cloud-edge computing adoption (CECA) for sustainable development in the higher education institutions of Africa: A high order SEM-ANN analytical approach," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    11. Gansser, Oliver Alexander & Reich, Christina Stefanie, 2021. "A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application," Technology in Society, Elsevier, vol. 65(C).
    12. Hong-Wen Lin & Ya-Cing Jhan & Yuan Tseng, 2019. "Behavioral intention of using one-stop mobile application: evidence from department stores," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 9(12), pages 401-412, December.
    13. Attié, Elodie & Meyer-Waarden, Lars, 2022. "The acceptance and usage of smart connected objects according to adoption stages: an enhanced technology acceptance model integrating the diffusion of innovation, uses and gratification and privacy ca," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    14. Adamantia Pateli & Naoum Mylonas & Aggeliki Spyrou, 2020. "Organizational Adoption of Social Media in the Hospitality Industry: An Integrated Approach Based on DIT and TOE Frameworks," Sustainability, MDPI, vol. 12(17), pages 1-20, September.
    15. Ritu Agarwal & V. Sambamurthy & Ralph M. Stair, 2000. "Research Report: The Evolving Relationship Between General and Specific Computer Self-Efficacy—An Empirical Assessment," Information Systems Research, INFORMS, vol. 11(4), pages 418-430, December.
    16. Natarajan, Thamaraiselvan & Balasubramanian, Senthil Arasu & Kasilingam, Dharun Lingam, 2017. "Understanding the intention to use mobile shopping applications and its influence on price sensitivity," Journal of Retailing and Consumer Services, Elsevier, vol. 37(C), pages 8-22.
    17. Foshay, Neil & Kuziemsky, Craig, 2014. "Towards an implementation framework for business intelligence in healthcare," International Journal of Information Management, Elsevier, vol. 34(1), pages 20-27.
    18. Christian Maier & Sven Laumer & Jason Bennett Thatcher & Jakob Wirth & Tim Weitzel, 2022. "Trial-Period Technostress: A Conceptual Definition and Mixed-Methods Investigation," Information Systems Research, INFORMS, vol. 33(2), pages 489-514, June.
    19. Gao, Tao (Tony) & Rohm, Andrew J. & Sultan, Fareena & Pagani, Margherita, 2013. "Consumers un-tethered: A three-market empirical study of consumers' mobile marketing acceptance," Journal of Business Research, Elsevier, vol. 66(12), pages 2536-2544.
    20. Seok Chan Jeong & Beom-Jin Choi, 2022. "Moderating Effects of Consumers’ Personal Innovativeness on the Adoption and Purchase Intention of Wearable Devices," SAGE Open, , vol. 12(4), pages 21582440221, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:9:y:2020:i:9:p:289-:d:402707. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.