IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i9p5136-d800438.html
   My bibliography  Save this article

More Income, Less Pollution? How Income Expectation Affects Pesticide Application

Author

Listed:
  • Xiaoshan Su

    (School of Management, Zhengzhou University, Zhengzhou 450001, China)

  • Jingyi Shi

    (School of Management, Zhengzhou University, Zhengzhou 450001, China)

  • Tianxi Wang

    (Business School, University of Edinburgh, 29 Buccleuch Place, Edinburgh EH8 9JS, UK)

  • Qinghui Shen

    (School of Management, Zhengzhou University, Zhengzhou 450001, China)

  • Wentao Niu

    (School of Management, Zhengzhou University, Zhengzhou 450001, China)

  • Zhenzhen Xu

    (School of Architecture and Built Environment, Deakin University, Geelong 3219, Australia)

Abstract

Farmers are still the foundation of China’s current “small, scattered, and weak” agricultural production pattern. As such, increasing guidance for reduction response behavior is central to reducing agricultural pesticide use. Following this pesticide reduction logic, four of the most widely promoted pesticide reduction technologies, including light trapping, biopesticide application, healthy crop growth, and insect-proof net technologies, were selected, and a theoretical analysis framework of farmers’ willingness to adopt these technologies was constructed based on the theories of value perception and planned behavior. An ordered logistic regression model is used to explore key factors behind current pesticide reduction technology perceptions, technology response willingness, and behavioral decisions of farmers in China, with survey data from 516 farmers in Henan Province. The results show that among the four pesticide reduction technologies, healthy crop growth technology is the most-appealing one for farmers, followed by insect-proof net technology and biopesticide application technology. The least-appealing one for farmers is the light trapping technology. Farmers’ perceived degree of income improvement from technology adoption is the main determinant of their willingness, which is positively significant at a 1% confidence level in all four models. In addition, farmers’ willingness to respond to technologies is also significantly influenced by farmers’ perception of technical operational ability, perception of risk from adopting technology, government-related subsidies, government technical training guidance, trust in government promotion of technology, and perception of the government’s role in improving the external environment for adopting technology.

Suggested Citation

  • Xiaoshan Su & Jingyi Shi & Tianxi Wang & Qinghui Shen & Wentao Niu & Zhenzhen Xu, 2022. "More Income, Less Pollution? How Income Expectation Affects Pesticide Application," IJERPH, MDPI, vol. 19(9), pages 1-23, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5136-:d:800438
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/9/5136/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/9/5136/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roba Argaw Tessema & Károly Nagy & Balázs Ádám, 2021. "Pesticide Use, Perceived Health Risks and Management in Ethiopia and in Hungary: A Comparative Analysis," IJERPH, MDPI, vol. 18(19), pages 1-14, October.
    2. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    3. 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.
    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. Yayan Xie & Yang Su & Feng Li, 2022. "The Evolutionary Game Analysis of Low Carbon Production Behaviour of Farmers, Government and Consumers in Food Safety Source Governance," IJERPH, MDPI, vol. 19(19), pages 1-16, September.
    2. Siyu Gong & Bo Wang & Zhigang Yu, 2022. "Whether the Use of the Internet Can Assist Farmers in Selecting Biopesticides or Not: A Study Based on Evidence from the Largest Rice-Producing Province in China," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    3. Xiuling Ding & Apurbo Sarkar & Lipeng Li & Hua Li & Qian Lu, 2022. "Effects of Market Incentives and Livelihood Dependence on Farmers’ Multi-Stage Pesticide Application Behavior—A Case Study of Four Provinces in China," IJERPH, MDPI, vol. 19(15), pages 1-19, August.

    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. Paul Juinn Bing Tan, 2013. "Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan," SAGE Open, , vol. 3(4), pages 21582440135, October.
    2. Hajiheydari, Nastaran & Delgosha, Mohammad Soltani & Olya, Hossein, 2021. "Scepticism and resistance to IoMT in healthcare: Application of behavioural reasoning theory with configurational perspective," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    3. Wang, Guoqiang & Tan, Garry Wei-Han & Yuan, Yunpeng & Ooi, Keng-Boon & Dwivedi, Yogesh K., 2022. "Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. 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.
    5. Petschnig, Martin & Heidenreich, Sven & Spieth, Patrick, 2014. "Innovative alternatives take action – Investigating determinants of alternative fuel vehicle adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 68-83.
    6. Sanjeev Verma, 2015. "Harnessing the Benefit of Social Networking Sites for Intentional Social Action: Determinants and Challenges," Vision, , vol. 19(2), pages 104-111, June.
    7. Queiroz, Maciel M. & Fosso Wamba, Samuel, 2019. "Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA," International Journal of Information Management, Elsevier, vol. 46(C), pages 70-82.
    8. Sarv Devaraj & Ming Fan & Rajiv Kohli, 2002. "Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics," Information Systems Research, INFORMS, vol. 13(3), pages 316-333, September.
    9. Bierstaker, James & Janvrin, Diane & Lowe, D. Jordan, 2014. "What factors influence auditors' use of computer-assisted audit techniques?," Advances in accounting, Elsevier, vol. 30(1), pages 67-74.
    10. Dalal Bamufleh, 2023. "Modelling the Acceptance and Use of Electronic Medical Records from Patients’ Point of View: Evidence from Saudi Arabia," International Journal of Business and Management, Canadian Center of Science and Education, vol. 16(7), pages 1-12, February.
    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. Keen, C. & Wetzels, M., 2001. "Exploring the Preference Structure for Online and Traditional Retail Formats," Working Papers 01.18, Eindhoven Center for Innovation Studies.
    13. repec:zbw:bofrdp:2006_032 is not listed on IDEAS
    14. Sabi, Humphrey M. & Uzoka, Faith-Michael E. & Langmia, Kehbuma & Njeh, Felix N., 2016. "Conceptualizing a model for adoption of cloud computing in education," International Journal of Information Management, Elsevier, vol. 36(2), pages 183-191.
    15. Guych Nuryyev & Yu-Ping Wang & Jennet Achyldurdyyeva & Bih-Shiaw Jaw & Yi-Shien Yeh & Hsien-Tang Lin & Li-Fan Wu, 2020. "Blockchain Technology Adoption Behavior and Sustainability of the Business in Tourism and Hospitality SMEs: An Empirical Study," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
    16. Donmez, Birsen & Matson, Zannah & Savan, Beth & Farahani, Ellie & Photiadis, David & Dafoe, Joanna, 2014. "Interruption management and office norms: Technology adoption lessons from a product commercialization study," International Journal of Information Management, Elsevier, vol. 34(6), pages 741-750.
    17. 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).
    18. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    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. Humphrey M. Sabi & Faith-Michael E. Uzoka & Kehbuma Langmia & Felix N. Njeh & Clive K. Tsuma, 0. "A cross-country model of contextual factors impacting cloud computing adoption at universities in sub-Saharan Africa," Information Systems Frontiers, Springer, vol. 0, pages 1-24.
    21. Nuray Cakirli Akyüz & Ludwig Theuvsen, 2020. "The Impact of Behavioral Drivers on Adoption of Sustainable Agricultural Practices: The Case of Organic Farming in Turkey," Sustainability, MDPI, vol. 12(17), pages 1-20, August.

    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:jijerp:v:19:y:2022:i:9:p:5136-:d:800438. 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.