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Towards an Understanding of the Behavioral Intentions and Actual Use of Smart Products among German Farmers

Author

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  • Sirkka Schukat

    (Agribusiness Management, Department of Agricultural Economics and Rural Development, University of Göttingen, 37073 Göttingen, Germany)

  • Heinke Heise

    (Agribusiness Management, Department of Agricultural Economics and Rural Development, University of Göttingen, 37073 Göttingen, Germany)

Abstract

Innovative technologies in the context of smart farming are expected to play a significant role in the adaptation of the agricultural sector to climate change and sustainable agriculture. However, the adoption of smart farming solutions, in this case so-called smart products, depends indispensably on the acceptance of farmers. For this reason, it is important to develop an understanding of what determinants are decisive for farmers in the adoption of these technologies. In order to address this research gap, farmers in Germany were surveyed via a large-scale online survey in 2020 (n = 523). Based on an extended version of the Unified Theory of Acceptance and Use of Technology, a Partial Least Squares (PLS) analysis was performed. The results indicate that hedonic motivation significantly influences farmers’ behavioral intention to use smart products. In addition, behavioral intention is affected by social determinants and the personal performance expectations of smart products. Trust, as well as facilitating conditions, also has an impact on behavioral intention. Furthermore, facilitating conditions are an important determinant of the actual use behavior. In addition, use behavior is influenced by behavioral intention. It was further found that technology readiness plays a significant role in the adoption of smart products. Moderating effects of age, work experience, and farm size were identified that influence farmers’ willingness to use smart products. The study holds important managerial implications for technology companies in the field of smart farming and can help develop approaches for tailored technical solutions that meet farmers’ needs.

Suggested Citation

  • Sirkka Schukat & Heinke Heise, 2021. "Towards an Understanding of the Behavioral Intentions and Actual Use of Smart Products among German Farmers," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6666-:d:573334
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    as
    1. 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.
    2. Sheng, Yu & Davidson, Alistair & Fuglie, Keith & Zhang, Dandan, 2016. "Input Substitution, Productivity Performance and Farm Size," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 60(3), July.
    3. Markus Blut & Cheng Wang, 2020. "Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage," Journal of the Academy of Marketing Science, Springer, vol. 48(4), pages 649-669, July.
    4. Eastwood, C. & Kenny, S., . "Art or Science? Heuristic versus Data Driven Grazing Management on Dairy Farms," Extension Farming Systems Journal - EFS Journal, Australasian Farm Business Management Network, vol. 5(1).
    5. Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
    6. Feder, Gershon & Just, Richard E & Zilberman, David, 1985. "Adoption of Agricultural Innovations in Developing Countries: A Survey," Economic Development and Cultural Change, University of Chicago Press, vol. 33(2), pages 255-298, January.
    7. Ronaghi, Mohammad Hossein & Forouharfar, Amir, 2020. "A contextualized study of the usage of the Internet of things (IoTs) in smart farming in a typical Middle Eastern country within the context of Unified Theory of Acceptance and Use of Technology model," Technology in Society, Elsevier, vol. 63(C).
    8. Uematsu, Hiroki & Mishra, Ashok K., 2011. "Use of Direct Marketing Strategies by Farmers and Their Impact on Farm Business Income," Agricultural and Resource Economics Review, Cambridge University Press, vol. 40(1), pages 1-19, April.
    9. Busse, M. & Schwerdtner, W. & Siebert, R. & Doernberg, A. & Kuntosch, A. & König, B. & Bokelmann, W., 2015. "Analysis of animal monitoring technologies in Germany from an innovation system perspective," Agricultural Systems, Elsevier, vol. 138(C), pages 55-65.
    10. Feder, Gershon, 1980. "Farm Size, Risk Aversion and the Adoption of New Technology under Uncertainty," Oxford Economic Papers, Oxford University Press, vol. 32(2), pages 263-283, July.
    11. Cavallo, Eugenio & Ferrari, Ester & Bollani, Luigi & Coccia, Mario, 2014. "Attitudes and behaviour of adopters of technological innovations in agricultural tractors: A case study in Italian agricultural system," Agricultural Systems, Elsevier, vol. 130(C), pages 44-54.
    12. Mohamed Gamal Aboelmaged, 2009. "An empirical analysis of ERP implementation in a developing country: toward a generic framework," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 3(4), pages 309-331.
    13. Tiffin, Richard & Balcombe, Kelvin, 2011. "The determinants of technology adoption by UK farmers using Bayesian model averaging: the cases of organic production and computer usage," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(4), pages 1-20.
    14. Eastwood, C.R. & Chapman, D.F. & Paine, M.S., 2012. "Networks of practice for co-construction of agricultural decision support systems: Case studies of precision dairy farms in Australia," Agricultural Systems, Elsevier, vol. 108(C), pages 10-18.
    15. Amir Heiman & Joel Ferguson & David Zilberman, 2020. "Marketing and Technology Adoption and Diffusion," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 21-30, March.
    16. Just, Richard E & Zilberman, David, 1983. "Stochastic Structure, Farm Size and Technology Adoption in Developing Agriculture," Oxford Economic Papers, Oxford University Press, vol. 35(2), pages 307-328, July.
    17. Rose, David C. & Sutherland, William J. & Parker, Caroline & Lobley, Matt & Winter, Michael & Morris, Carol & Twining, Susan & Ffoulkes, Charles & Amano, Tatsuya & Dicks, Lynn V., 2016. "Decision support tools for agriculture: Towards effective design and delivery," Agricultural Systems, Elsevier, vol. 149(C), pages 165-174.
    18. Lee Cronbach, 1951. "Coefficient alpha and the internal structure of tests," Psychometrika, Springer;The Psychometric Society, vol. 16(3), pages 297-334, September.
    19. Sabuhoro, Jean Bosco & Wunsch, Patti, 2003. "Computer Technology Adoption By Canadian Farm Businesses: An Analysis Based on the 2001 Census of Agriculture," Agriculture and Rural Working Paper Series 28039, Statistics Canada.
    20. Richard Perrin & Don Winkelmann, 1976. "Impediments to Technical Progress on Small versus Large Farms," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 58(5), pages 888-894.
    21. Sheppard, Blair H & Hartwick, Jon & Warshaw, Paul R, 1988. "The Theory of Reasoned Action: A Meta-analysis of Past Research with Recommendations for Modifications and Future Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(3), pages 325-343, December.
    22. McBride, William D. & Daberkow, Stan G., 2003. "Information And The Adoption Of Precision Farming Technologies," Journal of Agribusiness, Agricultural Economics Association of Georgia, vol. 21(1), pages 1-18.
    23. 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.
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