IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2023i1p268-d1308769.html
   My bibliography  Save this article

Kano Model Analysis of Digital On-Farm Technologies for Climate Adaptation and Mitigation in Livestock Farming

Author

Listed:
  • Pia Münster

    (Infernum, Interdisciplinary Distance Learning Program in Environmental Sciences, FernUniversität in Hagen, Universitätsstraße 33, 58097 Hagen, Germany)

  • Barbara Grabkowsky

    (Center of Sustainability Transformation in Areas of Intensive Agriculture, University of Vechta, Driverstrasse 22, 49377 Vechta, Germany)

Abstract

In the EU, agriculture contributes significantly to greenhouse gas (GHG) emissions. In Germany, over half of the GHG emissions from agriculture can be directly attributed to livestock farming. To combat the progressing climate change, GHG emissions must be significantly reduced. Digital solutions, particularly decision support systems (DSS), are promising tools to assist livestock farmers in achieving the globally agreed GHG reduction goals. However, there is a lack of studies addressing DSS requirements for reducing GHG emissions in livestock on the farm level. Users’ feedback on technologies can support identifying areas for enhancement and refinement. This study identifies, categorizes, and ranks fourteen DSS features aimed at supporting GHG reduction based on their impact on customer satisfaction. A quantitative online questionnaire using the Kano model surveyed livestock farmers’ satisfaction or dissatisfaction levels with these features. Results gathered from 98 responses across German federal states highlighted the significance of data authority and integrability, with their absence causing dissatisfaction. Multi-target optimization emerged as an attractive feature, positively impacting satisfaction. Connectivity and market perspective, however, appeared indifferent. The findings guide DSS developers in prioritizing attributes crucial for customer satisfaction. It also helps to focus on must-have attributes to preserve customer satisfaction and ensure successful GHG reduction implementation.

Suggested Citation

  • Pia Münster & Barbara Grabkowsky, 2023. "Kano Model Analysis of Digital On-Farm Technologies for Climate Adaptation and Mitigation in Livestock Farming," Sustainability, MDPI, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:268-:d:1308769
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/1/268/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/1/268/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Henner Gimpel & Dominikus Kleindienst & Niclas Nüske & Daniel Rau & Fabian Schmied, 2018. "The upside of data privacy – delighting customers by implementing data privacy measures," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 437-452, November.
    2. Nicola Gindele & Susanne Kaps & Reiner Doluschitz, 2015. "Strukturelle Veränderungen in der Landwirtschaft – Reaktion der landwirtschaftlichen Betriebsleiter sowie ableitbare Konsequenzen für den Landwirt als Unternehmer," Journal of Socio-Economics in Agriculture (Until 2015: Yearbook of Socioeconomics in Agriculture), Swiss Society for Agricultural Economics and Rural Sociology, vol. 8(1), pages 11-20.
    3. 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.
    4. Stacie Petter & William DeLone & Ephraim McLean, 2008. "Measuring information systems success: models, dimensions, measures, and interrelationships," European Journal of Information Systems, Taylor & Francis Journals, vol. 17(3), pages 236-263, June.
    Full references (including those not matched with items on IDEAS)

    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. Matthew Houser, 2022. "Does adopting a nitrogen best management practice reduce nitrogen fertilizer rates?," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 39(1), pages 79-94, March.
    2. Ali, Jabir, 2011. "Adoption of Mass Media Information for Decision-Making Among Vegetable Growers in Uttar Pradesh," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 66(2), pages 1-14.
    3. Jenkins, Amanda & Velandia, Margarita & Lambert, Dayton M. & Roberts, Roland K. & Larson, James A. & English, Burton C. & Martin, Steven W., 2011. "Factors Influencing the Selection of Precision Farming Information Sources by Cotton Producers," Agricultural and Resource Economics Review, Cambridge University Press, vol. 40(2), pages 307-320, September.
    4. Vecchio, Yari & De Rosa, Marcello & Adinolfi, Felice & Bartoli, Luca & Masi, Margherita, 2020. "Adoption of precision farming tools: A context-related analysis," Land Use Policy, Elsevier, vol. 94(C).
    5. Kristina Beethem & Sandra T. Marquart-Pyatt & Jennifer Lai & Tian Guo, 2023. "Navigating the information landscape: public and private information source access by midwest farmers," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 40(3), pages 1117-1135, September.
    6. Asare, Eric & Segarra, Eduardo, 2017. "Adoption and Extent of Adoption of Georeferenced Grid Soil Sampling Technology by Cotton Producers in the Southern US," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252773, Southern Agricultural Economics Association.
    7. Sushanta Roy Chowdhary, 2022. "Evaluating the Education Management Information System: A study on Non- Government College-level in Bangladesh," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 6(3), pages 445-452, March.
    8. Chowdhury, Iftekhar Uddin Ahmed & Wang, Tong & Jin, Hailong & Smart, Alexander J., 2020. "Exploring the Determinants of Perceived Benefits of Rotational Grazing in the U. S. Great Plains," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304487, Agricultural and Applied Economics Association.
    9. Surajit Bag & Muhammad Sabbir Rahman & Susmi Routray & Santosh Kumar Shrivastav & Soni Agrawal, 2024. "Exploring the potential of blockchain‐enabled smart contracts for achieving net‐zero emissions: An empirical study," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 3965-3985, July.
    10. Ranjan, Ram, 2007. "Technology Adoption Against Invasive Species," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 39(Special), pages 1-14, October.
    11. J Blasch & B van der Kroon & P van Beukering & R Munster & S Fabiani & P Nino & S Vanino, 2022. "Farmer preferences for adopting precision farming technologies: a case study from Italy," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(1), pages 33-81.
    12. Ali, Mir B. & McBride, William D. & Daberkow, Stan G., 2003. "Implications Of Remote Sensing Imagery And Crop Rotation For Nitrogen Management In Sugar Beet Production," 2003 Annual meeting, July 27-30, Montreal, Canada 22052, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    13. Margherita Masi & Marcello Rosa & Yari Vecchio & Luca Bartoli & Felice Adinolfi, 2022. "The long way to innovation adoption: insights from precision agriculture," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-17, December.
    14. Yari Vecchio & Giulio Paolo Agnusdei & Pier Paolo Miglietta & Fabian Capitanio, 2020. "Adoption of Precision Farming Tools: The Case of Italian Farmers," IJERPH, MDPI, vol. 17(3), pages 1-16, January.
    15. Brittani Edge & Margarita Velandia & Christopher Boyer & James Larson & Dayton Lambert & Roland Roberts & Bradley Wilson & Michael Buschermohle & Burton English & Roderick Rejesus & Larry Falconer, 2018. "Automatic Section Control Technologies and GPS Auto-guidance Systems Adoption in Cotton Production," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 10(7), pages 282-282, June.
    16. Murage, A. W. & Obare, Gideon A. & Chianu, J. & Amudavi, David Mulama & Midega, C. A. O. & Pickett, J. A. & Khan, Zeyaur R., 2012. "The Effectiveness of Dissemination Pathways on Adoption of “Push-Pull” Technology in Western Kenya," Quarterly Journal of International Agriculture, Humboldt-Universitaat zu Berlin, vol. 51(1), pages 1-21, February.
    17. Stuart, D. & Denny, R.C.H. & Houser, M. & Reimer, A.P. & Marquart-Pyatt, S., 2018. "Farmer selection of sources of information for nitrogen management in the US Midwest: Implications for environmental programs," Land Use Policy, Elsevier, vol. 70(C), pages 289-297.
    18. Slamet Soedarsono & Sri Mulyani & Hiro Tugiman & Didik Suhardi, 2019. "Information Quality and Management Support as Key Factors in the Applications of Continuous Auditing and Continuous Monitoring: An Empirical Study in the Government Sector of Indonesia," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 13(3), September.
    19. Jan Hendrik Betzing & Matthias Tietz & Jan Brocke & Jörg Becker, 2020. "The impact of transparency on mobile privacy decision making," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(3), pages 607-625, September.
    20. Nancyprabha Pushparaj & V. J. Sivakumar & Manoraj Natarajan & A. Bhuvaneskumar, 2023. "Two decades of DeLone and Mclean IS success model: a scientometrics analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2469-2491, June.

    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:jsusta:v:16:y:2023:i:1:p:268-:d:1308769. 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.