IDEAS home Printed from https://ideas.repec.org/a/spr/circec/v4y2024i3d10.1007_s43615-024-00357-7.html
   My bibliography  Save this article

Innovation and Networks in the Bioeconomy: A Case Study from the German Coffee Value Chain

Author

Listed:
  • Terese E. Venus

    (University of Passau)

  • Caroline Beale

    (Technical University of Munich)

  • Roberto Villalba

    (Technical University of Munich)

Abstract

The transition to a circular bioeconomy requires innovation across many sectors, but social dynamics within a sector’s network may affect innovation potential. We investigate how network dynamics relate to the perceptions and adoption of bioeconomy innovation using a case study from the food processing sector. Our case study of the German coffee value chain represents a technologically advanced sector with a strong sustainability focus and potential for residue valorization, which is an important dimension of a sustainable circular bioeconomy. We identify three distinct views (pioneers, traditional and limited users) related to residue valorization, map linkages between actors using social network analysis, and highlight barriers to innovation. We collected data through an online survey and semi-structured interviews with key actors in the coffee roasting sector. Within the social network analysis, we find that public waste managers are closely linked to the most influential actors, state actors such as the customs and tax offices can quickly interact with others in the network and promote the spread of information (highest closeness centrality) and specific roasters play an important role as intermediaries for efficient communication (highest betweenness centrality). Finally, we identify four main barriers including the structure of the coffee network, inconsistencies in federal waste regulations, economies of scale, and visions of sustainability. To support a sustainable bioeconomy, we recommend that policy makers revise the primary regulatory frameworks for waste (e.g., German Recycling Act) to clarify how to classify food residues, their disposal structures and broaden their use streams.

Suggested Citation

  • Terese E. Venus & Caroline Beale & Roberto Villalba, 2024. "Innovation and Networks in the Bioeconomy: A Case Study from the German Coffee Value Chain," Circular Economy and Sustainability, Springer, vol. 4(3), pages 1751-1772, September.
  • Handle: RePEc:spr:circec:v:4:y:2024:i:3:d:10.1007_s43615-024-00357-7
    DOI: 10.1007/s43615-024-00357-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43615-024-00357-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s43615-024-00357-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Timothy Conley & Udry Christopher, 2001. "Social Learning Through Networks: The Adoption of New Agricultural Technologies in Ghana," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 668-673.
    2. Basu, Arnab K. & Hicks, Robert L., 2008. "Label Performance and the Willingness to Pay for Fair Trade Coffee: A Cross-National Perspective," Discussion Papers 44336, University of Bonn, Center for Development Research (ZEF).
    3. Andrea Landherr & Bettina Friedl & Julia Heidemann, 2010. "A Critical Review of Centrality Measures in Social Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(6), pages 371-385, December.
    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. Giuseppe Maggio & Marina Mastrorillo & Nicholas J. Sitko, 2022. "Adapting to High Temperatures: Effect of Farm Practices and Their Adoption Duration on Total Value of Crop Production in Uganda," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(1), pages 385-403, January.
    2. Liverpool-Tasie, Lenis Saweda O. & Winter-Nelson, Alex, 2009. "Poverty status and the impact of social networks on smallholder technology adoption in rural Ethiopia," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49357, Agricultural and Applied Economics Association.
    3. Takahashi, Ryo, 2021. "How to stimulate environmentally friendly consumption: Evidence from a nationwide social experiment in Japan to promote eco-friendly coffee," Ecological Economics, Elsevier, vol. 186(C).
    4. Leakey, Roger & Kranjac-Berisavljevic, Gordana & Caron, Patrick & Craufurd, Peter & Martin, Adrienne M. & McDonald, Andy & Abedini, Walter & Afiff, Suraya & Bakurin, Ndey & Bass, Steve & Hilbeck, Ange, 2009. "Impacts of AKST on development and sustainability goals," Book Chapters,, International Water Management Institute.
    5. Hannes Koppel & Günther Schulze, 2013. "The Importance of the Indirect Transfer Mechanism for Consumer Willingness to Pay for Fair Trade Products—Evidence from a Natural Field Experiment," Journal of Consumer Policy, Springer, vol. 36(4), pages 369-387, December.
    6. Fang, Di & Richards, Timothy, 2016. "New Maize Variety Adoption in Mozambique: A Spatial Approach," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235388, Agricultural and Applied Economics Association.
    7. Zainab Asif & Radhika Lahiri, 2021. "Dimensions of human capital and technological diffusion," Empirical Economics, Springer, vol. 60(2), pages 941-967, February.
    8. Takahashi, Ryo & Todo, Yasuyuki & Funaki, Yukihiko, 2018. "How Can We Motivate Consumers to Purchase Certified Forest Coffee? Evidence From a Laboratory Randomized Experiment Using Eye-trackers," Ecological Economics, Elsevier, vol. 150(C), pages 107-121.
    9. Chih‐Sheng Hsieh & Lung‐Fei Lee & Vincent Boucher, 2020. "Specification and estimation of network formation and network interaction models with the exponential probability distribution," Quantitative Economics, Econometric Society, vol. 11(4), pages 1349-1390, November.
    10. Paul Pecorino, 2016. "A Portion of Profits to Charity: Corporate Social Responsibility and Firm Profitability," Southern Economic Journal, John Wiley & Sons, vol. 83(2), pages 380-398, October.
    11. Erjon Nexhipi, 2022. "The difference in consumer attitudes of locally grown apples with imported apples. the case of Korca Region, Albania:," Technium Social Sciences Journal, Technium Science, vol. 37(1), pages 250-264, November.
    12. Jared Hutchins & Brent Hueth, 2023. "100 years of data sovereignty: Cooperative data governance and innovation in US dairy," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(3), pages 1551-1576, September.
    13. Lars Petersen & Jacob Hörisch & Kathleen Jacobs, 2021. "Worse is worse and better doesn't matter?: The effects of favorable and unfavorable environmental information on consumers’ willingness to pay," Journal of Industrial Ecology, Yale University, vol. 25(5), pages 1338-1356, October.
    14. Enid M. Katungi & Catherine Larochelle & Josephat R. Mugabo & Robin Buruchara, 2018. "The effect of climbing bean adoption on the welfare of smallholder common bean growers in Rwanda," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(1), pages 61-79, February.
    15. Etilé, Fabrice & Teyssier, Sabrina, 2013. "Corporate social responsibility and the economics of consumer social responsibility," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement (RAEStud), Institut National de la Recherche Agronomique (INRA), vol. 94(2).
    16. Ali, S. Nageeb, 2018. "Herding with costly information," Journal of Economic Theory, Elsevier, vol. 175(C), pages 713-729.
    17. Zhang, Yang & Du, Xiaomin, 2017. "Network effects on strategic interactions: A laboratory approach," Journal of Economic Behavior & Organization, Elsevier, vol. 143(C), pages 133-146.
    18. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(4), pages 1201-1236.
    19. Paul Pecorino, 2013. "Monopolistic Competition and Public Good Provision with By‐product Firms," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 22(4), pages 875-893, December.

    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:spr:circec:v:4:y:2024:i:3:d:10.1007_s43615-024-00357-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.