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Achieving Double Bottom-Line Performance in Hybrid Organisations: A Machine-Learning Approach

Author

Listed:
  • Eline Auwera

    (K.U. Leuven)

  • Bert D’Espallier

    (K.U. Leuven)

  • Roy Mersland

    (University of Agder)

Abstract

Drawing on a global sample of microfinance institutions (MFIs), this paper offers insights into the trade-off versus synergy debate of adopting multiple institutional goals in hybrid organisations. Additionally, it unravels which organisation- and country-specific determinants associate with top joint performance using machine-learning techniques. We find that the synergy versus trade-off debate is not dichotomous. Rather, MFIs can be strong both socially and financially but not while charging low interest rates. In our sample, 17% of MFIs serve a low-income clientele in need with a diverse range of services while remaining financially sustainable and ask a close-to-average interest rate. These organisations are larger and more mature as well as financially prudent in that they minimize both financial and credit risk. Moreover, they are located in countries where their services can create larger benefits regarding their underlying goals.

Suggested Citation

  • Eline Auwera & Bert D’Espallier & Roy Mersland, 2024. "Achieving Double Bottom-Line Performance in Hybrid Organisations: A Machine-Learning Approach," Journal of Business Ethics, Springer, vol. 190(3), pages 625-647, March.
    • Handle: RePEc:kap:jbuset:v:190:y:2024:i:3:d:10.1007_s10551-023-05410-3
    DOI: 10.1007/s10551-023-05410-3
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