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Data Envelopment Analysis and Social Enterprises: Analysing Performance, Strategic Orientation and Mission Drift

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Abstract

This study endorses the use of data envelopment analysis, which uses benefit-of-the-doubt weighting to evaluate the social, economic and overall performance of social enterprises. This methodology is especially useful for creating composite indicators based on multiple outputs expressed in different measurement units, and allows for enterprise-specific weighting of the different objectives. Applying this methodology on a unique longitudinal dataset of Flemish sheltered workshops suggests that social enterprises may face different types of mission drift. Further, our results show that top-performing social enterprises are more economically and socially efficient than low performers. These top performers also have a stronger economic orientation, which sheds new light on the balance between social and economic orientations in social enterprises.

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Notes

  1. To obtain balanced panel data, we limited our study to 2004 and, where possible, missing data were collected by contacting the firms or examining online annual accounts (available at: https://www.nbb.be/nl). It proved impossible to fill in the missing values for one particular firm. As a consequence, eight observations (for the same firm) were lost. Excluding this firm from our analysis did not influence the efficiency scores, so we decided to keep it in the dataset. In summary, we had 542 observations of 55 firms over a period of 10 years, with eight missing values for one organisation.

  2. In order to get good discriminatory power out of DEA models, several scholars have established rules of thumb for the number of inputs and outputs to select and their relation to the number of decision-making units (DMU) (Sarkis 2007). The most strict rule is a total number of DMU of twice the product of the number of input and output variables. Our model with two inputs, two outputs and 55 DMU satisfies this rule. Imbalances in the magnitude of inputs and outputs may also cause issues in DEA models. One of the best ways to avoid such issues is to have inputs and outputs of the same or similar magnitude (Sarkis 2007). Therefore, we mean-normalised all variables, as commonly applied in the DEA literature (see Cherchye et al. 2007a).

  3. This approach is followed by papers in top journals to sort firms into quartiles by their aggregate performance and compare the top, mid and bottom quartiles (e.g. La Porta et al. 1997; Gelb and Strawser 2001). For instance, this approach has also been used by La Porta et al. (1997) to compare 49 countries, and by Harrison and Rouse (2016) to compare DEA efficiency scores.

  4. According to the Kolmogorov-Smirnov test, differences in the distribution of top, mid and bottom quartiles were significant at a p value < 0.01.

  5. We thank an anonymous reviewer for pointing this out to us.

  6. We thank an anonymous reviewer for pushing our thinking on this point.

  7. We thank an anonymous reviewer for pointing this out to us.

  8. We thank an anonymous reviewer for raising this point.

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Acknowledgements

We would like to thank editor Robert Phillips, guest editors Luca Mongelli, Tomislav Rimac, Francesco Rullani, and Ramus Tommaso, and the anonymous reviewers for their constructive feedback. We would also like to thank the audience at the Academy of Management Conference (Atlanta, 2017) and at the 1st IESE-LUISS Business School Conference on Responsibility, Sustainability and Social Entrepreneurship (Rome, 2017) for their thoughtful reflections and comments on earlier versions of this paper. Lastly, we are also grateful to the Flemish Department of Work and Social Economy for providing access to the data and to the sector organisations Samen Sociaal Tewerkstellen and Groep Maatwerk for their support and feedback.

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Authors and Affiliations

  1. IÉSEG School of Management, LEM (UMR-CNRS 9221), Rue de la Digue 3, 59000, Lille, France

    Johan Bruneel

  2. KU Leuven, E. Sabbelaan 53, 8500, Kortrijk, Belgium

    Matthias Staessens, Johan Bruneel & Laurens Cherchye

  3. University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Denmark

    Pieter Jan Kerstens

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  1. Matthias Staessens
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Table 4 Correlation matrix of overall efficiency with different weighting restrictions
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Staessens, M., Kerstens, P.J., Bruneel, J. et al. Data Envelopment Analysis and Social Enterprises: Analysing Performance, Strategic Orientation and Mission Drift. J Bus Ethics 159, 325–341 (2019). https://doi.org/10.1007/s10551-018-4046-4

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