Cited by 11Entrepreneurship Studies and InfluencesCommunity Development and Social ImpactInnovation and Knowledge Management

Organizational Closure Competencies and Scaling: A Realist Approach to Theorizing Social Enterprise

Seelos, Christian, Mair, Johanna·2014·Social Entrepreneurship and Research Methods

Abstract

Purpose: Social entrepreneurs create novel approaches to social problems such as poverty. But scaling these approaches to the dimension of the problem can be a difficult task. In the social enterprise sector, the subject of scaling has become a key dimension of organizational performance. This chapter advances the scholarly literature on the scaling of social enterprises, a literature which is currently in an embryonic stage and characterized by conceptual ambiguity and fragmented perspectives. Methodology/Approach: We engage realist philosophy of science to develop mechanism-based causal explanations of the scaling performance of social enterprises. We also develop a coding scheme to guide systematic empirical analysis and highlight the explanatory power of counterfactuals. Counterfactuals have been largely neglected in empirical research as they represent mechanisms that are enabled but remain unobservable – in a state of suppression or neutralization of their effects. Findings: We question the ability of organizations to “socially engineer” desired outcomes and introduce a new construct – organizational closure competence. Anchored in realism, this construct provides a basis for productive approaches to social engineering. We elaborate on the importance of organizational closure competencies for scaling, derive a series of propositions, and develop ideas for future research and for practice. Research, Practical and Social Implications: Applying a realist lens allows us to add empirical rigor to research on social enterprises and scaling. Our approach constitutes a move from rich narratives to causal models and informs the way we design and evaluate efforts to address important societal challenges. Originality/Value of Chapter: This chapter demonstrates how to operationalize realist philosophy of science for causal explanations of complex social phenomena and better utilize its theoretical and practical value.

Integrity signals

Registry

Crossref · 10.1108/s1479-838720140000009014

Cites

76 of 76 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

2 of the 78 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Seelos, C., & Mair, J.
Venue
Social Entrepreneurship and Research Methods
Published
2014-07-21
Pages
147-187
OpenAlex
W2490485165
Cited by
11
References
78
Citations by year
2026: 1 · 2024: 2 · 2022: 3 · 2021: 3 · 2020: 1 · 2016: 1
FWCI
1.33
Type
book chapter
Language
en
Open access
closed
Locations
  • Emerald Group Publishing Limited eBooks (publishedVersion)
  • Research methodology in strategy and management (publishedVersion)
  • OPUS 4 (Zuse Institute Berlin) (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Entrepreneurship Studies and Influences
  • Social Sciences › Economics, Econometrics and Finance › Finance › Community Development and Social Impact
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Innovation and Knowledge Management
Keywords
social enterprises, scaling, social engineering, counterfactuals, causal mechanisms, poverty reduction, organizational performance

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review