Exploring the social innovation process in a large market based social enterprise
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the organising of social innovation in a large market-based social enterprises from the perspective of dynamic capabilities and social transformation. Design/methodology/approach This paper analyses the process by which Desjardins Group launched the Desjardins Environment Fund as the first investment fund in North America to integrate environmental screening. It uses longitudinal single case analysis and a theoretical framework based on Teece’s three dynamic capabilities. Findings Results show that dynamic capabilities can be conceived as stages in the process of social innovation. Sensing refers to the capability to identify a societal demand for social transformation. Seizing capability is about shaping societal demand into a commercial offer. Reconfiguring concerns organisational innovation to integrate actual and new knowledge through innovative routines. Microprocesses of both path dependency and path building are in action at each of the three stages. Practical implications This paper shows that managing dynamic capabilities is central to social innovation in the context of a large social business and provides genuine managerial input via an analysis of the microprocesses at work in the social innovation process. Originality/value This paper contributes to the operationalization of Teece’s dynamic capabilities model. In mobilising a framework in the field of management of innovation, it contributes to the understanding of the process of social innovation and develops the organisational mechanism for multiscalarity of social innovation as a condition for social transformation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".