Business-Led Social Innovation in the Work Integration Field: The Role of Large Firms and Corporate Foundations
Bibliographic record
Abstract
The overall aim of this article is to understand the type of roles that corporate actors (large corporations and corporate foundations) play in social innovation processes taking place in the field of work integration. Its first specific goal is to depict the dynamics of the field in Spain. In order to achieve it, we describe the field and characterize the roles of relevant actors using strategic action field theory. The second goal consists of understanding how large firms and corporate foundations can contribute innovative solutions to the field. “Juntos por el Empleo”, a collective impact initiative to promote the work integration for the most vulnerable groups of population in Spain, is explored as an illustrative example. This cross-sector partnership, led by Accenture Foundation, encompasses the efforts of over 1000 organizations, including corporate actors. Data collection methods combine secondary sources, direct observations and in-depth interviews. Results of this qualitative research show a broad variety of innovative ways through which firms and corporate foundations can contribute to the work integration of the disadvantaged, such as participating in the design of tools or programs, disseminating sought after profiles, providing specialized training for particular job positions, designing personalized work paths, acting as large employers for low-qualified people, and finally mobilizing collective efforts and creating new resources through cross-sector partnerships. However, not all these alternatives are equally developed at this point. This paper contributes to fill a research gap about the roles played by corporate actors in social innovation processes and outcomes.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| 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".