Affordance of conciliation: increasing the social impact of hybrid organizations
Bibliographic record
Abstract
Purpose Hybrid organizations offer an innovative approach to promote social impact. However, hybrids face the challenge of reconciling the dual mission (social/financial). The purpose is to understand how hybrids and information and communication technologies (ICT) interact, unveiling opportunities ICT offers for hybrids regarding the dual mission. Design/methodology/approach We used affordance theory and adopted a predominantly inductive approach inspired by the so-called “Gioia template.” The research design was based on semi-structured interviews with entrepreneurs, specialists, and people working in institutes, foundations, and accelerators, i.e. social actors operating in the hybrid organizational ecosystem in Brazil. Findings Our findings suggest that the affordances of the relationship between organizational and ICT resources act as facilitators. A theoretical contribution is conceptualizing “affordance of conciliation,” indicating how ICT resources might facilitate achieving social/financial goals, thus minimizing efforts to reconcile mission duality. Furthermore, we list categories and aggregate dimensions and elucidate how results aligned with goals are generated through the process-based model. We show that ICT has a significant role in helping hybrids overcome challenges. Originality/value Our results extend affordance theory with theoretical and practical implications. We highlight fundamental components that contribute to proposing the new concept of “affordance of conciliation.” We contribute to information systems literature by better understanding the social interactions between ICT and hybrids. Finally, we help hybrids understand the support of ICT resources to fulfill their dual mission.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".