Formal Innovation Structures and Innovativeness of Social Service Organizations in Times of the Pandemic
Bibliographic record
Abstract
Due to a surge in demand, the emergence of new needs among beneficiary groups and essential health safety measures, SSOs needed to substantially broaden their activities or find new ways of providing services to their clients during the COVID-19 pandemic. Formal Innovation Management Structures (FIMS) aim at regulating criteria and methods for resource allocation and aligning the process of generating and implementing novel ideas with the overall organizational strategy. While they offer legitimacy, shared language, and standardized procedures of resource allocation for innovation, they may also create slow, bureaucratic decision making structures which stand in contrast to the need to fast reactions during a crises. Against this background, we investigate the role of FIMS regarding innovativeness of Social Service Organizations (SSOs) in times of crisis. We draw on 16 interviews from a sample of 8 SSOs in Vienna, Austria. Moreover, an analysis of organizational documents provides further insights into innovation practices in these organizations. Our results show that the pandemic widely influenced innovation in SSOs, including new challenges and a boost for digitization. The role of FIMS has been ambivalent during this time, with our results hinting towards an influence on internal perceptions regarding the relevance of innovation, organizational decision-making and legitimization of innovation in times of crisis.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".