Management of the Digitalization of Municipal Services: Influence on Citizen Collective Intelligence and Social Innovation Resilience
Bibliographic record
Abstract
Studies on the digital transformation of organizational services have clearly revealed its effectiveness. However, the link between the management of the digitization of functions, citizen mobilization and social innovation is little investigated. Even research on the effectiveness of the management of the digitalization of municipal services is almost non-existent. To shed light on this gray area, research with municipal councillors and heads of departments of municipalities in Chad was conducted. The hybrid approach has been deployed. She facilitated the production of data through 15 semi-structured interviews and 210 surveys per survey. The approach of deconstructing the management of the digitalization of services into three constructs (management of the appropriation of social media, management of the operationalization of digital platforms and management of organizational agility) was used. The results showed that the management of social media appropriation and organizational agility promote the emergence and structuring of the dynamics of citizen collective intelligence. Then, the management of the operationalization of digital platforms is a real lever for the adaptation and resistance of the social innovation system. While the dynamics of collective intelligence increase the resilience of the social innovation system. However, the management of organizational agility has little significant contribution to citizen mobilization. This concerns the management of collective citizen intelligence, considered as a factor in the pooling of know-how which generates performance in social entrepreneurial innovation. The article can be useful to municipal councillors and department heads who will use it to improve citizen mobilization practices. While the government and its partners will find new directions to build the capacity of municipal services so that they are able to develop social innovation projects.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".