The value of technology affordances to improve the management of nonprofit organizations
Bibliographic record
Abstract
Purpose This paper aims to investigate the benefits generated by the use of new technologies by nonprofit organizations, with focus on how these artefacts can improve their ability to achieve their social mission. Design/methodology/approach To understand the potential use of technology by a nonprofit organization, the concept of affordance was applied. The authors propose a processual model of affordances’ interdependences that enrich the extant literature. Six nonprofit organizations in two Brazilian regions were deeply investigated using a multiple case study method. Findings The authors identified new sub-categories of technology affordances, which are not just related to nonprofit but that could be also applied to other types, including for-profit. Sub-categories of affordances seem to play different roles in the actualization process. The authors are not proposing determinist connections among sub-categories, but they argue that they sustain some sub-categories precede or create the condition for others to emerge. Originality/value Nonprofit organizations lack theoretical and empirical investigations on management in general and on technology management in particular. In its turn, the technology field does not pay much attention, both in terms of research and practice, to the specificities of the third sector where the nonprofit organizations operate. This process model of potential uses of new technologies that might favor nonprofit organizations contributes to the cross-fertilization between two distinct fields: third sector and technology management.
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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 | 0.007 | 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 | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| 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".