The Strategic Use of Information Technology by Nonprofit Organizations: Increasing Capacity and Untapped Potential
Bibliographic record
Abstract
How are nonprofits using information technology to enhance mission‐related outcomes and boost organizational performance? The authors examine a large‐scale survey of nonprofits’ technology planning, acquisition, and implementation to assess the strategic use of IT in these organizations. They evaluate nonprofits’ strategic technology‐use potential by examining IT‐related competencies and practices that are critical for the successful strategic employment of technology resources. Several promising developments are found, alongside significant deficits in the strategic utilization of IT, especially in the areas of financial sustainability, strategic communications and relationship building, and collaborations and partnerships. To boost IT’s mission‐related impact, nonprofits must enhance their organizational capacities in long‐term IT planning, budgeting, staffing, and training; performance measurement; Internet and Web site capabilities; and the vision, support, and involvement of senior 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".