How Can We Develop Contextualized Theories of Effective Use? A Demonstration in the Context of Community-Care Electronic Health Records
Bibliographic record
Abstract
We contribute to the shifting discourse in the literature on information system use, towards context-specific (rather than general) theories and effective use (rather than just use). Organizations are under great pressure to use information systems effectively but they have few theories to turn to for insights. Motivated by this need, we propose an approach for developing context-specific theories of effective use. The approach suggests that effective use can be theorized by: (1) understanding how a network of affordances supports the achievement of organizational goals, (2) understanding how the affordances are actualized, and (3) using inductive theorizing to elaborate these principles in a given context. We demonstrate the approach in the context of a Canadian health authority’s use of a community-care electronic healthcare record (EHR). We discovered that effective use in this context can be viewed at a high level as the accuracy and consistency with which users work with the EHR, and how they engage in reflection-in-action across a network of nine affordances. The key, however, is understanding how those elements interact with the multiple levels of data needed to achieve the organization’s various goals. Overall, we contribute by offering an approach for developing context-specific theories of effective use, demonstrating its usefulness in an important context, and discovering the importance of understanding in a new way the multilevel nature of information systems. The online appendix is available at https://doi.org/10.1287/isre.2017.0702 .
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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 | 0.031 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.044 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".