A Theory of Organization-EHR Affordance Actualization
Bibliographic record
Abstract
While organizations implement information technology (IT) to effect change, current theories of IT-associated organizational change pay insufficient attention to the change goals, the role of IT in organizational change, and the multilevel nature of change processes. We take a fresh look at IT-associated organizational change using grounded theory methods. Our longitudinal study of an electronic health record (EHR) system implementation in a multi-site medical group found user behaviors that did not fit well with existing theories. Instead, we found that they fit better with the concept of affordances from ecological psychology. In developing our affordance-based theory of IT-associated organizational change from our field data, we discovered three gaps in the affordance literature; namely, the lack of theory for (1) the process of actualizing an affordance’s potential, (2) affordances in an organizational context, and (3) bundles of interrelated affordances. This paper extends the theory of affordances to handle these three gaps and, in doing so, develops a mid-range theory for EHR-associated organizational change in a healthcare organization. While the resulting theory is specific to EHR implementations, it offers a template for other mid-range affordance-actualization theories and a more general affordance-actualization lens. Our affordance-actualization lens considers the materiality of the IT artifact, the non-deterministic process by which IT leads to organizational effects, the multilevel nature of IT-associated change processes, and the intentionality of managers and users as agents of change, and thus addresses important criteria for theories of IT effects in organizations. The paper also provides practical guidance for implementing EHR systems and other organizational systems.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".