Representation in Systems Development and Implementation: A Healthcare Enterprise System Implementation
Bibliographic record
Abstract
Today’s information systems are often large and complex, affecting many people within and beyond the organization. Participation in this context is increasingly challenging because of the complexity of involving all who might be affected by a new information system. As a result, systems of representation, in which individuals are chosen to represent others, are often put in place to manage the participation process. Research has considered particular challenges of “representative participation” (Mumford, 1983); however, there is little empirical research comprehensively examining these systems of representation. Who participates in these systems, how are they structured and how is this representative work undertaken? Most importantly, what are the impacts of these decisions on the representation systems that are built and on the participation that flows from them? The purpose of our research is to explore the structuring of systems of representation in IS development and implementation. Building on the work of Land and Hirschheim (1983) and Mumford (1983), and drawing on Habermas’ deliberative democracy (1998), this research explores the system of representation employed in a large Electronic Health Record implementation as empirical evidence. Healthcare is a critical context for studying information systems implementation because of its uniqueness and complexity (Chiasson and Davidson, 2004; LeRouge et al., 2007), and therefore serves as an important environment for this research. Our contributions, drawn from this Electronic Health Record project, include an understanding of three fundamental tasks for developing systems of representation: defining the constituency, selecting representatives, and determining how the representation relationship will be carried out. We demonstrate that systems of representation can be classified by different meanings of “represent.” These types – “represent as spokesperson,” “represent as example” and “represent as symbol” – differ in the purpose of representation, in who is involved and in how representation is undertaken. Most importantly, these types of systems differ in their participatory potential. The findings highlight the opportunities and challenges inherent in the construction and implementation of systems of representation. The paper concludes by exploring the implications of these findings for practitioners and researchers, suggesting that representation should be more than simply appointing representatives, and offering mechanisms for participation and influence. We call on researchers to view these systems of representation in more fine-grained ways to better understand what their complexities mean for contemporary system development and implementation.
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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 | 0.061 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".