A method for resolving organisation‐enterprise system misfits: An action research study in a pluralistic organisation
Bibliographic record
Abstract
Abstract Although off‐the‐shelf enterprise systems (ES) have been widely adopted in organisations, the extant literature repeatedly documents ES failures caused by misfits between organisational processes and the ES. Although some misfits can be identified early in the ES lifecycle, others emerge in the onward and upward phase (i.e., after the implementation) and, hence, must be resolved reactively. Prior research on misfits and resolution strategies has primarily focused on the implementation phase, often assuming that close‐to‐perfect information on the misfit's nature and characteristics is available. However, no study has examined how to effectively complete a shared diagnosis and resolution of misfits when diverging individual user perceptions are taken as the starting point. Such situations may be particularly pronounced in pluralistic organisations, where a variety of interdependent processes and potentially competing perceptions of processes are prevalent. The main objective of this study is to address this gap. To this end, we propose a pragmatic method for the diagnosis and resolution of misfits between organisational processes and enterprise systems, which builds on an actionable conceptualization of misfits. This method builds on theoretical concepts of affordances, affordance actualization, user participation, and change agentry. To demonstrate the feasibility and effectiveness of the proposed method, we conducted an action research study in a university hospital. Our analysis focused on a specific misfit involving the hospital's ES‐supported clinical processes. The findings suggest that the method effectively diagnoses and resolves misfits and optimises the resources required for their resolution through efficient management of user participation. We conclude with a discussion of the theoretical and practical contributions of our work.
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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 | 0.095 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".