Mutual Understanding in Information Systems Development: Changes Within and Across Projects1
Bibliographic record
Abstract
Although information systems development (ISD) projects are critical to organizations and improving them has been the focus of considerable research, successful projects remain elusive. Focusing on the cognitive aspects of ISD projects, we investigate how and why mutual understanding (MU) among key stakeholder groups (business and information technology managers, users, and developers) changes within and across projects, and how it affects project success. We examine relationships among project planning and control mechanisms; sensegiving and sensemaking activities by, and MU among, these stakeholder groups; and project success. Combining deductive and inductive approaches for theory building, we develop an initial model based on the literature and then modify it based on the results of a longitudinal embedded mixed-methods study of 13 projects at 2 organizations over a 10-year period. The results provide insights into the development of MU within projects, including (1) how MU changes during projects as a result of cognitive activities (sensegiving and sensemaking); (2) how planning and control mechanisms (and the associated artifacts) affect these cognitive activities; (3) how MU, and achieving it early in the project, affects success; and (4) how stakeholder engagement (in terms of depth, scope, and timing) affects the relationships in (1) and (2). The results also indicate that project management mechanisms, stakeholder engagement, and MU may change (either improve or deteriorate) across projects, depending on the disagreements among stakeholders in previous projects, the introduction of new project elements in subsequent projects, and the reflection on previous projects.
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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 | 0.077 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.002 | 0.003 |
| 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".