TOWARDS A MORE SITUATED IS DESIGN BY PRIORITIZING USE SITUATIONS
Bibliographic record
Abstract
Understanding user preferences for interactions with information systems (IS) is of particular interest in the field of Management Support Systems (MSS), where strong user preferences must be taken into account. However, managers’ IS preferences are underresearched in IS research. Thus, IS are not used very often on the management level. To close this gap, we propose a prioritization of MSS use situations, which generalize distinct classes of “similar” user-group preferences and result in non-functional requirements with respect to MSS. This article provides such a prioritization of MSS use situations from a manager perspective by applying the Analytic Hierarchy Process (AHP). Finally, associated design guidelines for a more situated MSS design are presented: (1) Maintain established MSS features for “alone, stationary” use situations. Then, incorporate “mobile” designs step by step. (2) Build a common MSS core with information at one click, while providing additional analyses on an individual basis. (3) Empower the “alone” MSS use case with a one-pager entry point. (4) To support manager “groupwork,” provide them with direct communication and manipulation tools. (5) Run MSS on convertibles with a smartphone as the “satellite” to attract first attention.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".