Bibliographic record
Abstract
In the April 2012 issue of the Journal of the Association for Information Systems, Michael Davern, Teresa Shaft, and Dov Te’eni published an article titled “Cognition Matters: Enduring Questions in IS Research”. Their paper reviewed much of the history of cognitive research in the IS discipline, especially that related to human-computer interaction and decision support systems. While we believe their article is excellent in many respects, we also believe that it omitted a great deal of the most basic cognitive research performed in the IS domain over the past 10-15 years, especially work in the area of systems analysis and design. Our purpose in this paper is to supplement the work of Davern et al. by discussing much of this recent work. We use two theoretical lenses to organize our review: basic cognition and behavioral decision-making research. Our review provides many illustrations of IS research in these areas, including memory and categorization (basic cognition) and heuristics and biases (behavioral decision making). The result, we believe, is a fuller picture of the breadth of cognition-based work in the IS discipline in general and systems analysis and design in particular. The paper provides further evidence of the importance of cognitive research in IS and suggests additional enduring questions for future investigations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.014 | 0.049 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".