Learning in Enterprise System Support: Specialization, Task Type and Network Characteristics.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we introduce two contingency factors --task type and network characteristics—that examine how individuals learn from experience. We hypothesize that task specialization and variation have positive impacts on IS professionals’ learning from experience. We further hypothesize that this performance effect of learning is contingent upon task type and characteristics of domain-specific knowledge networks. In particular, specialized experience will be more beneficial to learning when a task is a locating task-type or when network centrality is high. In contrast, varied experience will be more beneficial when a task is a diagnosing task-type or when network betweenness is high. The research model will be validated in the context of postimplementation enterprise system support. The study incorporates a social network perspective to study learning by experience, and contributes to the knowledge management field. Findings will provide practical insights on managing IT human capital and improving IS support services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it