Emotions and ERP information sourcing: the moderating role of expertise
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
Purpose – The purpose of this paper is to report on a laboratory experiment in which the paper investigated how expert and novice users differ in their emotional responses during use of an enterprise resource planning (ERP) system in a decision-making context, and how such a difference affects information sourcing behavior. Design/methodology/approach – In a simulated SAP business environment, participants’ emotional responses were physiologically measured based on electrodermal activity (EDA) while they made business decisions. Findings – Results show that both expert and novice users exhibit considerable EDA activity during their interaction with the ERP system, indicating that ERP use is an emotional process for both groups. However, the findings also indicate that experts’ emotional responses led to their sourcing information from the ERP, while novices’ emotional responses led to their sourcing information from other people. Research limitations/implications – From an academic standpoint, this paper responds to the recent call for more research on the role of emotions for information systems behavior. Practical implications – The paper discusses the implications of this finding for the development of ERP system trainings. Originality/value – Because emotions often do not reach users’ awareness level, the paper used EDA, a neurophysiological measure, to capture users’ emotional responses during ERP decision making, instead of using self-report measures that depend on conscious perception. Based on this method, the paper found that emotions can lead to different behavioral reactions, depending on whether the user is an expert or novice.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it