Personal Achievement Goals, Learning Strategies, and Perceived IT Affordances
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
What people perceive when they interact with technologies are not the features and functionalities of the technology but rather the behaviors it affords them. Affordance perception determines how organizational information technology (IT) is used by employees and the benefits they provide to organizations and their members. In this article, we explain how employees who pursue different personal goals and use various learning strategies come to perceive different IT affordances. We identify three distinct pathways: (1) performance-avoidance goals are positively associated with surface processing, which leads to perceptions of common in-role IT affordances; (2) performance-approach goals are positively associated with surface processing and effort regulation and these learning strategies lead to perceptions of common and specialized in-role IT affordances; and (3) mastery goals are associated with deep processing, effort regulation, and peer learning, which are positively associated with perceptions of specialized in-role and extra-role IT affordances. By identifying the different pathways to perceived affordances, the article identifies potential interventions that can help managers steer employees toward certain affordances and away from other, less desirable affordances.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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