Personal Achievement Goals, Learning Strategies, and Perceived IT Affordances
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.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".