The Creativity Model of Age and Innovation with IT: How to Counteract the Effects of Age Stereotyping on User Innovation
Bibliographic record
Abstract
Older users shy away from the post-adoptive use of information technologies much more often than their younger counterparts. This situation is alarming given that the workforce is aging rapidly and that organizational technologies are proliferating at the same time. Yet there is no clear explanation for older users’ lower post-adoptive use, which limits practitioners’ understanding of what can be done to assist them. This lack of understanding is especially problematic vis-à-vis user innovation. Successful firms like Microsoft, 3M, or Nike encourage their employees to innovate with IT in order to realize the full potential of their existing IT infrastructure. Thus the present paper advances the creativity model of age and innovation with IT. This model indicates that age differences in user innovation are accounted for by negative age stereotypes and their impacts on creative IT self-efficacy. The model further proposes that the indirect effect of age through creative IT self-efficacy on user innovation can be lessened by contextual factors, namely job control, IT support, and user enjoyment. The paper offers much-needed implications both for managers and for systems designers.
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How this classification was reachedexpand
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".