Helping Older Workers Realize Their Full Organizational Potential: A Moderated Mediation Model of Age and IT-Enabled Task Performance
Bibliographic record
Abstract
Evidence shows that older users have lower performance levels for IT-enabled tasks than younger users. This is alarming at a time when the workforce is rapidly aging and organizational technologies are proliferating. Since the explanation for these lower performance levels remains unclear, managers are not sure how to help older users realize their full potential as contributors to organizational success. The research model presented here identifies the declining information-processing speed of older workers as the cause of their reduced capacity to perform IT-enabled tasks. According to the model, IT experience and IT self-efficacy reduce the negative impacts of this decline, whereas IT overload and the effort cost of IT use aggravate them. To test the model, data were collected using three complementary studies. The results supported the model and indicated five ways that organizations can help older users improve their capacity to perform IT-enabled tasks. Additional data collected in interviews with human resources directors confirmed the relevance of these solutions.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".