Does generational thinking create differences in knowledge sharing and<scp>ICT</scp>preferences?
Bibliographic record
Abstract
Abstract Organizational strategies around employee retirement are often cast in generational terms (i.e., as knowledge transferred between older and younger generations). Within this context, research suggests generational differences in knowledge sharing preferences and in supporting information and communication technology (ICT) preferences. At the same time, others argue that the concept of generations is a myth, or a stereotype‐driven perception. Therefore, the objectives of this study were (1) to examine whether there are generational differences in knowledge sharing and ICT preferences and (2) to examine whether perceptions of younger and older generations' preferences match their actual preferences. Data were collected from 138 survey participants (Baby Boomers, Generation Xers, and Millennials) and analyzed using ANOVAs, effect sizes, and confidence intervals. Additionally, 13 interviews were conducted with Baby Boomers and analyzed using content and narrative analyses. Findings showed no reliable differences between the three generations' preferences for knowledge sharing modalities (i.e., in writing and verbally) and methods (i.e., in person and through various ICTs). The most preferred methods were email, in‐person, telephony, and instant messaging. Most interestingly, while all generations had an accurate perception of Millennials' sharing preferences, they all demonstrated a distorted perception of Baby Boomers' preferences. Moreover, the broader the generation gap, the greater the discrepancy in perception. These findings support the postulation that generational differences may be a matter of perception rather than actuality. The most significant implication for research and practice is to retire generational thinking and to propose several alternative organizational strategies in managing knowledge continuity.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".