Leading an intergenerational workforce: an integrative conceptual framework
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose a framework for understanding the particular issues associated with leading an intergenerational workforce. It presents promising strategies in the areas of talent management, decision making and internal communication to maximize the strengths and minimize the potential challenges of such a workforce. Design/methodology/approach This conceptual paper blends a review of descriptive research on generational differences and commonalities in work needs and preferences together with practice-based implications for management and organizational leadership. Findings A conceptual framework highlights generational issues as both individual- and organizational-level variables to be considered by leaders, and proposes that intergenerational leadership should strive toward achieving a balance between meeting individual and organizational needs. Specific management activities and approaches highlight opportunities for leaders to address generational needs, while paying attention to both commonalities and differences across generations, and create a positive intergenerational work environment. Originality/value No clear conceptual framework or model currently exists to help understand and organize the similarities and differences in needs and preferences across generations in a workforce. The paper also offers a series of practical recommendations for organizational leadership based on the proposed framework.
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.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".