Evaluating work-related drivers of Generation Y: A case of a multinational financial organisation
Bibliographic record
Abstract
There has been a growing body of research into generational differences within the work environment. While overall differences between generations and lately between Generation Y, in this study defined as those born between 1982 and 2000, and their predecessors are acknowledged, the extent and consistency of these differences remains unclear. To date, most research has been conducted in Anglo-Saxon countries, namely Canada, the US and the UK focussing low-income industries such as nursing, tourism, and retail. This study aims to address the gap in terms of geographical and industry focus by conducting research within a global financial institution in Germany. Taking a constructivist stance and utilising a multi-method approach, a monthly survey, running from March 2020 to February 2021, as well as semi-structured interviews have been used to capture the responses of the Generation Y participants on critical incidents affecting their work motivation, career satisfaction, and loyalty. The study confirms the current literature in terms of the decrease in work centrality and a stronger focus on the private life as well as the increased urge for continuous feedback, while adding a different perspective on remuneration. Simultaneously, this research discovers the importance of the team and its influence on the three drivers, work motivation, career satisfaction, and loyalty. The survey has been impacted by the coinciding start of the COVID-19 pandemic, which acts as a catalyst for the profound use of mobile working. Strongly changing the overall working context, the increased flexibility supports the importance of the private life for Generation Y leading to the concept of blending business and private tasks into daily routines. The first important contribution of this study is the development of a Generation Y dynamic interaction model at Triangle Germany visualising the dependencies between the themes and work-related drivers. Secondly, the study is contributing a new perspective on Generation Y working in the financial industry in Germany, which differs from previous literature. For policy and practice, the study critically assesses the increased feedback need of Generation Y, which has been found to be a driver of self-affirmation. Utilising the concept of work-life-blending, organisations can improve the co-existence of work and private life to better manage their workforce.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".