Working with motivation to increase performance.
Bibliographic record
Abstract
This research study examined how organizations might apply the wealth of research about employee motivation into business practice. Incorporating the knowledge from behavioural economics and psychological studies positively influences the following: employee motivation, productivity, well-being, engagement (Pink, 2011). In turn, this helps reduce the stunning loss of productivity, due to employee demotivation, quoted between USD 480-600 billion a year (State of the American Workplace 2016, Gallup). \nTo conduct my research study, I relied on qualitative research methods including literature review of scholarly sources, an overview of grey literature, with some insights from semi-structured interviews. My research looked to both North American and European sources. Scandinavian countries are known for their leadership in management practice (Eriksen et al, 2006) and attracting, developing, and retaining top talent (IMD, 2017). According to the Varieties of Capitalism framework, which outlines the differences in economic and political institutions, USA and Canada and the Nordic countries belong to two contrasting economies, and have profoundly distinct approach to law, development of labour market, inter-firm and employee relations (Hall, Soskice, 2001). This awareness is important to situate both approaches to company-employee relationship in economic and political context. I illustrated the ways the findings from behavioural economics and psychological studies have been harnessed in innovative ways. This manifests through creative management initiatives such as Results-Only Workplace Environment, reduced work hours, and Holacracy. This growing understanding of changing employee needs leads to the rise in team members’ motivation and furthers general engagement, decreases turnover and increases profit for business.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".