The Productive Workplace for Knowledge Workers: A focus on workplace design and environment across various age groups.
Bibliographic record
Abstract
The nature of work has changed and office designers are striving to find the ideal workplace design that meets the needs of knowledge workers. According to Thompson and Kay (2008) the issue of productivity is becoming of key interest in all sectors. In recent years, firms have begun to realise that a workplace environment that has been well designed is more likely to attract the highest calibre of worker and reduce staff attrition. (Gensler, 2005). A poorly-designed workplace can increase stress levels and negatively affect performance. As many as one- fifth of workplaces in the UK do not provide sufficient work place environments, and that at least one quarter of staff in the UK logged ‘serious’ complaints about factors such as poor layout, furniture, temperature and noise, among others (Myerson et al, 2011). Overall, British businesses are still considerably behind in creating workplaces that optimise employee satisfaction. (Arup, 2011). Improved workplace design can lead to a productivity increase Gensler (2005) and Bootle and Kalyan (2002) agree that billions of pounds are wasted each year due to the unproductive layout and design of some offices. There is a clear connection between the work environments and office users' productivity within the workplace. Most studies include the components of furniture, noise, lighting, temperature and spatial arrangements when considering that which affects productivity (Hameed and Amjad, 2009). However, there is no clear consensus as to which factors predominate. Employees of different generations respond differently to how their workplace environment is designed (Myerson et al, 2010). Almost 50% of today’s economy is knowledge-based and more workers are expected to be flexible, creative and communicative, (Greene and Myerson, 2011). The creation of work environments that result in satisfied and productive knowledge workers and end users requires information about user preferences concerning their work environments, and as the nature of work is changing, there is a need for updated research within this subject.Method: This paper will be based on research carried out on knowledge workers in 7 substantial companies within London. Results: Some results are already known and these include: employees are most productive when under pressure and in a buzzy environment; colleagues, design of office and quality of IT are the greatest factors that make employees unproductive.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".