Bibliographic record
Abstract
Purpose This purpose of this paper is to review the relationships between the physical and social characteristics of public and semi-public spaces and work behaviors of mobile knowledge workers employed by organizations. Design/methodology/approach This is a literature review of research from several disciplines on teleworking from multiple settings, particularly in public and semi-public spaces. Findings This review suggests that both the physical and social characteristics of public and semi-public spaces can constrain the cognitive work and communication of knowledge workers. The physical characteristics include amount of space, layout, ambient conditions and internet and Wi-Fi connectivity. To be effective, mobile workers perform different tasks at different workplaces that support those specific work tasks. Planning and coordinating work tasks for different workplaces is time-consuming and requires anticipation of constraints and effort to overcome obstacles encountered in these places. Research limitations/implications Little empirical research focusing on these new workplaces is available. There is much need for future research that uses larger, representative samples and a diversity of methods. As this paper is based on a review of a small number of studies currently available in peer-reviewed journals written in English, the findings should be considered tentative. Practical implications Understanding how the design of the physical workplace, work processes, organizational support and its interface with the virtual space support successful mobile work is crucial for organizations. Corporate real estate and facility managers of public and semi-public spaces should support mobile workers’ needs for internet and Wi-Fi connectivity and provide separate spaces for cognitive work and private business conversations. Originality/value This paper extends the research about teleworking from home to working in public and semi-public spaces.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".