Flexible Workplace Practices: Employees’ Experiences in Small IT Firms
Bibliographic record
Abstract
This paper examines how employees experience flexible workplace practices (FWPs), such as flex-time, in the context of small firms. Past research consistently documents that employees’ experiences vary according to whether or not the workplace culture is supportive of FWPs and work-life balance needs. Studies, however, typically use individual level data or focus on large companies. Little research has focused on the experiences of employees of small firms. Possibly, employees of small firms have somewhat unique experiences of FWPs because of the workplace context. Like past research, this paper considers how gender and age relations structure the workplace. Also taken into account are the control strategies that management employs over the workforce. Data are taken from a Canadian study on small information technology (IT) firms that employed between four and 21 individuals. A multiple case study of 17 firms is conducted using web-surveys, semi-structured interviews, case study reports, field notes, and HR policy documents. Three different workplace contexts emerged among study firms based on their flexibility and workplace culture with respect to time. Some of these workplaces reproduced hegemonic gender, age, and class expectations, whereas others somewhat challenged them. The three firm-types did not vary according to firm-specific characteristics, such as business specialization, but patterns with regard to age and gender characteristics of the owners and employees were evident. Employees’ experiences varied according to where they worked. The findings suggest that similar and different processes occur in small firms compared to the large companies often studied in the literature. Like large firms, small firms are not neutral or based on a consensus. Small firm employees, however, may be considerably more vulnerable.
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 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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".