Mapping the experiences of work-life balance: implications for the future of work
Bibliographic record
Abstract
Purpose This study aims to explore how information technology (IT) professionals perceive work-life balance (WLB) in a work-from-home (WFH) setup. Additionally, it explores what emotions one may associate with such changing work environments, which have high implications for organisational success. Design/methodology/approach The two primary research questions guided this research. An online questionnaire-based survey was conducted to collect the data so that respondents’ both subjective and objective perceptions were documented. Purposive cum snowball sampling was used to collect data from 262 IT professionals. However, the data was analysed using both qualitative (content analysis) and quantitative (chi-square) techniques. Findings The findings of this study are interesting in nature and reported the work-life experiences at various socio-demographic levels (age, gender, educational qualification, designation, work experience, income, type of family and the number of children). The comprehensive examination of the data obtained from diverse aspects related to remote work environments has shed light on crucial facets impacting IT professionals. A predominant observation derived from the study reveals a significant disparity in working hours between male and female respondents during remote work. This discrepancy is notable, with male employees tending to work longer hours (i.e. 10 or more hours daily) than their female counterparts. The investigation into respondents’ sleep patterns revealed that the majority slept between 5 h and 7 h daily, underscoring reduced sleep hours for IT professionals during remote work. This comprehensive study thus emphasises the multifaceted nature of gender-associated influences on work patterns, health and well-being during remote work scenarios among IT professionals. As remote work is the new normal, this study has high implications for future work arrangements and organisational success. Practical implications The findings of the study will assist managers in dealing with the work conflict issue of remote workers. Importantly, these managers should try eliminating or reducing workplace conflict, emotional exhaustion and social overload associated with remote work. Originality/value This study is a humble attempt to highlight the employee’s WLB in the context of WFH in an emerging market (i.e. India). Furthermore, emphasises practical issues associated with changing work paradigms and concludes with interesting recommendations for future work arrangements.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".