Women’s motivation at work in Asian countries: a configuration analysis
Bibliographic record
Abstract
Purpose The purpose of this study is to present empirical research on women’s motivation, specifically examining the perspectives of married and unmarried women in Asian countries. The study presents empirical findings on women’s motivation by building on Pinder’s (2014) motivational model. This model suggests that motivation stems from both internal (psychological) and external (environmental) influences. We refine and expand this model within a culturally specific Asian context. Design/methodology/approach We collected the primary data from 110 female employees working for UAE/Asian firms. The study examined data using qualitative comparative analysis (QCA), a configurational analysis approach that is an effective research tool for analysing complex causal linkages. Findings Our necessity analysis revealed that career aspirations (CA) ranked highest at 0.787, followed by work–life balance (WLB) at 0.747 and family and social influences (FSI) at 0.739. None of these met the cutoff score of 0.8 needed for inclusion as necessary conditions (Fiss, 2011; Ragin, 2008; Schneider and Wagemann, 2012). However, our sufficiency analysis found that women can feel motivated even without official support at work, especially by factors such as fairness, a balanced life and encouragement from their networks. Research limitations/implications The study’s limitations include the small sample size of 110 female managers from various Asian backgrounds in the UAE and other Asian countries, which may not be representative of the broader population across different regions or sectors. Additionally, the use of the qualitative comparative analysis (QCA) method can restrict the types of insights gained. Future studies should contain longitudinal analyses and larger samples to validate and build on these findings. Practical implications The findings are important to human resource (HR) professionals and policymakers in Asian organisations, especially in the healthcare, finance and technology sectors. HR policies should promote fairness, improve work–life balance and create supportive social environments. Such policies can help female employees feel more motivated at the workplace. Balancing aspiration and work–life creates a strong motivational force. Social implications The study provides valuable information for HR professionals to understand what motivates women in the workplace. Listening to the voices of married and unmarried women is crucial for developing strategies that support career growth and well-being, ultimately promoting gender equality. Originality/value Our findings provide a novel theoretical contribution by refining and expanding Pinder’s (2014) work motivation model using a culturally relevant QCA approach. Pinder’s (2014) motivational model is multidimensional, and we demonstrate that motivation for Asian women is driven by different combinations of social, organisational, and personal factors, which is different from traditional linear models. We found that motivation can still exist without formal support, especially when combined with fairness, strong family and social influences and work–life balance. This suggests that support from informal relationships can also drive motivation, which challenges the idea that structural support is always necessary for motivation. Our study adds to the existing literature on gender studies, enhances understanding of the Asian context, and proposes potential directions for future research.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".