Reproductive Health Management and Transformative Business Practice; A Literature Review and Future Avenues for Research
Bibliographic record
Abstract
A major reproductive health issue affecting millions globally is infertility (Zhang et al., 2022). Currently, in Canada, around 16% of women experience infertility (CFAS, 2022). The infertility treatment market generated $1,770.6 million in 2023 (Ghosh, 2024) and is projected to grow to 62.8 billion in 10 years (The Lancet, 2024). Despite the prevalence and high monetary, physiological, and psychological costs of infertility, studies in business, including consumer psychology, examining infertility/fertility preservation are scarce and is limited to qualitative studies. We aim to systematically review research on the fertility industry, including infertility treatments and fertility preservation, to assess the current state of research, identify the gaps in the literature, and explore potential future directions within the areas of consumer psychology and decision-making. Using a systematic domain-based review approach (Snyder, 2019; Paul & Criado, 2020), we analyzed articles on the Scopus and Business Source Complete databases. We reviewed 449 articles based on their title, abstract, and keywords. Our results, including final 39 articles, unveil various themes of research on the influence of physiological, psychological, socioeconomic, financial, career, legal, and technological factors, as well as access to information, on fertility preservation and treatment decisions. Furthermore, we evaluate the psychological, relational, and career outcomes associated with these factors and discuss their implications for business and for health systems. We suggest future research directions and areas for improvement to guide subsequent studies in this field. This literature review is the first to explore fertility healthcare services through the lens of consumer psychology and business research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".