Psychometric Pretesting of a SERVQUAL-Based Revisit Intention Scale Among Mainland Chinese Fertility Tourists Receiving Assisted Reproductive Technology Services in Malaysia
Objective: This pilot study evaluated the preliminary reliability, factorability, and distributional properties of a SERVQUAL-based questionnaire designed to measure revisit intention among Mainland Chinese fertility tourists receiving assisted reproductive technology services in Malaysia.
Methods and Materials: A cross-sectional pilot design was used in 2024. Thirty Mainland Chinese patients who had received or recently completed in vitro fertilization or preimplantation genetic testing services in Malaysian private fertility clinics were recruited through convenience sampling. The adapted questionnaire included service quality based on SERVQUAL dimensions, privacy, cultural proximity, cost, and revisit intention, measured on a five-point Likert scale. Internal consistency was assessed using Cronbach’s alpha and corrected item-total correlations. Factorability was examined using Kaiser-Meyer-Olkin statistics and Bartlett’s Test of Sphericity. Multivariate normality was evaluated using Mardia’s skewness and kurtosis statistics.
Findings: Internal consistency was satisfactory across all main constructs: service quality (α = 0.953), privacy (α = 0.934), cultural proximity (α = 0.949), cost (α = 0.942), and revisit intention (α = 0.947). All corrected item-total correlations exceeded 0.40. Sampling adequacy was acceptable for service quality (KMO = 0.848), privacy (KMO = 0.833), cultural proximity (KMO = 0.909), cost (KMO = 0.901), and revisit intention (KMO = 0.732). Bartlett’s tests were significant for all constructs (χ² = 192.303–383.564, p < 0.001). Mardia’s multivariate kurtosis indicated violation of multivariate normality, suggesting that covariance-based structural equation modeling is not appropriate at this pilot stage.
Conclusion: The adapted instrument demonstrated preliminary reliability and factorability. A larger validation study using exploratory and confirmatory factor analyses and variance-based structural modeling is recommended.
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