The MyACE questionnaire: development and validation of a bilingual tool to evaluate user perceptions of the MyUBAT mobile application

Abstract Background Pharmacy Value-added Services (PVAS) by the Ministry of Health Malaysia aim to reduce waiting times, improve medicine access, and ease congestion in public health facilities. The MyUBAT mobile application was launched to streamline PVAS registration and medication refills, but its user experience remains unevaluated. Objective To develop and validate a bilingual questionnaire (MyACE) assessing perceived usefulness (PU), perceived ease of use (PEOU), satisfaction, and functionality of MyUBAT among adult users. Methods This cross-sectional study developed a questionnaire integrating constructs from Technology Acceptance Model, Mobile App Rating Scale and System Usability Scale. Two rounds of content validation involving eight experts were conducted using the Item-level Content Validity Index (I-CVI). The bilingual (English and Malay) version underwent face validation with 10 adults from the general public via the Item-level Face Validity Index (I-FVI). A pilot study recruited end-users (N = 30) via convenience sampling at outpatient pharmacy units to evaluate internal consistency using Cronbach’s alpha and Corrected Item-Total Correlation (CITC). Key findings MyACE (20 items) showed strong content validity (I-CVI: 0.88–1.00; S-CVI/Ave: 0.96) and excellent face validity (I-FVI ≥ 0.83 for 19/20 items; S-FVI/Ave: 0.99; S-FVI/UA: 0.90). Pilot testing confirmed excellent overall reliability (α = 0.91), with strong domain-specific reliability for Functionality (α = 0.90) and Satisfaction (α = 0.80). Although PU (α = 0.55) and PEOU (α = 0.62) showed lower reliability, all items were retained after considering overall reliability and CITC results. Conclusion The MyACE questionnaire demonstrated good validity and excellent reliability, supporting its use as a bilingual instrument to evaluate user perceptions of the MyUBAT mobile application and providing a foundation for future large-scale validation.