This study aims to investigate the complex pathways to mHealth adoption among older adults through an innovative methodological integration of necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA), addressing critical gaps in understanding technology acceptance in ageing populations.
Data were collected from 290 older adults through a structured questionnaire measuring six key factors: perceived usefulness, perceived ease of use, subjective norms, privacy security, social interaction capability and personalisation. The study employed a sequential mixed-methods design, integrating NCA to identify necessary conditions and fsQCA to uncover sufficient configurations leading to high adoption intention.
The analysis revealed four distinct configurations leading to high adoption intention, demonstrating equifinality in technology acceptance. Perceived usefulness emerged as the most critical necessary condition (effect size = 0.312), while different combinations of factors provided alternative pathways to successful adoption. The results demonstrate significant variations between urban and suburban participants, particularly in configurations emphasising personalisation and social interaction features.
This study advances theoretical understanding of mHealth adoption by capturing complex, configurational pathways and challenging traditional linear models. The innovative combination of NCA and fsQCA provides a sophisticated framework for understanding technology acceptance among older adults, offering valuable insights for developing inclusive digital health solutions for ageing populations.
