Validating a measurement instrument for disruptive innovation in retail clinics: A PLS-SEM approach
Year: 2025
Type: article
Study design: Observational Study
Abstract: This study aims to evaluate the validity and reliability of the measurement instrument and the proposed model, titled “Framework of Factors Related to Disruptive Innovation in Retail Clinics,” within the primary healthcare sector in Peru. A quantitative, non-experimental, cross-sectional, and correlational design was employed. Data were collected through a survey instrument comprising Likert-scale items, whose content validity was assessed via expert judgment using Aiken’s V coefficient. Reliability was examined through internal consistency measures such as Cronbach’s alpha. A multivariate analysis was conducted on 101 cases, followed by the application of Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the structural model’s fit, validity, and predictive capacity. The instrument demonstrated validity and reliability, confirming its appropriateness for measuring variables related to disruptive innovation in retail clinics. The model exhibited satisfactory goodness-of-fit indices and predictive capacity, with most hypothesized relationships being statistically significant. The validated measurement instrument and the proposed framework effectively capture the key factors influencing disruptive innovation in retail clinics within the Peruvian healthcare context. The study highlights the importance of evaluating these variables to understand and foster innovation in low-resource healthcare settings. Healthcare policymakers and practitioners can utilize this validated instrument to assess and implement disruptive innovations more effectively. This contributes to improving access, efficiency, and quality of primary healthcare services in emerging economies like Peru.
Institution: National University of San Marcos
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4414335232
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