Beyond the Brand-Generic Divide: An Exploratory Factor Analysis of Determinants Underlying Low Generic Medicine Uptake in India

India is known as pharmacy of the world due to its manufacturing capacity of generic medicines as well as vaccines. India supplies about a fifth of the world's generic medicine exports by volume. Yet uptake of low-cost unbranded generics is low: branded generics made up about 87% (eighty-seven percent) of the Indian Pharmaceutical Market by value in 2023. Many researches have identified several barriers for usage but rarely focused on underlying latent core barriers. 

Exploratory Factor Analysis (EFA) tool is used on Likert-scale attitude items from surveys of pharmacists (N = 78, 23 items) and the general public (N = 581, 18 items) in Telangana, India. Kaiser- Meyer-Olkin (KMO), Bartlett's test of sphericity, principal-axis factoring with varimax rotation, and Cronbach's α were used to establish factorability, recover latent structure, and verify internal consistency respectively. 

Both datasets independently supported a four-factor solution. Pharmacist factors: (F1) Trust in Generic Medicines and the Quality-Assurance System (α = 0.937, 10 items); (F2) Market and Demand-Side Resistance (α = 0.901, 6 items); (F3) Direct Clinical Confidence in Generic Pharmacology (α = 0.881, 3 items); (F4) Operational and Logistical Barriers (α = 0.839, 4 items). Public data showed a parallel four-factor structure with α values of 0.801, 0.782 for the two primary factors. The four- factor solution explained 74.80 per cent of variance in the pharmacist data and 51.89 per cent in the public data. 

In this paper, Exploratory Factor Analysis (EFA) is utilized to analyze sixteen identified barriers to generic medicine usage, reducing them to four core latent constructs across both supply- and demand- side data. Methodologically, the paper argues for the routine integration of EFA in health-policy survey analysis, particularly when dealing with extensive barrier lists that lack dimensionality reduction. Finally, a Generic Medicine Uptake Index (GMUI) is empirically constructed and computed on a 0–100 scale as GMUI = 0.301·B_Doctors + 0.271·B_Pharmacists + 0.054·B_Public + 0.374·B_JAK, where block weights are derived from model-fit R2 values. These four latent factors ultimately serve as the pillars for policy formulation in a companion paper.