<p>Understanding how new products diffuse through the market is critical for effective planning and strategic decision-making. While traditional innovation diffusion models, such as the Bass model, have been widely used, they often assume a constant adoption rate and overlook real-world complexities such as delayed adoption, market heterogeneity, and repeat purchases. To address these limitations, this study introduces a dynamic product adoption framework that incorporates a time-varying Adoption Increment Factor (AIF). It encapsulates the speed at which the market responds to the introduction of a new innovation or product, reflecting the market’s receptivity to change and the success of adoption initiatives. Three model variants are developed based on different functional forms of AIF. These models are validated using real-world sales data from Acer personal computers and Samsung smartphones. Parameter estimation is performed using the Least Squares Estimation method, and the models are evaluated using MSE, MAE, and AIC as goodness-of-fit criteria. The results show that the sigmoid-based AIF model consistently provides the best fit, accurately reflecting both moderate and rapid adoption scenarios. This approach offers a more realistic representation of adoption behavior and holds significant value for both researchers and practitioners seeking data-driven insights into product diffusion dynamics.</p>