2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 12 , Issue 2 , PP: 62-76, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ramazan Yasar 1 * , Sergey Drominko 2
Doi: https://doi.org/10.54216/AJBOR.120205
By effectively including diffusion into the framework, this study further illustrates whether Optimal Control Theory may be used to identify and address the control of prices issue of technology items. There is a three-stage paradigm that it uses to describe the procedure of adoption process: consciousness, inspiration, and adopting itself. The process is described by the diffusion logistic functions; furthermore, the model takes into account price fluctuations’ sensitivity. The selling price is a choice variable constrained such that the total profit over the relevant planning horizon is optimised. In this paper, the Hamiltonian function is used in obtaining necessary optimality conditions with spectral learning effects and Costate equations complemented by the adoption rates by use of Pontryagin’s Maximum Principle. As the problem is formulated as the continuous optimization problem, it is discretized for its practical applications, and the model is solved with the help of LINGO 15.0 software. The data used to validate the model implemented was derived from historical sales records of the electronics and semiconductor industries to obtain a measure of realism. Analysed sensitivity studies show how variations in adoption parameters including the price elasticity and customer attrition affect adoption rates and profitability. As such, the study offers managerial implications for the management of private sector schemes to focus on the application of dynamic pricing strategies as the optimal balance between consumers’ perceived value and firm revenues. It provides managers with strong tools for the implementation of adoption into a new generation of technology-enabled markets, maximization of revenues, and sustaining of competitive advantage. Outperforming all analysed models, the suggested technique employing Optimal Control Theory obtains an accuracy of 96%. This proves that the suggested strategy is the best at forecasting when a product will be adopted.
LINGO , SVM , LR , KNN , RF , Optimal Control Theory
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