2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 11 , Issue 2 , PP: 38-52, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Saurabh Singh 1 *
Doi: https://doi.org/10.54216/AJBOR.110203
In order to analyses the diffusion of new technological products in rapidly changing market environments, this paper presents two new stochastic diffusion models: SDM1 and SDM2. The two models also utilize stochastic market size function in capturing rather random growth of potential users, inherent in most real-world markets. SDM1 apply the exponential distribution to model the market growth rate to consider the cases characterized by the high increase, while SDM2 adapt the Erlang distribution to reflect the S-shape to consider the long-term adoptions. The presented models rely on stochastic differential equations with recourse to calculus, and they adopt stochastic geometric Brownian motion and logistic growth function for adoption rates. This makes it possible to capture effects of learning as well as the non-regularity of adoption over time. The empirical results of benchmark models by using Apple iPhones and Samsung Galaxy smartphones sales data show the better performance of SDM1 and SDM2. The performance of the methodologies is measured using parameters, the goodness-of-fit tests and the forecast accuracy that all show that the proposed methods are very efficient. These models have a rich theoretical background, which comprises the foundation for explaining adoption patterns, which in turn will facilitate the behaviour of managers and policymakers towards understanding consumers, controlling inventory, and designing significant marketing strategies for technology products in a stochastic world. Both SDM1 and SDM2, the suggested algorithms, outperform the state-of-the-art techniques in terms of accuracy. SDM1 outperforms the other models with an accuracy of 95.32 percent. SDM2's greater accuracy in forecasting is shown by its outperformance of all techniques, which stands at 97.3%.
SDM2 , SDM1 , MAPE , SDE , RMSE , MSE , MAE
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