{"title":"A generalized product adoption model under random marketing conditions","authors":"Shiva, Neetu Gupta, Anu G. Aggarwal","doi":"10.1007/s13198-024-02499-1","DOIUrl":null,"url":null,"abstract":"<p>In marketing research, diffusion models are extensively utilized to predict the trend of new product adoption over time. These models are categorized based on their deterministic or stochastic characteristics. While deterministic models disregard the stochasticity of the adoption rate influenced by environmental and internal factors, we aim to address this limitation by proposing a generalized innovation diffusion model that accounts for such uncertainties. We validate our approach using the particle swarm optimization (PSO) technique on actual sales data from technological products. Our findings suggest that the proposed model outperforms existing diffusion models in forecasting accuracy.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":"2 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of System Assurance Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13198-024-02499-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In marketing research, diffusion models are extensively utilized to predict the trend of new product adoption over time. These models are categorized based on their deterministic or stochastic characteristics. While deterministic models disregard the stochasticity of the adoption rate influenced by environmental and internal factors, we aim to address this limitation by proposing a generalized innovation diffusion model that accounts for such uncertainties. We validate our approach using the particle swarm optimization (PSO) technique on actual sales data from technological products. Our findings suggest that the proposed model outperforms existing diffusion models in forecasting accuracy.
期刊介绍：
This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems.
Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.
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