Cresting the commoditization curve: a regression analysis and bass diffusion model approach
The Bass Diffusion Model is a commonly used framework for understanding the diffusion of new products and innovations in the marketplace. In this study, Bass Diffusion Model is applied to historical data from Evoqua, a leading company in water technologies that produces water treatment systems, prod...
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sg-ntu-dr.10356-1664692023-05-08T15:39:02Z Cresting the commoditization curve: a regression analysis and bass diffusion model approach Ho, Shu Xian Xia Kelin School of Physical and Mathematical Sciences Evoqua Water Technologies Pte. Ltd. Roger Foo xiakelin@ntu.edu.sg, roger.foo@evoqua.com Science::Mathematics::Analysis Business::Marketing::Consumer behavior The Bass Diffusion Model is a commonly used framework for understanding the diffusion of new products and innovations in the marketplace. In this study, Bass Diffusion Model is applied to historical data from Evoqua, a leading company in water technologies that produces water treatment systems, producing values of p = 0.00318 and q = 0.453. Similar figures of p = 0.00962 and q = 0.587 were obtained when fitted to OSEC system, which is considered a new technology. These parameters suggest that the adoption of the Onsite Sodium Hypochloride systems will be driven more by imitation than innovation, and that the majority of adoption will occur in the later stages of the diffusion process. This information can be used by the company to inform its marketing and promotion efforts, and to target both early and late adopters of the product. By leveraging the insights provided by the Bass Diffusion Model, Evoqua can mitigate the effects of commoditization and achieve sustained success in the marketplace. Bachelor of Science in Mathematical Sciences 2023-05-02T05:26:58Z 2023-05-02T05:26:58Z 2023 Final Year Project (FYP) Ho, S. X. (2023). Cresting the commoditization curve: a regression analysis and bass diffusion model approach. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166469 https://hdl.handle.net/10356/166469 en application/pdf Nanyang Technological University |
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Science::Mathematics::Analysis Business::Marketing::Consumer behavior Ho, Shu Xian Cresting the commoditization curve: a regression analysis and bass diffusion model approach |
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The Bass Diffusion Model is a commonly used framework for understanding the diffusion of new products and innovations in the marketplace. In this study, Bass Diffusion Model is applied to historical data from Evoqua, a leading company in water technologies that produces water treatment systems, producing values of p = 0.00318 and q = 0.453. Similar figures of p = 0.00962 and q = 0.587 were obtained
when fitted to OSEC system, which is considered a new technology. These parameters suggest that the adoption of the Onsite Sodium Hypochloride systems will be driven more by imitation than innovation, and that the majority of adoption will occur in the later stages of the diffusion process. This information can be used by the company to inform its marketing and promotion efforts, and to target both early and late adopters of the product. By leveraging the insights provided by the Bass Diffusion Model, Evoqua can mitigate the effects of commoditization and achieve sustained success in the marketplace. |
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Xia Kelin |
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Xia Kelin Ho, Shu Xian |
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Final Year Project |
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Ho, Shu Xian |
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Ho, Shu Xian |
title |
Cresting the commoditization curve: a regression analysis and bass diffusion model approach |
title_short |
Cresting the commoditization curve: a regression analysis and bass diffusion model approach |
title_full |
Cresting the commoditization curve: a regression analysis and bass diffusion model approach |
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Cresting the commoditization curve: a regression analysis and bass diffusion model approach |
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Cresting the commoditization curve: a regression analysis and bass diffusion model approach |
title_sort |
cresting the commoditization curve: a regression analysis and bass diffusion model approach |
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Nanyang Technological University |
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2023 |
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https://hdl.handle.net/10356/166469 |
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