The Use Of S Curves In Technology Forecasting And Its Application On 3D Tv Technology

İntepe, Gizem; Koç, Tufan

doi:10.5281/zenodo.1080624

Cited by 3 publications

(1 citation statement)

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“…Empirical analysis procedure Several studies apply models' growth to examine the diffusion of technologies and identify their progression over time (Aduba and Asgari, 2021;Chen et al, 2011;Frank, 2004;Intepe & Koc, 2012;Liu et al, 2011;Liu and Wang, 2010;Nieto et al, 1998). One of these models is the logistic model, which is used to estimate the evolution of a specific technology by fitting a growth curve to a dataset of technological performances (Chen et al, 2011).…”

Section: 3mentioning

confidence: 99%

Technological Phases of Quantum Technologies Driving Long-Term Development

Coccia

Roshani

2023

Preprint

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The goal of this study is to develop a technology analysis for examining the evolutionary phases of some critical quantum technologies to explain on-going technological development. Method applies S-shaped model based on logistic function that is estimated with patent data to analyze the phases of quantum technologies over the course of their technological evolution. Findings reveal that the technological cycle of recent quantum technologies has a shorter period in emergence phase and a longer period in growth and maturity phases than older quantum technologies. In particular, structure of technological cycle also shows that for quantum technologies originated after 1980, technological phase of emergence (to reach to the point of growth) is reduced to 52% of the total length of the cycle, compared to 68% of technologies originated before 1980, whereas the growth and maturity phases for technologies originated after 1980 have a higher percentage weight on the total duration of the cycle than technologies originated before 1980: growth stage is 22.78% of total duration of cycle in new technologies originated after 1980 vs. 15.76% in older technologies originated before the 1980; maturity stage is 25.32% vs. 16.08%, respectively of total technological cycle. Results here can provide theoretical implications to explain dynamics and structure of the technological evolution of emerging quantum innovations that support the technological forecasting for improving decisions of R&D investments in specific technologies that can be major sources of next technological, industrial, economic and social change.

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Section: 3mentioning

confidence: 99%

Technological Phases of Quantum Technologies Driving Long-Term Development

Coccia

Roshani

2023

Preprint

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Macro Patterns and Trends of U.S. Consumer Technological Innovation Diffusion Rates

Parvin

Beruvides

2021

Systems

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Macro-level trends and patterns are commonly used in business, science, finance, and engineering to provide insights and estimates to assist decision-makers. In this research effort, macro-level trends and patterns were explored on the diffusion rates of technological innovations, a component of a sorely under-studied question in technology assessment: When should a technological innovation be abandoned? A quantitative exploratory data analysis (EDA)-based approach was employed to examine diffusion market data of 42 U.S. consumer technological innovations from the early 1900s to the 2010s to extract general macro-level knowledge on technological innovation diffusion rates. A goal of this effort is to grow diffusion rate knowledge to enable the development of general macro-based forecasting tools. Such tools would aid decision-makers in making informed and proactive decisions on when to abandon a technological innovation. This research offers several significant contributions to the macro-level understanding of the boundaries and likelihood of achieving a range of technological innovation diffusion rates. These contributions include the determination that the frequency of diffusion rates are positively skewed when ordered from slowest to fastest, and the identification and ranking of probability density functions that best represent the rates of technological innovation diffusion.

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A Study on Modeling and Forecasting of Mobile Phone Sales Trends

Kim¹

2016

Journal of the Korea Academia-Industrial cooperation Society

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The Use Of S Curves In Technology Forecasting And Its Application On 3D Tv Technology

Cited by 3 publications

References 0 publications

Technological Phases of Quantum Technologies Driving Long-Term Development

Technological Phases of Quantum Technologies Driving Long-Term Development

Macro Patterns and Trends of U.S. Consumer Technological Innovation Diffusion Rates

A Study on Modeling and Forecasting of Mobile Phone Sales Trends

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