Forecasting technology diffusion with the Richards model

Marinakis, Yorgos

doi:10.1016/j.techfore.2011.02.010

Cited by 31 publications

(34 citation statements)

References 14 publications

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“…The Richards shape parameter (d) showed clear differences between the sexes and it was higher in males than the females, thus males reached inflection point more quickly than female. When shape parameter (d) =0, the model approximates the exponential growth function and when d=0.67, 1, 2, the model behaves like the von Bertalanffy, Gompertz, Logistic model, respectively, Marinakis (2012). Shape parameters estimated for ST male and female were close to 1, indicating that their growth curves look like Gompertz model.…”

Section: Model Parametersmentioning

confidence: 86%

Growth Patterns of Two Chinese Native Goose Breeds

Ibtisham

et al. 2017

Rev. Bras. Cienc. Avic.

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To study the development rules of Chinese native geese, two breeds, Shitou and Sichuan White geese were analyzed from 0 to 12 weeks of age. The growth curves were fitted with commonly used four kinds of nonlinear models (Logistic, Gompertz, Von Bertalanffy and Richards). The results showed that the growth curves were appropriately fitted with all four models but the Logistic and Richards both had the best fitting with growth curve (R 2 >0.99). Analyzing the fitting parameters of the Logistic and Richards, we found that male Shitou had the highest adult body weight while Sichuan White female had the lowest weight. In Shitou breed, Shape parameter Predicted with Richards model was corresponded with Gompertz curve, while in Sichuan breed it was in between Gompertz and Bertalanffy. Growth parameters predicted with Logistic model was much more closed to observed value as compared others. So overall logistic was the best model to analyze the growth curve in Chinese native goose and Shitou goose had excellent growth performance when compared to Sichuan White.

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Section: Model Parametersmentioning

confidence: 86%

Growth Patterns of Two Chinese Native Goose Breeds

Ibtisham

et al. 2017

Rev. Bras. Cienc. Avic.

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“…Several analyses on the diffusion of technologies showed that their growth could be approximated by a S-shaped curve. which is usually modelled and predicted using Bass, Gompertz, Logistic, Richards and other statistical models [25].…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, standard technology diffusion and forecasting models [19][20][21][22][23][24][25] cannot tackle this issue, since they are designed to assess the general potential of technologies associated with a good number of documents. Conversely, we want to predict that a technology that has no or very few initial publications in topic T will be adopted by researchers working on T.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Spreading of Technologies in Research Communities

Osborne

Mannocci

Motta

2017

Proceedings of the Knowledge Capture Conference

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Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution.

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“…This flexibility has ensured the popularity of the function in biology and related sciences ever since the inception, although not without criticism (Annadurai et al, 2000;Birch, 1999;Zwietering et al, 1990). The function has recently been used also to model the diffusion of technology (Marinakis, 2012). It is worth noting, moreover, that also Bass's (1969) classical model for the adoption of technology allows a flexible inflection point.…”

Section: Growth Curvesmentioning

confidence: 99%

“…In other words, the points offered by the logistic and Gompertz functions are as good hypotheses as any other inflection point that would be available by manipulating the fourth parameter in the Richards' function. Even when some theory is available to guide the analysis, it is far from being a straightforward endeavor to use the Richards' function to discriminate between different growth patterns (Marinakis, 2012). Moreover, in some applications it is difficult or even impossible to model growth with the function without violating theoretical premises (Birch, 1999), or to interpret the fourth parameter theoretically (Annadurai et al, 2000;Zwietering et al, 1990).…”

Section: Growth Curvesmentioning

confidence: 99%