The knowledge diffusion model associated with innovative knowledge

Tsai, Chih Ming

doi:10.1016/j.eswa.2009.03.044

Cited by 19 publications

(10 citation statements)

References 28 publications

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“…Using the arguments of Lundvall (1992), Malerba et al (1997), 5 Hu and Tseng (2007), 6 Tsai (2009) and Petit et al (2009) that elucidated the details of virtuous cycle among existing knowledge, knowledge production and benefits of the knowledge, this study identifies self-propagating behavior as the value of knowledge production that contributes to the expansion of diffusion potential. Positing value of knowledge as the determinant that accounts for diffusion potential requires quantification of science or technological knowledge production value that propagates the growth of carrying capacity.…”

Section: Concepts and Methodologymentioning

confidence: 95%

“…Production of new knowledge at time t stems from the investment to improve the initial knowledge stock (Tsai 2009 Fig. 2 The mechanism of self-propagating function in science and technological diffusion the growth of carrying capacity that develops into a higher level of knowledge in the next period through the following process:…”

Section: Concepts and Methodologymentioning

confidence: 99%

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Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

Wong

Goh

2010

Scientometrics

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Through theoretical analysis and empirical demonstration, this paper attempts to model the behavior of science and technology by investigating the self-propagating behavior of their diffusion for South Korea, Malaysia and Japan. The dynamics of the selfpropagating behavior were examined using the logistic growth function within a dynamic carrying capacity, while allowing for different effectiveness of potential influence of science and technology producers on potential adopters. Evidence suggests that the selfpropagating growth function is particularly relevant for countries with advanced science and technology, like Japan. While self-propagating growth was also found for South Korea, the diffusion process remained fairly static for Malaysia.

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Section: Concepts and Methodologymentioning

confidence: 95%

Section: Concepts and Methodologymentioning

confidence: 99%

Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

Wong

Goh

2010

Scientometrics

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“…K and k are the carrying capacity or any growth potential limitation and velocity, respectively. The current status and trajectories of technological production through S‐curve have been used by many researches …”

Section: Methodsmentioning

confidence: 99%

“…The current status and trajectories of technological production through S-curve have been used by many researches. [55][56][57] 3 | RESULTS AND DISCUSSION…”

Section: Assessing the Technology Orientation And Life Cyclementioning

confidence: 99%

Technological assessment and modeling of energy‐related CO₂ emissions for the G8 countries by using hybrid IWO algorithm based on SVM

Ghazvini

Madvar

Ahmadi

et al. 2020

Energy Science & Engineering

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Recently, energy‐related CO2 emissions are considered as one of the most crucial issues and are promptly augmented due to further urbanization. In this paper, in order to model and calculate yearly CO2 emission, an artificial neural network is used. For the first time, the IWO‐SVM method has been applied in modeling energy‐related CO2 emissions. In this regard, consumption of different energy sources such as renewable energy, natural gas, coal, and oil, and GDP of the G8 countries in various years (from 1990 to 2016) are regarded as input in the present study. For the aim of evaluating the exact ability of the SVM and SVM‐IWO models, the performance of these models in three different modes is carried out on the basis of the number of data in the test and train sections. For this purpose, implementations are split into three categories (a = 80% of the data for the train section and 20% for the test section; b = 70% of the data for the train section and 30% for the test section; and c = 60% of the data for the train section and 40% for the test section). Furthermore, five scenarios were selected on the basis of the number of input parameters and input parameters for achieving the best model. As indicated in the results in all scenarios, the correlation of the model with the hybrid invasive weed algorithm based on SVM is more favorable than that in the support vector machine model, due to better training of the SVM‐IWO model than the SVM model. Moreover, the technological orientations of the G8 countries to mitigate CO2 emissions are determined through patent analysis. While the patents have essential information, investigating the published patent by a country could be helpful for determination of technological orientations. Hence, all published patents by these countries are extracted and deeply analyzed. In the next step, to find out main technological approaches, all patents and their intents have been studied. Eventually, the technological life cycles and trends of each main technology are drawn.

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“…Performance expectancy is important to individuals as individuals are more likely to adopt and use IT innovation if they can gain benefit and improve performance (Venkatesh & Zhang, 2010). Typically, entrepreneurs are more concerned about the benefits they can gain from new opportunities that enhance enterprise performance (Tsai, 2009). Therefore, this study expects that entrepreneurs would consider using an IT innovation if it is useful and can enhance their enterprise's performance.…”

Section: Research Model and Hypothesesmentioning

confidence: 99%

Impact of Perceived Desirability, Perceived Feasibility and Performance Expectancy on Use of It Innovation: Technology Adoption Decisions and Use Behaviour

Moghavvemi

Phoong

Lee

2018

VJM

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Theoretical perspectives from the field of entrepreneurship can be used to examine entrepreneurs' intention to use IT innovations. This study collected 412 completed survey responses from entrepreneurs and used structural equation modelling to test the proposed technology acceptance decision model. The results showed the significant effect of perceived desirability, perceived feasibility and performance expectancy as the salient antecedents of intention to adopt and use IT innovation. This study examined the effect of external factors which prevent or facilitate the adoption and use of new technology. The moderating effect of the propensity to act is examined and the results indicated that when the individual propensity to act is high, taking action becomes more desirable and feasible. This study revealed that, in the current IS environment, individuals adopt and use a new system due to the attractiveness of the system and perceived feasibility, which are derived from intrinsic interest and affective beliefs.

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The knowledge diffusion model associated with innovative knowledge

Cited by 19 publications

References 28 publications

Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

Technological assessment and modeling of energy‐related CO₂ emissions for the G8 countries by using hybrid IWO algorithm based on SVM

Impact of Perceived Desirability, Perceived Feasibility and Performance Expectancy on Use of It Innovation: Technology Adoption Decisions and Use Behaviour

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The knowledge diffusion model associated with innovative knowledge

Cited by 19 publications

References 28 publications

Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

Modeling the behaviour of science and technology: self-propagating growth in the diffusion process

Technological assessment and modeling of energy‐related CO2 emissions for the G8 countries by using hybrid IWO algorithm based on SVM

Impact of Perceived Desirability, Perceived Feasibility and Performance Expectancy on Use of It Innovation: Technology Adoption Decisions and Use Behaviour

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Technological assessment and modeling of energy‐related CO₂ emissions for the G8 countries by using hybrid IWO algorithm based on SVM