Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences

Durmuşoğlu, Alptekin

doi:10.3390/su9050862

Cited by 5 publications

(5 citation statements)

References 47 publications

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“…The influence of The Clean Air Act on the volume of patenting in the chemical sector was studied using basic chemical utility patents to identify the impacts of the Act on patenting activities. Following fitting the Autoregressive Integrated Moving Average model, a significant outlier was discovered, concluding that the chemical sector reacted to changes extremely efficiently (Durmuşoğlu, 2017). Based on the results of an analysis comparing Holt-Winters Exponential Smoothing with an Autoregressive Integrated Moving Average model for technology change forecasting using USPTO patent data from 1996 to 2013, Holt-Winters Exponential Smoothing outperformed Autoregressive Integrated Moving Average (Smith and Agrawal, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Pandemic and Technological Change: Analysis of Patent Applications

YILDIRIM>

2023

Verimlilik Dergisi

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Purpose: , This study is aimed to measure Covid-19's impact on innovation and monitor technical change during the Pandemic through patent applications Methodology: Patent application annual total number of European Patent Office patent applications data on 35 different technological disciplines separated into five key categories from 2012 to 2021 were utilized in the analysis The Prophet forecasting model to forecast patent applications for 2020 and 2021 has been developed. The technological advancements and Pandemic impact on innovation were then analyzed using the realized and forecasted values. Findings: The study's findings indicate no apparent difference between actual numbers and forecasted values. In 2020 and 2021, more patent applications than predicted were filed in 15 technical fields and 16 technological domains, respectively. The study also found that semiconductors, audio-visual, and nanotechnology advancements have been notable during the Pandemic. Originality: The originality of this study lies in the use of the Prophet forecasting model based on EPO patent application values in the analysis of the effects of the pandemic on innovation and technological change for 35 different technological disciplines. Keywords: Technological Change, Innovation, Patent, Prophet model, Covid-19. JEL Codes: O30,O33,C22

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Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Pandemic and Technological Change: Analysis of Patent Applications

YILDIRIM>

2023

Verimlilik Dergisi

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“…First, through "(A) Time Series Analysis (TSA)", the frequency of patent applications by technology was extracted as time series data to predict promising future technology [21,24,[28][29][30][31]34,[38][39][40][41][42][43][44]. Then, through "(B) Social Network Analysis (SNA)", the relationship between nodes as a quantitative indicator through centrality indices was extracted, and promising detailed descriptive areas based on the extracted indicators were predicted [22,[24][25][26][27][28][29][30][31][32]34,39,40,43,[45][46][47][48][49][50]. In addition to the quantitative analysis methods, the qualitative analysis method of "(C) Technology Mapping Analysis (TM)" was also used in previous studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition to the quantitative analysis methods, the qualitative analysis method of "(C) Technology Mapping Analysis (TM)" was also used in previous studies. Through "(C) Technology Mapping Analysis (TM)", the importance of the level of technology for each area was obtained, and priorities for technology development were identified [21,24,[29][30][31]38,40,[45][46][47][48][49][50][51]. In addition to predicting promising technology, previous studies have identified vacant technology by analyzing patent information of analysis target nodes through "(D) Generative Topographic Mapping Analysis (GTM)", identifying empty technology [24,27,28,40,52].…”

Section: Literature Reviewmentioning

confidence: 99%

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

et al. 2022

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The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data.

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“…de Araújo et al (2007) proposed a method that selects the most accurate prediction model first and then performs a behavioral statistical test and a phase fix procedure to adjust the time phase distortions that appear in financial time series. Durmuşoğlu (2017) used outlier analysis to check whether there is a need for an update of the forecasting model on hand.…”

Section: Related Workmentioning

confidence: 99%

Updating technology forecasting models using statistical control charts

Durmuşoğlu

2017

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Purpose The purpose of this paper is to develop an approach that can detect abnormal deviations in the time series models for technology forecasting. The detected modifications provide a basis for understanding the determinants and impact of the corresponding change. Design/methodology/approach The proposed approach is based on monitoring residual values (the difference between the observation and the forecasted value) continuously using statistical control charts (SCCs). The residuals that are out of the expected limits are considered an alert indicating a remarkable change. To demonstrate the use of the proposed approach, a time series model was fitted to a number of TV-related patent counts. Subsequently, model residuals were used to determine the limits of the SCCs. Findings A number of patents granted in the year 2012 violated the upper control limit. A further analysis has shown that there is a linkage between the abnormal patent counts and the emergence of LCD TVs. Practical implications Change in technology may dramatically affect the accuracy of a forecasting model. The need for a parameter update indicates a significant change (emergence or death of a technology) in the technological environment. This may lead to the revision of managerial actions in R&D plans and investment decisions. Originality/value The proposed methodology brings a novel approach for abnormal data detection and provides a basis for understanding the determinants and impact of the corresponding change.

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Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences

Cited by 5 publications

References 47 publications

COVID-19 Pandemic and Technological Change: Analysis of Patent Applications

COVID-19 Pandemic and Technological Change: Analysis of Patent Applications

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

Updating technology forecasting models using statistical control charts

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