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      Forecasting new product diffusion using both patent citation and web search traffic

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      PLoS ONE
      Public Library of Science

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          Abstract

          Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product’s diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods.

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          Most cited references19

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          Predicting the Present with Google Trends

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            New Product Diffusion Models in Marketing: A Review and Directions for Research

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              Cross-Country Technology Diffusion: The Case of Computers

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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Writing – original draft
                Role: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                9 April 2018
                2018
                : 13
                : 4
                : e0194723
                Affiliations
                [001]Department of Information & Industrial Engineering, Yonsei University, Seoul, Republic of Korea
                University of Rijeka, CROATIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-3958-2269
                Article
                PONE-D-16-44056
                10.1371/journal.pone.0194723
                5890978
                29630616
                b64d35a0-dffa-43df-8761-12225f9f2d84
                © 2018 Lee et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 November 2016
                : 7 January 2018
                Page count
                Figures: 3, Tables: 5, Pages: 12
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;
                Award ID: 2016R1A2A1A05005270
                Award Recipient :
                This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIP) grant # 2016R1A2A1A05005270 ( http://www.nrf.re.kr/) to SYS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Engineering and Technology
                Mechanical Engineering
                Robotics
                Robots
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Forecasting
                Biology and Life Sciences
                Behavior
                Research and Analysis Methods
                Research Assessment
                Citation Analysis
                Engineering and Technology
                Equipment
                Communication Equipment
                Cell Phones
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Decision Making
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Decision Making
                Social Sciences
                Psychology
                Cognitive Psychology
                Decision Making
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognition
                Decision Making
                Computer and Information Sciences
                Computer Networks
                Internet
                Physical Sciences
                Materials Science
                Materials by Structure
                Ceramics
                Custom metadata
                Three types of data related to hybrid cars were collected in this study: patent data, Web search traffic data, and sales data. All the data were gathered in units of six-month periods. We selected eight representative frequently cited patents on hybrid car technology among the patents registered at the United States Patents and Trademark Office (USPTO, http://patft.uspto.gov/) from 1976 to 1998. We collected the citation frequency of the eight patents from 1999 to 2014. In addition, we collected monthly Google web search traffic data for “hybrid car” in the United States from 2004 to 2014, from the Google Trends website ( https://www.google.com/trends). The maximum web search traffic for this period was normalized to be 100. In this study, the monthly web search traffic was summed for six-month periods. Sales data of hybrid cars from 1999 to 2014 were collected from the U.S. Department of Energy website ( http://www.afdc.energy.gov/data/). The increase in the number of hybrid car sales refer to the diffusion of the hybrid car.

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