A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
- David C. Schmittlein & Vijay Mahajan, 1982. "Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 1(1), pages 57-78.
- Campbell, Richard S., 1983. "Patent trends as a technological forecasting tool," World Patent Information, Elsevier, vol. 5(3), pages 137-143.
- von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
- Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
- Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
- Bo Wang & Shengbo Liu & Kun Ding & Zeyuan Liu & Jing Xu, 2014. "Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 685-704, October.
- Chen-Yuan Liu & Jhen-Cheng Wang, 2010. "Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 21-36, January.
- Gao, Lidan & Porter, Alan L. & Wang, Jing & Fang, Shu & Zhang, Xian & Ma, Tingting & Wang, Wenping & Huang, Lu, 2013. "Technology life cycle analysis method based on patent documents," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 398-407.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Hansu Hwang & SeJin An & Eunchang Lee & Suhyeon Han & Cheon-hwan Lee, 2021. "Cross-Societal Analysis of Climate Change Awareness and Its Relation to SDG 13: A Knowledge Synthesis from Text Mining," Sustainability, MDPI, vol. 13(10), pages 1-21, May.
- Bernardo Nicoletti & Andrea Appolloni, 2023. "Artificial Intelligence for the Management of Servitization 5.0," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
- Menger Tu & Sandy Dall'erba & Mingque Ye, 2022. "Spatial and Temporal Evolution of the Chinese Artificial Intelligence Innovation Network," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
- Soyoung Kim & Boyoung Kim, 2020. "A Decision-Making Model for Adopting Al-Generated News Articles: Preliminary Results," Sustainability, MDPI, vol. 12(18), pages 1-14, September.
- Xin Du & Hengming Zhang & Yawen Han, 2022. "How Does New Infrastructure Investment Affect Economic Growth Quality? Empirical Evidence from China," Sustainability, MDPI, vol. 14(6), pages 1-30, March.
- Carmen Isensee & Kai-Michael Griese & Frank Teuteberg, 2021. "Sustainable artificial intelligence: A corporate culture perspective [Sustainable artificial intelligence: Eine unternehmenskulturelle Perspektive]," Sustainability Nexus Forum, Springer, vol. 29(3), pages 217-230, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Kim, Namwoon & Srivastava, Rajendra K., 2007. "Modeling cross-price effects on inter-category dynamics: The case of three computing platforms," Omega, Elsevier, vol. 35(3), pages 290-301, June.
- Sung Yong Chun & Minhi Hahn, 2008. "A diffusion model for products with indirect network externalities," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 357-370.
- Singhal, Shakshi & Anand, Adarsh & Singh, Ompal, 2020. "Studying dynamic market size-based adoption modeling & product diffusion under stochastic environment," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
- Meizhen Zhang & Tao Lv & Xu Deng & Yuanxu Dai & Muhammad Sajid, 2019. "Diffusion of China’s coal-fired power generation technologies: historical evolution and development trends," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 7-23, January.
- Dong, Changgui & Sigrin, Benjamin & Brinkman, Gregory, 2017. "Forecasting residential solar photovoltaic deployment in California," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 251-265.
- Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
- Negahban, Ashkan & Smith, Jeffrey S., 2018. "Optimal production-sales policies and entry time for successive generations of new products," International Journal of Production Economics, Elsevier, vol. 199(C), pages 220-232.
- Shun-Chen Niu, 2006. "A Piecewise-Diffusion Model of New-Product Demands," Operations Research, INFORMS, vol. 54(4), pages 678-695, August.
- John Hauser & Gerard J. Tellis & Abbie Griffin, 2006. "Research on Innovation: A Review and Agenda for," Marketing Science, INFORMS, vol. 25(6), pages 687-717, 11-12.
- Cambier, Adrien & Chardy, Matthieu & Figueiredo, Rosa & Ouorou, Adam & Poss, Michael, 2022. "Optimizing subscriber migrations for a telecommunication operator in uncertain context," European Journal of Operational Research, Elsevier, vol. 298(1), pages 308-321.
- Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
- Shigeno, Hidenori & Matsuzaki, Taisuke & Ueki, Yasushi & Tsuji, Masatsugu, 2023. "The Effect of the Covid-19 Pandemic on the Innovation Process of Small and Medium-sized Regional Firms," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 278018, International Telecommunications Society (ITS).
- Sohn, So Young & Lim, Michael, 2008. "The effect of forecasting and information sharing in SCM for multi-generation products," European Journal of Operational Research, Elsevier, vol. 186(1), pages 276-287, April.
- Berrin Aytac & S. Wu, 2013. "Characterization of demand for short life-cycle technology products," Annals of Operations Research, Springer, vol. 203(1), pages 255-277, March.
- Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
- Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
- Krishnan, Trichy V. & Feng, Shanfei & Jain, Dipak C., 2023. "Peak sales time prediction in new product sales: Can a product manager rely on it?," Journal of Business Research, Elsevier, vol. 165(C).
- Hyoung Jun Kim & Su Jung Jee & So Young Sohn, 2021. "Cost–benefit model for multi-generational high-technology products to compare sequential innovation strategy with quality strategy," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.
- Brito, Thiago Luis Felipe & Islam, Towhidul & Stettler, Marc & Mouette, Dominique & Meade, Nigel & Moutinho dos Santos, Edmilson, 2019. "Transitions between technological generations of alternative fuel vehicles in Brazil," Energy Policy, Elsevier, vol. 134(C).
- Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
More about this item
Keywords
artificial intelligence technology; promising technology; Bass diffusion model; USPTO analysis; technology diffusion; innovation;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5609-:d:383592. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.