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Artykuły w czasopismach na temat "Predicting Innovation"
Schwabsky, Nitza, Ufuk Erdogan i Megan Tschannen-Moran. "Predicting school innovation". Journal of Educational Administration 58, nr 2 (23.12.2019): 246–62. http://dx.doi.org/10.1108/jea-02-2019-0029.
Pełny tekst źródłaBlagojevic, Marija, Zivadin Micic i Momcilo Vujicic. "Cluster analysis of knowledge sources in standardized electrical engineering subfields". Serbian Journal of Electrical Engineering 13, nr 3 (2016): 405–22. http://dx.doi.org/10.2298/sjee1603405b.
Pełny tekst źródłaHang Do, Thuy, Tim Mazzarol, Thierry Volery i Sophie Reboud. "Predicting anticipated rent from innovation commercialisation in SMEs". European Journal of Innovation Management 17, nr 2 (6.05.2014): 183–208. http://dx.doi.org/10.1108/ejim-12-2012-0113.
Pełny tekst źródłaRojas-Córdova, Carolina, Boris Heredia-Rojas i Patricio Ramírez-Correa. "Predicting Business Innovation Intention Based on Perceived Barriers: A Machine Learning Approach". Symmetry 12, nr 9 (19.08.2020): 1381. http://dx.doi.org/10.3390/sym12091381.
Pełny tekst źródłaPark, Kyungbo, Jeonghwa Cha i Jongyi Hong. "Developing a Framework for Evaluating and Predicting Management Innovation in Public Research Institutions". Sustainability 15, nr 9 (27.04.2023): 7261. http://dx.doi.org/10.3390/su15097261.
Pełny tekst źródłaRamiz Abdinov, Vidadi Akhundov, Ramiz Abdinov, Vidadi Akhundov. "METHODOLOGY FOR ASSESSING THE IMPACT OF INNOVATIONS ON THE PRODUCTION OF THE REGION'S FINAL PRODUCTS". PIRETC-Proceeding of The International Research Education & Training Centre 21, nr 04 (9.11.2022): 33–38. http://dx.doi.org/10.36962/piretc21042022-33.
Pełny tekst źródłaSwart, Rachelle R., Maria JG Jacobs, Cheryl Roumen, Ruud MA Houben, Folkert Koetsveld i Liesbeth J. Boersma. "Factors predicting timely implementation of radiotherapy innovations: the first model". British Journal of Radiology 94, nr 1117 (1.01.2021): 20200613. http://dx.doi.org/10.1259/bjr.20200613.
Pełny tekst źródłaGashema, Bruce. "Predicting innovative work behaviors through transformational leadership". International Journal of Research in Business and Social Science (2147- 4478) 10, nr 1 (11.02.2021): 69–84. http://dx.doi.org/10.20525/ijrbs.v10i1.999.
Pełny tekst źródłaRani, Ruchi, Sumit Kumar, Rutuja Rajendra Kadam i Sanjeev Kumar Pippal. "A machine learning model for predicting innovation effort of firms". International Journal of Electrical and Computer Engineering (IJECE) 13, nr 4 (1.08.2023): 4633. http://dx.doi.org/10.11591/ijece.v13i4.pp4633-4639.
Pełny tekst źródłaPan, Han, Wu Xin i Yuping Li. "A review on the concept of consumer innovativeness". E3S Web of Conferences 251 (2021): 01080. http://dx.doi.org/10.1051/e3sconf/202125101080.
Pełny tekst źródłaRozprawy doktorskie na temat "Predicting Innovation"
Reagan, James L. "Predicting disruptive innovation| Which factors determine success?" Thesis, Shenandoah University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3680894.
Pełny tekst źródłaDisruptive Innovation theory explains how industry entrants can defeat established firms and quickly gain a significant share of their key markets, in spite of the fact that incumbents tend to be significantly more experienced and better resourced. The theory has been criticized for being underspecified: whilst the general mechanics of the phenomenon of disruptive innovation are clear, it has not been established which individual variables are essential to the process and which ones are merely ancillary. As a consequence, to date it has not been possible to build a predictive model on the basis of the theory managers can use to assess the disruptive potential of their own and their competitors' innovation projects. In this research project the predictive power of each of the main variables that are mentioned in the literature has been assessed on the basis of a qualitative analysis of five real world case studies. Only variables for which information can be collected using publicly available data before disruption happens have been retained. By clarifying the detail of disruptive innovation theory, this study has been able to address a key issue in the debate, namely, whether products that are more expensive and more complex than the market standard can ever be classified as 'disruptive innovations' or whether they should always be regarded as 'high-end anomalies'. In this study two distinct disruptive innovation strategies have been identified based on the current phase of the product life cycle, the current focus of mainstream demand and the market segments first targeted when coming to market. The first strategy entails growing an existing market by moving the focus of demand on to a secondary market driver as soon as customers begin to lose their willingness to pay a premium for upgrades in the performance areas they historically used to value. Early on in the product life cycle, disruptors can conquer the mainstream market 'from above' with products that are different and more reliable or more convenient but not simpler or cheaper. The second strategy creates a new separate market by offering a radically new type of additional functionality. Over time the new market replaces the old market. These products are likely to be expensive because of their small production run and difficult to use because they are the first models of their kind. High-end customers constitute a natural foothold market for these products as they are wealthy and highly skilled.
Heesen, Bernd. "Diffusion of innovations : factors predicting the use of e-learning at institutions of higher education in Germany". Berlin dissertation.de, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2833665&prov=M&dokv̲ar=1&doke̲xt=htm.
Pełny tekst źródłaHeesen, Bernd. "Diffusion of innovations factors predicting the use of e-learning at institutions of higher education in Germany". Berlin dissertation.de, 2004. http://deposit.d-nb.de/cgi-bin/dokserv?id=2833665&prov=M&dok_var=1&dok_ext=htm.
Pełny tekst źródłaChan, Tan Fung Ivan. "Predicting the Probability for Adopting an Audience Response System in Higher Education". ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/1529.
Pełny tekst źródłaBohling, Timothy R. "Predicting Purchase Timing, Brand Choice and Purchase Amount of Firm Adoption of Radically Innovative Information Technology: A Business to Business Empirical Analysis". Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/bus_admin_diss/3.
Pełny tekst źródłaOcal, Kubilay. "Predicting Employee Performance In Non-profit Sport Organizations: The Role Of Managerial And Financial Performance And The Mediating Role Of Support For Innovation And Individual Creativity". Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613403/index.pdf.
Pełny tekst źródłaSpears-Dean, Dorothy. "Predicting the Diffusion of Next Generation 9-1-1 in the Commonwealth of Virginia: An Application Using the Deployment of Wireless E9-1-1 Technologies". VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/183.
Pełny tekst źródłaMcSharry, Patrick E. "Innovations in consistent nonlinear deterministic prediction". Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342590.
Pełny tekst źródłaEustace, Paul Alan. "Structural mass of innovative concept aircraft". Thesis, Loughborough University, 2001. https://dspace.lboro.ac.uk/2134/7361.
Pełny tekst źródłaLe, An Hai. "Innovative neural network approaches for petrophysical parameter prediction". Thesis, Heriot-Watt University, 2004. http://hdl.handle.net/10399/326.
Pełny tekst źródłaKsiążki na temat "Predicting Innovation"
Christopher, Layne, Arquilla John, Rand Corporation i Arroyo Center, red. Predicting military innovation. Santa Monica, CA: RAND, 1999.
Znajdź pełny tekst źródłaBarbara, Collier, i U.S. Army Research Laboratory, red. ARL, predictive technology. Adelphi, Md: Army Research Laboratory, 1993.
Znajdź pełny tekst źródłaKiruluta, Andrew M. Predictive head movement tracking using innovations generated by Kalman filters. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1993.
Znajdź pełny tekst źródłaBrueck, Terrance M. Forecasting the future: Progress, change, and predictions for the water sector. Denver, Colo: Water Research Foundation, 2012.
Znajdź pełny tekst źródłaLee, Jinsuk. Lifetime prediction for degradation of solar mirrors using step-stress accelerated testing. Golden, Colo.]: National Renewable Energy Laboratory, 2011.
Znajdź pełny tekst źródłaLittleton, Eliza Beth. Predicting rapid decision-making processes required by the dismounted objective force warrior. Alexandria, VA: United States Army Research Institute for the Behavioral and Social Sciences, 2003.
Znajdź pełny tekst źródłaYŏng-sŏn, Kwŏn. Homo k'ŏnbŏjŏnsŭ: Che 4-ch'a sanŏp hyŏngmyŏng kwa mirae sahoe = Homo convergence. Kyŏnggi-do P'aju-si: Asia, 2016.
Znajdź pełny tekst źródła(Firm), Knovel, red. Case studies in novel food processing technologies: Innovations in processing, packaging and predictive modelling. Oxford: Woodhead Publishing, 2010.
Znajdź pełny tekst źródłaTransizione, dall'era industriale a quella post-industriale: Verso la Terza Guerra Mondiale o alla conquista del futuro? Milano: Nuovi autori, 1986.
Znajdź pełny tekst źródłaMaxence, Layet, Bultez Adams Philippe i Kaplan Frédéric, red. Futur 2.0: Comprendre les 20 prochaines années. Limoges: Fyp éditions, 2007.
Znajdź pełny tekst źródłaCzęści książek na temat "Predicting Innovation"
Gatignon, Hubert, David Gotteland i Christophe Haon. "Predicting New Product Acceptance". W Making Innovation Last: Volume 2, 211–71. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1007/978-1-137-57264-6_5.
Pełny tekst źródłaNg, Shien Wee, Hoa Khanh Dam, Morakot Choetkiertikul i Aditya Ghose. "Predicting Issues for Resolving in the Next Release". W Service Research and Innovation, 164–77. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76587-7_11.
Pełny tekst źródłaHiltunen, Elina. "Some Thoughts about Predicting the Future, Its Ease and Difficulty". W Foresight and Innovation, 17–25. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9781137337702_2.
Pełny tekst źródłaChadha, Akalbir Singh, Yashowardhan Shinde, Neha Sharma i Prithwis Kumar De. "Predicting CO2 Emissions by Vehicles Using Machine Learning". W Data Management, Analytics and Innovation, 197–207. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2600-6_14.
Pełny tekst źródłaBhosale, Hrushikesh, Aamod Sane, Vigneshwar Ramakrishnan i Valadi K. Jayaraman. "Distributed Reduced Alphabet Representation for Predicting Proinflammatory Peptides". W Data Management, Analytics and Innovation, 161–73. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1414-2_14.
Pełny tekst źródłaMaiti, Reetun, i Balagopal G. Menon. "Predicting Injury Severity in Construction Using Logistic Regression". W Data Management, Analytics and Innovation, 175–85. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1414-2_15.
Pełny tekst źródłaMohanty, Samuka, i Rajashree Dash. "Predicting the Price of Gold: A CSPNN-DE Model". W Smart Innovation, Systems and Technologies, 289–97. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6202-0_29.
Pełny tekst źródłaWilliams, Louis, Salman Waqar, Tom Sherman i Giovanni Masala. "Comparative Study of Pattern Recognition Methods for Predicting Glaucoma Diagnosis". W Innovation in Medicine and Healthcare, 93–103. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5852-8_9.
Pełny tekst źródłaPoovammal, E., Mayank Kumar Nagda i K. Annapoorani. "Predicting Property Prices: A Universal Model". W EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, 259–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19562-5_26.
Pełny tekst źródłaRadhakrishnan, Hari Kumar, C. P. Ramanarayanan i R. Bharath. "Machine Learning Based Automated Process for Predicting the Anomaly in AIS Data". W Data Management, Analytics and Innovation, 303–14. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2600-6_21.
Pełny tekst źródłaStreszczenia konferencji na temat "Predicting Innovation"
Ahmad Rafi, Mohamed Eshaq, i Scott Chase. "Transforming Grammars for Goal Driven Style Innovation". W eCAADe 2007: Predicting the Future. eCAADe, 2007. http://dx.doi.org/10.52842/conf.ecaade.2007.879.
Pełny tekst źródłaThomson, A., M. Haggith i Ravi Prabhu. "Innovation diffusion: predicting success of system development". W Proceedings. 15th International Workshop on Database and Expert Systems Applications, 2004. IEEE, 2004. http://dx.doi.org/10.1109/dexa.2004.1333545.
Pełny tekst źródłaYu, Chen-Hsiang, Jungpin Wu i Aa-Chi Liu. "PREDICTING LEARNING OUTCOMES WITH MOOCS CLICKSTREAMS". W 2nd Eurasian Conference on Educational Innovation 2019. International Institute of Knowledge Innovation and Invention Private Limited, 2019. http://dx.doi.org/10.35745/ecei2019v2.079.
Pełny tekst źródłaKorableva, Olga, Viktoriya Mityakova i Olga Kalimullina. "Designing a Decision Support System for Predicting Innovation Activity". W 22nd International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009565706190625.
Pełny tekst źródłaSrivatsa, Hosur Srinivasan, Arun R, Sandeep N i Vijaya Kumar S. "Predicting Green Innovation Levels Among Automobile and Earthmoving Sectors". W 2nd Indian International Conference on Industrial Engineering and Operations Management. Michigan, USA: IEOM Society International, 2022. http://dx.doi.org/10.46254/in02.20220283.
Pełny tekst źródłaVesic, Ana, Vuk Ignjatovic, Sava Lakicevic, Luka Lakicevic, Bojan Gutic, Hristo Skacev, Dusan Dotlic, Andrej Micovic, Marina Marjanovic Jakovljevic i Miodrag Zivkovic. "Predicting Plant Water and Soil Nutrient Requirements". W 2020 Zooming Innovation in Consumer Technologies Conference (ZINC). IEEE, 2020. http://dx.doi.org/10.1109/zinc50678.2020.9161433.
Pełny tekst źródłaHajek, Petr, i Jan Stejskal. "Predicting the innovation activity of chemical firms using an ensemble of decision trees". W 2015 11th International Conference on Innovations in Information Technology (IIT). IEEE, 2015. http://dx.doi.org/10.1109/innovations.2015.7381511.
Pełny tekst źródłaKekulanadara, K. M. O. V. K., B. T. G. S. Kumara i Banujan Kuhaneswaran. "Comparative Analysis of Machine Learning Algorithms for Predicting Air Quality Index". W 2021 From Innovation To Impact (FITI). IEEE, 2021. http://dx.doi.org/10.1109/fiti54902.2021.9833033.
Pełny tekst źródłaKlavans, Richard, Kevin Boyack i Caleb Smith. "Field Effects in Predicting Exceptional Growth in Research Communities". W 27th International Conference on Science, Technology and Innovation Indicators (STI 2023). International Conference on Science, Technology and Innovation Indicators, 2023. http://dx.doi.org/10.55835/643f1aa90f649f6042841876.
Pełny tekst źródłaKhalaf, Fatema, i Subhashini S. Baskaran. "Predicting Acute Respiratory Failure Using Fuzzy Classifier". W 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD). IEEE, 2023. http://dx.doi.org/10.1109/itikd56332.2023.10099746.
Pełny tekst źródłaRaporty organizacyjne na temat "Predicting Innovation"
Coughlan, Peter, William Gates i Jeremy Arkes. Innovations in Defense Acquisition: Asymmetric Information, Mechanism Design and Prediction Markets. Fort Belvoir, VA: Defense Technical Information Center, luty 2011. http://dx.doi.org/10.21236/ada563409.
Pełny tekst źródłaJoseph, Earl C., i Steve Conway. Create full-scale predictive economic models on ROI and innovation with performance computing. Office of Scientific and Technical Information (OSTI), październik 2017. http://dx.doi.org/10.2172/1405141.
Pełny tekst źródłaBitz, Cecilia M. An Innovative Network to Improve Sea Ice Prediction in a Changing Arctic. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2014. http://dx.doi.org/10.21236/ada617899.
Pełny tekst źródłaGoldsmith, Stephen, Susan Crawford i Benjamin Weinryb Grohsgal. Innovations in Public Service Delivery: Issue No. 4: Predictive Analytics: Driving Improvements Using Data. Inter-American Development Bank, lipiec 2016. http://dx.doi.org/10.18235/0000421.
Pełny tekst źródłaSánchez- Sesma, Francisco José, Hiroshi Kawase i Joseline Mena Negrete. Working Paper PUEAA No. 5. The collaboration between Mexico and Japan in earthquake engineering and seismology. Universidad Nacional Autónoma de México, Programa Universitario de Estudios sobre Asia y África, 2022. http://dx.doi.org/10.22201/pueaa.003r.2022.
Pełny tekst źródłaMattsson, Ann Elisabet, Scott A. Mitchell i Stephen W. Thomas. LDRD 102610 final report new processes for innovative microsystems engineering with predictive simulation. Office of Scientific and Technical Information (OSTI), sierpień 2007. http://dx.doi.org/10.2172/913217.
Pełny tekst źródłaХолошин, Ігор Віталійович, Наталя Борисівна Пантелєєва, Олександр Миколайович Трунін, Людмила Володимирівна Бурман i Ольга Олександрівна Калініченко. Infrared Spectroscopy as the Method for Evaluating Technological Properties of Minerals and Their Behavior in Technological Processes. E3S Web of Conferences, 2020. http://dx.doi.org/10.31812/123456789/3929.
Pełny tekst źródłaSandford, Robert, Vladimir Smakhtin, Colin Mayfield, Hamid Mehmood, John Pomeroy, Chris Debeer, Phani Adapa i in. Canada in the Global Water World: Analysis of Capabilities. United Nations University Institute for Water, Environment and Health, listopad 2018. http://dx.doi.org/10.53328/vsgg2030.
Pełny tekst źródłaSaville, Alan, i Caroline Wickham-Jones, red. Palaeolithic and Mesolithic Scotland : Scottish Archaeological Research Framework Panel Report. Society for Antiquaries of Scotland, czerwiec 2012. http://dx.doi.org/10.9750/scarf.06.2012.163.
Pełny tekst źródłaLandau, Sergei Yan, John W. Walker, Avi Perevolotsky, Eugene D. Ungar, Butch Taylor i Daniel Waldron. Goats for maximal efficacy of brush control. United States Department of Agriculture, marzec 2008. http://dx.doi.org/10.32747/2008.7587731.bard.
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