Deep learning diffusion by search trend: a country-level analysis

Authors

  • Carlos Kazunari Takahashi Escola Superior de Propaganda e Marketing - ESPM, São Paulo, (Brasil)
  • Júlio César Bastos de Figueiredo Escola Superior de Propaganda e Marketing - ESPM, São Paulo, (Brasil)
  • José Eduardo Ricciardi Favaretto Escola Superior de Propaganda e Marketing - ESPM, São Paulo, (Brasil)

DOI:

https://doi.org/10.24023/FutureJournal/2175-5825/2023.v15i1.695

Keywords:

Deep learning, Innovation diffusion, Search trend, Country-level analysis, BRICS, Google trends

Abstract

Purpose: The theory of diffusion of innovation is the theoretical lens discussed in this research to analyze the diffusion of the deep learning theme in the BRICS and OECD countries. As little has been developed to understand country-level analysis and a theme such as innovation, this research sought to fill this gap.

Originality/Value: This research demonstrates how it is possible to use Search Trends to analyze the diffusion of a thematic, enabling the extension of the diffusion of innovation theory beyond the sale of products.

Methods: Google Trends was used for data collection and to provide up-to-date information, and two different statistical models were used: clustering to identify patterns in the first analysis, and the Bass diffusion model, aiming at comparing countries considering the curve peak, the innovation coefficient, and the imitation coefficient.

Results: The findings of this research identified that China has the highest innovation coefficient among the members of the BRICS and Japan among the members of the OECD.

Conclusions: This study brought both a theoretical contribution, allowing the expansion of the diffusion of innovations that use a theme as an object of innovation, as well as a practical implication, enabling research in an accessible and democratic way.

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Author Biographies

Carlos Kazunari Takahashi, Escola Superior de Propaganda e Marketing - ESPM, São Paulo, (Brasil)

PhD Candidate at Escola Superior de Propaganda e Marketing (ESPM) in São Paulo (Brazil). He holds a Master degree in Business Administration from Instituto de Ensino e Pesquisa (Insper). His research interests include Diffusion of Innovation, Business Innovation, Artificial Intelligence, Technology, and Innovation Management.

Júlio César Bastos de Figueiredo, Escola Superior de Propaganda e Marketing - ESPM, São Paulo, (Brasil)

Professor of the Masters and Doctorate Program in International Management at Escola Superior de Propaganda e Marketing (ESPM). He holds a Ph.D. in Nuclear Physics from the University of São Paulo (USP). His research interests include Business Modeling and Simulation, which deals with the study and application of mathematical modeling and computer simulation techniques, with the development of models to understand the phenomena of marketing and administration in the global environment.

José Eduardo Ricciardi Favaretto , Escola Superior de Propaganda e Marketing - ESPM, São Paulo, (Brasil)

Researcher and Professor in Innovation Diffusion and Data Science at Escola Superior de Propaganda e Marketing (ESPM) in São Paulo (Brazil). He holds a Ph.D. in Management Information Systems from Fundação Getulio Vargas (FGV EAESP). His research interests include Diffusion of Innovations, Artificial Intelligence in global markets, Data Science, technology and innovation management, big data analytics, and stage level measurement of information and communication technology (ICT) in organizations.

References

Askitas, N., & Zimmermann, K. F. (2015). Health and well-being in the great recession. International Journal of Manpower, 36(1), 26–47. https://doi.org/10.1108/IJM-12-2014-0260

Bass, F. M. (1969). A New Product Growth for Model Consumer Durables. Management Science, 15(5), 215–227. https://doi.org/10.1287/mnsc.15.5.215

Bass, F. M. (2004). Comments on “A New Product Growth for Model Consumer Durables The Bass Model.” Management Science, 50(12_supplement), 1833–1840. https://doi.org/10.1287/mnsc.1040.0300

Blazquez, D., & Domenech, J. (2018). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change, 130, 99–113. https://doi.org/10.1016/j.techfore.2017.07.027

Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., & Zieba, K. (2016). End to End Learning for Self-Driving Cars. 1–9. http://arxiv.org/abs/1604.07316

Brynjolfsson, E., Geva, T., & Reichman, S. (2016). Crowd-Squared: Amplifying the Predictive Power of Search Trend Data. MIS Quarterly, 40(4), 941–961. https://doi.org/10.25300/MISQ/2016/40.4.07

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data and Society, 3(1), 1–12. https://doi.org/10.1177/2053951715622512

Chen, X. W., & Lin, X. (2014). Big data deep learning: Challenges and perspectives. IEEE Access, 2, 514–525. https://doi.org/10.1109/ACCESS.2014.2325029

Cheng, A.-C. (2012). Exploring the relationship between technology diffusion and new material diffusion: The example of advanced ceramic powders. Technovation, 32(3–4), 163–167. https://doi.org/10.1016/j.technovation.2011.10.008

Choi, H., & Varian, H. (2012). Predicting the Present with Google Trends. The Economic Record, 88(special issue June), 2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.x

Chumnumpan, P., & Shi, X. (2019). Understanding new products’ market performance using Google Trends. Australasian Marketing Journal, 27(2), 91–103. https://doi.org/10.1016/j.ausmj.2019.01.001

Cornell University; INSEAD; WIPO. (2019a). Analysis - Explore the interactive database of the gii-2019 indicators. https://www.globalinnovationindex.org/analysis-indicator

Cornell University; INSEAD; WIPO. (2019b). Global Innovation Index - Report. https://www.globalinnovationindex.org/gii-2019-report

Crane, A., Henriques, I., Husted, B. W., & Matten, D. (2016). What Constitutes a Theoretical Contribution in the Business and Society Field? Business and Society, 55(6), 783–791. https://doi.org/10.1177/0007650316651343

Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., & Zupan, B. (2013). Orange: Data Mining Toolbox in Python. Journal of Machine Learning Research, 14, 2349–2353. http://jmlr.org/papers/v14/demsar13a.html

Desmarchelier, B., & Fang, E. S. (2016). National Culture and Innovation diffusion. Exploratory insights from agent-based modeling. Technological Forecasting and Social Change, 105, 121–128. https://doi.org/10.1016/j.techfore.2016.01.018

Dos Santos, M. J. P. L. (2018). Nowcasting and forecasting aquaponics by Google Trends in European countries. Technological Forecasting and Social Change, 134(June), 178–185. https://doi.org/10.1016/j.techfore.2018.06.002

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056

Freeman, C., & Soete, L. (2009). Developing science, technology and innovation indicators: What we can learn from the past. Research Policy, 38(4), 583–589. https://doi.org/10.1016/j.respol.2009.01.018

Ganglmair-Wooliscroft, A., & Wooliscroft, B. (2016). Diffusion of innovation: The case of ethical tourism behavior. Journal of Business Research, 69(8), 2711–2720. https://doi.org/10.1016/j.jbusres.2015.11.006

Gefen, Karahanna, & Straub. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, 27(1), 51. https://doi.org/10.2307/30036519

Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 3354–3361. https://doi.org/10.1109/CVPR.2012.6248074

Geroski, P. A. (2000). Models of technology diffusion. Research Policy, 29(4–5), 603–625. https://doi.org/10.1016/S0048-7333(99)00092-X

Godec, P., Pančur, M., Ilenič, N., Čopar, A., Stražar, M., Erjavec, A., Pretnar, A., Demšar, J., Starič, A., Toplak, M., Žagar, L., Hartman, J., Wang, H., Bellazzi, R., Petrovič, U., Garagna, S., Zuccotti, M., Park, D., Shaulsky, G., & Zupan, B. (2019). Democratized image analytics by visual programming through integration of deep models and small-scale machine learning. Nature Communications, 10(1), 1–7. https://doi.org/10.1038/s41467-019-12397-x

Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010). Predicting consumer behavior with web search. Proceedings of the National Academy of Sciences of the United States of America, 107(41), 17486–17490. https://doi.org/10.1073/pnas.1005962107

Google. (2020a). FAQ about Google Trends data - Google Trends. https://support.google.com/trends/answer/4365533?hl=en

Google. (2020b). Google. https://www.google.com/

Google. (2020c). Google Trends. https://trends.google.com/trends

Google. (2020d). Google Trends “Deep Learning.” https://trends.google.com/trends/explore?hl=en&date=2014-01-01 2020-03-31&q=deep learning

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P. C., Mega, J. L., & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA - Journal of the American Medical Association, 316(22), 2402–2410. https://doi.org/10.1001/jama.2016.17216

Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69. https://doi.org/10.1038/s41591-018-0268-3

Hofstede, G. (2001). Culture’s recent consequences: Using dimension scores in theory and research. International Journal of Cross Cultural Management, 1(1), 11–17. https://doi.org/10.1177/147059580111002

Hu, Y. (2013). Hyperlinked actors in the global knowledge communities and diffusion of innovation tools in nascent industrial field. Technovation, 33(2–3), 38–49 https://doi.org/10.1016/j.technovation.2012.10.001

Im, S., Mason, C. H., & Houston, M. B. (2007). Does innate consumer innovativeness relate to new product/service adoption behavior? the intervening role of social learning via vicarious innovativeness. Journal of the Academy of Marketing Science, 35(1), 63–75. https://doi.org/10.1007/s11747-006-0007-z

Jahanmir, S. F., & Lages, L. F. (2016). The late-adopter scale: A measure of late adopters of technological innovations. Journal of Business Research, 69(5), 1701–1706. https://doi.org/10.1016/j.jbusres.2015.10.041

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415

Jun, S. P., Sung, T. E., & Park, H. W. (2017). Forecasting by analogy using the web search traffic. Technological Forecasting and Social Change, 115, 37–51. https://doi.org/10.1016/j.techfore.2016.09.014

Jun, S. P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological Forecasting and Social Change, 130(November 2017), 69–87. https://doi.org/10.1016/j.techfore.2017.11.009

Kong, D., Zhou, Y., Liu, Y., & Xue, L. (2017). Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country. Technological Forecasting and Social Change, 119, 80–97. https://doi.org/10.1016/j.techfore.2017.02.035

Kupfer, A., & Zorn, J. (2019). Valuable information in early sales proxies: The use of Google search ranks in portfolio optimization. Journal of Forecasting, 38(1), 1–10. https://doi.org/10.1002/for.2547

Kwon, S., Liu, X., Porter, A. L., & Youtie, J. (2019). Research addressing emerging technological ideas has greater scientific impact. Research Policy, 48(9), 1–16. https://doi.org/10.1016/j.respol.2019.103834

Lacasa, I. D., Jindra, B., Radosevic, S., & Shubbak, M. (2019). Paths of technology upgrading in the BRICS economies. Research Policy, 48(1), 262–280. https://doi.org/10.1016/j.respol.2018.08.016

Lassar, W. M., Manolis, C., & Lassar, S. S. (2005). The relationship between consumer innovativeness, personal characteristics, and online banking adoption. International Journal of Bank Marketing, 23(2), 176–199. https://doi.org/10.1108/02652320510584403

LeCun, Y. (2018). The Power and Limits of Deep Learning. Research Technology Management, 61(6), 22–27. https://doi.org/10.1080/08956308.2018.1516928

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

Lee, H., & Kang, P. (2018). Identifying core topics in technology and innovation management studies: a topic model approach. Journal of Technology Transfer, 43(5), 1291–1317. https://doi.org/10.1007/s10961-017-9561-4

Mahajan, V., & Muller, E. (1994). Innovation diffusion in a borderless global market: Will the 1992 unification of the European Community accelerate diffusion of new ideas, products, and technologies? Technological Forecasting & Social Change, 45(3), 221–235. https://doi.org/10.1016/0040-162590047-7

Mahajan, V., Muller, E., & Bass, F. M. (1990). New Product Diffusion Models in Marketing: A Review and Directions for Research. Journal of Marketing, 54(1), 1–26. https://doi.org/10.2307/1252170

Mahajan, V., Muller, E., & Srivastava, R. K. (1990). Determinants of Adopter Categories by Using Innovation Diffusion Models. Journal of Marketing Research, 27(1), 37–50. https://doi.org/10.1177/002224379002700104

Mavragani, A., & Tsagarakis, K. P. (2016). YES or NO: Predicting the 2015 GReferendum results using Google Trends. Technological Forecasting and Social Change, 109, 1–5. https://doi.org/10.1016/j.techfore.2016.04.028

Meade, N., & Islam, T. (2006). Modelling and forecasting the diffusion of innovation – A 25-year review. International Journal of Forecasting, 22(3), 519–545. https://doi.org/10.1016/j.ijforecast.2006.01.005

Michalakelis, C., Varoutas, D., & Sphicopoulos, T. (2010). Innovation diffusion with generation substitution effects. Technological Forecasting and Social Change, 77(4), 541–557. https://doi.org/10.1016/j.techfore.2009.11.001

Ministry of Foreign Affairs - Brazil. (2020). BRICS – Brazil, Russia, India, China, South Africa. https://www.gov.br/mre/en/subjects/international-mechanisms/inter-regional-mechanisms/brics-brazil-russia-india-china-south-africa?set_language=en

Naseri, M. B., & Elliott, G. (2013). The diffusion of online shopping in Australia: Comparing the Bass, Logistic and Gompertz growth models. Journal of Marketing Analytics, 1(1), 49–60. https://doi.org/10.1057/jma.2013.2

Omar, M., Mehmood, A., Choi, G. S., & Park, H. W. (2017). Global mapping of artificial intelligence in Google and Google Scholar. Scientometrics, 113(3), 1269–1305. https://doi.org/10.1007/s11192-017-2534-4

Organisation for Economic Co-operation and Development. (2020). Our global reach. http://www.oecd.org/about/members-and-partners/

Papagiannidis, S., Gebka, B., Gertner, D., & Stahl, F. (2015). Diffusion of web technologies and practices: A longitudinal study. Technological Forecasting and Social Change, 96, 308–321. https://doi.org/10.1016/j.techfore.2015.04.011

Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: A critical review and research directions. International Journal of Research in Marketing, 27(2), 91–106. https://doi.org/10.1016/j.ijresmar.2009.12.012

Perlin, M. S., Caldeira, J. F., Santos, A. A. P., & Pontuschka, M. (2016). Can we predict the financial markets based on google’s search queries? Journal of Forecasting, 36(4), 454–467. https://doi.org/10.1002/for.2446

R Core Team. (2020). R: A Language and Environment for Statistical Computing. https://www.r-project.org

Rogers, E. M. (1976). New Product Adoption and Diffusion. Journal of Consumer Research, 2(4), 290–301. http://www.jstor.org/stable/2488658

Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.

Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827–1843. https://doi.org/10.1016/j.respol.2015.06.006

Schaer, O., Kourentzes, N., & Fildes, R. (2019). Demand forecasting with user-generated online information. International Journal of Forecasting, 35(1), 197–212. https://doi.org/10.1016/j.ijforecast.2018.03.005

Shi, X., Fernandes, K., & Chumnumpan, P. (2014). Diffusion of multi-generational high-technology products. Technovation, 34(3), 162–176. https://doi.org/10.1016/j.technovation.2013.11.008

South Africa Government. (2020). BRICS (Brazil, Russia, India, China, South Africa). https://www.gov.za/about-government/brics-brazil-russia-india-china-south-africa-1

Takieddine, S., & Sun, J. (2015). Internet banking diffusion: A country-level analysis. Electronic Commerce Research and Applications, 14(5), 361–371. https://doi.org/10.1016/j.elerap.2015.06.001

Talukdar, D., Sudhir, K., & Ainslie, A. (2002). Investigating New Product Diffusion Across Products and Countries. Marketing Science, 21(1), 97–114. https://doi.org/10.1287/mksc.21.1.97.161

Teece, D. J. (2018). Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Research Policy, 47(8), 1367–1387. https://doi.org/10.1016/j.respol.2017.01.015

The World Bank Group. (2020a). The World Bank Data - OECD members. https://data.worldbank.org/region/oecd-members

The World Bank Group. (2020b). World Bank national accounts data, and OECD National Accounts data files. https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?most_recent_value_desc=true

Tsai, B. H. (2013). Modeling diffusion of multi-generational LCD TVs while considering generation-specific price effects and consumer behaviors. Technovation, 33(10–11), 345–354. https://doi.org/10.1016/j.technovation.2013.05.002

Valente, T. W. (1996). Social network thresholds in the diffusion of innovations. Social Networks, 18(1), 69–89. https://doi.org/10.1016/0378-8733(95)00256-1

van Oorschot, J. A. W. H., Hofman, E., & Halman, J. I. M. (2018). A bibliometric review of the innovation adoption literature. Technological Forecasting and Social Change, 134(June), 1–21. https://doi.org/10.1016/j.techfore.2018.04.032

Ward, J. M. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845

World Economic Forum. (2019). A Framework for Developing a National Artificial Intelligence Strategy Centre for Fourth Industrial Revolution. August, 20. http://www3.weforum.org/docs/WEF_National_AI_Strategy.pdf

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Published

2023-03-24

How to Cite

Takahashi, C. K., Figueiredo, J. C. B. de, & Favaretto , J. E. R. . (2023). Deep learning diffusion by search trend: a country-level analysis. Future Studies Research Journal: Trends and Strategies, 15(1), e0695. https://doi.org/10.24023/FutureJournal/2175-5825/2023.v15i1.695

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