Clustering techniques and innovation-based comparison in Londrina and Region companies
DOI:
https://doi.org/10.5433/1679-0375.2024.v45.49522Keywords:
innovation, clustering, k-means, hierarchical clusteringAbstract
Innovation is often considered a cornerstone for success across various companies. However, research focused on measuring and describing innovation frequently relies on classical statistical techniques. In this context, this study applied unsupervised machine learning techniques to cluster companies in the Londrina region, investigating how variables related to innovation differ among the identified clusters. Data were collected through a survey instrument adapted from CIS 4 and PINTEC, encompassing 26 responding companies, although 23 were analyzed in this study. Four clustering algorithms were employed: k-means, k-means+PCA, hierarchical, and hierarchical+PCA. Regarding the results, the hierarchical+PCA algorithm showed improved separation between service and manufacturing companies. Clusters identified with the value ``"1" indicated concerns related to regular investment in R&D, collaborations for innovation, and requests/registrations of patents in the last three years. Analyzing demographic characteristics revealed that clusters identified by hierarchical+PCA exhibited a higher presence of service sector companies, while cluster 1 showed a prevalence of industries, suggesting that these possess more innovative characteristics in the Londrina region.
Metrics
References
Aarstad, J., & Kvitastein, O. A. (2020). Enterprise R&D investments, product innovation and the regional industry structure. Regional Studies, 54(3), 366–376.
Acs, Z. J., & Audretsch, D. B. (Eds.). (2003). Handbook of Entrepreneurship Research (pp. 55-79). Springer.
Aidoo, E. N., Appiah, S. K., Awashie, G. E., Boateng, A., & Darko, G. (2021). Geographically weighted principal component analysis for characterising the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana. Heliyon, 7(9), e08039.
Akman, G., Yorur, B., Boyaci, A. I., & Chiu, M.-C. (2023). Assessing innovation capabilities of manufacturing companies by combination of unsupervised and supervised machine learning approaches. Applied Soft Computing, 147, 110735.
Alam, S., Dobbie, G., Koh, Y. S., Riddle, P., & Ur Rehman, S. (2014). Research on particle swarm optimization based clustering: A systematic review of literature and techniques. Swarm and Evolutionary Computation, 17, 1–13.
Anaconda. (2023). Anaconda: The Operating System for AI.
Anzola-Román, P., Bayona-Sáez, C., & García-Marco, T. (2018). Organizational innovation, internal R&D and externally sourced innovation practices: Effects on technological innovation outcomes. Journal of Business Research, 91, 233–247.
Barney, J. (2010). Gaining and sustaining competitive advantage (4th ed.). Pearson.
Basberg, B. L. (1987). Patents and the measurement of technological change: A survey of the literature. Research Policy, 16(2–4), 131–141.
Bolívar-Ramos, M. T. (2017). The relation between R&D spending and patents: The moderating effect of collaboration networks. Journal of Engineering and Technology Management, 46, 26–38.
Ceccagnoli, M. (2009). Appropriability, Preemption, and Firm Performance. Strategic Management Journal, 30(1), 81–98.
Claudino, T. B., Santos, S. M. dos, Cabral, A. C. de A., & Pessoa, M. N. M. (2017). Fostering and limiting factors of innovation in Micro and Small Enterprises. RAI Revista de Administração e Inovação, 14(2), 130–139.
Condrobimo, A. R., Sano, A. V. D., & Nindito, H. (2016). The Application Of K-Means Algorithm For LQ45 Index on Indonesia Stock Exchange. ComTech: Computer, Mathematics and Engineering Applications, 7(2), 151.
Confederação Nacional da Indústria [CNI]. (2021). Inovação na indústria: Pesquisa com líderes empresariais.
da Silva, A. L., & Guerrini, F. M. (2021). Reference model for building innovation networks in information technology. Gestão & Produção, 28(3), 1–20.
de Castro, L. N., & Ferrari, D. G. (2016). Introdução à mineração de dados: Conceitos básicos, algoritmos e aplicações. Saraiva Uni.
de Carvalho, H. G., dos Reis, D. R., & Cavalcante, M. B. (2011). Gestão da inovação. Aymará.
de Castro, R. G., da Silva, J. F., & Paula, F. O. de. (2020). Inovação de serviço e seu impacto no desempenho financeiro. Pretexto, 21(1), 86–102.
Eszergár-Kiss, D., & Caesar, B. (2017). Definition of user groups applying Ward’s method. Transportation Research Procedia, 22, 25–34.
Etzkowitz, H., & Zhou, C. (2017). Hélice Tríplice: inovação e empreendedorismo universidade-indústria-governo. Estudos Avançados, 31(90), 23–48.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37–53.
Furtado, A., Quadros, R., & Domingues, S. A. (2007). Intensidade de P&D das empresas brasileiras. Inovação Uniemp, 3(6), 26–27.
Galvão, N. D., & Marin, H. d. F. (2009). Técnica de mineração de dados: uma revisão da literatura. Acta Paulista de Enfermagem, 22(5), 686–690.
Goldschmidt, R., Passos, E., & Bezerra, E. (2015). Data mining: conceitos, técnicas, algoritmos, orientações e aplicações (2nd ed.). Gen LTC.
Governo Federal. (2022). Serviços crescem pelo quarto mês seguido, aponta IBGE.
Granato, D., Santos, J. S., Escher, G. B., Ferreira, B. L., & Maggio, R. M. (2018). Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective. Trends in Food Science & Technology, 72, 83–90.
Huang, X., Ma, L., Li, R., & Liu, Z. (2020). Determinants of Innovation Ecosystem in Underdeveloped Areas—Take Nanning High-Tech Zone in Western China as an Example. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 135.
Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95.
Iata, C., & Cunha, C. J. C. de A. (2018). A Atuação da Tríplice Hélice em Santa Catarina pela Visão dos Núcleos de Inovação Tecnológica (NITs) do Estado. Navus. Revista de Gestão e Tecnologia, 8(4), 180–188.
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning (8th ed.). Springer.
James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning with Applications in Python (Vol. 1). Springer.
Kinoshita, K. F., Cirani, C. B., & da Silva, W. N. (2013). A Inovação em Serviços no Brasil: uma Comparação Internacional. Faculdade de Economia, Administração, Contabilidade e Atuária da Universidade de São Paulo, Seminários em Administração [Anais]. 16 SEMEAD Seminários em Administração, São Paulo, Brasil.
Kon, A. (2016). Ecossistemas de inovação: A natureza da inovação em serviços. Revista de Administração, Contabilidade e Economia Da Fundace, 7(1), 15–27.
Liu, Y., Liang, C. C., & Phillips, F. (2020). Precursors of intellectual property rights enforcement in East and Southeast Asia. Industrial Marketing Management, 90, 133–142.
Luzzini, D., Amann, M., Caniato, F., Essig, M., & Ronchi, S. (2015). The path of innovation: purchasing and supplier involvement into new product development. Industrial Marketing Management, 47, 109–120.
Ma, X., Hao, Y., Li, X., Liu, J., & Qi, J. (2023). Evaluating global intelligence innovation: An index based on machine learning methods. Technological Forecasting and Social Change, 194, 1–17.
Maćkiewicz, A., & Ratajczak, W. (1993). Principal components analysis (PCA). Computers & Geosciences, 19(3), 303–342.
Mairesse, J., & Mohnen, P. (2010). Using Innovation Surveys for Econometric Analysis. In B. H. Hall, & N. Rosenberg Handbook of the Economics of Innovation (pp. 1129–1155, Vol. 2). Elsevier.
Najafi-Tavani, S., Najafi-Tavani, Z., Naudé, P., Oghazi, P., & Zeynaloo, E. (2018). How collaborative innovation networks affect new product performance: Product innovation capability, process innovation capability, and absorptive capacity. Industrial Marketing Management, 73, 193–205.
Organization for Economic Co-operation and Developmen [OECD]. (2018). Oslo Manual 2018: Guidelines for Collecting, Reporting and Using Data on Innovation (4th ed.). OECD.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830.
Raschka, S. (2015). Python Machine Learning. Packt Publishing Ltd.
Rhoden, I., Weller, D., & Voit, A. K. (2022). Spatio-Temporal Dynamics of European Innovation—An Exploratory Approach via Multivariate Functional Data Cluster Analysis. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 1–23.
Robinson, S., & Stubberud, H. A. (2012). Issues in innovation for Norwegian SMES. Journal of International Business Research, 11(1), 53–62.
Roux, M. (2018). A Comparative Study of Divisive and Agglomerative Hierarchical Clustering Algorithms. Journal of Classification, 35(2), 345–366.
Santos, R. de O., Gorgulho, B. M., Castro, M. A. de, Fisberg, R. M., Marchioni, D. M., & Baltar, V. T. (2019). Principal Component Analysis and Factor Analysis: differences and similarities in Nutritional Epidemiology application. Revista Brasileira de Epidemiologia, 22, 1–14.
Shannon, W. D. (2007). Cluster Analysis.
Silva, L. A. (2016). Introdução à Mineração de Dados com aplicações em R (1st ed.). Gen LTC.
Sinaga, K. P., & Yang, M.-S. (2020). Unsupervised K-Means Clustering Algorithm. IEEE Access, 8, 80716–80727.
The pandas development team. (2020). Pandas.
Waskom, M. (2021). Seaborn: Statistical data visualization. Journal of Open Source Software, 6(60), 3021.
Zaini, W. M. F., Lai, D. T. C., & Lim, R. C. (2022). Identifying patent classification codes associated with specific search keywords using machine learning. World Patent Information, 71, 1–10.
Zengin, K., Esgi, N., Erginer, E., & Aksoy, M. E. (2011). A sample study on applying data mining research techniques in educational science: Developing a more meaning of data. Procedia - Social and Behavioral Sciences, 15, 4028–4032.
Zhou, J., & Luo, Q. (2023). Influence factor studies based on ensemble learning on the innovation performance of technology mergers and acquisitions. Mathematics and Computers in Simulation. [In press], 1–23.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ana Paula Barbosa de Morais, Matheus Santos Dias, Bruno Samways dos Santos, Rafael Henrique Palma Lima, Pedro Rochavetz de Lara Andrade
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Copyright Declaration for articles published in this journal is the author’s right. Since manuscripts are published in an open access Journal, they are free to use, with their own attributions, in educational and non-commercial applications. The Journal has the right to make, in the original document, changes regarding linguistic norms, orthography, and grammar, with the purpose of ensuring the standard norms of the language and the credibility of the Journal. It will, however, respect the writing style of the authors. When necessary, conceptual changes, corrections, or suggestions will be forwarded to the authors. In such cases, the manuscript shall be subjected to a new evaluation after revision. Responsibility for the opinions expressed in the manuscripts lies entirely with the authors.
This journal is licensed with a license Creative Commons Attribution-NonCommercial 4.0 International.
