PLS-SEM model on business demand for technological services and R&D and innovation activities

    Juan J. García-Machado Affiliation
    ; Włodzimierz Sroka Affiliation
    ; Martyna Nowak Affiliation


The aim of the current study is to search for the elements that determine the companies’ demand for technological services, and by doing so, to contribute to the advancement of a closer University-Company partnership in the sphere of activities in research, development and innovation. Based on the PLS-SEM methodology, an explanatory-predictive model was drawn up, which concluded that the four most influential variables are: the influence of the environment, market conditions, the technology adoption decision and the economic characteristics of the company. The originality and main contributions of this work lie in the construction and design of the proposed model, particularly the application of both the Confirmatory Tetrad Analysis and the Global Goodness-of-Fit measures adapted for the scope of PLS-SEM, both aiming to elaborate on its use and to provide a model that could be used by other researchers in different regions. By implementing this type of analysis, it is possible to better understand the drivers that push the choice of enterprises concerning the demand for technological services and, subsequently, policymakers, academy, and R&D agencies, as well as corporations leading to better strategies for closer and stronger cooperation and collaboration among themselves.

Keyword : technology demand, technology adoption, R&D&I, PLS-SEM, CTA-PLS

How to Cite
García-Machado, J. J., Sroka, W., & Nowak, M. (2023). PLS-SEM model on business demand for technological services and R&D and innovation activities. Technological and Economic Development of Economy, 29(1), 1–22.
Published in Issue
Jan 12, 2023
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Acebo, E., Miguel-Dávila, J.-Á., & Nieto, M. (2021). The impact of University–Industry relationships on firms’ performance: A meta-regression analysis. Science and Public Policy, 48(2), 276–293.

Albort-Morant, G., Henseler, J., Cepeda-Carrión, G., & Leal-Rodríguez, A. (2018). Potential and realized absorptive capacity as complementary drivers of green product and process innovation performance. Sustainability, 10(2), 381.

Alhassany, H., & Faisal, F. (2018). Factors influencing the internet banking adoption decision in North Cyprus: An evidence from the partial least square approach of the structural equation modeling. Financial Innovation, 4(1), 1–21.

Ali, F., Rasoolimanesh, S., Sarstedt, M., Ringle, C., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514–538.

Alsajjan, B., & Dennis, C. (2010). Internet banking acceptance model: Cross-market examination. Journal of Business Research, 63(9–10), 957–963.

Ametic. (2017). Objetivo Aprovechar Sinergias del Sector Público y Privado.

Barclay, D., Thompson, R., & Higgings, C. (1995). The Partial Least Square (PLS) approach to causal modelling: Personal computer adoption and use as an illustration. Technology Studies, 2(2), 285–309.

Basque Institute of Statistics. (2020, January 29). Internal R&D expenditure (% GDP) by country. 2007–2018.

Bellini, E., Piroli, G., & Pennacchio, L. (2019). Collaborative know-how and trust in university–industry collaborations: Empirical evidence from ICT firms. The Journal of Technology Transfer, 44(6), 1939–1963.

Bollen, K., & Ting, K. (1993). Confirmatory tetrad analysis. Sociological Methodology, 23, 147–175.

Bollen, K., & Ting, K. (2000). A tetrad test for causal indicators. Psychological Methods, 5(1), 3–22.

Campbell, D., & Fiske, D. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.

Cohen, J. A. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). New York University.

Cohen, J. A. (1992). A power primer. Pyschologival Bulletin, 112(1), 155–519.

Cohen, W. M., Nelson, R. R., & Walsh, J. P. (2002). Links and impacts: The influence of public research on industrial R&D. Management Science, 48(1), 1–23.

Conde-Pumpido Touron, R., & Cerezo García, L. (2019). Investigación y Transferencia de Conocimiento en las Universidades Españolas 2017. CRUE Universidades Españolas.

Cygler, J., & Wyka, S. (2019). Internal barriers to international R&D cooperation: The case of Polish high tech firms. Forum Scientiae Oeconomia, 7(2), 25–45.

Dijkstra, T., & Henseler, J. (2015a). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10–23.

Dijkstra, T., & Henseler, J. (2015b). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316.

Domańska, A. (2018). Cooperation between knowledge-based institutions and business: Empirical studies and network theories. Forum Scientiae Oeconomia, 6(2), 81–94.

Duque, P. (2020, January 18). Ciencia y Universidad: elogio de la cooperación. El País.

Dutrénit, G., Vera-Cruz, A., Álvarez, J., & Rodríguez, L. (2003). Estrategia Tecnológica y Demanda de Investigación Básica a las Universidades y Centros: El Caso de Dos Empresas en México. El Trimestre Económico, 70(280), 835–877.

Etzkowitz, H. (2003). Innovation in innovation: The Triple Helix of university-industry-government relations. Social Science Information, 42(3), 293–337.

Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy, 29(2), 109–123.

Etzkowitz, H., Webster, A., Gebhardt, C., & Terra, B. R. C. (2000). The future of the university and the university of the future: Evolution of ivory tower to entrepreneurial paradigm. Research Policy, 29(2), 313–330.

Europa Press. (2018). La inversión de España en I+D+i se mantiene en el 1,2% del PIB, mismo valor de 2006, según IEE. Cienciaplus.

Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G-Power 3.1: Tests for correlation and regression analyses. Behaviour Research Methods, 41(4), 1149–1160.

Fernández, M. (2019, December 2). La investigación arrastra los pies hacia la empresa. El País.

Figueroa-García, G., García-Machado, J. J., & Pérez-Bustamante Yábar, D. C. (2018). Modeling the social factors that determine sustainable consumption behaviour in the community of Madrid. Sustainability, 10(8), 2811.

Francis, P. (2010). Mercados cambiantes. Forum de Comercio Internacional.

García-Machado, J. J. (2017). Assessing a moderating effect and the global fit of a PLS model on online trading. Marketing of Scientific and Research Organizations, 26(4), 1–34.

García-Machado, J. J., Roca, J., & de la Vega, J. (2012). User satisfaction of online trading systems: An empirical study. In A. Gil-Lafuente, J. Gil-Lafuente, & J. Merigó-Lindahl (Eds.), Studies in fuzziness and soft computing: Vol. 286. Soft computing in management and business economics (pp. 313–326). Springer.

García-Machado, J. J., Sroka, W., & Nowak, M. (2021). R&D and innovation collaboration between universities and business – A PLS-SEM model for the Spanish province of Huelva. Administrative Sciences, 11(3), 83.

González de la Fe, T. (2009). El modelo de Triple Hélice de relaciones universidad, industria y gobierno: Un análisis crítico. ARBOR Ciencia, Pensamiento y Cultura, 185(738), 737–755.

González Hermoso de Mendoza, A. (2011). La Innovación: un factor clave para la competitividad de las empresas. Consejería de Educación de la Comunidad de Madrid, Ed. Madrid: Innovatec – CEIM Confederación Empresarial de Madrid-CEOE.

Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioural Research, 26(3), 499–510.

Gudergan, S., Ringle, C., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis in PLS path modelling. Journal of Business Research, 61(12), 1238–1249.

Hair, Jr. J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, G. V. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM). European Business Review, 26(2), 106–121.

Hair, Jr. J. F., Hult, G. T., Ringle, C., & Sarsted, M. (2017). Primer on partial least squares structural equation modelling (PLS-SEM) (2nd ed). Sage Publications.

Hair, J., Hult, G., Ringle, C., Sarstedt, M., Castillo, J., Cepeda, G., & Roldán, J. (2019a). Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM) (Segunda ed.). SAGE Publications, Inc & OmniaScience.

Hair, J., Ringle, C., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–151.

Hair, J., Risher, J., Sarstedt, M., & Ringle, C. (2019b). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

Henseler, J. (2017). Adanco 2.0.1 User manual. Composite Modeling GmbH&Co.

Henseler, J. (2018). Partial least squares path modeling: Quo vadis? Quality & Quantity, 52(1), 1–8.

Henseler, J., Hubona, G., & Ray, P. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20.

Hu, L., & Bentler, P. (1998). Fit indices in covariance structure modeling: Sensitivity to under parameterized model misspecification. Psychological Methods, 3(4), 424–453.

Iqbal, A. M., Khan, A. S., Abdullah, J., Kulathuramaiyer, N., & Senin, A. A. (2022). Blended system thinking approach to strengthen the education and training in university-industry research collaboration. Technology Analysis & Strategic Management, 34(4), 447–460.

Jarvis, C., MacKenzie, S., & Podsakoff, P. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.

Jirčikova, E., Pavelkova, D., Bialic-Davendra, M., & Homolka, L. (2013). The age of clusters and its influence on their activity preferences. Technological and Economic Development of Economy, 19(4), 621–637.

Klein, A., Horak, S., Bacouël-Jentjens, S., & Li, X. (2021). Does culture frame technological innovativeness? A study of millennials in triad countries. European Journal of International Management, 15(4), 564–594.

Kline, R. B. (1998). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261.

Kollmuss, A., & Agyeman, J. (2002). Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behaviour? Environmental Education Research, 8(3), 240–260.

Labra Lillo, E. (2015, May 18). Ciencia y Tecnología y las PYMES ¿Un asunto de cultura o una estrategia? CONICYT:

Lai, V. S., & Li, H. (2005). Technology acceptance model for internet banking: An invariance analysis. Information & Management, 42(2), 373–386.

Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141.

Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204.

Leydesdorff, L. (2011). The Triple Helix, Quadruple Helix,..., and N-tuple of helices: Explanatory models for analyzing the knowledge-based economys. Journal of Knowledge Economics, 1–21.

Liu, N., He, Y., & Xu, Z. (2019). Evaluate Public-Private-Partnership’s advancement using double hierarchy hesitant fuzzy linguistic PROMETHEE with subjective and objective information from stakeholder perspective. Technological and Economic Development of Economy, 25(3), 386–420.

López-Hurtado, J. (2014). Modelos interpretativos de la relación estado-empresa-universidad. Clío América, 8(15), 111–122.

Magotra, I., Sharma, J., & Sharma, S. (2018). Investigating linkage between customer value and technology adoption behaviour: A study of banking sector in India. European Research on Management and Business Economics, 24(1), 17–26.

Marone, L., & Gonzales del Solar, R. (2007). Crítica, creatividad y rigor: Vértices de un triángulo culturalmente valioso. Interciencia, 32(5), 354–357.

Nitzl, C. (2016). The use of partial least squares structural equation modelling (PLS-SEM) in management accounting research: Directions for future theory development. Journal of Accounting Literature, 37(1), 19–35.

Porras Bueno, N. (2016). Metodología. Modelos de ecuaciones estructurales basados en la covarianza (CB-SEM) con STATA. Univ. Huelva, Mimeo.

Rawashdeh, A. (2015). Factors affecting adoption of internet banking in Jordan: Chartered accountant’s perspective. The International Journal of Bank Marketing, 33(4), 510–529.

Rigdon, E. (2005). Structural equation modeling: Nontraditional alternatives. In B. Everitt & D. Howell (Eds.), Encyclopedia of statistics in behavioural science. Wiley.

Ringle, C. M. (2016). Advanced PLS-SEM topics: PLS multigroup analysis (Working paper, Noviembre). University of Seville.

Ringle, C., Wende, S., & Becker, J. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. Retrieved from

Rogger, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Roldán, J., & Sánchez-Franco, M. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. In M. Mora, O. Gelman, A. Steenkamp, & M. Raisinghani (Eds.), Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193–221). IGI Global.

Sábato, J. (1997). Bases para un régimen de tecnología. REDES, IV(10), 117–153.

Salgado Beltrán, L., & Espejel Blanco, J. E. (2016). Análisis del estudio de las relaciones causales en el marketing. Innovar, 26(62), 79–94.

Sarstedt, M., Hair, J. F., Ringle, C. M., Thielec, K. O., & Gudergand, S. P. (2016). Estimation issues with PLS and CB-SEM: Where the bias lies! Journal of Business Research, 69(10), 3998–4010.

Sharma, S., & Govindaluri, S. (2014). Internet banking adoption in India. Journal of Indian Business Research, 6(2), 155–169.

Valdivieso Taborga, C. E. (2013). Comparación de los modelos formativo, reflexivo y de antecedentes de evaluación estudiantil del servicio de docencia. Revista de Metodos Cuantitativos Para la Economía y la Empresa, 16, 95–120.

Vega Jurado, J. M., Fernández de Lucio, I., & Huanca López, R. (2007). La relación Universidad-Empresa en América Latina: ¿Apropiación incorrecta de modelos foráneos? Journal of Technology Management & Innovation, 2(3), 97–109.

Venkatesh, V., & Davis, F. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204.

Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: U.S. Vs. China. Journal of Global Information Technology Management, 13(1), 5–27.

Verhoef, P., Parasuraman, A., Lemon, K., Roggeveen, A., Tsiros, M., & Schlesinger, L. (2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of Retailing, 85(1), 31–41.

Yoldi, M. (2016, June 1). Nuevas ayudas para que la empresa se lance a proyectos de I+D+i. El País.

Yu, C. S. (2012). Factors affecting Individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2), 104–121.