Perceived external R&D purchases and internal R&D expenditures as innovation drivers in large Ecuadorian firms
-
DOIhttp://dx.doi.org/10.21511/ppm.24(4).2026.21
-
Article InfoVolume 24 2026, Issue #4, pp. 325–343
- 22 Views
-
3 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Type of the article: Research Article
Abstract
This study examines the relationship between external R&D purchases and internal R&D expenditure with technological and non-technological innovation in large Ecuadorian companies, using internal R&D expenditure as a mediating variable and company age as a moderating variable. A structured survey was administered in 2024 to the CEOs of 380 large companies in Ecuador’s three main business centers: Guayaquil, Quito, and Cuenca. Data were analyzed using PLS-SEM. The results confirmed that internal R&D expenditure is the main driver of reported innovation, with large direct effects on technological (β = 0.545; p < 0.001; f² = 0.434) and non-technological innovation (β = 0.526; p < 0.001; f² = 0.400). External R&D purchases directly affect non-technological innovation (β = 0.178; p < 0.001) and internal R&D expenditure (β = 0.308; p < 0.001; f² = 0.105), with significant indirect effects on both types of innovation (β = 0.168 and 0.162; p < 0.001). The model explains 37.9% of the variance in technological innovation and 37.5% of the variance in non-technological innovation (Q² > 0). Firm age negatively moderates the external R&D purchases–technological innovation relationship (β = −0.141; p = 0.045), a pattern consistent with younger firms relying more on external R&D purchases for technological innovation, although this interpretation requires caution given the moderate significance of the effect. Therefore, internal R&D expenditure relates to reported outcomes of innovation, while external R&D purchases appear to contribute mainly to the development of internal R&D.
- Keywords
-
JEL Classification (Paper profile tab)O32, O31, D83, O14
-
References52
-
Tables11
-
Figures3
-
- Figure 1. Research model
- Figure 2. Analysis of the proposed model after applying partial least squares
- Figure 3. Simple slopes analysis
-
- Table 1. Convergent validity analysis
- Table 2. Discriminant validity analysis
- Table 3. Hypothesis testing
- Table 4. Mediating effects
- Table 5. Structural path estimates with industry-sector controls
- Table 6. PLS-SEM model goodness of fit indicators
- Table A1. Distribution of participating firms by economic sector and business hub (ISIC classification)
- Table A2. Distribution of participating firms by years of operation
- Table B1. Questionnaire
- Table C1. Results of Harman’s single-factor test for common method bias
- Table C2. Inner model collinearity statistics (Full collinearity VIF)
-
- Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. SAGE.
- Aliasghar, O., Sadeghi, A., & Rose, E. (2023). Process innovation in small- and medium-sized enterprises: The critical roles of external knowledge sourcing and absorptive capacity. Journal of Small Business Management, 61(4), 1583-1610.
- Álvarez-Aros, E. L., & Bernal-Torres, C. A. (2017). Modelo de innovación abierta: Énfasis en el potencial humano [Open innovation model: Focus on human potential]. Información Tecnológica, 28(1), 65-76. (In Spanish).
- Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
- Becerra, L. (2024). The innovative environment and its relationship with the technological gap among emerging countries: A scientific literature review. In 5th South American International Conference on Industrial Engineering and Operations Management.
- Bianchini, S., Llerena, P., & Patsali, S. (2019). Demand-pull innovation in science: Empirical evidence from a research university’s suppliers. Research Policy, 48(suppl), Article 100005.
- Cassiman, B., & Veugelers, R. (2006). In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition. Management Science, 52(1), 68-82.
- Chaithanapat, P., Punnakitikashem, P., Khin Khin Oo, N. C., & Rakthin, S. (2022). Relationships among knowledge-oriented leadership, customer knowledge management, innovation quality and firm performance in SMEs. Journal of Innovation & Knowledge, 7(1), Article 100162.
- Cheah, J.-H., Thurasamy, R., Memon, M. A., Chuah, F., & Ting, H. (2020). Multigroup analysis using SmartPLS: Step-by-step guidelines for business research. Asian Journal of Business Research, 10(3), 1-19.
- Chen, S., & Yu, D. (2022). The impacts of ambidextrous innovation on organizational obsolescence in turbulent environments. Kybernetes, 51(3), 1009-1037.
- Coba, G. (2022, October 27). Aumenta en 23% la creación de empresas en el país [Increases business creation in the country by 23%.]. Primicias. (In Spanish).
- Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128-152.
- De la Vega, I. (2018). Estado y dinámicas de los sistemas tecnocientíficos: El caso de los países de la Alianza del Pacífico [Status and Dynamics of Technoscientific Systems: The Case of the Pacific Alliance Countries]. Revista del CLAD Reforma y Democracia, 70, 29-60. (In Spanish).
- Denicolai, S., Ramirez, M., & Tidd, J. (2016). Overcoming the false dichotomy between internal R&D and external knowledge acquisition: Absorptive capacity dynamics over time. Technological Forecasting and Social Change, 104, 57-65.
- Dimakopoulou, A. G., Gkypali, A., & Tsekouras, K. (2024). Technological and non-technological innovation synergies under the lens of absorptive capacity efficiency. Journal of Business Research, 176, Article 114593.
- Ding, Z., Li, M., Yang, X., & Xiao, W. (2023). Ambidextrous organizational learning and performance: Absorptive capacity in small and medium-sized enterprises. Management Decision, 61(11), 3610-3634.
- Erena, O. T., Kalko, M. M., & Debele, S. A. (2023). Organizational factors, knowledge management and innovation: Empirical evidence from medium- and large-scale manufacturing firms in Ethiopia. Journal of Knowledge Management, 27(4), 1165-1207.
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
- Fulgence, B. E., Hu, X., Larbi-Siaw, O., Tuo, S. J., & Gnahe, F. E. (2023). Impact of knowledge absorptive capacity on innovative performance in SMEs: Mediating effect of cluster environment. Kybernetes, 52(12), 6001-6024.
- García, J., Silva, M., Parga, N., & Bojórquez, L. (2021). El liderazgo emprendedor y la innovación en empresas manufactureras [Entrepreneurial Leadership and Innovation in Manufacturing Firms]. Contabilidad y Negocios, 16(31), 131-144. (In Spanish).
- Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101-107.
- Gifford, E., Ljungberg, D., & McKelvey, M. (2022). Innovating in knowledge-intensive Giffordentrepreneurial firms: Exploring the effects of a variety of internal and external knowledge sources on goods and service innovations. Industrial and Corporate Change, 31(5), 1259-1284.
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019a). Multivariate data analysis. Cengage Learning.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019b). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Hameed, W. U., Nisar, Q. A., & Wu, H.-C. (2021). Relationships between external knowledge, internal innovation, firms’ open innovation performance, service innovation and business performance in the Pakistani hotel industry. International Journal of Hospitality Management, 92, Article 102745.
- 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.
- Hervas-Oliver, J.-L., Sempere-Ripoll, F., & Boronat-Moll, C. (2021). Technological innovation typologies and open innovation in SMEs: Beyond internal and external sources of knowledge. Technological Forecasting and Social Change, 162, Article 120338.
- Ijomah, T., Idemudia, C., Eyo-Udo, N., & Anjorin, K. (2024). Innovative digital marketing strategies for SMEs: Driving competitive advantage and sustainable growth. International Journal of Management & Entrepreneurship Research, 6(7), 2173-2188.
- Instituto Nacional de Estadística. (n.d.). Encuesta sobre Innovación en las Empresas– Conjunto de datos [Business Innovation Survey – Dataset]. Catálogo de datos. (In Spanish).
- Jansen, J., Heavey, C., Mom, T., Simsek, Z., & Zahra, S. (2023). Scaling‐up: Building, leading and sustaining rapid growth over time. Journal of Management Studies, 60(3), 581-604.
- Kero, C., & Bogale, A. (2023). A systematic review of resource-based view and dynamic capabilities of firms and future research avenues. International Journal of Sustainable Development and Planning, 18(10), 3137-3154.
- Khraishi, A., Paulraj, A., Huq, F., & Seepana, C. (2023). Knowledge management in offshoring innovation by SMEs: Role of internal knowledge creation capability, absorptive capacity and formal knowledge-sharing routines. Supply Chain Management: An International Journal, 28(2), 405-422.
- Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10.
- Kowalski, A. M. (2021). Dynamics and factors of innovation gap between the European Union and China. Journal of the Knowledge Economy, 12(4), 1966-1981.
- Latifah, L., Setiawan, D., Aryani, Y. A., Sadalia, I., & Al Arif, M. N. R. (2022). Human capital and open innovation: Do social media networking and knowledge sharing matter? Journal of Open Innovation: Technology, Market, and Complexity, 8(3), Article 116.
- Li, Q., & Gao, H. (2023). How external knowledge acquisition contribute to innovation performance: A chain mediation model. Sage Open, 13(4).
- Lubis, N. (2022). Resource based view (RBV) in improving company strategic capacity. Research Horizon, 2(6), 587-596.
- McNeeley, S. (2012). Sensitive issues in surveys: Reducing refusals while increasing reliability and quality of responses to sensitive survey items. In L. Gideon (Ed.), Handbook of Survey Methodology for the Social Sciences (pp. 377-396). New York: Springer.
- Messeni, A., Ardito, L., & Savino, T. (2018). Maturity of knowledge inputs and innovation value: The moderating effect of firm age and size. Journal of Business Research, 86, 190-201.
- Milojević, M., Omarova, B., Trifonov, I., Tsvetkova, L., & Duisebayeva, A. (2025). The influence of innovative components of intellectual capital on commercial success. Journal of Innovation and Entrepreneurship, 14(1), Article 125.
- Nascimento, L. D. S., Trizotto, R. C. A., Pufal, N. A., Camboim, G. F., & Zawislak, P. A. (2025). Which innovation capabilities are relevant for technological and non-technological innovation? Implications for manufacturing firms’ financial performance. Journal of Manufacturing Technology Management, 36(2), 406-430.
- Paus, E., & Robinson, M. (2024). The challenge of productivity-based development: Innovation gaps and economic structure in Latin America. The European Journal of Development Research, 36(2), 277-305.
- Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.
- Pu, K., & Liu, W. (2023). Is absorptive capacity the “panacea” for organizational development? A META analysis of absorptive capacity and firm performance from the perspective of constructivism. PLOS ONE, 18(2), Article e0282321.
- Ramalho, T. S., Tarraco, E. L., Yokomizo, C. A., & Bernardes, R. C. (2019). Analysis of the innovation value chain in strategic projects of the Brazilian Army. Revista de Gestão, 26(4), 409-428.
- Ritter-Hayashi, D., Knoben, J., & Vermeulen, P. A. (2021). Temporary employment: Curse or blessing for a firm’s absorptive capacity? Technological Forecasting and Social Change, 173.
- Sancho-Zamora, R., Hernández-Perlines, F., Peña-García, I., & Gutiérrez-Broncano, S. (2022). The impact of absorptive capacity on innovation: The mediating role of organizational learning. International Journal of Environmental Research and Public Health, 19(2), Article 842.
- Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society Series B: Statistical Methodology, 36(2), 111-133.
- Vîrjan, D., Manole, A. M., Stanef-Puică, M. R., Chenic, A. S., Papuc, C. M., Huru, D., & Bănacu, C. S. (2023). Competitiveness– The engine that boosts economic growth and revives the economy. Frontiers in Environmental Science, 11, Article 1130173.
- Warwick, R. (2016). Routine innovation: Complex processes from policy development to implementation. International Journal of Business and Globalisation, 17(4), 541-555.
- Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 73(6), 913-934.
- Zahra, S. A., & George, G. (2000). Absorptive capacity: A review and reconceptualization. Academy of Management Proceedings, 2000(1), K1-K6.


