Structural embeddedness and innovation commercialization in Tanzanian universities: The moderating role of network embeddedness

Mary John; Liliane Pasape; Akinyi Lydia Sassi

doi:10.51867/AQSSR.3.3.44

Authors

Mary John Nelson Mandela African Institution of Science and Technology (NM-AIST), Tanzania Author
Liliane Pasape Nelson Mandela African Institution of Science and Technology (NM-AIST), Tanzania Author https://orcid.org/0000-0002-8898-9874
Akinyi Lydia Sassi Nelson Mandela African Institution of Science and Technology (NM-AIST), Tanzania Author

DOI:

https://doi.org/10.51867/AQSSR.3.3.44

Keywords:

Innovation Commercialization, Network Embeddedness, Network Theory, PLS-SEM, Structural Embeddedness, Tanzanian Universities

Abstract

This study examines the relationship between structural embeddedness and innovation commercialization among researchers across five Tanzanian public universities, drawing on Granovetter's theory of social embeddedness. It tests three hypotheses: the direct influence of structural embeddedness on commercialization (H0₁), the moderating role of network embeddedness on this relationship (H0₂), and the direct effect of network embeddedness on commercialization (H0₃). Data were collected from 274 academic staff across five public universities and analysed using partial least squares structural equation modelling with 5,000 bootstrap subsamples. The measurement model satisfied all convergent and discriminant validity criteria. Structural embeddedness positively and significantly influenced innovation commercialization (beta = 0.314, p < 0.001), supporting H1, and network embeddedness exerted a significant positive direct effect (beta = 0.362, p < 0.001), confirming H3. The moderation analysis yielded a significant negative interaction (beta = -0.100, p = 0.013): the positive influence of structural embeddedness weakened as network embeddedness intensified, suggesting a substitution effect in which researchers already deeply embedded in their networks derive diminishing returns from additional structural positioning. The model explained 46.1% of variance in innovation commercialization. We conclude that structural and network embeddedness are not simply additive but interact in ways that redistribute the returns to positional advantage, extending network theory by showing its dimensions can function as partial substitutes rather than complements in a developing-country university context. We recommend that Tanzanian universities and research administrators prioritise strengthening the overall quality and density of academic-industry networks, through shared infrastructure, cross-institutional platforms and industry engagement programmes, over interventions that target only individually well-positioned researchers, since the observed substitution effect implies that network-level investment yields broader and more equitable commercialization gains.

References

Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425-455. https://doi.org/10.2307/2667105

Breschi, S., & Catalini, C. (2010). Tracing the links between science and technology: An exploratory analysis of scientists' and inventors' networks. Research Policy, 39(1), 14-26. https://doi.org/10.1016/j.respol.2009.11.004

Burt, R. S. (1992). Structural holes: The social structure of competition. Harvard University Press.

https://doi.org/10.4159/9780674029095

Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217. https://doi.org/10.1287/isre.14.2.189.16018

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

Cunningham, J. A., Menter, M., & Young, C. (2017). A review of qualitative case methods trends and themes used in technology transfer research. The Journal of Technology Transfer, 42(4), 923-956. https://doi.org/10.1007/s10961-016-9491-6

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149-1160. https://doi.org/10.3758/BRM.41.4.114

Fini, R., Grimaldi, R., Santoni, S., & Sobrero, M. (2011). Complements or substitutes? The role of universities and local context in supporting the creation of academic spin-offs. Research Policy, 40(8), 1113-1127. https://doi.org/10.1016/j.respol.2011.05.013

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104

Gilsing, V., Nooteboom, B., Vanhaverbeke, W., Duysters, G., & van den Oord, A. (2008). Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density. Research Policy, 37(10), 1717-1731. https://doi.org/10.1016/j.respol.2008.08.010

Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360-1380. https://doi.org/10.1086/225469

Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3), 481-510. https://doi.org/10.1086/228311

Granovetter, M. (1992). Problems of explanation in economic sociology. In N. Nohria & R. G. Eccles (Eds.), Networks and organizations: Structure, form, and action (pp. 25-56). Harvard Business School Press.

Guerrero, M., & Siegel, D. S. (2025). Prosocial technology transfer and academic entrepreneurship: Lessons learned and new directions. Academy of Management Perspectives, 39(3), 439-455. https://doi.org/10.5465/amp.2024.0079

Guerrero, M., Urbano, D., & Herrera, F. (2019). Innovation practices in emerging economies: Do university partnerships matter? The Journal of Technology Transfer, 44(2), 615-646. https://doi.org/10.1007/s10961-017-9578-8

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications. https://doi.org/10.1007/978-3-030-80519-7

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203

Hansen, M. T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82-111. https://doi.org/10.2307/2667038

Hayter, C. S., Nelson, A. J., Zayed, S., & O'Connor, A. C. (2018). Conceptualizing academic entrepreneurship ecosystems: A review, analysis and extension of the literature. The Journal of Technology Transfer, 43(4), 1039-1082. https://doi.org/10.1007/s10961-018-9657-5

Henseler, J., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling: A Multidisciplinary Journal, 17(1), 82-109. https://doi.org/10.1080/10705510903439003

Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. https://doi.org/10.1108/IMDS-09-2015-0382

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. https://doi.org/10.1007/s11747-014-0403-8

Hofstede, G. (1980). Culture's consequences: International differences in work-related values. SAGE Publications.

Inkpen, A. C., & Tsang, E. W. K. (2005). Social capital, networks, and knowledge transfer. Academy of Management Review, 30(1), 146-165. https://doi.org/10.5465/AMR.2005.15281445

Jaffe, A. B. (1989). Real effects of academic research. The American Economic Review, 79(5), 957-970.

Kadikilo, A. C., Nayak, P., & Sahay, A. (2024). Barriers to research productivity of academics in Tanzania higher education institutions: The need for policy interventions. Cogent Education, 11(1), Article 2351285. https://doi.org/10.1080/2331186X.2024.2351285

Kenny, D. A. (2018). Moderation. http://davidakenny.net/cm/moderation.htm

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10. https://doi.org/10.4018/ijec.2015100101

Kruss, G., McGrath, S., Petersen, I., & Gastrow, M. (2015). Higher education and economic development: The importance of building technological capabilities. International Journal of Educational Development, 43, 22-31.

https://doi.org/10.1016/j.ijedudev.2015.04.011

Mashoto, K. O. (2022). Review and assessment of intellectual property policy implementation in Tanzanian universities and research institutions of health and sciences. East African Health Research Journal, 6(1), 43-51. https://doi.org/10.24248/eahrj.v6i1.678

Maziku, J. W. (2021). A scientometric analysis of the science system in Tanzania [Doctoral dissertation, Stellenbosch University].

Moran, P. (2005). Structural vs. relational embeddedness: Social capital and managerial performance. Strategic Management Journal, 26(12), 1129-1151. https://doi.org/10.1002/smj.486

Ndege, N., Atela, J., & Hall, A. J. (2021). Scaling innovation hubs: Impact on knowledge, innovation and entrepreneurial ecosystems in Tanzania. Journal of Innovation Management, 9(2), 39-63. https://doi.org/10.24840/2183-0606_009.002_0005

Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 23(2), 242-266. https://doi.org/10.5465/AMR.1998.533225

Nsanzumuhire, S. U., Groot, W., Cabus, S., Ngoma, M.-P., & Masengesho, J. (2023). Toward the identification of mechanisms to ensure effective university-industry collaboration in sub-Saharan Africa. The Bottom Line, 36(2), 181-208. https://doi.org/10.1108/BL-06-2022-0085

Odei, M. A., & Novak, P. (2023). Determinants of universities' spin-off creations. Economic Research-Ekonomska Istraživanja, 36(1), 1279-1298. https://doi.org/10.1080/1331677X.2022.2086148

Obstfeld, D. (2005). Social networks, the tertius iungens orientation, and involvement in innovation. Administrative Science Quarterly, 50(1), 100-130. https://doi.org/10.2189/asqu.2005.50.1.100

Owen-Smith, J., & Powell, W. W. (2004). Knowledge networks as channels and conduits: The effects of spillovers in the Boston biotechnology community. Organization Science, 15(1), 5-21. https://doi.org/10.1287/orsc.1030.0054

Perkmann, M., & Walsh, K. (2007). University-industry relationships and open innovation: Towards a research agenda. International Journal of Management Reviews, 9(4), 259-280. https://doi.org/10.1111/j.1468-2370.2007.00225.x

Perkmann, M., Salandra, R., Tartari, V., McKelvey, M., & Hughes, A. (2021). Academic engagement: A review of the literature 2011-2019. Research Policy, 50(1), Article 104114. https://doi.org/10.1016/j.respol.2020.104114

Phelps, C., Heidl, R., & Wadhwa, A. (2012). Knowledge, networks, and knowledge networks: A review and research agenda. Journal of Management, 38(4), 1115-1166. https://doi.org/10.1177/0149206311432640

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. https://doi.org/10.1037/0021-9010.88.5.879

Radko, N., Belitski, M., & Kalyuzhnova, Y. (2023). Conceptualising the entrepreneurial university: The stakeholder approach. The Journal of Technology Transfer, 48, 955-1044. https://doi.org/10.1007/s10961-022-09926-0

Romero-Sánchez, A., Perdomo-Charry, G., & Burbano-Vallejo, E. L. (2024). Exploring the entrepreneurial landscape of university-industry collaboration on public university spin-off creation: A systematic literature review. Heliyon, 10(19), Article e27258. https://doi.org/10.1016/j.heliyon.2024.e27258

Rowley, T., Behrens, D., & Krackhardt, D. (2000). Redundant governance structures: An analysis of structural and relational embeddedness in the steel and semiconductor industries. Strategic Management Journal, 21(3), 369-386.

https://doi.org/10.1002/(SICI)1097-0266(200003)21:3<369::AID-SMJ93>3.0.CO;2-M

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research (pp. 587-632). Springer. https://doi.org/10.1007/978-3-319-57413-4_15

Sassi, M., & Mshenga, P. M. (2025). Unlocking the potential of university-industry collaborations in African higher education: A comprehensive examination of agricultural faculties. Industry and Higher Education, 39(1), 102-114. https://doi.org/10.1177/09504222241254694

Stuart, T. E., Hoang, H., & Hybels, R. C. (1999). Interorganizational endorsements and the performance of entrepreneurial ventures. Administrative Science Quarterly, 44(2), 315-349. https://doi.org/10.2307/2666998

Tanzania Commission for Universities. (2021). Status of higher education in Tanzania: Enrolment, staffing, and institutional report. Tanzania Commission for Universities.

Uzzi, B. (1996). The sources and consequences of embeddedness for the economic performance of organizations: The network effect. American Sociological Review, 61(4), 674-698. https://doi.org/10.2307/2096399

Uzzi, B. (1997). Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 42(1), 35-67. https://doi.org/10.2307/2393808

Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observation: Causality-structure-prediction (Vol. 2, pp. 1-54). North-Holland.

Zhou, X. (2022). Moderating effect of structural holes on absorptive capacity and knowledge-innovation performance: Empirical evidence from Chinese firms. Sustainability, 14(10), Article 5821. https://doi.org/10.3390/su14105821

Zucker, L. G., Darby, M. R., & Armstrong, J. S. (2002). Commercializing knowledge: University science, knowledge capture, and firm performance in biotechnology. Management Science, 48(1), 138-153. https://doi.org/10.1287/mnsc.48.1.138.14274