Uncovering the Unequal Knowledge Transfer Network of the U.S. Higher Education Institutions: A Social Network Analysis of MIT

• In Event: Critical Perspectives and Globalizing Implications: Varied Explorations of Global Education and Power

Proposal

1. Rationale
Driven by technology advancements, the transfer of knowledge has fundamentally improved in both speed and scope, accelerating the formation and growth of the knowledge economy. Since the 2000s, to accommodate the global knowledge economy, U.S. higher education institutions (HEIs) have adopted the soft power mechanism of building their global knowledge transfer network by collaborating with different organizational stakeholders (Knight & Wit, 2018; Menashy & Verger, 2019).
Although it is indisputable that the U.S. HEIs’ global knowledge transfer can alleviate global inequality by promoting global access to advanced knowledge, several studies (Torres & Rhoads, 2006; Takeuchi et al., 2020; Tight, 2022) have criticized that U.S. HEIs have controlled the knowledge flow to other stakeholders, particularly stakeholders in the Global South, and maintained a hegemonic position in the global knowledge transfer network.
While previous studies (Stein, 2017; Mignolo, 2009) have focused on U.S HEIs and inequality in the global knowledge transfer network, few have systematically investigated the networks they built. To address this gap, this study utilizes social network analysis (SNA) to identify key organizational stakeholders and key features of U.S HEIs knowledge transfer networks. The Massachusetts Institute of Technology (MIT) is chosen as a case due to its prominent position in the global knowledge economy and its commitment to driving significant change through knowledge transfer. Specifically, two research questions are addressed: What are the critical features of MIT’s knowledge transfer network? Who are the key stakeholders in the network?
2. Methodology
This study employed SNA, a method examining the linkages (edges) among actors (nodes). The network database consists of data from publicly available documents from Global MIT, an MIT-affiliated organization which has established 25 collaborative programs to transfer knowledge and strengthen MIT’s global engagement. Each program has its own website that documents its collaborative partnerships.
This study defines actors as organizational stakeholders listed on the Global MIT and the specific program websites. Each actor was labeled using two attributes: organizational type and location. Ultimately, this study identified 790 unique actors engaged in the MIT global knowledge transfer network.
Network analysis was conducted using Gephi, an open-source network visualization and analysis platform to illustrate the network structure and identify prominent actors who are extensively involved in relationships with others (Wasserman & Faust, 1994).
3. Results
3.1 Overall Network Structure
The MIT knowledge transfer network has a star-like structure with low-density connectivity (network density = 0.003).
Approximately 15 clusters can be identified. The nodes at the center of the clusters are program-related organizations affiliated with MIT. The cluster distribution reveals that despite all 25 programs originating from MIT, they have few linkages, potentially creating barriers to effective knowledge transfer and interaction.
To better understand the network structure and identify the key actors, I calculate each node’s centrality, including betweenness centrality (which measures the extent to which a node lies on the shortest paths between other nodes), closeness centrality (which measures how close a node is to all other nodes in the network), and eigenvector centrality (which measures the extent to which a node is connected to other well-connected nodes).
Overall, MIT and its affiliated organizations are key players in the network, consistently occupying the top 10% positions across all centrality metrics. This dominance is expected because this is an MIT-centered knowledge transfer network.
On top of that, Mitsubishi Heavy Industries, a Japanese automobile manufacturer, has outstanding performance in closeness centrality (20th) and eigenvector centrality (35th). It is influential since it is connected to other well-connected nodes and can spread knowledge quickly and efficiently.
The results of betweenness centrality analyses strongly indicate the gatekeepers, which link different clusters together. In addition to MIT-related organizational actors, 16 organizational actors rank in the top 10% of betweenness centralities---four civil society organizations (CSOs) (e.g., Community Jameel), four foundations (e.g., Bill & Melinda Gates Foundation), three universities and research institutes (URIs) (e.g., Universidad del Valle de Guatemala), two businesses (e.g., HP), two government entities (e.g., Peruvian Ministry of Education), and one international organization (e.g., World Bank).
3.2 Types of Organizations
The network includes multiple organizational types. As the most frequently occurring organizational type, 280 business actors are included in the network, signifying the growing partnership between HEIs and industry that has developed. As the traditional actors in knowledge transfer, 257 URIs and 97 government entities are included. Moreover, non-state actors actively participated in the network, with 109 CSOs, 29 foundations, and 11 international organizations. Based on the numerical counts, the MIT knowledge transfer network reflects diverse participation, especially from the private sector.
Furthermore, I visualized the network according to the organizational type. All organizations in the center network and clusters are URIs, indicating their control of the knowledge flow within the network. Business actors are present in most clusters, especially the large ones. While they may not be at the cluster's center, they dominate several large clusters. Other than that, CSOs and governments are also important nodes in the clusters, but none is dominant.
3.3 Locations of Organizations
I also understand the network geographically by employing the income classification criteria from the World Bank (2023), which divides locations into four categories: low-income, low-middle income, upper-middle income, and high-income countries. The results show 529 high, 174 upper-middle, 61 lower-middle, and 12 low, respectively. 14 actors that lack location information were categorized as "other." Based on the numerical calculation, this study finds that the network predominantly represents high and upper-middle-income countries. Lower-middle and low-income countries are greatly less represented.
The visualization also strengthened this finding. All key nodes in the center of the network center are from high-income countries. However, all nodes from low and lower-middle are at the periphery. Thus, the network is primarily controlled by actors from high-income and upper-middle income countries. In contrast, those from low-income countries are both fewer in number and marginalized in network position. The result confirms the results of previous studies, which argue that the hegemonic power of global knowledge transfer is greatly seized by actors in the global North, particularly US HEIs (Rhoads & Torres, 2006; Demeter, 2022).

Author

• Shiqi Wu, University of California Los Angeles