Scholar using the following keywords: “investment in Africa”, “FDI”, “ODA”,
“international cooperation”, “flow of aid” and “aid for Africa”. Date of publication
and number of citations are used as initial filters. We use a second filter to select
papers that address FDI or ODA at the regional level, thus excluding local and
context-specific information. Finally, based on relevance to the topic and after
reading the abstracts, we identify 35 papers for a more thorough analysis through
concept mapping and social network analysis (see Steps 2–3).
During Step 2, we follow the literature-mapping process outlined by Hart (2018)
that uses concept maps as visual representations of the relationship between concepts
and processes (Sect. 5.3.2). Such concept maps can help transform declarative
knowledge (i.e. what the topic is about) into procedural knowledge
(i.e. understand the classification and relationships between the elements of the
topic). By mapping the multiple ideas of different authors, it is possible to unveil
common interactions, trends and landscapes, between them, thus enabling a better
understanding of the relationships between individual studies (Hart 2018). The
conceptual maps are discussed in more depth in Sect. 5.3.2. In summary, the
coloured text represents the concepts and the connectors are linked to concepts
with arrows. The relationship between concepts is determined by the direction of
these arrows and is explained by the connectors between the arrows. Whenever a
concept is identified as a positive determinant to attract FDI or ODA in the reviewed
literature, it is highlighted in green. Conversely, concepts highlighted in red are
identified in the literature as factors that drive away FDI and ODA. The conceptual
map is generated using the free CMapTools software, developed by IHMC Public
Cmaps.
During Step 3, we employ SNA to interpret the relationships between the factors
outlined above (Sect. 5.3.3). In particular, SNA allows mapping how frequently
these concepts relate to each other in the reviewed documents (i.e. how strongly
authors agree on these relationships) through estimating the degree centrality. Nodes
(i.e. the concepts identified in the literature) are represented by the labelled coloured
circles, which are connected to each other through ties (i.e. directional links and
relationships between concepts), which are represented by the lines and arrows
(Sect. 5.3.3). The diameter of each node and the font size of its label represent the
degree centrality of each concept (i.e. the number of connections). The social
network analysis is developed using the open-source Gephi Graph Visualization
and Manipulation software.
5.3 Results and Discussion
5.3.1 ODA Flows and Academic SDG Research Priorities
in Africa
Between 2000 and 2013, a total of USD 465 billion in ODA was directed to Africa
(USD 550 billion if debt-related assistance is considered). Approximately, 41% of
5 Determinants of Foreign Investment and International Aid for Meeting the. . .
165
“international cooperation”, “flow of aid” and “aid for Africa”. Date of publication
and number of citations are used as initial filters. We use a second filter to select
papers that address FDI or ODA at the regional level, thus excluding local and
context-specific information. Finally, based on relevance to the topic and after
reading the abstracts, we identify 35 papers for a more thorough analysis through
concept mapping and social network analysis (see Steps 2–3).
During Step 2, we follow the literature-mapping process outlined by Hart (2018)
that uses concept maps as visual representations of the relationship between concepts
and processes (Sect. 5.3.2). Such concept maps can help transform declarative
knowledge (i.e. what the topic is about) into procedural knowledge
(i.e. understand the classification and relationships between the elements of the
topic). By mapping the multiple ideas of different authors, it is possible to unveil
common interactions, trends and landscapes, between them, thus enabling a better
understanding of the relationships between individual studies (Hart 2018). The
conceptual maps are discussed in more depth in Sect. 5.3.2. In summary, the
coloured text represents the concepts and the connectors are linked to concepts
with arrows. The relationship between concepts is determined by the direction of
these arrows and is explained by the connectors between the arrows. Whenever a
concept is identified as a positive determinant to attract FDI or ODA in the reviewed
literature, it is highlighted in green. Conversely, concepts highlighted in red are
identified in the literature as factors that drive away FDI and ODA. The conceptual
map is generated using the free CMapTools software, developed by IHMC Public
Cmaps.
During Step 3, we employ SNA to interpret the relationships between the factors
outlined above (Sect. 5.3.3). In particular, SNA allows mapping how frequently
these concepts relate to each other in the reviewed documents (i.e. how strongly
authors agree on these relationships) through estimating the degree centrality. Nodes
(i.e. the concepts identified in the literature) are represented by the labelled coloured
circles, which are connected to each other through ties (i.e. directional links and
relationships between concepts), which are represented by the lines and arrows
(Sect. 5.3.3). The diameter of each node and the font size of its label represent the
degree centrality of each concept (i.e. the number of connections). The social
network analysis is developed using the open-source Gephi Graph Visualization
and Manipulation software.
5.3 Results and Discussion
5.3.1 ODA Flows and Academic SDG Research Priorities
in Africa
Between 2000 and 2013, a total of USD 465 billion in ODA was directed to Africa
(USD 550 billion if debt-related assistance is considered). Approximately, 41% of
5 Determinants of Foreign Investment and International Aid for Meeting the. . .
165