


































Bunch_2020_UF_International_Educator


Dossou Kpanou et al.   Advancements in Agricultural Development 
  Volume 1, Issue 3, 2020 
  agdevresearch.org 

1. Bidossessi M. G. Dossou Kpanou, Graduate Research Associate, University of Georgia, 203 Lumpkin House, 145 Cedar 
Street, Athens, GA, 30602, bd8245@uga.edu, https://orcid.org/0000-0002-7969-0811      

2. Kathleen D. Kelsey, Professor and Director of the Impact Evaluation Unit, University of Georgia, 203 Lumpkin House, 145 
Cedar Street, Athens, GA, 30602, kdk@uga.edu, https://orcid.org/0000-0001-9683-6993    

3. Kyle L. Bower, Postdoctoral Research Associate, University of Georgia, 203 Lumpkin House, 145 Cedar Street, Athens, GA, 
30602, kbower@uga.edu, https://orcid.org/0000-0001-6043-356X  
 

42 

 

Evaluation of a Federally Funded Research Network Using 
Social Network Analysis 

B. Dossou Kpanou 1, K. Kelsey2, K. Bower3,   
 

 
  

Abstract 

United States federal agencies fund research to promote discovery and innovation. Most agencies 
require collaboration because teams promote productivity to a greater degree than singular 
researchers. However, the functionality and productivity of collaboration is poorly understood. The 
purpose of this study was to evaluate the collaborative structure of a federally funded entomology 
research team to determine the characteristics of the network structure and its impact on research 
collaboration using social network analysis (SNA) methodology. An online survey and interviews 
were used to collect data. The theories of social network, strong and weak ties, and scientific 
collaboration were employed to determine the degree of collaboration among team members. We 
found a low-density pattern of collaboration that was associated with: (a) a centralized pattern, (b) 
the presence of sub-teams functioning like sub-networks, and (c) the presence of less interactive 
members. Our results confirm that the SNA approach was useful for evaluating network 
collaboration with innovative indicators to assess the dynamics of scientific collaboration. The study 
was limited by non-response. Future research should focus on collecting SNA data longitudinally of 
the whole network to determine how networking structure and benefits evolves over time, and how 
strong and weak ties impact scientific discovery. 
 
 
 

Keywords 

Scientific collaboration, network connectivity, evaluation. 
 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   43 
 

Introduction and Problem Statement 
 
United States (U.S.) federal agencies fund research to promote discovery and innovation. Most 
agencies require teams to collaborate on successful awards because they promote productivity 
to a greater degree than research projects executed by a single investigator (Contandriopoulos 
et al., 2018; Lee & Bozeman, 2005). Academic collaboration is also important as it fosters 
scientific discovery and generates knowledge (Katz & Martin, 1997; Lee & Bozeman, 2005). 
Accordingly, agencies such as the U.S. Department of Agriculture (USDA, 2019) and the National 
Institute of Food and Agriculture (NIFA) seek to enhance the productivity and efficiency of 
scientific research by requiring collaboration as stated in their request for proposals (Katz & 
Martin, 1997; Lee & Bozeman, 2005).  
 
The development of new scientific networks, or the continuation of existing collaborations, 
depends on the success and productivity of previous collaboration. Researchers who have 
worked together in the past are likely to continue their partnerships on future scientific 
projects (Contandriopoulos et al., 2018), while research networks are strengthened by their 
ability to secure additional funding having demonstrated past success.  
 
Despite the plethora of evidence supporting scientific effectiveness among collaborative 
research teams, their structure and functionality has not been adequately evaluated in the 
literature. Social network analysis (SNA) is a viable methodological approach that provides 
researchers with scientific tools to evaluate the structure of collaboration within networks 
(Borgatti et al., 2018). SNA enabled our exploration of emergent characteristics of a federally 
funded research team focused on solving an invasive pest problem and provided an 
opportunity to understand the functionality of this network. Furthermore, the evaluation of 
ongoing scientific networking provided evidence needed to improve research productivity and 
future collaboration among the team.  
 

Theoretical and Conceptual Framework 
 
This study integrated principles from social network theory (Borgatti et al., 2009 & 2018), 
strong and weak ties theory (Granovetter, 1973; 1983; Rademacher & Wang, 2014), and 
scientific collaboration theory (Olson et al., 2008) to develop a comprehensive approach for 
describing a social network originated for the purpose of executing a federally funded research 
project to address a major pest infestation damaging fruits in the U.S. 
 
Social network theory (SNT) explains the essence of interactions between members of a 
network and emphasizes the study of network characteristics, configurations, and architectural 
features (Borgatti et al., 2018). Additionally, SNT moves beyond the attributes of individuals to 
emphasize the relationships that form within a network by mapping connections (Schmidt, 
2007) between team members, or ties, that constitute the channels for exchanging resources 
such as knowledge, funding, and access to infrastructure (Borgatti et al., 2018). The exchange of 
resources is strongly influenced by a member’s position within the network and is vital for 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   44 
 

accessing benefits associated with networks (Hansen, 2009). Furthermore, the sociometry of 
social networks provides strong indicators of productivity (Hansen, 2009) because it gives 
insight into the collaboration structure of relationships among members who are involved in 
creating, disseminating, and using knowledge that adds to team functionality (Dunn, 1983). 
 
Strong and weak ties theory guided our analysis of network connectivity and information flow 
(Granovetter,1973, 1983; Rademacher & Wang, 2014). Strong ties within a network suggest 
frequent interrelations that assume better collaboration within a network (Borgatti, 2018; 
Granovetter, 1973). Alternatively, weak ties are characterized by distant and infrequent 
interrelations between members of a network, suggesting low reciprocity among members 
(Granovetter, 1973; Rademacher & Wang, 2014). While weak ties appear less advantageous, 
they benefit a network by providing a bridge between two unknown, or weakly connected, 
members (Leij & Goyal, 2011). 
 
Scientific collaboration theory is underpinned by communication theory (Olson et al., 2008; 
Olson & Olson, 2000; Sonnenwald, 2007). Research indicates that remote scientific contributors 
communicate less frequently than contributors working at the same institution (Ding et al., 
1998; Katz, 1994; Olson et al., 2008), which leads to difficulties in sharing knowledge and 
resources among collaborators. This theory helped us to understand how team members in this 
study communicated from geographically disparate locations, placing their ability to 
communicate at a disadvantage. Olson et al. developed the theory of remote scientific 
collaboration, which recognized digital communication as an effective way for remote scientific 
researchers to exchange information, data, ideas, and results. Their theory indicated that 
communication technologies such as videoconferencing, e-mail, and instant messaging helped 
to circumvent the geographical distance by allowing ongoing conversations among members 
(Ding et al., 1998; Katz, 1994). Olson and Olson (2000) noted that despite access to advanced 
communication technologies, remote collaboration remains challenging. 
 

Purpose 
 
The purpose of the study was to evaluate the collaborative structure of a federally funded 
scientific research network. Accordingly, the primary research question was to what extent did 
the current characteristics of the federally funded research network structure impact the 
research collaboration? This question guided our evaluative inquiry into how the research 
project fostered transdisciplinary collaboration and knowledge sharing among team members. 
 

Methods 
 
The study was conducted through a sequential mixed-methods evaluation design (Mertens, 
2018). Our approach was exploratory with a stronger emphasis placed on quantitative data. 
Qualitative data was used to triangulate the survey and observational findings (Fraenkel et al., 
2012). The sequential design allowed us to identify factors impacting the cohesion of the 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   45 
 

network via survey and then gather explanatory data via interviews that aided our 
interpretation of the results. 
 
Data Collection 
The evaluation team began data collection with participant observations (Patton, 2001) during 
monthly team meetings, webinars, conferences, and email correspondence. Observational data 
gathered before and after the survey data added to the contextualization of relationship 
patterns observed in the network sociograms (Figures 1 and 2) and helped us to frame the 
survey questions, known as an ethnographic sandwich approach (Borgatti et al., 2018). 
 
To recruit for the survey, all team members (N = 52) including research faculty (n = 16), post-
doctoral research associates (n = 7), advisory board members (n = 15), laboratory technicians (n 
= 7), a graduate research associate (n = 1), and undergraduate students (n = 6) were first 
informed of the study during monthly team meetings and then sent a formal invitation to 
participate via email with the survey link embedded. Two follow-up emails were sent to non-
responders. When necessary, we called participants to encourage survey completion (Dillman 
et al., 2008). Thirty-six participants completed the self-administered survey through Qualtrics® 
for a 69% response rate.  
 
Purposeful sampling (Mertens & Wilson, 2012) resulted in telephone interviews with five 
faculty and one graduate student. The interviews were recorded with consent and lasted an 
average of 36 minutes each. While the student and faculty were asked similar questions, the 
semi-structured interview protocols differed slightly taking into consideration their specific 
roles. Advisory board members did not respond to our requests for interviews, so we emailed 
all of them requesting written responses to five open-ended questions regarding their overall 
interaction with the project. Three of 15 advisory board members responded by providing 
written responses.  
 
Data Analysis 
Survey data were cleaned by removing missing data. Non-respondents who were referred to by 
other team members were kept in the dataset to ensure completeness of the network analysis 
(Borgatti et al., 2018). To protect participants' privacy, all names were changed. Once the data 
were properly formatted, we used UCINET 6 software to design the corresponding sociograms 
(Figures 1 and 2) and computed the following SNA indicators: (a) density, (b) degree (including 
in-degree and out-degree), (c) degree centralization (including indegree-centralization and 
outdegree-centralization), (d) betweenness, (e) dyad reciprocity, and (f) betweenness 
centrality.  
  
Qualitative interview data were transcribed through an online program, Otter.ai (Lang, 2020). 
Transcripts and observation notes were then uploaded into ATLAS.ti 8 for Windows, where the 
data were stored, managed, and analyzed (Friese, 2019). The coding process focused on 
acquiring meaning from interviewees’ perspectives by identifying significant quotations related 
to collaboration within the data (Linneberg & Korsgaard, 2019). We performed line-by-line 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   46 
 

coding by using the group code collaboration and two main codes, valuing collaboration and 
barriers/difficulties to collaboration. 
 

Findings 
 
Overall, the network had a low-density value, which at first glance implies that it was less 
connected and less cohesive than a high-density network. However, through our mixed-method 
analysis we identified three explanatory factors which contributed to a nuanced understanding 
of the low-density network, including: (a) a centralized network pattern, (b) the presence of 
sub-teams functioning like sub-networks, and (c) the presence of less-interactive members. 
 
We first describe the a less connected network pattern using the SNA metric of density and 
present two sociograms to illustrate this finding (Figures 1 and 2). Next, we explore the 
centralized pattern of the network and the presence of sub-teams using specific SNA indicators 
and the sociograms. Finally, we introduce qualitative data to explain how the less-interactive 
members contributed to reducing the overall network density. 
 
The Less Connected Structure of the Research Network 
Most of the team members had previously collaborated on a federally funded project, 
therefore, it was necessary to examine the structure of the current network against a baseline. 
To obtain a baseline, the question, “Who have you worked with prior to this project?” was 
included in the survey. Results from this question made it possible to assess changes in 
interactions and interconnections within the team between two time periods. Table 1 
compares the indicators characterizing the structure of the network at baseline and at the time 
of data collection. Findings revealed that the baseline had a density of 13.3% while that of the 
current network was 18.2%, suggesting that the overall professional connections within the 
network evolved over time but remained relatively low.  
 
Table 1 
Comparison of Indicators Characterizing the Structure of the Network  

Indicators 
Network 
Baseline At the time of data collection 

Density 0.133 0.182 
Number of ties (Degree) 326 445 
Degree Centralization 0.287 0.776 
In-centralization 0.323 0.294 
Out-centralization 0.261 0.835 
Dyad Reciprocity 0.374 0.422 

 
 
While the network density had increased and was visually perceptible when comparing the two 
sociograms (Figures 1 and 2), most of the members who were peripheral at baseline remained 
peripheral, thus, maintaining the low density of the whole network. The peripheral members 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   47 
 

were mainly advisory board members (names starting with letter A), students (names starting 
with letter S), and laboratory technicians (names starting with letter L). Generally, the 
indicators of the current network showed improvement in the number of ties, degree 
centralization, and dyad reciprocity between the baseline and at the time of the study. On 
average, at the time of data collection, each member shared information and data with nine 
other members of the network, higher than 6.5 at baseline. In addition, when compared to its 
initial structure (Figure 1), Figure 2 visually supports the findings indicating that the current 
research network has a more connected and centralized structure. 
 
Figure 1 
Sociogram of Members’ Professional Acquaintances Prior to the Project (Baseline) 

 
Note. Names starting with I are the principal investigators (red), names starting with C are the 
co-principal investigators (pink), names starting with P are postdocs (orange), names starting 
with A are the advisory board members (yellow), names starting with L are laboratory staff 
(green triangle), names starting with S are students (green square), and black circles are non-
respondents. 
 
 
 
 
 
 
 
 
 
 
 
 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   48 
 

Figure 2 
Sociogram of Members’ Professional Acquaintances at the time of Study  

 
Note. Names starting with I are the principal investigators (red), names starting with C are the 
co-principal investigators (pink), names starting with P are postdocs (orange), names starting 
with A are the advisory board members (yellow), names starting with L are laboratory staff 
(green triangle), names starting with S are students (green square), and black circles are non-
respondents. 
 
The Network Centralization as a Factor of the Network Low Connectedness 
The structure of the research network, characterized by a centralized pattern with few central 
members and several peripheral members, explains the low connectedness of the network. 
Initially, the baseline network (Figure 1) exhibited a structure with relatively low degree 
centralization, 0.287. At the time of data collection, the network became more centralized, 
which is substantiated by a relatively high degree centralization value of 0.776. For instance, 
central members, namely Ivan (the project director), Ismael, Peter, and Alice (Figure 2) played a 
key role in disseminating information and data within the network. In particular, the findings 
revealed that with a degree centrality of 65 and a betweenness centrality score of 407.19, Ivan 
was the most central member of the research network. 
 
Additionally, out-degree and in-degree centralization values supported the centralization 
pattern of the network around a few central members. The out-centralization, which is the total 
number of connections going out from all members, was 0.835 (Table 1), suggesting that there 
were high outgoing interactions compared to the total possible outgoing interactions. On the 
other hand, the in-degree centralization, which is the total number of connections being 
received by members, was 0.294 for the whole network, meaning there were fewer incoming 
interactions compared to the total possible incoming interactions. Both changes were 
significant at the 0.05 level (p-value = 0.002 for out-degree, p-value = 0.000 for in-degree). 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   49 
 

The Sub-Teams as a Factor Affecting the Network Connectivity 
The dyad reciprocity was 0.422, which means that from all possible relationships between all 
pairs of members 42% of the connections occurred. This indicates a relatively low reciprocity 
among the network and supports the low connectivity finding of the whole network. 
Furthermore, we explored sub-teams’ characteristics from four sub-teams (Table 2). While we 
did identify more than four sub-teams within the network, we were unable to detect significant 
results due to small sub-team size (3 or fewer members), low interaction within the sub-teams, 
and low response rates within the sub-teams.  
 
Table 2 
Characteristics of Sub-Teams 

Sub-Teams # of Members Density Dyad Reciprocity 

Sub-team 1 4 0.91 0.83 

Sub-team 2 6 0.57 0.70 

Sub-team 3 5 0.55 0.37 

Sub-team 4 8 0.83 0.67 

Note. The four sub-teams had at least four members each. 
 
The density of each sub-network was higher than the whole network density value (18%). Also, 
three of the four sub-teams had higher dyad reciprocity than the whole network value (42%), 
indicating that networking was stronger within sub-teams than the whole network. For 
example, sub-team 1 and sub-team 4 densities regarding information sharing were respectively 
91% and 83%, suggesting well-connected sub-teams with a high flow of information supported 
by their relatively high dyad reciprocity of 83% and 67%. Also, apart from sub-team 2, all other 
sub-teams reported having frequent interactions substantiated by high levels of internal 
interactions (Table 3).  
 
Table 3 
Frequency of Interaction within Sub-Teams 

Sub-teams # of Members Rarely Sometimes Often Very often Most often 

Sub-team 1 4 2 1 3 0 6 

Sub-team 2 6 10 5 0 2 4 

Sub-team 3 5 0 0 2 9 5 

Sub-team 4 8 0 21 17 15 3 

Note. The four sub-teams had at least 4 members. 

 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   50 
 

Low connectivity within sub-teams (e.g., sub-team 3) and less frequent interactions (e.g., sub-
team 2) is explained by the absence of relationships between some sub-team members and by 
the few reciprocal relationships between them. This impacted the connectivity of the whole 
network. Participant observations and interviews further supported this finding. For instance, 
during meetings and conferences we observed self-distinction made between the university 
research teams. i.e. the researchers identified themselves based on their employer’s affiliation 
versus members of the whole network (Entomology team). Further, interview data supported 
this pattern of collaboration. For example, Charlie (faculty) stated, “truth be told, we have been 
working more independently than we did before. We are working more on being parallel with 
the [other sub] teams”. Faculty reported that in practice, because their collaboration 
encompassed independent research, the sub-teams worked separately from the whole 
network. Although we cannot determine if this distinction will affect the research outputs over 
time, it explains the emerging pattern of interactions with more intense collaborations within 
the sub-teams. 
 
The Meaning of Less Interactive Members 
The qualitative data provided insights into the nature of interactions within the whole network 
and explained the effect of less interactive members on low connectivity by contextualizing 
interactions with each other and within sub-teams. While sub-team and group (faculty, student, 
advisory board) members’ interactions with each other were infrequent, they were productive.  
 
Student activity was most important to bolstering research outputs. Faculty reported that their 
student advisees, both graduate and undergraduate, were deeply involved in the research 
activities. Faculty were most connected to their student advisees. However, interactions 
between students and faculty beyond their sub-teams was limited and had the effect of 
decreasing the whole network density value.  
 
Sub-team 4 worked on the economic aspect of the project, differing from the other sub-teams 
who were involved in entomology experiments. Sub-team 4 interactions with the whole 
network were limited because of their unique mission. Lucas, graduate student in sub-team 4, 
reported that collaborations with other sub-teams would occur later in the project. He said, “A 
lot of their lab studies are not going to be directly applicable to our economic stuff. It is going to 
be over the next year that [we are] going to be collaborating a lot more with [the other sub-
teams].” Lucas added that interactions between his sub-team and the other sub-teams would 
determine who they would most likely work with in the future.  
 
Advisory board members’ infrequent interactions with faculty also lowered the whole network 
density value. Participant observations noted low attendance of advisory members at 
conferences and webinars. Faculty said that advisory members were more interested in 
receiving information regarding pest solutions than discussing emerging research findings, 
which was the focus of most of the team meetings and webinars. This finding was also 
triangulated by advisory board members in their responses to the questionnaire. Of the three 
who responded, two wrote they only interacted with the research sub-team located in their 
state of residence and the third did not mention any interaction with faculty. Although advisory 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   51 
 

board members’ involvement in research meetings was infrequent, faculty expressed the 
importance of their engagement during farm demonstrations and commented on their positive 
contributions toward research conducted on their farms.  
 
While the mostly unilateral transfer of information from faculty to students and advisory board 
members led to low density values, knowledge and resources were shared bilaterally to 
promote the advancement of research that benefited the whole network. 
 

Conclusions, Discussion, and Recommendations 
 
Overall, the whole network demonstrated a relative low-density value, which suggests a weakly 
connected network. According to Borgatti et al. (2018), the judgment of high or low 
connectivity as measured by network density depends on the context. To better understand the 
relatively low connectivity of the network, we explored the centralized pattern of the network, 
the presence of sub-teams, and the meaning of less interactive members within the network.  
 
We expected the whole network to have a high-density value that theoretically supports a well-
connected network because it was a closed network focused on solving an invasive pest issue 
(Borgatti et al., 2018). However, the overall low density of the network was associated with the 
centralized pattern of the network. The central members, including the project director (Ivan), 
sent information and data to members more frequently than they received it. Thus, they 
facilitated the interconnection between sub-teams, resulting in a positive influence on 
collaboration, building on weak ties theory (Zamzami & Schiffauerova, 2017) that suggests 
scientists who are central within their networks have a positive impact on the flow of 
information across sub-teams who may not otherwise be connected in the same whole 
network. 
 
The low connectedness value of the whole network can also be explained by the presence of 
sub-teams, which functioned like mini networks within the whole network. Sub-team internal 
interactions were governed by pre-assigned research questions at project initiation. Therefore, 
there was little need for sub-teams to communicate frequently with other sub-teams to 
accomplish their work. This finding is consistent with Hoang et al. (2019) who concluded that 
working on the same research topics is an essential factor that encourages interaction between 
faculty. Given the importance that funding agencies, especially NIFA, place on promoting 
collaborative research, we recommend greater collaboration between sub-teams by creating 
overlapping research questions that require input from the whole network (Contandriopoulos 
et al., 2018; Defazio et al., 2009). 
 
Consistent with the literature regarding the value of cohesion inherent among weakly tied 
members (Granovetter, 1973, 1983), our finding suggests a relatively strong collaboration 
among the whole network despite its relatively low connectivity value. While students and 
advisory board members did not regularly interact with faculty, they contributed significantly to 
the overall research agenda. Therefore, we conclude that the quality of a scientific 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   52 
 

collaboration between and among sub-team members did promote the advancement of 
scientific discovery and fostered future collaboration, which is indicative of a strong network 
collaboration (Defazio et al., 2009; Hoang et al., 2019; Olson et al., 2008).  
 
We recommend that researchers working within a network include overlapping research 
questions and implement activities that require frequent input from all network members. Such 
an approach should foster better interaction between sub-networks, foster participation of all 
members, improve overall network connectivity, and foster future collaboration among team 
members. 
 
The study was limited by non-respondents. Sub-team 3 remains underexplored due to low 
interaction within the sub-team and the whole network. Including non-respondents in the 
sociograms (Figures 1 and 2) influenced the number of ties; and therefore, it is possible we may 
have overstated density values (Borgatti et al., 2018). Nevertheless, the contributions of this 
study toward better understanding the structure and function of collaborative teams highlights 
the contextual factors that negatively impacted the overall connectivity of the research 
network. Future research should focus on collecting SNA data longitudinally of the whole 
network to determine how the network evolves over time, and how strong and weak ties 
impact scientific discovery over the life of the project. 
 

Acknowledgements 
 
This research was supported by funding from the USDA-NIFA Organic Agriculture Research and 
Extension Initiative (Award # 2018-51300-28434). 
 

References 
 
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet 6 for windows: Software for social 

network analysis [Computer software]. Analytic Technologies. 
http://www.analytictech.com/archive/ucinet.htm   

 
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks (2nd ed.). Sage 

Publications. 
 
Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social 

sciences. Science, 323(5916), 892–895. https://doi.org/10.1126/science.1165821  
 
Contandriopoulos, D., Larouche, C., & Duhoux, A. (2018). Evaluating academic research 

networks. Canadian Journal of Program Evaluation, 33(1), 69–89. 
https://doi.org/10.3138/cjpe.42159 

 



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   53 
 

Defazio, D., Lockett, A., & Wright, M. (2009). Funding incentives, collaborative dynamics and 
scientific productivity: Evidence from the EU framework program. Research Policy, 
38(2), 293–305. https://doi.org/10.1016/j.respol.2008.11.008  

 
Dillman, D. A., Smyth, J. D., & Christian, L. M. (2008). Internet, mail, and mixed-mode surveys: 

The tailored design method (2nd ed.). Wiley & Sons. 
 
Ding, Y., Foo, S., & Chowdhury, G. (1998). A bibliometric analysis of collaboration in the field of 

information retrieval. International Information & Library Review, 30(4), 367–376. 
https://doi.org/10.1080/10572317.1998.10762484  

 
Dunn, W. N. (1983). Social network theory. Knowledge, 4(3), 453–461. 

https://doi.org/10.1177/107554708300400306  
 
Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in 

education (8th ed.). McGraw-Hill Higher Education. 
 
Friese, S. (2019). ATLAS.ti 8 Mac user manual. 

https://downloads.atlasti.com/docs/manual/manual_a8_mac_en.pdf?_ga=2.31688247.
844522840.1558027933-883190210.1548880313  

 
Granovetter, M. S. (1983). The strength of weak ties: A network theory revisited. Sociological 

Theory, 1, 201–233. https://doi.org/10.2307/202051  
 
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 

1360–1380. https://doi.org/10.1086/225469  
 
Hansen, T. (2009). Applying social network theory and analysis in the struggle for social justice. 

Peace Research, 41(1), 5-43. www.jstor.org/stable/23607964  
 
Hoang, D. T., Nguyen, N. T., Tran, V. C., & Hwang, D. (2019). Research collaboration model in 

academic social networks. Enterprise Information Systems, 13(7-8), 1023-1045. 
https://doi.org/10.1080/17517575.2018.1556812 

 
Katz, J. S. (1994). Geographical proximity and scientific collaboration. Scientometrics, 31(1), 31–

43. https://doi.org/10.1007/bf02018100    
 
Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18. 

https://doi.org/10.1016/s0048-7333(96)00917-1  
 
Lang, S. (2020). Otter [Computer software]. http://otter.ai  
  
Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. 

Social Studies of Science, 35(5), 673–702. https://doi.org/10.1177/0306312705052359  



Dossou Kpanou et al.   Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v1i3.65   54 
 

Leij, M. V. D., & Goyal, S. (2011). Strong Ties in a Small World. Review of Network Economics, 
10(2), 1–42. https://doi.org/10.2202/1446-9022.1278 

 
Linneberg, M. S., & Korsgaard, S. (2019, March 16). Coding qualitative data: A synthesis guiding 

the novice. 
https://www.researchgate.net/publication/332957319_Coding_qualitative_data_a_synt
hesis_guiding_the_novice 

 
Mertens, (2018). Mixed methods design in evaluation. Sage Publication. 
 
Mertens, D. M., & Wilson, A. T. (2012). Program evaluation theory and practice: a 

comprehensive guide. Guilford Press. 
 
Olson, J. S., Hofer, E. C., Bos, N., Zimmerman, A., Olson, G. M., Cooney, D., & Faniel, I. (2008). A 

theory of remote scientific collaboration. Scientific Collaboration on the Internet, 73–97. 
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.8012&rep=rep1&type=
pdf  

 
Olson, G. M., & Olson, J. S. (2000). Distance matters. Human–computer interaction, 15(2-3), 

139–178. https://doi.org/10.1207/s15327051hci1523_4  
 
Patton, M. Q. (2001). Qualitative research and evaluation methods. Sage Publications. 
 
Rademacher, M. A., & Wang, K. Y. (2014). Strong-tie social connections versus weak-tie social 

connections. Scholarship and Professional Work -Communication. 103, 1213-1216. 
http://digitalcommons.butler.edu/ccom_papers/103 

 
Schmidt, A. (2007). Social network theory. Encyclopedia of Governance, 893–895. 

https://doi.org/10.4135/9781412952613.n503  
 
U. S. Department of Agriculture. (2019). National Institute of Food and Agriculture FY 2018 

Annual Report. https://nifa.usda.gov/sites/default/files/resource/NIFA-2018-Annual-
Report.pdf 

 
Sonnenwald, D. H. (2007). Scientific collaboration. In:  B. Cronin (Ed.), Annual Review of 

Information Science and Technology, 41, (pp. 643-681). 
https://www.researchgate.net/publication/220040763_Scientific_collaboration  

 
Zamzami, N., & Schiffauerova, A. (2017). The impact of individual collaborative activities on 

knowledge creation and transmission. Scientometrics, 111(3), 1385–1413. 
https://doi.org/10.1007/s11192-017-2350-x  

 
© 2020 by authors. This article is an open access article distributed under the terms and conditions of 
the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). 


