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Worley et al.  Advancements in Agricultural Development 
  Volume 2, Issue 1, 2021 
  agdevresearch.org 

1. Barbara Worley, Graduate Research Assistant, University of Georgia, 405 College Station Rd., Athens, GA 30602, 

bworley@uga.edu, https://orcid.org/0000-0002-8490-2743 

2. Nick Fuhrman, Professor, University of Georgia, 405 College Station Rd., Athens, GA 30602, fuhrman@uga.edu 
https://orcid.org/0000-0002-0969-0541 

3. Jason Peake, Professor, University of Georgia, 405 College Station Rd., Athens, GA 30602, jpeake@uga.edu,  
https://orcid.org/0000-0002-1431-5447 

 
83 

 

A Quantitative Approach to Identifying Turfgrass Key Players 
B. Worley1, N. Fuhrman2, J. Peake3  

 
 

  

Abstract 

The purpose of this study was to systematically identify key players and media channels within the 
turf industry to constitute the diffusion of innovations in emerging turf research and technologies. 
Online survey questions were structured using Borgatti’s work developing the KeyPlayer™ (TM 
Analytic Technologies) software to determine individual contribution to a network and thus network 
cohesion. Turf industry professionals were asked to identify who they trust when they have 
questions regarding turfgrass. Researchers directly contacted 282 participants via email, collecting 
239 responses. The top 25 key players, the number of distinct persons reached in the network, and 
the percent of the network reached were calculated for the entire sample and each strata of the 
sample (including golf course superintendents, landscapers, turf producers, Extension, and Others—
including Extension Specialists, Turfgrass Faculty, and Sales representatives. Of the 422 unique 
names mentioned in the survey, key player data showed that the top 25 key players were 1 or 2 
steps away from 305 distinct persons in the network (72.3% of the network). With their influence on 
the larger network, these individuals will now be enlisted to aid in the diffusion of emerging new turf 
research and technologies. 

Keywords 

Diffusion of Innovations, elaboration likelihood model, link-tracing network sampling, network 
identification, decision-making model 



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Introduction and Problem Statement 
 

In science communication an “opinion leader” is “influential because others in the organization 
listen to them for advice or information” regarding an innovation (Stone et al., 1999, pp. 135, 
138). The identification of a list of opinion leaders or key players – those with influence in a 
community – is a clear concept that is difficult to manifest in a real-world, diffusion of 
innovation scenario. Agriculture development and diffusion of innovation models thus far have 
primarily focused on the importance of engaging key players in efforts to disseminate research-
based information (Ruth et al., 2018). However, less work has been published on how to 
identify those key players. 
 
Science is communicated in a variety of channels to the public, and how the receiver decodes 
the message depends on outside noise and the source from which the information is being 
disseminated. A range of issues regarding the source of scientific news include “(1) scientists as 
sources and resources, (2) journalists and their role in utilizing sources and resources, (3) public 
information officials as sources and resources, and (4) the science policy climate under which 
scientists, journalists, and public information officials work” (Logan, 2001, p. 144). Considering 
the increased availability of pseudo-science and misinformation about emerging and new 
technologies, it is of paramount importance to ensure that accurate information reaches the 
intended audience using the relevant channels (Schiele, 2020). 
 
When new agricultural technologies are released into the market, there are important shifts in 
outreach efforts needed to reach broader audiences focused on key players and media 
channels to constitute the diffusion of innovation (Schwartz et al., 2020). Determining who is 
best suited to disseminate information throughout the network via social engagement can only 
be understood by knowing who within the network garners trust and is regarded as a leader.  
 

Theoretical and Conceptual Framework 
 
Turfgrass is an Agricultural and Natural Resource (ANR) commodity with a distinct population of 
stakeholders, gate keepers, and communication preferences and channels. This research is 
phase one of an effort to test a Decision-Making Model (DMM) for ANR (Ruth et al., 2018) that 
has yet to be applied the turfgrass industry. When planning how turfgrass advancements can be 
most effectively disseminated, a model is needed that reveals a path for information flow (Ruth 
et al., 2018). To apply this model, key players must be identified. Previous studies of the golf 
course superintendent stratum identified perceived transformational leadership traits as well 
as their position as “frontline decision makers in the industry’s response to environmental 
issues” (Lenhardt et. al, 2011; Millington & Wilson, 2013, p. 456). 
 
This work utilizes Rogers (2003) Diffusion of Innovations Theory (DoI), where Rogers defines the 
process of diffusion as “1) an innovation 2) communicated through certain channels 3) over 
time 4) among members of a social system” (p. 12). Rogers (2003) states that communication is 
“a process in which participants create and share information with one another in order to 



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reach a mutual understanding” (p. 5). The innovation of new turfgrass cultivars must be 
communicated using various channels over time (dynamic) among various strata within the 
turfgrass industry. Individuals go through different stages when deciding whether or not to 
adopt new innovations (Masambuka-Kanchewa, 2020, p. 118). These five stages of the DoI, as 
outlined by Rogers (2003), are “knowledge, persuasion, decision, implementation, and 
confirmation” (p. 20).  
 
In order to acquire knowledge about the innovation they must be aware of the innovation’s 
existence and then have knowledge about the innovation (Rogers, 2003, p. 173). Rogers (2003) 
refers to this as “awareness knowledge” (p. 173). “The effectiveness of communication 
channels in diffusing innovations depends on which stage a person is in during the decision-
making process” (Masambuka-Kanchewa, 2020, p. 118). Therefore, the manner by which 
awareness is created about an innovation is determined by the communication channel that is 
used in delivering information regarding the innovation. As such, tailored and intentional 
messaging for target audiences is critical (Lamm et al., 2019).  
 
Though the role of the innovator in Roger’s theory is creating new ideas and, in this case, those 
developing new cultivars, it is the opinion leader – those that have been identified as key 
players - that have the most influence on communicating the new research to various industry 
strata. The persuasion stage of diffusion is thus salient to this research as clients must have an 
active cognitive need for seeking new information, leading to either a desirable or undesirable 
opinion of the innovation (Rogers, 2003, pp. 174–175). As illustrated in the DMM (Ruth et al., 
2018), the DoI outlines the “perceived characteristics of innovation” having an impact on the 
persuasion stage as “relative advantage, compatibility, complexity, trialability, and 
observability” (Rogers, 2003, p. 170). Key players have the potentiality of becoming change 
agents if the communication they disseminate leads to the adoption of the innovation (Rogers, 
2003, p. 175). Knowing the key players in the southeastern turfgrass industry will ensure more 
efficient communication and potential adoption of new innovations.  
 
The Elaboration Likelihood Model (ELM) (Petty & Cacioppo, 1980) purports that people make 
decisions about new innovations based on Central Route Processing where they listen to and 
evaluate the message pros and cons; or Peripheral Route Processing where they listen to social 
norms, rule of thumb, and cues. The ELM tests the “motivation to process a message and the 
“ability to process the message”, thus overlapping with Rogers’ idea of persuasion to determine 
motivation and ability to process the communication (Stone, 1999, pp. 194–195). 
 

Purpose 
 
The overall purpose for the larger study is to refine and focus a DMM for ANR Science and 
Technology, that could later be adopted by other fields, by integrating the theories of Diffusion 
of Innovations and the Elaboration Likelihood Model with our findings from link-tracing 
network sampling and KeyPlayer™ computer software (Borgatti, 2008; Petty & Cacioppo, 1980; 
Rogers, 2003). In order to accomplish that, the purpose of this study is to identify key players in 



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the southeastern turf industry and determine their preferred communication preferences. 
Knowing their communication preferences will be important in order to determine the best 
route for dissemination of communications regarding new turfgrass technologies. As such, the 
objectives for this study are to: 
1. Identify influential individuals of the turf industry, referred to as “key players,” by 

professional affiliation. 
2. Describe the relationship between key players by professional affiliation and their 

communication preference. 
 

Methods 
 
This quantitative study aimed to target turf producers, Extension Agents, golf course 
superintendents, landscapers, and home builders as part of a larger study to determine a 
communication and decision-making model for innovations in turfgrass. An online survey was 
distributed in four rounds beginning Spring 2020 to the sample using the Tailored Design 
Method (Dillman et al., 2014), and data were collected using Qualtrics. Online survey questions, 
presented to the sample through an introductory email that provided a link to the instrument, 
were structured using Borgatti’s work developing the KeyPlayer™ software to determine “the 
contribution of a set of actors to the cohesion of the network” (Borgatti, 2006, p. 21). A panel of 
Agricultural Leadership, Education and Communication faculty were used to review the 
questions as well as provide feedback on the content, criterion, and content validity. 
 
Respondents (n = 239) were asked which of the roles (strata) they most closely identified with 
in the turf industry. Options for response included Extension agent, turf producer, golf course 
superintendent, landscaper, home builder, researcher, and other. When “other” was selected 
respondents were given a textbox to self-identify their role; for example, responses included 
Extension Specialist, Turfgrass Faculty, and Sales Representative. Quantitative descriptive 
research questions included in the instrument were demographic and solicited participants to 
identify who they trust when they have questions regarding turfgrass. Additionally, as part of a 
larger study, respondents were asked questions regarding the format they preferred receiving 
communication regarding turfgrass, how often they preferred receiving information, and what 
time of year (season) is best to receive information.  
 
The researchers used a variant of respondent-driven sampling known as link-tracing sampling to 
reach individuals across various parts of the turfgrass industry (Gile & Handcock, 2010, p. 285). 
The initial sample for Round 1 was selected from prior contacts in the population of interest 
from team members of the five universities that are part of a turfgrass Specialty Crop Research 
Initiative grant. Throughout four rounds of inquiry, researchers directly contacted 282 
participants via email, collecting 239 responses. In Round 2, researchers utilized professional 
organizations for turfgrass producers and golf course superintendents. These organizations 
would not allow researchers access to the membership listservs; however, they would send 
emails on behalf of the researchers. From this, a population frame of 712 for Round 2 could be 
determined. Table 1 details each of the four rounds of data collection, including response rate. 



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Table 1 
 
Response Rates for each Round of Data Collection 
 Round 1 Round 2 Round 3 Round 4 
Sample 45 712 116 42 
Responses 37 172 19 11 
Response Rate 82.20% 24.60% 16.40% 26.20% 

 
Limitations of the research were confounded by the global pandemic of COVID-19 that limited 
data collection to being electronically based. Identifying which strata to survey was limited to 
the researchers’ knowledge of contacts. Determining where to start the sampling may have 
skewed the proportion of respondents in certain professional segments of the industry or 
strata. Kirchherr and Charles (2018) refer to using prior personal or professional contacts in the 
absence of a sampling frame to serve as the seeds of the sample (p. 4). Because of this and the 
utilization of a non-probability sampling methodology, bias may have occurred. To address 
these limitations, data collection continued until a redundancy of responses of the same 
subjects was seen (S. Borgatti, personal communication, January 8, 2020). An additional 
limitation is that strata was self-reported by the subjects given the option to select only one 
choice. Some subjects may have difficulty identifying themselves within a single stratum, when 
in reality they play different roles in different strata yet were only allowed to select a single 
stratum in the instrument.  
 
Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 26 and 
Excel, and social network analysis was performed. Respondents were assigned a unique 
identifier as were those that they listed as trusted individuals. Four hundred and twenty-two 
unique names were identified. This data was placed into the KeyPlayer™ to create a statistical 
and graphical representation of the nodes (points of contact within the network) represented.  
 

Findings 
 
The findings are presented respective to the objectives of this study.  
 
Identification of Key Players in Strata of the Turfgrass Industry 
Table 2 details the top 25 key players, the number of distinct persons reached in the network, 
and the percent of the network reached. Of the 422 unique names mentioned in the survey, 
key player data showed that the top 25 key players were 1 or 2 steps away from 305 distinct 
persons (72.3% of the network). In other words, the node that is most central, within 1 or 2 
nodes from others, provides a larger reach over the network.  
 
 
 
 
 
 



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Table 2 
 
Top 25 Key Players in the Turfgrass Network 

Number Key Playera Influencedb % of Network 
1 
 

5 
 

90 
 

21.3 
 2 6 126 29.9 

3 23 157 37.2 
4 27 176 41.7 
5 30 190 45.0 
6 36 213 50.5 
7 61 198 46.9 
8 65 206 48.8 
9 69 220 52.1 

10 102 227 53.8 
11 195 234 55.5 
12 202 240 56.9 
13 225 246 58.3 
14 232 252 59.7 
15 246 258 61.1 
16 249 263 62.3 
17 252 268 63.5 
18 261 273 64.7 
19 264 278 65.9 
20 266 283 67.1 
21 274 288 68.2 
22 300 293 69.4 
23 324 297 70.4 
24 332 301 71.3 
25 349 305 72.3 

a Number assigned to each individual 
b Number of persons influenced by increase in reach. 
 
To graphically display the key player data results, Pajek (Mrvar & Batagelj, 2016) analysis and 
visualization software was used. Pajek creates various 2-D and 3-D visualization outputs; our 
study used a 2-D SVG format with the Kamada-Kawai optimization network layout.  
 
In the 2-D Pajek model, seen in Figure 2, the more connected the node under this model, the 
more centrally located. Single outliers are shown for reasons including that data collection 
ended at round four or that those nodes were included as unique persons but their data of 
those they trust was not collected for the model. 
 
 
 
 



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Figure 1 
 
Pajek 2-D of Turfgrass Network 

Note. Key Players are indicated by their corresponding numerical identifier. 
 
We were able determine the key players within each stratum (as indicated in the instrument), 
by segmenting the sample into the five strata (golf course superintendent, Extension agent, turf 
producer, landscaper, and other) as identified by respondents from the instrument and running 
a KeyPlayer™ analysis to determine bi-directional trust (i.e., who is influenced by the strata, and 
who the strata influences). As shown in Table 3 and Table 4, Golf course superintendents and 
Other emerged as the strata with the most influence over the network, with a total combined 
influence of all five strata equally greater than 100.0% due to cross-strata influence. Further, 
cross-strata influence is revealed indicating that key players emerge within individual strata to 
which they do not necessarily identify. For example, respondent 2 is the top key player within 
their strata (turf producer), as well as the landscaper strata. Likewise, respondent 6 is the top 
key player within their strata (other), as well as the Extension strata. Respondent 6 identified 
themselves as “Extension Specialist” in the text box that was provided for the other strata. The 
level of influence increases with the person identified as being closer to the top of the list. 
Moving down the list, the strength of influence decreases. These findings ultimately reveal the 
key players in the turf industry who can impact the maximum amount of the network through 
unique connections, as a whole, and within and between each stratum.  
 
 
 

Pajek

5

6

23
27

30

36 61

65

69

102

195

202

225

232

246

249

252

261

264

266

274 300

324

332

349



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Table 3 
 
Top 25 Key Players within Each Stratum 

Golf Course 
Superintendent Landscaper Other Turf Producer Extension 

5 2 6 2 6 
61 3 20 24 9 
69 4 23 36 18 

102 5 27 60 21 
113 8 36 78 40 
182 9 44 112 46 
200 10 48 120 71 
202 11 50 123 94 
233 12 137 132 109 
243 13 170 153 148 
249 14 197 165 154 
252 20 205 207 164 
264 23 220 213 179 
266 24 224 265 195 
268 25 231 274 202 
274 27 261 316 207 
281 29 268 324 244 
300 31 325 330 264 
303 33 346 337 277 
315 35 368 338 287 
332 36 387 339 293 
349 37 395 352 346 
358 196 396 354 364 
372 204 398 382 398 
385 228 410 410 399 

 

 
  



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Table 4 
 
Network Reach, Influence, and Identification 
 Golf Course 

Superintendents Landscapers Other 
Turf 

Producers Extension 
% of Network reacheda 48.6 9.2 31.5 14.2 12.3 
# of persons influencedb 205 39 133 60 52 

# within stratum identifiedc 6 2 12 4 2 
a The % of the entire turfgrass network reached, by which each strata were influenced or 
influential. 
b The number of persons influenced, within 1 or 2 steps, by the top 25 key players of each 
strata. 
c The number of key players that emerged within each stratum, possessing the same identity, is 
indicated by number, having influence on their own stratum. 
 
Relationship Between Key Players by Stratum and their Communication Preference 
The preferred method of communication was established across all strata and within each 
stratum, in relation to the second objective. As shown in Table 5, the findings revealed that the 
preferred communication channel across all strata was “Face to Face conversations with 
professionals” (interpersonal communication) at 51.4%. 
 
Table 5 
 
Top Communication Channel by Strata 
Strata Preferred communication channel Percent of strata 
Extension Agents Email correspondence with professionals 38.9 

Turf Producers Communication with peers  42.1 

Golf Course Superintendents Face to Face conversations with 
professionals 

62.0 

Landscapers Face to Face conversations with 
professionals 
Longer (more than 1 full day) conferences 

50.0 
 
 

Othera Face to Face conversations with 
professionals 
Research Articles 

43.2 

Note. An equal proportion of the Landscapers and Other strata had preference for two 
channels. 
a Other included those who self-reported as being anything other than the strata listed in the 
instrument. 
 



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Conclusions, Discussion, and Recommendations 
 
Through the use of the KeyPlayer™ (TM Analytic Technologies) program (Borgatti, 2008), we 
identified key players in the turfgrass industry to maximize diffusion of innovation and increase 
adoption rates of new cultivars and turf technologies. This study found that there are key 
players that exist within the southeastern turfgrass network that can be targeted to 
disseminate communications of new turf industry technologies and research throughout the 
network. Social network analysis through the use of KeyPlayer™ and the concept of group 
centrality, as used in this study, can be used as an “identifier of opinion leaders” (Abdel-Ghany, 
2012, p. 12–13).  
 
A primary finding from this study was that the top 25 key players in the network are 1 to 2 steps 
away from 72.3% of the network. Thus, these top key players have the potential to serve as 
opinion leaders due to their central positioning within the network and established 
relationships among others in the industry. When considering Rogers (2003) Diffusion of 
Innovations theory where 16.0% to 50.0% of the population represents the early majority 
adopters, identifying 25 individuals who influence 72.3% of the network as opinion leaders 
should diffuse the emerging technology and research well into the early majority if not the late 
majority (50.0% to 84.0%) of subjects.  
 
Now that key players and who the key players trust have been identified within the turfgrass 
network it is possible to trace which key players trust which members of the research team. The 
members of the research team will reach out to KeyPlayers who trust them to solicit their 
participation in a steering committee to disseminate new turfgrass innovations. 
 
Recommendations associated with future research are outlined addressing demographics, 
strata selection and response, and communication channels. Respondents were asked to self-
identify in the demographics section, but were not asked to provide the stratum of those they 
listed they “trust” when answering “Who do you trust when you have questions regarding 
turfgrass?” By providing this information in the future, if those individuals are not captured in 
subsequent rounds of query, their stratum will still be noted. This will provide a more detailed 
analysis of the communication preferences of the strata represented in the network. A 
recommendation to address the strata indicated as other is to eliminate the option of providing 
that selection in the instrument, thereby guiding individuals to identify with one of the strata 
defined. 
 
A recommendation for determining generational differences among respondents is in line with 
the literature. Lamm et. al (2019) suggested identifying characteristics of the target audiences 
and developing targeted communication based on specified needs and interests. Perhaps 
greater variation exists between contextual variables (age, gender, etc.) of subjects than 
between strata. Further, we did not seek to determine change agents, but rather to identify key 
players based on level of trust within the network. As Flynn et. al (1996) noted in their study, 
opinion leaders and opinion seekers are distinct groups (p. 2). However, in this study we sought 



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to determine the trusted individuals within the network. Future research on determining how 
key players act as change agents is recommended. 
 
Preference in communication channels should be more closely examined in conjunction with 
examining adoption rates of new technologies. Determining why face to face communication is 
preferred will give researchers insight in to how turfgrass information can be more effectively 
disseminated. Additionally, alternative to face-to-face communication should be explored and 
discussed across strata due to the implications of this form of interpersonal communication 
during a global pandemic. The model for decision-making by each stratum of the turfgrass 
industry should be refined with each iteration and the decision-making model should be revised 
to increase adoption rates.  
 
We are now building a customized training and diffusion plan for new turfgrass technology and 
research. This systematic approach to communication in the turfgrass industry can be 
implemented and will enable opinion leaders to disseminate information of new cultivars and 
other industry related research-based information. This decision-making model will be 
implemented and studied to continuously improve its reliability and validity. 
 

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