































   Advancements in Agricultural Development 
  Volume 5, Issue 3, 2024 
  agdevresearch.org 

 

1. Megan Cantrell, Lecturer, Agricultural Education and Communication Department, University of Florida, 113B Bryant Hall, 

PO Box 112060, Gainesville, FL 32611. megancantrell@ufl.edu,  https://orcid.org/0000-0001-8511-4442 
2. Lauri M. Baker, Associate Professor, Agricultural Education and Communication Department, UF/IFAS Center for Public 

Issues Education in Agriculture and Natural Resources. University of Florida,1408 Sabal Palm Drive, Level 2, Gainesville, FL 

32611, lauri.m.baker@ufl.edu,  https://orcid.org/0000-0002-4241-607732 
3. Cheng-Xian Yang, PhD Candidate, Agricultural Education and Communication Department, UF/IFAS Center for Public Issues 

Education in Agriculture and Natural Resources GRA, University of Florida,1408 Sabal Palm Drive, Level 2, Gainesville, FL 

32611, yang.c1@ufl.edu,  https://orcid.org/0000-0003-4297-5810 
1 

 

Time To Get Some ZZs? Navigating Generation Z Values in 
Extension Programming through a Systems Lens 

 
M. Cantrell1, L. M. Baker2, C. Yang3 

 
 

Article History 
Received: October 26, 2023 
Accepted: April 1, 2024 
Published: April 16, 2024 
Modified: May 30, 2024 
 
 
Keywords 
systems thinking; mental models; Q-
Methodology; land-grant university   

Abstract 
The purpose of this study was to identify programming values of 
Generation Z college students to offer insight into future programming 
and adaptation of current systems within Extension to prepare for the 
next generation of stakeholders. Researchers approached this study with 
the philosophy that Extension should aspire to be a learning organization 
and continually seek to understand current and future stakeholders to 
navigate societal changes. Q-methodology was used to quantitatively 
capture student priorities for 42 programming areas with three 
subcategories of people, environment, and economy and jobs. An initial 
sample of 158 was paired down to a P-set of 21. Results of the Q-Sort 
indicated three factors of group priorities: (a) Economic and People-
Centric; (b) People-Environment Centric with Limited Focus; and (c) 
Environmental-Centric. Results show a need for programming related to 
the environment and the protection of natural resources (Factor 3) and 
addressing societal concerns like drug and alcohol abuse (Factor 1). For 
non-urban and prior Extension user audiences (Factor 2) developing 
programming that connects environmental issues with people and 
societal issues is important. Future research should be conducted in other 
Extension systems to develop a broad understanding of Generation Z’s 
programming needs. 

mailto:megancantrell@ufl.edu
https://orcid.org/0000-0001-8511-4442
mailto:lauri.m.baker@ufl.edu
https://orcid.org/0000-0002-4241-607732
mailto:yang.c1@ufl.edu
https://orcid.org/0000-0003-4297-5810


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Introduction and Problem Statement 
 
Cooperative Extension is tailored to meet the needs of communities, and as community 
stakeholders shift and evolve, so should Extension programming priorities, as these are 
designed to work together to bring about positive change. Extension is a process that provides 
scientific knowledge from the Department of Agriculture and land-grant colleges to surrounding 
communities and the public to solve issues of the farm, home, and community (Collins, 2012; 
Hightower, 1973; USDA NIFA, 2024). By engaging in communities, Extension can lead positive 
change in states, the nation, and globally. It aims to connect the research to issues faced by 
people, allowing more people to benefit from scholarship (Collins, 2012; USDA NIFA, 2024). The 
mission of Extension is to contribute to helping communities acquire knowledge in agriculture, 
people and natural resources, and the life sciences (UF/IFAS Extension, 2022). Based on the ten-
year roadmap, the priority missions of the University of Florida’s Institute for Food and 
Agricultural Sciences (UF/IFAS) Extension are to promote food and environmental awareness; 
cultivate youth potential and community development; and to improve a healthier lifestyle for 
communities (UF/IFAS Extension, 2012). As younger adults enter the workforce, it is necessary 
to understand the thoughts of Generation Z as the next generation of Extension users, agents, 
and volunteers. Generation Z is defined as individuals born between 1995 and 2010 (Seemiller 
& Grace, 2016). The literature surrounding this generation continues to develop, particularly 
surrounding the impact of the COVID-19 pandemic (Dolot, 2018). Literature pre-dating the 
pandemic indicates Generation Z is a motivated, socially active generation that is marked by 
entrepreneurship and positive views on social change (Seemiller & Grace, 2016). Generation Z 
is the primary generation engaged in higher education and currently entering the workforce. 
Understanding current college students' perspectives on Extension topics can help adjust future 
Extension priorities and highlight topics that are viewed as important by this key demographic 
area as they become more prevalent Extension users. While prior work indicates college-aged 
students are not familiar with Extension, once exposed to Extension they see value in Extension 
(McLeod-Morin et al., 2023) and continue to be an important audience group to consider for 
building a future for Extension systems beyond current generations of Extension users (Baker et 
al., 2011; McLeod-Morin, 2023).  
 

Theoretical and Conceptual Framework 
 
Researchers approached this study with the philosophy that Extension should aspire to be a 
learning organization as defined by Senge’s five disciplines of learning organizations: Building a 
shared vision, systems thinking, mental models, team learning, and personal mastery (Senge, 
2006).  Innovation in systems thinking is key in agriculture, health, and human sciences to 
respond to rapidly changing demands and knowledge that reaches people who depend on it. 
Extension, in partnership with USDA NIFA, translates research into action to build a system that 
offers cutting-edge discoveries from research to turn knowledge into practice (USDA NIFA, 
2024). All five of Senge’s five disciplines of learning organizations are critical because of the 
interlinked and integrated needs of a learning organization (Senge, 1994), and in the full data 
collection process of the UF/IFAS Extension system all five have been utilized. However, the 

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portion of the study reported here related to Generation Z college students focused on two of 
the five: systems thinking and mental models. 
 
In organizational management and change literature, systems thinking is defined as the concept 
that organizations are integrated, dynamic systems that nest within each other (Burke, 2013). 
Thus, an organization cannot think about individual needs without recognizing the 
consequences to other parts of the system and to the universal system (Senge, 1994). To 
develop a full picture of what is happening within a connected system, opposing viewpoints and 
all different but interconnected types of thinking deserve consideration. Mental models are 
similarly complex and operationalized as the core values of all employees and stakeholders that 
shape the organizational identity. An organization’s success is dependent on its ability to adapt 
to new mental models when there is a shift in values of employees and stakeholders (Senge, 
1994). When individuals within an organization develop the capacity to challenge and reshape 
their mental models, it fosters a culture of continuous learning and adaptation, enabling the 
organization to navigate complex challenges effectively. The current study proposes that a shift 
may occur when asking Generation Z their preferences for future programming and education 
for Extension. If new priorities are set by one generation, systems thinking proposes that 
because of the interconnected systems, this change in one area can ripple through the entire 
organization (Sterman, 2000). While understanding the viewpoints of past generations and 
current stakeholders will always remain important, looking to the future may offer a greater 
understanding of where priorities will be in the future. This holistic perspective helps in 
anticipating and addressing emerging issues, enhancing resilience, and making informed, 
proactive decisions to prepare for the future. By combining mental model transformation with 
systems thinking, while building on current literature of existing mental models and systems 
thinking of Extension and connected entities like USDA NIFA organizations can not only adapt to 
change but also proactively shape their strategies and structures to thrive in an ever-evolving 
landscape (Senge, 2006; Sterman, 2000). 
 

Purpose 
 
The purpose of this study was to identify programming values of Generation Z college students 
related to Extension to offer insight into future programming and adaptation of current systems 
within Extension to prepare for the next generation of stakeholders. With the assistance of 
software, we divided Generation Z college students into different subfactors based on their 
perceived differences using self-reported demographic data. In addition to examining the 
Extension items that each subfactor was concerned with, this study also analyzed which items 
were less prioritized. Based on the results, this study could further inform the current 
development of Extension programming areas and discuss potential future priority areas. 
 

Methods 
 
Q-methodology was determined as the best technique to aid researchers in quantitatively 
capturing student opinion at a moment in time while enrolled at a land-grant institution (Watts 

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& Stenner, 2012). To better understand Generation Z’s perceptions of extension topics, we 
used 42 programming areas for UF/IFAS Extension, originally developed by Narine et al. (2020), 
and further refined by Harder et al. (2023). These 42 programming areas served as a pre-
determined concourse (Watts & Stenner, 2012). The programming areas were categorized into 
three programming areas of focus: people (f = 15), economy and jobs (f = 11), and the 
environment (f = 16).  A total of 158 undergraduate students enrolled at the University of 
Florida participated in ranking the program areas on a scale of most important to least 
important. The forced distribution was established based on the 42 programming areas used as 
statements, which established the Q-Set for this study (see Figure 1). The two additional 
statements over the 11 nodes were placed in the highest and lowest scores giving a more polar 
distribution over the 42 statements. 
 
Figure 1 
 
Forced Distribution Used to Collect Data in the Q-Sort Process 

 
 
Due to the nature of data saturation within Q-Methodology, deeper analysis was difficult with 
the full dataset. Thus, researchers took a purposive subsample with unique identifiers to make 
a less homogenous population as their P-Set (Watts & Stenner, 2012), which is the 
recommended practice in Q-Methodology (Stenner et al., 2008).  Priority was placed on having 
representation of gender, location, and prior Extension experience. This resulted in 21 
participants which aligns with the Q-methodology's standard of having a P-set for saturation 
that is equivalent to half of the sorting statements to allow proper statistical analysis (Watts & 
Stenner, 2012). These participants’ Q-sorts were entered in KADE software for analysis. Factor 
extraction was completed using a principal components analysis, resulting in three factors that 
had significant eigenvalues, which is indicative of the factor’s significance statistically (Watts & 
Stenner, 2012). One of the participants did not correlate strongly enough with any factor 
resulting in 20 students' data that were included in the factor rotations at a significance of 5% 
(p < .05) (Watts & Stenner, 2012). The three factors explained 52% of study variance with a 
more substantial break between factor three and four with 7% of the variance explained at the 
next factoring. Factors were then rotated on all data in the study excluding Q-sorts with a factor 
relevance which was less than .60 and factor loadings that were less than .40 on the remaining 
factors (Watts & Stenner, 2012). Factor analysis was completed using Q-sort values, with 
particular attention to distinguishing statements for each factor and factor arrays. Analysis of 

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these elements of the data were given a title to best represent the opinions of the individuals in 
each factor.  
 
Limitations exist for this study focused on the population of interest and a lack of details for 
priority areas for Extension. All participants were enrolled in a required writing course in the 
College of Agricultural and Life Sciences at the University of Florida which limits an 
encompassing understanding of all members of Generation Z who will be Extension users. The 
population also skews to the older sector of Generation Z which may have differing opinions 
and interest in Extension priority areas than the younger members who are still in secondary 
school. Q-methodology with a pre-determined concourse prevents participants from adding 
additional items which may reflect a differing priority area. This particularly highlights the 
current Extension priority areas but does not address a potential gap of other areas.  
 

Findings 
 
The sample was predominantly female (f = 14; 70%), four were males (20%), two identified as 
non-binary (10%). Half were juniors (f = 10; 50%), five (25%) were sophomores, four (20%) were 
seniors, and one was a freshman (5%). These students grew up almost equally in rural (f = 7; 
35%), subdivision (f = 6; 30%), and downtown/in town (f = 7; 35%) areas. Over three-fourths of 
participants had yet to participate in programming from Extension (f = 16; 80%). Based on the 
factoring results, the final factors one to three included six, six, and eight students respectively. 
We extracted three factors with between six and eight defining variables and composite 
reliability of above 0.80 (see Table 1). Factor Q-sort values where each factor placed the 
statement on the distribution board, which is its perceived value (see Table 2). 
 
Table 1 
 
Factor Characteristics 

Characteristics 
Factors 

1 2 3 
No. of Defining Variables 6 6 8 
Average Reliability Coefficient .80 .80 .80 
Composite Reliability .96 .96 .97 
Standard Error of Factor Z Scores .20 .20 .17 

 
  

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Table 2 
 
Factor Q-Sort Values for Each Statement 

No. Statement 
Factors 

1 2 3 
1 Preserving farmland -2 -5 1 
2 Protecting water quality -2 -5 3 
3 Protecting air quality -2 -4 2 
4 Ensuring safe food handling practices to prevent foodborne illness 1 -4 1 
5 Assisting farmers in agricultural production -2 -3 -2 
6 Strengthening the local food system 1 -4 -3 
7 Promoting economic development -1 -3 -5 
8 Ensuring individuals have access to affordable healthy food 3 -3 -1 
9 Helping youth develop leadership, citizenship, and other life skills 4 -3 -3 
10 Getting more adults involved in mentoring youth 3 -2 -4 
11 Reducing obesity through educational programs 1 -2 -1 
12 Helping consumers make healthy food choices 2 -2 -1 
13 Providing physical fitness education 2 -2 -1 
14 Preventing chronic disease 4 -1 3 
15 Helping rural communities improve their quality of life 0 -1 -4 
16 Assisting local government with land use decisions -3 -1 -2 
17 Helping communities be better prepared for natural disasters 0 -1 0 
18 Helping households become more energy efficient 0 -1 0 
19 Building the capacity of community nonprofits 2 0 -4 
20 Helping urban communities improve their quality of life 0 0 -5 
21 Assisting local businesses with land use decisions -3 1 -2 
22 Preserving foods for home use (e.g., canning, dehydrating) 1 1 0 
23 Helping first-time homeowners make smart financial decisions 0 2 -2 
24 Addressing alcohol abuse 0 2 0 
25 Addressing prescription drug abuse -1 5 2 
26 Addressing illegal drug abuse -1 4 0 
27 Addressing mental health 5 3 3 
28 Preventing suicide 3 4 4 
29 Building healthy families 4 5 2 
30 Strengthening couple and/or marital relationships 3 4 1 
31 Teaching healthy relationship skills to teens 5 3 1 
32 Strengthening workforce readiness 1 1 -1 
33 Helping households reduce water use -1 2 -3 
34 Composting, reducing, and recycling consumer goods -1 1 3 
35 Strengthening the financial well-being of small businesses 2 3 0 
36 Controlling invasive plants -3 3 1 
37 Protecting the coastal environment -3 2 4 
38 Protecting freshwater resources (e.g., lakes, rivers, springs, wetlands) -4 0 4 
39 Reducing saltwater intrusion -4 1 -3 
40 Protecting the marine environment -5 0 5 
41 Controlling invasive pests (e.g., animals, insects) -4 0 2 
42 Protecting natural habitats and ecosystems -5 0 5 

 

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Factor one, Economic and People-Centric Factor, explained 23% of the variance, with six 
defining variables, a composite reliability of 0.96, and an Eigenvalue of 4.73. Students in factor 
one prioritized people over the environment. Extension topics related to people and economy 
and jobs got significantly higher Z scores. Results indicated this factor prioritizes nurturing 
youth and addressing food and health issues. In addition, it should be noted that the economy 
and job topics with priorities in this factor had a relationship to the people and community 
elements, such as ensuring individuals have access to affordable healthy food and building the 
capacity of community nonprofits. On the contrary, all the lowest priority areas for this factor 
fell into the environment category. Demographics in factor one included four juniors, one 
senior, and one sophomore; only the sophomore was a prior Extension user. Four of them were 
females and the other two were male. All rural/urban options were represented within this 
factor, and they were from different College of Agricultural and Life Sciences majors. This 
Economic and People-Centric factor shows a commonality in this group of individuals to 
strongly emphasize economic growth, job creation, and social well-being, while showing less 
concern for environmental sustainability (see Table 3). 
 
Table 3 
 
Economic and People-Centric: Factor High and Low Priority Areas 
 

 

 
Low Z High Z Category 

Helping youth develop leadership, citizenship, and other life skills 
 

X People 
Ensuring individuals have access to affordable healthy food 

 
X Econ. and jobs 

Helping consumers make healthy food choices 
 

X People 
Getting more adults involved in mentoring youth 

 
X People 

Building the capacity of community nonprofits 
 

X Econ. and jobs 
Providing physical fitness education 

 
X People 

Strengthening the local food system 
 

X Econ. and jobs 
Ensuring safe food handling practices to prevent foodborne illness 

 
X Econ. and jobs 

Reducing obesity through educational programs 
 

X People 
Promoting economic development 

 
X Econ. and jobs 

Composting, reducing, and recycling consumer goods X 
 

Environment 
Controlling invasive plants X 

 
Environment 

Reducing saltwater intrusion X 
 

Environment 
Controlling invasive pests (e.g., animals, insects) X 

 
Environment 

Protecting freshwater resources (e.g., lakes, rivers, springs, 
wetlands) 

X 
 

Environment 

Protecting natural habitats and ecosystems X 
 

Environment 
Protecting the marine environment X 

 
Environment 

Note. Categories are highlighted in colors to show differentiation visually: People = pink; 
Economy and jobs = yellow; Environment = green 

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Factor two, People-Environment Centric with Limited Focus, explained 19% of the variance, 
with six defining variables, a composite reliability of 0.96, and an Eigenvalue of 4.09. Students in 
factor two highly prioritized Extension topics about people and the environment, particularly 
when the two are connected. They also had one high priority for the economy and jobs, but it 
was focused on a human element (helping first-time homeowners make smart financial 
decisions). Most of the lowest priorities for this factor were economy and job, followed closely 
by some environmental issues. However, it is worth noting that the environmental 
programming areas that this factor prioritized included a human element, such as “helping 
households reduce water use,” while those with lower priorities did not have a human element 
identified (e.g., protecting air or water quality). Students in factor two were from all years of 
school and all different majors across the University of Florida. Three of them were prior 
Extension users and two were unsure. All samples in this factor were female and lived outside 
of cities. This People-Environment Centric with Limited Focus factor represents individuals who 
strongly value the well-being of people and the environment but do not place a high priority on 
specific issues related to food and air quality (see Table 4). 
 
Table 4 
 
People-Environment Centric with Limited Focus: Factor High and Low Priority Areas   

Low Z High Z Category 
Addressing prescription drug abuse 

 
X People 

Addressing illegal drug abuse 
 

X People 
Controlling invasive plants 

 
X Environment 

Helping first-time homeowners make smart financial decisions 
 

X Econ. and jobs 
Helping households reduce water use 

 
X Environment 

Addressing alcohol abuse 
 

X People 
Reducing saltwater intrusion 

 
X Environment 

Assisting local businesses with land use decisions 
 

X Environment 
Addressing mental health X  People 
Ensuring individuals have access to affordable healthy food X 

 
Econ. and jobs 

Assisting farmers in agricultural production X 
 

Econ. and jobs 
Strengthening the local food system X 

 
Econ. and jobs 

Protecting air quality X 
 

Environment 
Protecting water quality X 

 
Environment 

Preserving farmland X 
 

Environment 

Note. Categories are highlighted in colors to show differentiation visually: People = pink; 
Economy and jobs = yellow; Environment = green 

 
 
Lastly, factor three, Environmental-Centric, explained 10% of the variance, with eight defining 
variables, a composite reliability of 0.97, and an Eigenvalue of 2.15. Students in factor three 
prioritized environmental topics above all else. Most of the lowest-priority topics for this factor 

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were economy and job, with only one other significantly low priority in the environmental 
category (Helping households reduce water use). This study inferred that factor three was so 
environmentally focused, they did not prioritize programming related to the residential 
element of water use due to prior connections to this or related topics. Two sophomores, four 
juniors, and two seniors were in factor three, with different majors across the college. None of 
the students were prior Extension users. The gender of this factor included two males, four 
females, and two non-binary students. All rural/urban options were represented within this 
factor. The Environmental-Centric factor can be described as individuals who prioritize 
environmental sustainability above all else and who do not prioritize economic growth, job 
creation, and people-centric aspects of society (see Table 5). 
 
Table 5 
 
Environmental-Centric: Factor High and Low Priority Areas  

Low Z High Z Category 
Protecting natural habitats and ecosystems 

 
X Environment 

Protecting the marine environment 
 

X Environment 
Protecting freshwater resources (e.g., lakes, rivers, springs, 

wetlands) 

 
X Environment 

Protecting the coastal environment 
 

X Environment 
Controlling invasive pests (e.g., animals, insects) 

 
X Environment 

Protecting water quality 
 

X Environment 
Protecting air quality 

 
X Environment 

Preserving farmland  X Environment 
Strengthening couple and/or marital relationships X 

 
People 

Teaching healthy relationship skills to teens X 
 

People 
Strengthening the financial well-being of small businesses X 

 
Econ. and jobs 

Strengthening workforce readiness X 
 

Econ. and jobs 
Helping first-time homeowners make smart financial decisions X 

 
Econ. and jobs 

Helping households reduce water use X 
 

Environment 
Building the capacity of community nonprofits X 

 
Econ. and jobs 

Helping rural communities improve their quality of life X 
 

Econ. and jobs 
Helping urban communities improve their quality of life X 

 
Econ. and jobs 

Promoting economic development X 
 

Econ. and jobs 

Note. Categories are highlighted in colors to show differentiation visually: People = pink; 
Economy and jobs = yellow; Environment = green 

 
Conclusions, Discussion, and Recommendations 

 
This sample of Generation Z, college-students born between 1997 to 2012 at the University of 
Florida enrolled in a course in the College of Agriculture and Life Sciences, fell into three 
different factors based on ranking of values and need for programming. These factors were: (a) 

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Economic and People-Centric; (b) People-Environment Centric with Limited Focus; and (c) 
Environmental-Centric. All factors were less likely to value programming related to the 
economy and job issues. This could reflect their stage of life as full-time students who are not 
engaged with the workforce in a full-time capacity during their time at a university, which 
supports previous work in this area (Baker et al., 2011; McLeod-Morin et al., 2023). However, 
when looking to build programming for the future for Extension, those who are less likely to 
have been Extension users (Factors 1 & 3) had clear programming focus areas of interest in 1) 
economic development when focused on people aspects and 3) environmental programming, 
which confirms this audience continues to offer potential value in engaging in Extension 
programming earlier once exposed positively (Baker et al., 2011; McLeod-Morin et al., 2023). 
Interestingly, those who were more likely to have used Extension before (Factor 2) valued more 
variety in programming. This may speak to the nature of Extension as a whole and the value 
placed on the importance of a variety of programming areas. Additionally, it may provide 
support for introducing Extension programming and resources available to current students 
through a larger-scale initiative while they are still students on campus.  
 
Results speak to a clear need for programming related to the environment and the protection 
of natural resources (Factor 3). Additionally, there is a clear need for building people and 
addressing societal concerns like drug and alcohol abuse (Factor 1). For non-urban and prior 
Extension user audiences (Factor 2), it will be important to develop programming that connects 
environmental issues with people and societal issues. Programming for economy and job areas 
may also gain more traction and interest with these Gen Z audiences if they connect to how 
these affect the environment and/or people. These elements are not currently as explored 
within the scope of Extension but should be examined as Extension reconsiders its future scope 
of work. While these results are not surprising given the culture of Generation Z (Seemiller & 
Grace, 2016), these may require a system thinking approach (Senge, 2006) in integrating within 
the current Extension programming structure. Competition for funding of programs and 
expansion of programs is always a balance, and new mental models may need to be adopted to 
respond to the values of Generation Z stakeholders within the current Extension system. The 
Extension system's success depends on adapting to stakeholders' changing needs and values 
(Burke, 2013; Senge, 1994; 2006). This requires ongoing flexibility as stakeholders move into 
new life stages. 
 
Beyond a programmatic standpoint, it is important to note that many undergraduate students 
in this research who are enrolled in a college related to agriculture and related sciences were 
unfamiliar or unsure if they had personally engaged with Extension. Being a prior Extension-
user had an impact on students' perspectives related to programming. Information, and 
potentially direct engagement, regarding Extension should be included in colleges of 
agriculture, particularly land-grant institutions to ensure each student is aware of the Extension 
system and what it may offer to them in their future careers and personal life as they continue 
to develop and become contributing members of society.  
 
Further research into other generations' priorities would be a useful addition to the literature, 
particularly if it was possible to compare those who are Extension users and non-Extension 

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users. Replication of this study on a national scale, or within other land-grant institutions, could 
create a broader understanding of programmatic priorities of Generation Z as they enter the 
workforce. Q-Sort methodology offers potential for other Extension systems to explore 
programming needs and priorities through repeating this process, which could offer deeper 
understanding of current and future needs and priorities for Extension, USDA NIFA, and other 
partnering organizations across the United States. 
 

Acknowledgments 
 
Funding for this research was provided by the University of Florida Institute for Food and 
Agricultural Sciences (UF/IFAS) and specifically by the Office of the Dean and Director of 
Extension at UF/IFAS and the Office of the Senior Vice President for UF/IFAS. 
 
Cantrell, M. - conducted data analysis and interpretation, writing of the results and methods, 
and editing; Baker, L. M. - developed the study design, collected data, wrote the literature 
review, discussion, and conclusions; Yang, C. - supported data collection, cleaned the data, 
contributed to writing and editing.  
 
This article was modified after initial publication to provide additional details about how the 
instrument was developed. Two additional references were added. 
 

References 
 
Baker, L. M., Irani, T., & Abrams, K. (2011). Communicating strategically with generation me: 

Aligning students’ career needs with communication about academic programs. NACTA 
Journal, 55(2), 32–39. https://www.jstor.org/stable/pdf/nactajournal.55.2.32.pdf   

 
Burke, W. W. (2013). Organization Change: Theory and practice. SAGE Publications. 

Foundations for Organizational Science publication series (3rd Edition). 
 
Collins, C. S. (2012). Land-grant extension as a global endeavor: Connecting knowledge and 

international development. The Review of Higher Education, 36(1), 91–124. 
https://doi.org/10.1353/rhe.2012.0065 

 
Dolot, A. (2018). The characteristics of Generation Z. E-mentor, 2(74), 44–50. 

http://dx.doi.org/10.15219/em74.1351 
 
Harder, A., Craig, D., Israel, G., Benge, M., & Caillouet, O. (2023). Exploring the possibilities of a 

standardized questionnaire for assessing residents’ needs. Journal of Extension, 61(2), 
Article 1. https://doi.org/10.34068/joe.61.02.01 

 
Hightower, J. (1973). Hard tomatoes, hard times: A report of the agribusiness accountability 

project on the failure of America's land grant college complex. Schenkman Publishing. 

https://doi.org/10.37433/aad.v5i3.415
https://www.jstor.org/stable/pdf/nactajournal.55.2.32.pdf
https://doi.org/10.1353/rhe.2012.0065
http://dx.doi.org/10.15219/em74.1351
https://doi.org/10.34068/joe.61.02.01


Cantrell et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i3.415   12 
 

 
McLeod-Morin, A., Baker, L. M., Boyer, C., Zagonel, A., & Lindsey, A. B. (2023). Rewarding 

relationships: A quasi-experimental design evaluating the impact of an extension public 
relations seminar. Journal of Human Sciences and Extension, 11(1), 4. 
https://doi.org/10.55533/2325-5226.1373   

 
Narine, L. K., Ali, A. D., & Hill, P. A. (2020). Application of a three-phase needs assessment 

framework to identify priority issue areas for Extension programming. Journal of 
Extension, 58(4), Article 24. https://tigerprints.clemson.edu/joe/vol58/iss4/24  

 
National Institute of Food and Agriculture [NIFA]. (2024). Cooperative extension system. United 

States Department of Agriculture. https://www.nifa.usda.gov/about-nifa/how-we-
work/extension/cooperative-extension-system  

 
Seemiller, C., & Grace, M. (2016). Generation Z goes to college. John Wiley & Sons. 
 
Senge, P. M. (2006). The fifth discipline: The art and practice of the learning organization. 

Broadway Business.  
 
Senge, P. M. (1994). The fifth discipline fieldbook: Strategies and tools for building a learning 

organization. Crown Business. 
 
Sterman, J. (2000). Business dynamics: System thinking and modeling for a complex world. 

McGraw-Hill Education. 
 
Stenner, P., Watts, S., & Worrell, M. (2008). Q Methodology. In C. Willig, & W. Stainton Rogers 

(Eds.), The Sage Handbook of Qualitative Research in Psychology (pp. 215-239). Sage 
Research Methods. https://doi.org/10.4135/9781848607927 

 
University of Florida Extension [UF/IFAS Extension]. (2022). About 

Extension.https://branding.ifas.ufl.edu/downloads/uploads/Extension%20Brochures/Ex
tension/Extension-Overview-Booklet-2021.pdf  

 
University of Florida Extension [UF/IFAS Extension]. (2012). Shaping solutions for Florida’s 

future: The UF/IFAS Extension roadmap 2013–2023. https://pdec.ifas.ufl.edu/teams  
 
Watts, S., & Stenner, P. (2012). Doing q methodological research: Theory, method and 

interpretation. SAGE Publications. https://dx.doi.org/10.4135/9781446251911 
 
 
© 2024 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/). 
 

https://doi.org/10.37433/aad.v5i3.415
https://doi.org/10.55533/2325-5226.1373
https://tigerprints.clemson.edu/joe/vol58/iss4/24
https://www.nifa.usda.gov/about-nifa/how-we-work/extension/cooperative-extension-system
https://www.nifa.usda.gov/about-nifa/how-we-work/extension/cooperative-extension-system
https://doi.org/10.4135/9781848607927
https://pdec.ifas.ufl.edu/teams
https://dx.doi.org/10.4135/9781446251911

