































   Advancements in Agricultural Development 
  Volume 6, Issue 3, 2025 
  agdevresearch.org 

 

1. Everett M. Martin, Graduate Student, University of Arkansas, 1120 W. Maple St., Fayetteville, AR 72701, 

emm016@uark.edu,  https://orcid.org/0009-0002-9370-5781 
2. Christopher M. Estepp, Professor, University of Arkansas, 1120 W. Maple St., Fayetteville, AR 72701, estepp@uark.edu,  

 https://orcid.org/0000-0001-7268-9044 
3. Will Doss, Assistant Professor, Texas Tech University, Box 42131, Lubbock, TX 79409 will.doss@ttu.edu,                       

 https://orcid.org/0000-0002-3163-7528 
4. Donald M. Johnson, University Professor, University of Arkansas, 1120 W. Maple St., Fayetteville, AR 72701, 

dmjohnso@uark.edu,  https://orcid.org/0000-0003-2592-654X 
 
 
 

 
73 

 

Unpacking Research Impact in Agricultural Education: 
Implications for Role Perception and Career Advancement 

 
E. M. Martin1, C. M. Estepp2, W. Doss3, D. M. Johnson4 

 
 

Article History 
Received: July 10, 2025 
Accepted: September 29, 2025 
Published: October 7, 2025 
 
 
Keywords 
faculty research impact; research 
metrics; professorial rank;  
SDG 4: Quality Education  

Abstract 
This study investigates research impact and academic rank progression 
within agricultural education disciplines, employing metrics such as the 
h-index, i10 index, and total citations. Grounded in Vroom's expectancy 
theory, the research emphasizes the significance of role perception and 
instrumentality in motivating faculty toward research impact and career 
advancement. The study collected data from publicly available Google 
Scholar profiles of 126 AAAE members, spanning the ranks of assistant, 
associate, and full professors. Mean total citations were 120.81 (SD = 
110.27) for assistant professors, 685.78 (SD = 682.10) for associate 
professors, and 1800.63 (SD = 1315.75) for professors. Mean h-index 
values were 5.00 (SD = 3.03), 11.72 (SD = 4.51), and 19.86 (SD = 6.81), 
respectively. Forward subset regression with leave-one-out cross-
validation and forward subset logistic regression minimizing AIC were 
used to identify factors influencing research impact and academic rank 
transitions. Years since first publication (YSFP), faculty size, R1 status, and 
disciplinary focus predicted research impact. Logistic regression models 
showed YSFP was the only significant variable associated with both rank 
transitions. These results describe relationships between experience, 
institutional resources, and sub-disciplinary involvement in shaping 
research impact and career progression. 

mailto:emm016@uark.edu
https://orcid.org/0009-0002-9370-5781
mailto:estepp@uark.edu
https://orcid.org/0000-0001-7268-9044
mailto:will.doss@ttu.edu
https://orcid.org/0000-0002-3163-7528
mailto:dmjohnso@uark.edu
https://orcid.org/0000-0003-2592-654X


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Introduction and Problem Statement  
 
Research productivity and impact have long been standard metrics in faculty members' hiring, 
promotion, and tenure processes (Kotrlik et al., 2002; Moher et al., 2018). Schimanski and 
Alperin (2018) noted the significance of research productivity in personnel decisions has 
increased over time. Additionally, research productivity has been a key indicator of the 
academic prestige of researchers, institutions, and academic disciplines (Birkenholz & 
Simonsen, 2011; Burris, 2004; Lindner et al., 2020).  
 
Traditionally, researchers evaluated research productivity based on the number of publications 
authored (Kotrlik et al., 2002); however, many institutions have begun prioritizing impact factor 
metrics such as the h-index and i10 index to quantify the reach and influence of researchers’ 
work (Moher et al., 2018; Schimanski & Alperin, 2018). The h-index measures the impact of a 
researcher's publications based on the number of citations received and is defined as the 
highest number of publications of a scientist that received h or more citations (Schreiber, 2008). 
The i10 index, specific to Google Scholar, quantifies the number of articles a researcher has 
published that have been cited at least 10 times.  
 
While these metrics offer a means of assessing research impact, their interpretation can be 
challenging without an appropriate baseline for comparison. This challenge is particularly 
relevant as our disciplines refine research impact (The American Association for Agricultural 
Education [AAAE], 2023) amid shifting institutional expectations (Stein, 2023). Faculty often 
navigate complex roles, balancing research with substantial teaching, service, and, in some 
cases, extension, yet are assessed using metrics whose meaning is ambiguous without 
appropriate baselines (Alvarez Jr., 2020; Lindner et al., 2020; Love et al., 2022; Sorcinelli, 2007). 
Therefore, the problem addressed in this study is the absence of grounded, discipline-specific 
benchmarks for interpreting h-Index, i10 index, and citation counts across academic ranks in 
the agricultural education disciplines. To address this gap, we examined research impact using 
these indicators across assistant, associate, and full professor ranks. 
 

Theoretical and Conceptual Framework  
 
For faculty navigating the promotion and tenure process, unclear benchmarks for research 
impact, inconsistent institutional expectations, and the laden pressures of service highlight the 
need for a framework that explains faculty members’ motivation for career advancement 
(Birkenholz & Simonsen, 2011; Jackson et al., 2017; Moher et al., 2018). Accordingly, to explain 
how perceived expectations and reward pathways shape effort and advancement, we drew on 
Vroom’s (1964) expectancy theory. According to Vroom, motivation has three influences, 
expectancy, instrumentality, and valence. Expectancy is a person’s judgment that increased 
effort will result in increased performance. Instrumentality is the belief that increased 
performance will result in a desired outcome while valence is the importance an individual 
place on that outcome. Porter and Lawler (1968) later added role perception as to the theory to 
help capture whether people know what truly counts as successful performance. Role 

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perception therefore describes the actions one must take, and the standards one must meet, to 
achieve desired outcomes. 
 
Role perception and instrumentality were particularly relevant for this study. Instrumentality 
assesses whether enhancing individual research metrics, such as h-index, i10 index, or citation 
counts, is worth pursuing and will be acknowledged and rewarded by an individual’s institution 
and discipline. Specifically, understanding research impact benchmarks at different academic 
ranks, such as assistant, associate, and full professor, can help faculty align efforts with the 
expectations for advancement. Further, by examining the distribution of h-indexes, i10 indexes, 
and total citations within our agricultural education disciplines, this study provides a clearer 
picture of where faculty currently stand within their academic rank and what functional goals 
and standards can be expected moving forward, building role perception within our disciplines. 
With greater role perception, faculty members can make better informed decisions about 
allocating their time, engaging in research, and pursuing professional development 
opportunities. 
 
The application of expectancy theory to research impact data can contribute to our 
understanding of faculty motivation and career advancement. Further, it can offer tangible 
guidance for faculty, administrators, and the discipline in navigating the evolving landscape of 
research impact in academic careers. 
 

Purpose  
 
The purpose of this study was to examine research impact within agricultural education 
disciplines using the h-index, i10 index, and total citations across academic ranks. The findings 
from this study have the potential to provide faculty members with actionable benchmarks to 
evaluate their own research trajectories and situate their progress within broader disciplinary 
expectations. In alignment with expectancy theory, these models can serve as tools to clarify 
role perceptions and help faculty understand how their scholarly efforts may translate into 
career advancement. The following research objectives guided the study: 
1. Compare faculty publication metrics (h-index, i10 index, and total citations) across academic 

ranks. 
2. Determine what factors have the greatest predictive influence on research impact. 
3. Determine what factors have the greatest predictive influence in distinguishing between 

academic ranks. 
 

Methods  
 
We identified all AAAE members listed in the online directory who had public Google Scholar 
profiles (AAAE, n.d.). For the purposes of this study, the target population was defined as 
tenure-track faculty holding the academic ranks of assistant, associate, or full professor. 
Individuals not listed as one of these ranks was excluded from the search and subsequent 
analysis. Our search yielded profiles for 37 assistant, 46 associate, and 42 professors (n = 126). 

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We determined professorial ranks and faculty size by consulting university websites and 
recorded each member’s metrics from their Google Scholar profile, including the h-index, i10 
index, and total citations. We note that Google Scholar’s accuracy depends on automated 
matching and author curation, which can introduce omissions or misattributions; therefore, 
these metrics should be interpreted as best-available estimates (López-Cózar et al., 2014; 
Sauvayre, 2022). For Research Objective 1, we described each metric by academic rank using 
the mean, standard deviation, and confidence intervals. 
 
We selected variables for Objectives 2 and 3 based on open-access availability and prior 
evidence on antecedents of faculty productivity and evaluation. Career stage (Years Since First 
Publication) is a well-established driver of output and advancement (Chen et al., 2006; Tien & 
Blackburn, 1996). Further, departmental capacity and university research expectations (Faculty 
Size, R1) worked to capture how resources and collaboration structures are linked to higher 
impact (Birkenholz & Simonsen, 2011; Bland et al., 2005; Jamali et al., 2016; Lee & Bozeman, 
2005; McGill & Settle, 2012). Disciplinary home and AAAE regional context (AGED, Region) were 
used to explore the impact of regional expectations and norms. Gender was included to gauge 
impact of documented disparities in impact and promotion (John et al., 2016; Love et al., 2022). 
Collectively, these variables index institutional and experiential factors that shape role 
perception and the perceived instrumentality of research efforts, aligning with our expectancy-
theory lens (Porter & Lawler, 1968; Vroom, 1964).To minimize variations in cited works, we 
collected data during two specific time periods: October 14–15, 2023, and December 27–28, 
2024. 
 
Table 1 

Variables and Coding Used in this Study 
Variable  Description 
h-index Number of h papers cited h times 
i10 index Number of papers cited 10 times 
Total Citations Cumulative number of citations across all manuscripts 

Total Citations Per Year  Total cumulative citations divided by years since first 
publication 

Years Since First Publication  Total years since first publication or PhD dissertation 
Gender Coded as 1 = Male, 0 = Female 
Region Coded by AAAE region; 1 = Yes, 0 = No 
Discipline (AGED) Coded as 1 = Agricultural Education, 0 = Other 
R1 Institution (R1) Coded as 1 = Yes, 0 = No 
Academic Rank Each rank coded as 1 = Yes, 0 = No 
Δ h-index Change in h-index from Oct. 2023 to Dec. 2024 
Δ i10 index Change in i10 index from Oct. 2023 to Dec. 2024 
Δ Total Citations Change in Total Citations from Oct. 2023 to Dec. 2024 
Faculty Size Total number of faculty within the department 

 

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For Objective 2, we applied forward subset regression with leave one out cross validation 
(LOOCV) to predict each impact metric, selecting models that minimized LOOCV mean squared 
error (Derksen & Keselman, 1992; James et al., 2013; Liu & Motoda, 2007). For Objective 3, we 
used forward logistic subset selection with the Akaike Information Criterion (AIC) to distinguish 
(a) assistant vs. associate and (b) associate vs. professor, evaluating performance via confusion 
matrices (James et al., 2013; Singh et al., 2021). 
 
Our convenience sample of public Google Scholar profiles (n = 126) limits generalizability and 
may introduce self selection bias. Open access data excluded potentially relevant predictors 
(Jackson et al., 2017; Sinclair et al., 2014; Wilsdon, 2015). Finally, the subset selection 
methodology explicitly aims to maximize predictive capacity (Draper & Smith, 1966; James et 
al., 2013) rather than facilitate traditional hypothesis testing. Thus, our analysis focuses on 
identifying predictors that provide the best model fit. 
 

Findings  
 
Our faculty sample was nearly gender balanced, with 51.6% male and 48.4% as female. A 
majority of faculty (64.3%, n = 81) had agricultural education as their primary discipline, and 
84.1% (n = 106) were affiliated with a R1 institution. The average faculty size was 15 members 
(SD = 10.98). Table 2 summarizes the universities included, number of faculty from each 
institution with Google Scholar profiles, and corresponding R1 Carnegie Classifications 
(American Council on Education, n.d.) within the sample. 
 
Assessing our first objective, Table 3 presents the means, standard deviations, and 95% 
confidence intervals for each scholarly metric by academic rank. The data revealed clear and 
substantial increases in research impact across ranks, with distinct and well-defined 
demarcations observed. Notably, the 95% confidence intervals for the i10 index, h-index, total 
citations, and years since first publication did not overlap between ranks, indicating statistically 
significant differences (p < .05). Additionally, significant differences (p < .05) were evident 
between assistant and associate ranks for i10 Index change and total citations change. 
However, overlap in the 95% confidence intervals was observed for citations per year, i10 index 
change, and total citations change between associate and professor ranks. Furthermore, h-
index change showed an overlap across all ranks. 
  

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Table 2 

Universi6es, Faculty with Google Scholar Profiles, and Carnegie R1 Classifica6ons 
University Faculty with Google Scholar Profiles (n) R1 Status (2021) 
Auburn University 2 Yes 
California State University, Fresno 1 No 
Clemson University 2 Yes  
Illinois State University 1 No 
Iowa State University 6 Yes  
Kansas State University 1 Yes  
Louisiana State University 1 Yes  
Michigan State University 1 Yes  
Middle Tennessee State University 1 No 
Mississippi State University 1 Yes  
Montana State University 1 Yes  
Murray State University 1 No 
New Mexico State University 3 No 
North Carolina A&T State University 1 No 
North Carolina State University 3 Yes 
North Dakota State University 2 No 
The Ohio State University 7 Yes 
Oklahoma State University 6 Yes 
Oregon State University 1 Yes 
The Pennsylvania State University 3 Yes 
Purdue University 6 Yes 
Sam Houston State University 1 No 
Sul Ross State University 1 No 
Tarleton State University 2 No 
Texas A&M University 12 Yes  
Texas A&M University–Kingsville 1 No 
Texas Tech University 4 Yes 
University of Arizona 4 Yes 
University of Arkansas 4 Yes 
University of Arkansas at Pine Bluff 1 No 
University of Florida 10 Yes 
University of Georgia 7 Yes 
University of Idaho 1 No 
University of Kentucky 2 Yes 
University of Minnesota 2 Yes 
University of Missouri 3 Yes 
University of Nebraska 2 Yes 
University of Nevada, Las Vegas 1 Yes 
University of Tennessee 4 Yes 
Utah State University 4 No 
Virginia Polytechnic Institute and State 

University 
6 Yes 

Washington State University 1 Yes 
West Virginia University 2 Yes 

Note. Carnegie Classificapons were updated in 2025; the classificapons reported here are based 
on the 2021 Carnegie Basic Classificapon system. 

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To address objective two, forward stepwise regression with leave-one-out cross-validation 
(LOOCV) was utilized to identify factors with the greatest combined predictive capacity on 
research impact (total citations, h-index, and i10 index (n = 126)). Candidate variables for this 
model were: years since first publication, faculty size, R1 status, AAAE regional affiliation, 
Agricultural Education as primary discipline, and gender. 
 
Total Citations  
Table 4 presents the results for the total citations final subset model. The model included the 
predictors years since first publication (YSFP), Faculty Size, Agricultural Education discipline 
(AGED), and R1 research institute (R1). 
 
Table 4 

Regression Results for Predic6ve Factors of Total Cita6ons 
Variable Estimate (β) SE t p 
Intercept -673.44 270.61 -2.489 0.014 
YSFP 52.93 8.91 5.395 <0.001 
Faculty Size 16.67 8.07 2.065 0.041 
AGED 371.24 178.13 2.003 0.047 
R1 433.12 239.53 1.807 0.072 

 
This model explained a significant portion of variation in total citations, F(4,121) = 15.96, p < 
.001, R2 = .324. The final model yielded a LOOCV mean squared error of 845,565, 
corresponding to an average predictive deviation of approximately 920 citations. Compared to 
the null model (intercept only), which had a LOOCV error of 1,216,254, this represents a 
substantial improvement in predictive accuracy and generalizability. 
 
The final regression model produced the following predictive equation for Total Citations: 
Total Citations = -673.44+ YSFP (52.93) + Faculty Size (16.67) + AGED (371.24) + R1 (433.12) 
 
i10 index 
Table 5 summarizes the results for the i10 index model. The final model included the predictors 
YSFP, Faculty Size, R1, Male, and Southern Region. This model accounted for a significant 
portion of the variance in the i10 index (F(3,122) = 15.38, p < 0.001, R2 = 0.391). The final model 
had a LOOCV mean squared error of 270, corresponding to an average predictive deviation of 
approximately 16.85. Compared to the null model, which had a LOOCV error 405.14, 
representing significant improvement in predictive accuracy and generalizability. 
 
  

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Table 5  

Regression Results for Predic6ve Factors of i10 index 
Variable Estimate (β) SE t p 
Intercept -9.819 4.431 -2.167 0.032 
YSFP 1.017 0.164 6.198 <0.001 
Faculty Size 0.405 0.145 2.790 0.006 
R1 6.470 4.315 1.500 0.136 
Gender (Male) 4.910 3.212 1.528 0.129 
Southern Region 3.520 2.971 1.185 0.239 

 
The final regression model produced the following predictive equation for i10 index: 
i10 index = -2.686 + YSFP (1.017) + Faculty Size (0.405) + R1 (6.470) + Gender (Male) (4.910) + 
Southern Region (3.520) 
 
h-index 
Table 6 presents the results for the h-index model. The final model included YSFP, Faculty Size, 
AGED, and R1. The model explained a significant portion of variation in h-index, F(4,121) = 
28.77, p < .001, R2 = .488. Under leave-one-out cross-validation, the final model achieved a 
mean squared prediction error (MSE) of 32.84, with an average deviation of 5.73, once again 
showing an improved fit when compared with 60.54 for the null model. 
 
Table 6 

Regression Results for Predic6ve Factors of h-index 
Variable Estimate (β) SE t p 
Intercept -0.175 1.683 -0.104 0.917 
YSFP 0.442 0.056 7.976 <0.001 
Faculty Size 0.175 0.052 3.493 <0.001 
AGED 2.552 1.142 2.223 0.027 
R1 2.936 1.492 1.973 0.051 

 
Our final model yielded the following equation to predict h-index: 
h-index = -0.175 + YSFP (0.442) + Faculty Size (0.175) + AGED (2.552) + R1 (2.936) 
 
As the h-index represents the most dynamic measure of research impact, this model provides a 
robust measure of scholarly impact. Further, the LOOCV error parameter is lower in the h-index 
model than the i10 index model while having the highest R2 value of all our models, indicating 
this model's enhanced ability to make accurate predictions within our discipline. Taken 
together, the results from these three models not only highlight key predictors of research 
impact but also help reinforce the theoretical lens of expectancy theory. By clarifying factors 
that most strongly predict research impact, these findings help faculty see a direct connection 
between their efforts and value outcomes, strengthening the perceived instrumentality of their 
research activities. Similarly, clarity of benchmarks for impact supports clearer role perception 

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by giving faculty a more concrete understanding of what is expected at different career stages 
and how their performance contributes to advancement.   
 
For research objective three, forward stepwise logistic regression was used, minimizing AIC as 
the selection criterion to identify factors with the greatest predictive capacity for distinguishing 
between academic ranks. Candidate variables included: h-index, years since first publication, 
faculty size, R1 status, AAAE regional affiliation, Agricultural Education as primary discipline, 
and gender. The h-index was included as the candidate variable to represent research 
performance as it represents both productivity and citation impact, providing a more balanced 
indicator of scholarly influence when compared to total citations or the i10 index. 
 
Assistant to Associate 
Assessing what variables have the greatest combined predictive capacity in distinguishing 
between assistant and associate professor ranks (n = 83), final logistic regression results are 
presented in Table 7. The final model in the forward stepwise procedure included the 
predictors YSFP, R1, AGED, and h-index. 
 
Table 7 

Logis6c Results for Predic6ve Factors to Dis6nguish Between Assistant and Associate 

Variable Estimate (β) SE Odds Ratio 

95% CI for Odds Ratio 

p LL UL 
Intercept -11.275 2.988 - - - < 0.001 
YSFP 0.951 0.282 2.588 1.490 4.497 < 0.001 
AGED 2.644 1.248 14.065 1.218 162.465 0.034 
R1 2.128 1.373 8.397 0.570 123.744 0.121 
h-index 0.196 0.124 1.217 0.955 1.552 0.113 

 
Further, to assess predictive accuracy, our model’s confusion matrix is presented in Table 8. 
 
Table 8 

Confusion Matrix for Assistant and Associate Model 
 Actual/Predicted Assistant Associate Accuracy 
Assistant 35 2 94.59% 
Associate 2 45 95.74% 

 
This model had an overall 95.24% predictive accuracy in distinguishing between assistant and 
associate ranks, while performing slightly better in predicting the associate rank. Further, this 
model revealed the importance of YSFP and being affiliated with the AGED discipline, being 
statistically significant and positively impacting the likelihood of being an associate professor. h-
index and R1 status were also retained in the final model.  Both were positive but non-
significant effects, suggesting they may relate to advancement, but their unique contributions 

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are modest once year in the profession and disciplinary affiliation are accounted for. Further, 
confidence intervals for AGED and R1 were particularly wide, reflecting limited precision in 
these estimates and should be interpreted with caution. 
 
Associate to Professor 
When assessing the variables that have the greatest combined predictive capacity in 
distinguishing between the associate and professor ranks (n = 88), the final logistic regression 
results are presented in Table 9. The final model in the forward stepwise procedure included 
the predictors YSFP, h-index, Faculty Size, and North Central. 
 
Table 9 

Logis6c Results for Predic6ve Factors to Dis6nguish Between Associate and Professor 

Variable Estimate (β) SE Odds Ratio 
95% CI for Odds Ratio 

p LL UL 
Intercept -8.015 1.802 - - - < 0.001 
YSFP 0.217 0.057 1.249 1.127 1.413 < 0.001 

h-index 0.374 0.107 1.454 1.213 1.855 < 0.001 

Faculty Size -0.068 0.098 0.934 0.860 1.001 0.072 

North Central -1.392 0.916 0.248 0.035 1.343 0.128 
 
To assess this model’s predictive accuracy, its confusion matrix is presented in Table 10. 
 
Table 10 

Confusion Matrix for Associate and Professor Model 
 Actual/Predicted Associate Professor Accuracy 
Associate 40 6 86.96% 
Professor 7 36 83.72% 

 
This model had an overall 85.39% predictive accuracy in distinguishing between associate and 
professor ranks while performing slightly better in predicting the associate rank. These results 
illustrate the slightly more complicated nature of distinguishing between associate to professor 
ranks when compared to assistant to associate. In this model, YSFP and h-index are strong 
predictors of promotion to professor, illustrating the impact of experiences and sustained 
scholarly impact. Conversely, the negative association between Faculty Size and North Central, 
though not significant, reflects the nuance of the interpretation of these models. It is unclear 
whether these negative associations reflect that these factors have direct negative implications 
or if they provide balance to the other factors in the model due to collinearity, illustrating a 
limitation of our methodology. 
 

 
 

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Conclusions, Discussion, and Recommendations  
 
While Google Scholar provides a practical source for examining scholarly output and research 
impact, its indexing and data quality have recognized limitations (López-Cózar et al., 2014; 
Sauvayre, 2022). Still, the present findings show how these metrics can guide the use of 
research impact indicators, provide a model for future studies regarding research impact, and 
offer insights into the development of role expectancy within our disciplines (Porter & Lawler, 
1968; Vroom, 1964). By quantifying progression in research impact (h-index, i10 index, total 
citations) across ranks, this study offers faculty and administrators clearer reference points for 
evaluating scholarly trajectories. Our analysis revealed clear progression in research impact (h-
index, i10 index, total citations) across ranks. However, annual impact metrics (yearly changes 
in i10 index, citations, h-index, and total citations per year) overlapped confidence intervals, 
indicating they offer a more nuanced comparison of scholarly performance between ranks. The 
consistent overlap in annual h-index changes further demonstrates the complexity in setting 
distinct research impact expectations. 
 
Regression analyses identified several consistent predictors of research impact: Years Since First 
Publication (YSFP), Faculty Size and R1 institutions significantly impacted total citations, i10 
index, and h-index. Being primarily in the Agricultural Education (AGED) sub-discipline increased 
total citations and h-index scores. These results show how institutional resources, 
collaboration, and disciplinary focus contribute to scholarly output and help shape role 
perception and the perceived instrumentality of research investment (Bland et al., 2005; 
Jackson et al., 2017; Jamali et al., 2016; Porter & Lawler, 1968; Vroom, 1964 ). 
 
Rank-progression analysis continues to sharpen this picture. For assistant to associate, Years 
Since First Publication (YSFP) and AGED were significant positive predictors, while h-index and 
R1 were positive but not significant; given the wide CIs for AGED and R1, these effects should 
be interpreted cautiously. For associate to professor, YSFP and h-index were significant and 
positive, indicating that time in the field and a sustained, cumulative citation record carry the 
greatest weight at senior promotion. Conversely, Faculty Size and North Central entered as 
negative, non-significant predictors, improving model fit but pointing to contextual complexity 
rather than simple linear relationships between setting and advancement. The differing 
predictor profiles across academic ranks highlights early advancement aligns more with time-in-
field and disciplinary context (YSFP, AGED), whereas senior promotion emphasizes a sustained, 
cumulative citation record (YSFP, h-index). Recognizing these rank-contingent expectations is 
essential for fair evaluation and targeted mentoring. 
 
While the study lacks generalizability, findings help expand our understanding of the complex 
nature of faculty research impact and career advancement. First, YSFP emerged consistently as 
a key factor, emphasizing sustained importance of commitment to research throughout faculty 
careers. Second, variables such as Faculty Size and R1 affiliations reinforce the critical role 
institutional context plays in impact (Bonn & Bouter, 2023; Jackson et al., 2017). Third, the 

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unique impact patterns of AGED faculty suggest distinct disciplinary strengths that could be 
drawn upon to enhance broader sub-disciplinary research development. 
 
Despite statistically significant predictors, our models explained only a moderate portion of 
impact variance with relatively high prediction errors, signaling potential overfitting (Hastie et 
al., 2009). This suggests the presence of influential but unmeasured factors such as teaching 
loads, service engagement, and intangible scholarly impacts (Kotrlik et al., 2002; Ramirez-
Montoya et al., 2023; Wisdom et al., 20215). Thus, additional exploration is needed to fully 
understand the determinants of research impact. 
 
Overall, findings support expectancy theory by clarifying the importance of role perceptions 
and instrumentality in motivating research impact and career advancement (Porter & Lawler, 
1968; Vroom, 1964). By establishing baseline research metrics, this study provides faculty 
clearer expectations and role perceptions critical for professional growth. Future studies should 
incorporate other dimensions of scholarly impact, such as teaching and service engagement, to 
help provide a more comprehensive understanding of research impact (Hind et al., 1974; 
Jackson et al., 2017; Wilsdon, 2015). Additionally, examining factors uniquely driving impact 
within diverse agricultural education sub-disciplines could provide targeted developmental 
insights. Lastly, methodological approaches exploring interaction effects between predictors 
could further clarify impact dynamics.  Nevertheless, this study contributes foundational 
insights into research impact metrics, offering clarity about professional expectations and 
informing faculty research development strategies aimed at career advancement within the 
agricultural education disciplines. 
 

Acknowledgments  
 
Funding Information: No external funding was received for this study. 
 
Conflict of interest: No conflicts of interest. 
 
Previous Dissemination: Portions of this data were presented at the 2025 AAAE National 
Research Conference, citation:  

Martin, E., Estepp, C.M., Doss, W., & Johnson, D.M. (2025). Unpacking research 
productivity in agricultural education: Implications for role perception and career 
advancement. Proceedings of the American Association for Agricultural Education 
National Research Conference, 518-531. 

 
Artificial Intelligence: No artificial intelligence was used in this article. 
 
Author Contribution Statement: E. Martin – conceptualization, methodology, formal analysis, 
investigation, data curation, writing – original draft, writing – review & editing, C. Estepp – 
conceptualization, methodology, writing – original draft, writing – review & editing, W. Doss – 
conceptualization, methodology, writing – original draft, writing – review & editing, D. Johnson 
– conceptualization, methodology, writing – review & editing. 

https://doi.org/10.37433/aad.v6i3.645


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