































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

 

1. Danny Morris, Assistant Professor, University of Tennessee at Martin, 554 University Street, Martin, TN 

38238, dmorri25@utm.edu,  https://orcid.org/0009-0008-0598-9790 
2. John C. Ricketts, Professor, Tennessee State University, 3500 John A. Merritt Blvd., Nashville, TN 

37209, jricket1@tnstate.edu,  https://orcid.org/0000-0002-0559-5803 
3. David Hochreiter, Ph.D. Candidate, Tennessee State University, 3500 John A. Merritt Blvd., Nashville, TN 

37209, dhochrei@tnstate.edu,  https://orcid.org/009-001-7899-9905 
 

59 

 

Factors Influencing Tennessee Farmers’ Adoption of 
Technology: A Survey of Tennessee Agricultural Enhancement 

Program Participants 
 

D. Morris1, J. C. Ricketts2, D. Hochreiter3 
 

 
Article History 
Received: July 10, 2025 
Accepted: August 10, 2025 
Published: September 29, 2025 
 
 
Keywords 
adoption; technology; farmers;  
SDG 9: Industry, Innovation, & 
Infrastructure   

Abstract 
Farmers adopt new technologies to be competitive and farm efficiently. 
This study explored what factors influence technology adoption among 
row crop and livestock farmers in Tennessee. Utilizing Rogers’ Diffusion 
of Innovations Theory, this study investigated the impact of economic 
benefits, cost, peer influence, compatibility, and demographic 
characteristics on the adoption decisions of farmers. This study employed 
a mixed-methods approach by combining the Delphi technique and 
survey research. Thirty experts participated in the Delphi and 675 farmers 
completed the quantitative instrument. The results of the Delphi study 
provided a list of technologies that farmers are currently looking to adopt 
along with what promotes and hinders adoption. Survey research 
revealed that economic benefits are the most influential factor in 
adoption, while cost and compatibility can serve as barriers. 
Demographic characteristics such as education level, farm size, farm 
income, and years of experience significantly influence adoption 
decisions. Binary logistic regression and Bayesian regression analyses 
indicated that adopter categories, innovativeness, economic factors, 
demographics, and socioeconomic factors significantly influence 
adoption decisions. The conceptual model developed from this study 
suggests the inclusion of various influential factors to improve the 
predictability of adoption decisions.  

 

mailto:dmorri25@utm.edu
https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Forcid.org%2F0009-0008-0598-9790&data=05%7C02%7Cjricket1%40tnstate.edu%7Cd14864ccf62c45954bec08ddf0b01f0c%7C7c539505f12946aea6cfecaf413b8b0d%7C0%7C0%7C638931361666774762%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=GEABpifoyF6o8e%2BSbl3UyBubdBJeCBFCFOPKy%2FJI4pg%3D&reserved=0
mailto:jricket1@tnstate.edu
https://orcid.org/0000-0002-0559-5803
mailto:dhochrei@tnstate.edu
https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Forcid.org%2F009-001-7899-9905&data=05%7C02%7Cjricket1%40tnstate.edu%7Cd14864ccf62c45954bec08ddf0b01f0c%7C7c539505f12946aea6cfecaf413b8b0d%7C0%7C0%7C638931361666783804%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Xq6vUBX%2B%2Bu1fJ4Ow5WTSwvMPDr7woC1i%2Ff61NMSv%2FNo%3D&reserved=0


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     60 
 

Introduction and Problem Statement 
 
The adoption of innovations and technologies by farmers enhances agricultural enterprises in 
multiple ways, from reduced inputs to increased production, profits, and sustainability. This 
research supports the United Nation’s goal to help farmers foster innovation to be more 
sustainable. Loevinsohn, et al. (2013, p. 2), described agricultural technology as “the means and 
methods of producing crops and livestock.” The goal of technology itself is the production of a 
good or service that is easier, less expensive, or less laborious while simultaneously increasing 
efficiency. Technologies are designed to save time and reduce effort by making the applicant's 
work easier than it would have been without the innovation (Bonabana-Wabbi, 2002). The 
factors that influence the decision to adopt or not adopt a technology are all influenced by an 
individual’s characteristics, access to information regarding the technology, peer influence, and 
a myriad of other factors (Rogers, 2003). Previous research on the transformation of agriculture 
has mostly concentrated on the technical aspects of integrating new technologies to enhance 
agricultural practices and output. A growing number of studies conducted in recent years have 
looked at how farmers are utilizing new technologies and the key factors that influence their 
decision to adopt technologies (Carlisle, 2016; Li et al., 2019; Yaseen et al., 2023).. According to 
Mwangi and Kariuki (2015, p. 209), “researchers should clearly state how they are defining 
technology adoption so that they can develop an appropriate tool to measure it.” Despite 
substantial work on technology adoption in agriculture, few statewide studies integrate farmer 
elicited technology priorities with multivariate adoption modeling that compares row-crop and 
beef farmers within the same study. This study addresses that gap by combining a three-round 
Delphi with a state-wide quantitative survey to describe how economic benefits, cost, 
compatibility, peer influence, and producer characteristics influence adoption decision in 
Tennessee.  
 

Theoretical and Conceptual Framework 
 
The theoretical framework for this study includes Rogers’ (2003; Rogers et al., 2014) Diffusion 
of Innovations  Theory and Ajzen’s (1991) Theory of Planned Behavior (TPB). The Theory of 
Rational Choice (TRC) was also used in this study to further explain the motivational behaviors 
of farmers when looking to adopt a new technology (Smith, 2012). The principal components of 
the  Diffusion of Innovations Theory incorporated in this study encompasses the definition of 
innovation, the social networks through which information is disseminated, influential factors 
in deciding to adopt an innovation, and the categories of adopters as defined by Rogers (2003). 
These foundational components of the theory are equally significant to the postulation of each 
respective theory. The framework for adopting innovations is best described as a theory of 
dissemination of information and individual behavior or as a theory of communication and 
collective behavior (Edison & Geissler, 2003; Nyairo et al., 2021). Hence, the incorporation of 
TPB into this study. 
 
According to Wright (2004), it can take prolonged periods for individuals to incorporate new 
technologies and concepts into routine practices and procedures. Once individuals are 

https://doi.org/10.37433/aad.v6i3.647


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     61 
 

introduced to an innovation, people begin their decision-making process, which includes 
discovery and understanding, evaluation of benefits and costs, selecting a choice, execution, 
and confirmation (Rogers, 2003). These internal processes are parts of cognitive behavior and 
have influences that go beyond the individual level. By communicating ideas with others 
through dissemination, the community, as a whole, engages in discovering the advantages and 
disadvantages of an innovation. While an individual’s decisions can be hard to observe, the 
adoption rate of an entire group within a social network such as the agricultural industry is 
more easily observed (Takahashi et al., 2020). 
 
The TRC assumes that individuals make informed decisions by performing a cost-benefit 
analysis of each alternative and choosing the option that maximizes their utility (Ajzen, 1991). 
In agricultural production, this theory suggests that a farmer will adopt an innovation if they 
think that it will provide more benefits than costs, otherwise known as a return on investment. 
An additional theory that is relevant to the adoption of new technology is the TPB. The TPB 
suggests that an individual's behaviors are primarily influenced by three factors: behavior, 
subjective norms, and perceived behavioral control (Doran et al., 2020). In agriculture, a 
farmer’s decision to adopt a new technology could potentially be influenced by their 
perceptions of how effective the technology is, the opinions of their fellow farmers, and how 
difficult it is to use. The TRC highlights the importance of knowing what behaviors farmers 
believe to be important and their expectations of how likely they can exude these behaviors 
(Pannell et al., 2006). 
 
The conceptual framework for the study was based on the work of Meijer et al. (2015, p. 1) 
who developed a conceptual model that captures “the characteristics of the farmer, the 
external environment, and the characteristics of agricultural innovations as the extrinsic factors 
that influence adoption.” There are numerous factors, both direct and indirect, that impact a 
farmer's adoption decision, including individual characteristics, environmental factors, features 
of the innovation, and an aggregation of the influence from both peers and other third parties. 
All these factors interact with one another to affect a farmer’s final decision. Combined, these 
“intrinsic and extrinsic factors interact and drive adoption and influence a farmers’ decision-
making process” (Meijer et al., 2015, p. 40). This work demonstrated how the four attributes of 
economic benefits, cost of technology, compatibility, and peer influence collectively affected a 
producer’s adoption decision. 
 

Purpose 
 
The primary purpose of this study was to identify the factors that had the greatest impact on 
Tennessee farmers’ decisions to adopt new technology. The research objectives included the 
following: 
1. Identify the key technologies that expert row crop and livestock producers prioritize for 

adoption on their farms. 
2. Describe the readiness and willingness of farmers to adopt new technologies based on 

adopter category and level of innovativeness. 

https://doi.org/10.37433/aad.v6i3.647


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     62 
 

3. Describe the influential factors impacting technology adoption. 
4. Explain farmers' decisions to adopt or not adopt technology based on adopter category, 

innovativeness, and influential factors. 
5. Develop a comprehensive model to illustrate the decision-making process regarding the 

adoption of specific technologies in row crops and beef cattle production. 
 

Methods 
 
This study asked experts what technologies were most important and farmers whether they 
would adopt or not adopt identified agricultural production practices. This study employed a 
mixed-methods approach to understand the factors influencing Tennessee farmers’ adoption of 
new technologies. A Delphi study (Dalkey & Helmer, 1963) and survey research employing 
descriptives, inferential statistics, and binary logistic regression were used to address the 
research objectives. The Delphi study was conducted with 28 expert farmers and 2 extension 
specialists, identified by the Tennessee Department of Agriculture, to determine the key 
technologies farmers are considering adopting. The study progressed through three rounds to 
reach a consensus, focusing on influential factors such as economic benefits, compatibility, 
cost, and peer influence. The first round involved open-ended questions, followed by ranking 
these factors in subsequent rounds. By the third round, a consensus was achieved for 
technologies that were important to the farmer participants (See Table 1) along with what 
factors ultimately had the greatest influence on adoption decisions.  
 
Table 1 
 
Technologies Reported from Delphi Study 
Technologies Livestock Farmers are Looking 
to Adopt 

Technologies Row Crop Farmers are Looking 
to Adopt 

Artificial Insemination Biologicals 
Embryonic Transfer Cover Crops 
Forage Innovations Data Management Software 
GIS Technologies Fertility Technology 
Invitro Fertilization GIS Technologies  
Medications/Vaccinations Pesticides 
Sexed Semen Precision Agricultural Technologies (PATs) 
Smartphone Apps Seed Genetic Traits 
 Smartphone Apps 
 Tillage Innovations 

 
Using Dillman’s (2000) system of five compatible contacts, the quantitative survey was 
distributed to 5,420 Tennessee farmers participating in the Tennessee Agricultural 
Enhancement Program, a successful cost-share program in the state. There were 675 responses 
for a return rate of 12.4%. Incomplete responses were removed from the database used for the 
regression analyses, which left (n = 675; 576 livestock farmers and 99 row crop farmers) actual 

https://doi.org/10.37433/aad.v6i3.647


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     63 
 

subjects in the study. Generally accepted protocols and procedures to mitigate nonresponse 
error were followed, and late respondents (the later 50%) were compared to early respondents 
as prescribed by Lindner et al. (2001) and Lindner (2002). Using an independent samples t-test, 
it was determined there was no significant difference for each independent variable between 
early (n = 427) and late (n = 241) respondents. The independent variables included adopter 
categories, innovativeness, economic benefits, cost, compatibility, peer influence, education, 
years of farming experience, operation size, and annual farm income. The dependent variables 
were the adoption decisions for specific technologies, which varied between row crop and 
livestock farmers.  
 
Survey participants were asked to self-categorize themselves in adopter categories (1 = 
Innovators, 2 = Early Adopters, 3 = Early Majority, 4 = Late Majority, 5 = Laggards). To avoid 
response bias, descriptions of each category were provided, not the name of each category. In 
addition, a 5-item innovativeness scale was completed by farmers. Innovativeness scores were 
interpreted as follows: Strongly Agree = 5.00 – 5.99, Agree = 6 – 6.49, Neutral = 6.50 – 7.49, 
Disagree = 7.50 – 8.49, Strongly Disagree = 8.50 – 10.00. The perceived importance of Economic 
benefits, Cost of technology, Compatibility, and Peer influence to adopting new technology 
used Likert-type items interpreted as: Very important = 5.00 – 4.51, Important = 4.50 – 3.51, 
Neutral = 3.50 – 2.51, Unimportant = 2.50 – 1.51, Very unimportant = 1.50 – 1.00. Binary 
responses (Adopt or Not Adopt) were collected to determine which specific technologies 
farmers would adopt.  
 
Descriptive statistics and binary logistic regression predicted the likelihood of technology 
adoption based on independent variables. Descriptive statistics, such as means and standard 
deviations, were reported for adopter categories, innovativeness, economic benefits, 
compatibility, cost, peer influence, education, years of farming experience, size of operation, 
and annual farm income. All tests used a significance threshold of α = 0.05 set a priori. The odds 
ratio (Exp(b)) was calculated to identify the magnitude of the relationship of adoption to the 
other independent variables in the study. A multicollinearity test was conducted using the 
Variance Inflation Factor to determine the level of collinearity between independent variables. 
A backward stepwise binary logistic regression procedure was used to predict adoption and 
each of the influential factors and demographics, for the purpose of better understanding what 
causes a farmer to adopt a technology. Finally, a Hosmer and Lemeshow Test was conducted to 
assess the goodness of fit of the regression logistic models (Hosmer et al., 2013). Bayesian 
regression was also employed for variables where binary logistic regression was inconclusive, as 
Bayesian regression is well suited to handle model data with small sample sizes and can provide 
accurate estimations (McNeish, 2016). 
 

Findings 
 
The Delphi study was utilized to generate a list of technologies that farmers were looking to 
adopt. The statewide survey revealed that livestock farmers emphasized medications, 
reproductive technologies, and forage innovations, while row crop farmers prioritized seed 

https://doi.org/10.37433/aad.v6i3.647


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     64 
 

genetic traits, cover crops, and precision agriculture technologies (PATs). Economic benefits 
were the primary driver of adoption decisions, followed by compatibility, cost, and peer 
influence, with insufficient economic returns as the main barrier. The participants were asked 
to rank technologies by importance, with lower mean scores indicating higher priority based on 
participant rankings (1 = most important, 10 = least important). The livestock farmers indicated 
that reproductive technologies, forage innovations, and medications were the technologies 
they were prioritizing to adopt for their operations. The row crop producers reported that seed 
genetic traits, cover crops, and precision agricultural technologies were the technologies that 
they were considering for adoption.  The findings suggest that livestock farmers prioritize 
technologies enhancing animal health and reproduction, while row crop farmers focus on yield-
enhancing and precision technologies, driven primarily by economic returns. 
 
Adoption Readiness by Adopter Category and Innovativeness 
Livestock farmers (n = 576) predominantly identified in the Early Majority (38.4%) and Late 
Majority (35.9%), with lower innovativeness scores (M = 8.79–9.16). Row crop farmers (n = 99) 
leaned toward being classified as the Early Majority (44.4%) and Early Adopters (23.2%), with 
higher levels of innovativeness (M = 7.00–8.96). ANOVA revealed significant innovativeness 
differences across adopter categories (p < 0.05). For livestock farmers, Early Adopters were 
more innovative than Late Majority (p < 0.01) and Laggards (p < 0.01). For row crop farmers, 
Innovators surpassed Early Majority (p < 0.02), Late Majority (p < 0.01), and Laggards (p < 0.04). 
Cohen’s d confirmed that level of innovativeness had a medium effect on technology adoption 
(Cohen’s d = 0.71 for livestock, 0.73 for row crop; p < 0.001). These results indicate row crop 
farmers are more innovative and more readily adopt new technologies, aligning with their 
larger, more commercial operations, while livestock farmers are more cautious, reflecting 
smaller farms. 
 
Influential Factors on Technology Adoption 
Livestock farmers commonly held bachelor’s degrees (39%), with the Early Majority having the 
highest education levels. Their operations averaged 50–100 head of cattle, with Laggards 
managing smaller herds (25–50 head). Experience increased from Innovators (10–15 years) to 
Laggards (20+ years), with income levels typically being below $50,000. Row crop farmers 
reported higher incomes (28% earned > $1,000,000) and larger operations (56% with 1,000–
5,000 acres), with 74% farming 20+ years. 
 
Influential factors included economic benefits, cost, compatibility, and peer influence. 
Participants were asked how influential each of these factors were on their decision to adopt 
new technologies. Economic benefits (M = 3.99; SD = 0.42 for row crop, M = 3.41; SD = 0.49 for 
livestock) and cost (M = 3.81, SD = 0.60; M = 3.47, SD = 0.52) were the most influential factors, 
followed by compatibility (M = 3.74, SD = 0.38; M = 3.09, SD = 0.34) and peer influence (M = 
3.39; SD = 0.53; M = 3.09, SD = 0.33). Those with higher education, larger operations, and 
higher incomes were more likely to adopt new technologies. Farmers with 10–15 years’ 
experience showed the highest adoption rates, suggesting a balance of experience and 
openness to innovation. 
 

https://doi.org/10.37433/aad.v6i3.647


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     65 
 

Predicting Adoption Decisions 
Table 2 summarizes significant predictors (p < 0.05) and their odds ratios (OR), indicating the 
likelihood of adoption per unit increase in the predictor. 
 
Table 2  
 
Significant Predictors of Technology Adoption 
Technology Group Predictor OR p-value Nagelkerke R² 
Artificial Insemination Livestock Operation Size (25–50 head) 2.65 0.03 0.15 
  Operation Size (200+ head) 4.05 0.01  
  Adopter Cat. (EM vs. Laggards) 6.01 <0.001  

Embryonic Transfer Livestock Education (MS vs. HS Diploma) 2.65 0.01 0.15 
  Operation Size (200+ head) 5.39 0.01  
  Adopter Cat. (EM vs. Laggards) 3.27 0.02  

Forages Livestock Adopter Cat. (EM vs. Laggards) 3.32 0.05 0.09 
  Years Farming (5–10 vs. 1–5 years) 8.62 0.05  

GIS Technologies Livestock Compatibility 2.17 0.01 0.02 
Medications Livestock Years Farming (20+ vs. <5 years) 5.21 0.03 0.10 
Sexed Semen Livestock Operation Size (100–200 head) 3.45 0.01 0.11 
  Adopter Cat. (EM vs. Laggards) 4.40 0.01  

Smartphone Apps Livestock Annual Income (>$250,000 vs. <$50,000) 7.81 0.05 0.13 
  Adopter Cat. (EA vs. Laggards) 3.58 0.02  

Biologicals Row Crop Innovativeness 20.04 0.03 0.75 
  Education (MS vs. HS Diploma) 9.86 0.03  
  Adopter Cat. (Innovators vs. Laggards) 23.45 0.00  
  Peer Influence 0.41 0.01  

Cover Crops Row Crop Cost 0.17 0.05 0.00 
Data Management Row Crop Adopter Cat. (EA vs. Laggards) 27.48 0.03 0.52 
GIS Technologies Row Crop Operation Size (1,000–5,000 acres) 16.50 0.04 0.12 
Note. Odds Ratio (OR) > 1 indicates increased adoption likelihood; OR < 1 indicates decreased 
likelihood. Nagelkerke R² reflects model explanatory power; 0.4 or higher suggest a strong 
relationship.  
 
For livestock technologies, larger operations are more likely to adopt artificial insemination  (OR 
= 2.65-4.05), embryonic transfer (OR = 5.39), and sexed semen (OR = 3.45, reflecting economies 
of scale. Early Majority farmers were more likely to adopt artificial insemination (OR = 6.01), 
embryonic transfer (OR = 3.27), forage innovations (OR = 3.32), and sexed semen (OR = 4.40) 
compared to Laggards, indicating faster adoption among moderately innovative farmers. Years 
of farming experience (20+ years, OR = 5.21) predicted medication use, likely due to established 
practices. Compatibility (OR = 2.17) was key for GIS technologies, emphasizing integration with 
existing systems. 
 
For row crop technologies, Innovativeness (OR = 20.04) and master’s-level education (OR = 
9.86) strongly predicted biologicals adoption, with Innovators (OR = 23.45) leading, though peer 
influence reduced adoption (OR = 0.41), suggesting skepticism among peers. Higher costs 

https://doi.org/10.37433/aad.v6i3.647


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     66 
 

decreased cover crop adoption (OR = 0.17), highlighting economic barriers. Early Adopters (OR 
= 27.48) drove data management adoption, reflecting their tech-savvy nature. Larger 
operations (OR = 16.50) readily adopted GIS technology, aligning with precision farming needs. 
For technologies with multicollinearity (fertility, pesticides, PATs, seed genetic traits, 
smartphone apps, tillage innovations), principal components (PC1: cost/economics; PC2: 
compatibility/operation size/income) were used. PC2 significantly predicted pesticide (OR = 
4.16) and smartphone app (OR = 3.85) adoption, indicating combined influence of farm scale 
and compatibility. Models showed strong explanatory power (Nagelkerke R Square = 0.08–0.75) 
and good fit (Hosmer-Lemeshow p > 0.05, except GIS Livestock, p = 0.00). Seed genetic traits 
results were inconclusive due to low variability. 
 
Comprehensive Adoption Model 
The conceptual model (see Figure 1) integrates significant findings and expands current 
understandings. Economic benefits consistently increased adoption, while cost and 
compatibility often reduced it. Adopter categories, peer influence, operation size, income, 
education, years of experience, and innovativeness had mixed effects, varying by technology. 
Newer technologies (e.g., biologicals) were favored by Innovators/Early Adopters, while older 
technologies (e.g., forage innovations) were adopted by Late Majority/Laggards. Principal 
components (PC1, PC2) confirmed the combined influence of economic factors, compatibility, 
and operation size. 
 
Economic benefits were the only factor that did not negatively influence the adoption of certain 
technologies. Compatibility had mixed effects across technologies, indicating that integration 
with existing systems can either facilitate or hinder adoption depending on the context. Cost 
had a varying impact on technology adoption. This finding is likely caused by differing levels of 
income amounts for row crop and livestock farmer subjects. This supports Kinyangi (2014) who 
suggested the influence of cost on technology adoption is dependent on whether the farmers 
possess the required resources to purchase the technology. For newer technologies, adopter 
categories positively influenced adoption. However, when analyzing older technologies, 
adopter categories had a negative influence on adoption. The model underscores that 
economic benefits and farm scale drive adoption, with adopter categories shaping technology-
specific patterns. Economic benefits and larger operations consistently drove adoption, with 
adopter categories influencing technology-specific outcomes. These findings provide insights 
for targeting extension efforts to enhance technology uptake among Tennessee farmers. 
 

Conclusions, Discussion, and Recommendations 
 
Using a Delphi study and survey research, we examined how farmers prioritize certain 
technologies and what factors, such as economic benefits, costs, compatibility, and peer 
influence, impacted their decisions. We found that the adoption of innovative technologies is 
essential for improving sustainability, reducing production costs, and increasing overall 
productivity. We affirmed numerous aspects of Rogers’ (2003) Diffusion of Innovations Theory. 
Consistent with Rogers’ adopter classifications, our findings indicated that Innovators and Early 

https://doi.org/10.37433/aad.v6i3.647


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     67 
 

Adopters were significantly more likely to adopt new technologies than the Late Majority and 
Laggards. The impact of peer influence alights with the Theory of Planned Behavior’s subjective 
norms, including perception of what fellow farmers value can either accelerate or hinder 
adoption. The mixed effects of compatibility are consistent with the perceived behavioral 
control since certain technologies are perceived to be easier to integrate and thus more readily 
adopted. The Theory of Rational Choice (Smith, 2012) opines that people make decisions by 
performing a cost-benefit analysis of various options and choosing the one that results in the 
highest levels of utility. This study’s findings relate to this theory by showing how farmers adopt 
technologies that provide more utility for themselves and their operations. Economic benefits 
were ranked as the most influential factor in adoption decisions for both livestock and row crop 
farmers. Costs, however, posed significant barriers for certain technologies, deterring adoption. 
Other factors, including compatibility with existing practices and peer influence, also played 
crucial roles in determining whether technologies would be embraced. Demographic 
characteristics, such as education level, years of farming experience, and operation size, were 
found to significantly influence technology adoption. Farmers with higher levels of education 
and larger operations were more likely to adopt new technologies, as were those with more 
years of farming experience. Farmers with 10 to 15 years of experience exhibited the highest 
adoption rates, likely reflecting a balance of practical experience and openness to new ideas. 
Larger operations also demonstrated a greater willingness to adopt advanced technologies. 
Figure 1 synthesizes our empirical results with Diffusion of Innovations, the Theory of Planned 
Behavior, and rational choice mechanisms, highlighting how perceived economic benefits and 
farm size interact with norms and compatibility constraints to shape adoption.  
 
  

https://doi.org/10.37433/aad.v6i3.647


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     68 
 

Figure 1 
 
Influential Factors to Technology Adoption Model 

 
Note. Adapted from Meijer et al. (2015) and modified with findings from the study.  
 
The bolded items in the model were found to be statistically significant to technology adoption. 
The plus symbols (+) signify variables that positively influenced adoption while the minus 
symbols (-) indicate variables that negatively influenced adoption. This study revealed the 
critical roles that economics, cost, compatibility, and peer influence have on technology 
adoption decisions. Through the Delphi study, technologies were identified that farmers are 
actively looking to adopt. Innovativeness was influential to technology adoption, but an 
improved measurement of innovativeness was recommended for future research. This 
innovativeness metric could include questions that asked about openness to new ideas, risk 
tolerance levels, management styles, and adoption experiences, and assess the farmers’ 
attitude toward continued learning. The study contributed to agricultural development by 
providing insight into the relationships between adopter categories, innovativeness, economics, 
compatibility, peer influence, and certain demographics influence a farmer’s decision to 
ultimately adopt or not adopt specific agricultural technologies. This work should be replicated 
in other states or regions to aid researchers in predicting technology adoption among farmers. 
The model needed to include the use of multivariate dependent variables to capture more 

https://doi.org/10.37433/aad.v6i3.647


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     69 
 

variance and improve its robustness, such as measuring different stages of adoption (no 
adoption, experimental use, partial adoption, and full adoption) to enhance accuracy and 
reliability. Given the importance of economic benefits as seen in this study and others (Bellon & 
Reeves, 2002; Karlan et al., 2014; Rogers, 2003), economic benefits need to be emphasized to 
promote the adoption of new technologies and in educational programs to increase adoption 
rates among farmers. We discovered compatibility negatively influences certain technologies, 
so educators should focus on addressing knowledge gaps to align existing and new 
technologies. Smaller operations were less open to adopting technologies, indicating a need for 
educational programs addressing scalability issues. Finally, initiatives to create a culture of 
innovation for farmers, tailored to different adopter categories and varying innovativeness 
levels, are recommended. 
 

Acknowledgments 
 
Funding Information: This work is supported by the United States Department of Agriculture 
National Institute of Food and Agriculture, NEXTGEN Program, Award #2023-7044-40157. 
 
Conflict of interest: There are no conflicts of interest. 
 
Previous Dissemination: This article is a truncated version of a dissertation at Tennessee State 
University. The dissertation was not yet published at the time of this article submission. 
 
Artificial Intelligence: AI was not used for this study. 
 
Author Contribution Statement: D. Morris – formal analysis, investigation, and writing original 
draft; J. Ricketts – formal analysis, writing, reviewing, and editing; D. Hochreiter – writing, 
reviewing, and editing. 
 

References 
 
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision 

Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T  
 
Bellon, M. R., & Reeves, J. (Eds.) (2002). Quantitative analysis of data from participatory 

methods in plant breeding. CIMMYT. https://books.google.com/books?id=iIGFJr4zz-EC 
 
Bonabana-Wabbi, J. (2002). Assessing factors affecting adoption of agricultural technologies: 

The case of Integrated Pest Management (IPM) in Kumi District, Eastern Uganda 
(Doctoral dissertation, Virginia Tech). http://hdl.handle.net/10919/36266 

 
Carlisle, L. (2016). Factors influencing farmer adoption of soil health practices in the United 

States: A narrative review. Agroecology and Sustainable Food Systems, 40(6), 583-613. 
https://doi.org/10.1080/21683565.2016.1156596  

https://doi.org/10.37433/aad.v6i3.647
https://doi.org/10.1016/0749-5978(91)90020-T
https://books.google.com/books?id=iIGFJr4zz-EC
http://hdl.handle.net/10919/36266
https://doi.org/10.1080/21683565.2016.1156596


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     70 
 

Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of 
experts. Management Science, 9(3), 458-467. https://doi.org/10.1287%2fmnsc.9.3.458 

 
Dillman, D. A. (2011). Mail and Internet surveys: The tailored design method--2007 Update with 

new Internet, visual, and mixed-mode guide. John Wiley & Sons.  
 
Doran, E. M., Zia, A., Hurley, S. E., Tsai, Y., Koliba, C., Adair, C., Schattman, R. E., Rizzo, D. M., & 

Méndez, V. E. (2020). Social-psychological determinants of farmer intention to adopt 
nutrient best management practices: Implications for resilient adaptation to climate 
change. Journal of Environmental Management, 276, 111304. 
https://doi.org/10.1016/j.jenvman.2020.111304 

 
Edison, S. W., & Geissler, G. L. (2003). Measuring attitudes towards general technology: 

Antecedents, hypotheses, and scale development. Journal of Targeting, Measurement, 
and Analysis for Marketing, 12, 137-156. 
https://doi.org/10.1057%2fpalgrave.jt.5740104 

 
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley 

& Sons. https://doi.org/10.1002/9781118548387  
 
Karlan, D., Osei, R., Osei-Akoto, I., & Udry, C. (2014). Agricultural decisions after relaxing credit 

and risk constraints. The Quarterly Journal of Economics, 129(2), 597-652. 
https://doi.org/10.1093/qje/qju002 

 
Kinyangi, A. A. (2014). Factors influencing the adoption of agricultural technology among 

smallholder farmers in Kakamega north sub-county, Kenya [Doctoral dissertation, 
University ofrobi]. https://erepository.uonbi.ac.ke/handle/11295/76086 

 
Li, H., Huang, D., Ma, Q., Qi, W., & Li, H. (2019). Factors influencing the technology adoption 

behaviours of litchi farmers in China. Sustainability, 12(1), 271. 
https://doi.org/10.3390%2fsu12010271 

 
Lindner, J. R. (2002). Handling of nonresponse error in the Journal of International Agricultural 

and Extension Education. Journal of International Agricultural and Extension Education, 
9(3), 55–60. https://doi.org/10.5191/jiaee.2002.09307 

 
Lindner, J. R., Murphy, T. H., & Briers, G. E. (2001). Handling nonresponse in social research. 

Journal of Agricultural Education, 42(4), 43–53. https://doi.org/10.5032/jae.2001.04043 
 
Loevinsohn, M., Sumberg, J., Diagne, A., & Whitfield, S. (2013). Under what circumstances and 

conditions does adoption of technology result in increased agricultural productivity? A 
systematic review (Version 1). The Institute of Development Studies and Partner 
Organisations. https://hdl.handle.net/20.500.12413/3208 

 

https://doi.org/10.37433/aad.v6i3.647
https://doi.org/10.1287%2fmnsc.9.3.458
https://doi.org/10.1016/j.jenvman.2020.111304
https://doi.org/10.1057%2fpalgrave.jt.5740104
https://doi.org/10.1002/9781118548387
https://doi.org/10.1093/qje/qju002
https://erepository.uonbi.ac.ke/handle/11295/76086
https://doi.org/10.3390%2fsu12010271
https://doi.org/10.5191/jiaee.2002.09307
https://doi.org/10.5032/jae.2001.04043
https://hdl.handle.net/20.500.12413/3208


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     71 
 

McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural 
Equation Modeling: A Multidisciplinary Journal, 23(5), 750-773. 
https://doi.org/10.1080/10705511.2016.1186549 

 
Meijer, S. S., Catacutan, D., Ajayi, O. C., Sileshi, G. W., & Nieuwenhuis, M. (2015). The role of 

knowledge, attitudes, and perceptions in the uptake of agricultural and agroforestry 
innovations among smallholder farmers in sub-Saharan Africa. International Journal of 
Agricultural Sustainability, 13(1), 40-54. 
https://doi.org/10.1080/14735903.2014.912493 

 
Mwangi, M., & Kariuki, S. (2015). Factors determining adoption of new agricultural technology 

by smallholder farmers in developing countries. Journal of Economics and Sustainable 
Development, 6(5). https://iiste.org/Journals/index.php/JEDS  

 
Nyairo, N., Pfeiffer, L., & Russell, M. (2021). Smallholder farmers’ perceptions of agricultural 

extension in adoption of new technologies in Kakamega County, Kenya. International 
Journal of Agricultural Extension, 9(1), 57-68. https://doi.org/10.33687%2f009.01.3510 

 
Pannell, D. J., Marshall, G. R., Barr, N., Curtis, A., Vanclay, F., & Wilkinson, R. (2006). 

Understanding and promoting adoption of conservation practices by rural landholders. 
Australian Journal of Experimental Agriculture, 46(11), 1407-1424. 
https://doi.org/10.1071/ea05037 

 
Rogers, E. (2003). Diffusion of innovations (5th ed.). The Free Press Simon & Schuster Inc. 
 
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An integrated 

approach to communication theory and research (pp. 432-448). Routledge. 
https://doi.org/10.4324%2f9780203710753-35 

 
Smith, A. (2012). Wealth of nations. Wordsworth Editions. 
 
Takahashi, K., Muraoka, R., & Otsuka, K. (2020). Technology adoption, impact, and extension in 

developing countries' agriculture: A review of the recent literature. Agricultural 
Economics, 51(1), 31-45. https://doi.org/10.1111/agec.12539 

 
Wright, V. (2004). How do land managers adopt scientific knowledge and technology? 

Contributions of the Diffusion of Innovations theory. In: N. Munro, P. Dearden, T. B. 
Herman, K. Beazley, & S. Bondrup-Nielson, (Eds.). Making ecosystem-based 
management work: Proceedings of the Fifth international conference on science and 
management of protected areas. SAMPAA. 
https://www.fs.usda.gov/rm/pubs_journals/2004/rmrs_2004_wright_v002.pdf  

 

https://doi.org/10.37433/aad.v6i3.647
https://doi.org/10.1080/10705511.2016.1186549
https://doi.org/10.1080/14735903.2014.912493
https://iiste.org/Journals/index.php/JEDS
https://doi.org/10.33687%2f009.01.3510
https://doi.org/10.1071/ea05037
https://doi.org/10.4324%2f9780203710753-35
https://doi.org/10.1111/agec.12539
https://www.fs.usda.gov/rm/pubs_journals/2004/rmrs_2004_wright_v002.pdf


Morris et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i3.647     72 
 

Yaseen, M., Manzoor, R., Shabbir, M., Hussain, S., & Maqsood, A. (2023). A review on adoption 
of technology and its impact on agricultural productivity. Journal of Agricultural 
Sciences, 18(3), 87-98. https://doi.org/10.3390/su152014792 

 
© 2025 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.v6i3.647
https://doi.org/10.3390/su152014792

