
































Microsoft Word - 329-Final.docx


   Advancements in Agricultural Development 
  Volume 4, Issue 3, 2023 
  agdevresearch.org 

 

1. Jamie Greig, Assistant Professor, The University of Tennessee, Knoxville, 320a Morgan Hall, 2621 Morgan Circle, Knoxville, 

TN 37996, jgreig@utk.edu,  https://orcid.org/0000-0002-7588-6374  
2. Kevin Cavasos, Postdoctoral Associate, The University of Tennessee, Knoxville, 236 Plant Biotechnology Building, 2505 E J 

Chapman Drive, Knoxville, TN 37996-4500, kcavasos@tennessee.edu,  https://orcid.org/0000-0002-8376-6954  
3. Christopher Boyer, Professor, The University of Tennessee, Knoxville, 302-I Morgan Hall, Knoxville, TN 37996, 

cboyer3@utk.edu,  https://orcid.org/0000-0002-1393-8589  
4. Susan Schexnayder, Senior Research Associate, The University of Tennessee, Knoxville, 238 Plant Biotechnology Building, 

2505 E J Chapman Drive, Knoxville, TN 37996-4563, schexnayder@utk.edu,  https://orcid.org/0000-0002-8803-7072  
103 

 

Diffusion of Innovation, Internet Access, and Adoption 
Barriers for Precision Livestock Farming among Beef Producers 

 
J. Greig1, K. Cavasos2, C. Boyer3, S. Schexnayder4 

 
 

Article History 
Received: March 24, 2023 
Accepted: August 3, 2023 
Published: August 21, 2023 
 
 
Keywords 
rural connectivity; beef producers' 
perceptions; technology trialability; 
adoption complexity; policy 
recommendations   

Abstract 
This study examined the relationship between internet access type and 
perceptions of Precision Livestock Farming (PLF) Technologies among 
beef producers in a specific state. Using data collected from an internet-
based survey of beef producers (n = 137), this study conducted an 
exploratory factor analysis to construct variables corresponding to 
Diffusion of Innovation (DOI) attributes that influence innovation 
adoption. Findings indicate producers with cable, cellular, and broadband 
internet access had more favorable perceptions of PLF technologies in 
terms of barriers to adoption, while those with no internet access or 
satellite connections reported higher perceived complexity with the use 
of PLF technologies. Trialability and observability varied across internet 
types, suggesting hands-on experience and practical demonstrations 
might be more impactful for certain groups. Beef producers with satellite 
internet connections were more likely to perceive the need to trial PLF 
technologies before adoption. This study highlights the importance of 
internet access in rural areas and its potential impact on the adoption of 
PLF technologies, offering valuable insights for industry stakeholders and 
policymakers to promote the adoption of PLF technologies. 

 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   104 
 

Introduction and Problem Statement 
 
Precision Livestock Farming (PLF) is a data-driven management approach that uses technology 
to optimize livestock performance and well-being. These technologies have the potential to 
benefit the livestock industry by enhancing animal welfare, increasing productivity, and 
reducing the environmental footprint of livestock operations (Berckmans, 2017; Neethirajan, 
2017). In recent years, there has been growing interest in understanding the factors that 
influence the adoption of these technologies among livestock producers (Banhazi et al., 2012; 
Eastwood et al., 2019). 

Internet access and connectivity are crucial for the successful implementation of PLF 
technologies, as they facilitate real-time monitoring and analysis of data. However, the 
availability and quality of internet access may vary among beef producers, potentially 
influencing their perceptions of PLF technologies and consequentially their adoption of PLF 
technologies (Groher, 2020). For instance, rural areas often have limited internet connectivity, 
which may act as a barrier to technology adoption (Vogels, 2021). 

Studies have identified several factors that influence the adoption of PLF technologies, 
including the cost of the technology, perceived ease of use, perceived usefulness, and 
compatibility with existing systems (Boehlj & Langemeier, 2021; Fastellini & Schillaci, 2020). 
Additionally, the availability of technical support, training, and knowledge sharing can 
significantly impact the adoption process (Läpple & Kelley, 2013; Rose et al., 2016). Moreover, 
social factors, such as the influence of peers, local networks, and the role of extension services, 
can also affect the adoption of farming technologies (Ramirez, 2013). Examination of the 
influence of internet access type on PLF technology adoption among beef producers, however, 
has been limited to date.  

An improved understanding of the technology-related factors that influence producers’ PLF 
technology adoption decisions is crucial for developing targeted strategies to facilitate the 
adoption of PLF technologies among beef producers. By addressing these barriers and tailoring 
support and interventions to suit the specific needs of producers, it is possible to enhance 
animal welfare, increase productivity, and reduce the environmental impact of livestock 
operations. 

Theoretical and Conceptual Framework 
 
The Diffusion of Innovation (DOI) theory has served as a foundation for research on technology 
adoption for several decades. Developed by Rogers (1962), this theoretical framework has 
proven useful in understanding how and why individuals and organizations adopt new 
technologies. By examining the five key attributes of a technology that affect adoption—
complexity, compatibility, relative advantage, trialability, and observability—researchers can 
gain insights into the adoption process and identify potential barriers or facilitators (Miller, 
2015).  



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   105 
 

Complexity, as an attribute of DOI theory, has been widely studied, with research indicating 
that technologies perceived as less complex are more likely to be adopted (Tornatzky & Klein, 
1982). This is because potential adopters may be hesitant to adopt a technology that they 
perceive as difficult to learn or use (Venkatesh et al., 2016). 

Compatibility is another essential attribute, as it addresses how well the new technology aligns 
with the existing values, needs, and practices of potential adopters (Tornatzky & Klein, 1982). 
Studies have shown that technologies that are more compatible with current practices are 
more likely to be adopted (Miller, 2015). 

Relative advantage, or the perceived benefit of the technology compared to existing 
alternatives, is a critical component of technology adoption (Miller, 2015). Research has 
demonstrated that potential adopters are more likely to adopt a technology when they 
perceive it to have a significant advantage over alternatives (Tornatzky & Klein, 1982). This can 
include benefits such as increased efficiency, cost savings, or improved performance (Moore & 
Benbasat, 1991). Cost-benefit analysis is often a consideration in determining the relative 
advantage of technology adoption, with expense being an important factor to weigh (Miller, 
2015). As Rogers notes, the cost of technology is an important factor to consider, as adopters 
will perform a cost-benefit analysis that will ultimately influence adoption decisions (Rogers, 
1962).  

Trialability, the ease with which potential adopters can test or experiment with the technology, 
has been shown to influence adoption rates (Miller, 2015). When potential adopters can try out 
a technology before adopting, they are more likely to understand the benefits and make a more 
informed adoption decision (Venkatesh et al., 2003). This is especially relevant for complex 
technologies, where hands-on experience can help to reduce perceived complexity (Moore & 
Benbasat, 1991). 

Lastly, observability is the extent to which the results of using the technology are visible to 
others (Rogers, 1962). Technologies with higher levels of observability tend to have higher 
adoption rates, as potential adopters can see the benefits firsthand and are more likely to be 
persuaded by the success of others (Miller, 2015). 

Diffusion of Innovation theory provides a comprehensive framework for understanding the PLF 
technology adoption process, including the perceived barriers to PLF adoption among beef 
producers. An improved understanding of how producers perceive individual PLF technologies 
with respect to the five DOI attributes (complexity, compatibility, relative advantage, 
trialability, observability), and whether and how these perceptions are influenced by internet 
connection type, can inform the design, manufacturing and marketing of PLF technologies and 
enable researchers and practitioners to better facilitate their successful adoption. 

 
 
 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   106 
 

Purpose 
 
The purpose of this study was to understand the barriers to the adoption of PLF technologies 
among beef producers in Tennessee and how these barriers differ across various types of 
internet access. The specific objectives of this study were to: (a) identify the DOI attributes that 
may serve as barriers to the adoption of PLF technologies among beef producers in Tennessee, 
(b) explore potential differences in the perceived barriers to PLF technologies adoption across 
different types of internet access, and; (c) assess the relationship between the type of internet 
access and the importance of perceived barriers to adopting PLF technologies. 

Methods 
 
Survey Design and Procedure 
This study collected data from a survey promoted via email in 2022 among Tennessee beef 
cattle producers and administered using the Qualtrics online survey platform. The survey 
targeted producers who participated in the Tennessee Agriculture Enhancement Program, a 
cost-share program that assists Tennessee agricultural producers in making long-term 
investments in their operations, and was designed to elicit respondent perceptions toward 
various PLF technologies and identify factors that influence their adoption. A pretest sample 
was first used to test the survey instrument and obtain feedback from producers to incorporate 
into the final version of the survey; it included six sections that addressed livestock numbers, 
farm size, cattle marketing, perceptions and use of PLF technologies, risk preferences, and 
socio-demographic information. Out of the 6,858 producers contacted by email, a total of 201 
responses were received. After removing respondents who did not complete responses to the 
PLF barrier and internet type questions, a total of 137 complete responses were retained for 
analysis. 

Exploratory Factor Analysis  
Factors perceived to be barriers to the adoption of PLF technologies were elicited with the 
question: “To what extent do you agree or disagree that the following are barriers associated 
with using any Precision Livestock Farming Technologies on your operation?” Ten potential 
barriers were provided and rated on a Likert scale of agreement (1 = Strongly disagree, 5 = 
Strongly agree). The importance of having real time continuous information on 11 specific 
aspects of respondents' operations was rated on a 7-point scale of agreement (1 = Not 
important, 7 = Extremely important) and elicited with the statement: “Rate the importance of 
having real-time, continuous information for your operation for the following.”  

Factors representing the five DOI attributes were extracted from survey responses using 
principal component analysis (PCA) (Watkins, 2018). Variables with high factor loadings, an 
indication of a strong relationship between them, were grouped under the same factor 
(Watkins, 2018). Variables representing each DOI attribute were then constructed by averaging 
the values of the variables comprising the respective attribute groupings. 

 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   107 
 

Descriptive Analysis 
Descriptive statistics were calculated to determine the distribution of internet access types 
among the surveyed beef producers in Tennessee and their perceived barriers to adopting PLF 
technologies. Cross-tabulation analysis was performed to examine the distribution of the 
barriers to adoption across different internet access types. Chi-square tests were used to 
determine whether observed differences in the distribution of barriers across internet access 
types were statistically significant (Upton & Cook, 2001). 

Multivariate and Post-hoc Tests 
Multivariate tests, including multivariate analysis of variance (MANOVA) and multivariate 
regression analysis, were conducted to assess whether the type of internet access significantly 
influenced the perceived barriers to adoption among Tennessee beef producers (Warne, 2014). 
Univariate tests, including ANOVA and multiple regression analysis, were used to examine the 
relationships between internet access types and each constructed DOI variable separately 
(Cardinal & Aitken 2005). Post-hoc tests, including Tukey's Honestly Significant Difference and 
Scheffé's test, were used to examine pairwise comparisons between different internet access 
types to identify which specific internet access types significantly differed from each other in 
terms of their influence on the DOI variables (Abdi & Williams, 2010; Klockars & Hancock 2000). 

Findings 
 
EFA Results 
In this study, we aimed to construct variables representing the five DOI attributes (complexity, 
compatibility, relative advantage, trialability, and observability) to better understand the 
barriers related to the adoption of PLF Technologies among Tennessee beef producers. 

The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's Test of Sphericity 
were used to assess the suitability of the data for factor analysis (Dziuban & Shirkey, 1974; 
Tobias & Carlson 1969). The rotated component matrix (Table 1) was used to interpret the 
factor loadings, which represent the correlations between the variables and the extracted 
factors. Variables with high factor loadings (ideally greater than 0.4 or 0.5) were grouped under 
the same factor. 

 
 
 
 
 
 
 
 
 
 
 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   108 
 

Table 1 
 
PLF Technology Barriers Rotated Component Matrix 
 Component 
 1 2 3 4 5 
The technologies are too complex for my employees to learn .862 .201 .028 .077 .024 
The technologies are too complex for me to learn .857 .217 .192 .088 .071 
I need to be able to try out the new technology before 

committing to it 
.665 .044 .510 .081 .036 

I don't have enough time to learn the new system .122 .892 .045 .145 .155 
I need to see the technology working on a farm before I 

consider it 
.234 .851 .242 -.039 -.023 

My operation is too small to benefit from these tools .136 .143 .893 .118 .101 
I am not sure the technology fits with my production operations .289 .555 .628 .008 -.102 
I don't have good internet .106 .046 .087 .957 .004 
The technologies are too expensive .192 .200 .158 .083 .842 
I do not trust that my data stay private .248 .301 .238 .335 -.502 

 
The final allocation of items to the DOI variables was determined as follows (Table 2): 
 
Table 2  
 
DOI Variables and Items 
DOI Barrier Variable 
 
 

Item(s) 
Complexity  
The technologies are too complex for me to learn (Barriers_complexforme) 
The technologies are too complex for my employees to 

learn 
(Barriers_complexforemployees) 

I don’t have enough time to learn the new system (Barriers_time) 
Compatibility  
I am not sure the technology fits with my production 

operations 
(Barriers_unsure) 

My operation is too small to benefit from these tools (Barriers_smalloperation) 
Relative Advantage  
The technologies are too expensive (Barriers_expensive) 
Trialability  
I need to be able to try out the new technology before 

committing to it 
(Barriers_trytech) 

Observability  
I need to see the technology working on a farm before I 

consider it 
(Barriers_seetechwork) 

 
 
 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   109 
 

Descriptive Statistics 
In the descriptive and selection phase for internet types, we aimed to understand the 
distribution of various internet access types among beef producers in Tennessee who 
participated in the survey. The binary variables representing the internet types were no 
internet, DSL, broadband, cable, satellite, and cellular. No participant selected dial-up, so this 
variable was removed from the analysis. 

Cellular was the most common type of internet service among respondents, followed by 
broadband and cable. Fewer respondents reported having DSL, satellite, or no internet service 
with the least common type of internet service among respondents being satellite (Table 3). 

Table 2 
 
Frequency of Internet Types Among Respondents (n = 137) 
Internet Type Frequency 
Cellular 55 
Broadband 37 
Cable 20 
DSL 16 
No internet 16 
Satellite 15 

*Respondents may have selected more than one internet type 
 
We attempted to determine whether specific barriers to technology adoption were associated 
with certain types of internet access by comparing the constructed DOI variables (Complexity, 
Compatibility, etc.) across the different internet types. As some respondents selected more 
than one type of internet, each type was examined independently. This is discussed further in 
the findings. Perceived complexity and compatibility of PLF technologies were lower for 
respondents with broadband (2.42, SD = 0.98), DSL (2.38, SD = 1.11), and cable (2.72, SD = 0.82) 
connections compared to no internet access (complexity: 2.77, SD = 0.88; compatibility: 3.34, 
SD = 0.79) and satellite (complexity: 2.80, SD = 1.30; compatibility: 2.93, SD = 1.28) (Table 4) 
 
 
 
 
 
 
 
 
 
 
 
 
 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   110 
 

Table 3 
 
Internet Types and Barrier Variable Means 

Internet Type Complexity Compatibility 
Relative 

Advantage Trialability Observability 
No internet access 2.77 

(0.88) 
3.34 

(0.79) 
3.81 

(1.05) 
4.38 

(0.72) 
4.06 

(0.85) 
DSL 2.38 

(1.11) 
2.91 

(0.95) 
3.38 

(1.15) 
3.69 

(1.20) 
3.44 

(1.21) 
Broadband 2.42 

(0.98) 
2.99 

(0.92) 
3.70 

(0.70) 
4.08 

(0.98) 
3.59 

(1.12) 
Cable 2.72 

(0.82) 
3.40 

(1.14) 
3.40 

(1.10) 
4.10 

(1.12) 
4.00 

(1.17) 
Satellite 2.80 

(1.30) 
2.93 

(1.28) 
3.40 

(1.40) 
3.20 

(1.21) 
3.20 

(1.08) 
Cellular 2.39 

(1.00) 
3.05 

(1.09) 
3.60 

(1.10) 
4.00 

(1.12) 
3.75 

(1.31) 
Note. Standard deviation in parentheses  
 
Relative advantage was rated highest by respondents with no internet access (3.81, SD=1.05), 
followed by those with cable (3.40, SD = 1.14), cellular (3.60, SD = 1.10), broadband (3.70, SD = 
0.70), DSL (3.38, SD = 1.15) and satellite (3.40, SD = 1.40). 

Trialability was rated highest by respondents with no internet access (4.38, SD = 0.72), followed 
by cable (4.10, SD = 1.12), broadband (4.08, SD = 0.98), cellular (4.00, SD = 1.12), DSL (3.69, SD = 
1.20) and satellite (3.20, SD = 1.21).  

Observability was rated highest by respondents with no internet access (4.06, SD = 0.85), 
followed by those with cable (4.00, SD = 1.17), cellular (3.75, SD = 1.31), broadband (3.59, SD = 
1.12), DSL (3.44, SD = 1.21) and satellite (3.20, SD = 1.08). 

In general, findings suggest perceived barriers to the adoption of PLF technologies decrease as 
the type of internet access becomes faster and more reliable. For example, the mean scores for 
complexity, compatibility, relative advantage, trialability, and observability are generally lower 
for broadband and cable internet compared to no internet access and DSL. Respondents with 
cable, cellular, and broadband internet access tend to have more favorable perceptions of PLF 
technologies, suggesting that access to these types of connections may encourage producers to 
adopt these technologies. Respondents with no internet access or a satellite connection rated 
complexity relatively higher than those with faster and more reliable types of access, suggesting 
technology adoption may be impacted by concerns among these groups regarding their ability 
to learn to use the technology. 

The importance of trialability and observability varies across internet types, with respondents 
having no internet access or using cable, cellular, and broadband connections placing a higher 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   111 
 

emphasis on these factors. This implies that hands-on experience and practical demonstrations 
of PLF technologies might be more impactful for these groups. Respondents with broadband, 
DSL, and cable connections rated perceived complexity and compatibility relatively lower. 

Multivariate and Post-hoc Tests 
This analysis applied the Pillai’s Trace test, as it is the most robust against violations of 
assumptions (Pillai & Sudjana, 1975); the results across all multivariate tests were consistent 
(Table 5). 
 
Table 4 
 
Pillai’s Trace Results 
Internet Type F(df1, df2) p-value 
Cellular 0.901(5, 131) 0.483 
Broadband 0.974(5, 131) 0.436 
Cable 0.832(5, 131) 0.529 
DSL 0.222(5, 131) 0.952 
No internet 0.695(5, 131) 0.628 
Satellite 2.390(5, 131) 0.041 

 
From these results, there is a significant difference in the DOI variables between the different 
internet types only for satellite internet (p = 0.041). For all other internet types, the p-values 
are greater than the typical significance level of 0.05, indicating no significant difference in the 
DOI variables. 

To better understand the specific differences between the satellite internet type and the 
others, this study conducted post-hoc tests, including Tukey’s HSD (Klockars & Hancock, 2000).  
The post-hoc tests identified which specific pairwise comparisons were significantly different. 
Based on the between-subjects effects for the satellite internet type, the only significant effect 
was found for the dependent variable “PLF Technology barriers – I need to be able to try out 
the new technology before committing to it” (F = 7.209, p = 0.008). The other dependent 
variables did not show a significant effect. 

ANOVA analysis indicated a significant effect of the independent variable “Internet satellite” on 
the dependent variable “PLF Technology barriers – I need to be able to try out the new 
technology before committing to it”, F(1, 135) = 7.209, p = 0.008, partial eta-squared = 0.051. 
The “Type III Sum of Squares” for the independent variable was 8.725, with one degree of 
freedom and a mean square of 8.725. The “Partial Eta Squared” value of 0.051 indicated that 
approximately 5.1% of the variance in the dependent variable can be explained by the 
independent variable, after controlling for the effect of any other variables in the model. 

Overall, the results suggest that exposure to satellite internet has a statistically significant effect 
on participants’ reported technology barriers, specifically their need to be able to try out new 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   112 
 

technology before committing to it. However, the effect size is relatively small, accounting for 
only about 5.1% of the variance in the dependent variable. 

Conclusions, Discussion, and Recommendations 
 
In this study, analysis accounted for the fact that respondents, based on their diverse working 
environments, may have access to more than one type of internet service. For instance, a 
producer might primarily utilize cellular internet in a production environment and a different 
type when working from an office. With this in mind, each internet type was treated as an 
independent variable in our multivariate analyses. This approach controlled for potential 
confounding variables, including instances of multiple internet access types, thereby reducing 
interference from other types. Subsequently, post-hoc tests were employed to statistically 
ascertain the differences in perceptions of PLF technologies across various internet types. These 
tests served dual purposes - they provided a deeper understanding of the unique impact of 
each internet type and also served to reconfirm the validity of our multivariate analyses. 
However, it is acknowledged that this method does assume a primary internet type for each 
producer, which may not always be the case given the possibility of multi-ISP usage. This 
represents an interesting dimension for future research. Exploring the specific patterns of 
multi-ISP usage among beef producers, and understanding how it influences their perception of 
PLF technologies, could yield important insights into the intricacies of technology adoption in 
this sector. 

The analysis revealed that respondents with cable, cellular, and broadband internet access 
generally have more favorable perceptions of PLF technologies, suggesting that access to these 
types of connections may encourage PLF technology adoption. On the other hand, respondents 
with no internet access or satellite connections reported a higher perceived complexity with 
respect to PLF technologies, which could serve as a barrier to their adoption. The importance of 
trialability and observability varied across internet types. Respondents with no internet access 
or using cable, cellular, and broadband connections placed a higher emphasis on these factors, 
implying that hands-on experience and practical demonstrations of PLF technologies might be 
more impactful for these groups. This corresponds with prior DOI research (Miller, 2015), and 
confirms that trialability and observability are key factors in the technology adoption process. 

The findings of this study are consistent with similar studies examining the adoption of PLF in 
the beef (Makinde et al., 2022), sheep (Kaler & Ruston, 2019), and swine (Akinyemi et al., 2023) 
industries and have significant implications for the adoption of PLF technologies and the impact 
of internet access in rural areas. By understanding the relationships between internet access 
types and perceptions of technology adoption, industry stakeholders and policymakers can 
design more targeted and effective interventions to promote the uptake of PLF technologies 
among agricultural producers. By focusing on improving internet access and connectivity, as 
well as addressing the specific barriers to adoption faced by producers with different internet 
access types, policymakers can contribute to the overall growth and competitiveness of the 
agricultural industry. 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   113 
 

These findings can be applied by industry stakeholders, such as technology providers, 
agricultural extension services and farmer organizations to better understand and address the 
needs of their target audiences. Consideration of the diverse range of internet access types 
among producers and the potential influence of access type on technology adoption can inform 
the development of effective strategies to facilitate the diffusion of PLF technologies and 
enhance the overall productivity and sustainability of the agricultural sector. 

Furthermore, these results suggest that targeted interventions and support, such as financial 
incentives, educational programs, and technology demonstrations might be more effective in 
promoting the adoption of PLF technologies among producers with different types of internet 
access (Pruitt et al., 2012). For instance, addressing cost barriers and providing opportunities 
for trialability and observability for certain users could improve their likelihood of adopting 
these technologies. 

In addition, policymakers and agricultural extension services should consider the specific needs 
and preferences of producers with different internet access types when designing and 
implementing initiatives to promote PLF technology adoption. This may include tailoring the 
content and delivery of educational materials and support services to suit the communication 
preferences and technological capabilities of producers with various internet access types. 

This study has several limitations that should be considered when interpreting the results. First, 
the sample size is relatively small, and the findings may not be generalizable to all beef 
producers in Tennessee or other rural areas. Second, the study relied on self-reported data, 
which may be subject to biases, such as social desirability bias (Vesely & Klöckner, 2020) and 
recall bias (Tarrant et. al, 1993). Lastly, the study explored the relationship between internet 
access types and perceptions of PLF technology adoption but did not delve into the underlying 
reasons for these relationships. Future research should aim to replicate these findings using 
larger and more diverse samples to confirm the relationships between Diffusion of Innovation 
factors and internet access types. Additionally, future research should investigate the specific 
factors and mechanisms that drive the observed differences in perceptions and adoption 
behaviors across different internet access types. 

Acknowledgements 
 
J. Greig – instrument design, formal analysis, investigation, writing-original draft; K. Cavasos - 
formal analysis, investigation, writing-review and editing; C. Boyer – instrument design, formal 
analysis, investigation, writing-review and editing; S. Schexnayder – instrument design, writing-
review and editing. 
 

References 
 
Akinyemi, B. E., Vigors, B., Turner, S. P., Akaichi, F., Benjamin, M., Johnson, A. K., Pairis-Garcia, 

M., Rozeboom, D. W., Steibel, J. P., Thompson, D. P., Zangaro, C., & Siegford, J. M. 
(2023). Precision livestock farming: A qualitative exploration of swine industry 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   114 
 

stakeholders. Frontiers in Animal Science, 4, [1150528]. 
https://doi.org/10.3389/fanim.2023.1150528  

Banhazi, T. M., Lehr, H.,  Black, J. L., Crabtree, H., Schofield, P., Tscharke, M., & Berckmans, D. 
(2012). Precision livestock farming: An international review of scientific and commercial 
aspects. International Journal of Agricultural and Biological Engineering, 5(3), 1–9. 
http://ijabe.org/index.php/ijabe/article/view/599/498 

Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 
7(1), 6–11. https://doi.org/10.2527/af.2017.0102 

Boehlje, M., & Langemeier, M. (2021, March 26). Adoption of precision agriculture technologies. 
farmdoc daily. https://farmdocdaily.illinois.edu/2021/03/adoption-of-precision-
agriculture-technologies.html 

Cardinal, R. N., & Aitken, M. R. F. (2005). ANOVA for the behavioural sciences researcher. Taylor 
& Francis Group. https://doi.org/10.4324/9780203763933  

Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor 
analysis? Some decision rules. Psychological Bulletin, 81(6), 358–361. 
https://doi.org/10.1037/h0036316 

Eastwood, C., Klerkx, L., Ayre, M., & Dela Rue, B. (2019). Managing socio-ethical challenges in 
the development of smart farming: From a fragmented to a comprehensive approach 
for responsible research and innovation. Journal of Agricultural & Environmental Ethics, 
32(5-6), 741–768. https://doi.org/10.1007/s10806-017-9704-5 

Fastellini, G., & Schillaci, C. (2020). Chapter 7 - Precision farming and IoT case studies across the 
world. In A. Catrignanò, G. Buttafuoco, R. Khosla, A. M. Mouazen, D. Moshou, & O. 
Naud, (Eds.),  Agricultural internet of things and decision support for precision smart 
farming (pp. 331–415). Elsevier Inc. https://doi.org/10.1016/B978-0-12-818373-
1.00007-X 

Groher, T., Heitkämper, K., & Umstätter, C. (2020). Digital technology adoption in livestock 
production with a special focus on ruminant farming. Animal, 14(11), 2404–2413. 
https://doi.org/10.1017/S1751731120001391 

Kaler, J., & Ruston, A. (2019). Technology adoption on farms: Using Normalisation Process 
Theory to understand sheep farmers’ attitudes and behaviours in relation to using 
precision technology in flock management. Preventive Veterinary Medicine, 170, 
104715–104715. https://doi.org/10.1016/j.prevetmed.2019.104715  

Klockars, A. J., & Hancock, G. R. (2000). Scheffé’s more powerful f-protected post hoc 
procedure. Journal of Educational and Behavioral Statistics, 25(1), 13–19. 
https://doi.org/10.2307/1165310 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   115 
 

Lane, D. (2010). Tukey’s honestly significant difference (HSD) test. In N. Salkind, Encyclopedia of 
research design (pp. 1566-1570). Sage. https://doi.org/10.4135/9781412961288 

Läpple, D., & Kelley, H. (2013). Understanding the uptake of organic farming: Accounting for 
heterogeneities among Irish farmers. Ecological Economics, 88, 11–19. 
https://doi.org/10.1016/j.ecolecon.2012.12.025 

Makinde, A., Islam, M. M., Wood, K. M., Conlin, E., Williams, M., & Scott, S. D. (2022). 
Investigating perceptions, adoption, and use of digital technologies in the Canadian beef 
industry. Computers and Electronics in Agriculture, 198, 1-12. 
https://doi.org/10.1016/j.compag.2022.107095 

Miller, R. L. (2015). Rogers’ Innovation Diffusion Theory (1962, 1995). In M. N. Al-Suqri & A. S. 
Al-Aufi (Eds.), Information seeking behavior and technology adoption: Theories and 
trends (pp. 261–274). IGI Global. https://doi.org/10.4018/978-1-4666-8156-9.ch016 

Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions 
of adopting an information technology innovation. Information Systems Research, 2(3), 
192–222. https://doi.org/10.1287/isre.2.3.192 

Neethirajan, S. (2017). Recent advances in wearable sensors for animal health management. 
Sensing and Bio-Sensing Research, 12, 15–29. 
https://doi.org/10.1016/j.sbsr.2016.11.004 

Pillai,  K. C. S., & Sudjana (1975). Exact robustness studies of tests of two multivariate 
hypotheses based on four criteria and their distribution problems under violations. The 
Annals of Statistics, 3(3) 617 – 636. https://doi.org/10.1214/aos/1176343126 

Pruitt, J. R., Gillespie, J. M., Nehring, R. F., & Qushim, B. (2012). Adoption of technology, 
management practices, and production systems by U.S. beef cow-calf producers. Journal 
of Agricultural and Applied Economics, 44(2), 203-222. 
https://doi.org/10.1017/S1074070800000274 

Ramirez, A. (2013). The influence of social networks on agricultural technology 
adoption. Procedia - Social and Behavioral Sciences, 79, 101–116. 
https://doi.org/10.1016/j.sbspro.2013.05.059 

Rogers, E. M. (1962) Diffusion of innovations. Free Press. 

Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., Twining, S., 
Ffloukes, C., Amano, T., & Dicks, L. V. (2016). Decision support tools for agriculture: 
Towards effective design and delivery. Agricultural Systems, 149, 165–174. 
https://doi.org/10.1016/j.agsy.2016.09.009 

Tarrant, M., Manfredo, M. J., Bayley, P. B., & Hess, R. (1993). Effects of recall bias and 
nonresponse bias on self-report estimates of angling participation. North American 



Greig et al.  Advancements in Agricultural Development 
 

10.37433/aad.v4i3.329   116 
 

Journal of Fisheries Management, 13(2), 217–222. https://doi.org/10.1577/1548-
8675(1993)013<0217:EORBAN>2.3.CO;2  

Tobias, S., & Carlson, J. E. (1969). Brief report: bartlett’s test of sphericity and chance findings in 
factor analysis. Multivariate Behavioral Research, 4(3), 375–377. 
https://doi.org/10.1207/s15327906mbr0403_8  

Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-
implementation: A meta-analysis of findings. IEEE Transactions on Engineering 
Management, EM-29(1), 28–45. https://doi.org/10.1109/TEM.1982.6447463 

Upton, G., & Cook, I. (2001).  Introducing Statistics (2nd ed). Oxford University Press. 

Venkatesh, V., Thong, J., & Xu, X. (2016). Unified theory of acceptance and use of technology: A 
synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 
328-376. https://doi.org/10.17705/1jais.00428 

Vesely, S., & Klöckner, C. A. (2020). Social desirability in environmental psychology research: 
Three meta-analyses. Frontiers in Psychology, 11, 1395–1395. 
https://doi.org/10.3389/fpsyg.2020.01395  

Vogels, E. A. (2021, August 19). Some digital divides persist between rural, urban and suburban 
America. Pew Research Center. https://policycommons.net/artifacts/1808201/some-
digital-divides-persist-between-rural-urban-and-suburban-america/2543052   

Warne, R (2014). A primer on multivariate analysis of variance (MANOVA) for behavioral 
scientists. Practical Assessment, Research & Evaluation, 19(17). 
https://doi.org/10.7275/sm63-7h70  

Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black 
Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807 

 
 
© 2023 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/). 
 


