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Looney et al.  Advancements in Agricultural Development 
  Volume 3, Issue 3, 2022
  agdevresearch.org 

1. Lauren M. Looney, Graduate Student, Oklahoma State University, lauren.looney@okstate.edu  

 https://orcid.org.0000-0002-6007-914X  
2. Paul M. Montgomery, Graduate Student, Oklahoma State University, paul.montgomery@okstate.edu 

 https://orcid.org/0000-0001-7371-6489   
3. M. Craig Edwards, Professor, Oklahoma State University, craig.edwards@okstate.edu,  

 https://orcid.org/0000-0002-4436-4450   

4. D. Brian Arnall, Professor, Oklahoma State University, b.arnall@okstate.edu,  https://orcid.org.0000-0002-6294-8150   
5. William R. Raun, Professor, Oklahoma State University, deceased 

40 

 

Producers’ Adoption Behaviors for Precision Agriculture (PA) 
Technologies to Improve Nitrogen Use Efficiency: Diffusion of 

Innovations Theory as an Explanatory Lens 
 

L. M. Looney1, P. M. Montgomery2, M. C. Edwards3, D. B. Arnall4, W. R. Raun5 
 

  

Abstract 

Advancements in precision agriculture technologies enable producers to achieve higher yields; 
however, in some cases, these innovations have not reached widespread adoption despite years of 
availability. We sought to understand producers’ adoption experiences with two precision 
agriculture technologies: Nitrogen (N)-Rich Strips and the Sensor Based Nitrogen Rate Calculator 
(SBNRC). These technologies can help producers optimize their application of nitrogen fertilizer on 
growing crops, especially small grains such as wheat. Using Rogers’ (2003) diffusion of innovations 
theory as an explanatory framework, this descriptive-exploratory study examined the adoption 
behaviors of producers from two midwestern states. Rogers’ (2003) theoretical lens guided 
instrument development and interpretation of results. To better understand the effects of change 
agents’ actions and potential adopters’ behaviors during the innovation-decision process, more 
research is needed regarding disenchantment discontinuance and replacement discontinuance, the 
potential for pro-innovation bias, and of the innovation attribute compatibility. The future 
development of precision agriculture technology with the perceptions of potential adopters in mind, 
especially those averse to adoption and continuance, may assist in overcoming barriers to 
widespread diffusion. 
 
 
 

Keywords 

Innovation-decision process, nitrogen fertilizer, sustainability 



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Introduction and Problem Statement 
 
Lee et al. (2021) reviewed Precision Agriculture (PA) adoption literature from 1999 to 2020 as 
published in peer-refereed journals. They concluded that producers’ adoption of PA was not 
occurring at its highest potential because communication was lacking between change agents 
and farmers regarding the technology’s attributes (Lee et al., 2021). Nitrogen (N)-Rich Strips 
(NRS) and the Sensor Based Nitrogen Rate Calculator (SBNRC) are examples of PA tools 
available for more than 20 years (Desta et al., 2017). These technologies help producers 
optimize their use of N, the most limiting nutrient for plant growth, and augment more 
sustainable production practices. If adopted together, the tools represent a technology cluster 
(Rogers, 2003). Widespread use of such, however, is limited, uneven, or non-existent among 
producers who ostensibly would benefit from adopting the technologies. NRS are “an integral 
part of correctly determining the appropriate amount of N needed by the crop mid-season” 
(Desta et al., 2017, para. 1) and could prevent producers from under-applying or over-applying 
N fertilizer. Using the sensors, handheld and other modes, to calculate N rates, is made possible 
by the NRS indicating whether a crop responds to the application of additional fertilizer.   
 
Understanding why producers adopt or reject an innovation can guide the development of new 
tools and techniques, reveal needed modifications of existing technologies, and inform 
researchers and extension educators about how to better serve their stakeholders. Camp 
(2001), however, asserted that “a major stumbling block for many researchers in 
conceptualizing research is the development of an adequate theoretical framework for a study” 
(para. 2). Another failing can be using data collection instruments without embedding relevant 
observables or variables of interest resonating with a study’s theoretical frame. We sought to 
avoid this oversight by using Rogers’ (2003) diffusion of innovations to guide this investigation.       
 

Theoretical and Conceptual Framework 
 
Diffusion of innovations theory has been used in many studies across various settings and 
contexts, including for the advancement of agriculture and its many practices and forms 
worldwide (Rogers, 2003). Often, effectiveness of the diffusion of new technologies can be 
attributed to the efforts of change agents affecting potential adopters’ willingness to adopt and 
implement such innovations (Rogers, 2003). Rogers (2003) described change agents as 
individuals who influence clients’ innovation-decisions toward a new tool or practice as deemed 
desirable by a change agency. Change agents work to guide potential adopters through the 
innovation-decision process during which they either adopt or reject an innovation (Rogers, 
2003). They usually describe and often demonstrate the characteristics or attributes of an 
innovation that influence its rate of adoption (Rogers, 2003). 
 
Rogers (2003) described five characteristics or attributes, terms he used interchangeably, most 
likely to influence the adoption of an innovation, especially during the persuasion stage of the 
innovation-decision process: relative advantage, compatibility, observability, trialability, and 
complexity. Relative advantage is how beneficial an innovation is perceived by potential 



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adopters, especially as compared to the innovations superseding it. Compatibility is how well an 
innovation is perceived by potential adopters as meeting their needs and conforming with 
existing attitudes, practices, and societal norms. Observability reflects the extent to which 
potential adopters can see innovations perform and their results prior to adoption. Trialability, 
either partially or entirely, is the opportunity for potential adopters to use an innovation before 
deciding to adopt. Complexity is the degree of difficulty potential adopters perceive associated 
with implementing an innovation and such negatively influences the rate of adoption (Rogers, 
2003).  
 
At first, change agents usually engage potential adopters with knowledge of an innovation and 
then use its attributes with the intent to persuade them to decide to adopt it. If an individual 
chooses to adopt, they move to the implementation stage (Rogers, 2003). Thereafter, the 
adopter may transition to the confirmation stage for continued adoption and use, or instead 
discontinuance may occur if the innovation was not sufficiently compatible or failed to provide 
the relative advantage it initially promised. Rogers (2003) called this disenchantment 
discontinuance, i.e., the later decision to actively reject an innovation was due to an adopter’s 
dissatisfaction with its performance. Another reason for adopters discontinuing the use of an 
innovation may be replacement discontinuance (Rogers, 2003); in this instance, the former 
innovation is superseded by another that arises which adopters prefer more. 
 
In the diffusion of new innovations, communication channels and the effectiveness of  
change agents’ efforts are often imperative to informing and persuading potential adopters. For  
these innovations, the channels employed to reach producers included interpersonal and mass  
media. Oklahoma State University Plant and Soil Science extension personnel used interpersonal 
forms of communication to connect with producers, and their university’s online resources acted 
as channels of mass media communication, e.g., the issuance of bulletins and use of social media. 
These resources served as a landing page for existing users and potential adopters to gain 
knowledge and understanding of the PA technologies. Such was described by Rogers (2003) as 
how-to knowledge regarding an innovation’s proper use or implementation.  
 
Personal connections and relationships among members of a social system also can be 
important catalysts influencing an innovation’s adoption (Rogers, 2003). Interactions between 
and among change agents, opinion leaders, and potential adopters, which often involve 
creating awareness-knowledge, using persuasion, decreasing perceptions of complexity or 
uncertainty, and offering opportunities for observability and trialability, can positively affect an 
innovation’s rate of adoption (Rogers, 2003).   
 

Purpose 
 
We sought to understand producers’ adoption behaviors regarding this PA technology in two 
midwestern states. Our four objectives included (a) understanding producers’ perceptions of 
the technologies’ attributes, as defined by Rogers (2003); (b) knowing when, how, and from 
whom they had learned about and received training on the technologies; (c) knowing whether 



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their adoption was continuing, or rejection had occurred and why; and (d) identifying ways to 
improve diffusion, adoption, and use of these and related technologies in the future.      
 

Methods 
 
This was a descriptive-exploratory study. Other than mostly anecdotal data, very little 
information existed about adopters’ perceptions of the PA technologies we studied, i.e., N-Rich 
Strips and SBNRC. In 2020, we developed a survey instrument to collect data on the adoption 
behaviors of crop producers in two midwestern states regarding PA technologies. Our Qualtrics 
online questionnaire included forced-choice (Yes/No), Likert-type scale, and open-ended items. 
Producers’ perceptions of Rogers’ (2003) key attributes of an innovation were assessed by nine 
response items. Five-point, Likert-type response scales were developed to measure producers’ 
views on relative advantage and complexity of the PA technologies, N-Rich Strips and the 
SBNRC. For example, their perceptions of the relative advantage (Rogers, 2003) associated with 
using the PA technologies were measured using the following response scale: 1 = No value to 5 
= High value. And Rogers’ (2003) attribute of complexity was assessed with a perceived level of 
difficulty scale: 1 = Not difficult to 5 = Very difficult. Other items were developed to determine 
the year during which producers likely adopted, as an indicator of innovativeness (Rogers, 
2003); from whom they learned about the innovations; number of related trainings attended; 
and their highest levels of education. A panel of experts, including two plant and soil scientists 
and an agricultural education and extension educator, reviewed the instrument to verify its 
face and content validity. A group of key informants (Rogers, 2003), including producers and 
crop service providers, also provided guidance during the instrument development phase.   
 
The online questionnaire was distributed through a listserv of subscribers (N = 564) who had 
requested to receive updates on research and extension meetings from the Plant and Soil 
Sciences department at Oklahoma State University. Twenty-five requests were returned as 
undeliverable or defective; so, messages requesting participation in the study were delivered to 
539 individuals.   
 
These listserv subscribers were a purposive sample of producers who had shown interest in the 
Extension and research efforts of plant and soil scientists at Oklahoma State University, 
including PA technologies, during a 10-year period. In purposive sampling, “the researcher 
specifies the characteristics of the population of interest and locates individuals with those 
characteristics” (Johnson & Christensen, 2017, p. 268). In this study, however, the possibility of 
coverage error existed due to its sampling frame (Dillman et al., 2009), i.e., adopters who were 
not listserv subscribers went unsolicited and some subscribers may not have adopted nor been 
interested in PA technologies. This limits the generalizability of our results.  
 
Dillman et al. (2009) recommendations for data collection were followed: a prenotice electronic 
mail message was sent to the intended respondents; an invitation message with a link to the 
online survey questionnaire was sent next; and two follow up, reminder electronic mail 
messages were sent to increase the response rate. Sixty or 11.13% of the producers responded 



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to the questionnaire. They farmed in three counties of Kansas and in 33 Oklahoma counties. As 
appropriate, some data were analyzed using descriptive statistics via the Qualtrics data 
collection and management platform; Yes/No and Likert-type responses to the online survey 
questionnaire were counted and related percentages calculated. Respondents’ answers to the 
instrument’s open-ended questions were sorted and categorized according to corresponding 
innovation attributes, as negotiated and determined by the researchers. Rogers (2003) 
assertions about and descriptions of attributes were consulted by members of the research 
team to determine the placement of respondents’ comments in the attributional categories. 
One researcher had taught Rogers’ (2003) diffusion of innovations theory at the graduate level 
for two decades and two others were graduate students who completed said course.    
 

Findings 
 
Most of the producers were White/non-Hispanic males (f = 49, 81.67%) and reported having 
earned bachelor’s degrees or higher levels of education. The group’s mean age was 57 years, 
they had farmed for about 30 years on average, and two-thirds farmed 1000 acres or less. The 
crop for which most used the technologies was wheat (f = 52, 53.61%), forages were second (f = 
9, 9.28%), and corn third (f = 8, 8.25%). The most cited communication channel for knowing 
about the PA technologies was the Oklahoma Cooperative Extension Service (f = 51, 85.00%). As 
a group, the producers reported attending an average of about two Extension meetings each 
year. With the possibility of recall problem (Rogers, 2003) notwithstanding, producers’ self-
reported adoption of N-Rich Strips ranged from 1995 to 2019; their adoption spiked around 
2005 and again from about 2010 to 2012 (see Figure 1).  
 
Figure 1 
 
Producers’ Self-Reported Adoption of N-Rich Strips Over Time, 1995 to 2020 

 
Note. Forty-two of the 60 respondents (70.00%) indicated their year of adoption.   
 
As a potential complementary tool to further improve nitrogen use efficiency, the  
producers could have also adopted the SBNRC to use in tandem with N-Rich Strips, i.e.,  



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implemented a technology cluster (Rogers, 2003). Slightly more than one-fifth of the  
respondents (f = 13; 21.67%) indicated they were using the calculator, almost one-fourth (f =  
14; 23.34%) had used it but stopped, one-half (f = 30; 50.00%) reported having never used the  
technology, and three did not respond. Producers’ perceptions of the relative advantage 
(Rogers, 2003) associated with using the N-Rich Strips was measured with a Likert-type scale: 1 
= No value to 5 = High value. If considering the scale’s real limits, the producers perceived the 
N-Rich Strips had Average value (M = 3.17; SD = 1.14) [see Table 1]. Rogers’ (2003) attribute of 
complexity was assessed with a perceived level of difficulty response scale: 1 = Not difficult to 5 
= Very difficult. Applying the same real limits, both technologies were viewed as Slightly difficult 
to use: N-Rich Strips, M = 1.75 (SD = 1.01); and the SBNRC, M = 1.83 (SD = 0.92) [see Table 1]. 
 
Regarding producers’ opportunities to observe the use of N-Rich Strips prior to adoption,  
70% (f = 42) said Yes, and nearly two-thirds (f = 15) of the SBNRC users had first observed it  
in practice before adopting. As for using the PA technologies on a trial or limited basis before  
making their initial adoption decisions, 60% (f = 36) of the producers had tried the N-Rich Strips 
as had nearly two-thirds (f = 15) of the SBNRC users (see Table 1). Both attributes – 
observability and trialability – support the likelihood of adoption (Rogers, 2003).  
 
Table 1 
 
Producers’ Quantified Perceptions of Selected Attributes of Two PA Technologies 
Attributes    NRS a  SBNRC b 
    M  SD  M  SD 
Relative Advantage:  
How much value do N-Rich 
Strips bring to your farming 
operation?   

 3.17  1.14     

           
Complexity: 
How difficult do you find N-Rich 
Strips or the SBNRC b to use? 

 1.75  1.01  1.83  .92 

           
Attributes   NRS a SBNRC b 
    f  %  f  % 
Observability: 
Were you able to observe N-
Rich Strips or the SBNRC b before 
using in your farm operation? 

Yes 
 

No 
 

42 
 

18 

 70.00 
 

30.00 

Yes 
 

No 

15 
 

8 

 65.22 
 

34.78 

           
Trialability: 
Were you able to use N-Rich 
Strips or the SBNRC b on a trial 
or limited basis? 

Yes 
 

No 

36 
 

24 

 60.00 
 

40.00 

Yes 
 

No 

15 
 

8 

 65.22 
 

34.78 

           



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a N-Rich Strips; b Sensor Based Nitrogen Rate Calculator.   
 
The questionnaire included open-ended items for which the producers provided 73 responses; 
67 addressed the N-Rich Strips and five related to the SBNRC. We categorized these responses 
regarding which of Rogers’ (2003) key attributes of an innovation the data aligned with or 
implied. In the case of the attributes relative advantage and compatibility, the producers’ 
comments varied from evidence or support for such to perceived lack of the attributes. This 
overall analysis and categorization resulted in 27 statements aligned with relative advantage; 
34 comments expressed perceptions related to compatibility; and 11 implied that the attribute 
of complexity existed, and one did not (see Table 2). Fifty-five percent (f = 33) of the producers 
reported discontinuance of their adoption of the N-Rich Strips, which also likely implied 
discontinuing use of the SBNRC for those who had adopted the calculator to complement use of 
the strips.  
 
Table 2  
 
Producers’ Perceptions of Selected Attributes of PA Technologies as Written Responses 

Producers’ Perceptions of Relative Advantage N = 18  
Easy indicator for need of topdressing wheat 
Improve nitrogen use efficiency 
To reduce fertilizer input cost 
Profits 
Increase profits 
Nitrogen efficiency 
Limited the amount of top dress on wheat 
Usually reduce[s] N application by about 20 lbs/acre most years 
I found that I was not doing too bad of a job of managing nitrogen application, so I have only had to make 

minor adjustments based on N-Rich strips. However, since it is an extremely low cost and easy technology 
to implement, the value gained from making even minor adjustments is still well worth the cost.   

It is a good measuring stick. 
Another way to check N 
Save money on N applied 
The use of N-Rich strips has saved the farm. 
Really helps on wheat. I know whether to spend money or not. 
Saves money 
Somewhat valuable as education tools for producers I consult with that don’t believe N can economically 

increase yields. 
Easy to use, also provides an early season yield estimate in addition to nitrogen recommendations.a 
Best way to leverage the information gained from my N strips.a 

 
Producers’ Perceptions of the Lack of Relative Advantage N = 9  
[I] had extension agent evaluate first year. [I] bought sensor for 2nd and 3rd year. [I] never saw any difference. 

[I] quit using [it]. 
N-Rich strips would not be worthless, but they have less value than other things that I can do with my time.  
Seem[ed] like my fertilizer program changed very little on cost. 
[I]n dry years it was too late to tell.  



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The times I applied N-Rich strips I couldn't tell if I had adequate nitrogen or the conditions just didn't allow 
for a proper evaluation. I never really saw a deficiency.  

[I] try to [N] manage by soil tests. 
[Requires] time/labor to apply. [I] hired coop to apply most years.   
I’m not sure how to value them. When the strips show up it’s time to apply nitrogen for maximum or optimum 

yield. 
[I] saw no difference, labor limitations, [and] wheat not profitable, so why micro-manage?a  
 
Producers’ Perceptions of Compatibility N = 3  
Can be used mostly to confirm I applied enough N 
Learned the technique in college and they are easy to set up  
Not difficult to use or apply. But as far as using them as an AG retailer, the results are too late. If I were a 

producer with my own applicator, it would be a great tool. The results from a N rich strip come too late for 
that year as a retailer. I have used them more for an after the fact tool. Did we get enough on or where we 
[were] short N? If we are short, then we my need to reevaluate the amount of N that producer is applying 
in that farming operation. 

  
Producers’ Perceptions of the Lack of Compatibility N = 31  
Just finding the time to put them out before the wheat came up 
Timing of the application as well as time associated with the application if applied myself has been the most 

significant factor. I’m also aware of consulting services who will apply/install the strips, but the cost has 
been prohibitive. Most consultants require additional services or an agreement for services other than the 
strips. 

Application of our nitrogen is from effluent which is pumped through center pivot. 
Not applicable for us. We use effluent for our nitrogen and application is through center pivot.  
By the time we see any differences it is too late to apply N in western Oklahoma. The ONLY application I can 

see working with N rich strips on dryland is Exactrix NH3 to provide immediate response. 
I rarely see my N-strips pop up until after jointing. I have had them show up as late as heading even after 

using the calculator to determine my fertilizer rates.  
In wheat, the system is very simple and easy. In corn, I find the system to be less valuable due to the need to 

delay mid-season N application later than ideal. The V7-8 growth stage in corn frequently coincides with 
the arrival of spring rains (May) for me. This causes problems getting the nitrogen applied. 

N-strip results have adversely been affected by lack of or excessive rainfall events. 
Nitrogen incorporating rains can be hard to come by.  
Sometimes they don’t show up, or they appear after my deadline to apply fertilizer.  
They are not difficult but the[y] are inconvenient from logistical reasons.   
Time and resource management, while working a full-time job off the farm. 
Timing and to many other things to do. 
Web page could be updated, and grazed wheat indicates a need for N later than ungrazed wheat. 
Between read time and getting the nitrogen needed applied in a wet year there can be a lag time because of 

ground conditions unless the farmer has a way to apply the fertilizer themselves most will still do a weed 
and feed app because of application cost.   

Snow cover prevented a reading, drought years provided misleading data, plan to graze some years and N-
Strips not really designed for grazing. 

The response is too late to be able to correct the issue. 
Don’t apply much N in the fall. 
Useful but they slow us down so that is a drawback. 
We have a lot to do, and they slow us down so less valuable the more they slow us down. 



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Many times, the strips did not become visible until after cattle were removed and top dress N applications 
had already occurred. 

We do not use N-Rich Strips since it is not feasible for our application.  
There are times that no response is observed, or response is late for adequate top dress application. 
Too much trouble 
Depends [on] if the farmer believes in them or not. There ha[ve] been Springs where the ground has been 

too wet to get on and by the time able to get it applied it has been late when able to finally get it done, and 
some felt they ha[d] lost yield. [At one time,] not enough sensors around [but] now with sensors getting 
cheaper and easier to use the “shiny” has worn off for some to come back to table to buy a new sensor. 
[With] wheat being double cropped back late not able to get strips put out in a timely fashion. 

It seems to be very late to apply N by the time I can see a difference. 
I’m trying to utilize grid sampling and rate applied fertilizer. 
I do soil samples. 
Getting urea into topsoil during dry weather periods 
Late applied N [was] ineffective without rain; [this] clobbered me in [a] dry year.a 
 

Producers’ Perceptions of Complexity N = 11 
 
 
 
  

[I am] not sure how to use them in a pasture setting or the benefit they would have when not trying to 
maximize production. 

Remembering to write down where I did the test. And remembering where I wrote it down. 
Got on the wrong software page. 
Hard to find, hard to interpret 
[A] hassle to put them out and more of a hassle to read and determine correct rate. 
Just the commitment to getting them done. Sometimes I hire nitrogen application and it’s difficult to get them 

to do them. 
Maybe I don’t have a good understanding of N rich strips? Wouldn’t it be the same as making another pass 

across the end of the field or for that matter just noticing when the manure patches show up? 
Most fields were grazed and many times the strip was hard to see. 
One issue was when taking the first reading after spring green up the recommendation called for 0 lbs. of N 

to be applied. A few weeks after spring green up, the N-Rich strip could be seen for a 1/2 mile in the field 
and the sensor then called for 100 lbs. This makes organizing spring top dress with N on wheat very difficult. 

To many numbers to mess with 
If it was eas[ier]a 
 
Producer’s Perception of the Lack of Complexity N = 1 
 Not really, just the time it takes. No real difficulties 

Note. a Producers’ responses regarding the SBNRC. Other responses related to the N-Rich Strips. 
 

Conclusions, Discussion, and Recommendations 
 
Change agents for these technologies should continue to stress the related attributes during 
trainings by using demonstrations and other promotional efforts to increase the likelihood of 
adoption. However, more work appears to be needed to avert producers’ negative 
reevaluations of the technologies, i.e., after having initially adopted but later rejecting such. 
Rogers (2003) described two types of discontinuance or later rejection of innovations as either 



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disenchantment or replacement discontinuance. Disenchantment discontinuance can be 
characterized as an adopter’s decision to reject an innovation after growing dissatisfied with its 
performance. While replacement discontinuance is when an adopter discontinues using an 
innovation due their having adopted an alternative in its place (Rogers, 2003).  
 
Although we did not explicitly measure the attribute compatibility with an anchored  
response scale, 34 of the producers’ 73 written responses described the PA technologies as 
either lacking or having compatibility with their farming practices, but the former was indicated 
overwhelmingly (see Table 2), which implied disenchantment with the technologies. For  
instance, some producers noted the need for rainfall soon after applying N, but that it did not 
always rain, nor did they irrigate. Issues also arose if their wheat was grazed by cattle for a 
time, which is a common practice in the producers’ states. So, the technologies were not as 
compatible with their farming systems as the producers may have thought when they adopted 
use of the strips and calculator. This perceived lack of compatibility warrants additional 
investigation of producers’ views on the attribute. In addition, those who described using soil 
testing or soil grid sampling as alternative methods for managing N use efficiency (see Table 2) 
could be interpreted as replacement discontinuance. However, more in-depth content analysis 
for emergent themes in the producers’ written comments as well as personal and focus group 
interviews may assist in more fully understanding producers’ perceptions and related behaviors 
during the confirmation stage (Rogers, 2003) of the innovation-decision process for these PA 
technologies.  
 
Moreover, the views of key informants (Rogers, 2003) should be sought out to understand what 
may have precipitated the spike in adoption of the N-Rich Strips that occurred around 2005 and 
again from about 2010 to 2012 (see Figure 1). If any behaviors of change agents are identified 
as unique to those time periods of increased adoption; such should be considered for use in the 
future. In addition, more effective ways are needed to increase survey questionnaire response 
rates with the targeted group, e.g., paper instruments completed during in-person meetings or 
instantaneous access to online questionnaires via QR codes (Hill et al., 2013) may lead to higher 
response rates. Further, randomly selecting producers’ locations from which to query fertilizer 
suppliers and crop service providers about trends in the purchase of N-fertilizer over time could 
assist in mitigating the recall problem (Rogers, 2003) confounding many ex post facto survey 
research studies. Rogers (2003) supported the use of archival data to address this threat to the 
veracity of findings and thereby avoid drawing spurious conclusions.   
 
The elephant in the room that frequently permeates and may confound or even obscure a  
clear-eyed understanding of an innovation’s introduction, diffusion, adoption, continuance, or 
rejection is the phenomenon of pro-innovation bias, which Rogers (2003) indicted as a criticism 
of diffusion research. As such, Rogers (2003) asserted: “Perhaps owing to the pro-innovation 
bias that pervades much diffusion inquiry (see Chapter 3), investigation of rejection behavior 
has not received much scholarly attention” (p. 178). Pro-innovation bias is the view often held 
by many change agencies, with agricultural scientists or cooperative extension services 
personnel no exception, that all members of a targeted social system, i.e., potential adopters, a) 
should adopt an innovation and do so rapidly and b) no reinvention of the innovation is needed 



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or should occur (Rogers, 2003). In other words, the potential adopters’ less than positive, 
skeptical, or hesitant perceptions of an innovation’s attributes coupled with preexisting socio-
cultural norms also likely to influence their adoption decisions are frequently overlooked, 
underappreciated, misinterpreted, or even summarily dismissed by the actors fomenting 
change. An assessment of the potential for pro-innovation bias regarding the PA technologies 
studied may better inform and even modify the behaviors of change agents promoting these 
innovations and increase the likelihood of producers continuing to implement such in the 
future.   
 

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© 2022 by authors. This article is an open access article distributed under the terms and conditions of 
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