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Article

A Correlational Study of Two U.S. State Extension Professionals’ Behavioral Intentions to Improve Sustainable Food Chains through Precision Farming Practices

by
Chin-Ling Lee
1,
Robert Strong
2,*,
Gary Briers
2,
Theresa Murphrey
2,
Nithya Rajan
3 and
Shelli Rampold
4
1
Taoyuan District Agricultural Research and Extension Station, Council of Agriculture, Taoyuan 32745, Taiwan
2
Department of Agricultural Leadership, Education, and Communications, Texas A&M University, College Station, TX 77843, USA
3
Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
4
Department of Agricultural Leadership, Education, and Communications, The University of Tennessee, Knoxville, TN 37996, USA
*
Author to whom correspondence should be addressed.
Foods 2023, 12(11), 2208; https://doi.org/10.3390/foods12112208
Submission received: 8 May 2023 / Revised: 24 May 2023 / Accepted: 29 May 2023 / Published: 31 May 2023
(This article belongs to the Special Issue Innovations in the Food System: Exploring the Future of Food Series II)
Browse Figures
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Abstract

:
Precision farming provides one of the most important solutions for managing agricultural production to advance global food security. Extending professionals’ competencies to promote precision farming practices can increase the adoption rate, ultimately impacting food security. Many studies have addressed barriers to the adoption of precision farming technologies from the farmers’ perspective. However, few are available data on the perspectives of extension professionals. Agricultural extension professionals play an important role in innovative agricultural technology adoption. Thus, this study applied four constructs from the unified theory of acceptance and use of technology (UTAUT) model to investigate behavioral intentions to promote precision farming among extension professionals from two extension systems. In total, 102 (N = 102) agricultural extension professionals were surveyed. The results indicated that performance expectancy and social influence were individually significant predictors of extension professional behavioral intentions to promote precision farming technologies. There were no significant differences between the professionals of two extension systems. Gender, age, and years of service did not affect extension professionals’ intention to promote precision agriculture technologies. The data suggested the need for training programs to develop advanced competencies to promote agricultural innovation. This study contributes to the future professional development programs for extension professionals on communicating innovations to address food security and sustainability issues.

1. Introduction

Food security is an important issue worldwide. According to [1], the FAO anticipated the depreciation of food security by 2030. As the global population grows toward 9.6 billion by 2050 [2], many solutions have been proposed to meet future food needs while maintaining the environment [3]. Many challenges, such as extreme climates, threaten food security [4] and require global scientific innovation focusing on sustainable agricultural practices that support healthy agricultural dietary solutions [5]. Food experts indicated that while there is no single solution to food security, technology can play an important role in global food security and in maintaining agricultural sustainability [6]. Currently, agriculture has further opportunities to apply more intelligent tools because of the widespread use of digital technologies in various fields [7]. The advances in information and communication technologies have enabled farmers to access a large amount of site-specific data for their farms, which provide farmers with the opportunity to apply precision-farming approaches to make more accurate decisions to reduce unnecessary inputs and achieve production quantity and quality [8,9]. Thus, precision-farming technologies have received increased attention in providing solutions for food production to feed a growing global population [10,11,12,13].
Precision agriculture is the application of information technology to manage agricultural production to better advance global food security [11]. In addition, adopting precision agriculture technologies is necessary to adjust to [14] extreme climates that can bring new pests and diseases [15]. However, studies have noted that precision farming technologies require a variety of knowledge and skills among farmers and that the application of these technologies on farms may change farmers’ approach to farm management from an experience-driven to a data-driven practice [16,17].
Extension professionals serve as agricultural change agents that represent an organization in local communities by interfacing with community leaders and stakeholders [18]. The extent farmers observe members of their social system using precision-faming applications influences the cultural structure (change agents and opinion leaders of farmers [19]. Farmer are more willing to use an innovation that is promoted by an opinion leader in their social system versus one that is not. However, the difficulties in accessing the technology and extension service create barriers to adopting agricultural technology [20]. The adoption of precision farming technologies depends much on the extension’s dissemination of each innovation’s advantages [21]. Agricultural extension change agents are a factor that influences the decision-making process of farmers to adopt or reject innovations [22]. Lee et al. [23] found that extension professionals in the extension system lacked professional development in promoting the adoption of precision farming technologies by farmers. Lack of information dissemination and extension service will result in a low precision farming technologies adoption rate [24]. Emmanuel et al. [25] suggested that various institutions should train more extension professionals because they have significant impact on agricultural innovative technology adoption by farmers. Therefore, developing extension professionals’ competency to enhance production practices with precision farming technologies is a necessary part of improving services that meet the needs of farmers [26]. Many studies have addressed barriers to adoption of precision-farming technologies, including accessibility, socioeconomic status (e.g., age, gender, education), digital divide, misinformation, and availability [27]. However, there are few data on extension professionals’ behavioral intention to promote precision farming technologies to increase the adoption rate by farmers. In addition, due to the nature of the precision farming technology, adoption and diffusion vary by crop variety, region, and country [28]. Thus, this study’s purpose was (1) to explore the perspectives of agricultural extension professionals from two extension systems in the United States and (2) to identify the factors that influence their intentions to promote precision farming technology practices. The contribution of this study sought to improve precision farming information dissemination, extension professional development needs, and approaches to improve farmer adoption of precision farming innovations that optimized food chain and land sustainability.

2. Theoretical Framework

The unified theory of acceptance and use of technology (UTAUT) model by Venkatesh et al. [29] was used to investigate the behavioral intentions of agricultural extension professionals, including county agents and state specialists, to promote precision farming technologies in their extension roles. The UTAUT model combines eight prior models or theories to explore technology adoption [29]. Many studies indicated that the UTAUT variables applied to determine users’ technology adoption vary in different contexts [30]. Many studies applying the UTAUT model in an agricultural context have focused on technology adoption by end users (i.e., farmers) [31], not by promotors. The UTAUT model has proved to be an appropriate contextualization tool to measure the adoption of agricultural technologies [32,33]. Four main constructs play important roles as variables influencing individuals’ acceptance behavior: performance, effort expectancy, social influence, and facilitating conditions.
Performance expectancy refers to the degree to which an individual believes using technologies improves performance [34]. Viewing the relevant models integrated into the UTAUT model, performance expectancy echoes the perceived usefulness of technology acceptance model [35] and relative advantage of innovation of diffusion theory [22].
Effort expectancy is the perception of ease of use. If individuals feel they don’t need to make a huge effort to use technology, their willingness to use it will increase [36].
Social influence is the extent to which individuals believe that people who are important to them think they should use technology. Prior studies have indicated that social influence can affect individuals’ behavior [37].
Facilitating conditions refer to the extent to which an individual believes that infrastructure to support the use of the technology exists. Studies have demonstrated that the higher the facilitating conditions provided, the greater the chances of technology adoption by end users [38,39].
Venkatesh et al. [29] identified four moderators—age, gender, voluntariness, and experience—that may affect the relationship between four key determining constructs and intention of innovation usage. Existing UTAUT studies supported age, gender, and experience as moderators in innovation adoption [40,41,42]. However, expectations about an individual to engage in precision farming technology promotion and adoption may change through age and previous work experience [43]. Thus, in this study, we modified the UTAUT model to include age, gender, and experience as independent variables instead of moderating variables. We used participants’ years of service in extension positions of the participants as a variable of experience. The modified UTAUT model is shown in Figure 1.
This study used this conceptual model to examine whether selected factors of extension professionals affect their behavioral intentions to promote precision farming technologies. Specifically, the objectives of this study were to:
(1)
Describe agricultural extension professionals’ performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intentions to promote precision farming technologies.
(2)
Investigate the relationship between performance expectancy, effort expectancy, social influence, and facilitating conditions on agricultural extension professionals’ behavioral intentions to promote precision farming technologies.
(3)
Examine the mean difference of four variables—performance expectancy, effort expectancy, social influence, and facilitating condition—between two extension system groups.
(4)
Predict behavioral intentions using independent variables (performance expectancy, effort expectancy, social influence, facilitating conditions, age, gender, and years of service).

3. Method

3.1. Population and Samples

The technology that can be applied to precision farming varies by crop and region [28]. Therefore, to have a more complete understanding the perspectives of agricultural Extension professionals in various fields toward the promotion of precision farming, this study selected two extension systems—the University of California’s Cooperative Extension (UC Extension) and the University of Tennessee Extension (UT Extension)—in the United States and conducted a survey design with an instrument distributed to 468 agricultural extension professionals in the UC and UT extension systems. The research procedures and instrument were approved by Texas A&M University under IRB 2022-0175M for the implementation of the study with these two extension systems.
The population comprised individuals identified as extension agents or specialists who may actively or possibly work with precision farming technologies with crop producers in their role in either the UC or UT extension systems. We used a list of UT extension professionals obtained from the University of Tennessee Extension Department and a list of California Extension professionals identified by the first author as the sampling frame. Random sampling was used to select participants from the sampling frame [44]. The sample consisted of 102 agents and specialists—51 from UT Extension and 51 from UC Extension.
Food crops were the primary crop among extension professionals who responded to the survey—32 of 37 UC extension professionals (n = 32, N = 37; 86%) and 39 of 46 UT extension professionals (n = 39, N = 46; 85%) responded with crop varieties they worked with. The extension professionals in the two extension systems differed in the types of crops they specialized in within the food crop category. According to the crop variety answered by the participants, the food crop category included vegetables (i.e., leafy green, onion, lettuce, etc.), fruits (i.e., watermelon, berries, etc.), and grains (i.e., rice, wheat, etc.). In total, 29 of 46 UT Extension professionals (n = 26, N = 46; 52%) reported that grains were the dominant food crops they specialized in and that fruits (n = 10, N = 46; 22%) were the least-reported crop for their work. UC extension professionals’ responses in terms of crops were the opposite of UT’s responses, with fruit reported as the primary crops in which extension professionals worked—with 25 of 37 UC Extension professionals responding (n = 25, N = 37; 68%)—while grains (n = 5, N = 37; 14%) were the least-reported crop varieties in which they specialized. Figure 2 depicted the types of crops in which the surveyed extension professionals from two extension systems were specialized.

3.2. Data Collection

A questionnaire was developed to operationalize and measure five constructs important in this study (performance expectancy, effort expectancy, facilitating conditions, social influence, and behavioral intention to promote precision agriculture technologies). Each construct was measured using a multi-item scale. To measure participants’ level of agreement with the provided statements for performance expectancy, effort expectancy, facilitating conditions, social influence, and behavioral intention constructs, we used a 5-point response scale of 1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, and 5 = strongly agree. The statements used to measure each construct were based on the literature [45] and were adjusted slightly to be appropriate for research objectives. We developed the questionnaire in the Qualtrics survey platform, and we administered the questionnaire via email. We followed Dillman et al.’s [46] five-step survey design method to contact participants and collect data.
A total of 102 participants accessed the instrument with a response rate of 22% (n = 102, N = 468). In total, 71 out of 102 participants responded with their gender, and more than half of the participants who answered the gender question were male (n = 51, f = 68.92%). We used a cross-sectional design in which we tested age differences based on U.S. Census age groups between young (<34 years), middle-aged (35 to 54 years), and older (>55 years) participants. In addition, we tested the experience variable using groups of lesser (<10 years), moderate (10 to 20 years), and high (>20 years) experience. In total, 72 out of 102 participants responded with their age and years of service. The age range of participants was 25 to 75 years, with a mean age of 49 years old (n = 72, SD = 14.95). The participants’ years of service as an extension agent or specialist ranged from less than 1 year to 49 years, with a mean of 17.59 years of service (n = 72, SD = 14.21). Table 1 summarizes some of the demographic characteristics of the sample.

3.3. Data Analysis

We used SPSS 28.0 to analyze the data collected through the Qualtrics survey platform. Descriptive and inferential statistics were used to analyze data [47]. Cronbach’s [48] alpha coefficients were calculated to measure the internal consistency of five constructs of this study, yielding coefficients of 0.93 for performance expectancy, 0.90 for effort expectancy, 0.72 for facilitating condition, 0.84 for social influence, and 0.96 for behavioral intention. In addition, Cronbach [48] indicated that reliability coefficients of 0.70 or higher are acceptable and that those of 0.80 or higher are good.
Davis’ [49] conventions were used to describe the magnitude of the correlation between pairs of variables: 0.01 ≥ r ≥ 0.09 = Negligible, 0.10 ≥ r ≥ 0.29 = Low, 0.30 ≥ r ≥ 0.49 = Moderate, 0.50 ≥ r ≥ 0.69 = Substantial, r ≥ 0.70 = Very Strong. ANOVA analysis and t-test were used to test the group mean differences of the significant independent variables. A Pearson correlation analysis was used to examine the relationship between constructs used in the UTAUT model. The multiple linear regression analysis was used to test whether four constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) have statistically significant power to predict the dependent variable, behavioral intention to promote precision-farming technologies. The regression model used in this study is shown below.
Y = β0 + β1X1 + β2X2 +β3X3 + β4X4 + e
Y = Dependent variable
β0 = Population Y intercept
βi = Population Slope Coefficient
Xi = Independent Variable
e = Random error

4. Results

4.1. Descriptive Results

The first objective of this study was to describe agricultural extension professionals’ performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intentions to promote precision farming technologies. Data collected from the Tennessee extension system showed that the highest-scoring construct was “Behavioral Intention” (M = 3.36, SD = 0.72); the lowest-scoring construct was “Facilitating Conditions” (M = 3.00, SD = 0.77). Only the grand mean of facilitating conditions construct was 2.78 (SD = 0.64), which was lower than 3.00; the other four grand means of constructs were all slightly higher than 3.0, indicating that the Tennessee respondents slightly agreed with the statements provided by the survey (Table 2).
Data collected from the UC extension system showed that, similarly to data collected from Tennessee participants, the highest-scoring construct was “Behavioral Intention” (M = 3.53, SD = 0.68); the lowest-scoring construct was “Facilitating Conditions” (M = 2.72, SD = 0.78). The grand means for all four constructs were slightly higher than 3.0, except for the construct of facilitating conditions, which had a grand mean below 3.0 (M = 2.72; SD = 0.78), indicating slight agreement among UC Extension respondents with the construct statements provided in this study (Table 3).

4.2. Inferential Results

4.2.1. The Relationships between UTAUT Constructs

A Pearson correlation coefficient was computed to determine the linear relationship between behavioral intention and performance expectancy, between behavioral intention and effort expectancy, between behavioral intention and facilitating conditions, and between behavioral intention and social influence. The results analyzed from data collected from Tennessee participants indicated significant positive relationships between behavioral intention and other four constructs, performance expectancy (r = 0.80, p < 0.01), effort expectancy (r = 0.53, p < 0.01), facilitating conditions (r = 0.50, p < 0.01), and social influence (r = 0.67, p < 0.01; see Table 4).
The Pearson correlation coefficient results analyzed from data collected from the UC extension participants indicated significant positive relationships between behavioral intention and the other four constructs, performance expectancy (r = 0.68, p < 0.01), effort expectancy (r = 0.49, p < 0.01), facilitating conditions (r = 0.55, p < 0.01), and social influence (r = 0.72, p < 0.01; see Table 5).

4.2.2. Determine the Mean Differences of Variables among Participants from Two Extension Systems

We conducted a series of independent sample t-tests to examine the mean difference of UTAUT variables (performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention) among participants from two different areas. The results of the t-tests showed no significant mean differences between the two groups for all variables (see Table 6).
The results in Table 1 show that the two groups of respondents who were middle-aged (35 to 54 years) and older (>55 years) with longer service made up the majority of respondents, and the number of respondents was the same. Therefore, we also conducted an independent sample t-test to investigate the mean difference in participants’ behavioral intentions from two different age groups with 30 participants in each group. The result of the t-test indicated no significant mean differences between the two groups (see Table 7).
The results in Table 1 show that the two groups of respondents with fewer than 10 years of service and those with more than 20 years of service made up the majority of respondents, and the number of respondents was similar. Therefore, we conducted an independent sample t-test to investigate the mean difference in participants’ behavioral intentions from two different years of service groups. The result of the t-test indicated no significant mean differences between the two groups (Table 8).
The results in Table 1 showed that more than 69% of respondents were male. Therefore, we conducted chi-squared analysis, and the results indicated that each independent variable—including performance expectancy (X2(4, N = 51) = 37.12, p < 0.01), effort expectancy (X2(4, N = 51) = 17.71, p < 0.01), social influence (X2(4, N = 51) = 19.83, p < 0.01), and promotion condition (X2(4, N = 51) = 14.19, p = 0.01)—had a significant relationship with the dependent variable, the behavioral intention of extension professionals to promote precision agriculture (see Table 9).

4.2.3. Determine the Predictor of Behavioral Intention to Promote Precision-Farming Technologies

For the final objective of this study, we sought to determine whether the seven independent variables (performance expectancy, effort expectancy, social influence, facilitating conditions, age, gender, and years of service) used in this study could be predictors of extension professionals’ behavioral intention to promote precision-farming technologies. To assess the multiple linear regression analysis, we coded four UTAUT predictors, performance expectancy, effort expectancy, social influence, and facilitating conditions as 1 = strongly disagree, 2 = disagree, 3 = neither, 4 = agree, to 5 = strongly agree. In addition, the moderator gender was coded as 1 = male, 2 = female, 3 = other, and 4 = rather not answer. The independent variable, age, was analyzed using the actual age of the participants as a quantitative variable rather than a categorical variable in this study to include more information.
We regressed the dependent variable, behavioral intention to promote precision agriculture technologies, on the independent variables. The table below illustrates the multiple linear regression model (R2 = 0.65, F = 15.67, p = < 0.01). The model explained 65% of the variance in behavioral intention scores. The results indicated that two constructs, performance expectancy (p < 0.01) and social influence (p < 0.01), significantly predicted behavioral intention. In addition, the other two constructs, effort expectancy (p = 0.58) and facilitating conditions (p = 0.07), were not significant predictors of behavioral intention. The regression analysis results also revealed no statistically significant interaction between the effects of moderators of age, gender, and years of service on individuals’ behavioral intention to promote precision farming technologies. Participants predicted behavioral intention to promote precision farming technologies was equal to 0.33 (intercept) + 0.36 performance expectancy + 0.36 social influence. The regression model explained that 65% of the variance of extension professionals’ intention to promote the technologies was due to their beliefs of performance expectancy and the social influence of precision farming technologies for agriculturalists (Table 10).

5. Discussions

The descriptive results revealed that the grand means for behavioral intention, performance expectancy, effort expectancy, and social influence indicated that extension professionals in both states slightly agreed with the statements provided by the instrument used in this study. However, the grand means of facilitating conditions in both states indicated that extension professionals slightly disagreed with the statements provided by the instrument, indicating that extension professionals believed that the lack of suitable technical and organizational environmental support would reduce their intention to promote precision agriculture.
According to the findings of this study, there were no significant differences between UT and UC extension participants. Age and years of service were not variables that affected extension professionals’ intentions to promote precision-farming technologies. In addition, older or younger extension professionals and those of different amounts of service experience did not significantly affect the extension professionals’ behavioral intention to promote precision farming technologies. The professionals surveyed are willing to promote farming technologies as long as they have opportunities to enhance their performance expectancy, effort expectancy, social influence, and facilitating conditions.
The regression results are consistent with the previous studies [50,51] applying the UTAUT model to the field of technology use, indicating that performance expectancy and social influence are significant predictors of technology acceptance. The finding indicates that an extension professional’s or others’ perceived expectation that an extension professional will benefit from improved performance may be a motivator for promoting the technology adoption.

6. Conclusions and Implications

The data revealed gaps in information dissemination, professional development needs for current and future professionals’ training opportunities, and strategies for improving farmer adoption. The adoption of precision farming technologies is one strategy for improving sustainable food chains and systems. Agricultural extension, change agents, and professionals serve essential roles in disseminating innovation attributes across local communities in the battle to sustain food chains and improve land sustainability. Given the low response rate and focus on two states, we recognize that the data can be generalized to only the two state extension systems investigated. However, findings can be used by others to guide future studies.
The very strong and substantial significant positive correlation coefficients of all UTAUT independent variables on behavioral intention indicated that extension professionals need to be supported with professional development training to enhance their competencies in promoting precision agriculture technologies. Extension professionals in both state extension systems had similar intentions to promote precision agriculture technologies to stakeholders [29]. The data informs extension administrators, program leaders, professional development staff, and specialists of variables necessary in professional development to improve extension professionals’ promotion of precision farming technologies.
The results indicated that there were no significant differences in the agreements of all constructs (performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention) used in this study between extension professionals from two state extension systems. In addition, gender, age, and years of service were not variables that affected extension professionals’ intention to promote precision farming technologies. Thus, we concluded that there is no need to target a specific age, gender, or years of service experience range and that we should instead use a generalized approach. Data indicated that extension professionals believed their behavior to promote precision farming technologies was related to performance expectancy, effort expectancy, social influence, and facilitating conditions. The regression model explained that 65% of the variance of extension professionals’ intention to promote the technologies was due to their beliefs of performance expectancy and the social influence of precision agriculture technologies for agriculturalists.
Many studies have examined the implementation of the UTAUT model with the technology’s acceptance [52]. Still, few research appear to have explored the acceptance of the promotion of precision agriculture from the agricultural extension professionals’ perspectives. Therefore, this study has expanded the UTAUT application in an agriculture context with new technology (precision farming).
The current study provides a primary understanding of factors influencing agricultural extension professionals’ behavioral intention to promote precision farming technology adoption. We further suggest training programs addressing the development of advanced competencies needed to promote agricultural innovation as supported by [26]. Agricultural program development specialists must be aware that age, gender, or years of service are not barriers to extension agents and specialists promoting precision farming technologies. The results of performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention [29] among extension agents and specialists from different states highlight the importance of how training programs can comprehensively apply to different areas to help effectively facilitate the diffusion of precision agriculture technologies and bridge the gap in precision agriculture information dissemination channels. Social network systems, such as mentorship, can be leveraged to encourage extension professionals to engage in precision agriculture dissemination. This study contributes to the future professional development programs for agricultural extension professionals on communicating agricultural innovations with educators, extension agents and specialists, producers, and stakeholders to address food security, climate change adaptation, and sustainability issues [21].

Author Contributions

Conceptualization, C.-L.L.; methodology, C.-L.L., R.S., G.B., T.M., N.R. and S.R.; formal analysis, C.-L.L.; discussions and conclusions development, C.-L.L.; data curation, C.-L.L.; writing—original draft preparation, C.-L.L.; writing—review and editing, R.S., G.B., T.M., N.R. and S.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by USDA Hatch Project TEX09890: The Adoption Impact of Food and Agricultural Sciences Curricula on Public Health.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Simola, A.; Boysen, O.; Ferrari, E.; Nechifor, V.; Boulanger, P. Economic integration and food security—The case of the AfCFTA. Glob. Food Secur. 2022, 35, 100651. [Google Scholar] [CrossRef]
  2. Food and Agriculture Organization of the United Nations. The Future of Food and Agriculture: Trends and Challenges; Food and Agriculture Organization of the United Nations: Rome, Italy, 2017. [Google Scholar]
  3. Crist, E.; Mora, C.; Engelman, R. The Interaction of Human Population, Food Production, and Biodiversity Protection. Science 2017, 356, 260–264. [Google Scholar] [CrossRef]
  4. Ganpat, W.G.; Ramjattan, J.; Strong, R. Factors Influencing Self-Efficacy and Adoption of ICT Dissemination Tools by New Extension Officers. J. Int. Agric. Ext. Educ. 2016, 23, 72–85. [Google Scholar] [CrossRef]
  5. Wolde, Z.; Wei, W.; Kunpeng, W.; Ketema, H. Local Community Perceptions Toward Livelihood and Water–Energy–Food Nexus: A Perspective on Food Security. Food Energy Secur. 2020, 9, e207. [Google Scholar] [CrossRef]
  6. Carthy, U.M.; Uysal, I.; Badia-Melis, R.; Mercier, S.; O’Donnell, C.; Ktenioudaki, A. Global Food Security—Issues, Challenges and Technological Solutions. Trends Food Sci. Technol. 2018, 77, 11–20. [Google Scholar] [CrossRef]
  7. Xin, J.; Zazueta, F. Technology Trends in ICT–Towards Data-Driven, Farmer-Centered and Knowledge-Based Hybrid Cloud Architectures for Smart Farming. Agric. Eng. Int. CIGR J. 2016, 18, 275–279. [Google Scholar]
  8. Asif, A.S.; Uddin, M.N.; Dev, D.S.; Miah, M.A.M. Factors Affecting Mobile Phone Usage by the Farmers in Receiving Information on Vegetable Cultivation in Bangladesh. J. Agric. Inf. 2017, 8, 33–43. [Google Scholar] [CrossRef]
  9. Azumah, S.B.; Donkoh, S.A.; Awuni, J.A. The Perceived Effectiveness of Agricultural Technology Transfer Methods: Evidence from Rice Farmers in Northern Ghana. Cogent Food Agric. 2018, 4, 1503798. [Google Scholar] [CrossRef]
  10. Benke, K.; Tomkins, B. Future Food-Production Systems: Vertical Farming and Controlled Environment Agriculture. Sustain. Sci. Pract. Policy 2017, 13, 13–26. [Google Scholar] [CrossRef]
  11. Erickson, B.; Fausti, S.W. The Role of Precision Agriculture in Food Security. J. Agron. 2021, 113, 4455–4462. [Google Scholar] [CrossRef]
  12. Gupta, M.; Abdelsalam, M.; Khorsandroo, S.; Mittal, S. Security and Privacy in Smart Farming: Challenges and Opportunities. IEEE Access 2020, 8, 34564–34584. [Google Scholar] [CrossRef]
  13. Raliya, R.; Saharan, V.; Dimkpa, C.; Biswas, P. Nanofertilizer for Precision Agriculture and Sustainable Agriculture: Current State and Future Perspectives. J. Agric. Food Chem. 2017, 66, 6487–6503. [Google Scholar] [CrossRef]
  14. Balafoutis, A.; Beck, B.; Fountas, S.; Vangeyte, J.; Van Der Wal, T.; Soto, I.; Gómez-Barbero, M.; Barnes, A.; Eory, V. Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics. Sustainability 2017, 9, 1339. [Google Scholar] [CrossRef]
  15. Sundström, J.F.; Albihn, A.; Boqvist, S.; Ljungvall, K.; Marstorp, H.; Martiin, C.; Nyberg, K.; Vågsholm, I.; Yuen, J.; Magnusson, U. Future Threats to Agricultural Food Production Posed by Environmental Degradation, Climate Change, and Animal and Plant Diseases—A Risk Analysis in Three Economic and Climate Settings. Food Secur. 2014, 6, 201–215. [Google Scholar] [CrossRef]
  16. Bulter, D.; Holloway, L. Technology and Restructuring the Social Field of Dairy Farming: Hybrid Capitals, ‘Stockmanship’ and Automatic Milking Systems. Sociol. Rural. 2016, 56, 513–530. [Google Scholar] [CrossRef]
  17. Eastwood, C.R.; Chapman, D.F.; Paine, M.S. Networks of Practice for Co-construction of Agricultural Decision Support Systems: Case Studies of Precision Dairy Farms in Australia. Agric. Syst. 2012, 108, 10–18. [Google Scholar] [CrossRef]
  18. Strong, R.; Wynn, J.T., II; Lindner, J.R.; Palmer, K. Evaluating Brazilian agriculturalists’ IoT smart agriculture adoption barriers: Understanding stakeholder salience prior to launching an innovation. Sensors 2022, 22, 6833. [Google Scholar] [CrossRef]
  19. Carolan, M. Publicising Food: Big Data, Precision Agriculture, and Co-Experimental Techniques of Addition. Sociol. Rural. 2015, 57, 135–154. [Google Scholar] [CrossRef]
  20. Tambo, J.A.; Abdoulaye, T. Climate Change and Agricultural Technology Adoption: The Case of Drought Tolerant Maize in Rural Nigeria. Mitig. Adapt. Strateg. Glob. Chang. 2012, 17, 277–292. [Google Scholar] [CrossRef]
  21. Mikwamba, K.; Dessein, J.; Kambewa, D.; Messely, L.; Strong, R. Collaborative Governance Dynamics in Innovation Platforms: Case of Malawi’s District Stakeholder Panel. J. Agric. Educ. Ext. 2021, 27, 255–275. [Google Scholar] [CrossRef]
  22. Rogers, E.M. Diffusion of Innovations, 5th ed.; Free Press: New York, NY, USA, 2003. [Google Scholar]
  23. Lee, C.-L.; Strong, R.; Dooley, K. Analyzing Precision Agriculture Adoption Across the Globe: A Systematic Review of Scholarship from 1999–2020. Sustainability 2021, 13, 10295. [Google Scholar] [CrossRef]
  24. Kanter, D.R.; Bell, A.R.; McDermid, S.S. Precision Agriculture for Smallholder Nitrogen Management. One Earth 2019, 1, 281–284. [Google Scholar] [CrossRef]
  25. Emmanuel, D.; Owusu-Sekyere, E.; Owusu, V.; Jordaan, H. Impact of Agricultural Extension Service on Adoption of Chemical Fertilizer: Implications for Rice Productivity and Development in Ghana. NJAS—Wagening. J. Life Sci. 2016, 79, 41–49. [Google Scholar] [CrossRef]
  26. Seitz, P.; Strong, R.; Hague, S.; Murphrey, T.P. Evaluating Agricultural Extension Agent’s Sustainable Cotton Land Production Competencies: Subject Matter Discrepancies Restricting Farmers’ Information Adoption. Land 2022, 11, 2075. [Google Scholar] [CrossRef]
  27. Pathak, H.S.; Brown, P.; Best, T. A Systematic Literature Review of The Factors Affecting the Precision Agriculture Adoption Process. Precis. Agric. 2019, 20, 1292–1316. [Google Scholar] [CrossRef]
  28. Roberts, P.C. Precision agriculture: A Challenge for Crop Nutrition Management. Plant Soil 2002, 247, 143–149. [Google Scholar] [CrossRef]
  29. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward A Unified View. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef]
  30. Attuquayefio, S.; Addo, H. Review of Studies with UTAUT as Conceptual Framework. Eur. Sci. J. 2014, 10, 249–258. [Google Scholar] [CrossRef]
  31. Ronaghi, M.H.; Forouharfar, A. A Contextualized Study of the Usage of the Internet of Things (IoTs) in Smart Farming in A Typical Middle Eastern Country within the Context of Unified Theory of Acceptance and Use of Technology Model (UTAUT). Technol. Soc. 2020, 63, 101415. [Google Scholar] [CrossRef]
  32. Faridi, A.A.; Kavoosi-Kalashami, H.; Bilali, H.E. Attitude Components Affecting Adoption of Soil and Water Conservation Measures by Paddy Farmers in Rasht County, Northern Iran. Land Use Policy 2020, 99, 104885. [Google Scholar] [CrossRef]
  33. Li, W.; Clark, B.; Taylor, J.A.; Kendall, H.; Jones, G.; Li, Z.; Jin, S.; Zhao, C.; Yang, G.; Shuai, C.; et al. A Hybrid Modelling Approach to Understanding Adoption of Precision Agriculture Technologies in Chinese Cropping Systems. Comput. Electron. Agric. 2020, 172, 105305. [Google Scholar] [CrossRef]
  34. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. Extrinsic and Intrinsic Motivation to Use Computers in the workplace. J. Appl. Soc. Psychol. 1992, 22, 1111–1132. [Google Scholar] [CrossRef]
  35. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  36. Moya, M.B.; Engotoit, B.; Mayoka, G.K. Mediating Effect of Price Value on Effort Expectancy and Behavioural Intentions to Use Mobile Communication Technologies by Commercial Farmers in Uganda. ICTACT J. Manag. Stud. 2018, 4, 754–763. [Google Scholar] [CrossRef]
  37. Giua, G.; Materia, V.C.; Camanzi, L. Smart Farming Technologies Adoption: Which Factors Play a Role in The Digital Transition? Technol. Soc. 2022, 68, 101869. [Google Scholar] [CrossRef]
  38. Shi, Y.; Siddik, A.B.; Masukujjaman, M.; Zheng, G.; Hamayun, M.; Ibrahim, A.M. The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory. Sustainability 2022, 14, 6640. [Google Scholar] [CrossRef]
  39. Wang, H.; Tao, D.; Yu, N.; Qu, X. Understanding Consumer Acceptance of Healthcare Wearable Devices: An Integrated Model of UTAUT and TTF. Int. J. Med. Inform. 2020, 139, 104156. [Google Scholar] [CrossRef]
  40. Lian, J.W.; Yen, D.C. Online Shopping Drivers and Barriers for Older Adults: Age and Gender Differences. Comput. Hum. Behav. 2014, 37, 133–143. [Google Scholar] [CrossRef]
  41. Ruggeri, K.; Kacha, O.; Menezes, I.G.; Kos, M.; Franklin, M.; Parma, L.; Landon, P.; Mattews, B.; Miles, J. In with the New? Generational Differences Shape Population Technology Adoption Patterns in the Age of Self-Driving Vehicles. J. Eng. Technol. Manag. 2018, 50, 39–44. [Google Scholar] [CrossRef]
  42. Zaremohzzabieh, Z.; Samah, B.A.; Omar, S.Z.; Bolong, J.; Mohamed Shaffril, H.A. Fisherman’s Acceptance of Information Effect of Age and Experience. J. Appl. Sci. 2014, 14, 873–882. [Google Scholar] [CrossRef]
  43. Pierpaoli, E.; Carli, G.; Pignatti, E.; Canavari, M. Drivers of Precision Agriculture Technologies Adoption: A Literature Review. Procedia Technol. 2013, 8, 61–69. [Google Scholar] [CrossRef]
  44. Creswell, J.W.; Guetterman, T.C. Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, 6th ed.; Pearson: New York, NY, USA, 2019. [Google Scholar]
  45. Irby, T.L.; Strong, R. Agricultural Education Students’ Acceptance and Self-Efficacy of Mobile Technology in Classrooms. NACTA 2013, 57, 82–87. Available online: https://www.jstor.org/stable/10.2307/nactajournal.57.1.82 (accessed on 9 March 2023).
  46. Dillman, D.A.; Smyth, J.D.; Christian, L.M. Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method, 4th ed.; John Wiley & Sons: Hoboken, NJ, USA, 2014. [Google Scholar]
  47. Fraenkel, J.R.; Hyun, H.H.; Wallen, N.E. How to Design and Evaluate Research in Education, 10th ed.; McGraw Hill Education: New York, NY, USA, 2019. [Google Scholar]
  48. Cronbach, L.J. Coefficient Alpha and the Internal Structure of Tests. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef]
  49. Davis, J.A. Elementary Survey Analysis; Prentice Hall: Hoboken, NJ, USA, 1971. [Google Scholar]
  50. El-Masri, M.; Tarhini, A. Factors Affecting the Adoption of E-Learning Systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educ. Technol. Res. Dev. 2017, 65, 743–763. [Google Scholar] [CrossRef]
  51. Hu, S.; Laxman, K.; Lee, K. Exploring Factors Affecting Academics’ Adoption of Emerging Mobile Technologies-An Extended UTAUT Perspective. Educ. Inf. Technol. 2020, 25, 4615–4635. [Google Scholar] [CrossRef]
  52. Williams, M.D.; Rana, N.P.; Dwivedi, Y.K. The Unified Theory of Acceptance and Use of Technology (UTAUT): A literature Review. J. Enterp. Inf. Manag. 2015, 28, 443–488. [Google Scholar] [CrossRef]
Foods 12 02208 g001 550
Figure 1. Modified Venkatesh et al.’s [29] UTAUT model used in this study.
Figure 1. Modified Venkatesh et al.’s [29] UTAUT model used in this study.
Foods 12 02208 g001
Foods 12 02208 g002 550
Figure 2. Crop variety worked on by surveyed extension professionals in two extension systems.
Figure 2. Crop variety worked on by surveyed extension professionals in two extension systems.
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Table
Table 1. Participants’ demographic characteristics.
Table 1. Participants’ demographic characteristics.
Characteristicsf%
UT ExtensionUC ExtensionTotal
Gender
 Male34175168.92
 Female8122027.03
 Prefer not to answer0334.05
100.00
Age
 Under 34 years751216.67
 35 to 54 years15153041.67
 55 years and older13173041.67
100.01
Years of Service as an Extension Agent/Specialist
 Less than ten years12203244.44
 10–20 years72912.50
 More than 20 years18133143.06
100.00
Table
Table 2. Descriptive statistics for UTAUT constructs of UT extension participants.
Table 2. Descriptive statistics for UTAUT constructs of UT extension participants.
ConstructsnMSD
Behavioral Intention443.360.72
Performance Expectancy453.330.75
Social Influence443.190.56
Effort Expectancy453.000.77
Facilitating Conditions 442.780.64
Note. Grand mean = 3.13, SD = 0.69, scale: 1 = strongly disagree, 2 = disagree, 3 = neither, 4 = agree, and 5 = strongly agree.
Table
Table 3. Descriptive statistics for UTAUT constructs of UC extension participants.
Table 3. Descriptive statistics for UTAUT constructs of UC extension participants.
ConstructsnMSD
Behavioral Intention373.530.68
Performance Expectancy383.340.87
Social Influence373.200.70
Effort Expectancy373.000.78
Facilitating Conditions372.720.78
Note. Grand mean = 3.16, SD = 0.76, scale: 1 = strongly disagree, 2 = disagree, 3 = neither, 4 = agree, and 5 = strongly agree.
Table
Table 4. Correlation between UTAUT constructs of UT extension participants.
Table 4. Correlation between UTAUT constructs of UT extension participants.
Performance
Expectancy		Behavioral
Intention		Social
Influence		Effort
Expectancy		Facilitating
Conditions
Performance Expectancy-
Behavioral Intention0.80 *-
Social Influence0.64 *0.67 *-
Effort Expectancy0.59 *0.53 *0.33 *-
Facilitating Conditions0.43 *0.50 *0.35 *0.62 *-
Note. * p < 0.05. Magnitude: 0.01 ≥ r ≥ 0.09 = Negligible, 0.10 ≥ r ≥ 0.29 = Low, 0.30 ≥ r ≥ 0.49 = Moderate, 0.50 ≥ r ≥ 0.69 = Substantial, r ≥ 0.70 = Very Strong (Davis, 1971).
Table
Table 5. Correlations between UTAUT constructs of UC extension participants.
Table 5. Correlations between UTAUT constructs of UC extension participants.
Performance
Expectancy		Behavioral
Intention		Social
Influence		Effort
Expectancy		Facilitating
Conditions
Performance Expectancy-
Behavioral Intention0.68 *-
Social Influence0.58 *0.72 *-
Effort Expectancy0.48 *0.49 *0.51 *-
Facilitating Conditions0.38 *0.55 *0.53 *0.67 *-
Note. * p < 0.05. Magnitude: 0.01 ≥ r ≥ 0.09 = Negligible, 0.10 ≥ r ≥ 0.29 = Low, 0.30 ≥ r ≥ 0.49 = Moderate, 0.50 ≥ r ≥ 0.69 = Substantial, r ≥ 0.70 = Very Strong (Davis, 1971).
Table
Table 6. t-test results for comparing UT extension participants and UC extension participants.
Table 6. t-test results for comparing UT extension participants and UC extension participants.
GroupnMSDtdfp
Performance Expectancy
  UC Extension383.340.75−0.04810.97
  UT Extension453.330.87
Effort Expectancy
  UT Extension453.000.770.07800.95
  UC Extension372.990.78
Social Influence
  UC Extension373.200.56−0.06790.95
  UT Extension443.190.70
Facilitating Conditions
  UT Extension442.780.640.35790.73
  UC Extension 372.720.78
Note. Scale: 1 = strongly disagree, 2 = disagree, 3 = neither, 4 = agree, and 5 = strongly agree.
Table
Table 7. t-test results for comparing participants’ age Groups.
Table 7. t-test results for comparing participants’ age Groups.
GroupnMSDtdfp
Behavioral Intention
 55 years and older303.460.75−1.12580.27
 35 to 54 years 303.260.67
Note. Scale: 1 = strongly disagree, 2 = disagree, 3 = neither, 4 = agree, and 5 = strongly agree.
Table
Table 8. t-test results for comparing participants’ years of service groups.
Table 8. t-test results for comparing participants’ years of service groups.
GroupnMSDtdfp
Behavioral Intention
 Fewer than 10 years of service323.720.571.64610.11
 More than 20 years of service313.470.65
Note. Scale: 1 = strongly disagree, 2 = disagree, 3 = neither, 4 = agree, and 5 = strongly agree.
Table
Table 9. A comparison of the chi-squared for four UTAUT constructs and behavioral intention construct in male participants.
Table 9. A comparison of the chi-squared for four UTAUT constructs and behavioral intention construct in male participants.
Negative IntentionModerate IntentionPositive Intention
n%n%n%X2p
Performance Expectancy
 Negative Opinion611.835.90037.12<0.01 *
 Moderate Opinion59.81733.347.8
 Positive Opinion0023.91427.5
Effort Expectancy
 Negative Opinion713.71121.612.017.71<0.01 *
 Moderate Opinion35.91121.61121.6
 Positive Opinion12.000611.8
Facilitating Conditions
 Negative Opinion9141427.547.814.190.01 *
 Moderate Opinion28815.71121.6
 Positive Opinion000035.9
Social Influence
 Negative Opinion5447.80019.83<0.01 *
 Moderate Opinion6181835.31223.5
 Positive Opinion0000611.8
Note. * p < 0.05. Three groups were used for each independent variable, coded as Negative Opinion = scale 1–2, Moderate Opinion = scale 3, and Positive Intention = 4–5. Three groups were used for Behavioral Intention, coded as Negative intention = scale 1–2, Moderate intention = scale 3, and Positive intention = scale 4–5. Scale: 1 = strongly disagree, 2 = disagree, 3 = neither, 4 = agree, and 5 = strongly agree.
Table
Table 10. Regression coefficients of all independent variables on behavioral intention.
Table 10. Regression coefficients of all independent variables on behavioral intention.
Independent VariableBetaSEꞵtp
Performance Expectancy0.360.100.423.75<0.01 *
Social Influence0.360.110.323.21<0.01 *
Facilitating Conditions0.180.100.201.840.07
Effort Expectancy0.050.090.060.550.58
Gender0.010.080.010.070.94
Age0.010.010.150.900.37
Years of Service−0.010.01−0.23−1.390.17
Note. * p < 0.05.
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Lee, C.-L.; Strong, R.; Briers, G.; Murphrey, T.; Rajan, N.; Rampold, S. A Correlational Study of Two U.S. State Extension Professionals’ Behavioral Intentions to Improve Sustainable Food Chains through Precision Farming Practices. Foods 2023, 12, 2208. https://doi.org/10.3390/foods12112208

AMA Style

Lee C-L, Strong R, Briers G, Murphrey T, Rajan N, Rampold S. A Correlational Study of Two U.S. State Extension Professionals’ Behavioral Intentions to Improve Sustainable Food Chains through Precision Farming Practices. Foods. 2023; 12(11):2208. https://doi.org/10.3390/foods12112208

Chicago/Turabian Style

Lee, Chin-Ling, Robert Strong, Gary Briers, Theresa Murphrey, Nithya Rajan, and Shelli Rampold. 2023. "A Correlational Study of Two U.S. State Extension Professionals’ Behavioral Intentions to Improve Sustainable Food Chains through Precision Farming Practices" Foods 12, no. 11: 2208. https://doi.org/10.3390/foods12112208

APA Style

Lee, C. -L., Strong, R., Briers, G., Murphrey, T., Rajan, N., & Rampold, S. (2023). A Correlational Study of Two U.S. State Extension Professionals’ Behavioral Intentions to Improve Sustainable Food Chains through Precision Farming Practices. Foods, 12(11), 2208. https://doi.org/10.3390/foods12112208

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