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Frontiers in Sustainable Food Systems ·

Volume 10 - 2026 | https://doi.org/10.3389/fsufs.2026.1935542

Development of a farmer-centric UTAUT scale for measuring farmers’ behavioural intention towards the adoption of fluoride-safe practices

  • Vicky Yadav1*corresponding author
  • Kalyan Ghadei1
  • Mani Prakash Shukla1
  1. Department of Extension Education, Banaras Hindu University, Varanasi, India
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Abstract

Fluoride contamination in groundwater is a serious problem in many parts of India and affects agriculture, livestock, and human health. Although the Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely used to explain technology adoption, no farmer-centric scale is available to measure farmers’ behavioural intention towards the adoption of fluoride-safe practices. Therefore, the present study was carried out to develop a farmer-centric UTAUT scale for this purpose. The initial statements were generated by reviewing UTAUT, UTAUT2, and other related technology adoption studies and were modified according to the agricultural context. Eighty-five statements were developed under seven constructs. After face validity, 66 statements were evaluated for content validity by a panel of 25 experts using Relevancy Weightage (RW), Mean Relevancy Score (MRS), and Overall Mean Relevancy Score (OMRS). Based on the predefined criteria, 49 statements were retained. A pilot survey was then conducted with 250 farmers from fluoride-affected areas using a five-point Likert scale. Exploratory Factor Analysis was performed using Principal Axis Factoring with Direct Oblimin rotation, followed by reliability analysis using Cronbach’s alpha. The KMO value (0.935) and Bartlett’s Test of Sphericity (p < 0.001) confirmed the suitability of the data for factor analysis. Exploratory Factor Analysis supported a seven-factor structure consistent with the adapted UTAUT framework, explaining 77.23% of the total variance. Based on the factor loading criterion, seven statements were removed, resulting in a final scale consisting of 42 statements. Cronbach’s alpha values ranged from 0.882 to 0.979, indicating satisfactory internal consistency across all constructs. The developed scale can be used to measure farmers’ behavioural intention towards the adoption of fluoride-safe practices in fluoride-affected areas. It can also help researchers, extension professionals, and policymakers to understand the factors influencing farmers’ adoption decisions and to plan suitable extension programmes. Further validation of the developed scale through Confirmatory Factor Analysis using an independent sample is recommended.

Keywords

1 Introduction

The increasing concern over fluoride contamination in groundwater has highlighted the need to understand farmers’ behavioural intention towards the adoption of fluoride-safe practices. Although the Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely applied to explain technology adoption across different domains, most existing measurement instruments were developed for information systems and other general technology applications. To the best of our knowledge, no standardized and validated instrument is available to measure farmers’ behavioural intention towards the adoption of fluoride-safe practices. Existing instruments do not adequately capture the agricultural and environmental context in which farmers make decisions regarding fluoride-safe practices. Therefore, there is a need to develop a context-specific, farmer-centric measurement instrument for assessing farmers’ behavioural intention in fluoride-affected areas. Accordingly, the present study focuses on developing a farmer-centric UTAUT scale for this purpose.

The study was carried out with the following objectives:

  • To review the literature on UTAUT and related technology adoption models for identifying the constructs and generating measurement items relevant to the adoption of fluoride-safe practices.
  • To develop a farmer-centric UTAUT scale by adapting the original UTAUT items to the context of fluoride-safe practices.
  • To assess the content validity of the developed scale through expert evaluation and retain the most relevant items.
  • To examine the underlying factor structure of the developed scale using Exploratory Factor Analysis (EFA).
  • To assess the internal consistency and reliability of the developed scale using Cronbach’s alpha.

1.1 Evolution of technology adoption theories

Many behavioural theories have been proposed over the years to explain how people make decisions about adopting a new technology. Each theory explains technology adoption from a different perspective and has contributed to understanding behavioural intention and adoption behaviour (see Table 1).

Table 1. Summary of technology adoption theories leading to UTAUT.

TheoryDeveloped byMain constructsLimitation
Theory of Reasoned Action (TRA)Fishbein and Ajzen (1975)Attitude, Subjective NormDoes not consider facilitating resources
Technology Acceptance Model (TAM)Davis (1989)Perceived Usefulness, Perceived Ease of UseFocuses mainly on information systems
Theory of Planned Behaviour (TPB)Ajzen (1991)Attitude, Subjective Norm, Perceived Behavioural ControlDoes not fully capture technological characteristics
Motivational ModelDavis et al. (1992)Intrinsic and Extrinsic MotivationLimited focus on social and organizational factors
Model of PC UtilizationThompson et al. (1991)Job Fit, Complexity, Facilitating ConditionsDesigned for personal computer use
Innovation Diffusion Theory (IDT)Rogers (1995), Moore and Benbasat (1991)Relative Advantage, Compatibility, Complexity, etc.Emphasizes innovation characteristics rather than behavioural intention
Social Cognitive Theory (SCT)Bandura (1986), Compeau and Higgins (1995)Self-efficacy, Outcome Expectations, Affect, AnxietyMore complex and not technology-specific
UTAUTVenkatesh et al. (2003)PE, EE, SI, FCIntegrates the strengths of previous models

1.2 Conceptual framework of the study

The Unified Theory of Acceptance and Use of Technology (UTAUT) proposed by Venkatesh et al. (2003) was selected for the present study because it explains the factors influencing an individual’s behavioural intention and adoption behaviour. Before UTAUT, several technology adoption theories such as TRA, TPB, TAM, IDT and SCT were developed to explain why people adopt a new technology or practice. However, UTAUT combines the key constructs of these theories into a single framework and has been widely used in different research areas to study technology adoption. Although UTAUT was originally developed for information system adoption, its application is no longer limited to information technology. Many researchers have successfully applied and extended the UTAUT framework in different fields by incorporating context-specific constructs whenever required (Williams et al., 2015; Dwivedi et al., 2020). Therefore, the original UTAUT framework was considered suitable for the present study. The adoption of fluoride-safe practices is also a behavioural decision. Before adopting these practices, farmers consider whether the practices will be beneficial, whether they are easy to implement, whether people around them encourage their adoption, and whether they have the required facilities and support. These factors are well explained by the UTAUT model. However, in the case of fluoride-safe practices, farmers’ opinion about the practice may also influence their decision. Previous studies have reported that farmers’ attitude plays an important role in agricultural technology adoption (Kutter et al., 2011, Aubert et al., 2012). Similarly, Shang et al., 2021, in their review of agricultural technology adoption studies, reported that attitude was one of the most consistent factors influencing farmers’ adoption decisions. Therefore, Attitude was included as an additional construct in the present study. Although farmers’ adoption decisions are influenced by broader social and institutional factors, the present study focuses on developing an individual-level behavioural intention scale. Therefore, UTAUT was considered an appropriate theoretical framework for the present study. To make the framework suitable for the agricultural context, the original UTAUT items were adapted by replacing the word “system” with “fluoride-safe practices” without changing the original meaning of the constructs. The original UTAUT considers Use Behaviour as the final outcome construct. In the present study, this construct was represented as Adoption, since the actual use of fluoride-safe practices is reflected through their adoption by farmers. Based on this framework, seven constructs were considered for scale development, namely Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Attitude, Behavioural Intention, and Adoption (Use Behaviour). In the present study, the term “fluoride-safe practices” refers to the recommended practices that help farmers reduce the harmful effects of fluoride contamination on human health, livestock, and agricultural productivity. These practices include the use of safe drinking water sources, suitable defluoridation methods, proper irrigation water management, appropriate crop and livestock management practices, and other fluoride mitigation measures recommended by agricultural and public health agencies (World Health Organization, 2017; Central Ground Water Board, 2023).

In the present framework, Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, and Attitude are considered determinants of Behavioural Intention. Behavioural Intention represents the farmer’s intention to adopt fluoride-safe practices, while Adoption (Use Behaviour) represents the actual adoption and continued use of these practices.

2 Methodology

2.1 Research design

The present study employed a quantitative methodological research design to develop and preliminarily validate a farmer-centric measurement scale based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The study aimed to develop a psychometrically sound instrument for measuring farmers’ behavioural intention towards the adoption of fluoride-safe practices.

The research followed a cross-sectional survey design, which is widely used in instrument development studies to collect quantitative data from respondents at a single point in time. The scale development process comprised systematic stages, including literature review, item generation, face validity, content validity, pilot testing, exploratory factor analysis, reliability analysis, and finalization of the measurement instrument.

Data were collected using a structured questionnaire. Responses to all scale items were recorded on a five-point Likert scale ranging from 1 = Strongly Disagree to 5 = Strongly Agree. Statistical analyses were performed using IBM SPSS Statistics (see Figure 1; Table 2).

Scale development process.
Figure 1. Scale development process.

Table 2. Operational definitions of constructs.

ConstructOperational definition
Performance expectancy (PE)Performance Expectancy is the degree to which a farmer believes that adopting fluoride-safe practices will improve agricultural productivity, livestock health, water quality, household well-being, and overall farming performance.
Effort expectancy (EE)Effort Expectancy is the degree to which a farmer believes that fluoride-safe practices are easy to learn, understand, implement, and maintain under local farming conditions.
Social influence (SI)Social Influence is the influence of family members, fellow farmers, village leaders, extension personnel, scientists, and other important people on a farmer’s decision to adopt fluoride-safe practices.
Facilitating conditions (FC)Facilitating Conditions refer to the availability of resources, training, technical guidance, financial support, infrastructure, and institutional assistance that help farmers adopt fluoride-safe practices.
Attitude (A)Attitude is a farmer’s positive or negative opinion about adopting fluoride-safe practices and whether the farmer considers these practices useful, beneficial, and worth adopting.
Behavioural intention (BI)Behavioural intention is a farmer’s willingness and future intention to adopt or continue using fluoride-safe practices in farming activities.
Adoption (AA)/use behaviourAdoption refers to the actual use and regular practice of fluoride-safe technologies or practices by farmers in their agricultural and household activities.

2.2 Item generation

The initial items were generated by reviewing the literature on technology adoption, particularly the Unified Theory of Acceptance and Use of Technology (UTAUT) developed by Venkatesh et al. (2003), UTAUT2 (Venkatesh et al., 2012), and other related studies in agriculture. Relevant studies on technology adoption, environmental management, and sustainable agricultural practices were also reviewed to identify suitable measurement items. The selected items were modified to suit the context of farmers’ adoption of fluoride-safe practices. Wherever required, the word “system” used in the original UTAUT scale was replaced with “fluoride-safe practices” without changing the meaning of the original construct. The adapted items were further reviewed by experts from Extension Education, Psychology, Environmental Science, Public Health, and Fluoride Research to improve their relevance and clarity. Initially, 85 items were developed under the seven constructs of the adapted UTAUT framework. These items were then taken forward for face validity and content validity assessment.

2.3 Face validity

The preliminary questionnaire was reviewed by subject matter experts to check whether the items were clear, easy to understand, and suitable for the study. Based on their suggestions, minor changes were made to the wording of a few statements before conducting the content validity assessment. No items were added or deleted during the face validity stage; the modifications were limited to improving the wording and clarity of the statements.

2.4 Content validity

For content validity, 25 experts from different disciplines, including Extension Education, Psychology, Environmental Science, Public Health, and Fluoride Research, were selected. The experts were chosen based on their experience and expertise in behavioural research, scale development, agricultural extension, fluoride contamination, environmental management, and public health. The preliminary questionnaire was circulated to the selected experts for evaluation. Each expert assessed the relevance of every statement using a five-point relevance continuum, where 1 = Non-relevant, 2 = Slightly Relevant, 3 = Moderately Relevant, 4 = Relevant, and 5 = Highly Relevant. The expert ratings were subsequently used to assess the content validity of the scale using Relevancy Weightage (RW), Mean Relevancy Score (MRS), and Overall Mean Relevancy Score (OMRS) (chowdhary et al., 2026; Kumari et al., 2026; Shukla et al., 2026). The present study followed the content validation approach that is widely adopted in Agricultural Extension research, where Relevancy Weightage (RW), Mean Relevancy Score (MRS), and Overall Mean Relevancy Score (OMRS) are commonly used to assess the relevance of newly developed items through expert judgment. The relevance ratings assigned by the experts were used to compute these indices for each item. Items satisfying the predetermined selection criteria, namely RW ≥ 0.80 and MRS greater than the OMRS, were retained for further analysis, whereas items failing to meet these criteria were discarded.

RW = MR + R + SWR + LR + NR MPS

MRS = MR + R + SWR + LR + NR n

OMRS = MR + R + SWR + LR + NR n x s

Whereas, RW = Relevancy Weightage, MRS = Mean Relevancy Weightage, OMRS = Overall Mean Relevancy Weightage, MR = Most relevant (5) R = Relevant (4), SWR = Somewhat Relevant (3), LR = Less Relevant (2), NR = Not Relevant (1); MPS = Maximum Possible Score (66 × 5 = 330); n = Number of judges (25); s = Number of Statements (66).

2.5 Pilot survey

After content validity, a pilot survey was conducted to assess the factor structure of the developed scale through Exploratory Factor Analysis (EFA). The survey was conducted in fluoride-affected villages that were not included in the main study area. The pilot survey was conducted in Nagaur district of Rajasthan, which was purposively selected because it is a fluoride-affected district and was not included in the main study area, thereby avoiding overlap with the respondents selected for the final survey. Out of the 15 blocks in the district, three blocks, namely Merta, Degana, and Makrana, were selected randomly. Subsequently, 11 villages were randomly selected from these blocks for conducting the pilot survey. Farmers residing in the selected villages and actively engaged in farming were interviewed using the structured questionnaire. Before the interview, the purpose of the study was explained to the respondents, and only those farmers who voluntarily agreed to participate were included in the survey. A total of 290 farmers were contacted, out of which 250 farmers responded to the questionnaire. Only farmers from fluoride-affected areas were included in the pilot survey. The sample size was decided based on the recommendation of at least five respondents per item for factor analysis (Costello and Osborne, 2005). Responses were collected using a five-point Likert scale ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The collected data were analysed using Principal Axis Factoring (PAF) with Direct Oblimin rotation.

2.6 Exploratory factor analysis

The retained items were analysed using Exploratory Factor Analysis (EFA) to identify the factor structure of the developed scale and to remove items that did not fit well with their respective constructs. Principal Axis Factoring (PAF) was used as the extraction method because the main objective of the study was to identify the common factors represented by the observed variables rather than simply reduce the data. Since the constructs of the adapted UTAUT model were expected to be related to each other, Direct Oblimin rotation was used. This rotation method allows the extracted factors to be correlated and is widely recommended for behavioural and social science research (Hair et al., 2019). The suitability of the data for factor analysis was assessed using the Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy and Bartlett’s Test of Sphericity. The retained items were evaluated based on factor loadings, communalities, and cross-loadings. Items that did not meet the predefined factor loading criterion or showed substantial cross-loadings were removed to obtain a meaningful factor structure (Worthington and Whittaker, 2006; Costello and Osborne, 2005).

The number of factors was assessed using the eigenvalue-greater-than-one criterion, scree plot, and parallel analysis. In parallel analysis, the observed eigenvalues were compared with the corresponding 95th-percentile eigenvalues obtained from randomly generated data, and factors were retained when the observed eigenvalue was higher than the random eigenvalue (Horn, 1965).

2.7 Reliability analysis

Following EFA, the internal consistency of each construct was assessed using Cronbach’s alpha and Corrected Item–Total Correlation (CITC) (Cronbach, 1951). Cronbach’s alpha values of 0.70 or above were considered indicative of acceptable reliability, while CITC values were examined to ensure that each retained item contributed adequately to the consistency of its respective construct.

3 Result

3.1 Item generation

The initial 85 items were developed by reviewing UTAUT, UTAUT2, and other related studies on technology adoption. The items were then modified to suit the agricultural context of fluoride-safe practices without changing the original meaning of the constructs.

3.2 Face validity

The preliminary questionnaire was reviewed by experts to ensure that the items were clear, easy to understand, and suitable for the study. Based on their suggestions, minor changes were made to the wording of some statements before conducting the content validity assessment. No items were added or deleted during the face validity stage. The modifications were limited to improving the wording, clarity, and readability of the statements to enhance their comprehensibility before the content validity assessment.

3.3 Content validity

Content validity was carried out to check the relevance of the developed items before the pilot survey. A total of 66 items were evaluated by a panel of 25 experts using Relevancy Weightage (RW), Mean Relevancy Score (MRS), and Overall Mean Relevancy Score (OMRS). Items with an RW value of ≥ 0.80 and an MRS value greater than the OMRS were retained. Based on these criteria, 49 items were selected, while 17 items were removed. The final set of 49 items was then used for the pilot survey and Exploratory Factor Analysis (EFA).

3.4 Suitability for factor analysis

The KMO value was 0.935, indicating that the sample was adequate for factor analysis. Bartlett’s Test of Sphericity was significant (p < 0.001), confirming that the data were suitable for Exploratory Factor Analysis (EFA) (see Table 3).

Table 3. KMO and Bartlett’s test.

Test/measureStatisticValue
Kaiser–Meyer–Olkin measure of sampling adequacy0.935
Bartlett’s test of sphericityApprox. chi-square14989.446
df1,176
Sig.0.000

3.5 Communalities

The extraction communalities ranged from 0.480 to 0.959, indicating that all retained items shared sufficient common variance with the extracted factors. As all values exceeded the recommended threshold of 0.40, the items were considered suitable for further analysis.

3.6 Factor correlation analysis

Since Direct Oblimin rotation was employed, the factor correlation matrix was examined to assess the relationships among the extracted factors (Table 4). The correlations among the seven factors ranged from −0.610 to 0.526, indicating moderate correlations among the extracted factors. These findings support the use of an oblique rotation method and suggest that the extracted factors are related while remaining conceptually distinct.

Table 4. Factor correlation matrix.

Factor correlation matrix
Factor1234567
11.000−0.455−0.1810.522−0.4590.2530.526
2−0.4551.0000.337−0.3560.2660.118−0.188
3−0.1810.3371.000−0.2130.056−0.085−0.319
40.522−0.356−0.2131.000−0.2870.4100.240
5−0.4590.2660.056−0.2871.000−0.423−0.610
60.2530.118−0.0850.410−0.4231.0000.496
70.526−0.188−0.3190.240−0.6100.4961.000

Extraction method: principal axis factoring.

Rotation method: Oblimin with Kaiser normalization.

3.7 Total variance explained

Exploratory Factor Analysis supported a seven-factor solution with eigenvalues greater than one, which was consistent with the theoretical structure of the adapted UTAUT model. The seven retained factors collectively explained 77.23% of the total variance. The decision to retain seven factors was based on the initial eigenvalues, theoretical expectations, and interpretability of the factor solution. To further examine factor retention, parallel analysis was conducted. The first seven observed eigenvalues were greater than the corresponding 95th-percentile random eigenvalues, whereas the eighth eigenvalue was lower than the random value, supporting the retention of seven factors (see Table 5).

Table 5. Total variance explained.

FactorInitial eigenvalue% varianceCumulative %
118.83338.00838.008
27.00413.86151.869
35.0449.93861.807
43.8957.38369.190
52.0513.76872.958
61.5292.59475.551
71.061.67677.227

The scree plot was also examined to further assess the number of factors to be retained (Figure 2). The plot showed a clear decline in eigenvalues up to the seventh factor, followed by a relatively flatter pattern, providing additional support for the seven-factor solution. The seven-factor structure was also consistent with the theoretically proposed constructs of the adapted UTAUT framework.

Scree plot for factor retention.
Figure 2. Scree plot for factor retention.

3.8 Pattern matrix

The pattern matrix revealed a clear seven-factor structure corresponding to the adapted UTAUT constructs. Based on the predefined criterion of a minimum factor loading of 0.70, most of the items demonstrated satisfactory loadings on their respective factors. A loading of 0.70 was adopted to retain only strongly loading items for the final scale. However, EE4 (0.607), EE5 (0.526), EE6 (0.687), SI1 (0.690), A1 (0.659), AA1 (0.643), and AA5 (0.636) did not meet the specified criterion and were excluded from the scale. The remaining items exhibited strong factor loadings ranging from 0.701 to 0.986, indicating satisfactory representation of their respective constructs (see Table 6).

Table 6. Pattern matrixa.

ItemFactor
1234567
PE10.986
PE20.948
PE30.899
PE40.923
PE50.890
PE60.905
PE70.936
EE10.701
EE20.837
EE30.744
EE40.607
EE50.526
EE60.687
EE70.713
SI10.690
SI20.942
SI30.897
SI40.743
SI50.812
SI60.789
SI70.801
FC10.828
FC20.887
FC30.919
FC40.835
FC50.771
FC60.774
FC70.819
A10.659
A20.804
A30.923
A40.755
A50.702
A60.755
A70.791
BI1−0.902
BI2−0.889
BI3−0.863
BI4−0.865
BI5−0.818
BI6−0.797
BI7−0.858
AA10.643
AA20.876
AA30.787
AA40.727
AA50.636
AA60.727
AA70.768

Extraction method: principal axis factoring.

Rotation method: Oblimin with Kaiser normalization.

a. Rotation converged in 10 iterations.

The Behavioural Intention items showed negative factor loadings in the pattern matrix. All items were positively worded and were scored in the same direction using a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree), and no reverse coding was applied. The negative loadings were due to the orientation of the extracted factor during Principal Axis Factoring with Direct Oblimin rotation and do not affect the interpretation of the construct. Since all Behavioural Intention items loaded strongly on a single factor, they were retained in the final scale.

3.9 Reliability

The internal consistency of the retained constructs was assessed using Cronbach’s alpha and McDonald’s omega. The Cronbach’s alpha values ranged from 0.882 to 0.979, while McDonald’s omega values ranged from 0.863 to 0.980, indicating good to excellent internal consistency across all constructs. Since the values were within the acceptable range, the final scale was considered reliable for measuring farmers’ behavioural intention towards the adoption of fluoride-safe practices (see Table 7).

Table 7. Reliability analysis of the final farmer-centric UTAUT scale.

ConstructNo. of ItemsCronbach’s AlphaMcDonald’s ωInterpretation
Performance expectancy (PE)70.9790.980Excellent
Effort expectancy (EE)40.8820.863Good
Social influence (SI)60.9530.949Excellent
Facilitating conditions (FC)70.9730.974Excellent
Attitude (A)60.9350.927Excellent
Behavioural intention (BI)70.9600.962Excellent
Adoption (AA)/Use Behaviour50.9690.959Excellent

The Item-Total Statistics were also examined to assess item redundancy. The results showed that deleting any individual item did not result in a meaningful improvement in Cronbach’s alpha; therefore, all retained items were considered appropriate based on both statistical evidence and conceptual relevance.

The average inter-item correlations for the constructs were also examined to assess item redundancy. The obtained values indicated satisfactory internal consistency without evidence of excessive item redundancy (Table 8).

Table 8. Final 42-Item Farmer-Centric UTAUT Scale.

Performance Expectancy (PE)Factor LoadingCronbach's Alpha
1. Using fluoride-safe practices will help increase crop productivity.0.9860.979
2. Adopting fluoride-safe practices will improve the quality of agricultural produce.0.948
3. Adopting fluoride-safe practices will improve soil health over time.0.899
4. Using fluoride-safe practices will improve the long-term sustainability of my farm.0.923
5. Adopting fluoride-safe practices will help protect the health of my family and consumers.0.890
6. Adopting fluoride-safe practices will improve my farm income in the long run.0.905
7. Overall, fluoride-safe practices will benefit my agricultural activities.0.936
Effort Expectancy (EE)
8. I would find fluoride-safe agricultural practices easy to understand.0.7010.882
9. Implementing fluoride-safe practices on my farm would be simple.0.837
10. It would be easy for me to become skilled in using fluoride-safe practices.0.744
11. The adoption process of fluoride-safe practices would be convenient for me.7.13
Social Influence (SI)
12. My family would support adoption of fluoride-safe agricultural practices.0.9420.953
13. Fellow farmers would encourage me to adopt fluoride-safe practices.0.897
14. Agricultural extension personnel encourage adoption of fluoride-safe practices.0.743
15. Farmers whose opinions I respect would approve adoption of fluoride-safe practices.0.812
16. Social discussions in my village influence my adoption decisions.0.789
17. Recommendations from trusted sources influence my decision to adopt fluoride-safe practices.0.801
Facilitating Conditions (FC)
18. I have access to the resources required to adopt fluoride-safe agricultural practices.0.8280.973
19. I have sufficient knowledge to adopt fluoride-safe practices.0.887
20. I can obtain technical guidance if I face problems.0.919
21. Information related to fluoride-safe practices is available to me.0.835
22. Training opportunities are available for learning fluoride-safe practices.0.771
23. Institutional support is available for adoption of fluoride-safe practices.0.774
24. Assistance is available whenever I need help in adoption.0.819
Attitude (A)
25. I believe adopting fluoride-safe practices would be beneficial.0.8040.935
26. I like the idea of adopting fluoride-safe agricultural practices.0.923
27. Adopting fluoride-safe practices would be a wise decision.0.755
28. I feel positive about fluoride-safe agricultural practices.0.702
29. Adopting fluoride-safe practices would be worthwhile.0.755
30. Fluoride-safe agricultural practices would create more problems than benefits.0.791
Behavioural Intention (BI)
31. I intend to adopt fluoride-safe agricultural practices in the near future.−0.9020.960
32. I plan to adopt fluoride-safe agricultural practices on my farm.−0.889
33. I am willing to adopt fluoride-safe agricultural practices.−0.863
34. I will make efforts to adopt fluoride-safe agricultural practices.−0.865
35. I am likely to adopt fluoride-safe agricultural practices if possible.−0.818
36. I will seriously consider adopting fluoride-safe practices.−0.797
37. I intend to use fluoride-safe practices whenever feasible.−0.858
Adoption (AA)/Use Behaviour
38. I regularly apply fluoride-safe practices in my farming activities.0.8760.969
39. I have adopted at least some fluoride-safe agricultural practices.0.787
40. I actively implement recommended fluoride-safe practices.0.727
41. I frequently use fluoride-safe methods in farming decisions.0.727
42. I have not adopted fluoride-safe agricultural practices.0.768

3.10 Final scale

Based on the results of content validity, exploratory factor analysis, and reliability analysis, the final farmer-centric UTAUT scale consisted of 42 items distributed across seven constructs. The retained items demonstrated satisfactory psychometric properties, including a well-defined factor structure and high internal consistency. Therefore, the final instrument was considered suitable for measuring farmers’ behavioural intention towards the adoption of fluoride-safe practices and can be used for further validation through Confirmatory Factor Analysis (CFA) using an independent sample.

4 Discussion

The present study was carried out to develop a farmer-centric UTAUT scale for measuring farmers’ behavioural intention towards the adoption of fluoride-safe practices. Although UTAUT has been widely used to study technology adoption, no scale was available to measure farmers’ behavioural intention towards fluoride-safe practices. Therefore, the original UTAUT model was adapted to the present study by modifying the statements according to the agricultural context. This approach is also supported by Venkatesh et al. (2003), who suggested that the wording of the original statements can be changed according to the study context without changing the meaning of the constructs. The initial statements were prepared after reviewing UTAUT, UTAUT2 and other related studies on technology adoption. The statements were modified by replacing the word “system” with “fluoride-safe practices” so that they could be easily understood by farmers. Similar adaptation of UTAUT statements has also been reported in studies conducted in different application areas (Venkatesh et al., 2012; Dwivedi et al., 2020). Before conducting the pilot survey, the developed statements were evaluated by experts to examine their relevance. Based on the opinion of the experts, only those statements that satisfied the predefined content validity criteria were retained for further analysis. Expert evaluation before factor analysis has also been recommended by Lawshe (1975) and Boateng et al. (2018) during scale development.

The EFA results supported the proposed seven constructs of the adapted UTAUT model. Most of the statements loaded well on their respective constructs, while a few statements with lower factor loadings were removed. This resulted in a clear factor structure representing Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Attitude, Behavioural Intention and Adoption. Similar findings have also been reported in previous studies where UTAUT was adapted for different technologies and different user groups (Venkatesh et al., 2003; Williams et al., 2015). One important modification made in the present study was the inclusion of Attitude as an additional construct. Although this construct was not included in the original UTAUT model, many later studies have reported that attitude plays an important role in behavioural intention, especially when people voluntarily decide whether to adopt a new practice. In the present study, farmers’ decision to adopt fluoride-safe practices is also influenced by their personal opinion and belief about these practices. Therefore, the inclusion of Attitude was considered appropriate (Dwivedi et al., 2016, 2020; Tamilmani et al., 2021). The reliability analysis also showed that all the retained constructs had good internal consistency. The Cronbach’s alpha values of all constructs were higher than the recommended value of 0.70, indicating that the retained statements consistently measured their respective constructs. Similar levels of reliability have been reported in previous UTAUT studies (Nunnally and Bernstein, 1994; Venkatesh et al., 2003).

The developed scale has practical significance beyond its psychometric properties. It provides a context-specific instrument to understand the behavioural factors influencing farmers’ intention to adopt fluoride-safe practices in fluoride-affected areas. The information generated through this scale can help researchers identify the factors that encourage or hinder adoption and support extension professionals in designing need-based awareness programmes, training activities, and capacity-building interventions. Since the adoption of fluoride-safe practices contributes to improving human health, livestock well-being, and sustainable agricultural production, the developed scale may also support evidence-based planning and decision-making by extension agencies and policymakers.

Although the present study focuses on developing a farmer-centric UTAUT scale, farmers’ behavioural intention is also influenced by the broader social and institutional environment. Previous studies have shown that community participation, institutional support, collective learning, stakeholder collaboration, and institutional trust play an important role in encouraging farmers to adopt sustainable agricultural practices (Karimi et al., 2025; Karimi et al., 2026). Therefore, alongside improving individual behavioural intention, strengthening extension services, local institutions, and participatory approaches can create a more supportive environment for promoting fluoride-safe practices in fluoride-affected areas.

The final scale consists of 42 statements under seven constructs and can be used to measure farmers’ behavioural intention towards the adoption of fluoride-safe practices in fluoride-affected areas. However, the developed scale was validated only through Exploratory Factor Analysis (EFA). Therefore, further validation using Confirmatory Factor Analysis (CFA) with an independent sample is recommended before applying the scale in future studies.

4.1 Policy and practical implications

The developed scale can be used by agricultural extension organizations, rural advisory services, researchers, and policymakers to identify the behavioural factors influencing farmers’ adoption of fluoride-safe practices. Understanding these factors may help in designing need-based awareness programmes, training activities, and extension interventions according to farmers’ behavioural needs. The findings may also support local governments in planning targeted interventions for fluoride-affected areas by strengthening institutional support, community participation, stakeholder collaboration, and participatory extension approaches to promote the adoption of fluoride-safe practices (Karimi et al., 2025; Karimi et al., 2026).

5 Conclusion

The present study was carried out to develop a farmer-centric UTAUT scale for measuring farmers’ behavioural intention towards the adoption of fluoride-safe practices in fluoride-affected areas. The original UTAUT statements were modified according to the agricultural context, and Attitude was included as an additional construct to better understand farmers’ behavioural intention. The developed items were first reviewed through face validity and content validity. Based on the opinion of experts, 49 items were retained for the pilot survey. Further, Exploratory Factor Analysis was carried out to identify the factor structure of the scale, and items with factor loadings below the selected criterion were removed. Reliability analysis showed that all the retained constructs had acceptable internal consistency. Finally, a farmer-centric UTAUT scale consisting of 42 items under seven constructs was developed. The contribution of the present study lies in developing the first farmer-centric UTAUT-based measurement instrument for assessing behavioural intention towards the adoption of fluoride-safe practices, thereby providing a context-specific tool for agricultural extension research in fluoride-affected areas.

The developed scale can be used to measure farmers’ behavioural intention towards the adoption of fluoride-safe practices in fluoride-affected areas. It can also be used by researchers and extension professionals to assess farmers’ behavioural intention and support future research and extension programmes related to fluoride-safe practices. The developed scale may also support extension organizations, rural advisory services, and policymakers in designing behavioural interventions and promoting the adoption of fluoride-safe practices in fluoride-affected areas. Since the present study includes only Exploratory Factor Analysis, further validation through Confirmatory Factor Analysis using an independent sample is recommended before using the scale in large-scale studies.

The present study represents the initial stage of scale development and provides preliminary evidence based on content validity, exploratory factor analysis, and internal consistency reliability. Therefore, further validation through Confirmatory Factor Analysis using an independent sample is recommended. Future studies should also examine additional psychometric properties, including construct validity, criterion or predictive validity, test–retest reliability, and measurement invariance across different farmer groups before the scale is applied in large-scale research, agricultural extension, or policy evaluation.

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Footnotes

Edited by: Mouloudj Kamel, University of Medea, Algeria

Reviewed by: Vahid Karimi, Tarbiat Modares University, Iran

Kassa Tarekegn Erekalo, University of Copenhagen, Denmark

Utkal Sapkota, Wageningen University and Research, Netherlands

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.

Ethics approval

Ethical approval was not required for the studies involving humans because this study involved adult farmers who voluntarily participated in the survey. Before data collection, the purpose of the study was explained to all participants, and verbal informed consent was obtained. Participation was entirely voluntary, and respondents were free to withdraw from the survey at any stage. The confidentiality and anonymity of the participants’ responses were maintained throughout the study. As this was a questionnaire-based survey involving adult participants and no personal or sensitive information was collected, formal ethical approval was not required in accordance with the institutional requirements. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements because The study involved a voluntary questionnaire-based survey with adult farmers. Verbal informed consent was obtained from all participants before data collection, and no personally identifiable or sensitive information was collected.

Author contributions

VY: Writing – review & editing, Writing – original draft, Formal analysis, Software, Visualization, Conceptualization, Methodology. KG: Supervision, Writing – review & editing, Data curation. MS: Writing – review & editing.

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Citation

Yadav, V., et al. (2026). Development of a farmer-centric UTAUT scale for measuring farmers’ behavioural intention towards the adoption of fluoride-safe practices. Frontiers in Sustainable Food Systems, Volume 10. https://doi.org/
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10 - 2026

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Copyright © 2026 Yadav, Ghadei and Shukla.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Vicky Yadav

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