Toward a standardised measure of farmers’ research orientation: scale development and validation

Introduction

Farmers have always lived in changing environments with uncertainty and disturbances (Kummer et al., 2017) [12] . The farming sector is always undergoing a rapid transformation due to changes in climate parameters, marketing systems, and technological advancements. To meet these challenges, continuous research and development on innovative solutions is important. The successful generation of agricultural innovations depends not only on scientific research institutions but also on farmers' orientation towards research, experimentation and innovation. Farmers can learn through experimentation, which is a crucial tactic for responding to change and enhancing adaptive farm management. By conducting and monitoring experiments, farmers can develop a deeper understanding of system dynamics, increase the number of options available in the event of a change, and improve their management practices based on the knowledge acquired (Darnhofer et al., 2010) [4] . The ability of farmers to search and engage with scientific knowledge, experiment with new ways to solve field-level problems, participate in on-farm research trials, and collaborate with agricultural research institutions plays a pivotal role in enhancing their capacity to invent and innovate. This can be called as the research orientation of farmers. It refers to the extent to which a farmer is oriented towards the endeavour of discovering new knowledge or collating existing facts through scientific study or systematic critical investigation (Devarakonda, 2015) [5] . But, according to Waters-Bayer et al. (2015) [23] , 'farmer-led research' is a process in which farmers, together with other support agents, for example, formal researchers, extensionists, investigate possible ways to improve the livelihoods of local people in the realm of agriculture and natural resource management. Among farmers, a strong orientation towards research indicates a favourable attitude towards experimentation, critical thinking in problem solving, and evidence-based decision making in farming. The historical development of locally adapted farming systems worldwide has been the result of continuous autonomous experimentation by farmers (Hoffmann et al., 2007) [7] . According to Kummer et al. (2017) [12] , farmers' experiments can be characterised as independent actions of farmers to attempt or implement something new at the farm, including assessing the success or failure of their own practices. Experiments have produced several agricultural breakthroughs, allowed farmers to adjust their farms to changing conditions, and increased local knowledge. These experiments are influenced by various intervening factors such as environmental, social and personal conditions (Leitgeb et al., 2014) [13] , and the personal motives can be intrinsic, like interest or curiosity, and extrinsic, like confronting challenges and problem solving (Kummer et al., 2017) [12] . www.extensionjournal.com According to Waters-Bayer et al. (2015) [23] , farmer-led research has led to greater food and nutrition security through higher and more dependable yields, higher household incomes, bringing more benefits to medium farmers, leading to increased crop diversity. Moreover, this farmer-led research has many such profound, self-reinforcing and longlasting impacts. In this backdrop, measuring farmers' research orientation gains importance to understand their inventive capacity, innovativeness and participation in the agricultural knowledge development process. Recognizing the importance of this trait, the present study aimed to develop a scale to measure farmers' research orientation in a systematic and reliable manner. By measuring farmer's research orientation, stakeholders in agricultural research, extension, education and policy can better understand the farmer's readiness for critical problemsolving, engagement in knowledge generation processes, participation in research activities, and scientific temper. This understanding can, in turn, help them tailor targeted strategies to nurture farmer-led research and innovation.

Methodology

In this study, research orientation is operationally defined as the extent to which an individual is inclined towards discovering new knowledge, critically evaluating existing knowledge and practices, and synthesising information through scientific inquiry or critical investigation. Likert's summated rating method (Likert, 1932) [14] , which has been widely used in agricultural extension research (Husain, 2007; Vivek and Bonny, 2023; Matla et al., 2025) [8,22,15] , was used to develop the scale. The method consists of a series of steps, including statement collection, relevancy testing, item analysis, and standardisation of the scale. Identifying and collecting statements or items related to research orientation is the first stage in a scale development process. Fifty-five statements, both positive and negative, regarding farmers' research orientation were generated based on literature review and discussions with subject-matter experts. Subsequently, the selected statements were revised in accordance with Edwards' (1957) [6] criteria for statement construction. Statements that were factual, contained double negatives, were irrelevant, or did not conform to Edwards' criteria were eliminated. Ultimately, 49 statements were retained for further analysis in the development of the research orientation scale for farmers. To assess the relevance of the statements, a Strongly Disagree with scores from 5 to 1, respectively; scoring was reversed for negative statements. Respondents were then arranged in descending order based on their total scores, and the highscoring and low-scoring groups were identified, each comprising 25 percent of the sample. These two groups served as criterion groups for evaluating the individual statements. Statements with higher t-values were selected for inclusion in the final scale. To standardise the scale, its validity and reliability were examined. A scale is considered reliable if it consistently yields the same results when applied repeatedly to the same sample. Here, Cronbach's alpha suggested by [3] panel of agricultural extension judges Cronbach (1951), and followed by many comprising experts from ICAR institutes and State Agricultural Universities (SAUs) researchers like Poornima and Husain, 2021; Vivek and Bonny, 2023; Boora et al., [19,22,2] was constituted. The 49 statements were 2024, and the split-half method were sent to the judges by mail and, where possible, delivered in person. Judges were asked to rate each statement on a five-point scale ranging from "most relevant" to "irrelevant," assigning scores of 5, 4, 3, 2, and 1, respectively. Statements with a relevance score above 80 percent of the threshold value (Preethi et al., 2014; Meethal and Thomas, 2024; Matla et al., 2025) [18,16,15] were retained, resulting in a final set of 30 statements. These statements underwent a comprehensive item analysis using the t-ratio approach suggested by Edwards (1957) [6] and subsequently followed by Ramakrishnan et al., 2020; Raj and Thomas, 2022; Meethal and Thomas, 2024 [21,20,16] . For this purpose, the statements selected after the judges' relevancy rating were administered to a total of 40 farmers. Responses were collected on a five-point scale -Strongly Agree, Agree, Undecided, Disagree, employed to measure the internal consistency of the scale. In the case of the split-half method of reliability checking, the final statements were divided into two halves by the oddeven method. The total score for each half was calculated separately. Then the split-half test reliability coefficient was calculated by the Pearson product-moment method, and the reliability coefficient of the whole test was estimated using the Spearman-brown prophecy formula (Kaplan and Saccuzzo, 2001) [11] . Validity refers to the extent to which an instrument accurately measures the construct it is intended to measure. A panel of experts considering the relevance of individual items within an instrument can be adopted as one of the approaches to examine content validity (Almanasreh et al., 2019) [1] . The content validity of statements was assessed by subject experts' opinions through a relevancy test (

Results and Discussion

A pool of 55 statements on the topic under study was initially edited and screened using the criteria enunciated by Edwards (1957) [6] , which reduced the number to 49 statements. These statements were then subjected to a relevancy test. Statements with a relevancy percentage above 80 percent were retained, yielding a final set of 30 statements. Item analysis was subsequently done using the t-ratio method suggested by Edwards (1957) [6] to identify the statements capable of discriminating between respondents with high and low research orientation. Thus, out of 40 respondents to whom the statements were administered for the item analysis, 10 respondents with the highest and 10 respondents with the lowest scores were selected to form the criterion groups. These two groups were then used to evaluate the discriminatory power of each statement. Items with a t-value greater than 2.87 at one percent level of significance were selected as final statements. Nineteen statements met this criterion. However, to further enhance the robustness of the scale, only those items with comparatively higher t-values (t > 4.5) were retained in the final scale. The 30 statements used for item analysis, along with their corresponding t-values and the items selected for inclusion in the final established through the content validity method, based on expert judgement. www.extensionjournal.com

Reliability

Cronbach's alpha is computed by comparing the variances for all individual item scores with the variance of the total score (Kaewkungwal, 2023) [10] .

The formula is α = (k/(k-1))(1-∑σyi2/σx2) k = the number of items σyi2 = the variance of item i, σx2 = the variance of the observed total scores Here, in this study, k = the number of items = 13 σyi2 = the variance of item i = 18.52 σx2 = the variance of the observed total scores = 251.84 Hence α = 0.97 The Cronbach's alpha value obtained was 0.97, which is close to 1, indicating that the scale is highly reliable and acceptable.

In the split-half method, the total score for each half of the scale was calculated separately. The correlation between the two halves was then computed using the Pearson productmoment method, and the reliability coefficient of the whole test was estimated using the Spearman-brown prophecy formula:

Reliability coefficiency (rSB) = 2 * rhh 1 + rhh Scale, are presented in Table 1. The final Scale consists of 13 items.

Standardisation of the scale

The reliability of the scale was examined using the split-half method and Cronbach's alpha method, while its validity was Where, rhh = Reliability coefficient of the half scale = 0.97 Reliability Coefficient (𝑟𝑆𝐵) = 0.98, which is close to 1, indicating high reliability of the scale.

Validity

The scale's validity was established through expert content validation by agricultural extension specialists. It was considered valid as it adequately represented the construct, supported by literature review and expert judgment. Additionally, the computation of t-values ensured the statements' high discriminating power. Therefore, the scale was accepted as a valid instrument for measuring the research orientation of farmers.

Conclusion

The present study successfully developed and standardised a scale to measure farmers' research orientation using the Likert method of summated rating. The final scale comprised 13 items that demonstrated strong content validity as established through expert evaluation, indicating that the construct was adequately represented. The reliability of the scale was confirmed through high internal consistency, as evidenced by Cronbach's alpha and split-half reliability coefficients. Furthermore, the significant t-values obtained during item analysis ensured the strong discriminating power of the statements. Taken together, these findings indicate that the developed 13-item scale is psychometrically sound and can be reliably used as a valid instrument for assessing the research orientation of farmers.

Acknowledgement

AcknowledgementThe authors acknowledge Kerala Agricultural University for providing the necessary support required for this study.

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