Best-Worst Scaling in Agricultural Research: A Review of Methods and Applications

November 2025, Volume 1, Issue II. doi: 10.65287/josta.202510.BD6A Best-Worst Scaling in Agricultural Research: A Review of Methods and Applications Anjana Bivas T Kerala Agricultural University Pratheesh P Gopinath* Kerala Agricultural University Manju Mary Paul Kerala Agricultural University Smija P K Kerala Agricultural University Durga A R Kerala Agricultural University Abstract Understanding stakeholder preferences is essential for designing effective agricultural policies, promoting technology adoption, and aligning market strategies with consumer demand. Best–Worst Scaling (BWS) is a robust stated preference method that captures preferences by choosing the most and least important attributes within a choice set. This review highlights the statistical foundations of BWS, including Random Utility Theory, and common analytical models such as Multinomial Logit, Latent Class Analysis, Random Parameter Logit, and Hierarchical Bayesian frameworks to estimate preference heterogeneity. A bibliometric analysis of BWS applications in agricultural research highlights increasing adoption, publication trends, and leading contributors in the field. The findings reveal that BWS provides actionable insights into consumer and farmer preferences, informing product development, policy formulation, and sustainable decision-making, and demonstrates its growing relevance as a rigorous tool for evidence-based agricultural research. Keywords: Best-Worst Scaling, Preference elicitation, Choice models, Consumer choices, Agricultural decision-making. 2Best-Worst Scaling in Agricultural Research: A Review of Methods and Applications 1. Introduction Agriculture serves as the foundation of global food systems, supporting human sustenance, economic stability, and environmental sustainability. Its effectiveness largely depends on the decisions and preferences of farmers, consumers, and policymakers. Choices such as selecting resilient crop varieties and inputs, implementing sustainable farming practices, and responding to consumer demand influence productivity and yields while shaping ecological outcomes, biodiversity conservation, and the equitable allocation of resources. Moreover, understanding these preferences enables producers and policymakers to align agricultural outputs with consumer needs, ensuring that market demand is met efficiently and sustainably.Gaining insights into stakeholder preferences is therefore essential for designing effective agricultural policies, promoting technology adoption, and developing marketing strategies (Custodio et al. 2023; Sawassi et al. 2025). Over the years, several preference elicitation and scaling techniques, such as Likert scales, rating, ranking, and pairwise comparison methods, have been widely employed in agricultural and consumer research. Likert and rating scales capture perceived importance or agreement, while ranking methods require respondents to order all items simultaneously, which can become cognitively demanding as the number of attributes increases. Pairwise comparisons simplify the task but still fail to reflect the relative importance among multiple competing attributes. As noted by Parvin (2016), these methods tend to be cognitively more demanding and are susceptible to response and scale-use biases, which can compromise the reliability and comparability of results across respondents. Best-Worst Scaling (BWS) is a stated preference method, developed by Jordan J Louviere in 1987, that addresses these limitations by asking respondents to identify both the most and least important items within a choice set. Unlike traditional rating or ranking methods, which require evaluating all items simultaneously, BWS divides the task into smaller, more cognitively manageable parts, simplifying the process and helping respondents make more distinct trade-offs between attributes(Erdem et al. 2012). This approach captures the extremes in the inclinations of respondents and reduces the cognitive burden on respondents, making it easier for the surveyor to obtain more accurate and reliable responses with reduced biases (Kiritchenko and Mohammad 2017), (Burton et al. 2021). Three main variants of BWS, Case 1 (Object case), Case 2 (Profile case), and Case 3 (Multiprofile case), facilitate identifying preferences according to the context and the nature of items or profiles to be evaluated. Case 1 is the simplest form of BWS, allowing respondents to choose the best and worst items in a list and make a relative ranking of the items Flynn and Marley (2014). Cases 2 and 3 deal with profiles which are combinations of levels of attributes. In the profile case, choices are made within the individual profiles, whereas in the multi-profile case, choices are made between whole profiles (Louviere et al. 2015;Cheung et al. 2016). While previous reviews have examined the methodological evolution and applications of BWS across disciplines (Schuster et al. 2024;Hollin et al. 2022;Beres et al. 2024), most have centred on health and policy domains. Comprehensive analyses focusing specifically on the agricultural sector remain limited. The present review aims to address this gap by systematically examining the use of BWS within agricultural research, highlighting methodological developments, thematic areas, and conceptual contributions. By integrating evidence and perspectives from existing studies, the review presents a coherent narrative on the expanding Journal of Sustainable Technology in Agriculture 3 role of BWS in shaping preference-based research within agriculture. 2. Statistical foundations of BWS The Random Utility Theory (RUT) underpins the fundamental principle of BWS Beres et al. (2024), which states that individuals make their choices based on the perceived utility characteristics or the characteristics of a product, which comprises observable attributes and some unobservable random component, as shown in Equation 1. 𝑈𝑖𝑗 =𝑉𝑖𝑗+𝜀𝑖𝑗 (1) where Uij is the total utility of the jth item for the ith consumer, Vij denote the utility from the observed characteristics, and �ij is the random component. The stochastic component implies that while exact choices cannot be predicted, the probability of each alternative being selected can be estimated statistically Adikari and Diawara (2024). In the context of BWS, this framework can be used to compare the ‘best’ and ‘worst’ choices of an individual.By requiring respondents to identify both extremes of preference within each choice set, BWS captures a fuller range of the utility distribution compared to traditional choice experiments. According to RUT, an item’s relative preference compared to others depends on how frequently it is chosen (Louviere et al. 2013). Recent methodological developments have further extended RUT to account for individual-level preference heterogeneity, allowing researchers to better understand differences in choices across respondents (Holmes et al. 2017). In agricultural research, RUT and BWS provide a framework for examining how different stakeholders evaluate competing options such as crop varieties, input combinations, market choices, or policy measures, based on perceived benefits and trade-offs. By linking observable attributes like yield, price, sustainability, and quality with individual preferences, BWS enables researchers to quantify the relative importance of these factors, offering valuable insights into agricultural decision-making. 3. Experimental Designs in BWS The efficiency of a BWS study depends critically on the construction of choice sets in the questionnaire. Asking respondents to rank a large number of items can create cognitive strain and reduce data quality. Parvin (2016) noted out that it is challenging for the respondents to rank more than seven items in a single choice set. To address this, BWS organizes the items into smaller and manageable choice sets, typically containing three to six items each, allowing respondents to make meaningful comparisons without fatigue. A statistically efficient design ensures that each item or attribute level appears an equal number of times across all choice sets, and that every possible pair of items co-occurs equally often with similar frequency Lee et al. (2008). This balance and orthogonality in the design facilitate the derivation of precise and reliable preference estimates. 4Best-Worst Scaling in Agricultural Research: A Review of Methods and Applications Early BWS studies used 2Jdesigns Flynn and Marley (2014) to generate choice sets, where J represents the number of items. However, as the number of items increases, the number of possible combinations grows exponentially, making such designs impractical for real-world surveys and burdensome for respondents. To overcome this challenge, recent studies employ Balanced Incomplete Block Design (BIBD) to generate combinations of items in each choice set, as it ensures the balanced occurrence and co-occurrence of objects. This approach is commonly used in the BWS object case (Case 1), where individual items are evaluated directly by respondents, allowing for manageable questionnaires without compromising statistical efficiency. In profile and multi-profile cases, the focus shifts from single items to combinations of attributes that define a product or alternative. Here, the profiles are created using Orthogonal Arrays (OA). If there are K attributes, each with Lk(k = 1, 2, …, K) levels, an LKOA can be used to create profiles while maintaining orthogonality. Each profile is considered as a choice set in BWS case 2, while in Case 3 multiple profiles are combined into choice sets using BIBD. This approach allows researchers to estimate the relative importance of individual attributes and their levels, while keeping the task manageable for respondents (Aizaki and Fogarty 2019; Aizaki 2021). 4. Statistical Models for Analysis Depending on the complexity of the data and objectives of the research, different analytical models are employed to extract insights from best–worst choices. These models range from simple counting approaches, which provide a basic understanding of preference patterns, to more sophisticated econometric models capable of capturing heterogeneity in preferences. 4.1. Counting approach The counting method, which involves the frequencies of each item being selected as ‘best’ and ‘worst,’ is a simple yet effective approach used to analyse BWS data Burns et al. (2022). The Best-Worst (B-W) score is obtained by subtracting the number of times an item is marked as worst (W�) from the number of times it is marked as best (B�), which is then divided by the total number of respondents (n) and the frequency of an item appeared in each choice set (r) to get the standardised score (Equation 2;Equation 3) (Massey et al. 2015;Yin et al. 2023). The Square root B-W score (Equation 4) and Standard B-W score (Equation 5) also represent the preferences Torok et al. (2023). 𝐵−𝑊 𝑆𝑐𝑜𝑟𝑒=𝐵𝑖−𝑊𝑖(2) Standard 𝐵−𝑊 score =𝐵𝑖−𝑊𝑖 𝑟×𝑛 (3) Sqrt. 𝐵−𝑊 score =√𝐵𝑖 𝑊𝑖(4) Journal of Sustainable Technology in Agriculture 5 Standard Sqrt. 𝐵−𝑊 score =Sqrt. 𝐵−𝑊 score max(Sqrt. 𝐵−𝑊 score)(5) 4.2. Modelling approaches Statistical models grounded in econometric theory and probabilistic frameworks enable rigorous estimation of preference parameters. Modelling approaches provide greater analytical precision and account for response heterogeneity, compared to count-based methods. These models estimate the likelihood of an individual choosing a particular item as ‘best’ or ‘worst’. The probabilistic model represented in Equation 6forms the theoretical foundation for the statistical models used in the analysis of BWS data. It considers a finite set of alternatives and estimates the probability of selecting an item as best, worst, or jointly as a best–worst pair based on underlying choice processes (Marley and Louviere 2005). BW𝑋(𝑥,𝑦)= 𝐵𝑋(𝑥)𝑊𝑋(𝑦) ∑ 𝑟,𝑠∈𝑋 𝑟≠𝑠 𝐵𝑋(𝑟)𝑊𝑋(𝑠) (6) where BX(x) is the probability that the alternative x is chosen as best in X, WX(y) is the probability that the alternative y is chosen as worst in X, and BWX(x, y) is the joint probability of the alternative x is chosen as best in X and the alternative y � x is chosen as worst in X. Advanced models can capture preference heterogeneity across respondents that arises due to geographic, socioeconomic, or behavioural factors. This can be used in agricultural studies to identify the difference in choices of farmers and consumers based on farm size, access to resources, and individual-level factors. Multinomial Logit Model (MNL) MNL is based on the assumption that the preferences of individuals in a population are homogeneous. The joint probability of selecting an item as best and another as worst within a choice set is used to estimate the preference for that item (Marley et al. 2015). Conceptually, the MNL model represents the probability of choosing a specific best–worst pair based on the relative utilities of the available items in a choice set. This probabilistic framework allows the model to capture the trade-offs respondents make when identifying their most and least preferred items. The resulting estimates provide a population-level measure of preference intensity of each item compared to a reference, making the MNL a fundamental model for advanced analyses of best–worst data (Cheung et al. 2019). Latent Class Analysis (LCA) LCA is a cluster analysis method used to uncover the unobserved heterogeneity among respondents when observable characteristics alone cannot form homogeneous groups. Based on this, respondents will be categorised into different latent classes sharing hidden attributes that 6Best-Worst Scaling in Agricultural Research: A Review of Methods and Applications can affect the selection. The optimal number of clusters is determined by statistical fit criteria such as Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), and McFadden’s Pseudo R-squared (�²), which are commonly used to assess model performance (Cheung et al. 2019;Barrowclough et al. 2023;Merlino et al. 2025). Hierarchical Bayesian Model (HB) HB models capture both within-respondent and across-respondent heterogeneity, producing individual-specific preference estimates (Lagerkvist et al. 2012;Yeh et al. 2020). Lagerkvist et al. (2012) demonstrated that the HB model outperforms the LCA by capturing both betweenand within-respondent preference variation. Its ability to estimate individual-level parameters from limited data makes it particularly useful for analysing best–worst choices. Random Parameter Logit (RPL) or Mixed Logit (MXL) Model RPL model provides a more accurate depiction of real-world choice behaviour by allowing for random differences in individual preferences and accounting for correlations among unobserved factors (Viciunaite 2023). It is a generalisation of MNL, used to estimate the individual heterogeneity in preferences. Unlike the MNL, which assumes uniform preferences across respondents, the RPL allows parameters to vary randomly within the population, implying that different people have different preferences. The preference heterogeneity can be estimated by finding the standard deviation of each parameter’s distribution (Cheung et al. 2019). Paired, Marginal, and Marginal Sequential Models for Profile Case Three different choice modelling methods, paired, marginal, and marginal sequential models, are used to analyse the responses obtained from a BWS case 2 survey. All models assume that each attribute level provides some utility to the respondent, but differ in how the best and worst levels are determined. The utility of the level chosen as worst is considered the negative of the utility of the level selected as best. The paired model identifies that the chosen best–worst pair represents the largest utility difference among all possible pairs in the set. The marginal model assumes that the best level has the maximum utility and the worst level the minimum utility among all levels, whereas in the marginal sequential model, the best level has the maximum utility among all levels and the worst level the minimum utility among the remaining levels. In all three models, the estimated coefficients indicate the relative preference of respondents for each attribute level, providing insights into the trade-offs made when identifying the most and least important options (Aizaki and Fogarty 2019). 5. Bibliometric Insights into BWS in Agriculture A bibliometric analysis was conducted using the bibliometrix package (Aria and Cuccurullo 2017) in R to examine studies employing BWS to elicit choices in agricultural contexts. The Dimensions database was selected for its broad coverage of multidisciplinary research (Hook Journal of Sustainable Technology in Agriculture 7 et al. 2018). Publications from 2011 to 2025 were retrieved using the search string: (“BestWorst Scaling” OR “BWS”) AND (“agriculture” OR “farm” OR “food”). The search was limited to peer-reviewed journal articles and conference papers published in English, ensuring the inclusion of relevant and high-quality academic outputs. Although larger datasets can be obtained from other databases, the export restrictions in these platforms limit their use for the present analysis. The selection of Dimensions thus ensured consistency, transparency, and replicability of the bibliometric workflow. After screening for relevance and removing duplicates, a total of 75 documents from 54 sources were retained for analysis. The chosen timespan captures both the emergence and evolution of BWS applications in agriculture, beginning with the earliest identifiable studies in 2011. The relatively small corpus reflects the recent adoption of BWS in this field and the method’s growing recognition as a robust alternative to traditional preference elicitation approaches. This also indicates a research gap and an opportunity for further exploration of BWS across diverse agricultural decision-making contexts. These records were analysed to interpret the research trends, current status, thematic distribution, advancements, and interrelationships among studies in this field. The analysis revealed an increasing trend in the scientific production, as shown in Figure 1, with an overall growth rate of 13.65% during the study period. The highest number of publications occurred in 2025, followed by 2020. From 2011 to 2017, the publication activity remained relatively stable and gained acceleration from 2018 onward, indicating a growing interest in BWS-based agricultural studies. Figure 1: Annual scientific production A total of 27 countries across the world have engaged in agricultural research employing BWS during this period. Japan (𝑛=15) recorded the highest number of publications, followed by Italy (𝑛=13) and the USA(𝑛=12). Figure 2illustrates the publication output of all the contributing countries, showing the trends in their research activity. The results indicate that while a few countries, particularly Japan, Italy, the USA, Germany, and the UK, dominate the field, several others are gradually adopting BWS in agricultural studies, reflecting its growing global relevance. 8Best-Worst Scaling in Agricultural Research: A Review of Methods and Applications Figure 2: Country-wise distribution of publications A total of 221 researchers contributed to the 75 examined articles, of which four were singleauthored. The average number of authors per article is 3.63. Out of the total, 31 authors have published more than one article. Borra and Massaglia (𝑛=6) are the authors with highest number of publications. Merlino (𝑛=5), Aizaki (𝑛=4), Blanc (𝑛=3), Madureira (𝑛=3), Nunes (𝑛=3), Peano (𝑛=3), Ujiie (𝑛=3), Veiga (𝑛=3), and Yang S (𝑛=3) are the other relevant authors in this field, as given in Figure 3. An international co-authorship of 37.33% was identified, indicating the strong level of cross-country collaboration among the authors. The reviewed studies were published in 54 journals. The most relevant journals among them were Foods (𝑛= 6), Journal of Dairy Science (𝑛=4), Agricultural Economics (𝑛=3), Food Quality and Preference (𝑛=3), Journal of Environmental Management (𝑛=3), Sustainability (𝑛=3), Agribusiness (𝑛=2), Agriculture (𝑛=2), Australian Journal of Agriculture (𝑛=2), British Food Journal (𝑛=2), and Land Use Policy (𝑛=2). The citations of the articles were analysed and found that, on an average, each article had 19.8 citations, with an average of 3.11 citations per document per year. Eight studies had more than fifty citations, with (Dekhili et al. 2011) recording the highest (140), followed by (Dumbrell et al. 2016) with 109 and (Massaglia et al. 2019) with 101. The citation network (Figure 4) revealed the interrelations among influential studies, highlighting the focal role of (Dekhili et al. 2011) in connecting multiple research clusters within the field. The bibliometric findings reveal a steady growth in the use of BWS within agricultural research over the past fifteen years. The method’s expanding adoption across countries and journals reflects its growing credibility as a reliable approach for capturing stakeholder preferences and supporting evidence-based decision-making. Although the number of retrieved publications is modest, this primarily reflects database-specific constraints and the relatively Journal of Sustainable Technology in Agriculture 9 Figure 3: Most relevant authors Figure 4: Network visualization of citations 16 Best-Worst Scaling in Agricultural Research: A Review of Methods and Applications References Adikari S, Diawara N (2024). “Utility in Time Description in Priority Best–Worst Discrete Choice Models: An Empirical Evaluation Using Flynn’s Data.” Stats,7(1), 185–202. doi: 10.3390/stats7010012. URL https://doi.org/10.3390/stats7010012. 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Journal of Sustainable Technology in Agriculture 23 Affiliation: Anjana Bivas T Agricultural Statistics College of Agriculture, Vellayani Thiruvananthapuram, Kerala India E-mail: [email protected] Pratheesh P Gopinath* Agricultural Statistics College of Agriculture, Vellayani Thiruvananthapuram, Kerala India E-mail: [email protected] URL: https://kau.in/people/sub-lt-dr-pratheesh-p-gopinath Manju Mary Paul Agricultural Statistics College of Agriculture, Vellayani Thiruvananthapuram, Kerala India E-mail: [email protected] URL: https://kau.in/people/manju-mary-paul Smija P K Agricultural Extension College of Agriculture, Vellayani Thiruvananthapuram, Kerala India E-mail: [email protected] URL: https://kau.in/people/smt-smija-pk Durga A R Agricultural Economics College of Agriculture, Vellayani Thiruvananthapuram, Kerala India E-mail: [email protected] URL: https://www.kau.in/people/durga-ar Journal of Sustainable Technology in Agriculture https://www.jostapubs.com/ PAPAYA Academic Press, Statoberry LLP, https://www.statoberry.com/papaya November 2025, Volume 1, Issue II Submitted: 2025-10-24 doi:10.65287/josta.202510.BD6A Accepted: 2025-11-14