Classifying Expected Benefits of Collective Organization: A Rule-Based Approach for Non-Wood Forest Product Collectors in Serbia

Although heterogeneity among non-wood forest product (NWFP) collectors is well established, less is known about how collectors’ expected benefits from association can be translated into operational support profiles. This study aims to operationalize NWFP collectors’ expected benefits from association into support-priority groups and to assess whether these groups differ in collectors’ stated willingness to participate in establishing an association, with implications for planning institutional support in Serbia. Data were collected through a 2022 survey of 639 NWFP-collecting households, of which 465 households with complete service-item data and at least one named service formed the primary analytical sample. Ten services were grouped into three indices (administrative, information and education, and market and infrastructure) and classified using a complete 27-rule framework implemented through fuzzy inference. The framework generated low (14.8%), medium (54.4%), and high (30.8%) support-priority groups. The medium support-priority group was characterized by a mixed pattern of expected benefits, while the high support-priority group reflected stronger expectations across complementary support domains. Stated willingness to participate in establishing an association, which was not used to construct the classification, was reported by 68.2% of the low, 73.0% of the medium, and 90.7% of the high support-priority group. The overall association was statistically significant (χ2(2) = 19.03, p = 7.38 × 10−5; Cramer’s V = 0.21), with higher adjusted odds of stated willingness in the high group than in either other group, whereas the low–medium contrast was not statistically significant. In a sensitivity analysis (N = 615), assigning 150 households with no recorded service selections to Low under a zero-score assumption reduced willingness in that group to 25.6%. Fuzzy and crisp implementations classified all 80 possible input profiles identically; the rule-based groups were not more strongly associated with willingness than alternative classifications. These findings show how expected association benefits can be translated into interpretable model-generated groups that may serve as exploratory input for planning differentiated association-based institutional support in Serbia.