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The aim of the paper is to propose a methodology that allows researchers and practitioners to structure a small amount of data in a way which aids understandings and allows predictions to be made.

The paper explores how formal concept analysis can be combined with fuzzy reasoning to make predictions based on small datasets. A dataset of nine regions in Norway described by six attributes is used. The paper focuses on regional disparities in labour market outcomes such as unemployment and wages.

The paper finds that unemployment tends to be concentrated in the most prosperous parts of the study area. These regions have high incomes and experience population growth. More rural regions have virtually no unemployment. The methodology proposed allows these patterns to be seen. The authors made predictions with an accuracy rate of over 75 per cent.

A common response to high unemployment in urban areas is to stimulate employment growth. The findings suggest that this will simply increase migration towards the cities. The net result will be no change in unemployment but an accelerated depopulation of more rural regions.

To the authors' knowledge, this is the first application of fuzzy reasoning to the topic of regional disparities. The methodology aids in the interpretation of small datasets. The methodology should be of interested to practitioners at the local level, who are interested in analysing their own region, even when limited data are available.

The purpose of this paper is to apply a mathematical method of formal concept analysis (FCA) to facilitate evaluation of potential partners, and to select the most appropriate partner for horizontal strategic alliances. Horizontal collaboration between ship design firms is important in relation to business cyclicality in the industry. The workload in ship design firms drops during the troughs of the shipbuilding cycle and increases dramatically during the peaks of the cycle.

The proposed method of partnership selection applies FCA, which is based on mathematical lattice theory. FCA allows firms to evaluate and select the best suitable partners for horizontal interfirm cooperation from several possible candidate firms. Utilization of FCA allows a firm to visually analyze a potential partner for a horizontal strategic alliance.

The contribution of this study to the literature is twofold. First, it contributes to the literature on the application of FCA in management field. Second, this study contributes to the partner selection literature. The contribution of the study is an alternative quantitative method for partner selection based on FCA. FCA compliments qualitative approaches in the process of alternatives evaluation and decision‐making regarding partner selection for horizontal collaboration.

Practitioners from ship design firms can use the FCA tool to facilitate decision‐making relating to the screening of potential partners for horizontal cooperation with regard to pre‐specified selected criteria.

FCA has been marginally applied to aid managerial decision making. The FCA tool is valuable for practitioners from ship design firms to manage the selection of partners for horizontal collaboration. The FCA tool is associated with numerous advantages, notably, relative simplicity and versatility of visual analysis when compared with other mathematical approaches such as the analytic hierarchy process, the analytic network process, optimization modeling, and fuzzy set logic.