Present research studies organizational context using lexical approach. The idea is that idioms reflect characteristics of context and situations. Goal. The research goal is to define characteristics of organizational context for companies representing different types of economic activity based on Russian idioms. Method. From a thesaurus of contemporary Russian idioms three experts selected 604 idioms, describing the “Work” domain. In the first study independent experts (N=10) sorted 604 idioms by similarity. Result allowed to create a similarity matrix for these idioms. In the second online study respondents (N = 845) chose from a list of idioms those, that best describe the organization they work in. Analyses of respondents’ biographical data showed seven most common types of economic activity presented in the research. We identified the most commonly used idioms for each of these types of economic activity. They were cluster analyzed (Ward’s method, Euclidean distance) to identify cluster structure of the most important characteristics of organizational context. Results. Seven clusters of organizational context emerged after statistical analysis (KL(7) = 2.99): “Hopelessness”, “Overload”, “Definitive processes”, “Role uncertainty”, “Engaging work process”, “Close interactions”, “Distrust”. Conclusions. Each type of economic activity has its own specific set of the most commonly used idioms. Those idioms are sensitive to the context differences between these types of economic activity. Results suggest that studying idioms is a heuristic approach to identifying significant characteristics of organizational context and thus, idioms are promising material for a further research in this field. Significance of the results. Results may be used to study different phenomena of organizational behavior and develop complex models that would account not only for the employee’s personality, but also important characteristics of organizational context. Consistently with the interactionist approach this allows to increase the predictive value of such models.
