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PurposeThe aim of the article is to determine research areas and to recognize the current direction in the development of maturity models, to indicate the key areas of organizational maturity models (OMMs) development and their classification as well as to pinpoint research gaps and areas of potential development of OMMs in the context of scientific research and the needs of management practitioners.Design/methodology/approachThe research was conducted using the literature review method, bibliometric analysis and visual mappings.FindingsThe empirical classification developed in this paper identified 12 categories based on management areas, constituting the criteria for classifying OMMs models, where OMMs are being developed: Information Technology, Project Management, Business Management and Strategy, Human Resource, Ergonomics, Health and Safety Management, Industry 4.0 concept, Knowledge Management, Process Management, Performance Management, Quality Management, Supply Chain Management, Risk Management and Innovation Management.Research limitations/implicationsThe main limitation is the analysis in the scope of topic OMMs including solely the Scopus and Thompson Reuters Web of Science database. Another shortcoming is conducting data analysis and classification based on the abstracts of the selected articles.Originality/valueThis work is a starting point to prospect trends for future revolving around the OMMs crossing different databases.

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The purpose of the article is to determine the Type I error and Average Run Length values for charts   and R, for which control limits have been determined based on the Skewness Correction method (SC method), with an unknown probability distribution of the qualitative feature being tested. The study also used the Monte Carlo Simulation, in which two sampling methods were used to obtain random input scenarios - matching theoretical distributions (selected skewed distributions) and bootstrap resampling based on a manufacturing company’s measurement data. The presented article is a continuation of Czabak-Górska's (2016) research. The purpose of the article was to determine Type I error value and ARL type A for chart   and R, for which the control limits were determined based on the skewness correction method. For this purpose, measurement data from a company producing car seat frames. Presented case study showed that the chart determined using the skewness correction method works better for the data described by the gamma or log-normal distribution. This, in turn, may suggest that appropriate distribution was selected for the presented data, thanks to which it is possible to determine the course and nature of the process, which is important from the point of view of its further analysis, e.g. in terms of the process capability.

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