The study of the economic process can be presented as a chain of reflections on the causes and consequences of the particular phenomenon’s occurrence, within the framework of which scientists try to study and understand the nature of cause-and-effect relationships and find out the mechanisms of their occurrence. This article discusses three well-known conceptual approaches to the assessment of causation in socioeconomic sciences: successionist causation, configurational causation, and generative causation. The author gives his own interpretation of these approaches, constructs graphic interpretations, and also offers such concepts as a linear sequence of factors, the causal field, and the causal space of factors in the economy and socioeconomic processes. Within the framework of these approaches, the development trends of these and new models are formulated, taking into account the transition of the world economy to a digital format. The article contains specific examples from the author of the causality models’ implementation in scientific research related to assessing the impact of corporate culture on the main indicators of an organization’s performance in various contexts.
Scientific research of any socio-economic and managerial process can be represented as a chain of reflections on the causes and consequences of this or that phenomenon's occurrence. At the same time, the authors can try not only to answer the question “why?” but also to study and understand the nature of cause-and-effect relationships, to find out the mechanisms of their occurrence, and also to get the answer to the question posed as accurately and reasonably as possible. Each author, using the accumulated experience, offers both qualitative and quantitative methods that allow him to obtain one or another assessment of causality. However, there are not enough articles devoted to a comprehensive review of the methods and technologies of cause-and-effect relationships in socio-economic processes. This article discusses three well-known conceptual approaches to the assessment of causation in socio-economic sciences: successionist causation, configurational causation and generative causation. The author gives his own interpretation of these approaches, builds graphic interpretations, and also offers such concepts as a linear sequence of factors, the causal field, and the causal space of factors in socio-economic processes. Within the framework of these approaches, a classification of mathematical and instrumental models for assessing the causality of relationships in socio-economic processes is given, and trends in the development of these and new models are formulated, taking into account the global transition to a digital format. All of these trends are based on the use of digital technologies in different formats and include descriptions of such formats. The article contains specific author’s examples of causality model implementation in scientific research related to economics and management.
dark patterns in the interfaces of users using sites and portals of online trading affect their behavior by companies that own digital resources. The authors propose to implement the detection of dark patterns on sites in user interfaces using cluster analysis algorithms using two methods for clustering many dark patterns in application interfaces: hierarchical and k-means. The complexity of the implementation lies in the lack of datasets that formalize dark patterns in user interfaces. The authors conducted a study and identified signs of dark patterns based on the use of Nelsen’s antisymmetric principles. The article proposes a technique for assessing dark patterns using linguistic variables and their further interval numerical assessment for implementing cluster data analysis. The last part of the article contains an analysis of two clustering algorithms and an analysis of the methods and procedures for applying them to clustering data according to previously selected features in the RStudio environment. We also gave a characteristic for each resulting cluster.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.