Matrix approach to digitalization of management objects

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-04-11 DOI:10.1108/jm2-02-2022-0057

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引用次数: 0

Abstract

Purpose The purpose of this study is to substantiate the matrix approach to digitalization of management objects based on identification of relevant qualitative characteristics of these objects and its dichotomies, which allowing determine the quantity and quality of their main variants, as well as the relationships between them. Design/methodology/approach Methods of classification and typology are selected as study methods, and binary matrices are used as the tool to determine the main variants of management objects, assign binary codes to it and form codes of more complex management objects on its basis, depending on the content of study tasks. Findings The main results of study include the classification of organization components; variants for choosing qualitative characteristics of chains components; adjusted content of methodology of qualitative research of management objects; sequences of “up” and “down” digitization of these objects; actual qualitative characteristics of e components of management objects and dichotomies; and variants of forming of ciphers of these objects. Practical implications The use of study results allows to reduce the complexity of substantiating and making managerial decisions in organization and supply chains, to structure these decisions by man-agement levels and positions and to reduce costs, time and lost profits for fulfilling orders of end consumers of products and/or services. Originality/value The originality of this study is confirmed by the substantiation of choice and use of actual qualitative characteristics of management objects and its dichotomies, which allow obtaining two variants of these objects and assigning them binary codes processed using computer software for management activities.

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管理对象数字化的矩阵方法

目的本研究的目的是在识别管理对象的相关定性特征及其二分性的基础上，证实管理对象数字化的矩阵方法，从而确定其主要变体的数量和质量，以及它们之间的关系。根据研究任务的内容，选择分类和类型学方法作为研究方法，使用二进制矩阵作为工具来确定管理对象的主要变体，为其分配二进制代码，并在此基础上形成更复杂的管理对象的代码。研究结果主要包括组织成分的分类；用于选择链组件的定性特征的变体；调整了管理对象定性研究方法论的内容；这些物体的“向上”和“向下”数字化序列；e管理对象组成部分的实际定性特征和二分法；以及形成这些对象的密码的变体。实际含义使用研究结果可以降低组织和供应链中证实和制定管理决策的复杂性，根据管理级别和职位构建这些决策，并减少履行产品和/或服务最终消费者订单的成本、时间和利润损失。独创性/价值本研究的独创性通过选择和使用管理对象的实际定性特征及其二分法得到证实，这允许获得这些对象的两种变体，并为其分配使用计算机软件处理的二进制代码，用于管理活动。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.