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  • 标题:The assessed usefulness of management accounting in Romania and Poland: a comparative contingency-based study
  • 本地全文:下载
  • 作者:Alina Almasan ; Cristina Circa ; Justyna Dobroszek
  • 期刊名称:ECONOMICS AND MANAGEMENT
  • 印刷版ISSN:1822-6515
  • 出版年度:2019
  • 卷号:30
  • 期号:3
  • 页码:253-264
  • DOI:10.5755/j01.ee.30.3.20483
  • 出版社:Kaunas University of Technology
  • 摘要:The purpose of this paper is to investigate how selected contingency factors, such as the company profile and the managers’ profile, impact on the usefulness of management accounting (MA) in two Eastern European countries: Romania and Poland. In order to achieve the objective of the paper, we employed a questionnaire-based survey addressed to managers of randomly selected companies from various industries. Data were processed by means of several statistical tests, as well as classification and regression trees (C&RT) and the results were interpreted in the historical context of the two countries. Our investigation showed that management accounting in both countries is oriented towards providing useful information for budgeting and cost control. There is no interest in preparing and communicating information meant to support strategy implementation. It was also found that the information delivered to management is seldom employed in supporting the decision-making process, where its usefulness is rated as relatively low. The most important predictor variable for the assessment of Polish managers is the origin of the company’s capital, followed by the education of the managers, while in Romania the assessment is mainly influenced by the managers’ function and the origin of capital. The paper contributes to the development of literature in this field by complementing the existing research on MA development in CEE countries, and identifying the internal factors that lead to managers from the two countries perceiving MA usefulness differently.
  • 关键词:Management Accounting Information; Usefulness; Assessment; Comparison; Contingency Factors; Classification and Regression Trees.
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