Business Intelligence and Analytics Systems in Management Accounting Contexts: A “Journey” Through Enablers, Barriers, and Organizational Dynamics ​

 


Abstract

This Doctoral Thesis comprises three interrelated papers that collectively contribute to advancing the understanding of the factors influencing the use of Business Intelligence & Analytics (BI&A) systems in Management Accounting (MA) contexts and of the implications of the use of BI&A systems for management accountants. 

The first paper is a literature review aimed at critically examining the state of the art on the enablers and barriers to the use of BI&A systems within MA contexts. Employing the Grounded Theory Literature Review method, a sample of 65 papers was analyzed and numerous enablers and barriers were identified and classified into three macro-categories: organizational-related factors, technical-related factors, and human-related factors. In addition to systematizing the existing literature on the enablers and barriers to the use of BI&A systems in MA contexts, this paper identifies existing gaps in the literature and suggests future avenues of research. The second paper aims to explore, through a cross-sectional field study, how and why BI&A systems may end up being used or not used for MA purposes. The paper shows that the factors capable of hindering or facilitating the use of BI&A systems do not come into play singly but interact with each other. Furthermore, these factors do not all have the same importance since some manifest with greater intensity, while others with lesser intensity. The third paper aims to explore, through a single in-depth case study, how BI&A systems reshape the organizational relevance of management accountants within companies. The paper demonstrates that the use of BI&A systems increases the organizational relevance of management accountants, due to the opportunity to devote more time to the analysis and interpretation of information, but it adds some nuances on the “costs” to be incurred to play such a more relevant role, i.e. new responsibilities on IT-related tasks and increased workload.



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Abstract

This Doctoral Thesis comprises three interrelated papers that collectively contribute to advancing the understanding of the factors influencing the use of Business Intelligence & Analytics (BI&A) systems in Management Accounting (MA) contexts and of the implications of the use of BI&A systems for management accountants. 

The first paper is a literature review aimed at critically examining the state of the art on the enablers and barriers to the use of BI&A systems within MA contexts. Employing the Grounded Theory Literature Review method, a sample of 65 papers was analyzed and numerous enablers and barriers were identified and classified into three macro-categories: organizational-related factors, technical-related factors, and human-related factors. In addition to systematizing the existing literature on the enablers and barriers to the use of BI&A systems in MA contexts, this paper identifies existing gaps in the literature and suggests future avenues of research. The second paper aims to explore, through a cross-sectional field study, how and why BI&A systems may end up being used or not used for MA purposes. The paper shows that the factors capable of hindering or facilitating the use of BI&A systems do not come into play singly but interact with each other. Furthermore, these factors do not all have the same importance since some manifest with greater intensity, while others with lesser intensity. The third paper aims to explore, through a single in-depth case study, how BI&A systems reshape the organizational relevance of management accountants within companies. The paper demonstrates that the use of BI&A systems increases the organizational relevance of management accountants, due to the opportunity to devote more time to the analysis and interpretation of information, but it adds some nuances on the “costs” to be incurred to play such a more relevant role, i.e. new responsibilities on IT-related tasks and increased workload.



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