Article review of “Data analytics by management accountants”: for advanced management accounting study

Article review of “Data analytics by management accountants”: for advanced management accounting study

 

How to present this article in Harvard reference format?

Spraakman, G., Sanchez-Rodriguez, C. and Tuck-Riggs, C.A., 2020. Data analytics by management accountants. Qualitative Research in Accounting & Management, [online] Available at: https://www.emerald.com/insight/1176-6093.htm

 

 What are the key research issues the article wants to address?

The article aims to address three key interrelated research questions (RQs) concerning the use of data analytics (DA) by management accountants (MA):

1.    What are the responsibilities of management accountants with respect to data analytics? This includes understanding whether more focused roles within management accounting are required in the context of DA.

2.    How does data analytics support inference, prediction, and assurance in management accounting tasks?

3.    How can management accountants ensure that data insights derived from data analytics are effectively turned into decisions that add value?

These research questions are designed to deepen the understanding of how the tasks and responsibilities of management accountants are affected by data analytics, moving beyond conceptual discussions to explore actual practices

 

Describe two main theories employed in this article.

The article primarily focuses on exploring the practical application of data analytics (DA) by management accountants (MA) rather than explicitly employing traditional formal theories. However, it builds its research framework and analysis based on two conceptual foundations or theoretical perspectives implicit in its approach:

1.    Management Accounting Theory: The article relies on an established definition of management accounting from the Institute of Management Accountants (IMA), which frames management accounting as a profession that involves partnering in management decision making, devising planning and performance systems, and providing expertise in financial reporting and control to assist in strategy formulation and implementation. This theoretical foundation infers that management accounting involves both financial and non-financial information, including external data, which is crucial for understanding the role of DA in expanding or changing MA responsibilities.

2.    Data Analytics Frameworks in Accounting: The paper draws on conceptualizations of data analytics as the use of information technology tools to perform data analysis, which ranges from simple descriptive statistics to more advanced analytical methods like clustering, regression, and factor analysis. It references perspectives from researchers like Pickard and Cokins (2017) and Schneider et al. (2015), which highlight DA's roles in enabling inference, prediction, and assurance within accounting tasks. This theoretical lens frames how DA supports management accountants in transforming raw data into actionable insights and supporting decision-making processes.

Thus, the article situates its research by combining a well-established definition of management accounting with emerging conceptual frameworks of data analytics in accounting research to examine how these intersect in practice. It does not explicitly apply formal theories but operates within these conceptual constructs to analyze empirical findings.

 

Describe 3 main claims of the article in terms of Toulmin's model of argument.

Using Toulmin’s model of argument, which includes Claim, Data (Evidence), Warrant (the reasoning connecting data and claim), Backing, Qualifier, and Rebuttal, the article’s three main claims can be described as follows:

1.    Claim 1: The responsibilities of management accountants (MA) have not fundamentally changed due to data analytics (DA), but their roles have expanded, particularly in data preparation and communication of analytic results.

• Data: Interviews with 29 MAs from 20 organizations show that MAs continue to analyze financial and non-financial data to support senior management but are increasingly preparing data for analytics and presenting results visually and clearly.
• Warrant: Because DA tools enable more detailed and complex analysis, MAs must expand their skills not only in technical analysis but also in effectively communicating insights to decision-makers.
• Backing: Literature citing the need for accountants to develop DA knowledge and skills supports this, as well as empirical findings showing expanded roles of MA.
• Qualifier: Though roles are generally expanding, adoption and use of DA vary significantly among organizations.
• Rebuttal: Some organizations remain traditional in their accounting approach, applying DA only modestly or not at all.

2.    Claim 2: Data analytics supports management accounting tasks by enhancing inference, prediction, and assurance, but adoption of advanced predictive analytics remains low.

• Data: Respondents predominantly use drill-down and trend analysis with financial and operational data; few use predictive analytics, which is still described as being in infancy and unfamiliar to many MAs.
• Warrant: Advanced DA techniques require skills and organizational readiness not yet widespread among MAs; Excel and ERP systems currently dominate as tools for data analysis.
• Backing: Prior research highlights the potential of DA for predictive accounting, but empirical evidence shows limited practical use so far.
• Qualifier: The potential for prediction is significant, but actual use is limited due to skill gaps and managerial focus on current state issues.
• Rebuttal: Some organizations and units (e.g., hospital 13) show advanced DA use including predictive analytics, indicating growing but uneven adoption.

3.    Claim 3: For data analytics to add value through MA, success factors include business knowledge, a cross-functional analytical perspective, and effective communication skills.

• Data: Interviewees emphasized the importance of understanding the broader business context, linking data across systems, and presenting findings clearly to senior management using visual tools.
• Warrant: Having technical analytic skills alone is insufficient; MA must translate data insights into actionable decisions, which requires these additional competencies.
• Backing: The difficulty in finding individuals with combined technical, domain, and communication skills is noted in prior literature and confirmed by respondents.
• Qualifier: These factors increase the likelihood of DA insights being turned into value-adding decisions.
• Rebuttal: Without such skills and perspectives, DA risks producing data overload or unhelpful results.

These claims integrate empirical evidence from qualitative interviews with literature-based reasoning to articulate the evolving role and effectiveness of management accountants using data analytics.

 ** references:  a collection of management accounting notes; a useful generative AI tool./ also consider another generative AI tool.