The Role of Business Analytics in Transforming Management Accounting Information into Cost Performance

Öz

The effects of e-commerce and big data on accounting and cost management should be evaluated comprehensively from various aspects. Increasing internet-based applications deeply affect both accounting and cost management. The effect of three basic variables is emphasized in the study. First, the relationship between business analytics and management accounting, and cost performance was evaluated. Business analytics can play an important role in the effectiveness of management accounting. The findings of the study show that descriptive and predictive analytics have positive effects on the planning, control, and cost management. The productivity increase seen in the planning and control functions of management accounting improves the cost performance in favor of the company. Managerial and practical evaluations have been made in the context of the inferences obtained from the research.

Anahtar Kelimeler

• Business analytics
• management accounting
• big data
• cost performance
• performance management
• information technology

Kaynakça

1. Aydıner, A.S., Tatoğlu, E., Bayraktar, E., Zaim,S., &Delen, D. (2019). Business analytics and firm performance: The mediating role of business process performance. Journal of Business Research, 96, 228-237.
2. Anderson, S.W. & Lanen, W.N. (1999). Economic transition strategy and the evolution of management accounting practices: The case of India. Accounting, Organizations and Society, 24 (5-6), 379-412.
3. Anderson, S.W., & Dekker, H.C. (2009). Strategic Cost Management in Supply Chains, Part 2: Executional Cost Management. Accounting Horizons, 29(3), 293-305.
4. Anderson, S.W., & Lanen.,W.N. (2002). Using electronic data interchange (EDI) to improve the efficiency of accounting transactions. Accounting Review, 77(4),703-729.
5. Appelbaum, D., Kogan,A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25,29-44
6. Atkinson, A. A., Kaplan, R.S., Matsumura, E.M. & Young, S.M. (2011). Management Accounting: Information for Decision Making and Strategy Execution. 6 Pearson Education.
7. Basu, A. (2013). Five pillars of prescriptive analytics success. Analytics Magazine, 8-12.
8. Baştürk, S. & Taştepe, M. (2013). Evren ve ömeklem. S. Baştürk (Ed.), Bilimsel Araştırma Yöntemleri (129- 159). Ankara: Vize Yayıncılık.

9. Brands, K. (2015). Business Analytics: Transforming the Role of Management Accountants. Management Accounting Quarterly, 16(3), 1-12.
10. Cadez,S.&, Guilding, C. (2008). An exploratory investigation of an integrated contingency model of strategic management accounting. Accounting, Organizations and Society, 33(7-8), 836-863.
11. Chae, B.K., Yang, C., Olson, D., & Sheu, C. (2014). The impact of advanced analytics and data accuracy on operational performance: a contingent resource based theory (RBT) perspective. Decision Support Systems, 59, 119-126.
12. Chen, I.J., & Paulraj, A. (2004). Towards a theory of supply chain management: the constructs and measurements. Journal of Operations Management, 22, 119-150.
13. Cokins, G. (2013). Top 7 trends in management accounting. Strategic Finance. 95(6), 21-30.
14. Cooper, R., & Slagmulder, R. (2004). Interorganizational cost management and relational context. Accounting, Organizations and Society, 29(1), 1-26.
15. Daniel, W.W., & Cross, C.L. (2018). Biostatistics: A Foundation for Analysis in the Health Sciences, 11th Ed., Wiley.
16. Davenport, T. & Harris, J.G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business School Press, Boston, MA.
17. Davenport, T. (2006). Competing on analytics, Harvard Business Review, 84(5), 150-151.
18. Dilla, W., Janvrin, D.J., & Raschke, R. (2010). Interactive data visualization: new directions for accounting information systems research. Journal of Information Systems, 24(2), 1-37.
19. Esfahbodi,A., Zhang, Y., & Watson, G. (2016). Sustainable supply chain management in emerging economies: Trade-offs between environmental and cost performance. International Journal of Production Economics, 181, 350-366.
20. George, G. , Haas, M. , & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321-326.
21. Granlund M. (2011). Extending AIS research to management accounting and control issues: A research note. International Journal of Accounting Information Systems, 12(1), 3-19.
22. Hedgebeth, D. (2007). Data-driven decision making for the enterprise: an overview of business intelligence applications, VINE, 37(4), 414-420.
23. Hindle, G. A., & Vidgen, R. (2018). Developing a business analytics methodology: A case study in the foodbank sector. European Journal of Operational Research, 268(3), 836-851.
24. Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130-141. https://data.tuik.gov.tr/Bulten/Index?p=Kucuk-ve-Orta- Buyuklukteki-Girisim-Istatistikleri-2019-37548 (access date: 5 May, 2020).
25. IBM. (2013). Descriptive, predictive, prescriptive: transforming asset and facilities management with analytics. In: Thought Leadership White Paper.
26. Jans, M., Alles, M., & Vasarhelyi, M. (2013). The case for process mining in auditing: Sources of value added and areas of application. International Journal of Accounting Information Systems ,14, 1-20.
27. Klatt, T., Schlaefke,M., & Moeller, K. (2011). Integrating business analytics into strategic planning for better performance. Journal of Business Strategy, 32(6), 30-39.
28. Krishnamoorthi, S.,& Mathew, S.K. (2018). Business analytics and business value: A comparative case study. Information & Management, 55, 643-666.
29. Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700-710.
30. MacKinnon, D.P. (1994). Analysis of mediating variables in prevention and intervention research, in Cazares, A. and Beatty, L.A. (Eds), Scientific Methods in Prevention Research, NIDA Research Monograph, Vol. 139, Government Printing Office, Washington, DC, pp. 127-153.
31. Maiga, A., & Jacobs, F.A. (2003). Balanced Scorecard, Activity- based Costing and Company Performance: An Empirical Analysis. Journal of Managerial Issues, 15(3), 283-301.
32. Maiga, A.S., Nilsson, A., & Ax, C. (2015). Relationships between internal and external information systems integration, cost and quality performance, and firm profitability. International Journal of Production Economics, 169, 422-434.
33. Nielsen, S. (2015). The Impact of Business Analytics on Management Accounting. (Available at SSRN 2616363).
34. Pape, T. (2016). Prioritising data items for business analytics: Framework and ap- plication to human resources. European Journal of Operational Research, 252 , 687-698 .
35. Redman, T.C. (2013). Data Driven: Profiting From Your Most Important Business Asset. Harvard Business Press.
36. Scapens, R.W., & Jazayeri, M. (2003). ERP systems and management accounting change: opportunities or impacts? A research note. European Accounting Review, 12(1), 201-233.
37. Shmueli, G., & Koppius, O. R. (2011). Predictive Analytics in Information Systems Research. MIS Quarterly, 35(3), 553-572.
38. Silvi, R., Moeller, K., & Schlaefke, M. (2010). Performance management analytics – the next extension in managerial accounting. available at: SSRN eLibrary, SSRN, doi: 10.2139/ ssrn.1656486
39. Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263-286.
40. Stenzel,J., &Stenzel.,C. (2004). Performance measurement and management in the reinsurance industry. Cost Management, 18(3), 28-35.
41. Sun, Z., Strang, K., & Firmin, S. (2017). Business analytics-based enterprise information systems. The Journal of Computer Information Systems, 57(2), 169-178
42. Taleizadeh, A.A., Noori-daryan, M., & Cárdenas-Barrón, L.E., (2015). Joint optimization of price, replenishment frequency, replenishment cycle and production rate in vendor managed inventory system with deteriorating items. International Journal of Production Economics, 159, 285-295.
43. Troilo, M., Bouchet, A., Urban, T. L., & Sutton, W. A. (2016). Perception, reality, and the adoption of business analytics: Evidence from North American professional sport organizations. Omega, 59, 72-83.
44. Vidgen, R., Shaw, S., & Grant, D.B. (2017). Management challenges in creating value from business analytics, European Journal of Operational Research, 261, 626-639.
45. Warren Jr., J.D., Moffitt, K.C., & Byrnes, P. (2015). How big data will change accounting. Accounting Horizons, 29(2), 397- 407.
46. Wong,C.Y., Boon-itt,S.,& Wong.,C.W.Y. (2011). The contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance. Journal of Operation Management, 29, 604-615.
47. Wonnacott, T.H. & Wonnacott, R.J. (1990). Introductory Statistics, 5th Ed. Wiley.
48. Zikopoulos, P. , Eaton, C. , DeRoos, D. , Deutsch, T. , & Lapis, G. (2012). Understanding big data: Analytics for enterprise class hadoop and streaming data . Maidenhead, UK: McGraw-Hill.