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Machine learning-based automation of accounting services: An exploratory case study

Author

Listed:
  • Bavaresco, Rodrigo Simon
  • Nesi, Luan Carlos
  • Victória Barbosa, Jorge Luis
  • Antunes, Rodolfo Stoffel
  • da Rosa Righi, Rodrigo
  • da Costa, Cristiano André
  • Vanzin, Mariangela
  • Dornelles, Daniel
  • Junior, Saint Clair
  • Gatti, Clauter
  • Ferreira, Mateus
  • Silva, Elton
  • Moreira, Carlos

Abstract

Machine Learning (ML) applied to Robotic Process Automation (RPA) and chatbot interfaces can generate significant value for many business processes. However, these technologies generate the intended return only with a carefully planned deployment. Current literature only contains a small number of case studies about how the adoption of ML-based automation services impacts employees’ behavior. In particular, no case studies look into the automation of manual tasks related to accounting management. This article reports a study conducted to understand users’ perceptions of an ML-enabled service to automate repetitive management tasks. The service was developed in a partnership between Unisinos University and Dell Inc. The study was conducted with a group of ten highly skilled employees from Dell with expertise in accounting processes and with IT background that frequently would use the automation service. The group participated in a presentation about the service and its interface and voluntarily answered a Technology Acceptance Model (TAM) questionnaire to evaluate the usability and ease of use. Results show that 10 out of 10 users agree that the service was easy to use. Also, 8 of them agree that its output is useful to reduce the manual labor required for statutory reconciliation. Furthermore, employees with an accounting management background were given access to the service, and three voluntarily answered an open-ended survey. In summary, employees agree that an automation service can reduce the time required to conduct management tasks but questioned the long-term usefulness and the ability to incorporate the process’s particularities. These results provided insights leading to ten lessons related to user experience, training and awareness, and service development.

Suggested Citation

  • Bavaresco, Rodrigo Simon & Nesi, Luan Carlos & Victória Barbosa, Jorge Luis & Antunes, Rodolfo Stoffel & da Rosa Righi, Rodrigo & da Costa, Cristiano André & Vanzin, Mariangela & Dornelles, Daniel & J, 2023. "Machine learning-based automation of accounting services: An exploratory case study," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:ijoais:v:49:y:2023:i:c:s1467089523000106
    DOI: 10.1016/j.accinf.2023.100618
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    References listed on IDEAS

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    1. Benedikt Berger & Martin Adam & Alexander Rühr & Alexander Benlian, 2021. "Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 55-68, February.
    2. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    3. Huang, Feiqi & Vasarhelyi, Miklos A., 2019. "Applying robotic process automation (RPA) in auditing: A framework," International Journal of Accounting Information Systems, Elsevier, vol. 35(C).
    4. Lee, In & Shin, Yong Jae, 2020. "Machine learning for enterprises: Applications, algorithm selection, and challenges," Business Horizons, Elsevier, vol. 63(2), pages 157-170.
    5. Braganza, Ashley & Chen, Weifeng & Canhoto, Ana & Sap, Serap, 2021. "Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust," Journal of Business Research, Elsevier, vol. 131(C), pages 485-494.
    6. Milad Mirbabaie & Stefan Stieglitz & Felix Brünker & Lennart Hofeditz & Björn Ross & Nicholas R. J. Frick, 2021. "Understanding Collaboration with Virtual Assistants – The Role of Social Identity and the Extended Self," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 21-37, February.
    7. Kokina, Julia & Blanchette, Shay, 2019. "Early evidence of digital labor in accounting: Innovation with Robotic Process Automation," International Journal of Accounting Information Systems, Elsevier, vol. 35(C).
    Full references (including those not matched with items on IDEAS)

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