IDEAS home Printed from https://ideas.repec.org/a/eee/ijoais/v49y2023ics1467089523000106.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1467089523000106
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.accinf.2023.100618?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Neubert, Mitchell J. & Montañez, George D., 2020. "Virtue as a framework for the design and use of artificial intelligence," Business Horizons, Elsevier, vol. 63(2), pages 195-204.
    2. Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    3. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    4. Emilio Abad-Segura & Mariana-Daniela González-Zamar, 2020. "Research Analysis on Emerging Technologies in Corporate Accounting," Mathematics, MDPI, vol. 8(9), pages 1-29, September.
    5. Costa Diogo António da Silva & Mamede Henrique São & Mira da Silva Miguel, 2022. "Robotic Process Automation (RPA) Adoption: A Systematic Literature Review," Engineering Management in Production and Services, Sciendo, vol. 14(2), pages 1-12, June.
    6. Shamim, Saqib & Yang, Yumei & Ul Zia, Najam & Khan, Zaheer & Shariq, Syed Muhammad, 2023. "Mechanisms of cognitive trust development in artificial intelligence among front line employees: An empirical examination from a developing economy," Journal of Business Research, Elsevier, vol. 167(C).
    7. Black, J. Stewart & van Esch, Patrick, 2020. "AI-enabled recruiting: What is it and how should a manager use it?," Business Horizons, Elsevier, vol. 63(2), pages 215-226.
    8. Berk Kaan Kuguoglu & Haiko van der Voort & Marijn Janssen, 2021. "The Giant Leap for Smart Cities: Scaling Up Smart City Artificial Intelligence of Things (AIoT) Initiatives," Sustainability, MDPI, vol. 13(21), pages 1-16, November.
    9. Syaiful Anwar Mohamed & Moamin A. Mahmoud & Mohammed Najah Mahdi & Salama A. Mostafa, 2022. "Improving Efficiency and Effectiveness of Robotic Process Automation in Human Resource Management," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    10. Roman Šperka & Michal Halaška, 2023. "The performance assessment framework (PPAFR) for RPA implementation in a loan application process using process mining," Information Systems and e-Business Management, Springer, vol. 21(2), pages 277-321, June.
    11. Paschen, Ulrich & Pitt, Christine & Kietzmann, Jan, 2020. "Artificial intelligence: Building blocks and an innovation typology," Business Horizons, Elsevier, vol. 63(2), pages 147-155.
    12. Desouza, Kevin C. & Dawson, Gregory S. & Chenok, Daniel, 2020. "Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector," Business Horizons, Elsevier, vol. 63(2), pages 205-213.
    13. Uklańska Anna, 2023. "Robotic Process Automation (RPA) – Bibliometric Analysis and Literature Review," Foundations of Management, Sciendo, vol. 15(1), pages 129-140, January.
    14. Fernandez Martinez, Roberto & Lostado Lorza, Ruben & Santos Delgado, Ana Alexandra & Piedra, Nelson, 2021. "Use of classification trees and rule-based models to optimize the funding assignment to research projects: A case study of UTPL," Journal of Informetrics, Elsevier, vol. 15(1).
    15. Yasheng Chen & Zhuojun Wu & Hui Yan, 2022. "A Full Population Auditing Method Based on Machine Learning," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    16. Ionut Anica-Popa & Liana Anica-Popa & Cristina Radulescu & Marinela Vrincianu, 2021. "The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 120-120, February.
    17. Li, Chia-Ying & Zhang, Jin-Ting, 2023. "Chatbots or me? Consumers’ switching between human agents and conversational agents," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    18. Ming‐Lang Tseng & Hien Minh Ha & Thi Phuong Thuy Tran & Tat‐Dat Bui & Chih‐Cheng Chen & Chun‐Wei Lin, 2022. "Building a data‐driven circular supply chain hierarchical structure: Resource recovery implementation drives circular business strategy," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2082-2106, July.
    19. Krakau, Jan & Feldmann, Carsten & Kaupe, Victor, 2021. "Robotic process automation in logistics: Implementation model and factors of success," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Adapting to the Future: Maritime and City Logistics in the Context of Digitalization and Sustainability. Proceedings of the Hamburg International Conf, volume 32, pages 219-256, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ijoais:v:49:y:2023:i:c:s1467089523000106. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-accounting-information-systems/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.