This thesis proposes a case study focusing on Robotic Process Automation (RPA) in the context of digital transformation. The study aims to address RPA from both technological and organizational perspectives. From a technological viewpoint, task selection is a crucial first step in RPA. In that context, process mining (PM) is a key technique for identifying suitable tasks, and this research aims to study how PM can aid in systematically selecting tasks for RPA.
Digital transformation is commonly used to refer to the profound changes that companies
must undergo to successfully adapt to new digital technologies [
From a technological perspective, RPA is based on small programs called bots that mimic
human tasks in interactions with diferent software systems [
Technology is not the only aspect to be considered in process automation. The organizational aspect is equally important, as automation afects people the most [ 11]. According to Ernst & Young, 30-50% of automation projects fail due to poor management approaches [12]. Achieving employee commitment and preventing any form of resistance is essential for automation projects [13]. The fear of losing their job, the resistance to new technologies, the need for new skills, the involvement of IT, or the impact on the enterprise architecture are some of the impacts that organizations should take into account [14]. From a strategic perspective, it requires a cultural change from the beginning of any RPA project [15].
This thesis aims to address these issues by presenting a case study carried out in a Spanish regional bank. Following a mixed research methodology, we will focus on analyzing the implementation of RPA from a technological and organizational perspective. On one hand, from a technological point of view, we will develop a quantitative case study with the aim of studying how PM can help identify suitable tasks for automation through RPA. On the other hand, from an organizational point of view, we will carry out a qualitative research, that seeks to study how a strategic management approach can aid to minimize the impact of RPA implementation on organizations. For that purpose, we will employ McKinsey’s 7S framework, which has been used in both digital transformation and change management contexts.
After the introduction of RPA, there are three types of tasks. One type of task is handled by
the information system using traditional automation, where the task has high frequency and
there is no human involvement. A second type would be a low-frequent task that is still done
manually. And the third type would be in the middle, where some of the human repetitive
tasks could be automated but traditional automation becomes expensive. There is where RPA
arises as a cost-efective technology [ 16]. At this point, it is essential a correct selection of
tasks that can be automated through RPA [17]. PM is a suitable technology that can aid to
identify those routine tasks [18]. It is based on event data, stored in an event log registered by
the information system, and it can be classified into four types: process discovery, conformance
checking, process reengineering, and operational support. The first one creates a process model
from event data. The second one detects the diferences between the event log and the process
model. Process reengineering, instead, tries to improve the process model based on event data.
And finally, operational support provides warnings and recommendations for the process [ 19].
We will focus on the first type, process discovery. However, the aim of using PM techniques in
RPA is for task discovery and not process discovery [20]. Task discovery is a time-consuming
job and is usually carried out by human teams [
Our study aims to respond to these challenges by providing a case study conducted within the context of a process-mining project. Specifically, the research question we want to answer is:
”How can the PM process discovery technique be applied for a systematic selection of suitable tasks for RPA?”
Organizational support is crucial in fostering individuals’ involvement in RPA projects [21]. Unfortunately, organizations find themselves with multiple silos, whether at an organizational, functional, technological, or management level, which makes RPA implementation dificult and change management necessary [15]. Indeed, automation afects people in organizations [11][13]. However, HR departments are involved in RPA projects in only 16% of the cases [15]. This diference contradicts the need to place individuals at the center of any long-term automation strategy [22].
From an organizational point of view, automation requires a strategic management approach [23][15]. However, there is currently limited literature addressing RPA from a strategic management perspective. Even more, current studies do not place suficient emphasis on a holistic consideration of the automation impacts on organizations, especially related to HRM function and employees [24].Consequently, there is a knowledge gap in the treatment of RPA from the perspective of strategic business management and the impact of process automation on organizations. From an organizational perspective, our research question is: ”What are the barriers and levers that organizations encounter in the implementation of RPA from the strategic management perspective?”
Our research will be conducted in partnership with a Spanish regional bank. This bank possesses extensive experience in RPA development and considers it a vital element of its business strategy.
To address the first research question, we will carry out a case study based on the methodology developed by Yin (2018) [25], a methodology already followed by other authors [26] [27][28]. The case study will be developed as a single-case study quantitative approach. The validity of the real-life case study method has been validated by researchers such as [29] [30][31].
Our research partner has recently implemented a PM software called Celonis. Based on the event logs recorded by his information system, their goal is to set the KPIs that allow them to establish the candidate tasks to be automated with RPA. Our work will consist of extracting and transforming the user logs, to later propose the task selection criteria in accordance with their business strategy. Once the tasks have been selected and the savings objectives established, we will proceed to implement them in the RPA software and then compare the achieved savings with the initially established objective. Consequently, our research will cover the end-to-end process, starting from establishing the task selection criteria using PM software and concluding with the final calculation of the benefits obtained with RPA.
Regarding the second research question, we selected a qualitative research methodology proposed by Miles and Huberman (1994) [32] to understand the experiences of bank employees with RPA. Concerning methodology, we can find qualitative research in RPA literature, such as [33][34][35]. We will use the semi-structured interview, which will allow us to collect the same general areas of information from each interviewee, and in turn, enough freedom to adapt the interview to our objectives [36]. The interview questions will be based on the 7S framework from McKinsey. This framework is widely used for strategic management in the field of digital transformation and change management[37, 38, 39]. The 7S framework gives a holistic vision of organizations through 7 interconnected elements [40]: Style, Skills, Systems, Structure, Staf, Strategy, and Shared Values.
The study aims to conduct no fewer than 30 interviews with individuals connected to the RPA project within the organization. Convenience sampling will be utilized, as it will be constrained by the possibilities presented by the bank [41]. In participant selection, two aspects of diversity will be considered. Vertically, by selecting individuals with diferent positions within the organizational structure. And horizontally, by including users from diferent departments of the company.
Our research aims to contribute to answering two gaps found in the research literature on RPA. First, from a technological perspective, the correct selection of the tasks to be automated is important. Unfortunately, this job remains manual and time-consuming. Our research aims to narrow the gap by presenting a case study where we use PM techniques to establish KPIs for the systematic selection of tasks. This case study introduces a novel approach that covers the entire process, from task selection criteria to the RPA implementation and its cost-saving evaluation. Secondly, RPA’s impact on organizations is a main issue, but there is a lack of literature from a strategic management point of view. We propose to study the RPA deployment using a qualitative methodology with a strategic framework such as McKinsey’s 7S model. Our innovative approach may generate future research lines linking strategic management and RPA deployment. Finally, one last contribution is the use of a mixed methodology, combining quantitative and qualitative research.
Concerning task discovery, professor Van der Aalst proposes the Pareto principle to classify
tasks based on their variants and frequency, proposing the use of RPA for those tasks that
are rather frequent and simple but it is not cost-efective for traditional automation [ 42]. PM
emerges as an ideal technology for selecting suitable tasks for RPA [18], as user interaction
recording provides information that can be used to discover tasks that can be automated with
RPA [43]. Although part of the tasks can be automated with RPA, there will still be complex
tasks only executed by humans creating a human-robot hybrid scenario [
Management support, communication, skills, or strategic approach appear to be among critical success factors for RPA [44]. A strategic approach should also consider employee engagement [23]. Despite the fact that employees are satisfied with the increase in value-added activities due to automation, many of them seem to fear its future impact. [34][45][46][22][21][47]. Some others see automation as a opportunity to expand their skills. [48].
Involving a company in research often poses several challenges. One of the primary obstacles is data accessibility and quality. Gaining access to it can be a complex process due to privacy concerns. Another dificulty lies in engaging employees in the research project. Employees’ participation may require their time and efort, which can be challenging.
On the other hand, we have identified some other problems that may arise in our research. From a technological point of view, the dificulties to obtain data and its quality is a problem to face. Process mining software selection is another point to take into account, as it is a company decision. From an organizational approach, employees’ reactions and involvement are crucial. At this point, organizational support for our research is essential.
However, we are aware of the limitations of developing our research in a single organization. The results are not generalizable, but they can help a better understanding of RPA impact in [10] W. van der Aalst, Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer-Verlag Berlin, Berlin, 2011. URL: https://link.springer.com/content/ pdf/bfm:978-3-642-19345-3/1. doi:1 0 . 1 0 0 7 / 9 7 8 - 3 - 6 4 2 - 1 9 3 4 5 - 3 , pages: 1-+ Publication Title: Process Mining: Discovery, Conformance and Enhancement of Business Processes WOS:000290417700001. [11] A. Jiménez-Ramírez, Humans, Processes and Robots: A Journey to Hyperautomation, in: Lecture Notes in Business Information Processing, volume 428, Springer Science and Business Media Deutschland GmbH, 2021, pp. 3–6. doi:1 0 . 1 0 0 7 / 9 7 8 - 3 - 0 3 0 - 8 5 8 6 7 - 4 _ 1 , iSSN: 18651356. [12] K. C. Partners, 19/8: RPA AND THE FUTURE, 2023. URL: https://www.
knowledgecapitalpartners.com/blog. [13] K. K. H. Ng, C.-H. Chen, C. K. M. Lee, J. R. Jiao, Z.-X. Yang, A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives, Advanced Engineering Informatics 47 (2021) 101246. URL: https://www. sciencedirect.com/science/article/pii/S147403462100001X. doi:1 0 . 1 0 1 6 / j . a e i . 2 0 2 1 . 1 0 1 2 4 6 . [14] J. Wewerka, M. Reichert, Robotic process automation - a systematic mapping study and classification framework, Enterprise Information Systems 17 (2023) 1986862. URL: https: //doi.org/10.1080/17517575.2021.1986862. doi:1 0 . 1 0 8 0 / 1 7 5 1 7 5 7 5 . 2 0 2 1 . 1 9 8 6 8 6 2 , publisher: Taylor & Francis _eprint: https://doi.org/10.1080/17517575.2021.1986862. [15] L. Willcocks, J. Hindle, M. Lacity, Becoming strategic with robotic process automation, SB
Publishing, Warwickshire, United Kingdom, 2019. OCLC: 1197938755. [16] W. van der Aalst, Hybrid intelligence: to automate or not to automate, that is the question, International Journal of Information Systems and Project Management 9 (2021) 5–20. doi:1 0 . 1 2 8 2 1 / i j i s p m 0 9 0 2 0 1 . [17] V. Leno, A. Polyvyanyy, M. Dumas, M. La Rosa, F. M. Maggi, Robotic Process Mining: Vision and Challenges, Business & Information Systems Engineering 63 (2021) 301–314. URL: https://link.springer.com/10.1007/s12599-020-00641-4. doi:1 0 . 1 0 0 7 / s 1 2 5 9 9 - 0 2 0 - 0 0 6 4 1 - 4 . [18] J. Geyer-Klingeberg, J. Nakladal, F. Baldauf, F. Veit, Process mining and Robotic process automation: A perfect match, volume 2196, 2018, pp. 124–131. ISSN: 1613-0073. [19] W. van der Aalst, Process mining and RPA: How to pick your automation battles?, in: Robotic Process Automation: Management, Technology, Applications, 2021, pp. 223–239. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127378467&doi=10.1515% 2f9783110676693-012&partnerID=40&md5=62ab1dcef82e851c1c7dc4256485b546. doi:1 0 . 1 5 1 5 / 9 7 8 3 1 1 0 6 7 6 6 9 3 - 0 1 2 . [20] D. Choi, H. R’Bigui, C. Cho, Candidate digital tasks selection methodology for automation with robotic process automation, Sustainability (Switzerland) 13 (2021). doi:1 0 . 3 3 9 0 / s u 1 3 1 6 8 9 8 0 . [21] D. Truong, F. Warmate, C. I. Okoye, Robotic Process Automation and its efect on Employees’ Attitude and Behaviour (2023). URL: https://www.researchgate.net/publication/ 366311730. doi:1 0 . 1 3 1 4 0 / R G . 2 . 2 . 1 3 8 5 6 . 2 5 6 0 0 . [22] A. Asatiani, E. Penttinen, J. Ruissalo, A. Salovaara, Knowledge Workers’ Reactions to a Planned Introduction of Robotic Process Automation—Empirical Evidence from an Accounting Firm, in: R. Hirschheim, A. Heinzl, J. Dibbern (Eds.), Information Systems Outsourcing, Springer International Publishing, Cham, 2020, pp. 413–452. URL: http://link. springer.com/10.1007/978-3-030-45819-5_17. doi:1 0 . 1 0 0 7 / 9 7 8 - 3 - 0 3 0 - 4 5 8 1 9 - 5 _ 1 7 , series Title: Progress in IS. [23] P. Hofmann, C. Samp, N. Urbach, Robotic process automation, Electronic Markets 30 (2020) 99–106. URL: https://doi.org/10.1007/s12525-019-00365-8. doi:1 0 . 1 0 0 7 / s 1 2 5 2 5 - 0 1 9 - 0 0 3 6 5 - 8 . [24] D. Fernandez, O. Dastane, H. Omar Zaki, A. Aman, Robotic process automation: bibliometric reflection and future opportunities, European Journal of Innovation Management (2023). URL: https://www.emerald.com/insight/content/doi/10.1108/EJIM-10-2022-0570/full/html. doi:1 0 . 1 1 0 8 / E J I M - 1 0 - 2 0 2 2 - 0 5 7 0 . [25] R. K. Yin, Case study research and applications: design and methods, sixth edition ed.,
SAGE, Los Angeles, 2018. [26] C. Flechsig, F. Anslinger, R. Lasch, Robotic Process Automation in purchasing and supply management: A multiple case study on potentials, barriers, and implementation, Journal of Purchasing and Supply Management 28 (2022). doi:1 0 . 1 0 1 6 / j . p u r s u p . 2 0 2 1 . 1 0 0 7 1 8 , publisher: Elsevier Ltd. [27] J. Jussila, V. Sillanpää, T. Lehtonen, S. Plc, N. Helander, L. Frank, An activity theory perspective on creating a new digital government service in Finland, in: 52nd Hawaii International Conference on System Sciences, 2019. URL: https://hdl.handle.net/10125/ 59729. [28] D. Fernandez, A. Aman, Impacts of Robotic Process Automation on Global Accounting Services, Asian Journal of Accounting and Governance 9 (2018) 123–132. doi:1 0 . 1 7 5 7 6 / a j a g - 2 0 1 8 - 0 9 - 1 1 , publisher: Penerbit Universiti Kebangsaan Malaysia (UKM Press). [29] W. van der Aalst, H. A. Reijers, A. J. M. M. Weijters, B. F. van Dongen, A. K. Alves de Medeiros, M. Song, H. M. W. Verbeek, Business process mining: An industrial application, Information Systems 32 (2007) 713–732. URL: https://www.sciencedirect.com/science/ article/pii/S0306437906000305. doi:1 0 . 1 0 1 6 / j . i s . 2 0 0 6 . 0 5 . 0 0 3 . [30] J. D. Weerdt, A. Schupp, A. Vanderloock, B. Baesens, Process Mining for the multi-faceted analysis of business processes - A case study in a financial services organization, Computers in Industry 64 (2013) 57–67. doi:1 0 . 1 0 1 6 / j . c o m p i n d . 2 0 1 2 . 0 9 . 0 1 0 . [31] M. Gotthardt, D. Koivulaakso, O. Paksoy, C. Saramo, M. Martikainen, O. Lehner, Current state and challenges in the implementation of smart robotic process automation in accounting and auditing, ACRN Journal of Finance and Risk Perspectives 9 (2020) 90–102. doi:1 0 . 3 5 9 4 4 / J O F R P . 2 0 2 0 . 9 . 1 . 0 0 7 , publisher: ACRN Oxford Ltd. [32] M. B. Miles, A. M. Huberman, Qualitative data analysis: an expanded sourcebook, 2nd ed ed., Sage Publications, Thousand Oaks, 1994. [33] A. Asatiani, O. Copeland, E. Penttinen, Deciding on the robotic process automation operating model: A checklist for RPA managers, Business Horizons (2022). URL: https: //linkinghub.elsevier.com/retrieve/pii/S0007681322000246. doi:1 0 . 1 0 1 6 / j . b u s h o r . 2 0 2 2 . 0 3 . 0 0 4 , publisher: Elsevier BV. [34] A. Bhargava, M. Bester, L. Bolton, Employees’ Perceptions of the Implementation of Robotics, Artificial Intelligence, and Automation (RAIA) on Job Satisfaction, Job Security, and Employability, Journal of Technology in Behavioral Science 6 (2021) 106–113. URL: https://doi.org/10.1007/s41347-020-00153-8. doi:1 0 . 1 0 0 7 / s 4 1 3 4 7 - 0 2 0 - 0 0 1 5 3 - 8 . [35] J. Wewerka, M. Reichert, Towards Quantifying the Efects of Robotic Process Automation, in: IEEE 24th International Enterprise Distributed Object Computing Workshop, 2020. [36] D. Turner, Qualitative Interview Design: A Practical Guide for Novice Investigators, The Qualitative Report 15 (2010) 754–760. URL: https://nsuworks.nova.edu/tqr/vol15/iss3/19. doi:1 0 . 4 6 7 4 3 / 2 1 6 0 - 3 7 1 5 / 2 0 1 0 . 1 1 7 8 . [37] O. Bismark, A.-G. Frank, O. A. Kofi, H. Eric, Utilizing Mckinsey 7s Model, SWOT Analysis, PESTLE and Balance Scorecard to Foster Eficient Implementation of Organizational Strategy. Evidence from The Community Hospital Group-Ghana Limited, International Journal of Research in Business, Economics and Management 2 (2018). URL: www.ijrbem. com, iSBN: 233-332-094905-7. [38] I. Gokdeniz, C. Kartal, K. Komurcu, Strategic Assessment based on 7S McKinsey Model for a Business by Using Analytic Network Process (ANP), International Journal of Academic Research in Business and Social Sciences 7 (2017). doi:1 0 . 6 0 0 7 / i j a r b s s / v 7 - i 6 / 2 9 6 7 , publisher: Human Resources Management Academic Research Society (HRMARS). [39] B. Kocaoglu, E. Demir, The use of McKinsey s 7S framework as a strategic planning and economic assestment tool in the process of digital transformation, Pressacademia 9 (2019). doi:1 0 . 1 7 2 6 1 / p r e s s a c a d e m i a . 2 0 1 9 . 1 0 7 8 , publisher: Pressacademia. [40] McKinsey, Enduring Ideas: The 7-S Framework, 2008. URL: https:// www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/ enduring-ideas-the-7-s-framework#. [41] B. Farrugia, WASP (Write a Scientific Paper): Sampling in qualitative research, Early Human Development 133 (2019) 69–71. URL: https://linkinghub.elsevier.com/retrieve/pii/ S0378378219301859. doi:1 0 . 1 0 1 6 / j . e a r l h u m d e v . 2 0 1 9 . 0 3 . 0 1 6 . [42] W. van der Aalst, On the pareto principle in process mining, task mining, and robotic process automation, in: DATA 2020 - Proceedings of the 9th International Conference on Data Science, Technology and Applications, SciTePress, 2020, pp. 5–12. doi:1 0 . 5 2 2 0 / 0 0 0 9 9 7 9 2 0 0 0 5 0 0 1 2 , titleTranslation: titleTranslation: titleTranslation:. [43] D. Choi, H. R’Bigui, C. Cho, Enabling the Gab Between RPA and Process Mining: User Interface Interactions Recorder, IEEE Access 10 (2022) 39604–39612. doi:1 0 . 1 1 0 9 / A C C E S S . 2 0 2 2 . 3 1 6 5 7 9 7 . [44] R. Plattfaut, V. Borghof, M. Godefroid, J. Koch, M. Trampler, A. Coners, The Critical Success Factors for Robotic Process Automation, Computers in Industry 138 (2022). doi:1 0 . 1 0 1 6 / j . c o m p i n d . 2 0 2 2 . 1 0 3 6 4 6 , publisher: Elsevier B.V. [45] L. A. Cooper, D. K. Holderness, T. L. Sorensen, D. A. Wood, Perceptions of Robotic Process Automation in Big 4 Public Accounting Firms: Do Firm Leaders and Lower-Level Employees Agree?, Journal of Emerging Technologies in Accounting 19 (2022) 33–51. doi:1 0 . 2 3 0 8 / J E T A - 2 0 2 0 - 0 8 5 , publisher: American Accounting Association. [46] A. Salih Aydıner, S. Ortaköy, Z. Özsürünç, Employees’ perception of value-added activity increase of Robotic Process Automation with time and cost eficiency: a case study, International Journal of Information Systems and Project Management 11 (2023) 30–49. URL: https://revistas.uminho.pt/index.php/ijispm/article/view/4714. doi:1 0 . 1 2 8 2 1 / i j i s p m 1 1 0 1 0 2 . [47] L. Waizenegger, A. A. Techatassanasoontorn, When robots join our team: A configuration theory of employees’ perceptions of and reactions to Robotic Process Automation, Australasian Journal of Information Systems 26 (2022). URL: https://journal.acs.org.au/index. php/ajis/article/view/3833. doi:1 0 . 3 1 2 7 / a j i s . v 2 6 i 0 . 3 8 3 3 . [48] J. Kokina, S. Blanchette, Early evidence of digital labor in accounting: Innovation with Robotic Process Automation, International Journal of Accounting Information Systems 35 (2019) 100431. URL: https://www.sciencedirect.com/science/article/pii/S1467089519301101. doi:1 0 . 1 0 1 6 / j . a c c i n f . 2 0 1 9 . 1 0 0 4 3 1 .