Category: Theses

Theses written by IMMIT students in the past.

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Thesis Väinö Saarinen

Subject: Information System Science

Title: Benefits from implementing a data labelling tool

Abstract: 

The amount of data in the world is constantly increasing, making data management more complex and demanding. Effective data utilization is crucial for in-depth analysis, logical reasoning, and decision-making processes. Data labelling is an essential part of this process, but it has traditionally been labour-intensive and resource-consuming. To manage always scarce resources more efficiently, companies are turning to data labelling tools to automate the process, enhance data management, and extract more value from their data.

This thesis aims to reason the benefits and risks associated with implementing a data labelling tool, specifically Microsoft Purview. The study employs a benefit measurement model and includes a pilot project conducted in a case company. Additionally, interviews with company professionals were conducted to provide further validation and professional insights into the benefits of data labelling.

The findings reveal several notable benefits of data labelling and data labelling tools. Firstly, labelling tools improve the quality and understanding of the data in hand, enhancing its utility. Secondly, automated labelling tools significantly accelerate the labelling process, reducing resource consumption compared to manual methods. Thirdly, data labelling offers broad advantages in data management, data governance, data loss prevention, data security and compliance management and data lifecycle management. Risks related to data labelling tool implementation includes accuracy of labelling, user adoption and engagement and beneficial resource allocation.

Key words: Data labelling, Benefit management, Data labelling tool

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Thesis Irene Manetti

Subject: International Master in Management of IT

Title: Enhancing Financial Audits through Deep Learning: Addressing Key Challenges and Improving Efficiency

Abstract: 

The financial audit (FA) process, traditionally based on manual procedures and reliant on professional judgment, faces challenges in the era of digitalization, due to the requirement of analyzing large volumes of complex data. This thesis investigates how deep learning (DL) can address challenges in the FA process, particularly focusing on large data volumes, manual procedures, and the subjectivity of professional judgment. Using the Task-Technology Fit (TTF) theory as a guiding framework, the study explores DL’s potential through a comprehensive research approach.

Through 13 EY expert interviews across various global locations, and a qualitative survey, the research identifies key challenges in current FA practices, and shows a fit with DL applications. DL shows promise in addressing these issues by automating tasks, managing data complexity and large data volumes, and providing auditors with data-driven recommendation.

Findings reveal that DL’s capabilities in natural language processing (NLP), computer vision, anomaly detection, recommendation systems, and big data analytics can address the identified FA challenges. Additionally, DL models are suggested for alleviating each challenge.

This study not only validates existing DL applications, but also introduces up to date FA challenges. This thesis provides a solid foundation for future research and practical applications in the field of financial auditing. The implications of these findings suggest that adopting DL can lead to more efficient and accurate FA processes.

Key words: Deep Learning (DL), Financial Audit (FA), Task-Technology Fit (TTF)

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Thesis Eemil Häikiö

Subject: Information Systems Science

Title: Adoption and Governance of AI-Powered Dashboards in Executive-Level Decision-Making

Abstract: 

AI-powered dashboards are business intelligence tools that collect and present data in a comprehendible manner to enhance decision-making. The integration of artificial intelligence (AI) in data-driven decision-making (DDDM) processes is continuously increasing in modern organizations, however, current research examining AI-powered dashboards does not address the deployment of the technology. To complement prior research, this thesis aims to provide a foundation for the implementation of AI-powered dashboards by investigating the current dashboard best practices, as well as the adoption and governance of AI-powered dashboards in the context of executive decision-making. The concepts of DDDM and AI function as a foundation regarding the purpose and functionality of AI-powered dashboards, whereas IT governance provides a basis for their adoption and governance. The research methodology encompasses a review of scientific literature addressing these concepts to enable the proposal of two distinct theoretical frameworks addressing the adoption and governance of AI-powered dashboards.

The results of this study highlight three critical areas regarding AI-powered dashboards: current best practices, adoption processes, and governance. Best practices show that AI-enabled dashboards are dynamic and versatile BI tools that enhance decision-making and operational efficiency through detailed visualizations and data analysis. Concerning adoption, the study emphasizes the importance of selecting a suitable framework tailored to organizational needs, suggesting that a combination of existing models might often be necessary to integrate AI and BI within organizational environments effectively. Results addressing the governance of AI-powered dashboards emphasize the importance of BI and AI governance. Although the development of AI governance frameworks is still in its early stages compared to BI and IT governance, the findings suggest that adopting flexible and diverse governance structures enables organizations to manage the security, transparency, and accuracy risks associated with AI technologies. 

Key words: Dashboard, Business intelligence, Artificial intelligence, Data-driven decision-making.

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Thesis Ronán Aardenburg

Subject: Cybersecurity; Information Management

Title: Going passwordless – a case study on the implementation of passwordless at Accenture

Abstract: 

In recent years, passwordless authentication has become an increasingly popular topic in the cybersecurity industry. An increase in the amount of cyberattacks has warranted a need for better authentication methods, and passwordless is perfectly suited for this issue. As one of the first companies in the world, Accenture has implemented passwordless authentication at a large scale.

There is a lack of existing research into implementing authentication systems, especially into passwordless authentication. This thesis investigates the implementation of passwordless authentication at Accenture, through interviews with implementation team members, IT service desk members, and ‘regular’ employees, several key lessons for future implementations have been identified.

The study has concluded that clear and concise communication is a key factor in an implementation, as end-users are prone to misunderstanding or ignoring important communication. Furthermore, the IT service desk must be involved as a main stakeholder in an implementation, as they have a heavy burden to support the implementation. To address the gaps in existing research, future studies should focus on the IT service desk and conduct additional case studies to increase the knowledge on passwordless authentication.

Key words: passwordless, authentication, implementation, end-user communication, FIDO2, passkeys.

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Thesis Elyas Razawi

Abstract: There are many different strategic objectives in managing an acquisition. One of the objectives that have grown in importance in recent years is the digital component of an acquisition. Especially in IT-driven acquisitions, spotting digital value levers and developing transformative capabilities help unlock more value from M&A. However, developing these digital dynamic capabilities in the context of M&A is novel and only practised by the most innovative firms.

This paper aims to uncover what role the digital dynamic capabilities play in the pre-signing phase of an IT-driven acquisition and whether there are any advantageous digital dynamic capabilities for IT-driven acquisitions. This thesis focuses on solving these questions by conducting informed grounded theory by studying multiple cases with semi-structured interviews and an extensive literature review.

From the analyses of the interviews and a confirmation of the validating focus group, insights into critical and supportive digital dynamic capabilities for the pre-signing phase of an IT-driven deal have been found, and a select few capabilities are picked to build a new theoretical model, namely formulating digital strategies, conducting IT due diligence, analysing the financial value of IT, leveraging digital knowledge inside the firm and external recruiting of digital expertise. These and other supporting digital dynamic capabilities are essential in unlocking value from IT-driven acquisitions.

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Thesis Lisa Janssen

Abstract: Both digital transformation and ESG (Environmental, Social, Governance) objectives are mainstream, yet critical to today’s economy. Multiple studies argue that there is a connection between digital transformation and ESG within the business context, but what that connection precisely entails remains a black box. The insurance industry is lagging in both digital transformation and ESG, so it could be assumed that insurance firms also lag in the possible connection. Several important factors were discovered by conducting an extensive literature review, several semi-structured interviews with experts in the field of insurance, and a validating document analysis.

There seems to be no direct influence between digital transformation and ESG if this is not included in the organization’s business strategy. To have the most significant possible impact on ESG objectives after a digital transformation, a practical roadmap has been designed as a support for insurance firms, where the first step is to include ESG objectives into the business strategy, then to change the organizational structure and company culture, to lastly be able to make more sustainable decisions and to invest in a greener IT infrastructure.

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Thesis Guillaume Manoukian

Abstract: This thesis examines the crucial issue of AI governance in companies and addresses the lack of clear governance frameworks that facilitate adopting and maximizing AI benefits.

Through a qualitative approach involving ten participants from the Bosch environment
(Belgium, Netherlands, Germany, and Portugal), this study investigates the implementation of AI-driven tools and proposes insights for effective governance. Companies should prioritize implementing Intelligent Process Automation by leveraging precise analyses and establishing clear internal guidelines. Moreover, creating dedicated teams, such as a data team, can significantly contribute to automating processes across different departments.

This research is built upon existing literature and knowledge in the field while providing unique insights by incorporating an internal perspective within Bosch. In practice, the recommendations from this study can be applied by creating specific teams within the company and emphasizing comprehensive documentation of AI processes and guidelines.

Further research could explore developing tailored approaches to suit specific company requirements. It is essential to acknowledge that a limitation of this study is the perpetual evolution of IPA.

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Thesis Victor Lhuilier

Abstract: In recent years, the predominant focus of organisations lied on automating business processes and augmenting productivity using ERP, SCM and CRM solutions. Nonetheless, to maintain competitiveness in the future, organisations must now surpass process automation by embedding them with greater intelligence through the integration of analytics into their processes and business application. Cutting-edge technologies like HTAP have facilitated the convergence of operational and informational environment leading to the creation of a new technology: embedded analytics.

Embedded analytics offers relevant and timely insights directly within the context of an application, reducing the need for users to switch between multiple tools or interfaces. Despite its rising popularity, research embedded analytics adoption is still inexistant. The thesis extended the UTAUT to explore factors influencing embedded analytics adoption in organisational contexts. The results indicated that performance expectancy, social influence and facilitating conditions are the primary drivers of embedded analytics adoption. It suggests that organisations and researchers should focus on enhancing performance expectancy by emphasising the usefulness and benefits of embedded analytics, leveraging social influence to promote adoption through influential figures and social norms, and providing facilitating conditions such as resources and support to remove barriers to adoption. Moreover, the findings show that company resources such as UX design, data quality and company mandate strengthen the aforementioned relationships.

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Thesis Camille Jennepin

Abstract: Organizational Sensemaking is still a hot topic for IS research field. This approach enables IS researchers to highlight the undergoing social and cognitive processes which an IS project goes through, and the way construction of meanings from these complex situations are achieved.

Mergers and acquisitions became progressively prevalent in today’s dynamic environment as organizations seek strategic growth and competitive advantage. Consequently, these events often trigger significant changes in the structure and functioning of the IS, leading to the need for restructuration projects.

To reach the research goals, a qualitative methodology approach utilizing a case study methodology and interviews will be employed. The selected case study organization will have undergone an M&A event followed by an IS restructuration project. In-depth interviews will be conducted with key stakeholders involved in both the M&A event and the later IS restructuration project. These interviews will provide rich insights into the experiences, perceptions, and interpretations of organizational members during the sensemaking process. The qualitative analysis of the interview data will involve thematic analysis to find patterns, themes, and categories related to sensemaking and its relationship to the M&A and IS restructuration.

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Thesis Loys Guglielmi

Abstract: This research study explores the nuanced impact of the Generative Artificial Intelligence’s emergence (GenAI) on employee stress levels, expectations, and challenges within the consulting and audit sector. The study was inspired by realizing the unregulated and under-acknowledged usage of GenAI tools, particularly ChatGPT, within Deloitte, a phenomenon termed ‘shadow use.’

The study strives to discern whether employees view this trend as a boon or a bane for
their professional roles and to identify the measures they consider necessary to ensure this
disruptive technology does not adversely impact their careers and work-life balance.
An interpretive exploratory research approach was employed to attain a comprehensive
and in-depth understanding of the issue at hand. This method involved conducting extensive interviews with Deloitte France employees across different hierarchical levels and an AI expert to gather diverse viewpoints. This qualitative research strategy gave an enriched perspective on how GenAI is perceived, used, and regarded within a well-established consulting firm among the market leaders.

The findings from the study reveal a spectrum of opinions and apprehensions among
employees about the advent of this technological innovation. While many consultants
acknowledge the significant potential of GenAI to automate tedious tasks, there is a general unease related to job security as the capabilities of AI continue to escalate. Various GenAI adoption challenges were identified during the research, with paramount emphasis on data confidentiality and the veracity of information. These concerns are especially significant in light of Deloitte’s responsibility to safeguard sensitive client data.

The study offers a set of pragmatic recommendations for Deloitte and analogous consulting firms. Those aim to help them facilitate an efficient, ethically compliant, and secure transition towards a generalization of GenAI in the workplace to minimize adverse impacts on employees’ well-being while optimizing productivity.