CLSBE - Dissertações de Mestrado / Master Dissertations
URI permanente para esta coleção:
Navegar
Percorrer CLSBE - Dissertações de Mestrado / Master Dissertations por Objetivos de Desenvolvimento Sustentável (ODS) "09:Indústria, Inovação e Infraestruturas"
A mostrar 1 - 10 de 380
Resultados por página
Opções de ordenação
- Accepting generative artificial intelligence in consulting services : an analysis of success factorsPublication . Herzog, Inessa; Lancastre, FilipaThe present study examines the success factors that influence the acceptance of Generative Artificial Intelligence (GenAI) among business consultants. While numerous research studies have developed and expanded models for general technology acceptance, there are still few studies that address the acceptance of GenAI in specific professional contexts such as the consulting industry. This work fills this research gap by developing a new model to investigate the acceptance factors of GenAI among business consultants. The model is based on established theories such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Task-Technology Fit (TTF) model, which have been combined into a new conceptual framework. For empirical verification, a quantitative study was conducted with 147 business consultants to identify key success factors on the intention to use and acceptance of GenAI. The results show that the fit between technology and tasks (Task-Technology Fit, TTF), the expected performance improvement (Performance Expectancy, PE) and the Behavioural Intention (BI) play a crucial role in the acceptance of GenAI in the business consulting context. The study highlights the need amongst companies for targeted training, practical use cases, and a strategic integration of GenAI into existing workflows to promote sustainable acceptance. The results provide both theoretical and practical implications for consulting firms to support the successful implementation of GenAI
- Accounting for intangibles : the role of capitalized R&D in explaining market valuePublication . Mühlhäuser, Louis Otto Hanskarl; Pape, UlrichThis study investigates whether capitalizing research and development (R&D) expenditures enhances the value relevance of financial information in U.S. capital markets. While R&D is a key driver of firm innovation and long-term value, it is typically expensed under U.S. GAAP, limiting its visibility in financial statements. A standardized capitalization and amortization approach is applied to firm-level R&D data to assess its impact on the explanatory power of earnings–price regressions. Based on a panel of U.S. firms from 1994 to 2023, the analysis shows that capitalizing R&D consistently increases the informativeness of reported earnings, particularly in more recent years. The findings indicate that markets increasingly price R&D as an unrecognized asset and that failing to account for it distorts valuation signals. These findings contribute to ongoing debates in financial reporting and intangible valuation by providing long-run empirical evidence on the effects of R&D capitalization on the capital market. The results have implications for analysts, investors, and accounting standard setters, highlighting the need for more transparent treatment of intangible-related expenditures in financial reporting.
- Achilles and the future of data economy : building trust and financial viability for data protection and monetizationPublication . Forte, João Domingos Mesquita Patena; Xavier, RuteThis thesis explores the viability of Achilles, a privacy-tech startup that combines personal data protection with the option to monetize anonymized user data. While digital platforms profit immensely from personal information, most users remain unaware that their data has market value or that they could benefit from it directly. Achilles addresses this imbalance by offering privacy tools alongside monetization features, aiming to empower individuals in the data economy. To assess adoption potential, a mixed-methods approach was employed: a quantitative survey of 241 respondents (focused on the 174 under-30 segment), 40 qualitative interviews, and an A/B branding experiment were conducted. The study evaluates user willingness to monetize data, preferred compensation models, trust barriers, and effective branding and acquisition strategies. Results show that only 7.5% of young users would not install an app like Achilles. While trust remains a major barrier, referrals and transparent design significantly improve perceptions. Branding tests revealed that bold, modern design (Achilles) beats traditional, conservative branding (DataGuardian) even though it enhances trust. The findings suggest that Achilles can gain traction through referral marketing, trust-based partnerships, and educational content. With the right strategy, Achilles has the potential to transform users from passive data sources into active participants in the digital economy.
- Adaptation of artificial intelligence in marketing for the automotive industryPublication . Ohanisian, Jerar; Xavier, RuteThe automotive industry is experiencing a dynamic shift due to rapid environmental changes. Organizations are adopting innovative solutions to navigate these extreme shifts successfully. Since 2023, Nissan and several other organizations have adopted a new business model to change their approach to the market, hoping to have more connectivity with the customer. The new business model is being adopted in Sweden as a demo market to prove its sustainability and effectiveness in a sustainable market. This research explores the new business model in detail, focusing on how the organization is collecting and segmenting customer data and building the customer database. Most importantly, it explains how implementing Artificial intelligence and Big data will enhance this approach. Building big data infrastructure will offer various benefits, including <Velocity, Veracity, Value, Variety, Volume.= meanwhile, implementing article intelligence will increase the marketing effects, making it more effective, cost-efficient, and reliable. Furthermore, AI and big data will play a significant role in collecting customer data, building segmentation for target audience based on demographic, psychographic, and behavioral patterns, while also aligning the target audience taking into account the market shifts and evolving customer needs.
- Adapting to challenges : Sanner’s strategic journey through a period of crisesPublication . Hahn, Jakob; Cardeal, NunoThis dissertation examines the adaptive strategies introduced by the Sanner Group during a period of crises, including the climate crisis, the Covid-19 pandemic and the Russo-Ukrainian war, which illustrates the growing impact of such crises on corporate strategy development. Following a pedagogical research approach, the constructed case study examines how Sanner, global market leader for desiccant closures and effervescent tablet packaging, adapted to changing environments by exploiting dynamic capabilities. Extensive research was conducted to analyse the impact of the crises on the business and its strategic responses. Additional evidence was gathered through interviews with the company's management team, which offered insights into the internal processes driving adaptive strategies. The teaching notes provide a structured proposal for effective knowledge transfer, enabling instructors to enhance students' understanding of related theoretical concepts through application to the case example. The findings indicate that Sanner effectively recognized and interpreted external developments by continuously monitoring the macroeconomic environment. Based on this, the company was able to take timely strategic action and leverage valuable resources and capabilities, facilitated by its organizational adaptability. As a result, Sanner not only managed the immediate crises successfully, but also engaged in a strategic renewal, which improved the company's resilience and competitiveness. The work thus highlights the critical role of dynamic capabilities in mitigating the adverse effects of crises and achieving long-term organizational success.
- The adoption of AI agents : from automation to autonomy, reshaping governance and strategic decision-making in FintTchPublication . Szakacs, Abel; Rajsingh, PeterThis study investigated how AI agents are transforming automation, governance, and strategic decision-making in the FinTech sector. Drawing on an extensive literature review, the research identifies key adoption drivers including efficiency, real-time responsiveness, compliance alignment, and innovation pressure. The findings were evaluated using a mixed methods approach, combining 13 semi-structured expert interviews with a survey of 112 professionals, and validated through triangulation across qualitative and quantitative data. Using data collected from the survey, a regression analysis and Sample T-test was conducted, indicating that individuals are more likely to adopt AI agents if they perceive them as useful, feel confident in their ability, receive organizational support, and experience ongoing adoption within their workplace (p < 0.05). Expert insights reinforced these drivers, emphasizing the role of modular agent design, organizational readiness, explainability, and real-time adaptability in successful deployment. The results suggest that AI agents are evolving from static automation tools to strategic, goal-oriented collaborators. However, their integration still faces challenges related to transparency, hallucination risks, and overreliance on automation, emphasizing the need for stronger regulatory alignment and governance frameworks. While the study offers a comprehensive understanding of responsible AI agent adoption, its scope is limited by non-probability sampling and rapid evolution of the technology, which may affect the generalizability of the findings. Future research should explore long-term agent behavior and coordination across industries. Overall, the findings underscore the growing strategic importance of AI agents in shaping the future FinTech.
- Adoption of AI tools in software development in Germany : curse or blessing for the SME sector?Publication . Blischke, Robin; Rajsingh, PeterThis study investigated the adoption of Artificial Intelligence (AI) tools in Germany's software development sector, focusing on small and medium-sized enterprises (SMEs). AI technologies are reshaping the industry, presenting opportunities and challenges. Using a mixed-methods approach, including 14 expert interviews and a survey of 262 participants, the research identified key factors affected by AI adoption, such as efficiency gains, code quality, competitive pressure, security risks, organizational readiness, technical debt, and ethical concerns. These factors were identified through a literature review, tested via expert interviews, and validated through a survey, adhering to the principle of triangulation. Currently, AI adoption in German SMEs remains in its infancy, primarily focused on enhancing productivity in routine tasks, with strategic integration still limited. Expert insights highlighted SMEs' agility in deploying off-the-shelf AI tools but noted constraints from limited resources and technical expertise. In contrast, large enterprises (LEs) leverage robust infrastructure and R&D investments for more comprehensive AI integration. While AI tools were viewed as an efficiency innovation, findings indicated their disruptive potential to democratize coding, bridge skill gaps, and drive long-term transformations. However, systemic barriers, including security vulnerabilities, ethical dilemmas, and insufficient organizational readiness, continue to hinder widespread adoption. By integrating dynamic capabilities and innovation theories, this research extrapolated AI’s trajectory from incremental efficiency gains to disruptive innovation, fundamentally altering workflows and competitive dynamics. The study offers actionable recommendations to foster readiness, address ethical and security concerns, and promote targeted upskilling for a transformative future.
- The adoption of blockchain in the supply chain of global manufacturing firms : motivations, challenges, and key success factorsPublication . Urzua, Ursula Judith Virgen; Lancastre, FilipaGlobal manufacturers face persistent blind spots that ultimately fuel disputes, delays, and costly workarounds. Although interest in blockchain is high and pilots are common, few initiatives scale across firms. In the academic literature, use cases and potential benefits are well mapped, yet firm-level accounts of how adoption decisions are made, and why promising efforts stall, remain scarce. Drawing on semi-structured interviews with supply-chain professionals in global manufacturing (n=21) and a Gioia analysis, the study reframes blockchain as shared evidence: attributable, time-stamped event records that trigger coded actions, rather than a generic database. Progress is shown to depend on a trial-first, measured rollout; executive-backed, quantified business cases; and co-authored operating rules that specify who records what, when, and with which proofs. Barriers include exposure aversion, weak posting discipline, uneven partner capability, bureaucratic drag, and concerns about governance trust. For managers, the findings distill a usable sequence: identify high-friction handoffs and the few boundary events that matter most, integrate their capture with existing systems, run a focused trial, measure before and after, and expand only when gains persist across lanes and sites; scale improves when joining is easy for partners and visibility is role-based and contractually bounded. Theoretically, the study casts blockchain as an evidence-centred coordination layer, maps a path from potential to routine use, shows how verifiable handoffs curb hidden action, and frames adoption as staged fit-and-feasibility checks tied to agency, affordance–actualization, and fit–viability models within a configurational view of success.
- Adoption of post-quantum cryptography in organizations : challenges and driversPublication . Zimmermann, Daniel; Rajsingh, Peter V.The emergence of quantum computing poses a significant threat to modern encryption standards, with the potential to render widely used cryptographic protocols obsolete. To mitigate this risk, organizations must transition to post-quantum cryptography (PQC), a process that requires technical, operational, and strategic adjustments. However, the pace and approach to adoption vary across industries and organizational size, influenced by both external pressures and internal capabilities. This study employs a mixed-methods approach, incorporating 14 expert interviews and survey responses from 37 cybersecurity professionals to examine the factors driving and inhibiting PQC adoption. The findings reveal that while regulatory mandates play a role, organizations are increasingly guided by risk exposure and reputational concerns. Key challenges include resource constraints, reliance on third-party vendors, and the complexity of cryptographic inventory assessments and legacy systems. Despite these challenges, the study found that PQC adoption will accelerate as risk awareness grows and implementation guidelines become available. It emphasizes the importance of a strategic, resilience-focused approach to cybersecurity, incorporating cryptographic agility, continuous assessments, and hybrid encryption solutions to navigate the evolving threat landscape.
- AI and the evolving skill set in consulting managerial insights on essential skills and human-AI collaborationPublication . Morawetz, Maximilian Johann; Almeida, Filipa deThis master thesis explores Artificial Intelligence (AI) in the management consulting industry and its impact on skill sets and human AI-collaboration. The study consists of three research questions, focusing on the emergence of new roles in management consulting as a results of AI integration, the difference between traditional roles and AI-enhances roles, and the managerial perception of the impact of human-AI collaboration on the efficiency and effectiveness on project outcomes. The overall objective is to explore the evolving skill set and provide managerial insights to adapt AI-driven changes. Semi-structured interviews with managers were conducted for qualitative research. The sample contains 18 participants in managerial positions from eleven consulting firms. The focus of the research was on capturing insights on emerging roles, evolving competencies, and the impact of AI on project efficiency and effectiveness. The results illustrate that AI shifts the demand towards consultants with enhanced data handling knowledge and AI expertise. While there is a greater emphasis on technical skills, soft skills such as critical thinking and contextual decision-making are equally important. Regarding human-AI collaboration, the results show that AI significantly enhances efficiency by automating tasks and assisting in creative thinking. The research contributes to the academic and managerial discourse by emphasising the need for deeper and more educated human-AI collaboration. This requires clear frameworks for skill development and the combination of technical proficiency and interpersonal competencies.