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Recent Submissions

Entry strategies to ecosystemic technology markets
(2026-02-15) Turkkila, Aleksi
School of Science | Master's thesis
Developing proprietary technologies that can become industry standards offers companies significant competitive and financial advantages. However, achieving widespread adoption in complex, ecosystem-driven markets is a major challenge. This thesis investigates how companies introduce and scale proprietary technologies within ecosystemic technology markets characterized by interdependent actors and value co-creation. The study aims to identify effective entry strategies and the ecosystem dynamics that firms leverage during this process. Using a multiple case study methodology based on secondary data from publicly available sources, the research examines six technology companies operating in various roles and stages of market entry. The analysis reveals a new model of technology introduction centered around integrator-driven ecosystems, where a key actor assembles value for end users by combining complementarities. Additional strategies identified include the use of technology branding, direct involvement in producing complementary products, and participation in formal standardization processes. This thesis contributes to the literature by integrating ecosystem theory with technology market entry strategy. It provides practical insights for firms seeking to position their technologies as de facto or formal standards in ecosystemic environments.
Simulating Stable Quasi-Isodynamic Design Stellarator Neutronics Using Serpent
(2026-02-06) Lehti, Petteri
School of Science | Master's thesis
This thesis presents a neutron transport study in a SQuID stellarator, modeled using the Monte Carlo simulation software Serpent 2. The main focus is on the tritium breeding efficiency and coil neutron response. A workflow for generating the neutron transport geometry based on a predefined magnetic field and coil set was developed and applied. The neutron transport geometry is composed of the first wall, breeding blanket, vacuum vessel, and coils. The studied SQuID proved to have insufficient shielding for the coils, even with an additional neutron absorber present in the structure. The irradiation was strongly localized on distinct hotspots, located in regions where the coils are closest to the plasma. Without the additional shielding, coils reached local fast flux peaks of over 1.1e11 cm^-2 s^-1 and nuclear heating of approximately 3.5 kW per cubic meter, both roughly two orders of magnitude over their respective design limits of 1e9 cm^-2 s^-1 and 50 W per cubic meter. The peak fast neutron flux and nuclear heating were decreased with additional boron carbide shielding, but they were still over the limits in all simulation cases. The lowest fast flux peak achieved was 2.4e10 cm^-2 s^-1, whereas the lowest nuclear heating peak was at 680 W per cubic meter. Most of the coil volume was well below the limits, even without additional shielding. The Dual-Coolant Lithium-Lead (DCLL) blanket demonstrated better shielding than the Helium-Cooled Pebble-Bed (HCPB) blanket. The studied configuration showed tritium breeding ratios in the range 1.35 to 1.45 with full breeding blanket coverage, significantly over the design target of 1.15. Higher TBRs were observed in HCPB blankets compared to DCLL. Additionally, the HCPB blanket demonstrated better energy multiplication, with energy multiplication factors of over 1.3 compared to 1.15 with DCLL.
Contesting Control Points: Industry Architecture and Value Capture in Generative AI
(2026-02-08) Tekoniemi, Matias
School of Science | Master's thesis
Generative artificial intelligence (generative AI) is reshaping the digital economy, but the decisive contest is not only about building the best AI model. This thesis shows that value capture is shaped by control of three hard-to-bypass bottlenecks: scarce compute and serving capacity, the rules for who gets access, and the default interfaces users work through. Using an industry architecture lens, the thesis maps the generative AI value stack and identifies the control points through which participation conditions and rents are set across layers. Empirically, the study conducts a theory-driven, qualitative multiple-case analysis of four consequential actors (OpenAI, Google, Meta, and Hugging Face) across the post-ChatGPT acceleration period (November 1, 2022–November 30, 2025). Public framing artifacts are compared against high-salience strategic actions (including product launches, partnerships, acquisitions, and investments) to make frame–action alignment and divergence visible. To reduce interpretive drift in public-source research, the analysis applies an a priori codebook, an explicit evidence hierarchy (salience weighting), and traceable matrix-based comparison across cases and themes. Across cases, the evidence converges on three recurring dynamics: (1) compute supply and specialized infrastructure act as primary conditioning constraints for frontier participation, (2) access is increasingly governed via enforceable boundary resources, safety and compliance regimes, and marketplaces that define participation rules, and (3) interface and workflow capture emerges as a central lever for demand routing and switching costs. Decentralization mechanisms (modularity, open access, and extensibility) broaden diffusion and increase substitutability, but in practice relocate rather than eliminate platform power, shifting it toward hubs, standards, and orchestration layers. The thesis contributes (1) a synthesized framework for contested industry architecture under discontinuity, (2) an operationalization of centralization and decentralization postures for analyzing strategic moves across layers, and (3) empirically grounded implications for where control points are likely to stabilize. The results support researchers studying emerging platform structures and inform policymakers and practitioners evaluating competition, dependency, and governance in the generative AI ecosystem.
Assessing physical climate risks in manufacturing supply chains: Perspectives and recommendations for manufacturing companies in view of climate change
(2026-02-09) Mäki, Juho
School of Science | Master's thesis
Acute and chronic climate risks attributable to anthropogenic climate change are becoming increasingly frequent and severe, which has implications for manufacturing companies and their supply chains. Despite the growing vulnerability of global supply chains to climate change, few manufacturing companies have extensively engaged with physical climate risk assessment in their supply chains. Without integrating physical climate risk assessment and supply chain risk management processes, companies are ill-equipped to proactively address these risks. In view of accelerating climate change, this thesis seeks to bridge gaps between supply chain risk management and the assessment of physical climate risks. Using a qualitative interview study, this thesis explores how manufacturing companies currently perceive physical climate risks in their supply chains. Additionally, this thesis explores how manufacturing companies currently assess these risks, and what approaches exist for assessing physical climate risks in supply chains. The findings reveal that there is a lack of systematic physical climate risk assessment in the supply chain risk management of manufacturing companies. It is found that the physical impacts of climate change are predominantly not considered in manufacturing companies' supply chain risk management. Furthermore, the study reveals heterogeneous perceptions about the impacts of physical climate risks on manufacturing company supply chains. The study also outlines practices for manufacturing companies to assess these risks as part of their supply chain risk management.
Carbon Leakage as Self-Selection: Firm Sorting and the Design of Rebating Policies
(2026-02-13) Junnila, Eelis
School of Business | Master's thesis
This thesis examines carbon leakage, the phenomenon in which firms shift production to regions with weaker climate policy to avoid the costs associated with carbon pricing. While carbon taxes and emissions trading systems reduce emissions by putting a price on carbon, unilateral policy can also change where production takes place, causing emissions, jobs, and output to “leak” abroad. A central argument of this thesis is that leakage is fundamentally a selection problem: firms are not identical, and differences in baseline emissions intensity, abatement costs, and relocation costs determine which firms relocate when carbon pricing tightens. To study this mechanism, the thesis applies the Roy model of self-selection to firms’ location choices. The framework delivers a simple threshold rule in which a firm relocates when its carbon-price exposure exceeds its firm-specific cost of moving. Under joint heterogeneity in emissions intensity, abatement efficiency, and mobility, the model characterises not only the overall extent of leakage but also the composition of movers and stayers. This composition matters for policy: the environmental and welfare consequences depend on whether regulation pushes out high-emission firms, low-cost abaters, or primarily the most mobile firms. The thesis then evaluates common leakage-mitigation instruments—output-based rebating and benchmark-based schemes that condition rebates on emissions performance. In the simpler cases, these policies map directly into the selection framework by shifting the relocation threshold and changing how firm characteristics load into the relocation incentive. For more complex emissions-based designs, the selection rule becomes nonlinear, so the thesis complements theory with simulations calibrated to match output protection for a representative firm. Across a range of heterogeneity scenarios, the results show that no single policy dominates: some schemes are robust at limiting relocation but weak on emissions, while others reduce emissions more strongly but require larger fiscal transfers. Overall, the thesis highlights that effective leakage policy must be designed around firm heterogeneity and selection, and that empirical work to pin down the key parameters governing exposure and mobility is essential for credible policy evaluation.