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Osuva on Vaasan yliopiston avoin julkaisuarkisto. Osuva sisältää Vaasan yliopiston omat julkaisut, opinnäytteet ja tieteellisten artikkeleiden rinnakkaistallenteet. Osuvaan sisältyy julkaisujen viitetietoja, tiivistelmiä ja kokotekstejä. Sähköisten arkistokokoelmien sisältö ei ole luettavissa verkossa.

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Viimeksi tallennetut

  • The role of leadership in supporting psychological well-being in WFH
    Mäkelä, Atte (2026-09-24)
    Kandidaatintutkielma
    The purpose of this study is to examine the role of leadership in supporting employee’s psychological well-being in WFH. As WFH has become more common due to advancements in ICTs and COVID-19 pandemic, understanding how leadership influences employees experience has gained importance. This literature review draws on existing research on leadership theories and psychological well-being. The theoretical framework focuses on a few key approaches: transformational leadership, e-leadership, LMX and destructive leadership. Additionally, psychological well-being is examined through eudaimonic perspective, which values aspects such as autonomy and meaningfulness. This study also considers physical distance and digitally mediated communication, which are key characteristics shaping leadership practices and employee experiences. The findings suggest that leadership plays a significant role in shaping employees’ well-being in a WFH context. Positive leadership approaches such as transformational leadership and high-quality LMX are associated with increased perceived support and work-engagement that directly affects well-being positively. In contrast, destructive leadership practices negatively affect employees’ well-being, particularly in WFH settings. Overall, the study highlights the importance of the quality of leadership in supporting employee well-being.
    Tämän tutkimuksen tarkoituksena on tarkastella johtajuuden roolia työntekijöiden psykologisen hyvinvoinnin tukemisessa etätyössä (WFH). Etätyön yleistyessä tieto- ja viestintäteknologian kehityksen sekä COVID-19-pandemian myötä on tullut yhä tärkeämmäksi ymmärtää, miten johtajuus vaikuttaa työntekijöiden kokemuksiin. Tämä kirjallisuuskatsaus perustuu olemassa olevaan tutkimukseen johtajuusteorioista ja psykologisesta hyvinvoinnista. Teoreettinen viitekehys keskittyy muutamaan keskeiseen lähestymistapaan: transformationaaliseen johtajuuteen, e-johtajuuteen, LMX-teoriaan sekä tuhoisaan johtajuuteen. Lisäksi psykologista hyvinvointia tarkastellaan eudaimonisesta näkökulmasta, joka korostaa muun muassa autonomiaa ja merkityksellisyyttä. Tutkimuksessa otetaan huomioon myös fyysinen etäisyys ja digitaalisesti välittynyt viestintä keskeisinä tekijöinä, jotka muovaavat johtamiskäytäntöjä ja työntekijöiden kokemuksia. Tulokset osoittavat, että johtajuudella on merkittävä rooli työntekijöiden hyvinvoinnin muotoutumisessa etätyökontekstissa. Positiiviset johtamislähestymistavat, kuten transformationaalinen johtajuus ja korkealaatuinen LMX-suhde, liittyvät lisääntyneeseen koettuun tukeen ja työn imuun, jotka suoraan parantavat hyvinvointia. Sen sijaan tuhoisat johtamiskäytännöt vaikuttavat kielteisesti työntekijöiden hyvinvointiin, erityisesti etätyöympäristöissä. Kaiken kaikkiaan tutkimus korostaa johtamisen laadun merkitystä työntekijöiden hyvinvoinnin tukemisessa.
  • Compound deployment risk analysis in direct air capture, green ammonia and long-duration energy storage
    Küfeoğlu, Sinan (Elsevier, 2026)
    Artikkeli
    Direct air capture (DAC), green ammonia and long-duration energy storage (LDES) projects have attracted large public commitments, yet project cancellations are mounting. This paper argues that deployment fails when several barriers bind at once, chiefly absent demand, uncompetitive cost, policy uncertainty and unbankable finance, and that a technology's prospects are set by its weakest barrier rather than its average strength. That logic is formalised in a Compound Deployment Risk Index, stress-tested for robustness, and paired with a levelised-cost model for each technology, using sourced data through mid-2026. The results show why each technology stalls. Capturing CO2 from air costs 480–1000 USD per tonne, more than any compliance scheme currently pays. Green ammonia becomes competitive as a shipping fuel only at a carbon penalty of 370 to 680 USD per tonne, above the 100 to 380 USD proposed by the International Maritime Organization. Furthermore, in Great Britain's storage auction, new battery chemistries were undercut by lithium-ion and pumped hydro at every procured duration. The policy conclusion is that creating demand is necessary but not sufficient: it must be paired with cost reduction and de-risked finance for a self-sustaining industry to emerge.
  • Reframing diversity, equity, and inclusion as essential practice for human progress in SMEs: complexity, agency, and emergent inclusion
    Narayan, Rumy; Šilenskytė, Aušrinė (toim.); Fletcher, Margaret (toim.); Butkevičienė, Jurgita (toim.); Jayton, Chenthuran (toim.) (Edward Elgar, 2026)
    Artikkeli
    Effective diversity, equity, and inclusion (DEI) integration requires a shift from linear, reductionist models toward complexity and systems thinking. In a global landscape increasingly shaped by populist discourses that challenge DEI, inclusion cannot remain a checkbox exercise. Instead, it must be a core practice for designing social systems capable of sustainable progress. This transformation demands a societal redesign that prioritizes nonlinearity, distributed agency, and paradox navigation. Utilizing complexity theory, this chapter argues that impactful DEI work mirrors the emergent nature of complex systems. It examines the failures of tokenism and highlights how small- and medium-sized enterprises (SMEs) and entrepreneurs are uniquely positioned to lead this shift. Unlike large corporations, SMEs can implement fundamental changes rapidly, bypassing the inertia of legacy management. Through the lens of adaptive self-organization and identity work, the chapter offers a roadmap for rewiring social architectures to ensure resilient, equitable futures.
  • A new way to analyze ESG reports: A theme based model supported by AI
    Kokkinen, Lauri (Elsevier, 2026)
    Artikkeli
    Sustainability reports are often long, complex, and poorly standardized, making it difficult to extract comparable and meaningful information across companies. Despite increasing regulatory pressure, including the EU's Corporate Sustainability Reporting Directive (CSRD) and European Sustainability Reporting Standards (ESRS), the quality, clarity, and usefulness of ESG disclosures remain questionable. Neither manual nor automated analyses have fully resolved these challenges. This study introduces a transparent and replicable methodological framework for organizing heterogeneous ESG reports into structured thematic summaries. Rather than applying predefined categories, the approach begins with the reports themselves: ESG disclosures are reviewed and similar passages grouped inductively into seven shared ESG themes. Theme-specific keywords and contextual filters are then derived to support AI-assisted retrieval across a larger dataset. The framework was applied to 393 sustainability reports from publicly listed EU companies. The findings demonstrate that the inductively derived thematic structure can be applied consistently across large datasets while preserving interpretability. The framework produces structured summaries showing which ESG topics are addressed, how extensively, and at what level of specificity, providing a foundation that users can begin to interpret and compare in a manner of their choosing. Developing and validating formal comparison methods lies beyond the scope of this study and represents a direction for future research.
  • Multi-task learning based deep neural network for short term power and thermal energy prediction of hybrid PV–Thermal systems
    Khan, Noman Mujeeb; Hamad, Qusay Shihab; Houran, Mohamad Abou; Khan, Muhammad Kamran; Zafar, Muhammad Hamza; Sanfilippo, Filippo (Elsevier, 2026)
    Artikkeli
    Accurate forecasting of power output and thermal energy in hybrid Photovoltaic–Thermal (PVT) plants is critical for optimising energy management and maximising efficiency. Traditional models often struggle to capture complex temporal dependencies and non-linear patterns in such systems. We propose the Inception Embedded Dual Attention Bi-LSTM (IDAMBi-LSTM) Model for enhanced power forecasting and thermal energy prediction in hybrid PVT plants to address these challenges. The model integrates inception layers to capture multi-scale temporal features, dual attention mechanisms to emphasise significant data points, and Bidirectional Long Short-Term Memory (Bi-LSTM) networks for handling sequential dependencies in both forward and backward directions. We conducted a comparative analysis with an Inception Embedded Attention Mechanism with GRU (IAM-GRU) and Inception Embedded Dual Attention GRU (IDAM-GRU) model to evaluate the performance and accuracy of our proposed approach. The evaluation was based on several performance metrics, including Root Mean Square Error (RMSE), R-squared (R2), Normalised Mean Square Error (NMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The results demonstrate that the proposed Inception Embedded Dual Attention Bi-LSTM Model outperforms the IAM-GRU model across all metrics, providing more accurate and reliable forecasts for both power output and thermal energy in hybrid PVT systems. This research highlights the potential of advanced deep learning techniques in improving the efficiency and predictability of renewable energy systems.