Abstract
Data augmentation is a crucial tool in time series forecasting, especially for deep learning architectures that require a large training sample size to generalize effectively. However, extensive datasets are not always available in real-world scenarios. Although many data augmentation methods exist, their limitations include the use of transformations that do not adequately preserve data properties. This paper introduces Grasynda, a novel graph-based approach for synthetic time series generation that: (1) converts univariate time series into a network structure using a graph representation, where each state is a node and each transition is represented as a directed edge; and (2) encodes their temporal dynamics in a transition probability matrix. We performed an extensive evaluation of Grasynda as a data augmentation method for time series forecasting. We use three neural network variations on six benchmark datasets. The results indicate that Grasynda consistently outperforms other time series data augmentation methods, including ones used in state-of-the-art time series foundation models. The method and all experiments are publicly available. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Entrepreneurial ecosystems are key drivers of regional development, yet limited attention has been paid to how startups experience these systems and how their positions within the ecosystem shape those experiences. This article examines the role of startups in the entrepreneurial ecosystem of the Porto Metropolitan Area, through an in-depth qualitative case study based on semi-structured interviews with founders, investors, intermediaries, and higher-education actors, complemented by contextual indicators analysis. The findings show that the ecosystem is experienced in differentiated ways across actors. Founders primarily experience challenges through market uncertainty, access to funding, and scale-up challenges, while academic entrepreneurs emphasize institutional pathways and knowledge translation processes. University actors focus on talent pipelines, graduates' employability, and collaboration with firms. Although the region benefits from strong early-stage support and a dense institutional infrastructure, a persistent scale-up gap and weak political embeddedness constrain startups' longer-term contributions to regional development, despite their economic significance. The article contributes to entrepreneurial ecosystem research by adopting an actor-centered, positional perspective and by highlighting how institutional diversity, within higher-education, shapes differentiated roles, while pointing to the limits of startup creation policies and the need for stronger coordination, improved scale-up finance, and more meaningful channels for startup participation in decision-making.

Abstract
As competitive and cooperative dynamics gain prominence in games, they present unique opportunities to study player behavior. This paper explores the orientations of different player types, as categorized by Bartles Taxonomy, through the lens of a Game With A Purpose (GWAP) called BartleZ. Bartle's Taxonomy identifies four distinct player types Achievers, Explorers, Socializers, and Killers. This study delves into how these different types approach competitive and cooperative gameplay, through structured dilemmas in BartleZ. Results with 45 participants, reveal that player orientations significantly influence engagement and decision-making. Achievers balanced both strategies; Explorers favored cooperation; Socializers consistently chose cooperation; and Killers preferred competition but adapted in some contexts. Overall, players leaned toward cooperation early on, with a shift toward competition as complexity increased. Our findings pinpoint the importance of tailoring GWAP mechanics with diverse player motivations, enhancing both engagement and problem-solving effectiveness.

Abstract
This study addresses a gap in literature by empirically examining the role of FLIGBY in the development of thinking skills and creativity, contributing to a more holistic understanding of the pedagogical value of serious games in higher education. Two research questions guide the study: (i) how FLIGBY assesses and contributes to the development of thinking skills; and (ii) how the pedagogical approach and challenges embedded in FLIGBY foster creativity. A mixed-methods design was adopted. Quantitative methods were first used to assess students' performance in thinking skills, which in FLIGBY are measured continuously through behavior-based analytics using the Master Analytics Profiler (MAP) system. In parallel, qualitative methods were employed to explore the development of creative competencies through thematic analysis of interview data. The results indicate that FLIGBY is an effective tool for the integrated development of thinking skills and creative competencies in higher education. Statistical analysis reveals moderate to high levels of cognitive skills such as emotional intelligence, leadership, and systemic decision-making, with strong and significant interrelationships among these dimensions. The thematic analysis further shows that FLIGBY fosters creativity by providing a safe environment for experimentation, adaptive decision-making, complex problem solving, and metacognitive reflection. Accordingly, the findings suggest that FLIGBY not only strengthens strategic cognitive skills but also stimulates creative and reflective processes transferable to real-world leadership and management contexts, offering important implications for educational practice and policy in higher education.

Abstract
SMEs in developing economies operate under persistently volatile environments where economic instability, regulatory uncertainte and technological disruptions threaten their survival. Here, sustainability shifts from long-term environmental or socioeconomic performance to strategic resilience. In this study, we investigate how dynamic capabilities condition the effect of business environmental forces on SME sustainability in Nigeria. Grounded in contingency and dynamic capability theory, this study adopts a quantitative, cross-sectional survey design using data from 285 Nigerian SMEs. It examines the direct effects of economic, legal and technological environmental forces, as well as the moderating roles of sensing and seizing, and learning and reconfiguration capabilities, on SME strategic resilience using PLS-SEM. The results show that economic, legal and technological turbulence significantly affect SME strategic resilience, with legal turbulence emerging as the strongest constraint. Findings further reveal that dynamic capabilities-sensing and seizing, learning and reconfiguration-significantly moderate the effect of environmental turbulence on SME strategic resilience and strengthen SME capacity in absorbing shocks, reconfiguring resources and sustaining operations under disruptions. This study contributes by reframing SME sustainability as strategic resilience amid environmental turbulence, differentiating external pressures into economic, legal and technological dimensions, and showing how dynamic capability bundles condition SME strategic resilience in a highly volatile developing-economy context. This study offers insights relevant to other emerging economies characterised by institutional instability, policy unpredictability and uneven technological development. It also broadens understanding of contingency and dynamic capability theory in developing economies and positions dynamic capabilities as vital for resilience-building, not just competitive advantage.

Abstract
Route optimization algorithms are essential to calculate the best routes and solve problems that may arise in logistics services. This study focuses on the problem of route optimization for both single and fleets of electrical motorbikes dedicated to urban services. The paper explores the most relevant and widely-known problems within route optimization, which match the requirements of the specified target problem, e.g., traveling salesman problems. This paper also explores the most widely used algorithms for solving the identified problem variations, namely exact methods such as Branch and Bound and Dijkstra's algorithm; and also heuristic and meta-heuristic algorithms. Route optimization issues and problem variations related to vehicle fleets are also addressed, including the Vehicle Routing Problem (VRP), Battery-Constrained Vehicle Routing Problem (BCVRP), and Vehicle Routing Problem with Time Windows (VRPTW). This study concludes that efficient algorithms for solving these problems can have several benefits, including cost reduction, resource optimization, and time efficiency.