Canadian Institute of Technology

This paper revisits the finance-growth nexus in Albania, providing an updated and more thorough empirical analysis of findings initially presented in previous research (2016). The original study revealed a statistically significant negative relationship between financial development and economic growth in Albania from 1994 to 2015, contradicting established beliefs. This study extends the time-series analysis to 2024 and utilizes a dynamic autoregressive distributed lag (ARDL) model to examine the persistence of the negative relationship while accounting for financial stability and institutional quality. The ARDL bounds test verifies a stable long-term cointegrating relationship among the variables. The results demonstrate that the adverse long-term correlation between financial development and economic growth has not only endured but is also substantial and highly significant. The analysis confirms that while trade openness is conducive to long-run growth, inflation has a significant negative impact. The study concludes that in Albania's post-transition context, the quality and efficiency of financial intermediation are more important than the amount of credit. These results show that we still need policies that focus on improving banking supervision to ensure that credit goes to investments that are productive and lead to long-term growth.

This article conducts a bibliometric study to systematically examine the evolution, intellectual paradigm, and future research trends in Agile Methodology and Digital Transformation. Data was gathered from scopus database, analyzed with the assistance of tools such as Biblioshiny and R Studio. The activity monitors various phases of growth (emerging, growth, mature) from 1986 through 2025, recording exponential academic interest, particularly post-2016. Core themes are Agile Manufacturing Systems, Industry 4.0, Artificial Intelligence, Big Data, and Internet of Things (IoT). Outcome shows that publications increased significantly, with notable peaks and new themes depicting integration with advanced technologies. Keyword analysis shows Agile practices becoming increasingly intermixed with digital strategies across industries, confirming Agile’s central role in organizational adaptability. Thematic mapping categorizes themes as motor, basic, niche, and emerging themes to offer strategic hints for potential avenues of future research. The research is a point of reference for researchers and practitioners alike, charting impactful books, thematic evolution, and possible research gaps in the rapidly evolving landscape of Agile and Digital Transformation.

Federated Learning (FL) enables collaborative model training across distributed IoT and edge devices while preserving data locality, making it attractive for privacy-sensitive and resource-constrained environments. However, the integration of Differential Privacy (DP) introduces a critical trade-off between privacy guaranties and model utility, which is further intensified by client heterogeneity, non-IID data distributions, and irregular participation. Existing DP-enabled FL approaches typically apply uniform noise budgets and static aggregation strategies, overlooking differences in client uncertainty, reliability, and contribution. In this work, we address this limitation by proposing a liminality-aware federated learning framework that adaptively assigns privacy noise and aggregation weights based on client-level uncertainty and participation behavior. Liminality is defined as a lightweight epistemic uncertainty measure derived from softmax entropy and prediction confidence, which requires no access to raw data and minimal additional computation. The framework combines uncertainty and behavior-aware signals to dynamically redistribute learning responsibility under DP constraints. The proposed approach is evaluated in highly heterogeneous synthetic FL settings with severe non-IID label and data size distributions across 80 clients. Experimental results show that the liminality-awareness strategy can improve the average accuracy by up to 7

This article aims to contribute to the emerging body of literature on digital leadership in higher education institutions (HEIs) in Albania and Montenegro, two less researched higher education systems in the Balkan region, which are undergoing a rapid process of digital transformation. The article is based on a conceptualization of digital leadership, understood as a process of strategic alignment between the vision of the institution, the governance of the institution, and the evaluative quality assurance approaches, rather than a process of technology adoption. The article uses a qualitative, deductive document/content analysis to systematically compare the national and institutional levels of strategy, governance, and quality assurance policies in the two countries. The results of the analysis reveal a similar trend in the formalized digitalization strategies in both countries, which are less translated to the evaluative approaches, particularly in the context of teaching quality enhancement and inclusion. While both systems demonstrate growing strategic awareness of digital transformation, differences appear in the level of coordination and accountability in the implementation of the strategies. The article aims to contribute to the literature on digital leadership, providing empirical evidence on the tension between the strategic approaches and the evaluative approaches in the context of digital transformation in small higher education systems.

Socioeconomic factors exert multifaceted influences on a country’s economic performance, shaping both its short-term fluctuations and long-term sustainability. Among the most significant determinants are education, fertility, birth rates, and labor force participation, which interact dynamically and affect broader macroeconomic outcomes. This study investigates the relationship between key socioeconomic variables and gross domestic product (GDP) per capita in the Durana region, Albania’s most densely populated and economically active area. Demographic changes in this region have been substantial, particularly following post-communist migration and sociopolitical transformations. The purpose of the study is to analyze the impact of socioeconomic factors on GDP per capita. The theoretical model of this study is based on Pardi et al. (2024). The methodology of the study is based on three methods: first difference, ordinary least squares (OLS), and generalized method of moments (GMM). The findings indicate considerable variability in coefficient estimates, with the GMM (Cruz & Ahmed, 2018) emerging as the most robust specification to analyze the impact of socioeconomic variables on GDP per capita. Results reveal that socioeconomic factors exert both immediate and lagged effects on economic performance: education contributes significantly over the long term, whereas fertility rates and labor force participation show the strongest short-term impacts on GDP per capita.