Purpose – This study undertakes a problematizing review (Alvesson & Sandberg, 2020) of research on digital taxation (e-Tax) in Africa. Moving beyond descriptive mapping, it critically examines the field’s intellectual foundations to uncover dominant assumptions, conceptual limitations, and geolinguistic biases. Theoretical Framework – Grounded in the problematization approach, the study draws on fiscal sociology, digital governance, and postcolonial perspectives to question how African e-Tax research engages with issues of power, inclusion, and digital transformation. Design/Methodology/Approach – A bibliometric analysis of 156 publications (2010–2024) indexed in Scopus was conducted using VOSviewer for co-citation, co-authorship, and keyword co-occurrence mapping. Quantitative mapping was complemented by a critical interpretive analysis to expose thematic patterns, omissions, and structural biases. Findings – The review reveals a field dominated by a “tax compliance” paradigm, concentrated in South African and Anglophone scholarship. It highlights three main weaknesses: (1) conceptual narrowness, overlooking trust, ethics, and digital inequality; (2) geographic and linguistic exclusion, particularly of Francophone and Lusophone Africa; and (3) a reactive stance toward technological change, with limited theoretical engagement. Originality/Value – This is the first critical assessment of African e-Tax research. It calls for a shift toward more reflexive, interdisciplinary, and inclusive approaches that question existing paradigms and address structural and epistemic imbalances.
The increasing complexity, disruption, and sustainability pressures affecting global supply chains have intensified interest in blockchain technology as a mechanism for improving transparency, traceability, and coordination among supply chain actors. This study addresses the need to better understand the evolution and future research trends of blockchain applications in supply chain management (SCM). The objective is to map the intellectual structure, thematic evolution, and emerging dynamics of this rapidly expanding research field. The analysis is based on a corpus of 2101 articles published between 2017 and 2024 and extracted from the Scopus database. Following the PRISMA protocol for data selection, VOSviewer and Harzing’s Publish or Perish tools were employed to conduct performance analyses (publications and citations) and scientific mapping analyses (co-citation, keyword co-occurrence, and collaboration networks). The findings reveal exponential growth in scientific production, accelerated by the COVID-19 pandemic. Research in this field is structured around four major thematic areas: supply chain performance, technological foundations, sustainability, and traceability, particularly within the agri-food sector. The analysis also highlights significant geographical polarization, dominated by China, India, and the United States, as well as a strong editorial concentration around technology- and sustainability-oriented journals. Foundational works by Saberi et al. (2019) and Dutta et al. (2020) play a central role in structuring the intellectual foundations of the field. This study contributes to the literature by providing an updated and comprehensive mapping of blockchain research in SCM while identifying persistent gaps related to empirical validation, disciplinary fragmentation, and the underrepresentation of developing countries. Beyond descriptive mapping, the findings suggest that blockchain research in SCM is progressively evolving from a technology-centered perspective toward a governance-, resilience-, and sustainability-oriented paradigm. Based on these findings, the study proposes a structured research agenda to guide future interdisciplinary, empirical, and collaborative investigations.
This study aims to analyze the dynamic relationship between inflation and Brazil’s basic interest rate (SELIC) from 2000 to 2025 within the country’s inflation-targeting framework. Grounded in monetary policy theory and inflation dynamics and explores how the Central Bank’s interest rate adjustments respond to inflationary pressures and other macroeconomic variables. The theoretical framework integrates models of inflation persistence and monetary policy transmission, emphasizing the role of credibility in inflation targeting. Methodologically, the study employs advanced econometric techniques, including Vector Autoregressive (VAR) models, Error Correction Models (ECM), and Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) models, to capture both short-term monetary reactions and long-term equilibrium relationships. Controls such as GDP growth, exchange rate volatility, fiscal shocks, and credit market responses are incorporated to ensure practical results. Findings reveal a statistically significant positive correlation between inflation and the SELIC rate, with an average policy response lag of nine months. Over the study period, inflation persistence has declined, indicating increased policy credibility and effectiveness of the inflation-targeting regime.Episodes of heightened financial volatility, notably the 2008 global crisis and recent fiscal challenges, substantially influence monetary policy stance and inflation outcomes. The research provides empirical evidence on Brazil’s monetary policy transmission mechanism, contributing to a understanding of how macroeconomic stability is maintained through coordinated policy actions. Its originality lies in combining long-span data with sophisticated econometric models and a set of macro and micro controls, updating and extending prior findings such in renowned past studies and data of institutions as the Central Bank of Brazil
Conventional methods of time series forecasting often strive in capturing many dynamics patterns that are existing in large real-world time series data. Consequently, Machine learning techniques are gaining popularity especially in forecasting enormous data, thereby making research interests in the economic and finance fields to be more attractive. This paper uses time series prediction techniques to forecast the prices of two World most popular crude oils namely; WTI and Brent by examining the market volatility. The features of GARCH, SVR, and LSTM are combined to capture many dynamics patterns in the WTI and Brent crude oils. Specifically, we use GARCH to model volatility clustering, SVR to improve short-term price prediction, and LSTM to forecast long-term price trends. The results show that the GARCH model can effectively capture volatility patterns, making it ideal for analyzing risk and market shocks. However, SVR performs in short-term price predictions under stable market conditions. LSTM, on the other hand, proved valuable for long-term forecasting, particularly in volatile periods.
This research note examines the Granger-causal relationship between the stock of active registered enterprises and real export performance in Çanakkale province, Turkey, over the period 2013–2023 (n = 11 annual observations). We apply the Toda–Yamamoto (1995) augmented VAR procedure — a commonly used procedure when integration and cointegration properties are uncertain in short samples — and obtain an initial result of unidirectional Granger-predictive precedence from enterprise stock to real exports (χ²(1) = 7.63, p = 0.031). We then subject this finding to systematic leave-one-out (LOO) sensitivity testing and find that the result disappears when any of three individual years (2016, 2019, or 2020) are excluded from the sample. We report this fragility transparently, argue that it is a direct consequence of the minimum effective sample of n = 9 observations after VAR lags, and interpret the initial result as motivation for a multi-province panel analysis rather than a policy-ready conclusion. We also discuss the conceptual limitations of using the total enterprise stock as a proxy for entrepreneurial activity and outline the exact data and methodological requirements for a confirmatory study.
