The study aims to examine how technological leapfrogging, as a smart and sustainable adaptation strategy, influences organizational resilience in small and medium-sized manufacturing enterprises (SMEs) of the developing economies. It also highlighted employees' digital capabilities as mediators...

This paper makes the case for measuring economic activity in real time in a middle-income setting using indicators alternative to the standard System of National Accounts measure of gross domestic product (GDP). GDP remains indispensable, yet its limits as a welfare proxy are amplified where...

Background: The primary objective of this study is to investigate the influence of big data analytics (BDA) on supply chain (SC) risk, SC ambidexterity, and SC resilience. It further examines the effects of SC risk and SC ambidexterity on SC resilience and explores their mediating roles in the...

We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using interpretable ML methods, we identify past flows and the...

We investigate whether satellite observations of nitrogen dioxide (NO₂) - a short-lived pollutant primarily emitted by fossil fuel combustion - can improve the forecasting of oil demand. After retrieving, cleaning, and aggregating daily satellite data, we integrate NO₂ into a range of...

The expansion of digital technologies has led to a digital transformation of the economy and society. E-commerce, driven by new digital technologies and the restrictions during the COVID-19 pandemic, has increased its share in the overall trade of goods and services, influencing economic growth....

Batteries are crucial for decarbonising energy storage and mitigating climate change. As the battery industry expands, it faces increasing complexity due to accelerated innovation cycles, rising competition, and growing technological diversity, emphasising the need for cost-effective strategic...

While big data analytics capabilities (BDAC) have been pivotal to retail companies, their effects on innovation capabilities and sustainable performance have not been investigated systematically thus far. Therefore, this study explores the influence of BDAC on the innovation capabilities and...

The financial sector faces mounting challenges in processing vast volumes of high-velocity data to support intelligent, real-time decision-making. Traditional machine learning models often fall short in accuracy, scalability, and responsiveness when dealing with large, dynamic financial...

This study investigates the direct and indirect relationship of big data analytics powered by artificial intelligence (BDA-AI) adoption on entrepreneurial resilience, with business model innovation serving as a mediator and technological turbulence as a moderator. Drawing on an integrated...

Entrepreneurship plays a significant role in the economic development of emerging economies, particularly by addressing persistent issues such as youth unemployment and growth challenges. Developing nations perceive their startup ecosystems as critical engines of economic progress. Policymakers...

Innovations in big data and algorithms are enabling new approaches to target interventions at scale. We compare the accuracy of three different systems for identifying the poor to receive benefit transfers - proxy means-testing, nominations from community members, and an algorithmic approach...

The rapid advancement of Big Data technologies has significantly influenced multiple sectors, with the retail industry being a key impact area. This study explores the relationship between Big Data analytics and consumer behavior, focusing on supermarket transaction data to extract macroeconomic...

Customer lifetime value is an important marketing metric and has applications in market segmentation, strategy development, and direct marketing programs, especially when customers are not under contract. In this research, we demonstrate the prediction of the lifetime value of patients in a...

Exhaustive hyperparameter search techniques such as grid search and random search are widely used in predictive analytics but become computationally expensive and often suboptimal in high-dimensional or nonlinear settings. This challenge is particularly clear in tuning Cycle Reservoir with Jumps...

This study uses natural language processing (NLP) techniques to analyze the political discourse of the surprise presidential candidate, focusing on linguistic patterns, sentiment distribution, and recurring themes. This study addresses the problem of how TikTok virality and algorithmic...

The integration of IoT and Big Data in education has significantly improved digital learning effectiveness and students' digital literacy. This study examines their impact on sustainability awareness among university students in Indonesia using a quantitative approach with PLS-SEM analysis. The...

This study examines the impact of digital technologies, particularly Artificial Intelligence (AI), Business Intelligence (BI), and Digital Transformation (DT), on enhancing supply chain performance in Oman's manufacturing and logistics sectors. Anchored in the Resource-Based View (RBV) and...

Big data technologies have greatly enhanced the effectiveness of humanitarian logistics. However, most research in this area has focused on developed countries, with limited application to emerging economies. This study aims to address that gap by systematically reviewing global literature to...