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Recent Submissions
Item Supply chain optimization(Emerald Group Publishing Ltd., 2024-07-24)Purpose: Supply chain (SC) research has boomed over the past two decades. Significant contributions have been made to the field from various analytical and decision-making perspectives. This paper, a comprehensive bibliometric study, aims to identify the key research contributors, institutions and themes. Design/methodology/approach: A comprehensive knowledge domain visualization of over 1,000 articles, published between 2000 and 2022, is carried out to construct a bird's eye view of the field in terms of research production, key authors, main publication outlets, geographic disparity of the contributions and emerging research trends. Additionally, collaboration patterns among researchers and institutions are mapped to highlight the communication networks underlying research initiatives. Findings: Results show an explosive growth in the number of articles tackling supply chain optimization (SCO) issues with a significant concentration of the contributions in a relatively small cluster of authors, journals, institutions and countries. Among the many important findings, our analysis indicates that mixed-integer linear programming is the most commonly used model, while robust optimization is the method of choice for handling uncertainty. Furthermore, most SC models are developed at only one level of the organizational hierarchy and consider only one planning horizon. The importance of developing integrated SCO systems is key for future research. Originality/value: The study fills the optimization techniques gap that exists in SC management bibliometric studies and presents a thematic map for the SCO research highlighting the various research foci.Item Improving Neural Network Using Jaya Algorithm with Opposite Learning for Air Quality Prediction(2024-01-01)The Multi-Layer Perceptron Neural Network (MLP) is the commonly used Feedforward Neural Network (FNN) for tackling classification and prediction problems. The efficiency of MLP relies on the appropriate selection of its weights and biases. Usually, a gradient-based technique is used for tuning the selection of these parameters during the learning process. This technique suffers from its slow convergence and being stuck in local optima. Predicting urban air quality is vital to prevent urban air pollution and improve the life of residents. The air quality index (AQI) is a quantitative air quality tool. In this paper, an enhanced Jaya optimization algorithm is used to improve the MLP outcome (called EOL-Jaya-MLP). The opposite-learning method is used to improve the algorithm search space exploration. A three-year dataset from air quality monitoring stations is used in this study. The proposed technique is compared against the original Jaya and six machine learning techniques. Interestingly, the EOL-Jaya-MLP outperforms other techniques when predicting the AQI.Item Money Supply, Banking and Economic Growth(2024-03-18)The primary objective of the research is to understand the interactions between money supply, banking and economic growth for effective policy interventions and business decisions. Based on annual data for the time period (2004-2021), descriptive analysis, correlations, causality tests and panel data regressions are analyzed for a sample from India, Saudi Arabia and UAE to draw conclusions. The results favored the intermediation theory and were contrary to the credit creation theory of banking. It was observed that the GDP of a country can be efficiently explained by financial soundness, broad money, loans and deposits for a country. Also, that the GDP of a country influences banking loans and deposits but not vice versa. The monetary policy of the sample was questioned by the finding that GDP causes banking loans and banking deposits but not vice versa. This important finding will add to the effectiveness in business decision making.Item Free Trade Agreements and Inequality(2024-07-03)This paper examines the potential effects of Brexit on income inequality in the United Kingdom (UK) and utilizes empirical methods to predict these effects. The study focuses on the impact of trade and Foreign Direct Investment (FDI) on income inequality, using macroeconomic data from 1971 to 2019. By employing cointegration techniques and an Error Correction Model on annual time series data, the analysis reveals that higher levels of trade have historically reduced income inequality in the UK over the long term. Consequently, changes in trade resulting from Brexit are expected to have a negative influence on the distribution of income and wealth in the UK. On the other hand, the study finds that higher FDI has only had a short-term negative effect on income distribution in the UK.Item The Influence of Urbanization and Environmental Factors on the Financial Performance of Retail and Automotive Industries in China(MDPI AG, 2024-11-21)This study probes the intersection of urbanization, environmental degradation, and corporate performance in China's retail and automotive sectors. Utilizing data from 23 regions spanning 2000-2022, the research elucidates the impacts of urban growth, infrastructure development, and particulate matter 2.5 pollution (PM2.5) on these industries. The study uses panel data from 2000 to 2022 and the corresponding analytical random effect model, along with pre- and post-estimation tests for the main results. The findings reveal that while urban population growth bolsters retail sales, it adversely affects the automotive industry. Urban infrastructure does not influence conventional sectors, yet it negatively impacts online retail sales. PM2.5 pollution depresses retail and automotive sales but boosts online sales, underscoring the rising environmental consciousness among consumers. The urban population significantly moderates the relationship between PM2.5 pollution and sales across sectors, hinting at unique consumption patterns in populated urban areas. This study provides critical insights for policymakers aiming for sustainable economic growth and encourages further research with more region-specific factors and extended time frames.
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