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Research Article
Lessons Learned from the American Federation of Labor Regarding Crisis Leadership and Innovative Managerial Practices in January 1899
Jovana Pavlovic*
Issue:
Volume 13, Issue 5, October 2025
Pages:
244-249
Received:
16 July 2025
Accepted:
5 August 2025
Published:
5 September 2025
Abstract: The study delves into the tactics and crisis handling exhibited by the American Federation of Labor (abbreviated as AFL) focusing on a week in January 1899 as detailed in the AFLs historical letters stored in the Library of Congress (document mss51185, on reel 18, in volume 26). The paper brings attention to lessons, for management, and social science by studying how the AFL handles strikes and national organizing challenges along with its responses to lockouts, in a crisis scenario focusing on decision making under pressure delegation of tasks democratic leadership and intelligence gathering. The research investigates historical events which include The Textile Strike in Augusta, the Ludington Lockout Crisis and Delaware labor intelligence efforts that demonstrate enduring examples of proactive Information Gathering, Strategic Benchmarking, Efficient Communication, Pragmatic Leadership, and Collaborative hotspots - effective management for modern research. Utilizing both the documents and insights, from scholars specialized in labor history reveals how AFL President Samuel Gompers (1850-1924) along with his associates showcased leadership abilities-managing swift decision making alongside seeking input, from others and cultivating inclusive unity-despite the challenges posed by industrial unrest. Samuel Gompers played a role, within the AFL back, in January 1899. During that time frame he contributed to the development of management strategies. Showcased crisis leadership skills.
Abstract: The study delves into the tactics and crisis handling exhibited by the American Federation of Labor (abbreviated as AFL) focusing on a week in January 1899 as detailed in the AFLs historical letters stored in the Library of Congress (document mss51185, on reel 18, in volume 26). The paper brings attention to lessons, for management, and social scie...
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Review Article
The Role of Big Data Technology in Revolutionizing the Insurance Industry in Kenya
Cyprian Omenge Nyambane*
Issue:
Volume 13, Issue 5, October 2025
Pages:
250-259
Received:
30 April 2025
Accepted:
27 June 2025
Published:
8 September 2025
Abstract: Recent years have witnessed the emergence of novel ideas and concepts of big data to face the remarkable rise of amounts of data in many business sectors. Meanwhile, the remarkable growth of internet use and social networks have even added not only huge amounts of data to different business sectors but have also added challenges to conventional data processing systems. To deal with the large amount of data, traditional processing techniques have proved to be inefficient and insufficient to provide accurate and meaningful information required for evidence-based decision making. The insurance industry has also heavily relied on processed data for accurate risk assessment, underwriting and pricing. However, the sector today is also dealing with huge amounts of accumulated data, both structured and unstructured, which has made traditional data processing techniques unable to handle. Big data Analytics is an aspect of innovation which has recently gained major attention from both academics and practitioners. Big data analytics is the process of examining big data to uncover information such as hidden patterns, correlations, market trends and customer preferences. These revelations can significantly impact an organization as it provides deeper insights into customer behaviour, operational efficiency, and market trends. This paper aims to assess the role of Big Data adoption in the insurance industry through literature review. The paper presents big data and insurance industry in Kenya, characteristics of big data, the technologies used in big data implementation. It also looks at the beneficial role of adopting big data technology in the insurance sector and concludes that big data analytics stands out as an enabler to insurance organisations in making data-based decisions and providing customized insurance products and services according to customer needs. Finally, the paper encourages future research to examine the levels of big data adoption and its effect in the insurance industry.
Abstract: Recent years have witnessed the emergence of novel ideas and concepts of big data to face the remarkable rise of amounts of data in many business sectors. Meanwhile, the remarkable growth of internet use and social networks have even added not only huge amounts of data to different business sectors but have also added challenges to conventional dat...
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Research Article
Experimental XGBoost Method for Predicting the Ghana Cedi Exchange Rate Against Major Developed Currencies
Issue:
Volume 13, Issue 5, October 2025
Pages:
260-270
Received:
30 June 2025
Accepted:
11 July 2025
Published:
19 September 2025
DOI:
10.11648/j.ijefm.20251305.13
Downloads:
Views:
Abstract: Exchange rate prediction is a crucial aspect of international finance, impacting decisions by governments, investors, and businesses. Accurate prediction supports the development of sound monetary policies, effective risk management, and strategic international trade planning. According to literature, traditional econometric models like ARIMAX and VAR often struggle to capture the complex, non-linear dynamics of foreign exchange markets. In contrast, machine learning methods, particularly Extreme Gradient Boosting (XGBoost), have shown superior performance due to their ability to handle large datasets, model non-linear relationships, and resist overfitting. This study evaluates the efficacy of the Extreme Gradient Boosting (XGBoost) model by predicting the GHc/USD, GHc/GBP and GHc/EUR exchange rates. Four different types of XGBoost models were employed on the financial data to determine the best performed model. The four different XGBoost models include, the XGBoost all feature, the XGBoost difference feature, the XGBoost ratio feature and the XGBoost lagged feature. The data sourced from Bank of Ghana and World Bank websites spans from January 2015 to March 2025. Findings from the study reveals that the XGBoost lagged feature and XGBoost all feature models outperformed the other two models, with an average R2of 99%, RMSE of 0.05, and MAE of 0.01. Gold price was the biggest contributor to the GHc/USD exchange rate with the feature important score of 80% followed by monthly interest rate 60%, Government debt 25%, M2 20%, price of oil 20%, BCI 15%, and CCI 10%. This result provides valuable insight for financial analyst and policymakers seeking to forecast/predict exchange rates and develop policies aimed at addressing exchange rate menace in Ghana.
Abstract: Exchange rate prediction is a crucial aspect of international finance, impacting decisions by governments, investors, and businesses. Accurate prediction supports the development of sound monetary policies, effective risk management, and strategic international trade planning. According to literature, traditional econometric models like ARIMAX and ...
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Research Article
Integrating Social Dynamics in Water Resource Management: Insights from Secondary Data
Chen Jian*,
Reagan Kapilya
Issue:
Volume 13, Issue 5, October 2025
Pages:
271-287
Received:
27 June 2025
Accepted:
1 September 2025
Published:
19 September 2025
DOI:
10.11648/j.ijefm.20251305.14
Downloads:
Views:
Abstract: This article examines the crucial role of social dynamics in managing water resources, highlighting the importance of considering these factors for effective governance. Through a comprehensive analysis of secondary data drawn from diverse case studies and existing literature, we elucidate how aspects such as stakeholder engagement, cultural values, power dynamics, and social networks shape water management practices. Our findings underscore the importance of integrating these social factors to foster sustainable and equitable governance of water resources. Employing the Social-Ecological Systems Framework, this study systematically examines the interactions between social elements and water use, access, and conservation efforts across various communities. We present a range of case studies that exemplify successful stakeholder engagement and collaborative governance, highlighting the promising outcomes that can arise from incorporating social dynamics into water management strategies. These case studies illustrate how participatory approaches can lead to improved decision-making processes, ensuring that the voices of all stakeholders are heard and valued. Our research indicates that facilitating community involvement and aligning water management policies with local cultural values can significantly enhance both ecological health and social equity. By recognizing the interdependence of social and ecological systems, we propose that water management practices should evolve to reflect local contexts and foster collaboration among diverse stakeholders. This integrative approach not only addresses immediate water-related challenges but also promotes long-term resilience and adaptability in response to changing environmental conditions. This article advocates for a paradigm shift in water resource management that prioritizes social dynamics alongside technical and ecological considerations. By embracing this holistic perspective, we can develop more effective and just water governance frameworks that contribute to achieving the Sustainable Development Goals. Our insights aim to guide policymakers and practitioners in rethinking their strategies, ultimately leading to healthier ecosystems and more equitable access to water resources for all.
Abstract: This article examines the crucial role of social dynamics in managing water resources, highlighting the importance of considering these factors for effective governance. Through a comprehensive analysis of secondary data drawn from diverse case studies and existing literature, we elucidate how aspects such as stakeholder engagement, cultural values...
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