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1 – 10 of 699Sonali Khatua, Manoranjan Dash and Padma Charan Mishra
Ores and minerals are extracted from the earth’s crust depending on the type of deposit. Iron ore mines come under massive deposit patterns and have their own mine development and…
Abstract
Purpose
Ores and minerals are extracted from the earth’s crust depending on the type of deposit. Iron ore mines come under massive deposit patterns and have their own mine development and life cycles. This study aims to depict the development and life cycle of large open-pit iron ore mines and the intertwined organizational design of the departments/sections operated within the industry.
Design/methodology/approach
Primary data were collected on the site by participant observation, in-depth interviews of the field staff and executives, and field notes. Secondary data were collected from the literature review to compare and cite similar or previous studies on each mining activity. Finally, interactions were conducted with academic experts and top field executives to validate the findings. An organizational ethnography methodology was employed to study and analyse four large-scale iron ore mines of India’s largest iron-producing state, Odisha, from January to April 2023.
Findings
Six stages were observed for development and life cycle, and the operations have been depicted in a schematic diagram for ease of understanding. The intertwined functioning of organizational set-up is also discovered.
Originality/value
The paper will benefit entrepreneurs, mining and geology students, new recruits, and professionals in allied services linked to large iron ore mines. It offers valuable insights for knowledge enhancement, operational manual preparation and further research endeavours.
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Omokolade Akinsomi, Mustapha Bangura and Joseph Yacim
Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of…
Abstract
Purpose
Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.
Design/methodology/approach
This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.
Findings
In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.
Originality/value
These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.
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Mohammed Ayoub Ledhem and Warda Moussaoui
This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric…
Abstract
Purpose
This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.
Design/methodology/approach
This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.
Findings
The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.
Practical implications
This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.
Originality/value
This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.
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The move raises risks for Noboa, who presented himself during his presidential campaign as environmentally aware. An ongoing dispute in Cotopaxi between indigenous communities and…
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DOI: 10.1108/OXAN-DB286229
ISSN: 2633-304X
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Geographic
Topical
Alejandro Ramos-Soto, Angel Dacal-Nieto, Gonzalo Martín Alcrudo, Gabriel Mosquera and Juan José Areal
Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application…
Abstract
Purpose
Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.
Design/methodology/approach
Standard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.
Findings
Results show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.
Originality/value
This paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.
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Dominique Mazé, Jorge Alcaraz and Ricardo E. Buitrago R.
This paper aims to investigate how emerging market multinational enterprises (EMNEs) are integrating and expanding into other emerging market host countries, focusing on Chinese…
Abstract
Purpose
This paper aims to investigate how emerging market multinational enterprises (EMNEs) are integrating and expanding into other emerging market host countries, focusing on Chinese mining companies in Peru.
Design/methodology/approach
Adopting a qualitative approach, an in-depth analysis of two Chinese state-owned enterprises’ strategies was conducted, building on stakeholder theory and the business ecosystem perspective.
Findings
This study reveals a reliance on high-level political lobbying rather than localized engagement strategies. However, findings point to increasing grassroots resistance among local stakeholders, undermining EMNEs’ bargaining power.
Originality/value
This paper argues for a paradigm shift toward inclusive, cooperative “translocal governance” approaches as empowered communities gain voice. Key contributions include advancing theoretical understanding of changing stakeholder relationships and power configurations in emerging countries, underscoring the rising significance of microlevel sociocultural embeddedness for MNE success and highlighting practical imperatives for EMNEs to embark on rapid localization strategies in Latin America. By elucidating multilayered integration realities in Peru, this interdisciplinary study yields contextualized insights and enriches perspective on the conditions and pathways for EMNEs to build sustainability in Global South emerging market environments.
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My-Linh Thi Nguyen and Tuan Huu Nguyen
This study examines the evidence of the impact of climate change on the financial performance of basic materials companies in Vietnam.
Abstract
Purpose
This study examines the evidence of the impact of climate change on the financial performance of basic materials companies in Vietnam.
Design/methodology/approach
The research sample includes eighty-two basic materials companies listed on the Vietnamese stock market from 2003 to 2022. This study used one-way and two-way fixed-effects feasible generalized least squares (FGLS) estimation methods.
Findings
Climate change, measured through variables including changes in temperature, average rainfall, greenhouse gas emissions and rising sea levels, has a negative impact on the financial performance of companies in this industry. The study also found that, with rising temperatures, the financial performance of steel manufacturing companies decreased less than that of coal mining and forestry companies, but increasing greenhouse gases and rising sea levels reduced the financial performance of steel companies. We did not find evidence of any difference in the impact of climate change on the financial performance of basic materials companies before and after the UN Climate Change Conference (COP 21). This is a new finding, which is consistent with empirical studies in Vietnam and different from previous studies in that it provides new evidence on the impact of climate change on the financial performance of basic materials companies in the Vietnamese market and cross-checks the impact of climate change by sector and over time.
Originality/value
To the best of our knowledge, this is one of the first articles on climate change and the financial performance of basic materials companies.
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More recently, this has involved widely publicised claims that Russia is skirting international sanctions by using African gold from mines seized by proxy forces to help finance…
Details
DOI: 10.1108/OXAN-DB286368
ISSN: 2633-304X
Keywords
Geographic
Topical
Saba Sareminia, Zahra Ghayoumian and Fatemeh Haghighat
The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring…
Abstract
Purpose
The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.
Design/methodology/approach
This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.
Findings
The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.
Originality/value
This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.
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Sara Pau, Giulia Contu and Vincenzo Rundeddu
This study aims to explore how closed factories could be transformed and provide a path for sustainable development for a territory. The authors focus on the case of the Great…
Abstract
Purpose
This study aims to explore how closed factories could be transformed and provide a path for sustainable development for a territory. The authors focus on the case of the Great Mine Serbariu, located in Carbonia (Sardinia), which used to be the largest coal mine in Italy between 1939 and 1964.
Design/methodology/approach
The authors adopt a qualitative research design based on an exploratory single-case study, drawing on interviews with the main stakeholders, on a survey conducted among 5,158 visitors, and on administrative documentation of the City Council.
Findings
The analysis of the Great Mine Serbariu case showed that the regeneration of an exhausted mine serves a model of sustainable development, especially for the redevelopment of other urban and industrial degraded areas. The Great mine Serbariu was restored and turned into a place of culture, tourism, research and higher education, with the Italian Cultural Centre of Coal Mining (ICCCM) establishing its headquarters in the heart of the former mine. It attracted almost 220,000 visitors, generating both domestic and international tourist flows and making an industrial heritage a real resource for the area.
Originality/value
This article advances the authors’ understanding of how closed industries could become an instrument for sustainable development on the social, economic, touristic and cultural levels. This study would help local governments with examples to enhance the historical resources to create a new identity that led to a sustainable development of an urban landscape, and to create networks with other comparable museums all over Europe to better exploit the touristic and cultural potential.
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