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1 – 10 of over 5000Sonali 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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Ramesh Chandra Das and Munjeti Benudhar Naidu
This study aims to comprehensively analyse the implementation and effectiveness of corporate social responsibility (CSR) policies within the context of the Indian coal mining…
Abstract
Purpose
This study aims to comprehensively analyse the implementation and effectiveness of corporate social responsibility (CSR) policies within the context of the Indian coal mining sector. Furthermore, it investigates the alignment between CSR initiatives and the unique challenges faced by the coal mining sector and examines the outcomes and impacts of these initiatives on the employees of the sector and their perspective on the situation.
Design/methodology/approach
This study adopts a comprehensive qualitative research method, including a review of the literature, case studies and stakeholder interviews. This study seeks to deconstruct the application of CSR policies.
Findings
The analysis developed a deeper understanding of the complexities surrounding CSR policies in the Indian coal mining sector, offering insights into strategies for enhancing the effectiveness and relevance of these initiatives while fostering sustainable development.
Practical implications
This study reveals a rich tapestry of theoretical implications and how they connect to important organisational and societal paradigms. The results of this qualitative analysis can work as a foundation for creating scales to measure the level of efficiency of CSR policies implemented by different companies. Furthermore, this study goes beyond theoretical knowledge and gives companies, regulators and communities information they can use. By looking at how CSR policies work in the real world, a road map for responsible resource extraction and community growth can be made.
Originality/value
The findings are unique in exploring the CSR initiatives and the unique challenges faced by the coal mining sector. This study offers insight on the employees of the sector and their perspectives on the situation and delves into the multifaceted dimensions of CSR practices.
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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
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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Xiaobo Shi, Yan Liu, Kunkun Ma, Zixin Gu, Yaning Qiao, Guodong Ni, Chibuzor Ojum, Alex Opoku and Yong Liu
The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.
Abstract
Purpose
The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.
Design/methodology/approach
The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.
Findings
The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.
Research limitations/implications
Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.
Social implications
The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.
Originality/value
Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.
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Na Xu, Yanxiang Liang, Chaoran Guo, Bo Meng, Xueqing Zhou, Yuting Hu and Bo Zhang
Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a…
Abstract
Purpose
Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.
Design/methodology/approach
Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.
Findings
Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.
Originality/value
This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.
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Birol Yıldız and Şafak Ağdeniz
Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show…
Abstract
Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.
Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.
Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.
Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.
Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).
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Hasanuzzaman, Kaustov Chakraborty and Surajit Bag
Sustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth…
Abstract
Purpose
Sustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth multifaceted economic, environmental and social efforts to accomplish the Sustainable Development Goals (SDGs). This research aims to identify the factors for sustainable improvements in coal mining operations. Secondly, this study examines the intensity of causal relations among the factors. Thirdly, this study examines whether causal relations exist among the factors to be considered for sustainable improvement in coal mining operations. Lastly, the study aims to understand how the factors ensure sustainable improvement in coal mining operations.
Design/methodology/approach
An integrated three-phase methodology was applied to identify the critical factors related to coal mining and explore the contextual relationships among the identified factors. Fifteen critical factors were selected based on the Delphi technique. Subsequently, the fifteen factors were analyzed to determine the contextual and causal relationships using the total interpretive structural modelling (TISM) and DEMATEL methods.
Findings
The study identified “Extraction of Coal and Overburden” as the leading factor for sustainable improvement in coal mining operations, because it directly or indirectly influences the overall mining operation, environmental impact and resource utilization. Hence, strict control measures are necessary in “Extraction of Coal and Overburden” to ensure sustainable coal mining. Conversely, “Health Impact” is the lagging factor as it has very low or no impact on the system. Therefore, it requires fewer control mechanisms. Nevertheless, control measures for the remaining factors must be decided on a priority basis.
Practical implications
The proposed structural model can serve as a framework for enhancing sustainability in India’s (Bharat’s) coal mining operations. This framework can also be applied to other developing nations with similar sustainability concerns, providing valuable guidance for sustainable operations.
Originality/value
The current study highlights the significance of logical links and dependencies between several parameters essential to coal mining sustainability. Furthermore, it leads to the development of a well-defined control sequence that identifies the causal linkages between numerous components needed to achieve real progress towards sustainability.
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The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning…
Abstract
The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning analytics and Big Data in the Saudi Arabian higher education. Examining learning analytics in higher education institutions promise transforming the learning experience to maximize students' learning potential. With the thousands of students' transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics in Saudi Arabia has caught the interest of scholars and researchers regionally and internationally. This chapter explores a Saudi private university in Jeddah, Saudi Arabia, and examines its rich learning analytics and discovers the knowledge behind it. More than 300,000 records of LMS analytical data were collected from a consecutive 4-year historic data. Romero, Ventura, and Garcia (2008) educational data mining process was applied to collect and analyze the analytical reports. Statistical and trend analysis were applied to examine and interpret the collected data. The study has also collected lecturers' testimonies to support the collected analytical data. The study revealed a transformative pedagogy that impact course instructional design and students' engagement.
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Napoleon Kurantin and Bertha Z. Osei-Hwedie
This chapter presents an investigation into the theory of labour market segmentation and income inequality in the Ghanaian mining sector. Mining activity especially gold mining…
Abstract
This chapter presents an investigation into the theory of labour market segmentation and income inequality in the Ghanaian mining sector. Mining activity especially gold mining has been a significant component of exports as well as employment and income earning in the three major mining regions of Ghana. While income growth is an economic benefit, the high incomes associated with the mining sector may lead to greater income inequality. This chapter provides an analysis of mining activity and income inequality in the Western, Eastern, and Ashanti regions of Ghana. The application of labour market segmentation and the Gini coefficient (a measure of inequality) for personal income are found to be significantly associated with the type and levels of mining employment. However, this observation is not linear as income inequality initially increases with mining activity before decreasing at medium to high levels of mining employment, thus following a Kuznets curve pattern. Segregating datasets for indigenous and expatriate staff reveals very different patterns of income inequality. It poignantly increases with indigenous and/or local community personnel relative to expatriate technical personnel at high levels of mining employment; income inequality is lower among the local community residents relative to nationals from other regions and/or from neighbouring countries. This means segmented labour markets (SLM) within the mining industry are likely to be a problem as they result in increased income inequality among locales relative to foreign expatriates.
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