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We develop a conceptual framework as a basis for thinking about the impact of extractive industries and emancipatory potential of alternative accounts. We then review selected alternative accounts literature on some contemporary issues surrounding the extractive industries and identify opportunities for accounting, auditing, and accountability research. We also provide an overview of the other contributions in this special issue.

Drawing on alternative accounts from the popular and social media as well as the alternative accounting literature, this primarily discursive paper provides a contemporary literature review of identified issues within the extractive industries highlighting potential areas for future research. The eight papers that make up the special issue are located within a conceptual framework is employed to illustrate each paper’s contribution to the field.

While accounting has a rich literature covering some of the issues detailed in this paper, this has not necessarily translated to the extractive industries. Few studies in accounting have got “down and dirty” so to speak and engaged directly with those impacted by companies operating in the extractive industries. Those that have, have focused on specific areas such as the Niger Delta. Although prior studies in the social governance literature have tended to focus on disclosure issues, it is questionable whether this work, while informative, has resulted in any meaningful environmental, social or governance (ESG) changes on the part of the extractive industries.

The extensive extractive industries literature both from within and outside the accounting discipline makes a comprehensive review impractical. Drawing on both the accounting literature and other disciplines, this paper identifies areas that warrant further investigation through alternative accounts.

This paper and other contributions to this special issue provide a basis and an agenda for accounting scholars seeking to undertake interdisciplinary research into the extractive industries.

The purpose of this paper is to examine the social sustainability strategies of multinational mining companies in addressing risks in areas of exploratory intensity and contributing to social capital in local communities.

This study is situated within an interpretivist paradigm and uses a qualitative research methodology, drawing on data from semi-structured interviews with multinational mining companies operating in Ghana and key stakeholder groups.

The results of this study show that multinational mining companies use strategies broadly categorised as social responsibility, social compliance, local content and relationship proximity to address challenges embedded in the extractive process.

By examining the strategies in addressing risks to sustainable social development in mining communities, this study contributes to fill the social sustainability gaps in mining research and adds to relevant literature.

Financial stability has become a focal point for central banks since the global financial crisis. However, the optimal mix between monetary and financial stability policies remains unclear. In this study, the “soft” approach to such policy mix was tested – how often monetary policy (in inflation reports) analyses financial stability issues. This paper aims to discuss the aforementioned objective.

A total of 648 inflation reports published by 11 central banks from post-communist countries in 1998-2019 were reviewed using a text-mining method.

Results show that financial stability topics (mainly cyclical aspects of systemic risk) on average account for only 2%of inflation reports’ content. Although this share has grown somewhat since the global financial crisis (in CZ, HU and PL), it still remains at a low level. Thus, not enough evidence was found on the use of a “soft” policy mix in post-communist countries.

Given the strong interactions between price and financial stability, this paper emphasizes the need to increase the attention of monetary policymakers to financial stability issues.

The study combines two research areas, i.e. monetary policy and modern text mining techniques on a sample of post-communist countries, something which to the best of the authors’ knowledge has not been sufficiently explored in the literature before.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.

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.

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.

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.

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.

This study aims to propose an approach to assess the security of supply (SS) in a coal-fired electricity generation supply chain subject to public price regulation in Brazil. This study characterizes the Brazilian scenario of coal-fired electricity generation, which represents less than 3.5% of the energy sources.

Data from six mining companies that supply a coal plant were analyzed in a case study. The risks were characterized and objectively estimated through a synthetic multidimensional index. Structural changes in the earnings before interest, taxes, depreciation, amortization and exploration indicator time series of coal companies (CC) were statistically detected.

Empirical evidence demonstrates that the supply chain has a low disruption risk (SS index equal to 0.74). However, when suppliers are individually analyzed, 48.64% of all coal shows moderated disruption risk, and 2.51% is under high risk. In addition, this study finds a drop in the financial results of CC related to public regulation of coal prices. This impacts the security of coal supply.

This study discusses the influence of legal and regulatory policy risks in a coal power generation supply chain and the implications of the SS index as a management tool.

A novel SS index is presented and empirically operationalized, and its dimensions – environmental, occupational, operational, economic-financial and supply capacity – are analyzed.

This study examines the evidence of the impact of climate change on the financial performance of basic materials companies in Vietnam.

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.

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.

To the best of our knowledge, this is one of the first articles on climate change and the financial performance of basic materials companies.

The purpose of this study is to examine the extent to which using accounting as a multidimensional practice that encompasses technical, social and moral dimensions facilitates the instigation and advancement of a culture of sustainable development.

A qualitative approach was used to analyse the case of Waratah Coal Pty Ltd vs Youth Verdict Ltd – a dispute over a lease to establish a coal mine. The study draws on Carnegie et al.’s (2021a, 2021b) multidimensional definition of accounting and the Carnegie et al.’s (2023) framework for analysis to explore how different parties drew on accounting concepts to support their position over the sustainability of the mining lease proposal.

A multidimensional perspective on accounting appears to have clear transformative potential and can be used to champion a culture of sustainable development. This approach also has broad societal, environmental and moral implications that transcend Western financial metrics. This study shows that relying solely on accounting as a technical practice to pursue economic benefits can result in contested arguments. Overall, this analysis illustrates how the wider public, and notably First Nations communities, might challenge accounting methodologies that marginalise cultural and social narratives.

This paper expands accounting research by demonstrating how fully embracing accounting’s capacities can create a space for hearing multiple voices, including those silenced by Western accounting practices. Specifically, this study presents a unique case in which the authors incorporate the voices and views of those affected by accounting-based decisions.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.

A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria.

The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors.

This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.