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The purpose of the work is to use a systematic process to identify the environmental and social impacts of small-scale alluvial gold mining projects using data from Ghana.

In this work, we used survey data collected from experts in the mining sector. This was followed by the use of a risk analysis approach to identify the significant and non-significant environmental and social impacts.

Seven key impacts associated with typical alluvial mining operations were identified. The first two are the loss of vegetation and the issue of airborne diseases from dust as a result of vegetation losses during the clearing of vegetation in the block out area. The third and fourth issues were loss of vegetation and airborne diseases as a result of vegetation losses during the removal of overburden. The fifth, sixth and seventh, most significant issues identified were the pollution from smoke fumes from the processing machines; and wastewater from the washing process. The last issue of significance was the dust pollution from the transportation of the washed gravel back to the mined pit.

One main limitation is that the data for this study were collected from Ghana.

The results indicate the need for proper and systematic measures to identify the environmental and social impacts of mining activities.

The work provides some insights into the strategies of identifying environmental and social impacts of mining activities. It is also one of the key works that systematically identify environmental and social impacts of small-scale alluvial gold projects.

Digitalization, innovation and changing customer requirements drive the continuous improvement of an organization's business processes. IT demand management (ITDM) as a methodology supports the holistic governance of IT and the corresponding business process change (BPC), by allocating resources to meet a company's requirements and strategic objectives. As ITDM decision-makers are not fully aware of how the as-is business processes operate and interact, making informed decisions that positively impact the to-be process is a key challenge.

In this paper, the authors address this challenge by developing a novel approach that integrates process mining and ITDM. To this end, the authors conduct an action research study where the researchers participated in the design, creation and evaluation of the approach. The proposed approach is illustrated using two sample demands of an insurance claims process. These demands are used to construct the artefact in multiple research circles and to validate the approach in practice. The authors applied learning and reflection methods for incrementally adjusting this study’s approach.

The study shows that the utilization of process mining activities during process changes on an operational level contributes to (1) increasing accuracy and efficiency of ITDM; (2) timely identification of potential risks and dependencies and (3) support of testing and acceptance of IT demands.

The implementation of this study’s approach improved ITDM practice. It appropriately addressed the information needs of decision-makers and unveiled the effects and consequences of process changes. Furthermore, providing a clearer picture of the process dependencies clarified the responsibilities and the interfaces at the intra- and inter-process level.

Process mining (PM) specialises in extracting insights from event logs to facilitate the improvement of an organisation's business processes. Industry trends show the proliferation and continued growth of PM techniques. To address the minimal attention given to developing empirically supported frameworks to assess the nature of impact in the PM domain, this study proposes a framework that identifies the key categories of PM impacts and their interrelationships.

The qualitatively derived framework is built, re-specified and validated from a diverse collection of 62 PM case reports. With multiple rounds of coding supported by coder corroborations, inductively extracted concepts relating to impact from a first set of 12 case reports were grouped into themes and sub-themes to derive an a-priori framework by adopting the balanced scorecard as a theoretical lens. Concepts from the remaining 50 case reports were deductively grouped to re-specify and validate the proposed PM impacts framework. Further analysis identified interrelationships between impacts, which extends our understanding of the identified PM impacts.

The proposed framework captures PM impacts in four main categories: (a) impact on the process, (b) customer impact, (c) financial impact, and (d) impact on innovation and learning. The authors extended this analysis to identify the interrelationships between these categories, which vividly demonstrates how impact on the process mediates the attainment of the other three impact types.

The need for a deeper understanding of PM impacts within the context of contemporary PM practice is addressed by this work. The PM impacts framework provides a classification of PM impacts into four categories with 19 subcategories. It also identifies direct, moderating and mediating relationships between categories and subcategories whilst highlighting the role of impact on the process as a precursor to the other types of PM impact.

This study explores how process managers perceive the adoption, use and management of process mining in practice. While research in process mining predominantly focuses on the technical aspects, our work highlights organizational and managerial implications.

We report on a focus group study conducted with process managers from various industries in Central Europe. This setting allowed us to gain diverse and in-depth insights about the needs and expectations of practitioners in relation to the adoption, use and management of process mining.

We find that process managers face four central challenges. These challenges are largely related to four stages; (1) planning and business case calculation, (2) process selection, (3) implementation, and (4) process mining use.

We point to research opportunities in relation to the adoption, use and management of process mining. We suggest that future research should apply interdisciplinary study designs to better understand the managerial and organizational implications of process mining.

The reported challenges have various practical implications at the organizational and managerial level. We explore how existing BPM frameworks can be extended to meet these challenges.

This study is among the first attempts to explore process mining from the perspective of process managers. It clarifies important challenges and points to avenues for future research.

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

The ten principles of good business process management (BPM) support organizations in planning and scoping the organizations' BPM approach. Derived from literature and expert panels, the principles received much attention both in research and practice. This article develops a measurement instrument to operationalize the principles and to support organizations in measuring the degree to which they incorporate the principles in their BPM approach, that way advancing their BPM capabilities.

The authors applied the scale-development methodology, because this methodology is an established approach consisting of various techniques to develop measurement instruments. First, the authors used established techniques to develop such an instrument. Then, the authors assessed the validity and reliability of the developed instrument through a field survey with 345 participants.

The authors developed a valid and reliable measurement instrument for the ten principles of good BPM. The field survey's results reveal that the measurement instrument meets all required methodological standards. The instrument, thus, can be applied to help process owners and managers to evaluate their BPM approach and plan future actions based on potential shortcomings. Future research can both use and further develop the instrument, which serves as a conceptualization of the principles.

This study is the first to provide a measurement instrument for assessing an organizations' BPM practice against the ten principles of good BPM, which have become established as a much-considered and widely-used source of reference both in academia and practice. The authors also discuss how the instrument compares to and distinguishes from existing approaches to qualify BPM approaches, thus communicating the significance of the instrument.

Business intelligence (BI) is a combination of computer systems and managerial processes to support decision-making. The balanced scorecard is a kind of business intelligence tool for performance measurement and management control aimed at balancing financial and non-financial as well as short- and long-term measures. The sustainable balanced scorecard is a modification of the original balanced scorecard developed to expressly consider governance, social, environmental and ethical issues, and therefore to allow sustainability concepts to be included within the strategy and the management of the organization. However, although the sustainable balanced scorecard is one of the most suitable tools for integrating sustainability within management, there are few examples of how to develop and implement it which can be used as reference models. To help solve this problem, this paper proposes a methodology for the development of a sustainable balanced scorecard, considering different phases such as planification, analysis, design or computer tool implementation, and describes the findings of three case studies.

The research was conducted using the qualitative multiple-case study method. This made it possible to establish the methodological issues regarding the performance and reporting of this study. Therefore, the research method for the conceptualization and execution of the case studies was divided into seven phases: definition of research goals and questions; proposed theoretical model; identification of units of analysis; case selection; definition of research methods and resources; fieldwork; data collection, classification of information and triangulation; formulation of the enhanced theory, model or methodology; and verification of the rigour and quality of the study.

Paper shows a methodology organized in phases, activities and tasks that allow a sustainable balanced scorecard to be planned, designed, built, computerized and controlled in order to integrate sustainability within the management systems of organizations.

This study contributes to the currently emerging sustainable balanced scorecard literature and practice and, more generally, to research on sustainability measurement and management. The methodology for sustainable balanced scorecard development and implementation showed in this paper contributes to the management and information systems theory because it makes it possible to overcome the shortcomings identified to date: it considers all the sustainability dimensions; it describes all the project life-cycle activities; it encourages stakeholders' participation; and it has been proved to work in real situations.

Purpose – To review the performance and growth of mining industry in India against current global vision and trend of the industrial growth internationally. Also, to evolve the strategic policy for evaluating Corporate Social Responsibility (CSR) programme initiatives taken by the Indian mining industry at large as well as the impacts thereof, with special reference to affected and most vulnerable mining belts in India.Design/methodology/approach – An attempt has been made in the chapter to have a holistic sectoral review of the overall performance of the mining industry in India for the past one decade, as well as its claimed impact on improvement of ecological quality and socio-economic growth in the mining belts. The chapter reviews the state of the impact of ‘CSR’ initiatives and programmes on environment as well as the mining community in terms of stakeholders’ involvement and protection of rights in developing socio-economic business equity. The chapter also critically analyses the policy dimensions – including mining industry's operational framework, which can attribute towards developing future strategy for sustainable development of the mining industry at large, through evolving a series of reform processes, adequately backed up by innovative CSR policy and programme initiatives, together with well-defined implementation, monitoring, evaluation strategies and standards.Findings – The mining industries in India have a huge potential for growth to support the other industries for which bulk of the raw materials are derived from this industrial segment. Several research and developmental studies conducted by different organisations spread across the globe have convincingly been able to link the prospect of industrial growth and long-term sustainability with the stakeholders’ participatory and proactive roles along with those of the industry for holistic and integrated socio-economic development of the mining areas. This has been possible through careful designing of the CSR programmes and initiatives by several mining companies in India (with varying degree of success and failures) with close monitoring and performance evaluation of the impact of the programmes in ecological, economical and sustainability terms against certain pre-designed standards. Such standards – as they are constantly evolving – should inter alia include ethical and transparency dimensions to ensure total involvement of the local community in the mining-affected areas. Proper compensation mechanisms and socio-economic growth of the mining community will not only improve productivity, but will also take care of ecological and economic safeguard of the mined coal blocks that are highly vulnerable to ecological degradation and economic exploitation. As for future strategy for sustainable industrial growth of the mining industry in India, there should be constant monitoring and evaluation of the various provisions of the various Acts related to mining, minerals, metals, energy, power, environment, etc. that are constantly under review and reforms processes with a view to guiding the future strategy. International co-operation in the mining sector will go a long way for sustainable growth and development of the mining industry in India for boosting the economic growth of the country.Research limitations/implications – Future research on the theme should focus on identification of replicable and sustainable model of CSR practices in the mining industry by developing illustrative business models on the basis of global experiences. Sustainability reporting and identification of better qualitative as well as quantitative parameters, tools and techniques to study the impact of CSR practices on the socio-economic growth of the affected mining community should be the focus of future research.Social implications – The findings (serving as messages) of this piece of research will certainly have an impact on society. This in turn, will, hopefully influence public attitudes, and by implications, it will also influence (corporate) social responsibility or environmental issues.Originality/value of the chapter – The chapter is innovative and, among other things, addresses some of recently reported burning issues affecting the interests of the mining industry on one hand, and the national economy of the affected countries on the other.

When improving business processes, process analysts can use data-driven methods, such as process mining, to identify improvement opportunities. However, despite being supported by data, process analysts decide which changes to implement. Analysts often use process visualisations to assess and determine which changes to pursue. This paper helps explore how process mining visualisations can aid process analysts in their work to identify, prioritise and communicate business process improvement opportunities.

The study follows the design science methodology to create and evaluate an artefact for visualising identified improvement opportunities (IRVIN).

A set of principles to facilitate the visualisation of process mining outputs for analysts to work with improvement opportunities was suggested. Particularly, insights into identifying, prioritising and communicating process improvement opportunities from visual representation are outlined.

Prior work focuses on visualisation from the perspectives – among others – of process exploration, process comparison and performance analysis. This study, however, considers process mining visualisation that aids in analysing process improvement opportunities.