The knowledge management landscape in the Greek coal mining industry

This paper presents a quantitative research (QNR) methodology, collaboratively conducted with a group of mining experts, to assess the knowledge management (KM) landscape in the Greek mining industry. The methodology involves a framework for structured data collection using questionnaires, statistical analysis, discussion on critical methodological aspects, and interpretation of results focusing on key aspects of interest for designers of KM systems. The paper concludes by summarizing the methodology’s outcome and proposing perspectives for further research. Background The coal mining industry remains globally a driver of high criticality for primary energy production, particularly in countries like Greece. During a mine’s life cycle, significant scientific and technological knowledge regarding the mining operations and broader socio-environmental applications is accumulated. As lignite mines enter the post-mining phase, leveraging diverse knowledge becomes essential for transforming the intensively mined sites in alignment with the principles of sustainable development and circular economy. However, Greek mining organizations face challenges in effectively managing and utilizing this knowledge, and this fact impacts production and competitiveness. Furthermore, KM in the Greek mining sector remains still underexplored.The objective of this paper is to introduce a prototype methodology based on the principles of QNR, aiming to (1) identify gaps in the literature regarding KM in the mining industry and (2) conduct a systematic survey and statistical analysis of KM practices in Greek mining organizations, focusing on KM theory’s key aspects and operation processes. The methodology involves a structured data collection framework utilizing questionnaires distributed to and completed by a team of subject-matter experts (SMEs) with long-term experience in the Greek mining industry. The motivation for this work stemmed from a prior pilot study employing qualitative research (QLR) to evaluate the main knowledge gaps (KGs) identified in the Greek mining sector. Methods The methodology was designed and developed by the following steps: Literature review: This step was done to understand how knowledge, KM and KGs are understood and associated, which KM methods and practices are applied in mining operations, how technology and social practices are fit to a mine under research, and how they all interact as functional elements in the context of mining organizations and operations. Workshops (WS): A first WS was carried out so that our team could cooperate with SMEs to discuss the results of the pilot study and to define the content and the scope of the QNR. This was followed by a second WS to define the research dimensions, exchange opinions on the template and structure of the questionnaire, discuss the comprehensiveness and clarity of questions, and evaluate the extent to which the selected questions cover the main views of KM in the Greek mining industry. Data collection: The numerical data related to the functions and factors of the KM landscape were collected upon receiving the properly completed questionnaires from the respondents. Data analysis: After all the questionnaire responses were received, the analysis and visualization of the collected data was performed using the MS Descriptive Statistics ToolPak. Interpretation of results: The frequencies of respondent’s preferences and the statistical calculation results demonstrated the criticality of the investigated aspects and encompassed the suggestions for the measures and actions to be taken as adequate for the KM introduction in local mining organizations. Experts’ judgement: The guidance and consultancy of SMEs have proven valuable. The SMEs brought ideas and insights from previous investigations and consulted on how the QNR can be organized efficiently and effectively. The contribution of SMEs to the data collection was particularly important because they reviewed the questionnaire, checked that the drafted questions were clear and properly composed, and that the addressed dimensions captured the most important factors and processes of KM. Figure 1 presents the workflow of the methodology consisting of the investigation tasks (A1, A2, … , A18), and the decision nodes (DN1 and DN2). Results and discussion The questionnaire delivered to the SMEs was structured in six dimensions: Knowledge understanding refers to the understanding of KM status in the curriculum of academic programs, the perceptions of the tacit and explicit knowledge, and the KM functions. Knowledge gaps refer to the essence and role of KGs in the mining industry. Knowledge acquisition refers to the perception of how data and information are acquired within partnerships established for the needs of knowledge and technology transfer (KTT). Knowledge organization and storage refers to how and by which technology/practice, data and information are stored and uploaded in digital/conventional archives of local mining organizations. Knowledge transfer and sharing refers to technologies and infrastructures used for the knowledge streams flowing into local mining organizations. Knowledge application refers to the role of stakeholders in mining projects. The delivery of questionnaires was successfully carried out (response rate: 84.21 percent). Also, the research consistency was proven satisfactory, as the Cronbach’s alpha test values were between 0.5 and 0.8. Conclusions Considering the data gathered from the questionnaires and the insights gleaned from data analysis, it is evident that the introduction of KM must align with a well-structured strategy. This strategy should be specific and adaptable to each mining organization’s functional system and organizational structure. Therefore, particular emphasis is placed on critical aspects that should capture the attention of KM strategy planners. They may include a relative feasibility study, early recognition of knowledge gaps, organizational settings, a knowledge base, networking, interfaces, human capital and synergies.From a statistical analysis of the collected data, the following main results are obtained: The theoretical and practical aspects of KM are missing from the academic programs of local universities. The KGs are of critical importance and address the effects of low performance and competitiveness, increase of mining operations cost, and obsolescence of accumulated knowledge. The personnel of mining organizations is positive about the perspective of introducing modern KM systems, along with the necessary organizational settings and reengineering methods and practices, and information and communication technologies means and tools, to enable the acquisition, refining, structuring, storage and (re)use of knowledge that is accumulated during and because of mining and post-mining operations and projects. References A list of all references is available in the full paper.

The knowledge management landscape in the Greek coal mining industry

Authored by: Philip-Mark Spanidis, Francis Pavloudakis and Christos Roumpos

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