A relay race or an ironman? A systematic review of the literature on innovation in the mining sector
Bibliographic record
Abstract
As global demand for minerals and metals surges, the mining industry is faced with the triple challenge of finite resources, societal resistance, and environmental considerations. To address these challenges the mining sector is relying on innovation and technological advancements to enable exploitation of so far inaccessible deposits, minimize energy consumption, and improve sustainability. This study presents a systematic review of the literature on innovation in mining and the unique management approach being adopted to respond to the above challenges. From an initial corpus of 4059 scientific articles, a reproducible process filtered out 222 documents, published between 1996 and 2023. The analysis reveals that the literature on innovation in the mining industry, predominantly qualitative and exploratory in nature, is showing a shift towards empirical validation using advanced quantitative methods. Mining innovation appears to be shaped by the industry's inherent features such as a conservative culture, market volatility, emphasis on productivity, and profitability. While the industry somewhat risk-averse approach favours stability and specialization which could hamper innovation, its simultaneous focus on productivity encourages innovation to achieve cost reductions, enhanced processes, and implementation of new technology. However, the uniformity of ore and the position of miners in the value chain is not favorable to product innovation and the focus tends to be on —mainly supplier-led— process innovation. The review also shows the potential benefits of non-technological innovations, including marketing and organizational changes although these are infrequent. The study concludes by addressing conceptualizations of innovation and mining management and highlighting gaps in research that focuses on regions, minerals, and innovation types. It also discusses the knowledge management implications in this context. • Analyzed 222 out of 4059 articles for mining innovation trends. • Revealed shift towards quantitative methods in mining research. • Identified unique challenges to innovation in mining vs. other sectors. • Highlighted mining's focus on process over product innovation. • Detailed mining's sustainability and social engagement complexities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".