Global growth in the mining industry is driving the demand for innovative industrial processes, skilled labor, and advanced management capabilities that are aimed at improving productivity. However, these advancements have also made mining one of the most high-risk and unpredictable sectors worldwide. Despite the implementation of risk management strategies, large-scale mining projects often fail due to unrecognized or underestimated risks. This study addresses these challenges by exploring a systematic risk assessment and safety management approach in the mining sector, using Nouvelle Gabon Mining in Gabon as a case study. We analyze the dispersion of identified risks and uncertainties that are often overlooked in traditional safety frameworks. Through a hierarchical classification of hazards, we illustrate the risk impacts across various operational levels. Advanced decision-making techniques, including multiple criteria ranking with alternative trace (MCRAT) and perimeter similarity (RAPS), are employed and tested against multiple-criteria decision-making (MCDM) approaches to assess their effectiveness in hazard control. In addition, this study integrates a cross-cultural perspective, examining how cultural dimensions such as individualism vs. collectivism, power distance, and uncertainty avoidance influence safety behavior, compliance, and risk perception. By analyzing safety behaviors across diverse cultural settings, we find that culturally adaptive safety management strategies significantly enhance compliance and reduce incident rates. Drawing on data from high-risk industries like mining, construction, and manufacturing, our research emphasizes the importance of incorporating cultural considerations into safety management frameworks to create safer workplaces globally. Furthermore, we propose an early warning model for manganese mining hazards based on an optimized adaptive neuro-fuzzy inference system (ANFIS), designed to predict and control risks at multiple levels within Gabon’s manganese mines, offering a robust, data-driven tool for hazard management and global safety improvement. Key strategies for improving safety management include cultural sensitivity in safety training, cross-cultural leadership styles, and the cultural adaptation of safety communication. First, safety training should be tailored to align with cultural norms and values to improve engagement and adherence to safety protocols across diverse employee groups. Second, leadership approaches must be adapted to cultural differences, aligning communication and motivation strategies to foster an inclusive and effective safety culture. Lastly, safety communication methods should be customized to cultural contexts, using visual aids and storytelling for high-context cultures, while employing clear, direct written communication for low-context cultures.
KeywordsSafety Behavior Management
Leavy, P. (2023) Research Design: Quantitative, Qualitative, Mixed Methods, Arts-Based, and Community-Based Participatory Research Approaches. Guilford Press.
Taherdoost, H. (2022) Research Methodologies: An Overview. International Journal of Academic Research in Management , 11, 10-27.
Kotronoulas, G., Miguel, S., Dowling, M., Fernández-Ortega, P., Colomer-Lahiguera, S., Bağçivan, G., et al . (2023) An Overview of the Fundamentals of Data Management, Analysis, and Interpretation in Quantitative Research. Seminars in Oncology Nursing , 39, Article 151398. https://doi.org/10.1016/j.soncn.2023.151398
Sardana, S. and Singhania, V. (2023) Mapping the Field of Research on Entrepreneurial Success: A Bibliometric Study and Future Research Agenda. International Journal of Business Science and Applied Management , 18, 53-79. https://doi.org/10.69864/ijbsam.18-2.176
Mulisa, F. (2022) Quantitative Research in Education. Educational Research Review , 17, 45-59.
Wallwey, C. and Kajfez, R. (2023) Quantitative Methods in Engineering Education. Journal of Engineering Education , 112, 23-40.
Khoa, B.T., Hung, B.P. and Brahmi, M.H. (2023) Qualitative Research in Social Sciences: Data Collection, Data Analysis and Report Writing. International Journal of Public Sector Performance Management , 12, 187-209. https://doi.org/10.1504/ijpspm.2023.132247
Fischer, C.T. and Guzel, A. (2023) Interpretive Approaches in Qualitative Research. Qualitative Psychology , 10, 78-92.
Aman Mezmir, E. (2020) Qualitative Data Analysis: An Overview of Data Reduction, Data Display, and Interpretation. Research on Humanities and Social Sciences , 10, 15-27.
Stoecker, R. and Avila, E. (2021) Mixed Methods in Community-Based Research. Community Development Journal , 56, 456-470.
Williams, C. (2007) Mixed Methods in Social Research. Social Science Research , 36, 456-472.
Creswell, J.W. (2016) Qualitative Inquiry and Research Design: Choosing among Five Approaches. Sage Publications.
Myers, J.L., Well, A.D. and Lorch Jr., R.F. (2013) Research Design and Statistical Analysis. Routledge.
Wisenthige, K. (2023) Research Design. In: Saliya, C.A., Ed., Social Research Methodology and Publishing Results : A Guide to Non-Native English Speakers , IGI Global, 74-93. https://doi.org/10.4018/978-1-6684-6859-3.ch006
Cross-Culture Perspective
Cultural Dimensions
Safety Compliance
Risk Perception
Safety Training
Cross-Cultural Leadership
Safety Communication
High-Risk Industries
Safety Performance
Ranganathan, P. and Aggarwal, R. (2018) Understanding the Properties of Diagnostic Tests—Part 2: Likelihood Ratios. Perspectives in Clinical Research , 9, 99-102. https://doi.org/10.4103/picr.picr_41_18
Rahi, S. (2017) Research Design and Methods: A Systematic Review of Research Paradigms, Sampling Issues and Instruments Development. International Journal of Eco-nomics & Management Sciences , 6, 1-5.
Berndt, A.E. (2020) Sampling Methods. Journal of Human Lactation , 36, 224-226. https://doi.org/10.1177/0890334420906850
Golzar, J., Noor, S. and Tajik, O. (2022) Convenience Sampling. International Journal of Education & Language Studies , 1, 72-77.
Stratton, S.J. (2023) Population Sampling: Probability and Non-Probability Techniques. Prehospital and Disaster Medicine , 38, 147-148. https://doi.org/10.1017/s1049023x23000304
Jager, J., Xia, Y., Putnick, D.L. and Bornstein, M.H. (2025) Improving Generalizability of Developmental Research through Increased Use of Homogeneous Convenience Samples: A Monte Carlo Simulation. Developmental Psychology . https://doi.org/10.1037/dev0001890
Zickar, M.J. and Keith, M.G. (2023) Innovations in Sampling: Improving the Appropriateness and Quality of Samples in Organizational Research. Annual Review of Organizational Psychology and Organizational Behavior , 10, 315-337. https://doi.org/10.1146/annurev-orgpsych-120920-052946
Penn, J.M., Petrolia, D.R. and Fannin, J.M. (2023) Hypothetical Bias Mitigation in Representative and Convenience Samples. Applied Economic Perspectives and Policy , 45, 721-743. https://doi.org/10.1002/aepp.13374
Weigold, A., Weigold, I.K., Jang, M. and Thornton, E.M. (2021) College Students’ and Mechanical Turk Workers’ Environmental Factors While Completing Online Surveys. Quality & Quantity , 56, 2589-2612. https://doi.org/10.1007/s11135-021-01237-0
Daikeler, J., Bošnjak, M. and Lozar Manfreda, K. (2019) Web versus Other Survey Modes: An Updated and Extended Meta-Analysis Comparing Response Rates. Journal of Survey Statistics and Methodology , 8, 513-539. https://doi.org/10.1093/jssam/smz008
Lehdonvirta, V., Oksanen, A., Räsänen, P. and Blank, G. (2020) Social Media, Web, and Panel Surveys: Using Non‐Probability Samples in Social and Policy Research. Policy & Internet , 13, 134-155. https://doi.org/10.1002/poi3.238
Griffin, M.A. and Neal, A. (2000) Perceptions of Safety at Work: A Framework for Linking Safety Climate to Safety Performance, Knowledge, and Motivation. Journal of Occupational Health Psychology , 5, 347-358. https://doi.org/10.1037//1076-8998.5.3.347
Ochoa Pacheco, P., Coello-Montecel, D. and Andrei, D.M. (2022) Validation of the Spanish Version of the Neal, Griffin and Hart Safety Behavior Scale. International Journal of Occupational Safety and Ergonomics , 29, 1402-1415. https://doi.org/10.1080/10803548.2022.2131277
Bensonch, C., Argyropoulos, C.D., Dimopoulos, C., Varianou Mikellidou, C. and Boustras, G. (2022) Analysis of Safety Climate Factors and Safety Compliance Relationships in the Oil and Gas Industry. Safety Science , 151, Article 105744. https://doi.org/10.1016/j.ssci.2022.105744
Vinodkumar, M.N. and Bhasi, M. (2010) Safety Management Practices and Safety Behaviour: Assessing the Mediating Role of Safety Knowledge and Motivation. Accident Analysis & Prevention , 42, 2082-2093. https://doi.org/10.1016/j.aap.2010.06.021
In, J. (2017) Introduction of a Pilot Study. Korean Journal of Anesthesiology , 70, 601-605. https://doi.org/10.4097/kjae.2017.70.6.601
Hajian-Tilaki, K. (2014) Sample Size Estimation in Diagnostic Test Studies of Biomedical Informatics. Journal of Biomedical Informatics , 48, 193-204. https://doi.org/10.1016/j.jbi.2014.02.013
Cohen, E. (1988) Traditions in the Qualitative Sociology of Tourism. Annals of Tourism Research , 15, 29-46. https://doi.org/10.1016/0160-7383(88)90069-2
Siegel, S. and Castellan. N.J. (1988) The Case of K Related Samples. In: Siegel, S. and Castellan Jr., N.J., Eds., Nonparametric Statistics for Behavioral Sciences , McGraw-Hill, 170-174.
Buchner, A., Erdfelder, E., Faul, F. and Lang, A.G. (2014) G* Power 3.1 Manual. https://www.psychologie.hhu.de/fileadmin/redaktion/Fakultaeten/Mathematisch-Naturwissenschaftliche_Fakultaet/Psychologie/AAP/gpower/GPowerManual.pdf
Greenland, S., Senn, S.J., Rothman, K.J., Carlin, J.B., Poole, C., Goodman, S.N., et al . (2016) Statistical Tests, P Values, Confidence Intervals, and Power: A Guide to Misinterpretations. European Journal of Epidemiology , 31, 337-350. https://doi.org/10.1007/s10654-016-0149-3
Sauro, J. (2015) SUPR-Q: A Comprehensive Measure of the Quality of the Website User Experience. Journal of Usability Studies , 10, 68-86.
Tan, F., Song, J., Wang, C., Fan, Y. and Dai, H. (2019) Titanium Clasp Fabricated by Selective Laser Melting, CNC Milling, and Conventional Casting: A Comparative in Vitro Study. Journal of Prosthodontic Research , 63, 58-65. https://doi.org/10.1016/j.jpor.2018.08.002
Franzese, M. and Iuliano, A. (2019) Correlation Analysis. In: Ranganathan, S., Gribskov, M., Nakai, K. and Schönbach, C., Eds., Encyclopedia of Bioinformatics and Computational Biology , Elsevier, 706-721. https://doi.org/10.1016/b978-0-12-809633-8.20358-0
Zou, K.H., Tuncali, K. and Silverman, S.G. (2003) Correlation and Simple Linear Regression. Radiology , 227, 617-628. https://doi.org/10.1148/radiol.2273011499
Cochran, W.G. (1963) Methodological Problems in the Study of Human Populations. Annals of the New York Academy of Sciences , 107, 476-489. https://doi.org/10.1111/j.1749-6632.1963.tb13293.x
Greener, S. (2008) Business Research Methods. BookBoon.
Arellano, L., Alcubilla, P. and Leguízamo, L. (2023) Ethical Considerations in Informed Consent. In: Ethics — Scientific Research , Ethical Issues , Artificial Intelligence and Education , IntechOpen, 1. https://doi.org/10.5772/intechopen.1001319
O’ Sullivan, L., Feeney, L., Crowley, R.K., Sukumar, P., McAuliffe, E. and Doran, P. (2021) An Evaluation of the Process of Informed Consent: Views from Research Participants and Staff. Trials , 22, Article No. 544. https://doi.org/10.1186/s13063-021-05493-1
Gupta, S., Kamboj, S. and Bag, S. (2023) Role of Risks in the Development of Responsible Artificial Intelligence in the Digital Healthcare Domain. Information Systems Frontiers , 25, 2257-2274. https://doi.org/10.1007/s10796-021-10174-0
Xu, A., Baysari, M.T., Stocker, S.L., Leow, L.J., Day, R.O. and Carland, J.E. (2020) Researchers’ Views On, and Experiences With, the Requirement to Obtain Informed Consent in Research Involving Human Participants: A Qualitative Study. BMC Medical Ethics , 21, Article No. 93. https://doi.org/10.1186/s12910-020-00538-7
Tabesh, M. (2015) Aggregation and Mathematical Programming for Long-Term Open Pit Production Planning. PhD Thesis, University of Alberta.
Osman, I.H. and Kelly, J.P. (1997) Meta-Heuristics Theory and Applications. Journal of the Operational Research Society , 48, 657-657. https://doi.org/10.1057/palgrave.jors.2600781
Thierens, D. (2004) Population-Based Iterated Local Search: Restricting Neighborhood Search by Crossover. Genetic and Evolutionary Computation — GECCO 2004, Seattle, 26-30 June 2004, 234-245. https://doi.org/10.1007/978-3-540-24855-2_21
Lourenço, H.R., Martin, O. and Stützle, T. (2001) A Beginner’s Introduction to Iterated Local Search. Proceeding of the 4 th Metaheuristics International Conference , Vol. 2, Porto, 16 July 2001, 1-6.