Section 3.3. Model of Integration of Loyalty Assessment Results into the Threat Management System of the Economic Security of an Industrial Enterprise
Keywords:
personnel loyalty index, economic security, industrial enterprise, risk management, transfer function, resilience, occupational safety, information security, human factor risk, performance indicators, scenario modelling, loyalty assessmentSynopsis
Industrial enterprises operate under growing uncertainty, where human factor risks interact with technological, financial and information vulnerabilities. The paper proposes a sociotechnical model that embeds personnel loyalty into the threat management system of economic security, treating loyalty as a leading indicator that precedes changes in key risk and performance metrics. The objective is to develop and formalize an integrated model that connects loyalty assessment results with process, financial and risk indicators in order to quantify the impact of loyalty dynamics on the integral economic security index of an industrial enterprise and to support anticipatory managerial decisions. The model combines a five layer conceptual framework, an integral loyalty index L with affective, normative, instrumental and behavioral components, and an integral economic security index S constructed from normalized financial, operational, safety, compliance and information security indicators. The mathematical mechanism uses transfer functions, elasticities, regression based estimation on panel data with control variables, robustness checks and scenario calculations that translate changes in L into shifts in S. Implementation follows a staged procedure of diagnosis, design, piloting, scaling and institutionalization with strict ethical and data governance requirements. The model demonstrates that loyalty can be treated as a quantitatively specified risk factor rather than a purely qualitative construct. Formal links between L and S show that increased loyalty is typically associated with lower incident rates, reduced turnover, improved process stability and higher operating margins. The approach yields an aggregated sensitivity parameter β that allows estimation of the expected change in S under given loyalty scenarios, including saturation effects at high security levels. The model is operationalized through dashboards, risk registers, escalation thresholds and targeted interventions at shop floor and corporate levels. The model forms a reproducible management contour that connects validated loyalty measurements with hard performance metrics, closes the loop from monitoring to action and learning, and strengthens the resilience and economic security of industrial enterprises under high uncertainty. Future work should test the model across sectors and countries, refine causal identification strategies, explore nonlinear and machine learning based transfer functions, integrate real time digital traces of behavior, and examine interactions between loyalty, leadership style and organizational culture in shaping economic security trajectories.
References
Allen, N. J., & Meyer, J. P. (1990). The measurement and antecedents of affective, continuance and normative commitment to the organization. Journal of Occupational Psychology, 63(1), 1-18. https://doi.org/10.1111/j.2044-8325.1990.tb00506.x
Christian, M. S., Bradley, J. C., Wallace, J. C., & Burke, M. J. (2009). Workplace safety: A meta-analysis of the roles of person and situation factors. Journal of Applied Psychology, 94(5), 1103-1127. https://doi.org/10.1037/a0016172
Committee of Sponsoring Organizations of the Treadway Commission. (2017). Enterprise risk management: Integrating with strategy and performance. COSO. https://www.coso.org/enterprise-risk-management
Hollnagel, E., Woods, D.D., & Leveson, N. (Eds.). (2006). Resilience Engineering: Concepts and Precepts (1st ed.). CRC Press. https://doi.org/10.1201/9781315605685
International Organization for Standardization. (2018). ISO 31000:2018 Risk management - Guidelines. ISO. https://www.iso.org/standard/65694.html
Meyer, J. P., & Allen, N. J. (1991). A three component conceptualization of organizational commitment. Human Resource Management Review, 1(1), 61-89. https://doi.org/10.1016/1053-4822(91)90011-Z
Meyer, J. P., Stanley, D. J., Herscovitch, L., & Topolnytsky, L. (2002). Affective, continuance and normative commitment to the organization: A meta analysis of antecedents, correlates and consequences. Journal of Vocational Behavior, 61(1), 20-52. https://doi.org/10.1006/jvbe.2001.1842
Nahrgang, J. D., Morgeson, F. P., & Hofmann, D. A. (2011). Safety at work: A meta analytic investigation of the link between job demands, job resources, burnout, engagement and safety outcomes. Journal of Applied Psychology, 96(1), 71-94. https://doi.org/10.1037/a0021484
Neal, A., & Griffin, M. A. (2002). Safety climate and safety behaviour. Australian Journal of Management, 27 (Suppl. 1), 67-75. https://doi.org/10.1177/031289620202701S08
Neal, A., Griffin, M. A., & Hart, P. M. (2000). The impact of organizational climate on safety climate and individual behavior. Safety Science, 34(1-3), 99-109. https://doi.org/10.1016/S0925-7535(00)00008-4
Reason, J. (1997). Managing the risks of organizational accidents. Ashgate. https://doi.org/10.4324/9781315543543
Weick, K. E., & Sutcliffe, K. M. (2007). Managing the unexpected: Resilient performance in an age of uncertainty (2nd ed.). Jossey Bass. https://archive.org/details/managingunexpect0000weic_e3o8
Zohar, D. (1980). Safety climate in industrial organizations: Theoretical and applied implications. Journal of Applied Psychology, 65(1), 96-102. https://doi.org/10.1037/0021-9010.65.1.96
Published
November 20, 2025
Series
Categories
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
