[4] Kovacova M, Lăzăroiu G, Sustainable
organizational performance, cyber-physical
production networks, and deep learning-
assisted smart process planning in Industry
4.0-based manufacturing systems, Economics,
Management and Financial Markets, Vol.16,
No.3, 2021, pp. 41-54.
[5] Levytska S, Akimova L, Zaiachkivska O,
Karpa M, Gupta S, Modern analytical
instruments for controlling the enterprise
financial performance, Financial and Credit
Activity Problems of Theory and Practice,
Vol.2, No.33, 2020, pp. 314-323.
[6] Hall J L, Kanaan D Z, State tax policy,
municipal choice, and local economic
development outcomes: A structural equation
modeling approach to performance
assessment, Public Administration Review,
Vol.81, No.3, 2021, pp. 459-474.
[7] Khan S A R, Zhang Y, Kumar A, Zavadskas
E, Sreimikiene D, Measuring the impact of
renewable energy, public health expenditure,
logistics, and environmental performance on
sustainable economic growth, Sustainable
Development, Vol.28, No.4, 2020, pp. 833-
843.
[8] Khan S A R, Razzaq A, Yu Z, Miller S,
Industry 4.0 and circular economy practices:
A new era business strategies for
environmental sustainability, Business
Strategy and the Environment, Vol.30, No.8,
2021, pp. 4001-4014.
[9] Lee I, Shin Y J, Machine learning for
enterprises: Applications, algorithm selection,
and challenges, Business Horizons, Vol.63,
No.2, 2020, pp. 157-170.
[10] Cui Z, Zhang J, Wu D, Cai X, Wang H,
Zhang W, Chen J, Hybrid many-objective
particle swarm optimization algorithm for
green coal production problem, Information
Sciences, Vol.518, 2020, pp. 256-271.
[11] Li W, Xu G, Xing Q, Lyu M, Application of
improved AHP-BP neural network in CSR
performance evaluation model, Wireless
Personal Communications, Vol.111, No.4,
2020, pp. 2215-2230.
[12] Liang W, Li T, Research on human
performance evaluation model based on
neural network and data mining algorithm,
EURASIP Journal on Wireless
Communications and Networking, Vol.2020,
No.1, 2020, pp. 1-14.
[13] Li W, Xu G, Zuo D, Zhu J, Corporate social
responsibility performance-evaluation based
on analytic hierarchy process-fuzzy
comprehensive evaluation model, Wireless
Personal Communications, Vol.118, No.4,
2021, pp. 2897-2919.
[14] Abudureheman A, Nilupaer A, He Y,
Performance evaluation of enterprises’
innovation capacity based on fuzzy system
model and convolutional neural network,
Journal of Intelligent & Fuzzy Systems,
Vol.39, No.2, 2020, pp. 1563-1571.
[15] Cheng L H, Cao D Q, Guo H M, Analysis of
coal mine occupational disease hazard
evaluation index based on AHP-DEMATEL,
Archives of Environmental & Occupational
Health, Vol.76, No.7, 2021, pp. 372-384.
[16] Khan O, Daddi T, Iraldo F, The role of
dynamic capabilities in circular economy
implementation and performance of
companies, Corporate Social Responsibility
and Environmental Management, Vol.27,
No.6, 2020, pp. 3018-3033.
[17] Tuffour J K, Amoako A A, Amartey E O,
Assessing the effect of financial literacy
among managers on the performance of small-
scale enterprises, Global Business Review,
Vol.23, No.5, 2022, pp. 1200-1217.
[18] Zhou Y, Zhou W, Lu X, Evaluation index
system of green surface mining in China,
Mining, Metallurgy & Exploration, Vol.37,
No.4, 2020, pp. 1093-1103.
[19] Inshakova A O, Frolova E E, Rusakova E P,
Kovalev S, The model of distribution of
human and machine labor at intellectual
production in industry 4.0, Journal of
Intellectual Capital, Vol.21, No.4, 2020, pp.
601-622.
[20] Ortiz-Barrios M, Cabarcas-Reyes J, Ishizaka
A, Barbati M, Rueda N, Jesús G, Zambrano
C, A hybrid fuzzy multi-criteria decision
making model for selecting a sustainable
supplier of forklift filters: A case study from
the mining industry, Annals of Operations
Research, Vol.307, No.1, 2021, pp. 443-481.
[21] Wu Y, Li X, Liu Q, Tong G, The analysis of
credit risks in agricultural supply chain
finance assessment model based on genetic
algorithm and backpropagation neural
network, Computational Economics, Vol.60,
No.4, 2022, pp. 1269-1292.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.180