The purpose of the scientific work is to study problems of eco-oriented management and design of intelligent management systems for ecological innovative enterprises. The approaches to formation of the eco-oriented management process are studied. These approaches investigation additionally proofs the importance for modern business conditions to design the management model that provide economic as well as environmental and social efficiency of enterprises. At the same time, the problems of an intelligent system organizational design are relevant for ecooriented management and using of artificial intelligence approaches in order to optimize management decisions. A system model of an intelligent system of eco-oriented management for innovative enterprises has been developed to improve their environmental efficiency, sustainability and safety, as well as to improve resource and socio-economic efficiency. Based on the system model, the organizational structure of the intelligent management system of ecological innovative enterprises has been developed. It will make it possible to design, construct and restructure the intelligent management systems of ecological innovative enterprises as a whole and their units in to adapt them to modern business conditions. The study of ecological sustainability of enterprises based on its integral index was tested. The methodological basis of the investigation is a set of general and special methods of scientific researches. The implementation of these methods is caused by the goal and logic of problems solving for eco-oriented management and design the system of eco-oriented management that are based on the using of environmental innovations. Markov chain theory was used as a mathematical tool to evaluate efficiency of eco-oriented mathematical tool, the software for evaluation and prediction of state of eco-oriented innovative enterprises development was presented and tested for adequacy. The prediction results can be used to support managerial decision-making, developed software can be incorporated into the structure of the intelligent management system of ecological innovative enterprises and applied for the study of ecological sustainability of enterprises. Eco-oriented management, system, innovative enterprise, ecological, economic and social COLINS-2021: 5th International Conference on Computational Linguistics and Intelligent Systems, April 22-23, 2021, Kharkiv, Ukraine ORCID: 0000-0003-3165-4384 (M. Odrekhivskyi); 0000-0002-3847-2762 (U. Kohut); 0000-0001-8826-8084 (U. Kostyuk)
efficiency, sustainable development
The extremely unsatisfactory environmental situation in Ukraine and in the world demands for the search for new approaches to enterprises management, focused on minimizing negative impact on the environment and ensuring sustainable development. Different forms and ways of improving production
2021 Copyright for this paper by its authors. and economic activities based on introduction of eco-innovations serve s a basis of sustainable development. Under such circumstances, it is urgent to develop an appropriate of intelligent management systems (IMS) for ecological innovative enterprises (EIE) to ensure rational decisions regarding environmental, economic sustainability and security of individual economic entities and the country as a whole. The designing of the IMS EIE, use of artificial intelligence elements in the system functioning to optimize management, formation of a system of criteria and indicators to evaluate the system efficiency are considered as important problems.
Eco-oriented management (EOM) of innovative activities at all organizational levels of national
innovation systems is an urgent issue in the current conditions of international socio-economic
development. EOM generates and activates all enterprise resources to achieve economic goals,
interrelated with purposes of sustainable nature management and development. At present, EOM is
becoming the central subsystem of the management system; whereas operation of all other subsystems
(personnel, financial, etc.) is based on the principles sustainable nature management and environmental
protection [
Eco-oriented management has been the subject of research by many scientists. In particular,
Yasnolob I. notes that as a part of a general management system, an environmental management system,
based on a system-environmental approach, ensures correlation of all management functions according
to sustainable development concept and environmental justice principles [
In a broad sense, EOM can be defined as a type of management, mainly oriented at formation and
development of ecological production, ecological culture and life sphere. This type of management
should be considered as a complex and perspective consideration of ecological problem as a part of the
economic policy of enterprises [
The principles of an efficient ecological management system include [
However, the problems of a mechanism formation for eco-oriented management of innovative enterprises are not sufficiently addressed and need further research, development and implementation in compliance with requirements and challenges of industry 4.0.
EOM at the level of innovative enterprises (IE) can be ensured by development of an appropriate
eco-oriented management system (EOMC). EOMC can promote IE to maintain and strengthen its stable
position at the market, form and meet eco-oriented consumer needs, respond adequately and promptly
to changes in the environment, provide sustainable development. Changes in the environment, high
competition and intensive development of eco-oriented demand force IE leaders to reorganize and
improve the organizational and economic mechanism of IE management to provide IE eco-oriented
activities to get, on the one hand, their profit, and on the other, preserve the environment and human
health. That is, nowadays, all IE shall become ecological, by ecological innovating enterprise (EIE) we
mean an enterprise of various organizational and legal forms, engaged in eco-innovation activities,
focused on the production of environmental products (provision of environmental services) in various
fields life. The EIE business strategy is aimed at profit earning with a focus on environmental
conservation and sustainable development [
EIE can be described as follows [
EIE identification criteria are based on [
The methodological basis of the investigation is a set of general and special methods of scientific researches. The implementation of these methods is caused by the goal and logic of problems solving for eco-oriented management and design the mechanism of eco-oriented management that are based on the using of environmental innovations, greening the activities of innovative enterprises for ensuring the sustainable development. The obtained scientific results are based on the implementation: a systematic approach, logical analysis and synthesis for researching the reference sources to determine approaches for the problem solving of forming IMS EIE; structural design methodologies for the analysis of various aspects of IMS as a complex organizational and economic system that implements eco-oriented management of EIE; methods of multilevel and multi-stage decomposition of IMS for construction of its model; the concept of hierarchical management and methods of functional analysis of IMS, for determination of the multiplicity of its functions, the heterogeneity of structural elements at all levels of the hierarchy, the diversity of forms of existence throughout the life cycle of EIE; formalization methods - economic-mathematical models of queuing theory, methods of hierarchical structures theory, set-theoretic models, differential equations, models of Markov processes - for quantitative evaluation, comparative analysis and forecasting of EIE states in general and its structural components; graphical method - for a visual representation of the dynamics and statics of the development of EIE or their structural units; decision-making methods - for decision-making on EIE states and the choice of methods for managing these states; methods of cognitology - for development the organizational structure of intellectual IMS. These aspects will allow the design, construction and reconstruction of IMS EIE in general and their units with the purpose of their adaptation to modern business conditions.
It is important to decide on the strategic vector of EIE development to form IMS EIE. For this, it is
relevant to perform: structural analysis of the IMS EIE as a complex system and consider its results; to
use quantitative evaluation, comparative analysis and prediction of EIE conditions in general and its
structural elements; functional consideration of IMS EIE, determined by the multiplicity of functions,
heterogeneity of structural elements, variety of forms of IE life cycle. That is, the analysis of IMS EIE
various aspects as a complex system, implementing IE eco-oriented management, it is recommended to
provide by multilevel and multi-stage decomposition of its model [
Analysis of systems, using decomposition methods, allows to break down their models into
subsystems and determine their structure and functions. The complexity that generated decomposition
and aimed efforts of scientists to get further knowledge about certain aspects of phenomena, also caused
the problem of the studied components integration into a complex system - the problem of synthesis
[
Thus, it is proposed to use decomposition methods for formation of IMS EIE (S) [
The following types of relations are observed between these sets of system model of IMS EIE: R1 (E,Vj) – correspondence relation that puts into correspondence to each sample of set E a sample of Vj, that is, from V1j і V2j; R2 (V1j,V2j) – correspondence relation between a given indicator name and its specific values at time moment t; R (Vj,W) – relation, which puts into correspondence to each element 3 of set Vj пa subset of values of set W elements at time moment t; R4 (W,F) – relation of order that sets the sequence of functions (actions, operations) in the process of achieving main goal G0.
SS1 GE
SS2
E GV1
SS3
V GV2
SS4 T
GW W
F
S
SS5 G
SS6
R GF
GG
The goal, in turn, can be formed as a requirement for achievement of specific values of indicators or states of performance for subsystems of IMS EIE and effective performance of certain functions of IMS EIE as a whole. That is, when simulation IMS EIE, it is necessary to distinguish such mandatory sets of components that would ensure the model completeness (1), where i = 1,2,…, N, N = 6.
PM ESS1 , ESS2 , ESS3 , ESS4 , ESS5 , ESS6 ,V SS1 ,V SS2 ,V SS3 ,V SS4 ,V SS5 ,V SS6 ,W SS1 ,W SS2 ,W SS3 ,W SS4 ,W SS5 ,W SS6 , F SS1 , F SS2 , F SS3 , F SS4 , F SS5 , F SS6 , GSS1 , GSS2 , CSS3 , GSS4 , GSS5 , GSS6 , RSS1 , RSS2 , RSS3 , RSS4 , RSS5 , RSS6
R(SS1, SS2 ), R(SS1, SS3 ),..., R(SSi,SSi1),..., R(SSi1, SSi ),..., R(SSN , SSN1).
The above patterns of structuring and functioning of IMS EIE subsystems are the initial rules for allocation of a system of elements and its model attributes. Regarding completeness of the mechanism system model, consideration is required at the upper levels of decomposition relative to the following objects: Е Е SS1 , Е SS2 , E SS3 , Е SS4 , E SS5 , E SS6 . (1) (2)
During decomposition of set E elements, for each element (E SS1 , E SS2 , E SS3 , E SS4 , E SS5 , E SS6 ) from set (2), it is proposed to select the entire set of system components – {V,Z,W,F,G,R}. Next decomposition of elements (2) and their components depend on the analysed subsystem type SSі, where і = 1,2,…,6. Hierarchy of structuring is a common feature. It can be formalized by a description in a theoretical and multiple language in the form of a relation tree, which can be an abstract level of a hierarchical model of subsystems, where the units of their characteristics and the mutual relations are specified. Here we propose a six-stage decomposition that can be represented as a model (Fig. 1), where the first stage is represented by graph ( GЕ ) – graph of elements, the second ( GV1 ) – indicators graph, the third ( GV 2 ) – graph of indicator values, the fourth ( GW ) – graph of states, the fifth ( GF ) – graph of functions (actions and operations), the sixth ( GG ) – tree (graph) of goals functioning of the IMS EIE. That is, the presented six-stage decomposition can set the rules for IMS EIE system model formation.
For graphical representation of IMS EIE (S) hierarchical levels it is proposed to perform three-level and two-stage decomposition and build a system S model in the form of three-level structure using system (macro), subsystem (meso) and micro-level (element level). That is, system S model is proposed to be presented in the form of a graph, where, after the first stage of system S decomposition as a complex system (macro-level), the second (meso) levels can accommodate hierarchical structures of all system S components, in our case, these are the following subsystems: SS1, SS2, ... , SS6. Elements of sets E SS1 , E SS2 , E SS3 , E SS4 , E SS5 , E SS6 (graph GЕ ) indicate the second stage of system S decomposition and, accordingly, to the third (micro) level of system S hierarchical structure. Similarly, further decomposition of all elements of set E of system S is performed, if they can be regarded as complex hierarchical structures.
The evaluation of system S efficiency can be performed based on the evaluation of EIE functioning
efficiency, in particular, on results of economic, environmental, scientific, technical, social, resource,
organizational and managerial types of efficiency, etc. [
That is, next graph ( GV1 ) is indicators graph and the third ( Gv2 ) is a graph of indicator values can represent, for example, a graph of EIE performance and, accordingly, a graph of performance. Based on EIE performance values, it is possible to analyse efficiency of system S functioning of eco-oriented management of EIE activities in general and its components and to make appropriate decisions on this basis.
The fourth graph (graph GW ) can be represented by EIE development stages and, accordingly, implementation states of EIE functions, actions and operations and, accordingly, efficiency states EIE and IMS.
Study of functions, actions and operations of IMS EIE is relevant, contributing to achievement of the EIE goal, and during decomposition IMS EIE may be the fifth graph (graph GF ). IMS EIE management functions, actions and operations are proposed to include: collecting, storing, processing and transmitting management information, forecasting and supporting the adoption and implementation of management decisions.
A goal tree of IMS EIE decomposition is proposed as the sixth stage (Fig. 2). This tree defines the main goal of G0 EIE and the ways to achieve it, that is, includes the goals of all elements and subsystems of EIE, in particular:
G1 – production of intelligent ecological products; G11 – generation of ecological ideas; G12 – scientific research work; G13 – design work; G14 – pilot work;
G2 – production of innovative ecological products; G21 – transfer of innovative eco-technologies; G22 – technological preparation of ecological production; G23 – ecological production; G24 – marketing; G1 G11 G12 G13 G14
G2 G21 G22 G23
G24
G3 – ecological training of personnel; G31 – formation of ecological competence of staff; G32 – training in ecological knowledge; G33 – ecological skills training (operations and operations); G34 – provision of eco-innovation certificates; G35 – acquiring eco-innovation skills; G36 – study of educational process and decision making;
G4 – implementation and use of eco-innovation; G41 – resource; G42 – process; G43 – product; G44 – market; G45 – managerial; G46 – organizational;
G5 – management; G51 – prediction; G52 – goal formation; G53 – planning; G54 – coordination; G55 – organization; G56 – stimulation; G57 – control; G58 – regulation; G59 – operational management.
IMS EIE analysis, using multilevel and multi-stage decomposition allows to break up IMS EIE model into subsystems and study the EIE functioning states at different levels of their organization, IMS EIE structure, its function, actions and operations. It causes the synthesis problem of studied IMS EIE components, involving use of integration methods to reproduce IMS EIE from its functional elements to study implementation processes of IMS EIE functions in general to meet present challenges of EIE development. That is, the analysis and synthesis will allow the design of IMS EIE, IMS EIE general construction and reconstruction and their subsystems, to adapt IMS EIE to modern business conditions.
The tools, required for IMS EIE formation and operation, can be based on object-oriented integrated and distributed databases and knowledge bases, hybrid expert systems, decision support systems (DSS), integrated neural networks and fuzzy logic tools. DSS allow you to model and automate decisionmaking processes, model and automate EIE organizational management. Distributed artificial intelligence, integrated intelligent information systems (IIIS) as multi-agent systems [21-23] is the most suitable class of models for IMS EIE implementation, its structure is presented in Fig. 3.
The structure of decision support subsystems (SS3 and SS4 subsystems) that are capable of decision making support and explanation include: the first and second generation knowledge subsystems, knowledge base, user interface, decision making subsystems and explanation. Necessary decisions, when using IMS EIE, will be made based on expert knowledge, which, respectively, can be highly qualified specialists in specialized fields of knowledge (knowledge of the first kind), as well as the knowledge obtained based on a priori information and the research results of EIE activities (knowledge of the second kind). This knowledge can be formalized and entered into the knowledge base as knowledge, on its basis SS3 subsystem supports decision-making on the states of EIE activities, SS4 subsystem - main effects implemented by SS6 subsystem.
Managerial decisions on EIE states in general, or any element of their hierarchy, in IMS EIE with proposed structure can be supported, using Monte Carlo simulation, discrete simulation, system dynamics and statics [24, 25], digital business models and visual simulation, operations research (simulation modelling, business games, stochastic programming), decision trees, impact diagrams, fuzzy logic tools, agent and multi-agent simulation [26-29].
Study of dynamic and static characteristics of real states of EIE development and their efficiency with subsequent decision-making is performed by means of IMS EIE. Data collection, their initial processing, to state an environmental problem, is performed using SS1 subsystem - diagnosing subsystem of EIE states. The storage and collected data processing and further evaluation and prediction of EIE states and their efficiency are performed using SS2 subsystem. If real studies fail to be implemented, then it is proposed to use virtual information from the information block and modulate virtual states and business processes of EIE using SS5 subsystem. It will promote further evaluation and prediction of possible states, make situational decisions and implement them using SS6 subsystem. If ecological problem is well-structured, then mathematical methods are used to process environmental information about it and subsequent choice of management decisions, and if the problem is poorly structured or unstructured, expert judgement is proposed to prepare options [30-32] and evaluations.
To evaluate and predict EIE states in general or their components, it is proposed to use a mathematical tool of Markov chain theory; applying systems of differential equations of Kolmogorov type. Based on this mathematical tool the corresponding software was developed and tested in the study of petroleum products concentration on biogeocenosis elements around wells operated in BoryslavSkhidnytske oil deposit [33, 34]. This mathematical tool can also be used to study IE environmental friendliness of before and after introduction of eco-innovations, allowing to evaluate their efficiency.
Decision support subsystem on EIE state (SS3)
Knowledge base
Tool of conclusions
Decision explanation
Accumulation of knowledge of the
1st kind Accumulation of knowledge of the
2nd kind
Expert
User interface Evaluation and prediction subsystem
of EIE states and their efficiency (SS2) Evaluation of EIE
states Information processing
Prediction of EIE
states Information
storage System interface
These studies are proposed to be performed based on calculation of integrated ecological indicators of enterprises [35-37], namely, integral indicator IPEVS, represented as formula (1) [35].
To evaluate and predict the EIE states of ecological sustainability based on the study of its integral index, these states are proposed to be presented as a graph with vertices (Fig. 4), identifying the states (Table 1): S1 – “very poor”; S2 – “poor”; S3 – “satisfactory”; S4 – “good”; S5 – “very good”.
This graph can be described by the system of Kolmogorov differential equations (1), and as t → ∞ and dP/dt = 0, by the system of algebraic equations (2), where λi, j are the intensities of the transition from state i to state j, i, j = 1 , 2,…, 5; i ≠ j; Pi is the probability of i states.
ddPt1 12 P1 21 P2 (1) (2)
The value of transitions from one state to the other, directly affected by eco-innovations, is the statistical information that can be obtained during the functioning of studied EIE. To evaluate and predict EIE ecological sustainability state, it is recommended to collect information at the beginning and at the end of the eco-innovation implementation. For the purpose of automated study of the dynamics of ecological sustainability states, the numerical solution of the differential equation system (1) is proposed to be performed by software tools, based on the fourth-order numerical Runge-Kutta method, included in the structure of the IMS EIE (Subsystem SS4). An automated study of EIE ecological sustainability state in static when t → ∞, a dP/dt = 0, is proposed to be performed based on computer solution of the system of algebraic equations (2) using software developed on the basis of the Gaussian numerical method. Based on the obtained dynamic and static characteristics of the state of ecological sustainability of enterprises can make appropriate forecasts and make optimal management decisions.
12 P1 21 P2 0 12 P1 (21 ,23 ) P2 32 P3 0 23 P2 (32 34 ) P3 43 P4 0 34 P3 (43 45 ) P4 54 P5
0 45 P4 54 P5 0
The proposed mathematical tool and the developed software to solve systems of differential (1) and algebraic (2) equations, were tested for relevance in real studies of the state of ecological sustainability of 100 enterprises in the Western region of Ukraine.. Graph of virtual states of ecological sustainability is shown in Fig. 5.
Prior to the introduction of eco-innovation, 30 enterprises were in state S1, 40 enterprises were in state S2, 15 enterprises were in state S3, 10 enterprises were in state S4 and 5 enterprises were in state S5, that is, the initial values of state probabilities (initial conditions of the studied process) were as follows:
Р1 = 0.3; Р2 = 0.4; Р3 = 0.15, Р4 = 0.1, Р5= 0.05 S1 25 2 2 S2 4 3 30 5
S3 35 3 20 1 S4
S5
When solving differential equations system (1), describing the state graph of the integral indicator of ecological sustainability (Fig. 5) and algebraic equations systems (2), we obtained dynamic and static characteristics of virtual state probabilities of ecological sustainability, presented in Fig. 6.
In this case, state of S5 is most likely, since value of P5 in statics is 0.9342, and P4 = 0.0467, P3 = 0.0093, P2 = 0.0092, P1 = 0.0006. Since S5 is a very good state, characterizing the absolutely sustainable development of the EIE, it can be considered that most EIE where eco-innovations are implemented, belong to enterprises where ecological issues of industrial activities are resolved.
Based on the computational experiment, it can be concluded that this mathematical tool and the software developed on its basis can find application as a mathematical and software IMS EIE to evaluate, predict and support environmental decision making.
Thus, the proposed approach to designing of IMS EIE, the study of EIE state in general and their structural elements, goals, efficiency, functions, actions and operations, indicates that IMS EIE should be considered as a complex system with a set of interdependent elements, their structure, strategic and operational activities aimed at achievement of EIE interim goals and overarching goals in a market environment and the constant impacts of a changing environment.
Designing of IMS EIE as a complex system is aimed at greening of production and technological processes of economic entities of different spheres and activities, industries, regions and the country as a whole in compliance with modern challenges. Use of IMS EIE will allow to make optimal management decisions on ecological and economic activity and increase the efficiency of enterprises functioning, increase their environmental and economic security, which is a practical effect of work.
The studied approaches to IMS EIE designing can be widely used for IMS EIE and EIE design, construction and restructuring in general and their structural elements, to adapt EIE to current economic conditions.
It is important to formulate criteria to evaluate efficiency of the IMS EIE and use of an integral indicator for evaluation and prediction of ecological sustainability of enterprises. Approaches to the study of dynamic and static characteristics of EIE ecological sustainability states, its evaluation and prediction, are relevant, for further optimal management decisions to improve the IMS EIE functioning and the EIE as a whole.
[20] Tsapuk О. Yu. 2013. System of criteria and indicators for evaluation of innovative development efficiency. Marketing and innovation management, N 3, 137-145. [21] Antonescu, M. 2018. Are business leaders prepared to handle the upcoming revolution in business artificial intelligence? Qual. Access Success, 2018, N 19 (53), 15–19. [22] Boitnott J. 2019. 7 innovative companies using A.I. to distrust their industries. URL: https:// www.inc.com/john-boitnott/7-innovative-companies-using-ai-to-disrupt-their-industries.html (accessed on 1 June 2019). [23] Jaehun Lee, Taewon Suh, Daniel Roy and Melissa Baucus. 2019. Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5, 44. URL: https://www.researchgate.net/publication/334620177. [24] Aliyev A.G. 2014. Economic-Mathematical Methods and Models under Uncertainty. Apple
Academic Press, 302 p. [25] De la Fuente A. 2000. Mathematical Methods and Models for Economists. Cambridge University
Press, 836 p. URL: https://www.twirpx.com/file/1180529/. [26] Ana Paula M. Tanajura, Valdir Leanderson C. Oliveira, Herman Lepikson. 2015. A Multi-agent Approach for Production Management. Industrial Engineering, Management Science and Applications, 65-75. [27] Aradea Dipalokareswara, Iping Supriana, Kridanto Surendro. 2014. An Overview of Multi Agent System Approach in Knowledge Management Model. International Conference on Information Technology Systems and Innovation, 2014. URL: https://www.researchgate.net/publication/268982254. [28] Dhanashree R. Kamble. 2013. Architectural Review On Multi Agent Knowledge Management.
International Journal of Scientific & Technology Research, Volume 2, Issue 6 (June 2013), 105 – 114. [29] F. Vendrell-Herrero, G. Parry, O. F. Bustinza, E. Gomes. 2018. Digital business models: Taxonomy and future research avenues. Strategic Change, Vol. 27, Issue 2. URL: https://doi.org/10.1002/jsc.2183. [30] Delgado M., Verdegay J.L., and Vila V. 1993. Linguistic decision making models. International
Journal of Intelligent Systems, Vol. 7, 479-492. [31] Larysa Cherchyk, Mykola Shershun, Nina Khumarova, Taras Mykytyn, Artur Cherchyk. 2019. Аssessment of forest enterprises performance: Іntegrating Economic Security and Ecological Impact. Entrepreneurship аnd Sustainability іssues, Vol. 6, N 4, 1784-1797. URL: http://doi.org/10.9770/jesi.2019.6.4(17)/. [32] Singh S., Olugu E. U. and Fallahpour A. 2014. Fuzzy-based sustainable manufacturing assessment model for SMEs. Clean Technologies and Environmental Policy, Vol. 16 (5), 847-860. URL: https://doi.org/10.1007/s10098-013-0676-5. [33] Odrekhivskyy M., Kohut U., Kochan R., Jancarczyk D and Gryga V. 2019. Problems of evaluation and Forecasting of the Biosystems states. 19th International multidisciplinary scientific Geoconference SGEM 2019. Nano, Bio, Green and Space: Technologies for Sustainable Future, (30 June – 6 July, 2019), Albena, Bulgaria. Vol. 19, Issue: 6.1, 859-865. [34] Odrekhivskyy, M., Kohut, U., Kochan, R., Karpinskyi, V. and Bernas, M. 2019. Problems of environmental innovation systems design. 19th International multidisciplinary scientific Geoconference SGEM 2019. Ecology, Economics, Education and Legislation, 30 June – 6 July, 2019, Albena, Bulgaria. Vol. 19, Issue: 5.3, 587-594. [35] Rudneva H.Y., Zaglada R.Y., Chiechienieva E.R. 2012. Evaluation the ecological Component of Sustainable development of Coal-mining Industry Enterprises. Bulletin of Zaporizhzhya National University. Economic Sciences, No. 4 (16), 62 - 68. [36] Jurgis K. Staniskis, Valdas Arbaciauskas. 2009. Sustainability Performance Indicators for Industrial Enterprise Management. Environmental Research, Engineering and Management, N 2 (48), 42 - 50. URL: https://www.researchgate. net/publication/49941561. [37] Hyunkee Bae, Richard S. Smardon. 2011. Indicators of Sustainable Business Practices.
Environmental Management in Practice. Edited by Dr. Elzbieta Broniewicz, 177-206.