CC BY
4DOI: 10.29141/2658-5081-2022-23-1-6 JEL classification: G32, G34
Irina N. Tkachenko Ural State University of Economics, Ekaterinburg, Russia
Aleksandr A. Zlygostev Ural Bank for Reconstruction and Development, Ekaterinburg, Russia
Stakeholder risk research tools in the light of companies' sustainable development
Abstract. Despite very extensive research on the issues of the stakeholder approach, so far there is no general understanding of the risks borne by company's stakeholders and no applied tools developed to address specific problems of recognising and analysing them. The paper aims to study stakeholder risks, evaluate them, model stakeholder risk networks, develop tools for determining the loyalty (satisfaction) of stakeholders, and establish risk priorities for stakeholders. The research methodology rests on the stakeholder approach, corporate governance theory and graph theory. The paper applies comparative and content analysis, methods of modeling, prioritisa-tion and visualisation of graphs. Based on Rebecca Yang's method modified by the authors, the study models stakeholder risk networks. Due to the modification, the method is able to take into account the factor of balance of stakeholders' interests. The method is tested on a business project of a particular company. The research results include specifying the mutual influence of risks in the network, determining key categories of risks and the most influential stakeholders, rating risks using analysis metrics and graphs, and developing a scheme for implementing the proposed tools in the management system. The theoretical and practical significance of the study lies in introducing the factor of balance (imbalance) of interests in modelling of stakeholder risk networks, as well as in providing recommendations on the use of these tools for sustainable development.
Keywords: sustainable development; stakeholder network; balance of interests; stakeholder risks; business development; corporate governance.
Acknowledgements: The research is funded by the Russian Foundation for Basic Research (RFFI) and Sverdlovsk oblast within the framework of the research project no. 20-410-660032-r_a "Innovation technological development of regional industry in the context of the transformation of business architecture and management technologies that produce knowledge and common values: Institutional and stakeholder aspects".
For citation: Tkachenko I. N., Zlygostev A. A. (2022). Stakeholder risk research tools in the light of companies' sustainable development. Journal of New Economy, vol. 23, no. 1, pp. 109-130. DOI: 10.29141/2658-5081-2022-23-1-6 Received January 10, 2022
Introduction
In the context of the COVID-19 pandemic, growing uncertainty and a variety of threats, it is of increasing relevance for both foreign and Russian companies to examine stakeholder risks and the adequate application of the results obtained in management practice. Corporate business, informed about the undoubted advantages of an integrated risk management system, is showing an intense interest in building it. Owners are becoming increasingly aware that the growth of their wealth and the long-term sustainability of business are ensured by the needs of various groups of stakeholders satisfied.
Lack of well-managed sustainability practices undermines the strength of business models and creates significant reputational and financial risks for investors. To manage a business effectively, one has to make risky decisions while adhering to the concept of acceptable risk. In this regard, there is a manifold increase in the importance of risk management based on keeping a balance of interests of a wide range of company stakeholders. It is worth noting that the need to balance interests is attributed to the fact that stakeholders are involved in coalition (network) interaction. At the same time, there are significant differences in the perceptions of each stakeholder about the expected or lost benefits and risks, which also hampers assessing both their contribution to the total stakeholder value and the degree of risk acceptability.
The purpose of the current research is to draw attention to the issue of stakeholder risks and suggest tools for modelling networks of these risks while taking into account the balance of interests of company (project) stakeholders, as well as the toolkit for determining the loyalty (satisfaction) of stakeholders and risk priorities for them. The proposed approach allows stakeholders to manage their risks and take responsibility for them, as well as provides an opportunity to consider the cross impact of stakeholder risks and build constructive relationships with stakeholders through ensuring a balance of interests, control and influence.
Literature overview on stakeholder risks
For over thirty years, the stakeholder approach has been widely discussed in numerous scientific publications. It has covered the issues on the role of stakeholders, the need to take account of their interests, the distinguishing features of their identification,
and the specificity of their analysis. However, there is a lack of studies on the applied aspects of the stakeholder model, namely, specific developments in the methodology for analysing and evaluating stakeholder value, as well as assessing benefits and risks for particular categories of stakeholders.
To establish the array of publications on stakeholder risks, we used content analysis for research papers released in 1980-2020 in the Google Scholar database with search queries in English and Russian for the keywords "stakeholder approach" (steykkholderskiy podkhod) and "stakeholder risks" (steykkholderskie riski) [Tkachenko, 2021]. It was found that in recent years the interest in the stakeholder approach among foreign researchers has been constantly growing, which resulted in a marked increase in the number of relevant publications (up to 7,000 articles in 2020). Studying stakeholder risks, however, was not very dynamic: one article in 1990, and 48 articles in 2020. The number of English-language publications on the stakeholder approach has always exceeded the number of Russian-language works (almost 100 times in 2020), as well as publications on stakeholder risks (25 times in 2020). All this indicates that risks for a wide range of stakeholders are poorly examined in academic literature.
Let us look at a number of significant works on the topic under consideration.
There are some publications that examine stakeholder risks within the methodological framework of the stakeholder approach. Operational and strategic decisions should be taken through finding a consensus with stakeholders and developing an agreed position on the company's key risks and evaluating a given level of risk appetite.
Mitchell et al. [2015] propose developing a transdisciplinary theory of value creation stakeholder accounting (VCSA) based on the distribution of risks between these parties. Value creation stakeholder partnership (VCSP) aimed at creating value is viewed as a promising mechanism of VCSA implementation; the connection between the value creation process, accounting and risks for stakeholders is established.
The work by Henry and Lundberg [2017], focused on the risk assessment procedure, is worth special attention. The authors attach supreme importance to the definition of stakeholder risks that is comparable to the identification of prioritized threats to national security, and formulate their own methodology for determining the value and risks for stakeholders.
Publications stating the problem of risk management for key stakeholders lack a developed toolkit applicable for assessing stakeholder risks in business practice [Ma-haraj, 2008; Schiffer, 2015], although these works emphasize the relevance of reducing the risks and the need for appropriate procedures taken by companies' boards of directors. A number of authors (see, for example, [Yadav, Sekhri, Curtis, 2007])
analyse how risks and benefits are distributed among stakeholders in the value chain and identify ways to correct imbalances while taking into account diverging interests.
Among the applied research with a well-developed toolkit, it is worth highlighting publications by Chinese authors, which substantiate the approach to modelling stakeholder risks in projects of interactive networks using social network analysis methods. The developed methodology allows identifying critical risks and related stakeholders, as well as improving the accuracy of the stakeholder and risk analysis [Yang, 2014; Yang, Zou, Wang, 2016]. Similar views on stakeholder risks are shared in publications devoted to establishing the impact of these risks in PPP mega-engineering projects [Aladag, I§ik, 2020] and studying the problem of stakeholder trust and risk perceptions [Zhou et al., 2018].
Woolridge, McManus and Hale [2007] propose a methodology for assessing stakeholder risks in projects and operational activity based on the application of subjective methods, such as surveys and expert assessments. The outcome-based stakeholder risk assessment (OBSRAM) model is founded on the results assessment. It allows analysing how changes in business processes affect stakeholders and taking into account their reaction to these changes. The method used lies in prioritizing the stakeholders with the highest stakeholder risk metric and gaps in stakeholder influences, as well as the gap in their risk perception. The actions aimed at identifying stakeholder risks constitute the basis of the method: the entire business is split into a number of key business processes that lead to the outcome, which underlies stakeholder risk management. To attain this objective, the authors investigate how the outcome of each business process affects each stakeholder.
Badran [2020] explores the influence of stakeholder risks by studying infrastructure projects risks using the method of neural networks modelling. The author demonstrates the opportunities of analysis and drawing conclusions regarding the impact of stakeholder risk categories on increasing the project time using neural networks. As shown in the article, inefficient stakeholder management can lead to delays in project implementation.
Address a number of publications concentrated on assessing the stakeholder value and risks. Fernández-Guadaño and Sarria-Pedroza [2018] apply economic-mathematical modelling to assess value creation for certain groups of stakeholders. Ramírez and Tarziján [2018] provide recommendations on evaluating the benefits for stakeholders in the light of the influence of exogenous and institutional risk factors. Neto et al. [2018] propose determining stakeholders' contribution using the value judgments of their representatives when making decisions about networking in the context of limited resources, risks and uncertainty.
The above publications emphasize the benefits of associating all possible types of risks with stakeholders creating these risks. The applied methodological recommendations for risk assessment, however, relate primarily to project stakeholders, but not organization stakeholders.
Ivashkovskaya is one of the Russian researchers who contributed most significantly to the topic under review. She extensively explores the corporate development strategies aimed at creating value for companies, while taking into account stakeholder value. The researcher stresses that stakeholder risk is associated with the relationship imbalance in the stakeholder network and the loss of trust of its participants. This risk lowers the value of intellectual and social capital for the company, which, in turn, leads to an increase in the cost of total financial capital and a decrease in the economic profit of shareholders [Ivashkovskaya, 2009, 2011, 2012, 2016].
Vashakmadze proposes evaluating stakeholder risks using the company's capitalization in the stock market, namely through the premium to the required return, which should correlate with this risk. Based on the results of econometric analysis, the author proves that the growth of stakeholder risk reduces the capitalization of the company [Vashakmadze, 2014].
When analysing the risks and opportunities of stakeholders in management practice, Baydakov [2016] looks at the risks of individual stakeholders. In his later publication, while focusing on the need to develop and improve effective tools for the stakeholder approach to resolve specific problems, the researcher discusses assessing the benefits received by each stakeholder from the company (or, vice versa, derived by the company from stakeholders); costs incurred by either the stakeholder or the company when providing resources and interacting with each other; and risks that arise in the interactions of the company (stakeholder) in comparison with its alternative opportunities [Baydakov, 2019]. Nevertheless, setting the task of assessing stakeholder risks, the author does not provide the tools to fulfil it.
The paper by Kogdenko [2018] is devoted to the analysis of stakeholder risks, which indicates the need to take account of these risks in the overall risk structure of a company and identifies the types of risks characteristic of each group of stakeholders. Based on the stakeholder approach, the author develops a methodology for analysing company risks. The proposed algorithms make it possible to link these risks with the company's sector, identify the most significant of them, calculate quantitative risk indicators and devise risk management methods. Despite the fact that the article only formulates stakeholder risks and does not scrutinize them in detail, this is one of the first attempts in the Russian research field to integrate such risks into the overall risk analysis system.
Russian-language publications often use generally accepted methods, such as the Mitchell-Agle-Wood model [Mitchell, Agle, Wood 1997], as a kind of template. Under this approach, stakeholders are categorized according to a combination of three attributes: power, legitimacy, and urgency [Abrosimova, Sedelnikova, 2011; Zilber-shteyn et al., 2016]. Stakeholder risks can be analysed using qualitative and quantitative content analysis: based on news texts of the largest information agencies, through identifying stakeholders, their interests, and emotional colouring of text messages [Ramenskaya, 2021].
Developing the stakeholder approach, we paid attention to the practical aspects of the analysis and modelling of stakeholder risks [Tkachenko, Pervukhina, 2020; Tkachenko, Pervukhina, Zlygostev, 2020; Tkachenko, Zlygostev, 2021]. Stakeholder risk is a risk of losing long-term and (or) short-term competitiveness of an organization due to the destruction or rupture of its relationships with key stakeholders. This particular type of risk is not found in most risk classifications. In our opinion, the reason for the stakeholder risk lies in the conflict of interests and (or) lack of communication with a significant group of stakeholders. The core factors in preventing and detecting this risk are monitoring and analysis of stakeholders, as well as a dialogue with them and balancing interests.
How to analyse stakeholders and monitor stakeholder risk? As indicated by the above review of scientific publications, there is no unequivocal answer to this question yet. Stakeholder risk is an underestimated problem that is poorly described in the literature. There are numerous aspects for considering and analysing particular stakeholders to detect stakeholder risk. The choice of tools depends on the situation and its context, as well as the goals of analysis. Researchers attempt to combine the factors to be taken into account by the management when formulating the coherent policy in the field of stakeholder relationships.
Research methods
As part of the study, stakeholder risk networks were modelled taking into account the factor of the imbalance of interests. This approach is characterized by the following distinguishing features. Firstly, it is oriented towards the qualitative characteristics of risks. Secondly, risks in this case are considered within the framework of a specific project or strategy. Thirdly, the method is based on subjective approaches to assessing stakeholder risks, i.e., the source of information for their evaluation is the subjective opinions, estimates and expertise of stakeholders (in contrast to, for example, financial risk assessments, where the information base is usually comprised of accounting reports and financial models, i.e., monetary economic facts). Another peculiarity is the focus on the way in which stakeholder risks affect each other, but not on the
organization as a whole: the relationships between risks in the network are studied in order to identify their key links. The outcome of the assessment is, therefore, not indices or financial (monetary) evaluation of risk, but a risk ranking established using graph analysis metrics.
Within such an approach, stakeholder risk refers to a possibility of a project or strategy deviating from target criteria associated with the activities of the stakeholder that manages this risk. From the standpoint of the risk network, this is a specific risk of a particular stakeholder, and one of the vertices of the graph of the entire network. In this sense, the risk in question is not some kind of integrating, aggregated indicator of the overall level of stakeholder risk of an organization or its stakeholder groups; it cannot be expressed financially, and therefore differs sharply from the traditional financial risk assessments. The risk network determines these risks through their relationships with other risks, without identifying the likelihood and significance of the stakeholder risk itself. This approach assumes that stakeholders manage their own risks and bear responsibility for them. It is necessary, therefore, to take into account the mutual influence of stakeholder risks and build constructive relationships with stakeholders by maintaining a balance of interests, control and influence. Risk modelling allows one to identify and prioritize critical nodes.
The algorithm of the methodology applied is based on Rebecca Yang's approach [Yang, 2014], which was modified by adding the factor of the stakeholder interests imbalance to the formula for the relationship of stakeholder risks (Figure 1).
Fig. 1. Algorithm for modelling stakeholder risk networks1
Let us look at the modelling stages in Figure 1 in more detail. 1. Identifying stakeholders and their risks includes:
• expert assessments and surveys of project stakeholders;
• identification of risk categories.
1 Source: own compilation using Rebecca Yang's methodology [Yang, 2014].
2. Establishing the interactions between risks and describing the strength of these relationships are based on the expert assessments and surveys of project stakeholders in order to identify the interaction factors: the strength of influence, its probability, and the degree of the imbalance in the risk holder's interests.
3. Visualization and modelling of risk networks using graphs implies identifying:
• graph node, i.e., a stakeholder risk with its own code S*R*, where (*) is the number of the stakeholder, risk;
• node shapes to indicate the stakeholder;
• node colour to indicate the risk category;
• a directed edge of the graph, which shows the direction of the risk relationship transferring the risk from one to another;
• the edge weight, which indicates the degree of conductivity of the risk: the higher it is, the easier the risk is transferred along the edge.
4. Risk ranking based on analytical graph metrics covers:
• out-status centrality / degree centrality;
• degree difference between out-status and in-status centralities;
• betweenness centrality.
The authors' modification of the method for modelling the stakeholder risk network consists in introducing the factor of maintaining the balance of stakeholder interests (in formula (1) below, this is "the degree of the interest imbalance"). The need to cover this factor is due to the imperfection of Yang's original methodology, which considers stakeholder risks with no reference to stakeholders themselves and their interests. The modification complements the field of risk analysis by taking into account the degree of consistency of stakeholder interests. Thus, the degree of risk impact is calculated as follows:
Risk impact = Risk significance x Risk probability x
x Degree of the interest imbalance. (1)
The degree of the interest imbalance is determined for each project (strategy) stakeholder by a survey and expert assessments using a five-point scale. It affects all communications of the stakeholder in the risk network it manages (both incoming and outcoming), with the exception of internal communications (when some risks of a stakeholder affects other risks of the same stakeholder). For internal risks, the imbalance of interests is assigned the average 3-point value (since it helps not to overestimate the metrics in stakeholder risk interactions within the same stakeholder).
When the estimates of the imbalance of interests in the link differ, the estimate with the maximum degree of this imbalance is selected. For instance, if stakeholder S1 with a 5-point imbalance of interests is associated with stakeholder S2 with a 4-point
imbalance of interests, a score of 5 will be used to calculate the cross impact of these risks.
The application of the methodology for modelling stakeholder risks networks and analytical methods for graph analysis has allowed compiling a list of key stakeholder risks serving as the basis for singling out priority risk categories and priority stakeholders responsible for risks. The ranking allows prioritizing risks to encourage their further development and analysis.
The following limitations and disadvantages are characteristic of this methodology:
• subjective assessments and views of stakeholders are of great importance, there is a lack of objective data and facts to support the assessments;
• there is a possibility of missing some stakeholder risks;
• the totality of risk interactions is studied, but not the probability of risks as an event in itself and not the damage from the realization of a risk in monetary terms. There is no monetary expression of risks;
• there is no general indicator of the level of a stakeholder risk.
The model reflects an alternative view on stakeholders and stakeholder risks that takes account of complex relationships between risks and redefine the role of stakeholders. The authors' modification opens up a new field for analysis, which covers the interests of stakeholders, since their mismatch enhances the conductivity of stakeholder risks in the network and creates the preconditions for new ones to emerge that were not previously considered. This approach can be applied when assessing stakeholder risks in projects and strategies of an organization.
Research results
Modelling risk networks in the light of the imbalance of interests. The presented methodology was tested in a company engaged in the banking sector using a project aimed at upgrading internal business processes. The project involved the revision of the software technology by the developer to increase the flexibility of the bank's IT process in order to enhance the efficiency of the sales departments and boost profits. The list of the project stakeholders is given in Table 1.
The list of stakeholder risks put together on the basis of a survey of the key project stakeholders is presented in Table 2. To designate the risks, the code S*R* is used, where (*) denotes the number of the stakeholder and its risk.
Once the list of risks is drawn up, their cross impact is determined, i.e., their links, direction, probability (score of 0 to 5), and risk significance (score of 0 to 5). Relationship scores were the result of expert assessments and discussions. In particular, the stakeholder S1 was assigned the interest imbalance with the score of 4, and all the rest were assigned the score of 3.
Table 1. Project stakeholders and their functions
Stakeholder Risk code Functions
Customer's manager S1 Controls problem solving and the outcome with the help of the developer's manager
Customer S2 Forms a software refinement request to the developer's manager and transfers the responsibility to control it to the customer's manager
Developer's manager S3 Monitors the developer's progress and reports the results to the customer's manager
Supervisor of the developer's manager S4 Supervises the developer's manager working
Developer S5 Refines the software technology according to the terms of reference from the developer's manager
Sales departments S6 Use software refinement to enhance the efficiency of transactions
Bank S7 Increases profit due to the growing efficiency of sales departments
Table 2. List of stakeholder risks
Stakeholder Risk code Risk Risk category
Customer's manager S1R1 Time-consuming testing. Extending deadlines of the project implementation Time related
S1R2 Testing error that will lead to calculation errors Quality issues
S1R3 Errors with access to/functioning of forms, testing time delay Time related
S1R4 Uncontrolled problem solving, slowdown in implementation Management
Customer S2R1 Uncontrolled problem solving, slowdown in implementation Management
Developer's manager S3R1 Prolonged processing of the refinement request and belated transfer to the developer, slowdown in implementation Time related
Supervisor of the developer's manager S4R1 Uncontrolled problem solving, slowdown in implementation Time related
Developer S5R1 Developer errors and incorrect calculation of parameters Quality issues
S5R2 Implementation delay Time related
Sales departments S6R1 A missing or incorrectly calculated parameter can distort the orientation and incentives for sales departments Management
Bank S7R1 Decline in the bank's profit Cost related
S7R2 Errors in the bank's software and IT Quality issues
Source: own compilation using the surveys of the project stakeholders.
Table 3 presents the relationship scores. The matrices are constructed in such a way that the rows denote the impacting risks, and the columns indicate the impacted risks. For instance, the influence of risk S1R2 on risk S1R1 is equal to 30, and this strength is calculated using formula (1) based on the relationship characteristics.
Table 3. Relationship matrix (strength, probability, interest imbalance factor)
Risks Impacted by
S1R1 S1R2 S1R3 S1R4 S2R1 S3R1 S4R1 S5R1 S5R2 S6R1 S7R1 S7R2
S1R1 - - - 4, 4, 3 - - - - - 4, 2, 4 - -
S1R2 2, 5, 3 - - - - - - - - 4, 3, 4 - -
S1R3 4, 2, 3 - - - - - - - - - - -
S1R4 - - - - - 2, 2, 4 4, 4, 4 - - - - -
o S2R1 2, 2, 4 - - - - - 2, 2, 3 - - - - -
-M O a ¡a HH S3R1 - - - - - - - - 5, 5, 3 - - -
S4R1 - - - - - 2, 2, 3 - - - - - -
S5R1 - 4, 3, 4 - - - - - - - - - -
S5R2 4, 2, 4 - - - - - - - - - - -
S6R1 - - - - - - - - - - 3, 4, 3 -
S7R1 - - - - - - - - - - - 3, 3, 3
S7R2 - - 3, 2, 4 - - - - - - - - -
The data in Table 3 were calculated by formula (1) and visualized as a network (graph) of risks (Figure 2). A graph is made up of vertices (shapes) and edges (lines connecting the shapes). Each vertex characterizes a certain stakeholder risk. The shape indicates the stakeholder category, and its colour points to the risk category.
Stakeholders O Customer s manager
□ Customer A Developer's manager ^ Supervisor
of the developer's manager O Developer O Sales departments O Bank
Risk category
■ Time
■ Management Costs
□ Quality
Fig. 2. Network of the project stakeholder risks
S6R1
S1R1
S2R1
S4R1
Prior to applying the risk network model in practice, it should be analysed. The purpose of the analysis is to find and indicate key risks by prioritizing the most significant graphs according to the analysis metrics:
1) betweenness centrality (BC);
2) out-status / degree centrality (DC);
3) degree difference (DD).
Consider each of them more closely.
1. Betweenness centrality is a metric of a node in a graph, which shows the incidence with which a given node is on the shortest path between the other nodes in the graph. This indicator is calculated by formula (2):
_ y Number of shortest paths for a given pair of nodes through node X
NodeX Aifpairs All shortest paths for a given pair . ( )
of network nodes
Each actor's BC score can be interpreted as a measure of potential control, as it quantifies to what extend that actor acts as a proxy for others. It is assumed that the subject lying in between many others can more likely control the impact or information flow in the network.
BC implies that communication in the network occurs along the shortest possible path and completely ignores the probability of communication between actors not on the shortest paths.
The metrics of the betweenness centrality for the graph within the case under study are presented in Table 4.
Table 4. Betweenness centrality
Stakeholder Risk code Risk category BC BC', %
Customer's manager S1R1 Time related 59 54
Sales departments S6R1 Management 27 25
Customer's manager S1R4 Management 26 24
Bank S7R1 Cost related 24 22
Developer's manager S3R1 Time related 21 19
Bank S7R2 Quality issues 21 19
Customer's manager S1R3 Time related 18 16
Developer S5R2 Time related 18 16
Customer's manager S1R2 Quality issues 9 8
Customer S2R1 Management 0 0
Supervisor of the developer's manager S4R1 Time related 0 0
Developer S5R1 Quality issues 0 0
Since our graph is directed (the links between the nodes have directions indicated by arrows), the shortest path is often the only one for a given pair, which explains the
predominance of integers in the BC metrics: BC is the betweenness centrality; BC is the weight sum of the betweenness centrality of all the nodes.
Based on the metrics from Table 4, a graph of the project stakeholder risks was constructed (Figure 3), centred according to the betweenness centrality (the closer the graph's vertex to the centre, the greater the betweenness centrality of the corresponding risk). Stakeholders S1 and S6, i.e., the customer's manager and sales departments, demonstrate the highest betweenness centrality and control over risks in the project network. At that, the key risk categories are related to time and management (S1R1, S6R1, S1R4), since they mediate a large number of risk and stakeholder relationships.
Stakeholders O Customer's manager
□ Customer
A Developer's manager Supervisor
of the developer's manager O Developer O Sales departments O Bank
Risk category
■ Time
■ Management Costs
□ Quality
Fig. 3. Graph of the project stakeholder risks based on the betweenness centrality (BC)
2. Out-status/degree centrality provides the number of outgoing relations (taking into account their weight) a node has with other nodes in the network (Table 5), i.e., the metric determines the degree to which a node affects its nearest neighbours, taking into account the weight of the links. In social network theory, degree centrality is often considered as an indicator of actor activity. In Table 5, DC denotes the number of outgoing relations (degrees) of a node; DC stands for the weight of out-degrees in the total sum of out-degrees of all nodes.
Table 5 is visualized as a graph centred according to the degree centrality: the greater the DC of the node, the closer the node is to the graph centre (Figure 4).
According to the DC metrics (see Table 5), stakeholder S1 (the customer's manager) was the most influential stakeholder, and the most influential risk categories were time, management, and quality (S1R1, S1R4, S1R2).
Table 5. Degree of nodes impact on the nearest nodes
Stakeholder Risk code Risk category DC DC', %
Customer's manager S1R1 Time related 80 15
S1R4 Management 80 15
S1R2 Quality issues 78 14
Developer's manager S3R1 Time related 75 14
Developer S5R1 Quality issues 48 9
Sales departments S6R1 Management 36 7
Developer S5R2 Time related 32 6
Customer S2R1 Management 28 5
Bank S7R1 Cost related 27 5
Customer's manager S1R3 Time related 24 4
Bank S7R2 Quality issues 24 4
Supervisor of the developer's manager S4R1 Time related 12 2
S7R1-.
"S7R2
■ 24 :: ;
' S2RÏ- -
o
S1R3-'
Stakeholders O Customer's manager
□ Customer
A Developer's manager ^ Supervisor
of the developer s manager O Developer O Sales departments O Bank
Risk category
■ Time
■ Management Costs
□ Quality
Fig. 4. Graph of the stakeholder risks based on the out-status / degree centrality (DC)
3. Degree difference (DD) is the difference between the in-degree and out-degree (edges) of the node. With a positive value, a node can impact other nodes generally, but has relatively low direct impact from the others, i.e., it acts more as a source of risks for the rest of the network than a transmitter (controller) of the influence of other risks. This metric allows identifying "independent risks" that are less affected by other risks in the network, but at the same time have a noticeable impact on others.
Out-degree (the sum of outcoming edges) shows the direct impact of a risk on others. The bigger the difference in degrees, the stronger the impact of the risk on the other risks compared to the total impact received from them.
The matrix of node relations supplemented with a diagonal (highlighted in colour, includes positive numbers) shows the sum of all outcoming relations of a node (Table 6). The remaining (negative) numbers demonstrate relations between the nodes (the impact is denoted as -10).
Table 6. Matrix of risk relations
Risks Impacted by
S1R1 S1R2 S1R3 S1R4 S2R1 S3R1 S4R1 S5R1 S5R2 S6R1 S7R1 S7R2
S1R1 80 - - -48 - - - - - -32 - -
S1R2 -30 78 - - - - - - - -48 - -
S1R3 -24 - 24 - - - - - - - - -
S1R4 - - - 80 - -16 -64 - - - - -
o S2R1 -16 - - - 28 - -12 - - - - -
-M O S3R1 - - - - - 75 - - -75 - - -
S4R1 - - - - - -12 12 - - - - -
HH S5R1 - -48 - - - - - 48 - - - -
S5R2 -32 - - - - - - - 32 - - -
S6R1 - - - - - - - - - 36 -36 -
S7R1 - - - - - - - - - - 27 -27
S7R2 - - -24 - - - - - - - - 24
Table 7 is compiled according to the relations matrix, which is visualized as a scatter plot (Figure 5).
Table 7. Degree difference (DD) and out-status / degree centrality (DC) of risks
Risks Metrics S1R1 S1R2 S1R3 S1R4 S2R1 S3R1 S4R1 S5R1 S5R2 S6R1 S7R1 S7R2
DD -22 30 0 32 28 47 -64 48 -43 -44 -9 -3
DC 80 78 24 80 28 75 12 48 32 36 27 24
Note: Top-3 influential DD risks are highlighted in grey.
60
<u
0
g 20
1 0
«u -20
Fig. 5. Influence of nodes by degree difference and out-status centrality
i • S 3R1 •
• S5R L » S1R4
S1R3 - S7 R1 S1R2
0 1 0 2 • °S7R2# 3 XV J. 0 4 0 5 0 6 0 7 0 80 9
S6R1 « Ç1 » B1
S4R1 • • S5R2
•
Out-status centrality
The risks of the developer's manager, the developer and the customer's manager demonstrate the greatest independence in terms of the degree difference. The key risks are related to the categories of time, quality, and management (S3R1, S5R1, S1R4). The degree difference made it possible to identify independent risks associated with the stakeholder "Developer". Despite its low degree of intermediation in the network, it has a serious influence on the project as having a high degree difference. The metrics analysed - the betweenness centrality, the out-status/degree centrality, and the degree difference - allowed identifying the key risks (Top-3 for each) in the context of stakeholder and risk categories of the project for their prioritization (Tables 8, 9).
Table 8. Key project risks by three prioritisation metrics
Metrics Risks Betweenness centrality (BC) Degree centrality (DC) Degree difference (DD)
Risk code S1R1, S6R1, S1R4 S1R1, S1R4, S1R2 S3R1, S5R1, S1R4
Stakeholders responsible for risks Customer's manager - 2, sales departments - 1 Customer's manager - 3 Developer's manager - 1, developer - 1, customer's manager - 1
Type of risk Time - 1, management - 2 Time - 1, management - 1, quality - 1 Quality - 1, time - 1, management - 1
Table 9. Priority risks of the project
Risk Metric-based priority BC DC DD
S1R1 BC, DC 59 80 -22
S1R4 BC, DC, DD 26 80 32
S3R1 DC 21 75 47
S1R2 DC 9 78 30
S5R1 DD 0 48 48
Taking into account the factor of interest imbalance enhances the "conductivity" of risks in the network for stakeholders with the imbalance of interests and related stakeholders. We have found that this factor primarily affected the DC and DD metrics, while the changes in the BC metric were insignificant. The proposed methodology has allowed prioritizing stakeholder risks to ensure their further development, management, and control. The project manager initiates the modelling of these risks to establish the "nodal" stakeholders and their risks in order to provide additional control and devise preparatory measures and responses. The toolkit is universal and can be applied in any projects of organizations irrespective of their profiles.
The toolkit application in management. We propose using the method of stakeholder risk network modelling in the context of the overall transition to sustainable
development of an organization with stakeholder interests taken into account. In our research, the presented methodology is only one of the tools applied. Risk networks modelling is part of our general scheme for the implementation of the management system focused on stakeholders' interests and stakeholder value creation (Figure 6). It is also one of the components of the company's transition to sustainable development within the stakeholder paradigm.
Board of Directors
HR
Corporate Secretary
I
Retail Business Office
I
Corporate Business Office
Measure index
T
Remuneration Committee
Stakeholder satisfaction indices
Sets objectives by metrics, analyses their achievement
Value creation for stakeholders (stakeholder benefits)
k
Value created and distributed across stakeholder groups
Key performance indicators -metrics for considering stakeholder interestSB
Methodological support for accounting for stakeholder value and stakeholder risks
Integrated report: 1 Report for stakeholders. ■ Reference information on the effectiveness of relationships with stakeholders and their balance
General Director
Uses in work and distributes goals among departments
Departments
-Galculate-
-Develops-
Corporate governance
Sustainability Director
Sustainability Committee
Management
Modelling networks of stakeholder risks in projects
Accounting for project stakeholder risks
- Use in work -
Heads and Project Leaders
] Stakeholders -Aa-+ Functions Q^) Tools Goal
Fig. 6. Implementation of the tools for taking account of the stakeholder interests
in the management system
In addition to modelling stakeholder risk networks, we also propose taking account of the parameters of stakeholder loyalty and value creation for stakeholders as key performance indicators (KPIs).
1. Accounting for the parameters of stakeholder loyalty.
We suggest measuring stakeholder satisfaction using the Net Promoter Score (NPS). The metric was developed by Fred Reichheld and first published in Harvard Business Review in 2003 [Reichheld, 2003].
The metric's popularity is attributed to its simplicity and data collection speed. Respondents are primarily asked the question "How likely is it that you will recommend our company to your friends and family?". The users answer with an 11-point scale ranging from 0 to 10. It takes no more than 30 seconds to answer. The second question is optional, which asks about the factors having the most profound effect on the assessment and revealing the stakeholder's values. Depending on the score given to the NP question, three categories of respondents can be distinguished: Detractors (respondents giving a 0 to 6 score), Passives (7 or 8 score), and Promoters, i.e., those who promote the organization (9 or 10 score). The NPS is calculated as the difference between the percentage of Promoters and Detractors. For each industry and in each market, this indicator has its own special features; therefore, it is incorrect to compare it directly. However, the average of 30 % is considered a satisfactory rate1 [Kechinov, 2020]. According to the metric's creator, companies with an NPS greater than 50 % are characterized by high growth rates. At the same time, one should not attach excessive importance to the absolute NPS measure; what is really important is its dynamics and comparison with its previous values.
The frequency with which stakeholders are surveyed depends on the needs of the company and the feasibility of measurements. Usually it varies in the range of 3-12 months. It is recommended to work with each group of respondents individually. It is necessary to get feedback from Detractors in order to identify possible options for improvement and growth. Extra attention should be paid to Passives to win their loyalty. Promoters are expected to advance the company in their social circle as 83 % of customers say they completely or somewhat trust the recommendations of friends and family2.
We believe that the NPS indicators by stakeholder groups will reflect the degree to which a balance of interests is achieved. Therefore, we consider it appropriate to include this indicator in the KPI to assess the performance of the organization's top
1 What the NPS is and why it is so important for your business. https://timeweb.com/ru/community/articles/ chto-takoe-nps. (In Russ.)
2 Global trust in advertising. The Nielsen Company. https://www.nielsen.com/us/en/insights/report/2015/glob-al-trust-in-advertising-2015/.
management in the context of implementing the sustainable development concept by taking into account stakeholder risks.
2. Accounting for value creation for stakeholders.
Stakeholder risks are associated with the stakeholders' perception of the created and distributed value of the organization. We propose taking into account the stakeholder-associated value and using this indicator to disclose information in the integrated report in order to build a dialogue with stakeholders and get a broader picture of the organization's activities. We also suggest using it as a KPI to assess the performance of the organization's top management focused on the interests of stakeholders. In [Tkachenko, Zlygostev, 2021], we analyse a possible approach to modelling the created and distributed value in the context of risks through considering stakeholders' contributions and benefits.
Conclusion
To implement the concept of sustainable development in terms of stakeholder interests and stakeholder risks, we have developed a toolkit for their modelling. It is based on Rebecca Yang's methodology for modelling stakeholder project risk networks [Yang, 2014], modified by adding such a factor as a balance of stakeholder interests, i.e., project participants. It is necessary to account for this factor, since it leads to an increase in the conductivity of risks in the network that are associated with dissatisfied stakeholders.
To improve stakeholder risk management in an organization, a general management scheme is developed, which includes: 1) using tools for modelling networks of stakeholder project risks, taking into account the balance of interests of its participants; 2) stakeholder loyalty indices based on the NPS metric; 3) modelling stakeholder value creation. It seems reasonable to use stakeholder loyalty indices and stakeholder value modelling as indicators for evaluating the performance of top management, as well as utilize them in the integrated report for stakeholders.
The proposed management approach will allow implementing sustainability principles from the position of the stakeholder interests.
References
Abrosimova E., Sedelnikova I. (2011). Sistemnyy analiz steykkholderov [System analysis of stakeholders]. Vestnik INZhEKONa. Seriya "Ekonomika" = Bulletin of Saint Petersburg State University of Economics, vol. 3, pp. 222-230. (In Russ.)
Baydakov S. L. (2016). Riski i shansy steykkholderov v praktike territorial'nogo upravleniya [Stakeholders' risks and chances in local area governing practice]. Innovatsii v menedzhmente = Innovations in Management, vol. 2, no. 8, pp. 22-27. (In Russ.)
Baydakov S. L. (2019). Prakticheskiy instrumentariy steykkholderskoy teorii v upravlenii biznesom [The business management instruments for practical usage based on stakeholder approach]. Innovatsii v menedzhmente = Innovations in Management, no. 2 (20), pp. 10-18. (In Russ.)
Vashakmadze T. (2014). Vliyanie steykkholderskogo riska na stoimost' kompanii [The influence of stakeholder's risk on the company value]. Finansovaya zhizn = Financial Life, vol. 2, pp. 43-48. (In Russ.)
Zilbershteyn O. B., Nevstruev K. V., Semenyuk D. D., Shklyar T. L., Yurkovskiy A. V. (2016). Analiz steykkholderov na primere rossiyskikh predpriyatiy [Analysis of stakeholders on the example of Russian enterprises]. Naukovedenie: internet-zhurnal = Studying Science: An internet journal, vol. 8, no. 3. http://naukovedenie.ru/PDF/42EVN316.pdf. (In Russ.)
Ivashkovskaya I. V. (2009). Modelirovanie stoimosti kompanii. Strategicheskaya otvetstvennost' sovetov direktorov [Modeling the value of the company. Strategic responsibility of boards of directors]. Moscow: Infra-M. 430 p. (In Russ.)
Ivashkovskaya I. V. (2011). Razvitie steykkholderskogo podkhoda v metodologii finanso-vogo analiza : garmonichnaya kompaniya [Incorporating stakeholder approach into financial analysis: The harmonized firm]. Korporativnye finansy = Journal of Corporate Finance Research, vol. 5, no. 3, pp. 59-70. (In Russ.)
Ivashkovskaya I. V. (2012). Steykkholderskiy podkhod k upravleniyu, orientirovannomu na prirashchenie stoimosti kompanii [Stakeholder approach to value-based management]. Korporativnye finansy = Journal of Corporate Finance Research, vol. 6, no. 1, pp. 14-23. (In Russ.)
Ivashkovskaya I. V. (2016). Finansovye izmereniya korporativnykh strategiy. Steykkholderskiy podkhod [Financial measurement of corporate strategies. Stakeholder approach]. Moscow: Infra-M Publ. 320 p. (In Russ.)
Kechinov M. (2020). Indeks NPS [Net Promoter Score]. https://vc.ru/marketing/189334-in-deks-nps-chto-eto-takoe-i-zachem-on-nuzhen. (In Russ.)
Kogdenko V. G. (2018). Issledovanie riskov kompaniy v ramkakh steykkholderskogo podkhoda k analizu [Investigating company risks within the framework of the stakeholder approach to analysis]. Ekonomicheskiy analiz: teoriya ipraktika = Economic Analysis: Theory and Practice, vol. 17, no. 6, pp. 1051-1072. (In Russ.)
Ramenskaya L. A. (2021). Vzaimodeystvie tsifrovykh platform s klyuchevymi zainteresovan-nymi storonami: kontent-analiz [Interaction between digital platforms and key stakeholders: A content analysis]. Upravlenets = The Manager, vol. 12, no. 5, pp. 96-106. DOI: 10.29141/22185003-2021-12-5-7. (In Russ.)
Tkachenko I. N. (2021). Aktualizatsiya steykkholderskogo podkhoda korporativnogo uprav-leniya v usloviyakh koronakrizisa: ot deklarirovaniya priverzhennosti k prikladnym modelyam [Rethinking the stakeholder approach to corporate governance in the coronavirus crisis: From commitment declaration to applied models]. Upravlenets = The Manager, vol. 12, no. 2, pp. 2-16. DOI: 10.29141/2218-5003-2021-12-2-1. (In Russ.)
Tkachenko I. N., Zlygostev A. A. (2021). Modelirovanie steykkholderskikh riskov: opyt ekonometricheskogo analiza [Stakeholder risk modeling: an econometric analysis experience]. Izvestiya Saratovskogo universiteta. Novaya seriya. Seriya "Ekonomika. Upravlenie. Pravo" =
Izvestiya of Saratov University. Economics. Management. Law, vol. 21, no. 3, pp. 271-287. https:// doi.org/10.18500/1994-2540-2021-21-3-271-287. (In Russ.)
AladagH., IçikZ. (2020). The effect ofstakeholder-associated risks in mega-engineeringprojects: A case study of a PPP airport project. IEEE Transactions on Engineering Management, vol. 67, issue 1, pp. 174-186. https://doi.org/10.1109/TEM.2018.2866269.
Badran Y. (2020). Analysis of construction and stakeholder risks for public private partnership projects in developing countries. A Thesis submitted in Fulfilment of the Requirements of the Degree of Doctor of Philosophy. Manchester: University of Salford. 386 p.
Fernández-Guadaño J., Sarria-Pedroza J. H.(2018). Impact of corporate social responsibility on value creation from a stakeholder perspective. Sustainability, vol. 10, issue 6, 2062. DOI: 10.3390/su10062062.
Henry W. H., Lundberg R. (2017). Integrating stakeholder values into strategic planning through comparative risk analysis. In: Abbas A., Tambe M., Winterfeldt D., von (eds.) Improving homeland security decisions. Cambridge University Press, pp. 32-53. D0I:10.1017/9781316676714.003.
Maharaj R. (2008). Critiquing and contrasting "moral" stakeholder theory and "strategic" stakeholder: Implications for the board of directors. Corporate Governance, vol. 8, no. 2, pp. 115-127. https://doi.org/10.1108/14720700810863751.
Mitchell R. K., Buren H. J., van, Greenwood M., Freeman R. E. (2015). Stakeholder inclusion and accounting for stakeholders. Journal of Management Studies, vol. 52, no. 7, pp. 851-877. https://doi.org/10.1111/j oms.12151.
Mitchell R., Agle R., Wood J. (1997). Toward a theory of stakeholder identification and salience: Defining the principle of who and what really counts. The Academy of Management Review, vol. 22, no. 4, pp. 853-886. https://doi.org/259247.
Neto L. S., Lessa F., Nardi E. P., Ferraz M. B. (2018). Stakeholder value judgments in decision-making on the incorporation, financing, and allocation of new health technologies in limited-resource settings: A potential Brazilian approach. Pan American Journal of Public Health, vol. 42, e102. https://doi.org/10.26633/RPSP.2018.102.
Ramírez C., Tarziján J. (2018). Stakeholder value appropriation: The case of labor in the worldwide mining industry. Strategic Management Journal, vol. 39, no. 5. D0I:10.1002/smj.2771.
Reichheld F. (2003). The one number you need to grow. Harvard Business Review, December. https://hbr.org/2003/12/the-one-number-you-need-to-grow.
Schiffer E. (2015). Understanding common network patterns to improve management of external and internal stakeholder risks (Chapter 29). In: Andersen T. (ed.) The Routledge companion to strategic risk management. 1st ed. London, Routledge.
Tkachenko I. N., Pervukhina I. V., Zlygostev A. A. (2020). Modeling the contribution and benefts of company stakeholders. Upravlenets = The Manager, vol. 11, no. 2, pp. 2-15. DOI: 10.29141/2218-5003-2020-11-2-1.
Tkachenko I., Pervukhina I. (2020). Stakeholder value assessment: Attaining company-stakeholder relationship synergy. In: Aluchna M., Idowu S. O., Tkachenko I. (eds.) Corporate governance in Central Europe and Russia. Springer, pp. 89-105. DOI: 10.1007/978-3-030-39504-9_6.
Woolridge R. W., McManus D. J., Hale J. E. (2007). Stakeholder risk assessment: An outcome-based approach. IEEE Software, vol. 24, issue 2, pp. 36-45. https://doi.org/10.1109/MS.2007.54.
Yadav P., Sekhri N., Curtis K. (2007). Barriers to access: An assessment of stakeholder risks and incentives in the value chain for Artemisin Combination Therapy (ACT) treatments. SSRN Electronic Journal. D0I:10.2139/ssrn.1008307.
Yang R. J. (2014). Stakeholder-associated risks and their interactions in complex green building projects: A social network model. Building and Environment, vol. 73, pp. 208-222. https:// doi.org/10.1016/j .buildenv.2013.12.014.
Yang R. J., Zou P. X. W., Wang J. (2016). Modelling stakeholder-associated risk networks in green building projects. International Journal of Project Management, vol. 34, issue 1, pp. 66-81. https://doi.org/10.1016/j.ijproman.2015.09.010.
Zhou Y., Pan M., Zhou D. K., Xue L. (2018). Stakeholder risk and trust perceptions in the diffusion of green manufacturing technologies: Evidence from China. The Journal of Environment & Development, vol. 27, issue 1, pp. 46-73. https://doi.org/10.1177 / 1070496517733497.
Information about the authors
Irina N. Tkachenko, Dr. Sc. (Econ.), Prof., Head of Economic Theory and Corporate Economics Dept., Ural State University of Economics, 62/45 8 Marta / Narodnoy Voli St., Ekaterinburg, 620144, Russia
Phone: +7 (343) 283-10-78, e-mail: Tkachenko@usue.ru
Aleksandr A. Zlygostev, Chief Economist of the Internal Treasury Dept., Ural Bank for Reconstruction and Development, 67 Sacco i Vanzetti St., Ekaterinburg, 620014, Russia Phone: +7 (343) 311-84-25, e-mail: letrus.alex@gmail.com
© Tkachenko I. N., Zlygostev A. A., 2022
