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23услуг бытового характера, до проведения, причем при значительной экономии средств и времени, форумов, конференций, саммитов, совещаний политического, экономического и научного характера, как на международном, так и на локальных уровнях.
Что касается сферы образования, где непосредственное общение педагога и ученика остается одним из основных составляющих всего процесса приобретения качественных знаний и навыков, «Удалёнка», очевидно, требует более подробного анализа на предмет сопоставления её позитивных и негативных сторон.
Выводы и предложения: Деятельность основных международных структур, большинства правительств, особенно развитых стран, обладающих значительными рычагами воздействия на ход и характер системообразующих экономических и социальных процессов крупных транснациональных компаний и конгломератов, на примере противодействия пандемии Covid-19 проявила полную несостоятельность и неподготовленность в отношении критических глобальных событий. Представляется жизненно необходимым создание на базе многочисленных и малоэффективных международных организаций, обладающих мощными финансовыми и организационными ресурсами, принципиально новой международной, инновационной по своей
сути структуры, способной не только реагировать на текущие глобальные и региональные катастрофы и критические события, но в первую очередь квалифицированно анализировать прошлые и текущие процессы и прогнозировать возможные надвигающиеся катаклизмы, разрабатывать и реа-лизовывать соответствующие результативные планы и программы действий.
Литература
1. Г. Талаквадзе, «30 лет», (социально-экономический обзор), изд. «Некери», Тбилиси, 2020 г.
2. Г. Талаквадзе, «Пандемия COVID-19: уроки и вызовы», труды VIII Международной Экономической Конференции IEC - 2020, «Бизнес-Инжиниринг» №3, стр.45-51, Тбилиси, 2020 г.
3. Г. Талаквадзе, З. Ломсадзе, И. Арчвадзе, «Приоритеты и ресурсы Грузии: мифы, история, перспективы», «Экономика природопользования и устойчивое развитие», ИЭПУР АН Украины, Киев, декабрь, 2020г.
4. Nacional Intelligence Counsel, "Global trends and Paradox of progress" www.dni.gov/nic/globaltrends/2020.
5. Декларация прав человека и гражданина, Учредительное Собрание Франции, Париж, 1789 г.
THE SOCIAL NETWORKS' EFFICIENCY IN THE PROCESS OF SOCIAL INTERACTION
CONSTRUCTION
Kogan C.
Kyiv National Pedagogical University named after M.P. Dragomanov,
Kyiv, Ukraine.
ABSTRACT
This article aims to define the criteria and conditions of social networks' efficiency in the construction of social interaction in a modern? or the current modern communicative environment. The author proceeds from the assumption that a social networks' function is to produce social capital. Therefore, social capital increasing is a criterion for the effectiveness of social networks. This paper shows how this result is achieved in the process of a two-stage network "game". The author considers quantitative and qualitative approaches to evaluating the effectiveness of social networks; he concludes that the pattern of social networks' development and functioning consists of two trend interactions: the networks' striving for closeness and openness. This paper presents the characteristics of multi-networks as areas of real social interaction and reveals the conditions for their effectiveness. The author proves that the multi-networks' three-areas structure, which consists of a close-knit core, developed semi-periphery, and wide periphery, is the most effective for social co-operation. This model combines the individualism and collectivism of the group members because it contributes to the social capital accumulation for each actor and society as a whole.
Keywords: network society, social actor, interaction, social capital, communication, social network, multinetwork.
Introduction
Social network analysis has become one of the most important areas of modern sociological science. Special attention from scientists to this area is explained by the rapid development of the Internet, such segments as online-networks. Researchers are interested in both the structure of these networks and their ability to influence change in society. As a result, they substantiated the concept of a network society as a new form of social interaction [1, 2]. The formation of this
type of social structure is a trend of modern world development and determines social processes in many countries that did not support the Internet revolution. Studying the structure of this society, scientists have not come to define both the general pattern of development and functioning of social networks and the specifics of their structure, which is the most effective for organizing social interaction. Therefore, the topic of this paper is relevant both in a scientific and applied practical context.
The purpose of the article is to define the criteria and conditions of social networks' efficiency in constructing modern social social interaction
Research methodology
Achieving this goal presupposes the consistent use of such scientific methods as content and comparative analysis, systemic and structural-functional approaches, as well as statistical computation. Content analysis was used for considering the main provisions of the social capital concept (P. Bourdieu, J. Coleman) from the standpoint of network theory (M. Jackson, M. Gradwell, K. Levin). The comparative analysis became useful in the study of various quantitative (D. Sarnov, R. Metcalfe, D. Reed, D. Gubanov, D. Novikov, and A. Chkhartishvili), and qualitative (R. Burt, J. Coleman, M. Granovetter, H. White) methods to evaluating the social network's effectiveness. By the systematic approach the author considers modern society as a social network system based on communication and argues the consistent pattern of network development and functioning. Following the structural-functional approach, he describes the properties and effective structure of multi-networks. The use of statistics in Ukrainians participation in social media allowed the author to calculate the ratio of subscribers to their activity and compared it with the election results to demonstrate that this indicator is a key in assessing the effectiveness of online-networks.
Discussion
The social capital increase as a performance criterion of a network game
It should be noted that online-networks really became an influential factor in social development, but are just a form of information usage and communication technologies (ICT) of the 21st century in the process of solving the eternal task of all communities - the organization of effective social interaction. Social interaction has always been a central category of sociological theory and practice. The classical scientific theories can be conditionally divided into two main paradigms: individualistic - that the activities of each participant are independent and determined by their interests and benefits, and collectivistic - that the participant, by definition, is a socialized subject and therefore his activity is guided by existing collective norms. The second paradigm describes the activity and explains its forms and directions exclusively in a social context.
Attempting to go beyond the specified controversy, it is necessary to proceed from the fact that each participant of social interaction makes a decision to participate in common activities and bears the corresponding costs associated with this role based on their own needs, interests, and values. Therefore, an incentive in the form of social capital is needed to motivate the participants in collective action to apply efforts to obtain results in common activities. The fact that this type of capital is generated exclusively as a result of communication and cannot be divided into individual and group components expresses the synergistic nature of this process.
P. Bourdieu's point of view states that social capital is a set of real or potential resources associated with
the ownership of a stable network of more or less institutionalized relations of mutual acquaintance and recognition. In other words, with the membership in a group [3, p. 250]. It is important to emphasize that the founder of the social capital concept initially associates it with the social networks' availability.
Developing this concept, J. Coleman argues that considering social capital as a resource for action is the only way to represent the social structure in the paradigm of rational activity. At the same time, Coleman proceeds from the fact that social capital is formed by trust. In addition to trust, he also defines that the expectations and obligations are some forms of social capital, as well as information channels, norms, and sanctions [4].
The above arguments prove that social networks, in which social capital is formed like honey in a honeycomb, serve as a structural basis to motivate a social interaction. Thus, even by belonging to a social network it is already becoming a kind of capital that can create a competitive advantage for some individuals or groups.. Better-connected people have the opportunity to receive more value. However, the question remains relevant: which connection should be considered the most valuable, that is, which network structure is most effective for accumulating social capital?
To find an answer to this question, it is advisable to consider the genesis, development, and functioning of social networks through the prism of game theory. The networks' participants are considered players which interact with others, seeking to obtain the maximum payoff. Such interaction is born as a result of communication between the players, in a process by which they exchange the available information, agree on the organization of cooperation, and the distribution of their own resources; it is a so-called network game. his game has two stages: the first stage communication forms a network of social interaction, and in the second stage, the network acts as a communication tool, accordingly structuring the communicative space. As M. Jackson points out, first a network formation game is set up, then when the network is formed it determines the results of the players' activities and their payoff, and then the network-based game begins [5, pp. 234240]. A game based on a social network is a game in which the actors are functioning as social network' nodes, and the communication edges reflect the level of their trust in each other or influence each other. Such forms of social capital as norms and sanctions can be considered as the rules of the game, which also affect the structure of the network. In general, in a dynamic game the social capital of each player explicitly depends on the actions of all players. Thus, network game products social capital.
The question arises: What should be the characteristics of the network for this game to be most successful? At first glance, it seems clear that the desire to increase their social capital encourages participants in the network game to form more branched and complex networks. However, it is necessary to point out that since the value of any social network is determined by its
ability to increase the social capital of actors, the network grows as long as it can effectively provide this process.
The growth dynamics of social networks can be represented by an S-shaped function, which contains three phases of development: the formation of a development base (slow growth of social capital), rapid growth, and saturation (slow growth) [6, p. 124]. This process, like many other processes in nature and society, has the limits of possible changes primarily due to the limited resources (limitation of the possibilities and capabilities of the social network). The limited resource - social capital in the network - leads to the fact that the players who previously played in the same team, and so cooperated to maximize the gain, begin to compete with each other.
In this context, the research of K. Levin is very useful. Having analyzed the forces acting in the social area and determining the social structure of the group, he pointed to the existence of two forces: the integration forces which are determined by factors that contribute to the goals of the group, and the disintegration forces which are determined by factors that interfere with the achievement of the goals of the group [7]. Therefore, in cooperation mode, players work together to achieve the desired results in a multiplayer game using a minimum amount of resources. In a competitive mode, which occurs when the goals of the players differ significantly, resources are spent inefficiently. It leads to dispersion and even destruction of the social network.
According to Bourdieu, the amount of social capital that is at the disposal of a certain agent depends on the size of the connection network that he can effectively mobilize, and on the amount of capital (economic, cultural, or symbolic) that each of those, who is connected with him, has [3, pp. 250-251]. Researchers who seek to find an algorithm for determining the effectiveness of a social network face the problem of accounting for the dynamics of network growth, and its value-communication content.
Quantitative and qualitative approaches to evaluating the social networks' effectiveness
The search for a solution to this problem has given rise to two main scientific approaches that can be called quantitative and qualitative. The first direction is developed mainly within the framework of technical sciences, and the second - social sciences.
Representatives of the quantitative approach, for example V. Breyer, proceed from the assumption that the effectiveness of a social network is determined by the potential availability of partners with whom any participant can contact if necessary [8, p. 202]. It should be noted that several attempts have been made to derive a formula that would measure the value of social networks with mathematical precision. At the same time, the change in the formulas proposed by scientists for calculating the effectiveness of social networks was determined by changes in communication channels and in network structures.
For example, a law formulated in the 1930s by the founder of the American National Broadcasting Company (NBC), D. Sarnov, proclaims that the value of a radio or television network grows in proportion to the
number of listeners/viewers [9]. However, the emergence of local computer networks has made adjustments to these calculations. The developer of Ethernet technology, R. Metcalfe, determined that the dependence of a network's value on a breadth of its distribution is not linear, but proportional to the square of users' number [10].
The development of the Internet has brought forth the issue of calculating a networks' effectiveness in this communication segment. In an attempt to solve this problem, D. Reed noted that since Internet networks provide an opportunity for the simultaneous connection of three or more individuals, then the efficiency of such networks should grow much faster. He added another component to the formula for calculating the value of a social network related to the association of many Internet users into groups [11]. But the rapid expansion of global online-networks in the early 2000s put on the agenda the question of finding a new algorithm for calculating the social networks' effectiveness. B. Briscoe, A. Odlyzhko, and B. Tilly criticized Metcalfe's and Reed's laws and offered their assessment, which is based on the so-called Zipf's law. his law ranks values of connections in networks in a certain way [12]. In turn, examining the structure of a network, which can represent a complex configuration of nonlinear connections, D. Gubanov, D. Novikov, and A. Chkhartishvili added another criterion for calculating the effectiveness of social networks - additivity. The researchers proceeded from the assumption that the level of social networks' efficiency, as a value that depends on the potential connections of all agents, should increase following an increase in the number of possible configurations (potential opportunities) of these connections in the network. Thus, they obtained the law of a more moderate increase in network efficiency compared to Zipf's law. For example, according to this calculation, for Fa-cebook, whose members' number reaches 1.3 billion users, the difference between this law and Zipf's law is about 13%. But for smaller networks, this difference will increase [13, pp. 11-14].
It should be noted that although these formulas find their practical application to the calculation of a whole class of networks united by a single technology, they still leave some questions open. For example, how to calculate the efficiency of a network under the condition that the real network in contrast to the simulated one is a scale-free graph where the number of participants either cannot be precisely determined or even goes to infinity.
There are also questions of a completely different order to which adherents of the quantitative approach do not answer. Indeed in real life there are many examples where the value of a network as a communication structure is measured not at all by the number of interconnected participants, but by the content and depth of communication. In a certain situation, participant X can be so influential that participant Y may find connecting with him more valuable to acquire social capital than an acquaintance with a thousand "small" participants. In a different situation, when only participant A completely absorbs the communication value frame of an-
other participant B, then for the latter even such a simple dual network can become the highest value. A similar situation is modeled by the expression: "I am ready to give my life for you!" At the same time, it is not at all necessary that participant A is ready for such self-sacrifice, because, in his communication frame, participant B can occupy a very small area. It is also clear that for a normal person, family ties are much more valuable than hundreds of acquaintances in chat rooms. In these cases, how can the network's value be determined in terms of increasing social capital and how can its effectiveness in the process of group interaction be defined? A vector in search of answers to these questions is given by supporters of a qualitative approach to assessing the cooperative effects of social networks.
According to J. Coleman, since the basis of social capital is trust, the closed social networks are the most effective communication constructs which provide the strongest connections between the subjects. Coleman cites as an example of two families in which both children and parents are friends. Under such conditions, parents can discuss the behavior of their children and take appropriate educational sanctions. Moreover, these sanctions from the side of one parent are enhanced by sanctions from the side of the other. Also, parents can look after their friends' children and monitor their behavior not only at school but also outside of it; it increases the total social capital of both families. Conversely, in open networks where parents are not personally connected, this does not happen. This also applies to other forms of social capital - reputation cannot arise in an open structure and collective sanctions that would ensure the reliability of interaction cannot be applied. But in a closed network, all participants in the interaction can join forces for a common positive result, thanks to the possibility of encouraging or sanctioning each other.
Considering the process of organizing a broader interaction Coleman points out that in this case, complex networks that are formed from several closed networks are effective. As an example, he cites an underground radical organization of South Korean students consisting of separate circles whose members were connected either by studying in one institution or living in the same city, or belonging to the same church parish [4].
It can be noted that most of the underground organizations are built according to similar rules. Similar closed social networks, in network analysis, are defined as clicks built on strong connections. Players in these networks are guided by clear rules of cooperative game, guaranteed not only by mutual trust but also by strict adherence to accepted norms and a high cost of reputation. Besides, they provide the formation of the participants identity, which can gradually rise from the family level to the organizational and even to the national level. An interesting example of the gradual formation of such networks was given in H. White's study. It shows how this process takes place while playing on the playground. Whitecalled such initial networks "netdom" (home network), demonstrating how more complex structures are made of them, like bricks [14, p. 208].
Based on the above conclusions, it would seem that one can make an unambiguous conclusion that the more closed social networks are more efficient, however; similar reasoning vividly illustrates the process of social construction at the initial level, it is somewhat simplistic because social life is too complex for all its interactions to be reduced to closed networks. Two families, which in J. Coleman's example constitutes a closed network that has many other connections in the social environment, which could affect the processes of raising children. Underground organizations expose themselves either through traitors or through agents, sooner or later. One and another are "holes" that open the network.
But do they always mean the destruction of the network, or, on the contrary, the opening of the network provides new opportunities for the growth of social capital? Developing the theory of M. Granovetter about the strength of weak ties [15], R. Burt proves that participants of a network, through which it can communicate with other networks, are nodes of social capital accumulation because the so-called indirect "weak" ties allow them to spread the network in breadth and thus make it more efficient. Thus, structural holes make it possible to mediate the flow of information between people and to control projects that bring people together from opposite sides of the hole. Having analyzed the level of rewards received by managers of large firms, Burt shows that the negative relationship between their relative wages and network closeness is statistically significant. Conversely, managers who were paid higher than expected for rank and age were typically managers with networks that involved the structural holes in the firm. Discussing with Coleman this ingenious approach allowed Burt to conclude that open networks are usually more efficient. He showed that with the help of structural holes, complex social structures are formed, and how they contribute to the progressive dynamics of social capital growth [16, pp. 15-19].
The comparison of the four types of network effectiveness carried out by Burt is very significant too. He compared an internally cohesive open network, which has external connections, internally cohesive closed network, internally disintegrated closed network, internally disintegrated open network, and concludes that the first one is the most efficient, and the fourth one is the least efficient [16, pp. 28-30]. At the same time, internally cohesive networks can be either centralized with a strong leader, or decentralized, but connected by dense communication channels. Disintegrated networks are distinguished by the fact that in them the communication of cooperation gives way to endless discussion and empty beams (chats).
Results
The consistent pattern of the development and functioning of networks as constructs of social interaction
Thus, the analysis of the social capital concept and the nodal positions of the discussion between adherents of different approaches to evaluating the social networks' effectiveness gives us a reason to talk about the existence of a certain pattern in the genesis, functioning, and development of social networks, which causes
its efficiency as integral social constructs. It consists of the interaction of two trends: the networks' striving for closeness and openness. In essence, this pattern is based on the existence of individualistic and collectivistic principles in human nature. This pattern is confirmed by the analysis of real social networks, for example, families. As already indicated, the family, which J. Coleman cited as an example of an elementary closed network in which social capital is produced, can be considered as such only in an artificial environment.
From a functional point of view, it is expressed in the fact that the social capital accumulation in the family largely depends on its connectivity with other social networks. rust and parents' authority (as a degree of reputation) are largely determined by how children evaluate the effectiveness of parental involvement in other networks: success at the work, a reputation in friendly companies, etc. Likewise, children, wishing to raise the level of their social capital in the family, try to do this by informing their parents about the results of their participation in other networks: at the school, sports club, etc. On the other hand, both parents and children strive to preserve the closed nature of their social networks (it is unlikely that the father will happily want to take the children with him to his corporate fest, and the children will allow their parents to access their pages in online-networks). If the family has a high level of social capital, it actively communicates with other networks, being proud of its achievements and striving to raise its status as a trans-network bridge. But if the level of social capital falls due to any internal conflicts, the family network tends to be closed (what is called "do not wash dirty linen in public").
In the process of family development, that is, the dynamics of the social network, it is possible to observe similar trends. When children grow up and start their own families they maintain their connection with parents and thereby build bridges between family networks, increasing the level of their openness. But also during this process, the former strong communication ties that form closed networks are weakened. Young families to a certain extent try to close their newborn networks from external influences (hence born the saying: "it is better to love parents from a distance").
Thus illustrating the co-existence of two trends -the networks' striving for closeness and openness - on the example of an elementary family network allows us to provide evidence in favor of our hypothesis in the most accessible form, both from the functional and dynamic points of view. It should be noted, those similar arguments can be found on all networks from religious communities to Facebook The synergistic affect of these trends characterizes the effectiveness of social networks as an integral factor of group interaction, along with taking into account some quantitative indicators.
The properties and effective structure of multinetworks as modern forms of social interaction
It is important to dwell on the fact that in reality each social subject performs certain roles in various social networks (at the work, in the family, with friends, etc.), so completely closed networks simply do not exist. Besides, we can state that the so-called autocratic
networks can ensure the integral interaction of their members-only narrowly functional and for a limited period. Subsequently, structural holes are necessarily formed in them. In our opinion, they would be better defined as trans-network communication bridges. Thanks to these bridges, social constructs are formed with many new properties.
First, each is a member of several social networks at the same time. Thus, complex structures are constructed in the form of branched multi-networks, which characterize group interactions really.
Secondly, interaction is established not only between the network's participants but also between networks. Online games are no longer played only at the individual level of single players but by teams. So, the interaction is carried out according to the new rules of the game.
Third, communication in multi-networks does not take place according to the "face-to-face" principle, it becomes mediated through various media, both as traditional as online. Strengthening the law of "six handshakes" by S. Milgram [17], modern media are forming global social multi-networks.
Fourth, the border between real and virtual networks is practically erased in modern multi-networks. For example, within the Facebook group sub-nets of graduates of a certain university or school, supporters of a common hobby, or fans of football clubs and show stars can be distinguished. At the same time, they often actively communicate in real social networks. Social activity in the web-space sometimes spills out into real life in the form of various flash-mobs and other actions, including protests.
Multi-networks are heterogeneous since the number and strength of connections between participants in them are different. It is known that the effectiveness of a heterogeneous system is determined by its structure. Speaking about the effective structure of multi-networks, it is necessary to focus on the fact that it consists of three areas: core, semi-periphery, and periphery. The core is a relatively closed network with strong connections and a small number of participants, each of which creates huge social capital for the other (family, closest friends). The effectiveness of the core for organizing group interaction is determined primarily by the qualitative categories of trust, respect, mutual understanding, love, as well as mutual responsibility, and clear collective norms and sanctions. The semi-periphery is formed through trans-network bridges, along which rather intensive connections between the core and its closest, and limited environment (relatives, friends, close colleagues, online-followers) are carried out. The multi-network's semi-periphery can include not only individual participants but also other cores, for example, friendship with families. The efficiency of the semi-periphery in the process of social capital accumulation depends on a compatible extent on both qualitative and quantitative parameters. A periphery is an area of weakness, often mediated connections, which tends to be scaleless. It includes acquaintances, "friends of friends", as well as cores and semi-peripheries of their
online-networks. The periphery's role in the multi-networks' effectiveness is computing using almost quantitative indicators, as is done in cooperative networks.
The hypothesis about the effectiveness of such a model of multi-networks is confirmed by the field researches of political practices. After analyzing the Occupy Wall Street action, L. Bennett, A. Segerberg, and Y. Yang pointed out the existence of social networks' core and periphery and revealed their functions. They argue that the push and pull of content through differently-located networks is at the core of networked gate-keeping and framing processes. And far from consisting of minimally-involved "clicktivism" peripheral networks can play a significant role in defining and responding to social events. The construction of attention and meaning between the core and periphery in technology-enabled crowds involves an iterative mix of selective attention, network interactions, narrative construction, and selective uptake by legacy media. Even in relatively supportive public environments, networked information flows can translate and transform key ideas as they move from the most committed direct participants to the most distant spectator publics, who pay attention and contribute through a combination of social and legacy media [18, pp. 660, 662]. But these authors do not mention the role of the semi-periphery.
Even so, the role of this structural component of multi-networks is revealed by the analysis of the last presidential and parliamentary elections in Ukraine. Generally, the 2019 Ukrainian elections are the most striking example of the successful use of the three-area multi-networks structure by Vladimir Zelenskiy and his team. It became a key factor in their victory. During the election campaign, players of "Ze" (Zelenskiy) multinetwork managed to take advantage of closed centralized networks' cohesion in the core area ("95 kvartal" show team), and the activity potential of semi-peripheral network participants (online-followers), and the flexibility of open decentralized networks in the periphery area (viewers of TV serial "Servant of the People"). Let's dwell on the analysis of the semi-periphery activity. Even though "Ze" was a network of "star" type from the moment of its inception, a certain balance between quantitative and qualitative indicators of participation was established in this area. In the spring of 2019, the official Facebook page of former Ukrainian President Poroshenko had 2,423,638 subscribers. While the official page of Zelenskiy's team was almost four times smaller - 582347, and the page "Ze! - Let's change the country togethef' - 139963 in total. But the effectiveness of social media is determined by the ratio of the number of subscribers and their activity. And this key indicator was 3 times larger on the Facebook page of Zelensky's team, and 14 times larger on the page "Ze! - Let's change the country together" than on Po-roshenko's Facebook page (31.46%, 137.88%, and 9.75%, respectively) [19]. The impulse of trust and emotional complicity turned out to be so powerful that they predetermined the fantastic victory of Zelenskiy in the presidential elections and then provided an unprecedented mono-majority in the history of Ukraine in the Verkhovna Rada (154 seats) for representatives of a political party that was not known literally on the eve of
the parliamentary elections. So skillful use of the tree-area multi-network structure and a winning network game made it possible to obtain victorious public support and electoral engagement.
Conclusions
The concept of social capital has the to possibly escape the contradiction between two main paradigms of social sciences - individualism and collectivism. The consideration of the social capital concept through the prism of network theory proves that social capital is a result of a two-stage network game. The analyses of discussion about the advantages and disadvantages of close and open networks show that it is groundless, because close networks are always transformed to open throw so-called structure holes, and each participant plays on several different social networks. The interaction between different social networks produces so-called multi-networks. There is the pattern of social networks' development and function which causes its efficiency - the interaction of two trends: the network's striving for closeness and openness. These trends construct more or less effective social networks' structures. The effectiveness of a real social network (and it will be a multi-network necessarily) is determined by the well-coordinated functioning of a cohesive core, a developed semi-periphery, and a wide periphery. The indicated structure of the network allows for the synergistic integration of individualism and collectivism because it serves as the most effective tool for accumulating social capital for each participant. As practice shows, the purposeful construction of such a three-area social multi-network is a necessary condition for organizing successful social interaction in a network society. This model combines the individualism and collectivism of the group members because it contributes to the social capital accumulation for each actor and society as a whole.
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