Importance of business communication

and tools of their increase

Mária Pomffyová

Matej Bel University, Institute of managerial systems, Affiliate Branch of the Faculty of Economics in Poprad, Nábrežie Jána Pavla II 2802/3, 058 01 Poprad
maria.pomffyova@umb.sk

Abstract

This article is focused to analyzing of the importance of business communication in SME and their role by increasing of business efficiency. We use a concept of social network methodology. We used analyze by mathematics model of matrix correlation analysis. Through this method we can analyze structure of these networks, of their centralities as well as their density. There are presented methods and tools designed to detection of closeness, degree and betweeness centralities as well as network density. Setting of these centralities visualizes social networks in common businesses communication systems. Business efficiency could be increased because of better information about properties of analyzed structure businesses communication system.

Key words: efficiency, management by information and communication, correlation analysis

Introduction

Nowadays enterprises have to meet competition, but not each of them is ready to meet it. The small and medium sized enterprises (SME) have been assigned growing roles in economic development in recent years. Many factors influence their success including industry structure, competition, entrepreneurial decision, employee relations, entrepreneurial objectives, organisational structure, as well as education, training and prior experience resulting in managerial skills of SME`s managers. The risk of doing business on foreign markets is closely related to the need of good knowledge in business area and terms of business in target country and leads to looking for simple solution for the risk elimination. We can say when firms should cooperate with foreign partners; it is needed to adopt themselves in a flexible ways to economic, legislative and social, demographics and cultural environment. Definitely, they need enough of information about possible ways and available support in starting business with business partners – it means knowledge about their partners; their culture and information what’s the key to any successful business deal.

In the competition process it is needful:

· to get hold of the right person first time,

· to be given the information that they need quickly, accurately and helpfully,

· to be able to interact with partners in a way that suits they best, whether in person, by phone, by SMS, by email or by accessing a website.

There has a very important role an effective utilization of enterprise communication network supported by ICT. It must serve support for

· clear and consistent processes for handling partners interactions,

· back office systems that are accessible through a common interface so these partners and product or service information are instantly retrievable,

· motivated staff who comes across as helpful and well informed.

The solutions lay in common technologies whose making sure that the right information is always available to managers and front line staff. This means finding the right solution of investments to evolving of network as well as people knowledge that can leave the managers free to concentrate on its core competencies. People – keeping staff motivated will ensure they deal more effectively with partners and want to behave in their jobs more loyal.

There it is needed to know where are weaknesses of the network and decide where is needful to invest finances or innovate functionalities of such network. This means to know the most significant nodes and their properties – centralities. To obtain these characteristics we can use optimization by graph-analyzing methodology.

Needs of information sources

In the international businesses dialogues put stress on communication in foreign languages as well as cross-cultural competences play an immensely important role in contemporary globalized world especially. Managers and their employees require more useful information. What are searched enterprises the most afraid is unknowing of conditions on foreign markets (92 % of enterprises which are afraid). Then they are fearful of competition (68 %). Language barrier is problem for 32 % respondents. The knowledge of foreign language itself is not sufficient to avoid misunderstandings. A good manager should know the culture of his/her business partners, which is the key to any successful business deal. The easiest way is to choose the country with similar conditions (legislation, demand, trade customs, etc.) on the market. In our opinion, this is why many Slovak small and medium sized enterprises the most often do trade with partners from Czech Republic. Then follows EU countries: Germany, Austria, Italy, etc. (Zimermanová, 2007).

Generally, they need right information to be successful by communication with their partners. We know people suffer from information overload; there’s much more information on any given subject than a person is able to access. As a result, people are forced to depend upon each other for knowledge. Know-who information rather than know-what, know-how or know-why information has become most crucial. It involves knowing who has the needed information and being able to reach that person (Singh, 2007).

In this context, understanding the formation, evolution and utilization of social networks becomes important. A social network is “a set of people (or organizations or other social entities) connected by a set of social relationships, such as friendship, co-working or information exchange. While in the enterprises effective utilization of information brings a topic position on the business market competition, it also needed effectively to manage one’s social network and through them gain access to the right pieces of information.

Information networks utilized by people worked by some social network, of particular, interest researchers working at the intersection of information systems, sociology and mathematics. These researchers study the uses of social networks and the ways in which they are mediated in society and in the workplace through information communication technologies (ICT) used by LAN, MAN, WAN) or the Internet.

The social network field is broad, and any literature review can only focus on a selection of tools how to increase its benefit. That paper presents our recent research of one’s enterprise network and we have to optimize its structure by using of such tools as analyzing of their centralities as well as ties density by utilizing of a mathematics model based upon a matrix correlation analysis. This analysis gives more opportunities to selection of needed changes in such network, about investments to network structure rebuilding, it means not only to ICT innovation as well as investments into people and their knowledge bases.

Social network theory

Business communication is strongly depended by model of social network in the enterprise. We can apply social network theory that views social relationships in terms of nodes and ties. Nodes are the individual actors within the networks, and ties are the relationships between the actors. There can be many kinds of ties between the nodes. In its most simple form, a social network is a map of all of the relevant ties between the nodes being studied. The network can also be used to determine the social capital of individual actors. These concepts are often displayed in a social network diagram, where nodes are the points and ties are the lines.

The power of social network theory stems from its difference from traditional sociological studies, which assume that it is the attributes of individual actors - whether they are friendly or unfriendly, smart or dumb, etc. – that’s matter. Social network theory produces an alternate view, where the attributes of individuals are less important than their relationships and ties with other actors within the network. This approach has turned out to be useful for explaining many real-world phenomena, but leaves less space for individual agency, the ability for individuals to influence their success; so much of it rests within the structure of their network.

Social networks have also been used to examine how company’s members interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different departments. These networks provide ways for companies to gather information deter or achieve information for and about competition and even collude in setting prices or policies.

Analysis of social network and its relation

If we can make a network analysis we must study social relations among a set of actors. Network researchers have developed a set of distinctive theoretical perspectives as well. Some of the hallmarks of these perspectives are:

· focus on relationships between actors rather than attributes of actors,

· sense of interdependence: global rather atomistic view,

· structure affects substantive outcomes,

· emergent effects.

Network theory is sympathetic with system theory and complexity theory. Social networks are also characterized by a distinctive methodology encompassing techniques for collecting data, statistical analysis, visual representation, etc.

Social relations can be thought of as dyadic attributes. Whereas mainstream social science is concerned with monadic attributes (e.g., income, age, sex, etc.), network analysis is concerned with attributes of pairs of individuals, of which binary relations are the main kind. Some examples of dyadic attributes:

· Social Roles: boss of, teacher of, friend of

· Affective: likes, respects, hates

· Cognitive: knows, views as similar

· Actions: talks to, has lunch with, attacks

· Distance: number of person between

· Co-occurrence: is in the same position as, has the same relation as

· Mathematical: is two links removed from

If we analyse networks we have to analyse such parameters as:

Picture 1 Network variables

1. Substantive effects of social network variables

· Attributes of ego network access to resources, mental/physical health

· Network closeness influence, diffusion

· Similarity of position similarity of risks, opportunities, outcomes

2. Substantive determinants of social network variables

· Personality centrality?

· Similarity friendship ties? (homophily)

· Reduction of cognitive dissonance transitivity?

· Strategic "networking"

3. Network determinants of network variables

· Relationship between density and centrality.

If we make an ego network analysis, it can be done in the context of traditional surveys. Each respondent is asked about the people they interact with, and about the relationships among these people. Ego network analysis is extremely convenient because it can be used in conjunction with random sampling, which enables classical statistical techniques to be used to test hypotheses.

Complete network analysis is where we try to obtain all the relationships among a set of respondents, such as all the friendships among employees of a given company. Most of the rhetoric surrounding network analysis is based on complete network. Techniques such as subgroup analysis, equivalence analysis and measures like centrality all require complete network.

Network analysis is conventionally criticized for being too much methodological and too little theoretical. Critics say that there are few truly network theories of substantive phenomena. This is not a well-considered argument, however, because when examples of network theories are presented, critics say “that's not really a network theory”. This is natural because theories that account for, say, psychological phenomena, tend to have a lot of psychological content. Theories that account for sociological phenomena have sociological independent variables. Only theories that explain network phenomena tend to have a lot of network content.

Settings of network centralities

One of the ways to understanding of social network needs includes accounts of centrality and of one node’s relationship to other nodes in a network. This is why Linton C. Freeman’s article on centrality in social networks is important (Freeman, 1978). Freeman explored how “graph centralization” is based on differences in point centralities. There are also outlined three competing theories regarding the definition of centrality based on degree of a point, control and independence.

Because social networks are fundamentally social tools in which people are constantly monitoring and growing their social network, most social network media depict growth using the degree of point definition. However, control and independence can be more useful definitions. For example, a person who controls information flows is more important than one who may have more friends in the network.

Degree centrality is defined as the number of links incident upon a node (i.e., the number of ties that a node has). In enterprise network this means counting the number of informed people it has in a social network. The more people are connected to given node, the more important node is.

Degree centrality can also indicate which members are the most useful or well connected and therefore the best information resources. It is often interpreted in terms of the immediate risk of node for catching whatever is flowing through the network. The greater a person's degree, the greater the chance that he will catch whatever is flowing through the network, whether good or bad. Nodes with degree centrality are not only more viewable as well as controllable but therefore the network better obtains any information whose may effectively exploit in the competition process. They better gather new innovation and knowledge. In general, the greater a person's degree, the more potential influence the network has, and vice-versa. For example, in enterprise network, a person who has more connections can spread information more quickly, and will also be more likely to hear more stuff. Any research points say, the better organisations gather more information by reception more then official reports.

To obtain better information about weaknesses as well as opportunities of our communication network we analysed such social network.

Our network has following structure (Picture 2):

Picture 2 Social network diagram

Social network can be interpreted as network with nodes and ties. If the network is directed (meaning that ties have direction), then we usually define two separate measures of degree centrality, namely indegree and outdegree. Indegree is a count of the number of ties directed to the node, and outdegree is the number of ties that the node directs to others. For positive relations such as friendship or advice, we normally interpret indegree as a form of popularity, and outdegree as gregariousness.

Also when we analyse information flows we must analyse number of ties for direct information and separately for back information flows in our network to obtain degree centrality definition in such system. It is necessary to avoid of system cycling.

Setting of centralities by using a matrix-analysis method

Firstly, we made a matrix answered to information flow relations in our analysed communication network presented by network diagram (Picture 2). Secondly we analysed a correlation matrix of information flow relation of network’s subjects.

Let that matrix be compact model of information graph. In the social network it is occurred time hold-up of information flows between input and output data accordingly to their background processing in the social network. Let these elements of social network are marked as subjects of that system - Y_i. Some relation between these subjects are described as oriented paths X_i,j.

Hence variable X_i,j is even to 1, if exists relation from node Y_i to Y_j node, and X_i,j is even to zero, if that relation absent. We obtain information matrix (e.g. matrix of relation between manager and employees see Picture 3):

Picture 3 Correlation matrix of information relation network

		Y1	Y2	Y3	Y4	Y5	Y6
	Y1	0	1	1	1	0	1
	Y2	0	0	1	0	0	0
M 1=	Y3	0	0	0	1	1	1
	Y4	0	0	0	0	1	0
	Y5	0	0	0	0	0	0
	Y6	0	0	0	0	0	0
	S	0	1	2	2	2	2

In the next, we summarize values in the columns. If sum in the columns is even to zero, those points are integrated to even hierarchy levels. As we can see from the Picture 3, the node Y₁ has the highest degree position.

To obtain next levels we calculated series of matrix n-exponentiation and consequently again we summarized values of columns answered to particular nodes. That process we repeated until we obtained a zero-matrix.

Number of exponentiation answered to number of hierarchy levels. We must exponentiate our matrix 5-times to obtain zero-matrix – so our network has five hierarchy levels. Gradually we determined distribution of nodes to several levels and assigned to nodes relevant information flows accordingly to existing ties of communication system. So we obtained new graph model of information network answered to existing information hierarchy levels (Picture 4):

Picture 4 Graph presentation of hierarchy model

That model waked an analysed network more transparently because of more visible hierarchy levels and ties in seem to be complicated graph diagram of analysed social network.

Next we configured matrix of ties counting rate. Number of ties indicates a number of information redundancies by network nodes (Picture 5):

Picture 5 Matrix of counting rate information ties

	Y1	Y2	Y3	Y4	Y5	Y6
Y1	0	1	2	2	2	2
Y2	0	0	1	1	2	1
Y3	0	0	0	1	2	1
Y4	0	0	0	0	1	0
Y5	0	0	0	0	0	0
Y6	0	0	0	0	0	0

As we can see the information redundancy had nodes Y₃,Y₄,Y₅,Y₆.

On the other hand, by analysing of existing network it is necessary to appreciate if redundancy is needful or needless. It is necessary to balance the positives and negatives of size and communication activity. A final question to consider is which type of membership activity and where (giant component, middle layer or among singletons) most affects communication network? Result of that analyse gives more opportunities to set up if that network worked effectively or financial resources or people potential are exploited efficiently.

Setting of other centralities

Graph-analysing method gives also next information. We can identify control centrality where control refers to the extent to which nodes depend on one specific node to communicate with other nodes. For example, if more employees are connected to each other only when that node serves as the bridge connecting them, then its centrality is high. It is the node that controls the communication flows.

In our network such centralities had nodes Y₁ and Y₃. Node Y₁ has degree centrality because of the most nodes are connected to it (Picture 4).

Next ones centrality is betweenness centrality. It is a centrality measure of nodes within a graph. Nodes that occur on many shortest paths between other nodes have higher betweenness than those that do not. The highest betweenness centrality has Y₃ and that serves as the bridge between the most nodes and controls the information flows.

And finally independence means that a node is closely related to all the nodes considered – so that it is minimally dependent on any single node and is not subject to control. This means it can reach the maximum number of people through the shortest number of links, without being dependent on a particular few nodes (Y₁).

In graph theory closeness is a centrality measure of a node within a graph. Nodes that are “shallow” to other nodes (that is, those that tend to have short distances to other nodes with in the graph) have higher closeness. In the network theory, closeness is a sophisticated measure of centrality. It is defined as the mean shortest path between a node and all other nodes reachable from it. If we used graph-analyse to set closeness centrality this centrality has a node Y₃.

Possibility of optimization by using a traffic model method

Different methods and algorithms can be introduced to measure closeness, like the random-walk centrality. That is a measure of the speed with which randomly walking messages reach a node from elsewhere in the network — a sort of random-walk version of closeness centrality. Closeness centrality can be regarded as a measure of how long it will take information to spread from a given node to other reachable nodes in the network.

There we can analyse time-delay needed to processing of all documents whose are processed by information network.

We used analyse by method of traffic model based on relationship between information flow and distribution flow. We tested the condition by transmission of random document to random department of our enterprise.

We can optimize a sum multiple of time-delay occurred by transmission of documents:

_{S n}

Tc = å å (t_i,j +tm_i,j)

^{j=1 i=n}

where:

T_c – total duration of document processing cycle,

n – count of departments,

S – count of documents,

t_i,j – time-duration of technological operation by document processing,

tm_i,j – time-delay of document staying at same department.

It is needed to achieve a minimum of total time-duration of document processing cycle. We compounded a matrix of traffic model which enable to look over several time-duration t_i,j of document processing and time-delay tm_i,j of document staying as well as sum of time-duration TDs and sum of time-delay TPn needed by several departments by document processing. We have to search such values of TDs and TPn whose sum is minimized.

There the expression

åTcD=åTcP

means the total sum of time-duration of document processing which must be even to total sum of time-delay needed to document processing on several departments.

If we analyse several rows and columns of the relevant matrix we may detect any weaknesses in document processing, alternatively some limitation occurring at several departments by document processing. It allows to detect ledges occurred by information processing and therefore gives more possibilities to optimize information flows in the enterprises.

Conclusion

Searching and setting of centralities in the social network gives more possibilities by managing of that network, as well as by data management, etc. by taking control by implementation of new information and communication technologies. Separation of weaknesses and some limitations of managed network provide better utilization of communication network in the business processes, negotiation or by decision making processes. It can be eliminated treatments of network nodes that may evoke some failures by more important dealings or statements. Setting of their centralities may eliminate constraints and threatens whose may occur by using such network to predict partner behaviour and identify new business opportunities mostly by doing business on foreign markets.

Social network in the enterprises has unique properties thanks to be a socio-technical system that is created by people with their specific characters but not only by exploitation of technical components and other communication tools.

Literature

1. SINGH, S. Social Networks and Group Formation. Theoretical Concepts to Leverage 2007/09/06. http://www.boxesandarrows.com/view/social-networks

2. Balog, M., Straka, M. Logistické informačné systémy. Košice: Epos. 2005. ISBN 80-8057-660-2.

3. Freeman, L. C. Centrality in Social Networks: Conceptual Clarification. Social Networks 1(3): 215-239. 1978

4. Chung, K., Hossain, L., Davis, J. Exploring Sociocentric and Egocentric Approaches for Social Network Analysis. http://www.cs.usyd.edu.au

5. ZIMERMANOVÁ, K. 2006. Foreign trade - what do expect small and medium sized enterprises? In: Proceedings from international scientific conference „Small and medium sized enterprises in era of globalisation and integration“. Banská Bystrica: UMB, Ekonomická fakulta, 2006. ISBN 80-8083-296-X.

6. What Is Social Network Analysis? http://www.analytictech.com/networks/whatis.htm