Structure and Characteristics of Transaction Network in Korean Non-Financial Industries

Transcription

1 Indian Journal of Science and Technology, Vol 9(26), DOI: /ijst/2016/v9i26/97364, July 2016 ISSN (Print) : ISSN (Online) : Structure and Characteristics of Transaction Network in Korean Non-Financial Industries Jung Jaeheon 1 and Chang Ji Sang 2* 1 Department of Business Administration, Pukyong Nat l University, Korea; highfly1@pknu.ac.kr 2 School of Economics and Trade, Kyungpook Nat l University, Korea; jschang@knu.ac.kr Abstract Background/Objectives: This paper investigates the structural similarities and differences of supplier networks for 9 industries for Korea on the basis of social network analysis. Methods/Statistical Analysis: There are well known two types of network- small world network, scale free network. We checked the key characteristics-likes in/out degrees and path length and other indexes related with the connectivity- to identify types for the 9 industries using a unique dataset that contains information on buyer and supplier linkages for more than 80,000 incorporated non-financial firms. Findings: Common characteristics for scale free networks are the degrees of nodes in the networks, which are the sales and purchasing transaction numbers for the firms in the industrial networks. They fit the power law, the key characteristic of scale free network for all 9 industries. The error tolerances of networks upon the hub removal are very weak for all 9 industries. This is another characteristic of scale free network. But the hub influences and the degrees of connections via hubs vary with industries. The networks of the assembly and processing industries such as automobile, electronics, and shipbuilding have strongest hub influence and firms in the networks are connected strongest via hub. This is also identified in the shortest average path lengths and weakest tolerance for hubs in these industries. Consumer goods industries such as food and fabrics and the industry of basic materials have longest average path length and strongest tolerance for hubs so that these industries show weakest hub influences. Service industries are in the position of middle. These hub influences are also reflected in the path length and outbound degree s relations with sales volumes. Application/Improvements: The industrial policy should adapt to different network characteristics. For example, the industrial network will be more hierarchical as the network has more characteristics of scale free network. Policies should consider this. Keywords: Inter-Firm Transaction Network, Scale Free Network, Social Network Analysis, Supplier Chain 1. Introduction Supply chain makes huge network consisting of the companies connected by the buying and selling transactions. We can analyze this network using the tools of social network analysis which started from sociology and developed to the well known thinking that social phenomenon can be explained in the same theories found in natural phenomenon. We can use the theoretical basis developed in the social network analysis to find the characteristics of the transaction network (see the brief overviews 1-3 ). Network types are known to be divided into three. First well known type is the random network. Here network connections are random. But in real world, large number of nodes is connected in high local clustering if we have very small number of random rewiring starting from complete regular network with connecting rules 4. This is small world network. However, Barabasi suggested new network type, the scale free network, where small number of hub nodes connects lots of other nodes and the number of hubs with more connections decrease rapidly following power function (power law) 1. Japanese researchers first tried to find the characteristics of the enterprise transaction networks. Nakano and White used regional inter-firm transaction data of 8,347 firms in Ohita, Japan 5. They checked the network characteristics and concluded that the network * Author for correspondence

2 Structure and Characteristics of Transaction Network in Korean Non-Financial Industries is closer to scale free network and it fits the power law, but the network is not scale free network because hub firms in scale free network organize the hierarchical scale free network and the network is not the result of preferential attachment as suggested by 1. Mizuno used 500,000 firm transaction data between 2008 and 2012 and checked the characteristics of the network 6. They concluded that the network has the characteristics of scale free network and they also checked the trends of network characteristics. There are also other papers dealing with the enterprise transaction network mainly in the view of physical network attributes We evaluate and explore the network structure of the major 9 industries in Korea using 83,221 transaction information in the year of 2011 provided by Korean Enterprise Data (KED) and constructing a network of corporate firm transactions for industries. This paper discusses the similarities and differences of the network structure among 9 industries in Korea. We start the paper by introducing the data for industrial network and background in chapter 2 and discuss the industrial differences on the basis of social network analysis in chapter 3 and Data Overview We use the data for more than 80 thousand firms, provided by Korean Enterprise Data. These include the transaction data used directly in this research and the data also include financial and employment related data. Transaction information for each firm consists of the firms for the sales as well as the sales volume portions up to 10 firms. We have used these for constructing the transaction network. We constructed networks for each 9 industries to find the characteristics of the industries through the network analysis. Networks are constructed for the Korea s representative assembly and processing industries such as automobile, electronics, and ship-building, then for consumer goods industries such as food and fabrics and the industry of basic materials, finally for service industries i.e. construction, retailers, and knowledge service. We used the first 3 digit of Korean Standard Industrial Classification (KSIC) as seen in Table 1. For calculating the indexes representing the characteristics of networks, we used only the largest component of each industrial network because certain indexes cannot be calculated if they are related with the nodes belonging to the different components. 3. Network Characteristics: Degrees of Nodes Table 1. Industries and the firms for the analysis Industry Group Assembly and processing industries Consumer goods and basic materials Service industries Industry Group Contents(SIC number:3 digit) Number of Firms Automobile Automobile Trailer(C30) 1,371(661) electronics Electronic parts Computers Video Audio Telecommunication(C26) 2,119(715) Shipbuilding Other Transportation Equipmnet(C31) 689(385) Food Food(C10), Beverage(C11) 1,198(205) Fabrics Fabrics(C13), Clothes(C14), Leather(C15), Lumber(C16) 3,108(472) Paper(C17) Printing(C18) Basic materials Cokes Petroleom Refinery(C19), Chemical Industries(C20), 4,926(866) Rubber Plastics Processing(C22), Non Ferrous Metal Processing (C23), Primary Metal Processing (C24), Metal Processing(C25) Construction Construction(F41,F42) 12,933(3,919) Retailers Retailers(G45,G47) 4,118(131) Service Industries Software(J58), Audiovisual Recording and 3,665(435) Distribution(J59), Broadcasting(J60), Telecom- munication(j61), System Integration and Management(J62), Data Processing(J63) Notes: The number of firms within the parenthesis in the last column is number of firms in the biggest component on the analysis. 2 Vol 9 (26) July Indian Journal of Science and Technology

3 Jung Jaeheon and Chang Ji Sang 3. Network Characteristics: Degrees of Nodes 3.1 Uneven Number of Incoming and Outgoing Transactions Outbound degree from each node in the network implies the number of sales transaction and inbound degree implies the purchasing number. This inbound and outbound transaction numbers varies and we are interested in the differences of the distributions for 9 industries. Table 2 outlines the distributions of the purchasing transaction numbers for 9 industries. We note first that the maximum number of purchasing transaction is very large and these numbers vary very much with industries. Comparing these distributions to normal distribution, the 1 st quartile is bigger and 3 rd quartile is smaller much than the values expected using standard deviation, so that we can conclude the distributions are narrow and concentrated at the center. The distributions of the numbers of sales transaction are similar to the purchasing number distributions, but very close to the normal distributions for all 9 industries as seen in the Table 3. Also the differences in the maximum numbers of the sales transactions between 9 industries are smaller than the differences in purchasing transaction number. Now we take a look at the characteristics of the 9 industrial networks. All networks show the characteristics of scale free network in that the nodes with more transaction numbers decrease following the power law. Power law implies that the node numbers with more than K transactions numbers decrease following the function K -r. Taking the log, we can estimate r as the coefficients of linear regressions. Table 2. Outline for purchasing transaction number for 9 industries Industry Automobilicbuilding Electron- Ship- Food Fabrics Basic Construc- Retail- Knowledge Materials tion ers Service Min st Quartile 0(-3.7) 0(-3.7) 0(-5.9) 0(-6.1) 0(-0.5) 0(-2.2) 0(-5.2) 0(-1.7) 0(-3.1) Median Mean rd Quartile 1(7.0) 1(6.3) 1(8.8) 1(2.8) 1(2.9) 1(4.7) 1(8.1) 0(4.6) 0(5.3) Max Standard deviation Notes: The number of firms within the parenthesis is number of quartiles calculated using standard deviations when assuming normal distribution. Table 3. Outline for Sales Transaction Numbers for 9 industries Industry Automobilicbuilding Electron- Ship- Food Fabrics Basic Construc- Retail- Knowledge Materials tion ers Service Min st Quartile 1(0.9) 1(0.6) 1(0.9) 0(0.4) 0(0.3) 1(0.6) 1(0.7) 1(0.7) 1(0.7) Median Mean rd Quartile 2(2.4) 2(2.0) 2(2.0) 2(1.7) 2(2.1) 2(1.9) 2(2.3) 2(2.2) 1(1.6) Max Standard deviation Notes: The number of firms within the parenthesis is number of quartiles calculated using standard deviations when assuming normal distribution. Vol 9 (26) July Indian Journal of Science and Technology 3

4 Structure and Characteristics of Transaction Network in Korean Non-Financial Industries Figure 1 shows the linear relations between the number of nodes and the purchasing transactions numbers and r values lie between 1 and 2 shown in Table 4. Despite the differences in industrial nature, industrial differences for r values look very small in Table 4. And the power law is shown to have so high explanatory power that most of the regression s R 2 s show high values more than 90%. But we note that the nodes (firms) with many purchasing transaction numbers seem to violate the power law in all industries and violation pattern look very similar in the sense that the node distributions are flat around firms with very high numbers of purchasing transactions. Despite all these slight violations, we may conclude that all the 9 networks have similar characteristics of scale free network in the view of purchasing transaction numbers. Figure 2 shows the distribution of nodes with more than certain K sales transaction numbers. The r values are higher than the case of the purchasing transaction number as seen in Table 5. These values for all industries are nearly the same lying between 2 and 3 except the industry of construction and knowledge service. The explanatory power R 2 s are also very high as for the cases of the purchasing transaction number in Table 5. The reason why the nodes with more purchasing and sales transactions numbers decrease in the scale free networks is due to preferential attachment 2. It is understood that the firms with more sales have more transaction numbers. Many supplier firms try to sell their commodities to the small number of firms with more sales because they can give the suppliers the chance of more Figure 1. Distributions of firms with more purchasing transaction numbers than a certain purchasing number (horizontal axis). 4 Vol 9 (26) July Indian Journal of Science and Technology

5 Jung Jaeheon and Chang Ji Sang R Figure 2. Distributions of firms with more sales transaction numbers than a certain sales number (horizontal axis). Table 4. Average number of purchasing transactions and explanatory power of the power law for 9 industries Industry Automobile Electronics Shipbuilding Food Fabrics Basic Materials Construction Retail-ers Knowledge Service Incoming # Table 5. Average number of sales transactions and explanatory power of the power law for 9 industries Industry Automobile Electronics Shipbuilding Food Fabrics Basic Materials Construction Retailers Knowledge Service Outgoing # R sales. In the same way, more firms may try to buy from the firm with more sales due to the better buying conditions. But in our industrial transaction networks, the firms with more sales may have more suppliers because they need more various materials and parts. Also the firms may have more customer companies because they succeed to have more sales with the effort of acquiring more customers. In either way, it is certain that all our industrial networks shows characteristics of the scale free network and this means that firms form a hierarchical network with the hubs of small numbers of firms having more power and playing central role despite with the differences in industrial nature in Korea 11. Vol 9 (26) July Indian Journal of Science and Technology 5

6 Structure and Characteristics of Transaction Network in Korean Non-Financial Industries Figure 3. Relations between sales degrees and sales volumes in assembly and processing industries. Figure 4. Relations between sales degrees and sales volumes in food and basic materials industries. 6 Vol 9 (26) July Indian Journal of Science and Technology

7 Jung Jaeheon and Chang Ji Sang Figure 5. Relations between sales degrees and sales volumes in service industries. 3.2 The Relationship Between Customer Links and Sales A company can increase sales by increasing the number of customers or by increasing sales volume for each customers. Some researchers argue that firms can have large sales when they have many customers. But others say that firms with customers with large sale don t have to have many customers because they have the lasting and strong tie with the large sale customers, which are hub firms in the scale free network. We checked the relationship between the number of customer links, i.e. outbound degree and the sales volume for 9 industries. For checking precisely, we divided the firms into 4 bins, that is firms with the sales volume in the upper 1 percentile, firms between 4.75 to 5.25 percentile, firms between 45 and 55 percentile, firms between 90 and 100 percentile. These are shown in Figure 3-5 respectively for assembly and processing, food and basic materials, service industries. The different dot shape in the figures represents the 4 bins. We found that for the limited cases of the firms with extremely large sales (belonging to the first bin), the outbound degree numbers are correlated negatively with sales for all 9 industries. The firms with extremely large sales are located at the upper level in the supply chain and don t have many customers in the same industry. Also we note that weak positive correlation was found only in the 3rd and last bin in some industries. Firms with small sales can increase their sales by acquiring more customers especially in consumer goods industries and the industry of basic materials. In these industries, power of the hub firms are not so strong enough to guarantee more sales for the firms, which maintains a strong and long lasting transactional relations with the hub firms. 4. Network Characteristics: Connectivity 4.1 Connectivity of Transactions and its Interpretations How closely two firms are connected by transactions can be measured by the path length. Path length is the number of firms through which two firms are connected using the Vol 9 (26) July Indian Journal of Science and Technology 7

8 Structure and Characteristics of Transaction Network in Korean Non-Financial Industries Figure 6. Distributions of the number of firms with path length smaller than the certain path number. arcs (arc exists between two firms if the firms have any transactions (purchasing or sales). To measure this path length, we picked up 6 firms randomly and counted the number of firms having the path lengths smaller than certain number p from the randomly chosen firm. Figure 6 shows the distributions of the numbers of firms within p for 6 random firms, measured by the ratio over total number of firms in each industrial network. Horizontal axis indicates the number p and the 45 degree line is a reference for comparison. Industrial differences is somewhat clear as seen in Figure 6. In case of the assembly and processing industries it can be seen in the upper side of the Figure 6 that the number of firms in 4 path lengths from a randomly chosen firm is more than sixty percent. For example, more than 60% of 661 in the biggest component are connected to the randomly chosen companies in 4 path length in the automobile industry. But the numbers of firms within 4 path length are slightly more than 20% in the consumer goods and basic material industries in the middle of the Figure 6. In case of the service industries in the lowest side in the figure, it is shown that more than 60% of firms are connected within 4 path lengths as in the assembly and processing industries. But we can see that certain firms have less than 10% of firms connected in 4 path lengths in the biggest component in construction and knowledge service industries. We find that the service industries have more variations in the connectedness even though they are very closely connected on average. Figure 7 shows the distributions of average path lengths for all firms in each industry. Most of the industries show similar distributions except in the industry of fabrics and basic materials. Average path length for the industry of basic materials and fabrics is longer than other industries to show the consistent results with previous analysis. The variations in the average path lengths is largest in fabrics. If more hubs exist or hubs have more connections with other nodes in the scale free networks, the path length will be shorter because firms use the hubs with many connections to reach other firms. Thus we may conclude that the transaction networks of the industry of basic materials and fabrics have smaller number of hubs or the hubs have smaller number of connections than the networks of other industries. 8 Vol 9 (26) July Indian Journal of Science and Technology

9 Jung Jaeheon and Chang Ji Sang Figure 7. Distributions of the average path length for 9 industries. 4.2 Sequential Hub Removal and Industrial Characteristics If we remove a firm from the connected network we analyze, the original network will be decomposed into disconnected networks (components). If a firm connected with more companies in transactions is removed, the network will be decomposed into more disconnected networks. The firm with higher closeness centrality is connected transactionally with more firms and it reach other companies in closer distances in the scale free network. So removal of the hub firms have greater impact on the network function including the connectivity, because they have higher closeness centrality 12. Thus if we sequentially remove firms in the order of closeness centrality, the number of disconnected networks will increse more rapidly than the case that we remove firms randomly. Similar approach was done in other paper 13. We compared the increasing speed in number of components in the case of the sequential removal in the order of the closeness centraliy with the case of random removal for 9 industries in Figure 8. In this figure, horizontal axis indicates the ratio of number of firms over 10 percent of total fims in each industrial network. Figure 8 shows that the speed increases rapidly initially in the case of removal in higher centrality order (upper curves in the figure) and more rapidly than in the case of random removal (lower curves in the figure) for all 9 industries. These shows that the characteristics of scale free network. Figure 8. Number of components increased by removal of nodes: sequential (upper curve) vs. random (bottom curve). Vol 9 (26) July Indian Journal of Science and Technology 9

10 Structure and Characteristics of Transaction Network in Korean Non-Financial Industries Figure 9. Relations between average path length and sales volume for the firms in each industrial network. But the differences in the speed of increasing between higher centrality and random removal vary with industries. The speed differences are much smaller in the industries of food and fabrics. This indicates that the centralities of hubs (with the highest closeness centrality) are lower especially in these industries. Also in these industries, the phase of rapid increase at higher centrality removal is relatively long, on the other hand this phase is relatively short in the assembly and processing industries. It means that a few number of hubs have very strong influences in assembly and processing industries, but many hub-like firms have relatively weak influences in consumer goods industries. 4.3 More Connection Leads to More Sales? If a firm has shorter average path length in the scale free network, the firm play a central role as a hub and may have more sales taking advantage of the position. Figure 9 shows the relationship of average path length with the sales after taking logarithms. The relation, that the firms with shorter path length have more sales, look clear in the assembly and processing industries. This relation becomes clearer especially in automobile industry. It also appears clear that the same kind of relationship exists in the cases of firms with large sales in the industries such as basic materials, retailers, knowledge service, and construction. But for other industries this kind of relation looks unclear. This analysis shows the central position in the network leads to more sales in all industries but consumer goods industries such as food and fabrics. The influences of hubs in the scale free networks appear clear in these industries. 5. Conclusion This research suggested that the transaction networks for 9 industries are scale free network as the result of constructing and checking the indexes of network using more than 80,000 data in Korea. But the hub influences and connectivity of the networks vary with industries. 10 Vol 9 (26) July Indian Journal of Science and Technology

11 Jung Jaeheon and Chang Ji Sang We found that most of the firms in the assembly and processing industries are connected through a few hub firms and the influences of the hubs are strongest. But on the other hand in the network of the consumer goods industries, we can see many hub-like firms with weakest influences. This result is consistent with the observations that the structural characteristics of Korean assembly and processing industries is hierarchical in the sense that the higher level hub firms have strong influence and lead the firms in the lower levels. Lots of firms compete intensively to have sales transactions with the hub so that hub centrality and hierarchical power becomes stronger. The connected relations from the higher level to lower level firms make a tree-like hierarchical network, a kind of scale free network as the value chain flows from the higher level to lower level in these industries 11. An interesting remaining research will be the trend in structural change of the network. Will the hub influence become stronger as the time goes? May the trend be different with industries? 6. Acknowledgement This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2013S1A3A ). 6. References 3. Barabasi A. The origin of bursts and heavy tails in human dynamics. Nature. 2005; 435(7039): Watt DJ, Strogatz SH. Collective dynamics of small world networks. Nature. 1998; 393: Nakano T, White DR. Center of Organizational Innovation: Columbia University: The large-scale network of a Tokyo industrial district: small-world, scale-free, or depth hierarchy? Working Paper. 2007; p Mizuno T, Souma W, Watanabe T. The structure and revolution of buyer-supplier networks. Plos one. 2014; 9(7): Fujiwara Y, Aoyama H. Large-scale structure of a nation-wide production network. European Physical Journal B. 2010; 77(4): Kim J-W, Park K-N. A Study on methodologies to develop an e-industrial cluster hub system using social networks. Indian Journal of Science and Technology. 2015; 8(21): Ohnishi T, Takayasu H, Takayasu M. Hubs and authorities on Japanese inter-firm network: characterization of nodes in very large directed networks. Progress of Theoretical Physics Supplement. 2009; 179(1): Saito YU, Watanabe T, Iwamura M. Do larger firms have more interfirm relationships? Physica. 2007; 383(1): Jung J, Hong J-P. A study on the network structure of the supplier-customer relations between flagship and small companies. Korean Small Business Review. 2015; 37(4): Albert R, Jeong H, Barabasi A-L. Error and attack tolerance of complex networks. Nature. 2000; 406: Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi A-L. The large-scale organization of metabolic networks. Nature. 2000; 407: Barabasi A-L, Albert R. Emergence of scaling in random networks. Science. 1999; 286(5439): Barabasi A-L. London: Plume: Linked: How everything is connected to everything else and what it means for business, science, and everyday life. 2002; p Vol 9 (26) July Indian Journal of Science and Technology 11