Evolution of the linkage structure of ICT industry and its role in the economic system: the case of Korea^*

* Roland Weistroffer is the accepting Editor for this article.

ABSTRACT

When assessing the structural role of an industry sector within an economic system, considering its relationship to other sectors is crucial. Among others, Information and Communication Technology (ICT) industry, one of the innovation accelerators or key engines of economic growth, is evaluated. Specifically, we analyze inter-industry production inducement linkages within a qualitative input–output analysis framework, since it is useful for understanding the key structure of an economic system. Our purpose is to understand the significant spillover structure of the Republic of Korea’s ICT industry within the national production system, as it has played an important role in the national economy and grown dramatically over the years. The findings from the structural analysis, focused on changes in links, are as follows: First, ICT manufacturing showed a higher degree of heterogeneity than ICT service sectors in its sensitivity effects structure, an indication that this sector needs to be utilized in various other industries. Second, the spectrum of industries having significant production inducement linkages with the ICT industry is limited and furthermore, the influence effects of the ICT manufacturing sector diminished considerably although the ICT industry’s sensitivity effects increased. Finally, intra-industry linkages within the ICT industry are gradually strengthened especially between ICT services and manufacturing. These findings call for sustained policy efforts to promote the virtuous circle in the overall inter-industry production inducement system, by increasing the utilization of products and services from other sectors by ICT sectors (especially ICT manufacturing) as well as the application of ICT in other sectors.

KEYWORDS:

• Qualitative input–output analysis
• linkage analysis
• local network
• ICT industry
• production inducement

Acknowledgements

This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government. [18ZE1100, Research on the Techno-Economic Analysis and Standardization of ICT Technology for the Enhancement of ICT R&D Competitiveness].

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Pil Sun Heo is a researcher at the Industry-Strategy Research Department, ETRI of Korea. He received his BA and MS in industrial engineering from Hanyang University and Seoul National University, respectively. Since joining ETRI in 2004, Mr Heo has been working in the areas of ICT industry analysis/policy, techno-economic analysis. His research interests include industrial policy, economic network in the field of ICT industry.

Duk Hee Lee is professor at School of Business and Technology Management and also Head of Graduate School of Innovation and Technology Management, Korea Advanced Institute of Science and Technology (KAIST). His research areas are network and complex economics, innovation ecosystems and ICT economics and policy. He has investigated the fitness of network science combined with econometric methods like social network analysis, system dynamics, agent-based model with the tasks in the economy. And he analyzed the mechanism behind market dynamics such as the direct interdependence among economic agents, irrational behaviors and the non-reductive phenomena between micro and macro economies. He has authored 54 journal papers, 90 conference papers and 24 books. He received the PhD degree in Economics at the State University of New York (Buffalo).

Notes

* Roland Weistroffer is the accepting Editor for this article.

1 Korea in this document refers to the Republic of Korea (South Korea).

2 The IT839 Strategy is a policy vision of the IT industry announced by the Korean Ministry of Information and Communication (MIC) in February 2004. IT839 refers to eight services, three infrastructure sectors and nine growth drivers. Later, an upgraded version of IT839, called the u-IT839 Strategy, was announced in April 2006 (Lee, Kim, & Park, 2009).

3 For reference, Kim and Park (2009) analyzed the characteristics of linkage within ICT industry and the role of that under Korean industrial systems, which is similar to our analysis in terms of analysis subject (ICT industry) and its position. But, in the network perspective, there are some difference in the content of the inter-industrial relationships and method of filtering concepts. The former is concerned with absolute (values) knowledge flows based on global filtering, and the latter relative (per unit) production inducement with focus on local filtering. Furthermore, the reference period is different in that the former study is of early 1980s to mid-1990s and our study for the period from the mid-1990s to the mid-to-late 2000s.

4 The Shannon disparity method (Lee et al., 2010), although it selects links that are significant for individual nodes as does the disparity filter method, does not offer a parameter like $\alpha$ for setting different levels of extraction. When this method was actually tried, the resulting backbone network comprised too many links, which negatively affected the ability to visually discern the structural characteristics.

5 Strength implies the sum of the weighted values of the link connected to a certain node. In a directed network, we can consider strength from the perspective of both out-degree ($s_i^{\mathrm{o}\mathrm{u}\mathrm{t}}=\sum _j{w}_{ij}$) and in-degree ($s_i^{\mathrm{i}\mathrm{n}}=\sum _j{w}_{ji}$).

6 Hwang and Kang (2011) showed that the distribution of out-degrees (in-degrees) in an inter-industry transaction network, extracted by applying a global threshold for transactions in intermediate goods, at the level of supply (demand) structure, has a power-law form.

7 The range of variation in the distribution of local heterogeneity can be calculated by using the formula ${\gamma }^{\mathrm{m}\mathrm{a}\mathrm{x}}-{\gamma }^{\mathrm{m}\mathrm{i}\mathrm{n}}$.

8 The global threshold was set to a value that keeps the number of links the same as the number of links within the backbone network extracted using the local disparity filter method, so that the two backbone networks, created using two different techniques, can be compared with each other.

9 This is due to the fact that when the links are evaluated in relative terms, the network density is maintained at a certain level even if the size of production inducement effects is reduced over time, as only those relationships that are deemed important for each industry are selected. This is the reason why when evaluating the ICT industry’s local linkage structure, it is imperative to consider the both cases (relative and absolute evaluations).

10 When generating the backbone of a network, if the average number of extracted links for a sector is $c$ on the column basis, and $r$ on the row basis, the maximum possible number of links that can exist on the (column ∩ row) basis is max(c, r). Therefore, the maximum number of links to other sectors that the ICT industry with $m$ number of subsectors can have is $m\times \text{max}(c,r)$. This value was about 6 (21.7% of ($n-1$), the maximum value of possible links) and 20 (26.1%), respectively, for the high (29 sectors) and medium (78 sectors) levels.