2.1.1 History of Convergence

Because of largescale installment of factories, fulfilling Adam Smith’s theory of industrial revolution, capitalism has gained a new drive for growth [3–5]. The typical factors determining the growth in mainstream economics have been labor and capital, represented by L and K, respectively. These factors act sometimes independently, but in many cases synergistically, for enhancing productivity and other economic and financial indicators in capitalism [6,8]. The synergetic action of the two factors is an alternative presentation of the role of technology in capitalism and its development. Based on this mechanism, capitalism entered the second and third industrial revolutions; in the former as the introduction of largescale facilities, and in the latter as information revolution [7]. Following from the history, the fourth revolution has provided insight on the area where technology will bring capitalism into the future, which has been clearly the tradition of convergence [8].

Prior to discussing the topic of convergence, which can be described as the indication of i-fashion, it is relevant to understand the importance of the technology factor. One critical nature of capitalism in understanding its economic growth dynamics is that markets can become saturated [9–11]. If markets are saturated, then it is difficult to achieve economic growth. For this urgent problem, one solution is to rely on population growth for economic development, and the other alternative for resolving market saturation is “technology” [16]. Technology can create new markets [15]. This is highlighted to show that there exist both demand and supply sides of technology.

The supply side of technology corresponds to people requiring a certain functionality and there being a specific technology fitting into that need. In comparison, the supply side of technology indicates that once a new technology emerges, typically people will find new approaches of using it. For example, i-fashion-related technology did not begin from nothing. Instead, there has been a series of intervals between the demand and supply sides of technology issues [11,12].

2.1.2 Preceding Trends Prior to Industry 4.0 and i-Fashion Trend

Since the first industrial revolution, for a long time, the economy and the industry have been following the legacy of the economies of scale, which has been the basis for almost every discussion and practice for industrial investment. [6,15] The trend toward the economies of scale was stimulated after World War 2. Moreover, the recovery of Germany and Japan after the war as well as the building of the strong industrial base of the U.S. are all potentially explainable by the theory of the economies of scale [17].

However, with industrial and economic development, there has been an inevitable trend emphasizing on the quality of production instead of on the volume in the economies of scale worldwide [13,14,17]. This trend has a historical origin, originating particularly from European industrial and institutional settings. It is named as flexible specialization [15, 18], providing strong insights about mass production. The remaining argument from this theory originated not only from its attractiveness as an alternative for mass production but also from mass production becoming the industrial norm based on political reasons [16,18]. The term of flexible specialization has its reference point in European institutions and history because the corresponding theory attempts to explain the trends in the textile and apparel industrial sectors in Italy and France. The attractiveness of the theory is that it shows how specialtybased industrial sectors, including textile and apparel sectors, have survived for hundreds of years with mass production system as the norm. When a mass production system fails, it is mostly believed that there is little chance for craft sectors to persist, including specialty apparel and textile sectors.

Although the economies of scale worldwide seem to dominate the industrial and economic development, this approach has a very serious drawback in its logic in the continuously increasing size of the market [17]. It has become clear since the 1970s and the 1980s that economies of scale hypothesisbased investment may not be effective for major industrial sectors, particularly during recession. New approaches have been developed to save mass production during recession to increase flexibility components. Now the game has been reversed. In the industrial history since the 1980s when the flexible specialization theory was established, mass production systems started to survive in the market by increasing their flexibility. Just-in-time production is a good example found in [19]. From the perspective of the decision makers, whether from the government or industries, motivated by the flexibility they focus on an “industrial district,” where the flexible technology and institutional settings are setup [20–22].

2.2.1. IT-based Convergence

Presently, in addition to the search for agileness as well as flexibility, the third and fourth industrial revolutions based on information technology have changed the scene as a game changer by offering a clear and nonmarket saturating solution to industrial sectors, including i-fashion sector [22]. One inimitable trend has been that information technologybased third revolution has changed the structure of industries, boosting the productivity of other sectors. This relation has clearly opened a pathway for the fourth industrial revolution to reform various industrial sectors, including i-fashion sectors.

2.2.2 General trend of convergence

The second trend worth mentioning is the natural trend of technology development, which has been driving different hightechnology sectors to move in a similar direction. For example, the precision machinery manufacturing sector and the medical equipment sector can share many potential areas in their development. Similarly, the convergence of the industrial sectors can be evidenced and is only limited by preconceived stereotypes. This potential convergence, if proved empirically, can be used to explain the convergence of different industrial sectors toward i-fashion.

Stage 1: Average Rates

Begin with an N × T matrix R of average rates(or comparable performance variable) for N officially-defined industries for years t=0 to T . (3 digit SIC codes are typically used.)

Stage 2: Convert to Time Series Data

Convert to an N ×(T - T)=N × P matrix G whose elements git are the rates of change of the performance variable for i=T to N industries for years i=T to T . Each row g is therefore a time-series of rates of change.

Stage 3: Clustering Method

Cluster the rows of G according to the Euclidean distance D=(Σt(git-gjt)2 criterion using Ward's method(a hierarchical agglomerative procedure that minimizes within-group variance relative to between-group variance at each step).

Step 4: Grouping

Choose an appropriate level of grouping based on the agglomeration schedule and marginal loss of information as clustering progresses. That is, stop clustering at K groups when the algorithm starts forcing dissimilar objects into awkward and unwieldy clusters.

This research presents how plotting of the rate of change of the average capital efficiency and the rate of change of the average efficiency of machinery investment and conducting time series-based clustering of the machinery investment efficiency can mutually confirm the potential convergence phenomenon toward i-fashion.

4.2.1. Positive Convergence Zone

It is possible to obtain several empirical findings and their implications when understanding the trends toward the i-fashion-oriented industrial developments. First, in Figure 1, the vertical axis is the ten-year average capital efficiency, and the horizontal axis is the ten-year average machinery investment efficiency. If certain sectors are located in zone 1, which comprises positive machinery investment efficiency and capital investment efficiency, then these sectors are located in the positive convergence zone in the framework of this analysis. This implies that these sectors in this convergence zone have relied on existing technologies instead of newer ones, which resulted in the high and positive values for both indicators. Second, the existence of multiple sectors in the same convergence zone implies that these sectors may have incentives to move similarly, and furthermore, we may find technological convergence potentials.

The industrial sectors located in the positive convergence zone include textile, apparel, leather, miscellaneous machinery, communications service, medical precision equipment, fabricated metal products, educational service, restaurant/hotels, repair services, and print/publishing sectors. It can be understood from this composition that if there is a trend toward i-fashion, it will be easy to find these sectors to at least comove in a similar direction. Based on a bolder forecasting, these sectors may converge because of technological reasons to form a new sector targeting sectors such as i-manufacturing, including i-fashion.

Figure 2.

Positive Convergence zone (group members at the Appendix)

4.2.2. Negative Convergence Zone

Figure 1 also shows an agglomeration on the left hand side, which in this study is referred as the negative convergence zone. Although the literal meaning of this term is negative, the sectors located in this zone share a unique commonality across them. First, during the 2009–2019 period, most heavy and chemical sectors (HCI sectors) are located in this zone, except the electronic components sector. Second, that the sectors are located in the negative convergence zone does not necessarily imply that they present low industrial performance individually. Third, concurrently, being located in the negative convergence zone implies that these sectors have made massive technology/facility-related investments in the same period. One possible interpretation of this negative convergence zone is called the productivity paradox, in which a series of massive investments in one period results in a relatively low technology-related productivity of the same sectors in the coming period. This occurs because absorbing the full potential of the technology investment is time consuming, particularly in the embodied sectors, and tacit knowledge is important for actualizing the outcomes. The pharmaceutical, electronics component, mining, nonmetal products, and repair of industrial machinery sectors that are included in this zone validate the plausibility of the scenario.

It can be expected that in the next decade or subsequently, some of the sectors in the negative convergence zone will move to the positive convergence zone, with the learning curve reasons reflected in the plots of the two indicators.

Figure 3.

Negative Convergence zone (group members at the Appendix)

4.2.3. Findings and Implications of Time Series Data-Tuned Cluster Analysis

The findings and implications of industrial convergence obtained from the times series data plotting need to be validated by another method[27]. To achieve this, in this study, a time series data-tuned cluster analysis was conducted to identify ten-year time series-based patterns of the industrial sectors in Korea. While Figure 1 shows a one shot analysis based on the average change in ten years, cluster analysis yields an evolution-like trajectory of the sectors because of the use of the Euclidean distances among the sectors for each year and their total for ten years.

Figure 4 shows two large groups: group 1 on the left hand side and group 2 on the right hand side. Group 1 can contain subgroups called as groups 1-1, 1-2, and 1-3 from the left to the right, and similarly, group 2 can contain groups 2-1, 2-2, and 2-3. In group 1, a mixed location of the sectors that were in the negative and positive convergence zones is found. The reason for the mix is quite natural and helpful in understanding industrial dynamics. Figure 1 shows one shot outcomes, such as scenes, whereas the cluster tree map shows the annual change rate of the machinery investment efficiency. Therefore, the latter collects the sectors with similar cumulative annual changes in the machinery investment efficiency over the ten-year period.

Figure 4.

Tree Diagram from Cluster Analysis of Korean industrial sectors in 2009–2019.

Despite the differences, if and only if relevant agglomeration of the industrial sectors is found similar to both in Figures 1 and 4, it will be a highly convincing hypothesis and prediction that these sectors will move on the same trajectory in the future. Particularly if i-fashion-related sectors feature relevant cocollection in both Figures 1 and 4, then based on their analysis, one can clearly state that the necessary conditions for convergence toward i-fashion are approaching. In this context, sufficient conditions for i-fashion can be determined additionally based on the investment responses from social demands.

It is noteworthy that in Figure 4, the textile, electronic medical equipment, nonmetal products, and miscellaneous machinery sectors are under group 1, which is in significant agreement with Figure 1. This presents a strong reason to state that there is a technological potential that these sectors that present similar behavior in the industrial measures can be merged to form the i-fashion sector and its related technologies. The only difference is that Figure 4 shows the covariation of the sectors in a high-resolution manner, whereas Figure 1 presents a one shot photolike results.