* Jin Park
o Time : November 24, Friday 12:00 ~ 1:30 p.m.

o Venue : 9705(The 7th floor seminar room)


Knowledge Cooperation with North Korea: its Trend and Tasks


-Abstract

During 1997~2005, North Korea participated in 76 economics-related seminars and training programs to acquire some knowledge for its economic survival. Surprisingly, their efforts to learn new knowledge have not been affected much by the nuclear issue. Knowledge cooperation with DPRK is expected to benefit not only North Korea but also the rest of the world. Based on categorization of knowledge cooperation projects (KCP), this paper analyzes the trend of knowledge cooperation projects with DPRK taking political and economic variables into account. As a conclusion, this paper proposes relevant strategies and tasks for promoting KCP with the North Korea.




*Seong-Ho Cho
o Time : November 24, Friday 2:30 ~ 4:30 p.m.

o Venue : 9705(The 7th floor seminar room)


1. On the stock return method to determining industry substructure: airline, banking, and oil industries


-Abstract

The importance of strategic group analysis as an analytical construct for theory-building has long been recognized, and some empirical methods have been proposed in the strategic management field. As insightful as their methods are, these methods suffer from some limitations including the arbitrary and subjective choice of critical strategic dimensions and variables which may not induce objective and replicable groupings (McGee and Thomas, 1986; Barney and Hoskisson, 1990; and Peteraf and Shanley, 1997).

In this paper, the stock return method (Cho and McKelvey, 1996) is further developed by resolving some of its limitations: lack of face validity and one year sample window. We apply the method to the airline, banking and oil industries over the period from 1988 to 1992 in order to examine the method’s effectiveness for detecting industry and its substructure in an objective way.

In our particular sample, the groups found show clear face validity, and the stability of groups is maintained across 1-, 2-, 3-, and 5-year windows. Given that objectivity and replicability are crucial in empirical studies, the stock return method may introduce a way to enhance the level of objectivity and replicability in the strategic group research methods


2. Detecting industry substructures: Case of banking, steel and pharmachtical industries

-Abstract

This paper further develops the stock return method proposed by Cho and McKelvey (1996). It is claimed that the method may detect industry substructure in an objective and effective way. Although they are statistically significantly different while avoiding any artifactual statistical results (Barney and Hoskisson, 1990; Peteraf and Shanley, 1997), the groups found in Cho and McKelvey (1996) fail to show clear face validity. Furthermore, the one-year sample window used in their previous study may be too short of a time to capture a sufficient number of outside disturbances that are the basis for grouping. In this paper, the sample period windows are extended from the previous one-year window to 6 different windows, namely 1-year, 2-year, 3-year, 5-year, 7-year and 9-year window spans. To test its stability on different time periods, clustering results of year 2000？04 are compared to those of year 1988-96. By formally applying different sample windows and time periods, the limitations of small window and unknown stability (Cho and McKelvey, 1996) are supposed to be dismantled. Furthermore, this paper examines whether daily returns or weekly returns are better to use in the stock return method.

Among the largest firms in their market capitalization, 30 sample firms listed in New York or American Stock Exchanges (NYAM) are carefully chosen from the steel, banking, and pharmaceutical industries. We find that the stock return method produces stable group classifications across different sample windows and time periods. In our particular sample, the groups found show clear face validity. As the time span increases from 1 year to 9 years, the group structures become clearer and tighter. The grouping structure found in the 1988-96 time period has been consistently maintained in the 2000-04 time period. Daily returns produce the same grouping results from weekly returns, but they are better because they can detect groups sooner or with smaller time windows.

Several conclusions are drawn from the study. First, the stock return method can effectively identify industry substructure as maintained in Cho and McKelvey (1996). The findings confirm that industry substructure can be reliably and validly separated, and that substructure stability has been longitudinally maintained across different sampling windows and time periods. Second, the identified group structure is not artifactual. The historically consistent results from our method using ‘hard’ market-equilibrium data render a high level of validity on our finding. Third, the findings are objective because the sample data used are ‘hard’ data, and the stock return method has no subjective decisions buried within it (including clustering methods).