Conjoint analysis for Contract Strategy for Culture Technology Enhancement Program in Korea

1. Conjoint analysis for Contract Strategy for Culture Technology Enhancement Program in Korea Yun Jong Kim*, Uk Jung** E-mail: yjkim@kistep.re.kr, ukjung@dongguk.edu *: KISTEP, Seoul, Korea, **: Dongguk University, Seoul, Korea

2. Overview (I) • As Culture Technology becomes one of the leading industries in its contribution to the economy, many countries are increasing national research investment through government-sponsored research projects. • Thus it becomes important to measure the importance that research participants attach to government-supported Culture Technology research project attributes. • Through some focus group discussions, a list of research project attributes for Culture Technology was identified as important for participants for more successful project results. • This study uses conjoint analysis based on survey results to show that there is preferential difference in research project attributes for different affiliations of participants. • While the research was based on Korean experience, the research technique can be generalized to research policy designs in other countries.

3. Culture Technology in Korea • The concept of CT in Korea was introduced in July 2001. It was immediately adopted by the Korean government as one of the next six core technological engines for economic growth in the 21st century. • The six technology (6T), high value-added technology-intensive industries expected to lead Korea's economic growth, were appointed for intensive support by the Korean government. • Information Technology (IT), Biotechnology (BT), Nanotechnology (NT), • Space Technology (ST), Environmental Technology (ET), Culture Technology (CT) • CT is defined as “…technologies used in the value chain of culture content from the planning, commercialization, loading to media platforms, to distribution, and in a wide sense, complex technologies which are necessary for enhancing the added value to cultural products, including knowledge and knowhow from humanities and social science, design, and arts as well as science and engineering” • Similar concepts are Entertainment Technology and Creative Technology in other countries.

4. Culture Technology in Korea : Movies

5. Culture Technology in Korea : Movies

6. Culture Technology in Korea : Games

7. Culture Technology in Korea : Screen Golf

8. Culture Technologies in Many Countries MIT Media Lab(America) IRCAM (France) 3C Research(England) ATR(Japan) ITP(America)

9. Culture Technology • Especially, CT is the most typical technology field which is requiring the convergence and fusion of several technologies. • Apple’s DNA • It’s in Apple’s DNA that technology is not enough. It’s tech married with liberal arts and the humanities. (2011. 3. 3. Keynote)

10. Culture Technology in Korea • Most government-supported CT research projects in Korea require different areas of knowledge to be put together and also require many different experts to work closely together. • Different areas of knowledge include humanities, Korean studies, engineering, marketing and management and so on. As the results, the research collaboration is a key mechanism for both knowledge production and diffusion in CT. • Thus, most CT research projects in Korea have basic attributes of inter-affiliated or interdisciplinary research requirements for research collaboration. • The main objective of this study is to explore how the research participants feel about the collaboration issue with inter-affiliated or interdisciplinary research requirements. • For this purpose, this research uses conjoint analysis, which is appropriate in measuring the importance level or utilitythat research participants attach to the government-supported research project attributes including interaffiliated or interdisciplinary research requirements.

11. Conjoint Analysis (I) • Conjoint analysis is often used to understand how consumers develop preferences for products or services with multi-attribute levels in the marketing field. • Two basic assumptions are made in conjoint analysis. • 1st, a product or service can be described as a combination of levels of a set of attributes. • 2nd, these attribute levels determine the respondents’ overall judgment of the product or service. • The attraction of using conjoint analysis is that it asks the respondents to make choices between products or services defined by a unique set of product/service attributes in a way resembling what they normally do-by trading off features, one against the other.

12. Conjoint Analysis (II) • Conjoint analysis produces two important results : • ① Utility of attribute • [It] is a numerical expression of the value the respondents place in an attribute level. • It represents the relative “worth” of the attribute. • Low utility indicates less value; high utility indicates more value. • ② Importance of attribute • [It] can be calculated by examining the difference between the lowest and highest utilities across the levels of attributes.

13. Attributes in Conjoint Analysis Model • Attributes of inter-affiliated or interdisciplinary research requirements are our main interest in this study. • Inter-affiliated research means the research in which researchers from different affiliations collaborate no matter whether researchers’ specialized research fields are same or not, • while interdisciplinary research means the research in which researchers with different research fields of specialization work together. • To design the tradeoffs to represent the utility of inter-affiliated or interdisciplinary research requirements, we put into the government-supported research project attributes the research fund size and period as important attributes in this study.

14. Hypotheses • We will explore the utility of different levels of attributes and the importance of attributes the overall respondents perceived. • Then those utilities will be investigated in different participants group. • Using these results, we will try to address the existence of preferential difference among different participants group on the government-supported CT research program attributes with the following research hypotheses; • Hypothesis 1. CT research participants from different affiliations would have different preference on the levels of public funding size and period. • Hypothesis 2. CT research participants from different affiliations would have different preference on whether the inter-affiliated or interdisciplinary research was required.

15. Empirical Study (I) • We set up the research project attributes as Research Period, Research Fund Size, Inter-affiliated Research Requirement, and Interdisciplinary Research Requirement, in order to study suitable supporting policies for CT. 3 levels 3 levels 2 levels 2 levels

16. Empirical Study (II) • Despite a careful selection of factors, there were still too many (3322 = 36) possible profiles for the respondents. • The SPSS generated a parsimonious orthogonal array of 9 profiles.

17. Empirical Study (III) • It was decided it would be useful to study the utilities perceived by the following three different groups of affiliation participating in the research project; 1) industries, 2) academia, and 3) government-supported research institutes(GRIs). • There were a total of 128 respondents. <Breakdown of affiliations>

18. Empirical Study (IV) • The relative importance levels of the various attributes are summarized. • Overall researchers generally consider the research period as the most important attributes, following by research fund size, inter-affiliation requirement, and interdisciplinary research requirement. <Graph of averaged importance of various attributes>

19. Empirical Study (V) • One-way ANOVA analysis with a Least Significant Difference (LSD) Test at the 0.05 significance level was performed to compare the preferences of researchers from different affiliations. <Comparison of means and ANOVA results for different affiliations>

20. Empirical Study (V) • One-way ANOVA analysis with a Least Significant Difference (LSD) Test at the 0.05 significance level was performed to compare the preferences of researchers from different affiliations. <Comparison of means and ANOVA results for different affiliations> No statistical difference at 0.05 significance level

21. Empirical Study (V) • One-way ANOVA analysis with a Least Significant Difference (LSD) Test at the 0.05 significance level was performed to compare the preferences of researchers from different affiliations. <Comparison of means and ANOVA results for different affiliations> difference in means at the 0.05 level for industry ([A]) and academia ([B])

22. Empirical Study (V) • The one-way ANOVA results can be interpreted as follows: <Comparison of means and ANOVA results for different affiliations> Compared to academia and GRI’s researchers, industry researchers had a higher constant value

23. Empirical Study (V) • The one-way ANOVA results can be interpreted as follows: <Comparison of means and ANOVA results for different affiliations> Industry researchers assigned higher utility to Medium research periods, whereas researchers from academia and GRIs assigned higher utility to Long research periods

24. Empirical Study (V) • The one-way ANOVA results can be interpreted as follows: <Comparison of means and ANOVA results for different affiliations> Industry researchers assigned lower utility to Inter-affiliation Research Requirements, whereas researchers from academia and GRIs assigned a higher utility.

25. Empirical Study (V) • The one-way ANOVA results can be interpreted as follows: <Comparison of means and ANOVA results for different affiliations> Industry researchers assigned lower utility to Interdisciplinary Research Requirements whereas researchers from academia and GRIs assigned higher utility.

26. Empirical Study (VI) • Hypothesis 1. CT research participants from different affiliations would have different preference on the levels of public funding size and period. • One can say that CT research participants from different affiliations have different preference on the levels of public funding period. • Industry researchersprefer medium research period to short or long period, whereas researchers from academia and GRIsprefer long research periods. • However, as for research fund size, there are no significant differences in preference among research participants from different affiliations.

27. Empirical Study (VII) • Hypothesis 2. CT research participants from different affiliations would have different preference on whether the inter-affiliated or interdisciplinary research was required. • Industry researchers are reluctant to participate in inter-affiliated and interdisciplinary research projects, whereas researchers from academia and GRIs prefer them. • These barriers that industry researchers feel about the collaborations are discernible from the analysis regarding inter-affiliation and interdisciplinary research requirements.

28. Conclusion • This research illustrates the usefulness of conjoint analysis in determining the utility values of government-supported CT research project attributes. • The study demonstrates how evaluators can use this research technique to reveal and measure the hidden needs of participants. • The segmentation of participants into different groups according to affiliation has many practical applications. • With other research techniques such as cluster analysis and multi-dimensional scaling, using conjoint analysis offers extremely interesting academic and evaluating research opportunities.

29. Thank you very much!!