Successful joint venture strategies based on data mining

Successful joint venture strategies based on data mining JIN HYUNG KIM, SO YOUNG SOHN* Department of Information & Industrial Systems Engineering Yonsei University 134 Shinchon-dong, Seoul 120-749 KOREA Abstract : The purpose of this study is to propose types of joint ventures that can increase the competiti veness of a company in the marketplace. We examine the characteristics of individual venture enterprises based on technology. We considered sixteen Technology Evaluation Attributes (TEA) in order to categori ze companies into four groups. Next, we used a multinomial logistic regression model to identify the sig nificant characteristics of a venture company that successfully predicts group membership. Based on this information, we propose various forms of joint venture which compliment each other and produce higher overall competence. The results of this study can provide important feedback information to academics, p olicy-makers, and venture capitalists. Keywords: Joint venture, Technology Evaluation Attributes (TEA), multinomial logistic analysis, factor analysis, cluster analysis 1. Introduction Small and medium enterprises (SME) play important roles in the national economy but possess inferior management conditions relative to the larger firms, due to less funds, staff, technology, and information available to them. Hence, the government in Korea has been implementing various forms of funding sources to resolve the financial difficulty of SMEs. In particular, the government has supported innovative SMEs and provided policy funds to venture companies which are established based on technology. While those venture companies supported by the policy fund often succeed in quantitative growth, they frequently fail in qualitative growth. It shows that what ventures need in order to be successful is not simply funding, but also quality strategies (Benfratello and Sembenelli, 2002). The results of this study can be used to provide not only strategies for joint venture companies, but also as important feedback information for academics, policy-makers and entrepreneurs. This paper is organized as follows. Section 2 proposes the analysis framework and performs an empirical data analysis. Finally, Section 3 discusses our study results along with a conclusion. ISSN: 1790-109 Page 263 ISBN: 978-960-6766-41-1

2. Pattern analysis of venture companies In order to get acquainted with the strengths and weaknesses of venture companies, we used a data set consisting of 231 Korean venture companies that were examined in 2004. Out of the 231 companies, just 83 companies possessed financial information and only those venture companies were evaluated in terms of their technology by using the sixteen Technology Evaluation Attributes (TEA) shown in Table 1. The attributes represent not only the technology but also capability of CEO, marketability of technology, and technology profitability. The TEA is calculated by professionals who measure all of the attributes in a - or 10-point Likert scale. Scales of two type which are evaluated in a 10-point scale are considered to be twice as valuable than those in a five point scale. Table 1. The 16 attributes of technology evaluation. Variable name Segment Attributes Score P1 Capability of Knowledge management score P2 ownership Technology ability score P3 Business management score P4 Fund supply score P P6 P7 P8 Human resource score Environment of technology development score Output of technology development score New technology success possibility score P9 Technology superiority score P10 Technology Quality Technology score commercialization P11 Marketability Market potential score P12 of technology Market characteristics score P13 Product competitiveness score 10 P14 Technology Sales plan score 10 P1 profitability Ordinary sale income P16 Profitability score We apply PCA and FA to the sixteen TEA scores of 83 companies. The first ten principal components explain 80% of the total variance and they were used for factor analysis. Table 2 shows rotated loading of those ten factors. The vast majority of the factor loadings associated with each component is 0. or above. The ten factors were given the following descriptive labels: Factor 1 (Technological level of the CEO); Factor 2 (Management capability of the CEO); Factor 3 (Marketing strategy); Factor 4 (Excellence of technology); Factor (Outcome of the technology development); Factor 6 (Market approach of the product); Factor 7 (Marketability of the technology); Factor 8 (Profitability of the technology); Factor 9(Market conditions); and Factor 10 (Success probability of the technology). The ten factors were used for clustering analysis used to divide the venture companies. K-means cluster analysis was used to identify group membership of venture companies, based on the factor score of the TEA. The determination of the appropriate number of groups is a key decision in cluster analysis (Woo et al., 1991). A sharp break in the efficiency of the classification was detected with regard to the four-cluster solution. The number of company groups is, therefore, reduced from 83 to 4. Table 3 shows the characteristics of the company group with regard to ISSN: 1790-109 Page 264 ISBN: 978-960-6766-41-1

the cluster means for each of the factor scores. Cluster 1, composed of 228 members, has good commercial capability (GC) but low scores in the other characteristics. Cluster 2 has 240 members which have high capability of owner, technology, and commercialization (GOTC) scores. Cluster 3 contains 189 companies which have good technology and high capability of owner (GT) scores, but low commercialization and profitability scores. Cluster 4 has 196 members which have good sales strategies (GS), but low values in the other characteristics. The evidence reported in Table 3 confirms that a different group of venture companies can be identified with regard to the characteristics of venture companies, based on TEA. Table 2. Factor analysis for TEA: rotated factor loadings. Factor1 Factor2 Factor3 Factor4 Factor Factor6 Factor7 Factor8 Factor9 Factor10 P2 0.909 0.037 0.029-0.076-0.034-0.043 0.110 0.021-0.010-0.003 P1 0.873-0.016-0.04-0.024 0.106 0.074-0.196-0.008-0.006-0.013 P4 0.00 0.862-0.020 0.107-0.140 0.07 0.039 0.093-0.00 0.167 P -0.114 0.76 0.24 0.008 0.449 0.140 0.043-0.04 0.070-0.201 P3 0.12 0.72 0.192-0.180 0.163-0.111 0.337 0.128 0.277-0.162 P14-0.021 0.069 0.87 0.072-0.004-0.024 0.112-0.04-0.023 0.110 P16 0.009 0.104 0.627 0.144-0.036 0.177 0.031 0.304 0.244 0.08 P9 0.001 0.079-0.016 0.842 0.09 0.02-0.047-0.011 0.0 0.023 P13-0.130-0.034 0.246 0.687 0.042-0.04 0.189 0.033 0.176 0.016 P7 0.060-0.041-0.08 0.096 0.867-0.079 0.102 0.063 0.098 0.132 P6 0.131 0.406 0.092 0.22 0.46 0.34-0.214-0.077-0.197-0.042 P11 0.014 0.0 0.061-0.008-0.023 0.93 0.106 0.026 0.072 0.037 P10-0.073 0.109 0.119 0.104 0.09 0.109 0.890-0.027-0.066 0.047 P1 0.011 0.086 0.081 0.002 0.038 0.010-0.018 0.960-0.01-0.014 P12-0.017 0.074 0.101 0.204 0.068 0.066-0.060-0.01 0.898 0.061 P8-0.016 0.022 0.161 0.030 0.087 0.036 0.037-0.009 0.08 0.939 Table 3. Clusters with mean factor values. Cluster Factor 1 Factor 2 Factor 3 Factor 4 Factor Factor 6 Factor 7 Factor 8 Factor 9 Factor 10 GC -0.324-0.683-0.479-0.241-0.303-0.73 0.388 0.07-0.142 0.129 GOTG 0.69 0.612 0.040 0.161 0.01 0.108 0.640 0.062 0.103 0.130 GT 0.308 0.067-0.42 0.47-0.328 0.319-1.028-0.066-0.138-0.223 GS -0.616-0.020 0.918-0.374 0.0 0.227-0.244-0.099 0.171-0.094 The group membership is used as the dependent variable in the multinomial logistic analysis, and ISSN: 1790-109 Page 26 ISBN: 978-960-6766-41-1

company characteristics are used as independent variables. Independent variables include those which have potential effects on the grouping of companies. Unlike a binary logistic model, in which a dependent variable has only a binary choice, the dependent variable in a multinomial logistic regression model can have more than two choices that are coded categorically, and one of the categories is taken as the reference category. We consider every case in changing reference category. While interpreting the results, it should be noted that coefficients for the reverse pairings of groups are identical to those given, but with reverse signs. For example, the coefficient of NEMP for GC relative to GOTC is reported in Table 6 as -0.0116, while the coefficient of NEMP for GOTC relative to GC is 0.0116. Table 6. Multinomial logistic regression models: relative to GOTC. Independent GOTC variable GC P-value GT P-value GS P-value NEMP -0.0116 0.0007-0.0192 <0.0001-0.00079 0.47 AGE -0.06 0.0043-0.1330 <0.0001-0.016 0.0004 EAD -1.219 <0.0001-0.4486 0.000-0.2433 0.0828 EXPO -0.0669 <0.0001-0.01 <0.0001-0.0790 <0.0001 ATR 0.4164 0.0008-0.0294 0.803 0.3979 <0.0001 NTC -0.0660 0.479 0.1666 0.0016 0.0294 0.601 NIP 0.00379 0.4171-0.0281 0.0027-0.0231 0.0002 TDL -1.664 <0.0001-1.031 0.0024-1.3663 <0.0001 POM 0.00866 0.0009 0.02292 0.170 0.00232 0.2036 POP -0.00097 0.6681-0.0020 0.1830-0.00494 0.0029 SIR 0.00171 0.3266 0.00386 0.0081 0.00403 0.006 ECIR -0.0022 0.232 0.0017 0.2127-0.0061 <0.0001 Table 7. Multinomial logistic regression models: remaining coefficients. Independent Vs. GC Vs. GT variable GT P-value GS P-value GS P-value NEMP -0.00689 0.103 0.00913 <0.0001 0.012 <0.0001 AGE -0.0622 0.018 0.021 0.248 0.0781 0.0029 SLD 0.764 0.0119 0.4267 0.0679-0.3347 0.2010 EAD 0.741 0.0001 0.9672 <0.0001 0.2390 0.2234 EXPO 0.0176 0.1470-0.0168 0.0604-0.027 0.0170 ATR -0.421 0.0023-0.0472 0.617 0.3976 0.0013 ECTR -0.0744 0.0042-0.0291 0.1826 0.0677 0.0084 NTC 0.212 0.000 0.1074 0.0999-0.1367 0.082 NIP -0.0318 0.0146-0.022 0.0010 0.0083 0.610 POM -0.0020 <0.0001-0.00631 0.001-0.0013 0.962 SIR 0.00191 0.0066 0.00198 0.0028 0.000133 0.6926 ECIR 0.0038 0.0109-0.00216 0.0142-0.0074 <0.0001 Both Table 6 and Table 7 show the results of the multinomial logistic regression model. Reference category in Table 6 is GOTC and those in Table 7 are GC and GT. In interpreting the result, we will consider attributes that are remarkably different, comparing one group to all alternative groups. GOTC group companies are generally at a high position consisting of older companies with a lot of CEO experience, technical development laboratories, abundant employees, and receiving external audits, but are weak in financial skill sets such as their assets turnover ratio. GC group companies are good at financial skill sets, such as assets turnover ratio and equity capital increasing rate, and have a good capability for production, but the size of the companies are small and their increasing sales rate is lower than the other groups. GT group companies have significant technical cooperation ability and high sales increase rate, but have a lower capability for production. GS group companies have greater financial skills than other groups and high sales increase rates, but possess less experienced CEOs, capability for ISSN: 1790-109 Page 266 ISBN: 978-960-6766-41-1

production, and number of industry properties. Table 8 shows the summary results of the multinomial logistic regression model. This table summarizes the weak and strong points of each group. Table 8. Summary results of multinomial logistic regression. Weakness Strength good financial skills and GC sales earnings ratio capability for production technical cooperation and GT capability of production sales increase rate experience of owner, capability for production financial skills and sales GS and number of industry increase rate properties CEO experience, technical development laboratories, GOTC financial skills abundant employees, and receiving external audits Based on our data analysis, we suggest types of joint ventures for the groups. Marxt and Link (2001) examined a successful type of venture when innovative ventures cooperate with a production system. This case is similar to combining GC with GT group companies. GC group companies have superior financial skills and capability for production while GT group companies equipped with highquality technologies need a production system or a developed production system. Successful factors affecting this kind of joint venture are structure, culture, and risk. The structure of cooperation is determined by the hierarchy of organization and by the process-oriented aspect. There are many individual companies in both GC and GT groups. In the case of selecting a partner from a matching group, Marxt and Link (2001) suggest that the partners need to have similar sizes and similar organizational structures. They need to have past experience in joint ventures. The cooperative fit of a culture should be accomplished by considering the different basic premises of the organizational culture. Marxt and Link (2001) point out the importance of little cultural distance between the partners. Finally, the technological capabilities, the know-how, and resources should be analyzed from a risk point of view. The sharing of risk needs to be symmetric between partner companies. Based on this information, Marxt and Link (2001) suggest the use of a checklist that can analyze and ensure similarity between partner companies. In addition, we considered a joint venture between GS and GOTC groups. GOTC is generally at an upper position over GS in the marketplace. Aloysius (2002) studied asymmetries with respect to the ability to fund, market power, and technological capital, and concluded that while two or more companies are asymmetric, joint ventures are not successful. Yo (2006), on the other hand, described factors that improve the outcome of a joint venture when the partners are in a complementary cooperative relationship. While GOTC is generally at an upper position over GS, it has lesser financial skills compared to GS. Thus, the two groups are in a complementary cooperative relationship. Yo (2006) explains that participating companies in a complementary joint venture need to ensure they are willing to invest the proper resources, such as time, energy, and management, for a joint venture to succeed. This implies that two or more companies can form a strategy for a successful joint venture if ISSN: 1790-109 Page 267 ISBN: 978-960-6766-41-1

they, even in an asymmetric situation, have a complementary cooperative relationship and have the willingness to invest necessary resources. As above, a joint venture strategy can increase the competitiveness of a company that complements its weaknesses with the strengths of another company. 3. Conclusion The aim of this study was to propose the formulation of joint ventures to increase a marketplace competitiveness. First, we briefly summarized the nature of a joint venture via a literature review. Next, we used data mining approaches to understand the characteristics of successful venture companies. The results of the factor analysis and subsequent cluster analysis allowed for the identification of four distinct groups of companies. Moreover, the use of multinomial logistic regression analysis revealed the strengths and the weaknesses of these four groups of companies. For these four groups, we proposed two different joint venture strategies: (1) cooperative ventures in an innovation and production system and (2) joint ventures in an asymmetric situation. These are focused toward group level strategies, and strategies are needed at the individual company level as well. It is important to identify the basic characteristics of venture companies for joint ventures as the two companies must understand their own strengths and weaknesses. This study will contribute to the planning of strategies to increase the competitiveness of joint ventures in order to resolve the problems faced by individual venture businesses. Sponsored research joint ventures in government have a positive impact on participating firms performance (Benfratello and Sembenelli, 2002). Government, therefore, must accomplish policy to support joint venture for research in order to increase technology competiveness of company. We focused our study on finding matching venture groups. In the future, further strategies for combining individual level companies will be required. Although we suggest a check-list approach to venture risk management, more research is required to determine the best possible venture analysis method. References [1] Aloysius, J. A., Research joint ventures: A cooperative game for competitors. European Journal of Operational Research, Vol.136, 2002, pp.91-602 [2] Benfratello, L., and Sembenelli, A., Research joint ventures and firm level performance, Research Policy, Vol.31, 2002, pp.493 07 [3]Woo, C.Y., Cooper, A.C., and Dunkelberg, W.C., The development and interpretation of entrepreneurial typologies. Journal of Business Venturing, Vol.6, 1991, pp.93 114. [4]Marxt, C., and Link, P., Success factors for cooperative ventures in innovation and production systems. Int. J. Production Economics, Vol.77, 2002, pp. 219 229 []Yo, K. C., Partnership and Business Performance of International Joint Venture. Korean Journal of International Commerce and Information, Vol.8, 2006, pp.1-14 ISSN: 1790-109 Page 268 ISBN: 978-960-6766-41-1