Research on Incubation Performance of China Information Technology Business Incubators with DEA

Journal of Modeling and Optimization 7:1 (2015) Research on Incubation Performance of China Information Technology Business Incubators with DEA Jiangping Wan 1, Guangwei Pan 2,Lianyu Liang 3 1. School of Business Administration, South China University of Technology, Guangdong, Guangzhou, 510640 2. School of Business Administration, South China University of Technology, Guangdong, Guangzhou, 510640 3.School of Business Administration, South China University of Technology, Guangdong, Guangzhou, 510640 E-mail:csjpwan@scut.edu.cn, guangwei_pan@foxmail.com,07245011@bjtu.edu.cn Abstract: Firstly, this paper evaluates the incubation performance of 50 national IT business incubators using SBM model of data envelopment. Then, 196 integrated business incubators are selected to make a comparison with IT business incubators. The conclusion is that the overall incubation performance of IT business incubators is low, which is much lower than that of the integrated business incubators. The main reason is the use and configuration of IT business incubators investment resources is inefficient. So, the IT business incubators need to use fund and technology platform better. Keywords: information technology; incubation; data envelopment ; SBM model; performance 1. Introduction With the advent of the Internet age, information technology has become an important driving force of the social development and economic growth of all countries. China regards information technology as one of the seven strategic emerging technology industries, but when compared with developed countries, there is a certain gap in information technology level. In the "2013 Global Information Technology Report (GITR)" released by the World Economic Forum, China's information technology level listed only 58 in 144 countries and regions [1]. In recent years, in order to promote the development of China's IT industry, China built a large number of information technology business incubators. However, because of China's information technology business incubator started late, at present both in theoretical research or practice level is obviously insufficient. Compared with the technology incubator in other fields, information technology incubator is normally in the creative development stage, whose hatching success rate is low and investment and marketing costs are getting higher and higher. It is necessary to explore the information technology incubator business and incubator performance. 2. Theoretical foundation and research status 2.1. Performance of business incubators Most foreign researches on the performance of business incubators adopt qualitative and case study. Roberts et al. evaluated the performance of business incubators in the US state of Idaho with case study, which argued that the development path of business incubators in different regions is different. Evaluating the performance of business incubators in the same region can promote regional incubator competition [2]. Chan and Lau proposed a framework of evaluation of technology business incubator with the qualitative, including resource centralization, resource sharing, public image, management consulting, public image, network platform, agglomeration effect, cost advantage, and financing support as index. They also studied 6 start-ups of Hong Kong Science Park to verify its effectiveness [3]. Study abroad generally use a large number of survey data or system research methods. Sung et al. studied 121 incubating and graduated enterprises of 7 business incubators in the South Korean city of Daejeon. They built "linear model" and "non-linear model" to evaluate business incubators with of the data collected in the survey [4]. Colombo and Delmastro studied the performance difference of 45 incubating enterprises and 45 non-incubating enterprises in Italy to measure the actual performance of the incubators. The study is illustrated that the input-output ratio of both types of enterprises is very close [5]. 33

7:1 (2015) Journal of Modeling and Optimization Researchers of Chinese scholars on the evaluation of the performance of incubator mostly concentrate on the of index and the evaluation method. Different scholars use different methods to establish the different evaluation index system and evaluation model, which can be divided into the subjective weighting evaluation and the objective weighting evaluation. (1) The subjective weighting evaluation includes the Delphi, analytic hierarchy process and fuzzy comprehensive evaluation etc., which the weights of indexes are obtained by the subjective judgment such as expert scoring. Li Daisong el. built the performance evaluation index system including 5 primary indexes and 24 secondary indexes. They evaluated the performance with Delphi technique and analytic hierarchy process to determine weight coefficients and used accelerations composition [6]. Huang Jiang and Wang Fei evaluated "SIFT Student Entrepreneurship Center" with analytic hierarchy process and the fuzzy comprehensive evaluation model which can deal with multi index and index with fuzziness evaluation [7]. (2) The objective weighting evaluation includes principal component, data envelopment (DEA), grey theory evaluation, the variation coefficient etc., whose determination of the weight is derived from the actual data, according to the relationship of index or the coefficient of variation. Yin Qun el. carried on the to the 45 national enterprises incubators in the Yangtze River Delta region of China with DEA, and put forward the suggestion of adjusting output for 33 DEA invalid incubators and carried out supper-efficient of DEA valid incubators [8]. Wang Jing estimated the technical of 140 incubators in China with DEA. He summarized the characteristics of four types of incubator and suggestions for improvement cluster to classify the measurement results [9]. Zhang Jiao el. calculated the technical, pure technical and scale of 180 national business incubators with the input oriented BCC model of DEA, and according to the results, the incubators are divided into four types using cluster [10]. In our understanding, different types of incubator has its particularity, it is short of the study of specific types of incubators. As one important part of business incubators, the information technology incubator has not caused enough attention of the scholars. Anymore, it is short of exploring the reasons for the result of evaluation performance of incubator. 3. Research design This paper quantitatively evaluates the performance status of 50 information technology business incubators with DEA. Then, 196 integrated business incubators are selected to make a comparison with IT business incubators. In view of the incubator performance gaps between individuals and comprehensive incubator, the improving suggestions are putted forward to promote the development of information technology business incubator in China (Figure 1). Evaluation of the performance of information and technology incubator Quantitative with DEA Information and technology incubator Pure technical Contrast Scale Integrated business incubator Projection Shadow price Suggestions to promote the development of information technology business incubator Figure 1. Research framework 34

Journal of Modeling and Optimization 7:1 (2015) 4. Analysis of the performance of information technology incubators 4.1 Evaluation index system and data source Considering the characteristics of the information technology enterprise incubator and referring Cao Xiyu (2001) [11], Li Daisong (2006) [6], Zhang Peng (2010) [12], the number of professional service personnel including professional technical personnel of incubator and mentors, total area of the site of incubator, total incubation fund and total investment of public technology service platform are as input indicators. Number of graduated enterprises, average income of graduated enterprises, number of the incubated business, number of approval of the intellectual property rights and total income of incubators are as output indicators (Table 1). Table 1. Input and output indicators of national information technology business incubators Indicators classification Evaluation indicators Manpower X1: number of professional service personnel Input indicators Material X2:total area of the site of incubator Out indicators Financial resources X3:total incubation fund X4:total investment of public technology service platform Y1:graduated enterprises Y2:average income of graduated enterprises Y3:number of the incubated business Y4:number of approval of the intellectual property rights Y5:total income of incubators The data source is from 2013 Chinese torch statistical yearbook which records 436 national business incubators. From the yearbook, we screen out 62 information technology business incubators, then get rid of 12 incubators whose index data are missing, finally, 50 study samples are obtained. 4.2 SBM model calculation The data are calculated with MaxDEA software, based on output-oriented SBM model with variable scale benefit (SBM-O-V), the results are illustrated in Table 2 and Figure 2. Table 2. Statistical characteristic of the performance of national information technology business incubators with DEA Min Max Mean Standard Number of enterprises value with DEA 0.0032 1 0.5848 0.4164 23 Pure technical 0.0036 1 0.7053 0.3904 30 Scale effect 0.0371 1 0.8299 0.2785 23 Figure 2. Number of information technology business incubators with different 35

7:1 (2015) Journal of Modeling and Optimization According to the principle of DEA, technical is the product of pure technical and scale, so the reason of technical in is pure technical in and scale in. For further of the relationship between technical and pure technical and scale of information technology business incubators, a correlation on the technical, pure technical and scale of 50 information technology business incubator is done with SPSS software (Table 3). Table 3. Correlation on the technical, pure technical and scale Technic Pure Scale al technical Pure technical Scale Pearson Correlation 1.778**.596** Sig. (2-tailed).000.000 Pearson Correlation N 50 50 50.778** 1 -.006 Sig. (2-tailed).000.968 Pearson Correlation N 50 50 50.596** -.006 1 Sig. (2-tailed).000.968 N 50 50 50 **Correlation is significant at the 0.01 level (2-tailed). For the information technology business incubators with technical in, projection can reach relatively effective state through adjusting resources or the number of output results. Through the amount needing to adjust dividing by the amount of original, the adjusted proportion of every element is illustrated in Table 4. Table 4. Result of projection (the adjusted proportion of inputs and outputs) () Indicators Incubator X 1 X 2 X 3 X 4 Y 1 Y 2 Y 3 Y 4 Y 5 I 1 38.03 0 0 0 0 252.90 0 49.71 0 I 2 40.45 0 0 0 0 91.32 0 16.99 0 I 3 19.29 0 9.95 45.91 0 0 0 0 317.58 I 5 0.79 0 93.09 73.94 0 28.84 0 0 1163.21 I 8 7.78 0 0 87.82 50.50 0 50.50 95.57 139900.13 I 9 0 0 0 73.14 0 0 0 34.35 3023.27 I 14 14.77 0 85.31 0 56.00 4.88 56.00 0 3154.70 I 15 0 14.81 0 0 0 0 2.90 11.07 1738.21 I 16 0 33.81 37.98 41.17 36.50 41.76 36.50 0 8930.69 I 18 0 13.70 81.61 35.90 46.40 42.93 46.40 0 794.10 I 20 0 0 0 0 339.50 31.50 378.00 15.83 745.16 I 22 34.81 8.57 0 25.33 0 0 0 2.54 1784.48 I 26 41.96 0 89.08 0 0 0 0 30.30 1330.44 I 27 3.79 0 71.59 0 159.00 0 159.00 0 4437.15 I 30 43.00 0 61.68 75.44 1780 56.43 2151.00 52.37 0 I 33 0 4.57 52.18 0 22.25 437.04 27.25 53.00 5342.95 I 38 52.19 45.72 0 79.20 107.25 37.75 131.75 0 1683.54 I 43 0 46.16 3.26 0 0 0 36.60 9.68 6197.36 I 47 0 0 0 54.21 0 12.75 0.25 4.88 354.55 I 49 0 12.17 49.02 81.57 0 0 3.00 3.48 2992.03 36

Journal of Modeling and Optimization 7:1 (2015) 4.3 Analysis of performance evaluation (1) It is illustrated in Table 2, the value of technical of 23 incubators is 1, which accounted for 46 of all samples and amounted to less than half within the 50 selected incubators. The average value of technical is 0.5848, it means that 41.52 of resources invested to the information technology incubators are wasted or make no contribution to output, and the incubation performance is low. By comparing the city of the incubators, the technical in incubators mostly are located in the more developed economy and the information technology enterprise incubator gathering area such as Beijing, Shanghai and Xi an etc. The low performance of information technology business incubators in China is universal. (2) It is illustrated in Figure 2, 20 technical in incubators are very low whose technical are lower than 0.3, and the technical standard deviations are larger. All those show information technology incubators in China have a serious polarization, and individual difference of incubation performance is very big. It should be much improved for the performance of information technology incubators in China. (3) The correlation coefficients of technical and pure technical (r=0.778) is greater than the correlation coefficients of technical and scale (r=0.596), it illustrates that the relevance of the technical and pure technical of information technology incubators in China is higher. In other words, the pure technical is the main factor influencing the technical of information technology incubators in China. First of all, incubators should improve the professional in operation, investment etc. then, increase of resource utilization and optimize the allocation of resources in order to enhance the performance of incubators. (4) It is illustrated in Table 4, 1 For all indicators, total income of incubators (Y5) is the indicators that most incubators need adjusting and the adjustment proportion is the largest, it illustrates that the income of information technology business incubator is too low. One reason is that the incubator service system does not conform to the incubated enterprises requirements and the service income is low, another reason is that most incubators are institutions which funded by the government and lack of enterprise and market experiences. So, first of all, the information technology business incubators should vigorously expand the business scope to increase revenue and improve management skills and self incubating ability. 2 For input indicators, total incubation fund (X3) and total investment of public technology service platform (X4) need to adjust significantly, which reflects that the funds invested to information technology business incubator have not been fully utilized. In our understanding, the one hand the incubators own operating is not high and assets are not fully activated, and the information technology business incubators need professional technical service platform. More funds need to be invested in early days. But most of the information technology business incubators are established late, and incubation fund and return on investment of the platform are not yet fully reflected. Therefore, the information technology business incubator funds need to be used rationally. The function of public technical service platform should be fully developed to improve the hatching performance. 4.4 Comparison between the performance of information technology incubators and integrated business incubators The 248 national integrated business incubators from 2013 Chinese torch statistical yearbook including hi-tech innovation service centers, university science and technology parks and the overseas students pioneer parks etc. are selected, then gets rid of 52 incubators whose index data are missing, finally, 196 study samples are obtained. Adding 50 information technology incubators, there are 246 business incubators in a total in order to further analyze the differences of the hatching performance between information technology business incubator as the representative of professional technology business incubator and integrated business incubator. The output-oriented SBM model with variable scale benefit (SBM-O-V) is applied to analyze the data. (1) The value of technical, pure technical and scale of the two kinds of incubators are illustrated in Table 5. As a whole, the technical of two kinds of incubator is low. Information technology business incubator technical and pure technical are lower than the comprehensive enterprise incubators. There is little difference between the scale. So the gap of two kinds of incubators performance mainly lies in the difference of pure technical. Table 5. Comparison between two kinds of incubators performance Pure technical Scale Integrated business incubators 0.3459 0.4252 0.8530 Information technology incubators 0.1154 0.2150 0.8001 37

7:1 (2015) Journal of Modeling and Optimization (2) The projection of two classes of the incubator is illustrated in Table 6. As can be seen from the table, compared to integrated business incubator, information technology incubators number of the incubated business (Y3) and total income of incubators (Y5) are obviously inadequate, which need to be improved. The adjustment proportion of information technology incubators graduated enterprises (Y1) and average income of graduated enterprises (Y2) is smaller than integrated business incubators, it is illustrated that information technology business incubators have more advantages on the aspect of the use of human resources and incubation site. Integrated business incubators Information technology incubators Table 6. The adjusted proportion of inputs and outputs of two kinds of incubators () X1 X2 X3 X4 Y1 Y2 Y3 Y4 Y5 18.72 16.55 57.76 52.98 46.82 50.18 180.52 9.74 9.14 43.08 63.98 26.58 11.02 1583.38 17.10 34.75 247.40 3441.51 5. Conclusions The overall incubation performance of IT business incubators in China is low. The main reason is that the use and configuration of IT business incubators investment resources are inefficient. The information technology business incubator need condense professional coach team, strengthen the use of funds and technology platform, and expand the business scope to increase revenue, foster more startups and improve enterprise innovation ability. For various reasons, the performance of information technology business is evaluated with statistical data in 2013. Further research may consider take longer range data to taking time series in order to thoroughly understand the change trend of China s information technology business incubator performance. References [1] World Economic Forum. The Global Information Technology Report 2013. [EB/OL][R]. [2014-4-2]. http://www.weforum.org/reports/global-information-technology-report-2013. [2] Roberts B., Reamer A. and Padden J. Iowa funded business incubators: an assessment of contributions for enterprise development [J]. Cedar Rapids (IA):Iowa Department of Economic Development,1990:20-26. [3] CHAN K F,LAU THERESA. Assessing technology incubator programs in the science park: The good, the bad and the ugly[j]. Technovation,2005,25(10):1215-1228. [4] TK Sung, DV Gibson, BS Kang.2003.Characteristics of Technology Transfer in Business Ventures: the Case of Daejeon,Korea[J]. Technological Forecastingand Social Change.2003, 70(5):449~466. [5]Colombo, M.G., Delmastro, M. How effective is technology incubators?:evidence from Italy[J]. Research Policy, 2002, 31(7):1103-1122. [6] Li Daison, Zhang Ge, Li Zhaohui. Study on Performance Evaluation of Enterprise Incubator[J]. Journal of Beijing University of Technology.2008,8(2):23-27. [7] Huang Jian, Wang Fei. Application of Fuzzy Mathematics in Incubatory Ability Appraisal of University Business Incubator[J]. Journal of Anhui Agricultural University(Social Science Edition).2007,(5):41-45. [8] Yin Qun, Zhang Jiao. Study on the performance of enterprise incubator in the region of Yangtze River delta[j]. studies in science of Science. 2010,(1):86-94. [9] Wang Jing. Evaluation on the Efficiency of the Business incubators[d]. Dalian university of technology,2012. [10] Zhang Jiao, Yin Qun. Differences Study on the Operational Efficiency of Business Incubators in China- Based on Data Envelopment Analysis and Cluster Analysis[J]. Science of Science and Management, 2010(5):171-177. [11] Cao Xiyu. Research on Business Incubator Hatching Capacity Evaluation [J]. Science & Technology Progress and Policy.2001(6):13-1. [12] Zhang Peng, Zhan Haojian. Performance Evaluation of Technology Business Incubator Based on DEA Model: A Case Study of Guangdong[J]. Science and Technology Management Research. 2014(14):78-81. 38