Research. Outsourcing. Lu Sun. Yanxin Xingang Wang. Roger Su. Abstract. of Training. the new analysiss

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1 ISSN 9-899X 0, Vol., No. The Key Elementss on Selection of Training Outsourcing Lu Sun Harbin University of Commerce,China Yanxin Zhang Harbin University of Commerce,China Xingang Wang Zhejiang Wanli University, Chinaa 6.com Roger Su Auckland University of Technology, Neww Zealand aut.ac.nz Received: June 7, 0 doi:0.96/jmr.vi.977 Accepted: July, 0 Published: October, 0 URL: : Abstract In this paper, we develop a new index system for the selectionn of g outsourcing. With the new system, we find key elements off the selection of outsourcing through factor analysiss are helpful to improve the accuracy of decisions of the contracting business, which make contracting businesses select s the vendor companies to meet their requirements. Keywords: Training outsourcing, Human resource management, Keyy element,, Factor analysiss 8

2 ISSN 9-899X 0, Vol., No.. Introduction In st century, the employee value hass become significant to businesss asset contracture. Therefore, many firms pay a lot to hire a high value staff andd organize course for existing staff to improve theirr knowledgee and skill. Staff is the main form off human resource development and is also onee of the ways to enhance the competitiveness of enterprises, however, simply relying on their own internal resources to train staff has been unable to meet the development needs of corporate human resources. Therefore, many firms prefer to use external organizations for personnel, whichh would reduce cost and improve management efficiency. Such, it is important for f a business to concern how they can select or choose an appropriate organization. Outsourcing was proposed by Gray Hamel and C. K. Prahaladd in 990, which refers to that enterprises outsourced some importantt but non-core business functions to professional outsourcing suppliers in order to improve the quality of services and products, short the production cycle and reduce costs, enterprises focus on those business that can create more value to maximize the potential efficiency and enhance the core competitiveness of companies (Lu, 009). Outsourcing mainly includes production outsourcing, marketing outsourcing, R & D outsourcing and human resource management outsourcing and so on. Training outsourcing as an important form means that enterprise entrust some or all of the business to the professional outsourcing provider by the way of bidding and the contract, monitor the entire process of activities andd evaluate the effect in order to achieve the targets ( Xu and Mei,007). Training outsourcing generally relates to the plan, the design of courses, time, the choice c of methods as well as a lecturer, the assessment and feedbackk of effectiveness andd so on. Training outsourcing can be a more specializedd division of labor, corporate c outsourced business to suppliers, which is i very helpful to simply the complexity of management and to improve the core competitiveness of enterprises allowing companies to focus on business advantages and to reduce the cost (Lin, Zhang and Wang, 008) ). From previous literature, exception of risk and motivation of outsource, o we do not find f any studies on key elements of supplier selection of outsourcing. In this paper, we will study what kinds of elements which may affect business to select a outsourcing, which will provide a theoretical knowledge to firms whenn they looking for a outsourcing provider. Method of Factor Analyses Factor analysis originated in the early 00 th century and was proposed by K. Pearson and C. Spearman to define and measure intelligence by the statistical analysis a (Lin and Qian, 00). According to the internal dependencies of the correlation matrix, factor analysis integrated some variables with intricate relationshipp to few factors. Factor analysis is statistical method to determine the essence and classificatio on of certain variables by b use of different factors. In a word, variables are grouped on the basis of the correlation, so variables within the same group have higher correlation and less correlation within different groups. Each variable represents a basic structure, this basic structure is called factor (Hou, 00). Therefore, factor 8

3 ISSN 9-899X 0, Vol., No. analysiss can be used to identify a few potential factors, whichh hidden inn a large number of observed variables. Generally, we can follows: use variables for factor analysis, its mathematical model is expressed as We assume several variables, X i (i=,,, p) a i F + + a im F m +ε (m p) ε i p, the expression as follows: X i =a i F + The second type of expression as follow: X X X p a a a m = a a a m a p a p a pm F ε F + ε F m ε p X=AF+ +ε The common factor is unobservable variable such as F,F, F m. Their coefficients (a ij ) are called factor loading, ε i is special factor and can not be included by the former several common factors. And, cov(f,ε)=0,f,ε i represents there is no correlation between variables. Two core issues of factor analysis are how to construct the factor variable and to t name explanation, there are four basic steps for factor analysis: ()determine whether the original numbers of variables is appropriate or not; ()construct factor of variables; ()make factor variables have interpretability by using rotation; ()calculate the score of factor variables (Sun, 008).. Literature Review Wang and Wang (007) point out that thee main factors about selecting providers included the reputation, financial condition, g experience, relevant documents, recruitment and, shared values, the relevant data, time and the commitments. Ren and Cun (008) use AHP on quantitative research on the selection of contractor andd find some factors such as cultural cohesion, motivating force and compatible degrees, staff, customers, contract management, brand, reputation, the recognition of industry, curriculumm design, effects are important for outsource provider selection. Tang, Sun and Gao (008) establish an index system of selection evaluation of outsourcing vendor including industry status, reputation, the richness degree of relevant experience, the proportion of qualified trainers, the completeness of materials, the level of o cross-cultural management, corporate values match, the communicat tion and coordination capacity off the team, the price level of and post- follow-up service. Qing Q and Deng (009)) studied decision-making selection of outsourcing provider byy using AHP, and they believe 8

4 ISSN 9-899X 0, Vol., No. main factors whichh will affect the decision of selecting outsourcing are reputation, price, financial condition, professional quality and cultural degree of coupling. Considering the enterprises actual needs of the outsourcing, suggestions from human resources experts and corporate executives, we in this paper develop a neww evaluation system which includes ten indicators to judge whether an enterprise select a proper external resourcing organization: the quality of service, cultural degree of coupling, culturall cohesion and driving forces, skills, the recognition off industry, experience, the position in the industry, the relevant data, reputation and brand. The new evaluation system emphasize more on characteristics of servicee and the cooperation between the contracting companies, which can more accurately reflect the focus of the outsourcers.. The Analysis of Key Elements of Training Outsourcing According to the above ten selection indicators of outsourcing, we build the Table below following the Likert scale, the scale use five points to score, where one, two, three, four, five respectively represents this indicator is not very important, unimportant, important, more important and most important, the greater score means that outsourcers are more satisfied with the services of g outsourcing supplier. Expert Review Committee compose of human resource managemen nt experts, senior executives scoredd this criterion, and this scale is sent to twenty people to answer, the effective data after a the recovery and analysis shown in Table : Table. The Selection Criteria of Training Outsourcing and Likert Scale Indicator code Supplier selection of criteria of outsourcing(name of index) Not very important unimportant important More important most important X The quality of services X cultural degree of coupling X Cultural cohesion and driving forces X Training skills and experience X Awareness of the industry X6 Teacher resource X7 The position in the industry X8 The relevant data X9 credit X0 brand 8

5 ISSN 9-899X 0, Vol., No. Table. The Original Data index number X X X X X X6 X7 X X8 X9 X The target of factor analysis extracted few representative factors from thee original indicator i variables, so it is necessary to test the correlation between the indicator variables before the use of factor analysis, if the correlation is not strong, indicator variabless is not suitable for factor analysis (Xu, and Li, 009). The correlation between variables wass verified by KMO and Bartlett s test. The value of the approximate chi-square is 6.77 (Table ), the degree of freedom is, the value off probabilityy is 0.000, the correlation matrix is not a unitt matrix, so those variables can be analyzed by factor analysis. KMO is an index too use compared the value of observation indicators with thee value of partial correlation coefficient, its value is closer to one, indicating that these variables are more appropriate to use factor analysis (Gong and Zhou, 00). The value of KMO is 0.70, which means the results off factor analysis are acceptable. 8

6 ISSN 9-899X 0, Vol., No. Table. KMO and Bartlett s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett s Test of Sphericity Approx Chi-Squaree df Sig When the main factors were extracted too substitute the information in thee original indicator variables, we obtain the cumulative variance and characteristic roots of each component (Table ). There are three characteristic roots of factors are more m than one and respectively called F, F and F. The explained variance of the fist principal componentt or the contribution rate is 6.8%, the t one of the second principal component c or the contribution rate is.8%, the one of the second principal component or the contribution rate is.%, moreover, the cumulative variance contribution rate of these three principal component is 89.% %, which shown F, F and F have covered 90% of content c of the raw dataa with a strong representation. Table. Total Variance Explained Compone nt Tota l Initial Eigenvalues % of Cumulative Varianc % e Extraction Sums of Squaredd Loadings Tota % of Cumulative l Varianc % e Rotation Sums of Squared Loadings Tota % of Cumulative l Varianc % e X X X X X X6 X7 X8 X9 X Extraction Method: Principal Componentt Analysis To calculate the factor loading matrix by principal component analysis, a these data can specify 86

7 ISSN 9-899X 0, Vol., No. the load of each factor on each variable, which is the extent (Table ). Table. Component Matrix Component X6.98 X.9.6 X X X X X X X X.8.77 Extraction Method: Principal Componentt Analysis. a. components extracted. However, the initial factor loading matrixx was carried on orthogonal rotation in order to make coefficient differentiate from zero to one, the rotated factor loading matrixx is shown in Table 6 below. Table 6. Rotated Component Matrix The evaluation index of outsourcing provider Component The quality of service(x) Relevant data(x8) The recognition of industry(x) Teacher source(x6) Training skills and experience(x) Cultural degree of coupling(x) credit(x9) brand(x0) The position of industry(x7) Cultural cohesion and driving forces( X) Extraction Method: Principal Componentt Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations 87

8 ISSN 9-899X 0, Vol., No. With factor extraction and orthogonal rotation, we obtain three t factors and the loading coefficient of ten indicators iss above 0.. The resultss of Rotatedd Component Matrix can make the meaning about factor variables clearer. F reflects the quality of services, the relevant data, the recognition of industry, faculty strength and skills and experience, so this factor variable is named as professional level. F presents reputation, brand and the position in the industry, so this t factor variable is named as the overall image. F mainly stands for the cultural degree of coupling, cultural cohesion and driving forces,, so the variable may be named corporate culture.. Conclusions Specialization and the development of information technology promote more intensee market competition; outsourcing has become one of the inevitable choices for various types of enterprises. However, how to effectively select a proper outsourcing provider has becomee significant. With ourr analysis and studies, we find that the contracting companies mainly take three indicators to select their appropriate outsourcing providers, see Table 7. Table 7. Selection Index System of Training Outsourcing The first level indicators % of variance explained The second level indicators Load factor score mean Professional standards 6.8 The quality of service Relevant dataa The recognition of industry Teacher resource Trainingg skills and experience The overall image.76 credit Brand The position in the industry Corporate culture.66 Cultural degree of coupling Culturall cohesion and driving forces 0.9 In the first column, the first level indicators are classified andd named byy the rotated factor loading matrix in Table 6. In the t second column, % of variance explained is the percentage of the explanatory power for ten indicator variables. In the third column, the second level indicators were obtained by previous comments of selection criteria. The load factor (column ) presents an index scale comparing thee second level indicators to the fist level indicators, and derived by factor analysis. 88

9 ISSN 9-899X 0, Vol., No. The finding of key elements of the selection of outsourcing through factor analysis are helpful to improve the accuracy of decisions of the contracting business, which make contracting businesses select the vendor companies to meet their requirements. As the same time, the choice of the key elements contribute to obtain appropriate indicators for enterprises in orderr to specify the relevant assessment criteria and refer to t the different weights of the various indicators, which may be moree operational selectionn and evaluation indicators of outsourcing provider selection. References Hou, L. (00). The research on performance evaluation of stock options for listed companies based on factor analysis. Reform & Opening, 0, -7 Guan, H. and Zhou, J. (00). Analysis of the competitiveness of regional financial f outsourcing based on factor analysis. International Economics and a Trade,, 9- Li, M. vendor and Huang, H. (009). An empirical research on indicators of selection. Reformation & Strategy, 07, - HRM outsourcing Lin, H., & Qian, Y. (00). The research on influencing factors of the coree competitiveness in service outsourcing bases based on factor analysis. Pioneering with Science & Technology Monthly, 09, 7-9 Lin, L., Zhang, P., & Wang, Z. (008). Outsourcing model selection of personnel for SMEs. Economic and Technological Cooperation, 0, 0- Lu, H. (009). Study on operating mode of outsourcing of Commercial Modernization,, 89 modern corporate. Qing, F., & Deng, Z. (009). The study on decision method of outsourcing suppliers s based on AHP. Modern Business Trade Industry, 8, 9-0 Ren, L., & Cun, X. (008). The application analysiss of AHP inn contractors for their choice. Journal of Yunnan Agricultural University (Social Science), 06, - Sun, X. (008). The study on competitiveness evaluation of service outsourcing. International Economics Trade, 07, 8- Tang, W., Sun, J., & Gao, P. (008). A fuzzy multi-attribut te decision-making 09, 9- Wang, Q., & Wang, Y. (007). The construction and use of o decision-making model for model for selection of outsourcing supplier. Technology Economics, corporate outsourcing. Training Corner, 0, 7-7 Xu, Y., & Li, W. (009). Refinement of the key risk factors in software outsourcing projects. Modern Business, 08, - Xu, Y., & Mei, J. (007). The research and decision model of outsourcing. Business Time,,