and Hung-Wen Chang 1 Department of Human Resource Development, Hsiuping University of Science and Technology, Taichung City 412, Taiwan 3

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1 A study using Genetic Algorithm and Support Vector Machine to find out how the attitude of training personnel affects the performance of the introduction of Taiwan TrainQuali System in an enterprise Tung-Shou Chen 1, Yih-Yeong Lin 2, Jeanne Chen 1, Chien-Che Huang 3, and Hung-Wen Chang 1 1 Department of Computer Science and Information Engineering, National Taichung Institute of Technology, Taichung City 404,Taiwan 2 Department of Human Resource Development, Hsiuping University of Science and Technology, Taichung City 412, Taiwan 3 Department of Industrial Education and Technology, National Changhua University of Education, Changhua City 500, Taiwan Abstract. After entering knowledge economy era, human resource has become one of the most important manufacturing elements of an enterprise, hence, human resource incubation has become one of the important jobs of enterprise development. However, most of the enterprises in Taiwan do not have clear assessment standard and analysis mechanism on educational training, which usually leads to bad implementation results. Therefore, in order to enhance enterprise s training-implementing ability, Taiwan has designed a set of Taiwan TrainQuali System (TTQS) using a training quality score card to perform the assessment and audit. It is important to understand whether the introducing TTQS mechanism to an enterprise can enhance business operation performance and enterprise s competitiveness, and it is also important to know also if the entire training-implementing attitude of the training-implementing personnel will affect the assessment and audit result of an enterprise. This study has investigated, through the attitude transition of training-implementing personnel who have participated in the learning curriculum, if the attitude transition will affect the current status of an enterprise and what kind of benefits it will bring. In this study, Genetic Algorithm is associated with Support Vector Machine technique to perform data mining and analysis. The experimental results show that within an enterprise, enterprise supervisor and training personnel of different standing points will have different thinking on the introduction of TTQS, and there is also significant difference on the educational training attitude too. For the difference of entire attitude change before and after the participation of TTQS curriculum, the data of the training personnel are better than that of the enterprise supervisor. Keywords: Data mining, human resources management, Genetic Algorithm, Support Vector Machine, Taiwan TrainQuali System (TTQS), Attitude

2 2 1 Introduction 1.1 Research background and motive In the severe global competitive environment, globalization and fast technological development has changed the survival environment of an enterprise, and the human resource quality within an enterprise is the key factor to maintain the competitiveness of an enterprise. The maintenance of long term competitiveness of an enterprise has to rely on the enhancement of human resource within an enterprise, hence, training becomes very important. However, when an enterprise is investing human resource of the employees, it should get devoted to the enhancement of the training quality more to enhance the training-implementing ability of an enterprise and to assist the employee to enhance their career competitiveness effectively. The educational training quality systems adopted internationally include: For the educational training quality management systems such as the human resource investment certification system of England (Investors In People, IIP) [8] and ISO10015 [9] can solve the quality issues of operation flows in human resource from the selection and investment, the resource involvement to the output and result [1]. In order to enhance the training-implementing ability of an enterprise, Taiwan has designed a set of Taiwan TrainQuali System (TTQS) [1] to assess and audit enterprises proposing training support projects each year, and appropriate financial support has been provided to enterprises that meet the support threshold [2]. From the students attending the TTQS promotional curriculum, student s attitude change before and after the learning is assessed, and the result will be used by the government and enterprise to enhance the quality of educational training. Due to different identities of students and different standing points of personnel in the enterprise, they will have very different attitudes on the implementation of TTQS. As a result, this study will investigate people of two different standing points to see if they have attitude change before and after the learning of TTQS promotional curriculum. 1.2 Research objective Currently, Taiwan is aggressively promoting TTQS. The objective for an enterprise to introduce TTQS is to assess and audit if the educational training of an enterprise has been associated with the goal of the enterprise and if the corporate vision has been associated with the training. According to the above statement, this research is going to take the enterprise supervisor and training personnel attending TTQS curriculum training class as the research targets, and the main objectives are: (1). Understand the current status of the introduction of TTQS into the enterprise. (2). Comparison of attitude between enterprise supervisor and training personnel before and after the implementation of TTQS. (3). Understand if TTQS curriculum is helpful to the enterprises.

3 3 1.3 Research flow The main objective of this research is to investigate the current status of the introduction of TTQS into an enterprise before the training of the enterprise supervisor and training personnel; meanwhile, the change of attitude for the enterprise supervisor and training personnel before and after the training is compared to analyze the attitude before and after training in each aspect, hence, the research flow is as shown in figure 1. In the flow, first, literature review is done on literature regarding TTQS, human resource, attitude, Genetic Algorithm and support vector machine, etc., meanwhile, after confirming the research objective, the method in the related literature is performed with analysis to find out advantages that can be learned and to design the survey questionnaire and to confirm the research method. Finally, experiment is carried out and conclusion is proposed. Research background, motive and objective Literature review Survey questionnaire design and data collection Perform experimental analysis Conclusions and suggestions Fig. 1. Research flow chart 1.4 Structure This research can be divided into five chapters. In addition to the introduction in the first chapter, the structure and content of the rest four chapters is described respectively as in the followings: chapter 2 is literature review, which includes the collection of research report, thesis, journal and book regarding TTQS, attitude, Genetic Algorithm and support vector machine, etc. Chapter 3 is research method, which includes the preparation of research structure and research target, survey questionnaire design and data collection and analysis method preparation, etc. Chapter 4 is analysis result, which includes the use of Genetic Algorithm to be associated with support vector machine to perform data analysis report. Chapter 5 is the conclusion and suggestion, which includes the general summarization of research conclusion and suggestion for this research to be used as reference or expanded research in the subsequent research.

4 4 Literature review 2.1 TTQS In this research, through the summarization of TTQS consultant data [4,14], it was found that after the introduction of TTQS for most of the enterprises in Taiwan, the common issues are: Insufficient support from supervisors of higher level, the lack of professional human resource department and personnel in the enterprise, there is a gap in the training ability of the sponsoring personnel and their concept in the training is not mature yet, the usual conflict caused in the job and training and low willingness for the employees to receive the training. The responsibility of the government is to help the enterprise to find out the trace from the existed operation so that the enterprise can be associated with TTQS system, hence, the incubation system of the enterprise can form an integrated and complete system, and the function of educational training can be enhanced to the level of enterprise strategy. In other words, let the business management personnel feel the help on the business operation from the educational training so that they will be willing to invest more resource to incubate the human resource, Only through long term implementation, can the quality be expanded and the enterprise can enter a mature period, finally, the human resource of an enterprise can be greatly enhanced, and the competitiveness of a country can be greatly reinforced [7]. 2.2 Attitude Attitude means the positive or negative evaluation on people, matters and things held by anyone. When I say: I love my job, it means that I have held a positive attitude towards my job, and according to the research performed by Robbins, we can find that attitude is composed of three components [15] as in the followings: (1). Cognitive component It is the belief of an individual on a certain target or event, and this belief is from the individual s thinking, knowledge, concept or learning. (2). Affective component It is the core part in the attitude, which means certain affective response from an individual as triggered by certain thing, in other words, it is the individual s love or hatred on certain thing. (3). Behavioral component It is the behavioral intention that is exposed outside for certain person, thing or object; according to the behavioral component, a different attitude will cause a different behavioral performance. Attitude contains, in addition to subjective affective factor, objective cognitive and behavioral factor [6]. There are two major reasons to study attitude : First, curiosity on people s method and content of thinking; second, the study of attitude can instruct the decision making, improve people s status, or solve the social problems.

5 5 2.3 Genetic Algorithm Genetic Algorithm was proposed by John Holland in 1975 [10], which originated from the theory of natural selection of theory of evolution as proposed by English scholar named Darwin, that is Survival of the fittest. Its theoretical design basis is mainly based on biological evolution mechanism, that is, the genetic evolution mechanism in the biological society is simulated and implemented through a scientific information method. Genetic Algorithm can be used to, after competitive evolution mechanism and after several generations of evolution, find out the optimal solution or better solution. In recent years, Genetic Algorithm is widely applied in solving the Combinatorial Optimization issue, in the utilization aspect, it is easier than the conventional optimization method. As long as the variables of the issue are converted into word strings, solution can then be found through Fitness Function. 2.4 Support vector machine Support Vector Machines (SVM)[5,17] is a machine learning system developed by statistical theory. SVM[13] has three advantages: First, SVM has better generalization capability, and good result can generally be obtained; second, the parameter and structure of SVM is to find the solution of second planning issue, which contains an unique and optimal solution; third, the training speed of SVM is very fast; meanwhile, in the classification algorithm, the advantages of SVM are that it is easier to be used, and it is commonly recognized as the one with best performance among the currently available classification methods. The SVM classification technique used in the article was LIBSVM [11] as developed by the research team led by professor Chih-Jen Lin of the department of information technology of National Taiwan University, which was applied in several Kernel Functions, for example, in Radial Basis Function (RBF), only two parameters need to be adjusted, and it will be relatively easier to be used as compared to other Kernel Functions. Moreover, it has better classification results compared to that of other Kernel Functions. Research Method 3.1 Research Hypothesis According to the above mentioned research problem, research objective and literature review result, this research has proposed the following research hypotheses for verification. (1). The attitude of enterprise supervisor and training specialist will have significant difference on the current status of an enterprise. (2). Enterprise supervisor and training specialist will have significant influence on the introduction of TTQS into the enterprise.

6 6 (3). Enterprise supervisor and training specialist show significant difference on the pre-test and post-test of TQS curriculum. The main objective of this research is to compare the attitude of enterprise supervisor and training specialist for the introduction of TTQS and to do sample study, to achieve such objective, we have selected enterprise supervisor and training specialist who have taken TTQS curriculum in central Taiwan area for survey before and after the acceptance of the curriculum. 3.2 Design of survey questionnaire Researchers have followed the objectives of this research to perform investigation through the use of questionnaire survey method. The first part is the filling of the basic information, which contains nine questions. The second part is, based on the related definitions from Small and Medium Enterprise Administration of the Ministry of Economic Affairs of Taiwan, to collect related literature data, to summarize enterprise characteristic, enterprise scale and the current status of the introduction of TTQS in an enterprise, meanwhile, closed type questionnaire survey method is adopted, a total of nine questions are prepared, and the survey questionnaire of this part is used only for pre-test. The third part is the problems designed according to three perspectives of attitude such as affective perspective, cognitive perspective and behavioral perspective as proposed by Robbins. Related literature data are collected to summarize the attitude item, meanwhile, scale method is adopted for a total of 24 questions. 3.3 Data analysis method After obtaining the data, manual method is adopted to process the data. Processing such as input, coding, proofreading and correction are performed, then GA is associated with SVM to perform the analysis. Experimental result The analysis result of entire attitude of enterprise supervisor and training specialist and of the accuracy rate of column correlation are as shown in table 1, as can be seen, training specialist has higher difference on post-test than that of enterprise supervisor, and for training specialist, age and count of educational training have more significant difference, but for enterprise supervisor, gender, major and employed department show more significant difference. For enterprise supervisor and training specialist, the analysis result of attitude cognitive perspective and of column correlation accuracy rate are as shown in table 2. It can be seen that the pre-test and post test difference of enterprise supervisor is higher than that of training specialist, and for enterprise supervisor, the differences in the followings are more significant, namely, Gender, Age, Educational background,

7 7 Major, Employed department, Count of educational training, etc. However, for training specialist, the followings are more significant, namely, Gender, Age, Employed department, Count of educational training, Money spent for educational training, etc. For enterprise supervisor and training specialist, the analysis result of attitude affective perspective and of accuracy rate of column correlation are as shown in table 3, and it can be seen that the pre-test and post-test difference of training specialist is higher than that of enterprise supervisor, and for enterprise supervisor, the more significant difference ones are, namely, Age, Educational background, Employed department, Count of educational training, etc. However, for training specialist, all the columns have pretty significant differences. Table 1. Analysis of entire attitude and of the accurate rate of column correlation Item enterprise supervisor training specialist Pre-test Post-test Pre-test Post-test Gender 56.4% 47.2% 49.2% 52.8% Age 52.3% 41.7% 52.3% 58.3% Educational background 50.8% 47.2% 52.3% 50.0% Major 47.7% 55.6% 50.8% 54.2% Employed department 50.8% 44.4% 50.8% 54.2% Accumulated years of working 50.8% 48.6% 49.2% 50.0% experience Count of educational training 46.2% 47.2% 49.2% 61.1% Money spent for educational training 44.6% 45.8% 55.4% 51.4% Table 2. Analysis result of attitude cognitive perspective and accuracy rate of column correlation Item enterprise supervisor training specialist Pre-test Post-test Pre-test Post-test Gender 63.1% 48.6% 58.5% 68.1% Age 69.2% 47.2% 53.8% 61.1% Educational background 49.2% 72.2% 60.0% 56.9% Major 64.6% 41.4% 52.3% 51.4% Employed department 36.9% 63.9% 61.5% 38.9% Accumulated years of working 58.5% 61.1% 60.0% 61.1% experience Count of educational training 69.2% 54.2% 70.8% 50.0% Money spent for educational training 47.7% 44.4% 60.0% 68.1% For enterprise supervisor and training specialist, the analysis result of attitude behavioral perspective and of accuracy rate of column correlation are as shown in table 4. It can be seen that the pre-test and post-test difference of training specialist is higher than that of enterprise supervisor, and for enterprise supervisor, the followings have higher significant differences, namely, Age, Major, Accumulated years of working experience, Count of educational training, Money spent for educational training, etc. But for training specialist, the followings have higher significant

8 8 differences, namely, Gender, Age, Educational background, Employed department, Count of educational training, etc. Table 3. Analysis result of attitude affective perspective and of accuracy rate of column correlation Item enterprise supervisor training specialist Pre-test Post-test Pre-test Post-test Gender 64.6% 65.3% 64.6% 68.1% Age 58.5% 66.7% 72.3% 61.1% Educational background 43.1% 72.2% 69.2% 62.5% Major 58.5% 65.3% 64.6% 50.0% Employed department 44.6% 68.1% 50.8% 56.9% Accumulated years of working 60.0% 62.5% 63.1% 47.2% experience Count of educational training 53.8% 61.1% 63.1% 56.9% Money spent for educational training 63.1% 58.3% 55.4% 66.7% Table 4. Analysis of attitude behavioral perspective and of accuracy rate of column correlation Item enterprise supervisor training specialist Pre-test Post-test Pre-test Post-test Gender 46.2% 44.4% 61.5% 45.8% Age 61.5% 48.6% 38.5% 68.1% Educational background 69.2% 66.7% 69.2% 41.7% Major 38.5% 68.1% 60.0% 61.1% Employed department 64.6% 68.1% 60.0% 68.1% Accumulated years of working 53.8% 66.7% 56.9% 59.7% experience Count of educational training 67.7% 47.2% 60.0% 40.3% Money spent for educational training 47.7% 58.3% 49.2% 47.2% Conclusion The experimental results show that within the enterprise, enterprise supervisor and training specialist of different standing points will have different thinking on the introduction of TTQS, and the attitude on educational training is also significantly different too. However, for the difference of entire attitude change after the participation of TTQS curriculum, the data of training specialist is better than that of enterprise supervisor. The above experimental results can be used as future reference for the government in preparing educational training strategy and for the implementing skill improvement of the training-implementing personnel in an enterprise. Since the research direction in this study is towards three major perspectives of attitude, hence, it is still room to be grown in the future study. In the future research, different variables can be studied, for example, L1, L2, L3 and L4 can be used to

9 9 study the feedback, professional skill and knowledge perspective, etc. gained by the enterprise after performing such actions, in other words, more extensive hypothetic research can be done. ACKNOWLEDGMENTS This work was supported partially by the National Science Council of Republic of China under grant NSC E References [1] Bureau of Employment and Vocational Training Council Of Labor Affairs Executive Yuan. Taiwan TrainQuali System(TTQS). [2] Carlson, D.S., Bozeman, D.P., Kacmar, K.M., Wright, P.M., and McMahon, G.C. (2000), Training motivation in organizations: An analysis of individual-level antecedents. Journal of managerial Issues, 12: [3] Chen, C.Y. (2002). Training and Development, Taipei: Futaba Book Gallery. [4] Chiang, C.C. (2008). Why businesses to promote business enterprise training and implementation status of Taiwan. Tunghai University EMBA Lecture. [5] Han, H.Y. (2003), Applying the Two-Stage Classification to Improve the SVM Classification Accuracy, Department of Industrial Management Science, National Cheng Kung University. [6] Hodgetts, R. M. & Altman, S. (1979). Organizational behavior. Orlando, FL: W. B. Saunders Co. [7] Hung, C.F. (2007). Taiwan TrainQuali System(TTQS) service group effectiveness evaluation. Employment security, 6:2, [8] Investors in people(iip). [9] ISO 10015, Quality management - Guidelines for training. [10] J. H. Holland, (1975). "Adaptation in Natural and Artificial Systems", Ann Arbor: The University of Michigan Press. [11] LIBSVM: a Library for Support Vector Machines, [12] Liao, J.C. (2004). By the two international standard ISO10015 and IIP to talk about the future of education training and improving the quality of human resources. Monthly Quality, 40:10, [13] M. E. Tipping, (2001). Sparse Bayesian Learning and the Relevance Vector Machine, Journal of Machine Learning Research, pp [14] Peng, C.S. (2007). Small and Medium businesses And training institutions into TTQS recommendations. Enterprise Training Contact Network Community. [15] Robbins, S. P. (1993). Organizational behavior (9th ed.). Upper Saddle River, NJ: Prentice Hall. [16] Vapnik, V.N.: The nature of statistical learning theory. Springer,1995.

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