Performance Evaluation On Human Resource Management Of China S Commercial Banks Based On Improved Bp Neural Networks

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1 Performance Evaluation On Human Resource Management Of China S *1 Honglei Zhang, 2 Wenshan Yuan, 1 Hua Jiang 1 School of Economics and Management, Hebei University of Engineering, Handan , P. R. China, hongleizhang@126.com 2 School of Information and Electrical Engineering, Hebei University of Engineering, Handan , P. R. China Abstract The function of human resource management (HRM) is to fully exploit human resources and to maintain the competitiveness of intellectual capital, thus, acquiring sustained competitive advantage and assuring the survival and development of commercial banks. Artificial neural network (ANN) is a computing tool widely used in various applications. However, few researchers apply ANNs in the performance evaluation of HRM. The paper presents a performance evaluation index system of HRM for commercial banks, and develops a novel approach of the performance evaluation of HRM based on BP neutral networks. Meanwhile, by adding the momentum factor and applying adaptive learning rate, the BPNN algorithm is improved so that it meets the training requirement and can effectively circumvent the local minimum problem of neural networks. Lastly, according to the actual data of eighty-six commercial banks, the paper proves the validity of the improved approach. Keywords: Commercial Banks, Human Resource Management, Performance Evaluation, Bp Neural Networks, Improved Algorithm 1. Introduction As the deepening of China's financial reform and financial opening degree, the competitive environment of banking system gradually forms and is increasingly fierce. Faced with a strong impact of foreign banks, how to improve the efficiency and competitiveness of domestic commercial banks becomes a serious problem to be solved. As we know, a key link to affect the efficiency of commercial banks is human resource management (HRM). Human resource management evaluation system is a very important part of the large-scale systems of commercial banks. Its function is to fully exploit human resource and to maintain competitiveness of intellectual capital, thus, acquiring sustained competitive advantage and assuring the survival and development of commercial banks. Therefore, many commercial banks start to attach importance to the performance evaluation of HRM and invest a lot of resources in it. Not only practitioners but also researchers have put attentions on the evaluation of human resource management. Ferris et al. [1] referred that HRM is the source of sustained competitive advantage for organizations operating in a global economy and the performance evaluation of HRM is one of the new directions for future work. Soltani et al. [2] highlighted the key generic criteria of a quality-driven HR performance evaluation system through a questionnaire survey of Scottish-based quality-driven organizations, which allowed the reader to map the most important issues in HR performance evaluation in a quality management context. Pakarinen [3] took a public knowledge organization as the object and collected data in four stages through individual and group interviews and questionnaires from senior management, personnel, policy makers, developers and customers, finally proposing a method of HRM evaluation. Ferris et al. [4] commented that performance evaluation is a formal accountability mechanism nested within a complex social, emotional, cognitive, political, and relationship context, which needs careful consideration and comprehension in order to fully sort out performance evaluation challenges and leverage possibilities, and proposed a framework for this area of scientific inquiry, which provides one basis of establishing the performance evaluation indexes system. Stone and Lukaszewski [5] developed an expanded model of their previous study for assessing the acceptance and effectiveness of electronic human resource management systems. McKenna, International Journal of Advancements in Computing Technology(IJACT) Volume4, Number11, June2012 doi: /ijact.vol4.issue

2 Richardson and Manroop [6] thought that the research field for performance management and evaluation is dominated by a one-dimensional approach located within positivist ontology, and analyzed alternative paradigms, which aimed to calling for innovation through paradigmatic diversity in PME. Ouyang [7] analyzed the content of human resources performance evaluation in construction projects, built an evaluation indexes system for HRM performance and applied data envelopment analysis to evaluate a company's performance of HRM. Artificial Neural Network (ANN) is a computing tool widely used in various applications [8, 9]. However, few researchers apply ANNs in the performance evaluation of human resource management. The paper firstly presents a performance evaluation index system of human resource management for commercial banks which is proposed in our previous studies, then develops a novel approach of performance evaluation of HRM based on BP neutral networks. Meanwhile, by adding the momentum factor and applying adaptive learning rate, the BPNN algorithm is improved so that it meets training requirement and can effectively circumvent the black-box operation of neural networks, which may overcome the deficiency of local minimum. 2. Performance evaluation index system of human resource management for commercial banks Table 1. Performance evaluation index system of human resource management of commercial banks Target First-level indexes Second-level indexes Third-level indexes A: Performance evaluation on human resource management of commercial banks B 1 : human resource management process B 2 : human resource management result C 1 : human resource acquisition C 2 : human resource return C 3 : human resource development C 4 : human resource maintenance C 5 : management effect C 6 :organization efficiency C 7 : organization goal D 1 : human resource strategy and planning D 2 : post and duty D 3 : recruitment management D 4 : performance management D 5 : salary management D 6 : the service level of human resource department D 7 : employee training and learning D 8 : employee's career development D 9 : labor relations D 10 : employee s health and safety D 11 : employee satisfaction D 12 : customer satisfaction D 13 : coordination of the internal relationship D 14 : absence rate D 15 : error rate D 16 : individual profit D 17 : return rate on investment of human resource D 18 : market share D 19 : enterprise profit D 20 : organization atmosphere Human resource management process refers to the various functions of human resource management. The evaluation of human resource management performance lies on how much human resource does and how is the process. It is divided into four categories, namely human resource acquisition, including strategy and planning of human resource, analysis and description of post and job, recruitment and selection, etc; human resource return, including performance management, payment and incentive, human resource services, etc; human resource development, including training and learning, employee s career development, etc; maintenance and protection of the human resource, including labor relations, employee s health and safety, etc. The result of human resource management is mainly to evaluate effect and efficiency of human resource management of commercial banks and to what extent the realization of enterprise performance and target. In the view of strategic human 305

3 resource management, enterprise human resource management is not only reflected in the performance of human resource department, but also closely related with the management effect, organization efficiency and realization degree of enterprise strategic target. Therefore, in the previous study we have constructed a performance evaluation index system of human resource management which contains three-level index, shown as Table 1 [10]. 3. BP neural networks 3.1. Brief introduction of BP neural networks The Back propagation (BP) neural network is not only the most widely used, but also one of the most maturely developed neural networks. It is a multi-network training with weights of the nonlinear differential function. 80 percent to 90 percent of artificial neural network models are BP network or its transfiguration in practical application. Back propagation was created by generalizing the Widrow- Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions. Input vectors and the corresponding target vectors are used to train a network until it can approximate a function, associate input vectors with specific output vectors, or classify input vectors in an appropriate way as defined by you. Networks with biases, a sigmoid layer, and a linear output layer are capable of approximating any function with a finite number of discontinuities. Standard back propagation is a gradient descent algorithm, as is the Widrow-Hoff learning rule, in which the network weights are moved along the negative of the gradient of the performance function. Properly trained back propagation networks tend to give reasonable answers when presented with inputs that they have never seen. Typically, a new input leads to an output similar to the correct output for input vectors used in training that are similar to the new input being presented. This generalization property makes it possible to train a network on a representative set of input/target pairs and get good results without training the network on all possible input/output pairs The improved BP algorithm As shown in Figure 1, the Backpropagation neural network is based on hierarchical structure, including an input layer, an output layer and one (or more) hidden layer. In order to overcome the slow convergence and local minimum, the paper adds the momentum factor and apply adaptive learning rate. Back-propagation process Link weights Adjust weights Adjust weights Teacher signal Input layer Hidden layer Forward calculation process Output layer Figure 1. The architecture of the BP neural network Momentum allows a network to respond not only to the local gradient, but also to recent trends in the error surface. Acting like a low-pass filter, momentum allows the network to ignore small features in the error surface. The learning algorithm of momentum factor mc is: W(x+1)=W(x)+ lr[(1-mc)d(x)+ mcd(x-1)] (1) 306

4 where W(k) is the weight and bias, D(k)= V v ( x) is the negative gradient at current X, V is the squared errors of network s actual outputs and expected outputs, D(x) is the search direction vector, D(x-1) is the previous change to the weight or bias, lr is the learning rate, mc is the momentum constant, which can be any number between 0 and 1. Without momentum a network may get stuck in a shallow local minimum. With momentum a network can slide through such a minimum. With standard steepest descent, the learning rate is held constant throughout training. The performance of the algorithm is very sensitive to the proper setting of the learning rate. If the learning rate is set too high, the algorithm may oscillate and become unstable. If the learning rate is too small, the algorithm will take too long to converge. An adaptive learning rate will attempt to keep the learning step size as large as possible while keeping learning stable. The learning rate is made responsive to the complexity of the local error surface. The formulas of the adaptive learning rate algorithm are: W(x+1)=W(x)+ lr (x)d(x) (2) Lr(x)=2 λ lr (x-1) (3) Λ=sign[D(x)D(x-1)] (4) First, the initial network output and error are calculated. At each epoch new weights and biases are calculated using the current learning rate. New outputs and errors are then calculated. As with momentum, if the new error exceeds the old error by more than a predefined ratio (typically 1.04), the new weights and biases are discarded. In addition, the learning rate is decreased (typically by multiplying by 0.7). Otherwise, the new weights, etc., are kept. If the new error is less than the old error, the learning rate is increased (typically by multiplying by 1.05) 3.3. The framework of applying improved BP neutral networks to evaluate the performance of HRM According to the algorithm idea of BP neural network, we can get the framework of applying improved BP neutral networks to evaluate the performance of HRM, as Figure 2 shows. Analyzing influencing factors on the performance of HRM in commercial banks Acquiring and preprocessing indexes data Constructing BP neural network and design improved BP algorithms Implementing performance evaluation of HRM Figure 2. The architecture of applying improved BP neural network to evaluate the performance of HRM 307

5 4. Computational experiments 4.1. Acquiring and preprocessing original data According to the performance evaluation index system of human resource management proposed in Section 2, we surveyed one hundred commercial banks in Handan on their performance of human resources management, and obtained eighty-six valid instances as Table 2 shows. In the table, for the values of quantitative indexes (e.g., absence rate, error rate, individual profit etc), the paper applies the binning technology to discrete their practical values into four levels: very good, good, general and poor, respectively represented by 4, 3, 2 and 1. For the qualitative indexes such as human resource strategy and planning, post and duty, recruitment management, and so on, this paper also divides them into four levels: very good, good, general and poor, respectively represented by 4, 3, 2 and 1. The performance level of commercial banks HRM is also divided into 4 (very good), 3 (good), 2 (general) and 1 (poor). Table 2. The original data Instances D 1 D 2 D 3 D 4 D 5 D 6 D 7 D 8 Values of indexes D 9 D 10 D 11 D 12 D 13 D 14 D 15 D 16 D 17 Levels D 18 D 19 D 20 X X X X X X X X X X X X X X X X X Results and analysis According to the above preprocessed values, we can implement the improved BP algorithm to train the network. The inputs of BPNN are the above eighty-six instances we have got and five hidden layers according to experience and one output neuron. The transfer function is log sigmoid transfer function (logsig). The training function applies improved BP algorithm with the momentum factor mc and adaptive learning rate. The training parameters are: performance goal is 1.0e-6; learning rate is 0.01; ratio to increase learning rate is 1.05; ratio to decrease learning rate is 0.7; momentum constant is 0.76; the maximum iterations is Input the data into the network and start training it. After 224 epochs, the improved BP network converges, as Figure 3 (b) shows. The comparison results with classic BPNN are shown as Table

6 (a) By the classic BPNN (b) By the improved BPNN Figure 3. The convergence curves of classic BPNN and improved BPNN Table 3. Results of classic BPNN and improved BPNN MSE Accuracy rate Error rate Epochs The running time (Sec.) Classic BPNN 3.56e % 38.98% Improved BPNN 1.04e % 14.84% Seen from Figure 3 and Table 3, the classical BP algorithm fails to achieve the performance goal 1.0e-6 within epoches, while the improved BP algorithm only uses 224 epochs to achieve the preseted training goal, which shows that the improved BP algorithm by adding the momentum factor mc and applying adaptive learning rate may overcome the local minimum problems of classic BP neutral networks. Meanwhile, the improved BP algorithm only consumes 21 seconds to train the network and produces the accuracy rate of 85.16%, which shows the quality and effectiveness of the improved BP algorithm. 5. Conclusions The function of human resource management (HRM) is to fully exploit human resources and to maintain the competitiveness of intellectual capital. Many commercial banks start to attach importance to the performance evaluation of HRM and invest a lot of resources in it. The paper develops a new approach to evaluate the performance of commercial banks HRM, which makes the following contributions: (1) presenting a performance evaluation index system of HRM for commercial banks; (2) developing a novel approach of the performance evaluation of HRM based on BP neutral networks; (3) improving the classical BP algorithm by adding the momentum factor and applying adaptive learning rate. The results of computational experiments show that the proposed approach is effective and efficient compared with the classical BP algorithm and effectively circumvent the local minimum problem of neural networks. 6. References [1] G. R. Ferris, W. A. Hochwarter, M. R. Buckley, G. H. Cook, D. D. Frink, Human resources management: some new directions, Journal of Management, vol.25, no.3, pp , [2] E. Soltani, R. van der Meer, J. Gennard, T. Williams, A TQM approach to HR performance evaluation criteria, European Management Journal, vol.21, no.3, pp , [3] T. Pakarinen, Performance evaluation and human resource management as change mechanisms in a public knowledge organization, Helsinki University of Technology,

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