# Performance Management for Inter-organization Information Systems Performance: Using the Balanced Scorecard and the Fuzzy Analytic Hierarchy Process

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3 and criteria, and construct fuzzy Positive Reciprocal Matrix. Step 4: Consistency Check The check methods are as follows: 4.1 Consistency Index (C.I.) According to Consistency Index (C.I.), C.I=0 indicate that evaluation has perfect consistency; C.I>0 indicate that evaluation has consistency; C.I. <0.1 indicate that evaluation has evaluation has tolerant bias. 4.2 Consistency Rate (C.R.) Saaty (1980) [4] supposed that Consistency Rate (C.R.) to evaluate the consistency of pairwise comparisons in a matrix among criterions. Under the condition of different rank of matrix, it produce different random index (R.I.). Under the condition of the same rank of matrix, the ratio of C.I. to R.I. is called C.R.. When C.R 0.1, the consistency level is acceptable. Step5: Calculate fuzzy weight value Utilize the Lambda-Max method which Csutora and Buckley (2001) [18] proposed, calculate the fuzzy weight of evaluation criterions. The steps of calculation are as follows: 5.1 When α=1, use α-cut to get median Positive Reciprocal Matrix. Then, calculate the weight use AHP method to get the weight matrix. 5.2 When α=0, use α-cut to get minimum positive reciprocal matrix and maximum positive reciprocal matrix. Then, calculate the weight use AHP method to obtain the weight matrix. 5.3 In order to make sure that calculated weight value is fuzzy number, therefore, adjusted the coefficient. 5.4 After obtained adjusted coefficient, calculate minimum positive reciprocal weight matrix and maximum positive reciprocal weight matrix of every measurement dimension. 5.5 Combing adjusted minimum, maximum and median values to get the fuzzy weight in kth evaluation member and kth measurement dimension. 5.6 Utilize average method to integrate the fuzzy weight of evaluation members and measurement dimensions. IV. RESULTS Based on previous studies on applying the BSC approach to information systems, this study used the 17 indicators as performance evaluation indicators to construct the research model and develop the questionnaire items based the model. The 17 indicators are showed in Figure 1. Figure 1. The hierarchical framework of the BSC performance evaluation criteria The 25 related managers of the enterprises in Taiwan s information and electronic industries which located in the Technology Park, Industrial Park, and Export Processing Zone in Taiwan were selected, the propose framework was explained to these experts and each expert was separately asked to do pairwise comparisons through the use of personal interviews for the final data analysis. Next, calculate the weights of model dimensions and indicators using Fuzzy AHP method. The results showed in Figure 2. The results demonstrated that the importance weights of all dimensions were ordered as follows when managers measure IOIS performance: the financial, the internal processes, the learning and growth and the customer dimension. IOIS focus on the financial and internal processes dimensions and related performance indicators. The managers of Taiwan s information and electronic industries pay more attentions on the traditional financial and internal processes dimension, and less on the learning and growth and the customer dimension. However, academic researchers recently stress the importance of

4 learning and customers for business administration for future development [3]. Additionally, the importance weights of the top 5 performance indicators were ordered as follows: increase the inventory turnover rate, reduce information exchange costs, reduce transaction costs, reduce IT repetition investment expenses, and accelerate the process speed related to working with suppliers and customers. All performance indicators are larger than For performance indicators, consistent with the suggestions of Lu et al. (2006) [19], the managers still emphasized operational efficiency and capability on IOIS performance measurement. followings. First, this study contributes to the IOIS theory, as it offers the model for assessing IOIS performance. The proposed model offers a strong linkage between strategic plans and performance measures based BSC, and also considering inner and outer organizational perspectives. The model provides a holistic viewpoint of the firms by simultaneously considering its performance from four perspectives: the learning and growth, internal business processes, customer, and financial perspectives. Furthermore, the enterprises then use the model to develop strategies and blueprints for self-evaluation. Second, this study uses the fuzzy AHP method to assess IOIS performance. The fuzzy AHP method can conquer this problem which decision makers are often uncertain in assigning the evaluation score in using the AHP method. The managers in Taiwan s information and electronic industries use the fuzzy AHP method is liable lead to better balanced scorecard consequences. For Taiwan s information and electronic industries, the proposed model can serve as a useful modeling instrument in analyzing coupled improvement problems. Identifying the dimensions and performance indicators offers an important piece of information which would assist managers in Taiwan s information and electronic industries to better understand the main facets of IOIS performance and adopt the right action to enhance the total performance. The model not only aid the companies in Taiwan s information and electronic industries to respond to the changes much faster, but also supplies the management teams to move forward with confidence. This study has some limitations. First, drastically changing environments influences dependency relationships of dimensions and indicators and the accuracy of the proposed model. Second, the proposed model is used in enterprises in which IOIS have already been deployed. If IOIS have not been deployed, one might not be able to apply the model effectively. Figure 2. The result of this study V. CONCLUSION The results of this study supply two contributions as

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