過去企業績效評估機制大多都只著重在財務面績效，在此劇變的資訊時代，企業不僅必須制定一套自我評估的機制，更需進行策略性、主動以及長遠的管理。資料包絡分析法可藉由投入產出之相互關係評估各單位的經營績效，而平衡計分卡可提供資料包絡分析建構衡量財務及非財務的產出指標。因此，本研究欲建構之績效評估(Performance Evaluation)模式，以平衡計分卡(Balanced Scorecard, BSC)建構資料包絡分析法(Data Envelopment Analysis, DEA)之產出項，對台灣上市資訊電子公司進行效率分析，經過資料包絡分析法之運算，再以敏感度分析與迴歸分析篩選出重要變數，由其產生相對效率值納入資料融合(Data Fusion)模式中，以類神經網路(Artificial Neural Network, ANN)做為資料融合之技術，利用類神經網路最小誤差權重值(Weight)來分析各變項的權重值，提供決策者作為策略規劃之依據，以建構一套具回饋性的績效評估模。
研究結果顯示，86-95年產業之平均整體效率為0.958，純技術效率為0.971，規模效率亦達0.986，表示存在資源使用無效率的狀況，且部份廠商未達最適營運規模狀態，顯示資訊電子產業在經營策略及管理決策上仍有需要改善的空間。
本研究亦採用生產力指數(Malmquist Productivity Index, MPI)分析跨期間生產力變動的情形，整體產業之平均生產力變動率為1.013，表示在86-95年資訊電子產業總體生產力持續成長。除此，透過整合不同資料來源建立之融合模式能降低預測誤差，亦可得知進行資料融合能提高績效評估模型的正確性及辨識力。

Most literatures of performance management focus on financial performance. In contrast, the Balanced Scorecard structured by four perspectives can provide a more comprehensive view of business performance.
The purpose of this study is to develop a useful framework to evaluate operating performance of the four output perspectives of the balanced scorecard using data envelopment analysis, and then using sensitivity analysis and regression analysis of selected important variables in the corresponding value of the relative efficiency into data fusion model of the artificial neural network.
The results show that average overall efficiency of industrial was 0.958 for year 1997-2006, average pure technical efficiency was 0.971, and average scale efficiency was 0.986. The figures indicated some firms in the industry have room for improvement.
This study also used productivity index for evaluating of the efficiency change. The total factor productivity was 1.013. Furthermore, the study results showed that the data fusion approach can improve the effectiveness of the performance evaluation model.

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