資訊科技，主要是利用電腦科學和通訊技術來設計、開發、安裝和實施資訊系統及應用軟體。資訊科技的研究範疇包括科學、技術、工程以及管理等學科；而資訊科技主要的應用範圍包括電腦硬體和軟體、網路和通訊技術、應用軟體開發工具等。因此，資訊科技也常被稱為資訊和通訊技術。現今，電腦和網際網路的普及，人們日漸普遍的使用電腦及網際網路來生產、處理、交換和傳播各種形式的資訊。換句話說，資訊科技已經成為人類日常生活中不可或缺的一個部份。有鑑於此，深入了解資訊科技公司的生產力與效率，並作績效評估對於資訊科技公司的管理者及投資者都非常重要。至今，著墨於資訊科技公司的績效評估研究並不多，因此，本研究將利用傳統資料包絡分析法來對資訊科技公司做經營效率的評估。本研究將以標準普爾五百指數資訊科技公司來做為受評估的決策單位。然而，傳統資料包絡分析法是從不合適的權重去做推導，而得到的不公平模型。因此，此研究將利用多目標決策之資料包絡分析法來評估標準普爾五百指數資訊科技公司之經營效率。利用多目標決策之資料包絡分析法來評估之下，每個決策單位將會在相等的標準下做評估，而評估結果將會比傳統資料包絡分析法還要更公平。本研究將藉由歷年的財務報表對標準普爾五百指數資訊科技公司的領導廠商作績效評估。除此之外，本研究將會利用多目標決策之資料包絡分析法與傳統資料包絡分析法做比較，並做更準確性的修正。在未來，本研究所評估的結果將會做為公司管理者或是投資者的一個重要的管理或投資依據。

Information technology (IT) is defined as the obtainment, procedure, storage and propagation of sounding, drawing, and textual information by combining microelectronics-based computing and telecommunications. Nowadays, IT is starting to spread further from the conventional personal computer and network technologies to integrations of other fields of technology such as the use of cell phones, televisions, automobiles, etc. In other words, IT has penetrated in daily life of human beings and become one part of the whole society. The importance of IT has become momentous. Therefore, to understand the performance of efficiency and productivity of the IT firms is critical for managers as well as for personal investors. Until now, there are very few researches tried to analyze final performance of the IT firms. As a result, this research intends to use traditional Data Envelopment Analysis (DEA) CCR or BCC models to evaluate the performance of IT firms. The Decision Making Units (DMUs) on this research are chosen from IT firms in S&P 500. However, the traditional DEA models are not fair models from the aspect of improper weight derivations. Thus, this paper intends to analyze the efficiency of IT firms in S&P 500 efficiencies by using multiple objective programming (MOP) based Data Envelopment Analysis (DEA). In a MOP based DEA approach, DMUs will be evaluated based on an equal standard and the results will be evaluated more fairly. The world’s leading IT firms in S&P 500 will be evaluated based on publicly available financial reports of the fiscal year. In addition, the newly developed MOP can improve the traditional DEA’s unfair weights problems and benchmark the efficiency of IT firms in S&P 500 correctly. In the future, performance evaluation results can be served as foundations for investment strategies definition.

Banker, R. D., Charnes, A., Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
Barros, C.P., Dieke, P.U.C. (2008). Technical efficiency of African hotels. International Journal of Hospitality Management, 27 (3), 438–447.
Barros, C.P., Mascarenhas, M.J. (2005). Technical and allocative efficiency in a chain of small hotels. International Journal of Hospitality Management, 24 (3), 415–436.
Bharadwaj, A., Bharadwaj, S., Konsynski, B. (1999). Information Technology Effects on Firm Performance as Measured by Tobin's q. Management Science 45(7), 1008-1024.
Bowlin, W. F. (1987). Evaluating the efficiency of US airforce real-property maintenance activities. Journal of Operational Research Society, 38(2), 127-135
Bowonder, B. Y., Yadav, S. (1999). R&D spending patterns of global firms. Research Technology Management, 42(6), 44-55.
Brooks, K. W. (1979). Delphi technique: Expanding applications. North Central Association Quarterly, 54(3), 377-385.
Bruton N. (2004). Managing the IT services process, Butterworth Heinemann, 95.
Bryd, T., Marshall, T. (1997). Relating information technology investment to organizational performance: a causal model analysis. Omega, 25, 43–56.
Brynjolfsson, E. (1993). The productivity paradox of information technology: Review and assessment. Comm.ACM, 36 (12), 66–77.
Cardy, R. L., Dobbins, R. H. (1995). Human resources, high technology, and a quality organizational environment: Research agendas. The Journal of High Technology Management Research, 6(2), 261-279.
Caves, D. W., Christensen, L. R., Diewert, W. E. (1982a). Multilateral comparisons of output, input, and productivity using superlative index numbers. The Economic Journal, 92, 73-86.
Caves, D. W., Christensen, L. R., Diewert, W. E. (1982b). The economic theory of index numbers and the measurement of input, output and productivity. Econometric, 50(6), 1393-1414.
Charnes, A., Clark, T., Cooper, W. W., Golany, B. (1985). A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the US Air Force. Annals of Operations Research, 2(1), 95-112.
Charnes, A., Cooper, W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2 (6), 429–444.
Chiang, C. I., Tzeng, G. H. (2000a). A New Efficiency Measure for DEA: Efficiency Achievement Measure Established on Fuzzy Multiple Objectives Programming. Journal of Management, 17(2), 369-388.
Chiang, C. I., Tzeng, G. H. (2000b). A multiple objective programming approach to data envelopment analysis. In Y. Shi and M. Zeleny (Eds.), New Frontiers of Decision Making for the Information Technology Era, Singapore: World Science Publishing Company, 270-285.
Chiang, C.I., Tzeng, G.H. (2000). A multiple objective programming approach to data envelopment analysis. In Shi Y, New frontiers of decision making for the information technology era, Hong Kong: World Science.
Chiang, C.I., Tzeng, G.H. (2003). A new efficiency measure for DEA: efficiency achievement measure established on fuzzy multiple objectives programming. Journal of Management, 17(2), 369-388 .
Chu, M. T., Shyu, J. Z., and Khosla, R. (2008). Measuring the relative performance for leading fabless firms by using data envelopment analysis. Journal of Intelligent Manufacturing, 19(3), 257-272.
Chu, M. T., Shyu, J. Z., Khosla, R. (2008). Measuring the relative performance for leading fabless firms by using data envelopment analysis. Journal of Intelligent Manufacturing, 19(3), 257-272.
Chu, Y. J. (2007). The analysis of high-technology industry. Taipei: Wunan.
Coelli, T., Prasada Rao, D. S., O’Donnell, C. J., Battese, G. E. (2005). An introduction to efficiency and productivity analysis. New York: Springer.
Collin S. M. H. (2004). Dictionary of ICT, Bloomsbury Publishing Plc, 4th Edition.
Cooper, W., Seiford, L.M., Zhu, J. (2004). Data envelopment analysis. Boston: Kluwer Academic Publishers, 1–39.
Dalkey, N. C. (1972). The Delphi method: an experimental application of group opinion. In: N. C. Dalkey, D. L. Rourke, R. Lewis, and D. Snyder (Eds.), Studies in the Quality of Life. MA: Lexington Books.
Dalkey, N., Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458-467.
Deeds, D. L. (2001). The role of R&D intensity, technical development and absorptive capacity in creating entrepreneurial wealth in high technology startups. Journal of Engineering and Technology Management, 18(1), 29-47.
DeLone W. H., McLean E. R. (1992). Information Systems Success: The Quest for the dependent variable. Information Systems Research, 3(1), 60-95.
Emrouznejad, A. (1995). Data Envelopment Analysis Homepage. http://www.DEAzone.com.
Farbey, B., Land, F., Targett, D. (1999). Moving IS evaluation forward: learning themes and research issues. Journal of Strategic Information Systems, 8, 189–207.
Fare, R., and Hunsaker, W. (1986). Notions of efficiency and their reference sets. Management Science, 32(2), 237-243.
Fare, R., Grabowaski, R., Grosskopf, S. (1985). Technical efficiency of Philippine agriculture. Applied Economics, 17(2), 205 – 214.
Fare, R., Grosskopf, S., Lindgren, B., Roos, P. (1992). Productivity changes in Swedish pharmacies 1980-1989: A Non-Parametric Malmquist approach. The Journal of Productivity Analysis, 3(1), 85-101.
Fare, R., Grosskopf, S., Lindgren, B., Roos, P. (Eds.). (1994). Data Envelopment Analysis. Boston: Kluwer Academic Publishers.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(3), 253-291.
Flynn, B., Schroeder, R., Sakakibara, S. (1994). A framework for quality management research and an associated measurement instrument. Journal of Operations Management, 11, 339–366.
Golany, B., Roll, Y. (1989). An application procedure for DEA. OMEGA: International Journal of Management Science, 17(3), 237-250.
Grinstein, A., Goldman, A. (2006). Characterizing the technology firm: An exploratory study. Research Policy, 35, 121-143.
Grover, V., Im, K.S., Dow, K.E. (2001). Research report: a reexamination of IT investment and the market value of the firm—an event study methodology. Information Systems Research, 12(1), 103–17.
Harbour, J. L. (2009). The performance Paradox: understanding the real drivers that critically affect outcomes, Productivity Press Taylor & Francis Group.
Harry P. (1980). Performance Measurement Principles and Techniques: An Overview for Local Government. Public Productivity Review, 4(4), 312-339.
Helms M. M. (2006). Encyclopaedia of Management, 5th Edition, Thomson Gale
Jones, J., Hunter, D. (1995). Qualitative research: Consensus methods for medical and health services research. British Medical Journal, 311(5), 376-380.
Jorgenson D. W. (2001). Information Technology and the U.S. Economy. The American Economic Review, 91(1), 1-32.
Judd, R. C. (1972). Forecasting to consensus gathering: Delphi grows up to college needs . College and University Business, 53(1), 35-38.
Kaplan R., Norton D. (1996). The balanced scorecard: translating strategy into action. Boston: Harvard Business School Press.
Kasul, R. A., Motwani, J. G. (1995). Performance measurements in worldclass operations: A strategic model. Benchmarking. An International Journal, 2(2), 20–36.
Keen, PCW. (1991). Shaping the future. Boston: Harvard Business School Press.
Kenneth I., Oxley (2008). Resolving the productivity paradox. Mathematics and Computers in Simulation, 78, 2-3.
Kohli R., Devaraj S. (2003). Measuring Information Technology Payoff: A Meta–Analysis of Structural Variables in Firm–Level Empirical Research. Information System Research, 14(2), 127-145.
Kohli R., Sherer S.A. (2002). Measuring payoff of information technology investments: research issues and guidelines, Communications of the Association for Information Systems, 9, 241–268.
Kollberg, B. (2007). Performance Measurement Systems in Swedish Health Care Services, Doctoral Dissertation, Linköping University, Linköping.
Kumar, K. (1990). Post-implementation evaluation of computer based IS: current practices. Communications of ACM, 33, 203–12.
Lee, Y. S., Huang, J. C., Hsu, Y. S. (2008). Using modified delphi method to explore the competition strategy for software companies of Taiwan. Journal of Informatics & Electronics, 13(1), 39-50.
Lewin, A.Y., Minton, J.W. (1986). Determining organizational effectiveness: another look, and an agenda for research. Management Science, 32 (5), 514–538.
Linstone, H. A., Turoff, M. (1975). The Delphi method: Techniques and applications, reading. MA: Addison-Wesley.
Liu, L. C., Lee, C., Tzeng, G. H. (2004). DEA approach for the current and the cross period efficiency for evaluating the vocational education. International Journal of Information Technology and Decision Making, 3(2), 353-374.
Martinsons, M., Davison, R., Tse, D. (1999). The balanced scorecard: a foundation for the strategic management of information systems. Decision Support Systems, 25, 71–88.
Meepadung, N., John, C. S., and Tang, D. B. K. (2009). IT-based banking services: Evaluating operating and profit efficiency at bank branches. Journal of High Technology Management Research, 20, 145–152.
Mintzer, I. M. (1992). Confronting climate change: risks, implications, and responses. Cambridge: Cambridge University Press.
Motiwalla, L., Khan, MR. (2002). Financial impact of e-business initiatives in the retail industry. Journal of Electronic Commerce in Organization, 1 (1), 55–73.
Murry, J. W., Hammons, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423-436.
Ohta, H., Yamaguchi, T. (1995). Multi-goal programming including fractional goal in consideration of fuzzy solutions. Journal of Japan Society for Fuzzy Theory and System, 7, 1221-1228.
Osei, B., K, M. and Ko, M. (2004). Exploring the relationship between information technology investments and firm performance using regression splines analysis. Information and Management, 42 (1), 1–13.
Park, S., Hartley, J., Wilson, D. (2001). Quality management practices and their relationship to buyers’ supplier ratings: A study in the Korean automotive industry. Journal of Operations Management, 268, 1-18.
Parker, R. P., Grimm, B. T. (2000). Recognition of Business and Government Expenditures on Software as Investment: Methodology and Quantitative Impacts.
Perego, A. (2009). IS Performance Management Systems: An Action Research Perspective. Sprouts: Working Papers on Information Systems, 9(63).
Peterson, R. A. (2000). Constructing effective questionnaires. CA: Sega Publications.
Phillips, P.A. (1999). Performance measurement systems and hotels: a new conceptual framework. International Journal of Hospitality Management, 18 (2), 171–182.
Rai, A., Patnayakuni, R., Patnayakuni, N. (1997). Technology investment and business performance. Communications of the ACM, 40 (7), 89–97.
Ray, S. C. (2004). Data Envelopment Analysis. NY: Cambridge University Press.
Sakawa, M., Yano, H. (1985). Interactive decision making for multi- objective linear fractional programming problems with parameters. Cybernetics and Systems: An International Journal, 16, 377-394.
Sakawa, M., Yumine, T. (1983). Interactive fuzzy decision-making for multi-objective linear fractional programming problems. Large Scale Systems, 5, 105-114.
Serrano-Cinca, C., Fuertes-Calle´n, Y., and Mar-Molinero, C. (2005). Measuring DEA efficiency in Internet companies. Decision Support Systems, 38, 557-573.
Sigala, M. (2004). Using data envelopment analysis for measuring and benchmarking productivity in the hotel sector. Journal of Travel & Tourism Marketing, 16 (2/3), 39–60.
Sircar, S.L., Turnbow, J., Bordoloi, B. (2000). A framework for assessing the relationship between information technology investments and firm performance. Journal of Management Information Systems, 16 (4), p.69-97.
Skinner, W. (1966). Production under pressure. Harvard Business Review, 6, 139–146.
Soh, C., Markus, M.L. (1995). How IT Creates Business Value: A Process Theory Synthesis, Proceedings of the Sixteenth International Conference on Information Systems.
Specht, Hammers P., Hoff G. (2005), Information Technology Investment and Organizational Performance in the Public Sector. Public Information Systems, 127-142.
Sumanth, D. J. (1994). Productivity engineering and management. New York: McGraw-Hill.
Sung, W. C. (2001). Application of Delphi method, a qualitative and quantitative analysis, to the healthcare management. Journal of Healthcare Management, 2(2), 11-19.
Tangen, S. (2003). An overview of frequently used performance measures.Work Study, 52(7), 347-354.
Tangen, S. (2005). Demystifying productivity and performance. International Journal of Productivity and Performance Management, 54(1), 34-46.
Thore, S., Phillips, F., Ruefli, T. W., and Yue, P. (1996). DEA and the management of the product cycle: The U.S. computer industry. Computers & Operations Research, 23(4), 341-356.
Wheelwright, S. (1978). Reflecting corporate strategy in manufacturing decisions. Business Horizons, 21(1), 57–66.
Willcocks, L. P., Lester, S. (1997). In Search of Information Technology. Productivity: Assessment Issues. The Journal of the Operational Research Society, 48(11), 1082-1094.
Wober, K.W. (2002). Benchmarking in Tourism and Hospitality Industries: The Selection of Benchmarking Partners. CABI Publishing, Oxford.
Wong, W. P., Wong, K. Y. (2008). A review on benchmarking of supply chain performance measures. Benchmarking: An International Journal, 15 (1), 25-51.
Yu, J. R., Tzeng, Y. C., Tzeng, G. H., Yu, Z. Y., Sheu, H. J. (2004). A fuzzy multiple objective programming to DEA with imprecise data. International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 12(5), 591-600.
Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55.