Measuring Information Technology's Indirect Impact on Firm Performance

It has been recognized that the link between information technology (IT) investment and firm performance is indirect due to the effect of mediating and moderating variables. For example, in the banking industry, the IT-value added activity helps to effectively generate funds from the customer in the forms of deposits. Profits then are generated by using deposits as a source of investment funds. Traditional efficiency models, such as data envelopment analysis (DEA), can only measure the efficiency of one specific stage when a two-stage production process is present. We develop an efficiency model that identifies the efficient frontier of a two-stage production process linked by intermediate measures. A set of firms in the banking industry is used to illustrate how the new model can be utilized to (i) characterize the indirect impact of IT on firm performance, (ii) identify the efficient frontier of two principal value-added stages related to IT investment and profit generation, and (iii) highlight those firms that can be further analyzed for best practice benchmarking.

[1]  Rajiv D. Banker,et al.  Returns to scale in different DEA models , 2004, Eur. J. Oper. Res..

[2]  Kenneth L. Kraemer,et al.  Executives’ Perceptions of the Business Value of Information Technology: A Process-Oriented Approach , 2000, J. Manag. Inf. Syst..

[3]  Heather A. Smith,et al.  The relationship between information technology use and organizational performance , 1993 .

[4]  Ernst R. Berndt,et al.  Productivity measurement with adjustments for variations in capacity utilization and other forms of temporary equilibrium , 1986 .

[5]  Terry Anthony Byrd,et al.  A framework for measuring the efficiency of organizational investments in information technology using data envelopment analysis , 2000 .

[6]  Marion G. Sobol,et al.  The relationship between computerization and performance: A strategy for maximizing the economic benefits of computerization , 1983, Inf. Manag..

[7]  Lorin M. Hitt,et al.  Productivity, Business Profitability, and Consumer Surplus: Three Different Measures of Information Technology Value , 1996, MIS Q..

[8]  Peter. Weill,et al.  Strategic Investment in Information Technology: An Empirical Study , 1990 .

[9]  Joe Zhu,et al.  Super-efficiency and DEA sensitivity analysis , 2001, Eur. J. Oper. Res..

[10]  Robert J. Kauffman,et al.  An evaluative framework for research on the performance effects of information technology investment , 1989, ICIS '89.

[11]  A. Charnes,et al.  Data Envelopment Analysis Theory, Methodology and Applications , 1995 .

[12]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[13]  Chris F. Kemerer,et al.  Recent Applications of Economic Theory in Information Technology Research Recent Applications of Economic Theory in Information Technology Research , 2022 .

[14]  L. Seiford,et al.  An investigation of returns to scale in data envelopment analysis , 1999 .

[15]  Clayton M. Christensen,et al.  Strategies for Survival in Fast-Changing Industries , 1998 .

[16]  E. Berndt,et al.  High-tech capital formation and economic performance in U.S. manufacturing industries : an exploratory analysis , 1992 .

[17]  Robert J. Kauffman,et al.  Opening the "Black Box" of Network Externalities in Network Adoption , 2000, Inf. Syst. Res..

[18]  L. Seiford,et al.  Profitability and Marketability of the Top 55 U.S. Commercial Banks , 1999 .

[19]  Stanley Zionts,et al.  Use of Data Envelopment Analysis in assessing Information Technology impact on firm performance , 1997, Ann. Oper. Res..

[20]  E. Brynjolfsson,et al.  Paradox Lost? Firm-Level Evidence on the Returns to Information Systems Spending , 1996 .

[21]  Lorin M. Hitt,et al.  Beyond the productivity paradox , 1998, CACM.

[22]  Varun Grover,et al.  From business reengineering to business process change management: a longitudinal study of trends and practices , 1999 .

[23]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[24]  Erik Brynjolfsson,et al.  The productivity paradox of information technology , 1993, CACM.

[25]  Joe Zhu,et al.  Multi-factor performance measure model with an application to Fortune 500 companies , 2000, Eur. J. Oper. Res..

[26]  S. Harris,et al.  Organizational Performance and Information Technology Investment Intensity in the Insurance Industry , 1991 .

[27]  F. Lichtenberg.  The Output Contributions of Computer Equipment and Personnel: A Firm- Level Analysis , 1993 .

[28]  Robert J. Kauffman,et al.  Measuring Gains in Operational Efficiency from Information Technology: A Study of the Positran Deployment at Hardee'S Inc. , 1990, J. Manag. Inf. Syst..

[29]  Rajiv D. Banker,et al.  Case Study of Electronic Banking at Meridian Bancorp , 1991 .

[30]  Kaj Grønbæk,et al.  CSCW challenges: cooperative design in engineering projects , 1993, CACM.

[31]  Charles B. Fleming,et al.  Opening the Black Box: Using Process Evaluation Measures to Assess Implementation and Theory Building , 1999, American journal of community psychology.

[32]  R. Veliyath.  Hypercompetition: Managing the Dynamics of Strategic Maneuvering , 1996 .