Author List: Kohli, Rajiv; Devaraj, Sarv;
Information Systems Research, 2003, Volume 14, Issue 2, Page 127-145.
Payoffs from information technology (IT) continue to generate interest and debate both among academicians and practitioners. The extant literature cites inadequate sample size, lack of process orientation, and analysis methods among the reasons some studies have shown mixed results in establishing a relationship between IT investment and firm performance. In this paper we examine the structural variables that affect IT payoff through a meta-analysis of 66 firm-level empirical studies between 1990 and 2000. Employing logistic regression and discriminant analyses, we present statistical evidence of the characteristics that discriminate between IT payoff studies that observed a positive effect and those that did not. In addition, we conduct ordinary least squares (OLS) regression on a continuous measure of IT payoff to examine the influence of structural variables on the result of IT payoff studies. The results indicate that the sample size, data source (firm-level or secondary), and industry in which the study is conducted influence the likelihood of the study finding greater improvements on firm performance. The choice of the dependent variable(s) also appears to influence the outcome (although we did not find support for process-oriented measurement), the type of statistical analysis conducted, and whether the study adopted a cross-sectional or longitudinal design. Finally, we present implications of the findings and recommendations for future research.
Keywords: Business Value IT; Discriminant Analysis; Firm-Level; Information Technology Payoff; Logistic Regression; Meta-Analysis; Process-Orientation
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#209 0.309 results study research information studies relationship size variables previous variable examining dependent increases empirical variance accounting independent demonstrate important addition
#271 0.157 technology investments investment information firm firms profitability value performance impact data higher evidence diversification industry payoff return findings decisions greater
#102 0.138 choice type functions nature paper literature particular implications function examine specific choices extent theoretical design discussion value widely finally adopted
#215 0.117 data classification statistical regression mining models neural methods using analysis techniques performance predictive networks accuracy method variables prediction problem measure
#93 0.101 performance results study impact research influence effects data higher efficiency effect significantly findings impacts empirical significant suggest outcomes better positive
#227 0.065 commitment need practitioners studies potential role consider difficult models result importance influence researchers established conduct investigated establishing appear clearly determining
#11 0.062 structural pls measurement modeling equation research formative squares partial using indicators constructs construct statistical models researchers latent analysis results sem