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Proposition 1. IT payoff will differ among the industry sector of the firms .

R. Kohli , and S. Devaraj , “Measuring Information Technology Payoff: A Meta-Analysis of Structural Variables in Firm-level Empirical Research,” Information Systems Research 14 (2), 2003, pp. 127-145. Proposition 1. IT payoff will differ among the industry sector of the firms .

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Proposition 1. IT payoff will differ among the industry sector of the firms .

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  1. R. Kohli, and S. Devaraj, “Measuring Information Technology Payoff: A Meta-Analysis of Structural Variables in Firm-level Empirical Research,” Information Systems Research 14 (2), 2003, pp. 127-145.

  2. Proposition 1. IT payoff will differ among the industry sector of the firms. • Proposition 2. Studies using larger sample sizes will show greater IT payoff. • Proposition 3. Studies using primary data sources will show greater IT payoff than those using secondary data sources. • Proposition 4. Studies with profitability-based dependent variables will have different IT payoff than those that measure productivity or both. • Proposition 5A. Studies applying regression or economic models will have higher IT payoff than those applying correlation-based analyses. • Proposition 5B. Studies with longitudinal designs will have greater IT payoff than those with cross-sectional designs. • Proposition 5C. Studies capturing IT assets and IT impacts (process orientation) will have higher IT payoff than those lacking process orientation.

  3. Meta Methodology • Development of a framework listing factors that contribute to explaining IT payoff; • Selection of studies to be included in the analysis; • Documentation and coding of the various characteristics of studies included in the analysis; • Statistical meta-analyses through regression (logistic and ordinary least squares) and discriminant analysis procedures; • Documentation of findings from the two statistical procedures, and directions for future research.

  4. Methodology • 66 studies published between 1990 and 2000 • dependent variablea classification, sample size, asset identification, and industry.

  5. Models • Logistic regression (LR) is preferred when assessing the contribution of variables. • Discriminant Analysis (DA) is suitable for understanding and explaining research problems that involve a single categorical-dependent variable and several independent variables. • Continuous IT Payoff Variable: OLS Regression We developed a percentage-based “continuous” IT payoff variable by categorizing the study outcome (or dependent) variables into subcategories

  6. Future Implications • future studies should consider gathering data from primary sources. • researchers should gather larger samples comprising of longitudinal or panel data to assess the lag effects of IT payoff. • productivity-based dependent variables are better suited to assess payoff results than those based on profitability

  7. Issues • Lag necessary to the model but data was not collected for that purpose. • Sample size to high to codify for it

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