A Meta-Analysis of the Effects of IT Investment on Firm Financial Performance
Bibliographic record
Abstract
ABSTRACT We use meta-analysis techniques to examine research choices that affect findings with respect to the return on IT investment. Recent research has established that IT investment is substantially related to firm financial performance. We find, however, that the relationship between IT investment and performance varies, depending on how both financial performance and IT investment are measured. Despite criticism of accounting measures as indicators of IT payoff, we find that the relationship is often stronger in studies that employ accounting measures rather than market measures of firm performance. This difference is driven by research that focuses on the process-level impacts of IT investment. Furthermore, the relationship is also stronger when IT investment is measured as IT strategy or spending, rather than IT capability. We discuss the practical implications of the results of our meta-analysis and suggest new directions for future theory development and research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.041 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".