On the Assessment of the Strategic Value of Information Technologies: Conceptual and Analytical Approaches1
Bibliographic record
Abstract
This study compares two conceptual (resource-centered and contingency-based) and two analytical (linear and nonlinear) approaches that can be used to assess the strategic value of information technology. Two hypotheses related to these approaches are developed and tested based on matched survey data collected from the CEOs and CIOs of 110 firms. The results indicate that the resource-centered and contingency-based approaches provide complementary understanding of the strategic value of IT. On the one hand, the contingency-based approach is better at explaining the impact of cost-related IT applications on firm performance. Alignment between business strategy and information systems strategy on cost reduction was found to have a significant negative association with firm expense. On the other hand, the resource-centered perspective has a stronger predictive ability of IT impact on firm revenue and profitability. Our results indicate that investments in growth-oriented applications were directly and positively related to firm revenue. An ANOVA test indicates that the nonlinear approaches provide additional insights that help to better understand the relationship between alignment and performance. The response surface method (RSM) shows that high-end strategic alignment (i.e., fit occurring when business strategy and IT strategy are both high) leads to superior performance compared to low-end strategic alignment (i.e., fit occurring when business strategy and IT strategy are both low). We discuss the implications of this study for research and practice and conclude with suggestions for future research directions.
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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 | 0.022 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".