Antecedents and outcomes of strategic IS alignment: an empirical investigation
Bibliographic record
Abstract
Prior research argues that alignment between business and information systems (IS) strategies enhances organizational performance. However, factors affecting alignment have received relatively little empirical attention. Moreover, IS strategic alignment is assumed to facilitate the performance of all organizations, regardless of type or business strategy. By using two studies of business firms and academic institutions, this paper: 1) develops and tests a model relating alignment, its antecedents, and its consequences and 2) examines differences in these relationships across organizational types and strategies. Findings indicate that alignment depends on shared domain knowledge and prior IS success, and also support the expected positive impact of alignment on organizational performance. Differences across Prospector, Analyzer, and Defender business strategies are examined. A key research contribution is the empirical demonstration that the importance of alignment, as well as the mechanisms used to attain alignment, vary by business strategy and industry. In past alignment studies, controlling for industry has not been uncommon. The findings suggest that future research studies should also control for business strategy. The article also empirically demonstrates that past implementation success influences alignment. In addition, it highlights the influence of a process variable, strategic planning, on the development of shared knowledge and, consequently, on alignment. This paper examines strategic issues related to the management of technology. Data from multiple surveys are used to test the extent to which strategic planning, shared business-IS knowledge, prior IS success, and other variables consistently enhance IS alignment. The study also provides empirical support for the popular argument that IS alignment improves organizational performance. It extends the current literature by examining the extent to which these findings hold across firm strategies and industries. The authors argue that not all firms are equally well served by allocating scarce resources to improve IS alignment.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".