Information Technology Alignment and Innovation: 30 Years of Intersecting Research
Bibliographic record
Abstract
Business-IT alignment (hereafter alignment) and information technology-enabled innovation (hereafter innovation) are essential for firm performance and competitive advantage. During the past 30 years, alignment and innovation literature streams have grown and become important areas of inquiry in the Information Systems field. Nevertheless, both literature streams have remained separate; it is unclear where and how the two streams overlap. To our knowledge, none of the existing review articles has systematically examined this overlap or how each literature stream informs the other. In this monograph, we bridge this gap and present findings from a review of the alignment and innovation literature streams published between 1990 and 2020 in the Senior Scholars’ Basket of Eight Journals of the Association for Information Systems. We summarize approaches, challenges, and opportunities seen in the alignment and innovation literature streams. Our analysis reveals that alignment scholars tend to overlook the complexities inherent in the process of innovating and view innovation as a black box. Meanwhile, innovation scholars assume different organizational components during the innovation process seamlessly work together to support alignment. We conclude that scholars in both camps should consider undertaking studies that examine aligning and innovating as interdependent processes: aligning involves coordination and cooperation among business units, and in many cases, innovations are needed to achieve alignment. Similarly, innovating with information technology jolts the organization out of its previous alignment and requires aligning in parallel to innovating to restore alignment. We end the monograph by presenting guidance to both scholars and practitioners interested in alignment and IT-enabled innovation.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.032 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".