Does IT Matter to Acquisitions? The Impacts of IT Distance on Post-Acquisition Performance
Bibliographic record
Abstract
Although researchers have examined the role of dyadic dynamics (i.e., interactions between the acquirer and the target firm) in the success of acquisitions, little attention has been devoted to the role of information technology (IT). In this study, we extend this literature by examining how pre-acquisition IT distance (i.e., the difference between the enterprise IT systems of the two firms that reflects the system incompatibility and resulting costs of system integration) affects the acquirer’s post-acquisition performance. To measure IT distance, we used a word-embedding technique to map each firm’s IT systems portfolio to a low-dimensional embedding space and calculate the distance between the firms in that space. Using data on U.S. firms’ acquisition activities over seven years, we found that IT distance is negatively associated with the acquirer’s post-acquisition performance. Also, the adverse effect of IT distance is stronger for acquisitions motivated by operational synergies, compared to those seeking non-operational synergies. This finding supports our fundamental premise that IT distance disrupts post-acquisition synergy creation, and more so when the combined firm has a greater need for tight integration to create acquisition synergies. This research contributes to the merger and acquisition (M&A) literature in management and IS by introducing a novel concept of IT distance and by theorizing and empirically examining its performance implications in acquisitions. The findings of this study can inform practitioners on how to devise IT strategies in corporate acquisitions to mitigate IT risks and achieve greater post-acquisition performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".