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 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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".