Seizing Growth Opportunities: A Risky Business? Effects of Cloud Sourcing on Mergers and Acquisitions
Bibliographic record
Abstract
Recent research has shown that enterprise information technology (IT) can drive strategic growth through mergers and acquisitions (M&As). An implicit assumption underlying this research is that firms own their IT infrastructure. Challenging this assumption, however, the emerging trend of cloud sourcing suggests that IT may be owned by third-party vendors. Since third-party ownership of IT can introduce significant transaction costs and operational inefficiencies, cloud sourcing, unlike in-house enterprise IT, may be considered unlikely to drive M&A growth. However, the unique combination of IT infrastructural and service flexibilities that cloud sourcing provides could help in reducing the risk of integration failure posed by M&As, thereby driving M&A growth. Grounded in the transaction cost economics and resource-based views of the firm, respectively, these arguments illustrate the conflicting theoretical viewpoints offered by prior literature. This study seeks to improve our theoretical understanding of the relationship between cloud sourcing and M&A growth by addressing the theoretical conflict. Analyzing a dataset of cloud sourcing deals and M&As comprising 4,075 observations from 673 firms, our research finds that highly standardized cloud services, i.e., SaaS (software as a service), public, and globalized clouds, have a positive impact on M&As, particularly in information industries. Overall, these results indicate that it is only under conditions of enhanced flexibility afforded by a standardized platform and informationally rich operating environments that cloud sourcing positively affects M&A growth. Support for the theoretical propositions is further established through interviews with industry experts and mechanism tests, which reveal that cloud sourcing has a positive impact, specifically on M&As requiring intensive integration, and is associated with reduced disclosure of M&A risks in annual reports. Finally, consistent with our view that cloud sourcing smooths post-M&A integration, this research also finds that firms with cloud sourcing have relatively stronger post-M&A 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".