Fooled by the hype? The influence of technology hype on acquisition premiums in digital M&As
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Given the rapidly evolving nature of digital technologies, the valuation of digital target firms in mergers and acquisitions (M&As) is particularly uncertain and complex. Adopting a socio-cognitive perspective, we argue that the cognitive burden of processing complex and uncertain information surrounding a digital technology creates a susceptibility for managers to rely on easily accessible expectations and media claims about these technologies, consistent with an availability heuristic. Consequently, managers incorporate excessively optimistic expectations from technology hype into their valuation assessments, leading them to pay higher acquisition premiums. We further propose that in-depth digital technology knowledge among the top management and prior experience in acquiring digital target firms alleviate the cognitive burden of assessing digital target firms, thereby reducing managers’ reliance on overly optimistic expectations associated with technology hype. Using a sample of digital M&As by S&P 1500 firms, we find support for these propositions. Additional analyses further reveal that digital M&As executed during hype phases generate lower post-acquisition returns than those completed outside hype phases. Overall, this study contributes to a better understanding of when and why heuristics may bias decision-making.
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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) | 0.000 | 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 it