Bibliographic record
Abstract
This dissertation investigates the roles of corporate digital assets in corporate decision-making and value creation. In Chapter 1, I examine the role of corporate digital assets in facilitating financial reporting decisions. In Chapter 2, I evaluate how corporate digital assets contribute to financial performance and firm value and whether accounting book earnings appropriately reflect the economic value of corporate digital assets. Chapter 1 studies the impact of corporate data accumulation and utilization on voluntary disclosure decisions. Using detailed website cookie information obtained from U.S. firms' websites in real time, I investigate how corporate data accumulation and utilization affect voluntary disclosure decisions. Cookies infuse first-hand, granular, and real-time data into managers' information sets and have the potential to enrich internal information about customers and sales operations. I show that the number of cookies is positively related to the frequency and the likelihood of issuing management sales forecasts. Using FinBERT-based measures, I find that the usage of cookies is also associated with a larger percentage of qualitative disclosure regarding customers, marketing, and products in 10-K filings. Next, I provide evidence that cookie-collected data are more useful if the collected data are of stronger relevance for disclosure and of larger volume. Additional analyses indicate that data analytic technology assists firms in better translating cookie-collected data into external disclosure, while data privacy protection regimes impair the usefulness of cookie-collected data. Using the California Consumer Privacy Act as a quasi-natural experiment, I provide additional evidence for the causal relation between cookies and voluntary disclosure. Overall, the paper sheds light on the value of corporate data assets in disclosure decisions and speaks to the effects of data analytic technology as well as the potential impacts of data privacy regulations. Chapter 2 examines how corporate digital assets contribute to financial performance and firm value and assesses if accounting earnings reflect the true economic value of digital assets. By combining website cookie information with website traffic data, I construct firm-specific measures of the volume of consumer data that firms collect through website cookies. I find that the volume of cookie-collected consumer data is positively associated with firms’ gross profitability and operating efficiency. In addition, it exhibits robust value relevance and positive relations with valuation multiples and future stock returns, suggesting that the market gives higher valuation to firms with more digital assets. Further, I show that the size of cookie-collected consumer data is positively associated with firms’ SG&A expenses but not intangible assets. As a result, cookie-collected consumer data negatively relate to bottom-line earnings (after SG&A). The distinct effects of digital assets on market valuation and accounting earnings imply that current accounting standards may fail to reflect the true economic value of digital assets. Overall, this study provides initial empirical evidence for the value of corporate digital assets and offers implications for future accounting standards and reporting regulations regarding digital assets.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".