The Impact of E-Commerce Announcements on the Market Value of Firms
Bibliographic record
Abstract
Firms are undertaking growing numbers of e-commerce initiatives and increasingly making significant investments required to participate in the growing online market. However, empirical support for the benefits to firms from e-commerce is weaker than glowing accounts in the popular press, based on anecdotal evidence, would lead us to believe. In this paper, we explore the following questions: What are the returns to shareholders in firms engaging in e-commerce? How do the returns to conventional, brick and mortar firms from e-commerce initiatives compare with returns to the new breed of net firms? How do returns from business-to-business e-commerce compare with returns from business-to-consumer e-commerce? How do the returns to e-commerce initiatives involving digital goods compare to initiatives involving tangible goods? We examine these issues using event study methodology and assess the cumulative abnormal returns to shareholders (CARs) for 251 e-commerce initiatives announced by firms between October and December 1998. The results suggest that e-commerce initiatives do indeed lead to significant positive CARs for firms' shareholders. While the CARs for conventional firms are not significantly different from those for net firms, the CARs for business-to-consumer (B2C) announcements are higher than those for business-to-business (B2B) announcements. Also, the CARs with respect to e-commerce initiatives involving tangible goods are higher than for those involving digital goods. Our data were collected in the last quarter of 1998 during a unique bull market period and the magnitudes of CARs (between 4.9 and 23.4% for different subsamples) in response to e-commerce announcements are larger than those reported for a variety of other firm actions in prior event studies. This paper presents the first empirical test of the dot com effect, validating popular anticipations of significant future benefits to firms entering into e-commerce arrangements.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".