The Effect of Enterprise Systems Implementation on the Firm Information Environment
Bibliographic record
Abstract
Proponents of Enterprise Systems argue that these systems improve the information environment of the firm. However, evidence on these benefits is largely anecdotal and survey-based. In this paper, we use an archival research design to assess the impact of enterprise systems on a firm’s internal information environment as reflected in the production of management earnings forecasts. Specifically, we hypothesize that if enterprise systems improve management’s access to decision relevant internal information, management should be able to produce higher quality earnings forecasts. Consistent with disclosure theory and the purported technical characteristics of enterprise systems, we find evidence that enterprise system implementations are subsequently associated with significant increases in the likelihood of management forecast issuance and the accuracy of the forecasts. Additional robustness tests support the argument that improvements in management forecasts are due to improvements in the firm’s internal information environment rather than enhancements in management’s ability to manage earnings. Prior literature and professional guidance points to the common challenges and importance of measuring the benefits of information technology investments. Our study responds to these challenges by providing an initial basis for considering management forecast qualities as a measurable proxy for improvements in the firm’s internal information environment that result from information technology investments.
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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 | 0.013 | 0.084 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".