ERP system implementation announcements: does the market cheer or jeer the adopters and vendors?
Bibliographic record
Abstract
Purpose – The purpose of the study is to examine the implementation of Enterprise Resource Planning (ERP) on the announcement of firms’ stock market returns. The authors investigate the stock market reaction on ERP adopters and ERP vendor firms in the USA during 1990-2010. The study examines firm- and non-firm-specific factors including the role of the financial analyst in explaining the determinants of the cumulative abnormal returns surrounding ERP announcements of adopting firms. Design/methodology/approach – Data on ERP system implementation announcements of 112 US firms for the period 1990-2010 were collected from LexisNexis Academics. The authors estimate abnormal returns using an event study methodology for each of the ERP announcements based on the Fama–French three-factor and Fama–French-momentum four-factor models for ERP adopters and for vendors. Subsequently, the authors explain the determinants of abnormal returns in terms of firm and non-firm behavioral variables using cross-section regression methodology. Findings – The empirical results establish that cumulative abnormal returns of US firms on ERP system implementation announcements are positive, signifying that investors view this decision positively and that ERP implementation contributes to enhanced business value in the future. On the contrary, the impact of ERP announcements on vendors is muted. We find that the extent of financial analyst coverage negatively impacts abnormal returns, while the extent of stock market liquidity has a significant positive impact on abnormal returns. Research limitations/implications – This study is based on a sample of ERP implementing firms which are predominantly large firms and on technology provided by one vendor that is predominantly monopolistic. Practical implications – Firms’ attitudes toward implementing an ERP system for future efficiency gains and the implications on the stock market (and indirectly, on the cost of equity of adopters) provide valuable insights for firms and stock markets. Originality/value – This study brings clarity to the debate on stock market impacts of ERP implementation announcements – stock markets cheer such announcements. The study also contributes to the literature by examining firm-specific factors (such as performance, size and leverage) and non-firm-specific factors (such as market risk and analyst coverage) in explaining the determinants of abnormal returns of firms announcing ERP investment.
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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 | 0.010 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".