The Moderating Effect of Context on the Market Reaction to it Investments
Bibliographic record
Abstract
This paper examines the moderating effects of firm and IT characteristics on the market reaction to IT investment announcements. A special emphasis has been placed on the potential interaction effects of these two types of variables, since the previous event studies have paid limited attention to the possibility that they interact and jointly alter investors' perceptions in relation to IT investment announcements. Very recently, several authors have noted the importance of interaction effects on theory development for IS research. Their assessments are particularly relevant to IT-value event studies, since the market reaction to IT investment announcements involves a complex process shaped by the interaction of firm and IT characteristics. Based on the previous studies in IS, finance and accounting, a firm's growth potential and uncertainty are used as proxies to represent firm characteristics, while IT strategic role and asset-specificity of IT are chosen as the variables reflecting IT characteristics. Three other variables (discloser information, firm size and industry) are included to control for their effects. We develop eight hypotheses based on the examinations of the main and interaction effects of firm and IT characteristic variables on the shareholder's reaction to IT investment announcements. The results of the main effects indicate that a firm's growth prospects, uncertainty, the strategic role of IT and discloser information are significantly related to cumulative abnormal returns (CARs), while no significant effect was observed for asset-specificity of IT resources. Interestingly, however, interaction effects reveal that the stock market reacts with a discount to announcements of IT investments that are characterized as highly asset-specific in the presence of uncertainty. In addition, the market reacts more favourably to investments with a transformational IT strategic role when the firm faces greater uncertainty. One of our main contributions in this study is to provide a finer level of granularity with regard to the market reaction to IT investments by considering the interaction as well as the main effects of firm and IT characteristics.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".