Management Emotion and Firm’s Strategic IT Investment Propensity
Why this work is in the frame
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Bibliographic record
Abstract
Different from prior IS business value literature that has largely taken rational decision-making for granted, this paper investigated the impacts of management emotion on firms’ propensity of strategic IT investment. Based on 191 annual reports of 32 companies from three industries in a 6-year period (i.e. 2010–2015 fiscal year), we applied sentiment analysis to retrieve emotion tunes embedded in each report and analyzed their relationship with both the volume and the composition of three types of strategic IT investment signals (automate, informate, and transform) embedded in the annual report of the subsequent year. Results show that positive management emotion promotes firm’s propensity of all types of strategic IT investments and increases weights of informate and/or transform IT among all strategic IT investments. With positive management emotion, firms also show propensity of investing in strategic IT different from the industry’s dominant IT strategic role.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it