The key role of corporate IT reputation in driving organizational performance
Bibliographic record
Abstract
In this research, we study corporate IT reputation and its impact on performance. The topic is of interest because (1) the corporate IT reputation as measured by the firm’s perceived ability to develop and sustain its IT capability reputation could be linked to organizational performance; (2) the identification of new IT success factors is needed for a better understanding of the antecedent factors leading to performance; and (3) the importance of senior leadership and reputation has been observed from a CEO’s perspective and on organizational performance but few research addressed the contribution of corporate IT reputation to organizational performance. To do so, we conducted an online survey (n=297), and performed analyses through SmartPLS, The model explains more than 40% of the organizational performance. The main findings illustrate that corporate IT reputation is directly linked to organizational performance and indirectly through mediating variables such as IT strategic alignment, IT orientation and IT business value. With this research, we identify corporate IT reputation as an additional factor explaining the contribution of IT to organizational performance. Second, we add to previous works on IT strategic alignment and its impact on organizational performance. Third and final, we underline the importance of prior IT executive experience in other firms as a key driver of corporate IT reputation and organizational performance.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".