Board Information Technology Governance Mechanisms and Firm Performance among Iraqi Medium-Sized Enterprises: Do IT Capabilities Matter?
Bibliographic record
Abstract
This paper aims to investigate the perceptions of Iraqi medium-sized enterprises’ board members on how board information technology governance mechanisms affect their companies’ performance with the help of IT capabilities as a mediator. The study is based on a survey of 223 board members using a stratified random sampling technique. The Structural Equation Model (SEM) method results show that board IT governance structure and board IT governance relational have a significant direct and indirect positive relationship with firm performance through IT capabilities. Contrariwise, IT capabilities do not interfere with the relationship between board IT governance processes mechanisms and firm performance. Our study contributes to the IT business literature by addressing new relationships and providing empirical evidence that explains the inconsistent and mixed results of prior studies. Moreover, it extends and complements these prior studies by considering three board IT governance mechanisms, four IT capabilities, and merges the two dimensions of firm performance in a developing country that offers different institutional settings and litigation environment. The study findings offer notable implications for business practitioners and industry leaders to enhance the IT environment and maximize their corporate outcomes. In addition, these findings draw the attention of the board members, management, and corporate general assemblies to recognize the importance of intensifying the investment in IT capabilities to gain superior firm performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".