Business Competence of Information Technology Professionals: Conceptual Development and Influence on IT–Business Partnerships1
Bibliographic record
Abstract
This research aims at improving our understanding of the concept of business competence of information technology professionals and at exploring the contribution of this competence to the development of partnerships between IT professionals and their business clients. Business competence focuses on the areas of knowledge that are not specifically IT-related. At a broad level, it comprises the organization-specific knowledge and the interpersonal and management knowledge possessed by IT professionals. Each of these categories is in turn inclusive of more specific areas of knowledge. Organizational overview, organizational unit, organizational responsibility, and IT–business integration form the organization-specific knowledge, while interpersonal communication, leadership, and knowledge networking form the interpersonal and management knowledge. Such competence is hypothesized to be instrumental in increasing the intentions of IT professionals to develop and strengthen the relationship with their clients. The first step in the study was to develop a scale to measure business competence of IT professionals. The scale was validated, and then used to test the model that relates competence to intentions to form IT-business partnerships. The results support the suggested structure for business competence and indicate that business competence significantly influences the intentions of IT professionals to develop partnerships with their business clients.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".