Factors That Influence the Social Dimension of Alignment Between Business and Information Technology Objectives1
Bibliographic record
Abstract
The establishment of strong alignment between information technology (IT) and organizational objectives has consistently been reported as one of the key concerns of information systems managers. This paper presents findings from a study which investigated the influence of several factors on the social dimension of alignment within 10 business units in the Canadian life insurance industry. The social dimension of alignment refers to the state in which business and IT executives understand and are committed to the business and IT mission, objectives, and plans. The research model included four factors that would potentially influence alignment: (1) shared domain knowledge between business and IT executives, (2) IT implementation success, (3) communication between business and IT executives, and (4) connections between business and IT planning processes. The outcome, alignment, was operationalized in two ways: the degree of mutual understanding of current objectives (short-term alignment) and the congruence of IT vision (long-term alignment) between business and IT executives. A total of 57 semi-structured interviews were held with 45 informants. Written business and IT strategic plans, minutes from IT steering committee meetings, and other strategy documents were collected and analyzed from each of the 10 business units. All four factors in the model (shared domain knowledge, IT implementation success, communication between business and IT executives, and connections between business and IT planning) were found to influence short-term alignment. Only shared domain knowledge was found to influence long-term alignment. A new factor, strategic business plans, was found to influence both short and long-term alignment. The findings suggest that both practitioners and researchers should direct significant effort toward understanding shared domain knowledge, the factor which had the strongest influence on the alignment between IT and business executives. There is also a call for further research into the creation of an IT vision.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".