Exploring the factors in aligning information systems and organizational strategies in tall organizational structures in an uncertain environment
Bibliographic record
Abstract
Abstract Factors affecting the alignment of information systems (ISs) and organizational strategies (i.e., strategic alignment) vary depending on the organizational contexts, comprising both the structural and environmental contexts. Previous studies of strategic alignment were largely focused on organizations with flatter organizational structures and low environmental uncertainty. Therefore, findings from previous studies might not be applicable to organizations with tall organizational structures and in uncertain environments, like Iranian organizations. To this end, we aimed to discover less‐explored factors in achieving strategic alignment based on the contexts of Iranian organizations, using grounded theory. We conducted semi‐structured interviews with 29 experts in different organizations to illustrate and substantiate the development of an eightfold framework that specifies the factors for achieving strategic alignment. Specifically, the developed eightfold framework resulted in identification of 8 factors and 51 elements in achieving strategic alignment. Here, we identified five factors in achieving strategic alignment in addition to the factors reported by previous research (i.e., senior management, communication, and partnership), namely IS leadership, IS capabilities, organizational development, human resources, and IT infrastructure. The results indicate that these factors play a prominent role in determining the challenges and discerning the momentous requirements for achieving strategic alignment in organizations with tall structures in uncertain environments.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".