Strategic information systems planning model for building flexibility and success
Bibliographic record
Abstract
Purpose This paper provides a model for IS planning for building flexibility and success, by considering volatile environment and the possibilities for leveraging the user's cognitive capabilities. Design/methodology/approach A review of existing IS planning models is given to identify the shortcomings in building flexibility and success. A model is evolved by hypothesizing user involvement in IS planning leads to IS flexibility; and flexibility in IS enables organizational flexibility and IS success. The control variables were considered at the user, IS, and organizational levels. The proposed model was examined by a questionnaire survey, in which 296 users and planners from 42 organizations participated. Findings The study results validated the proposed model that IS success and organizational flexibility could be achieved through IS flexibility, which could be generated by involving users in IS planning. Also, the study results have shown that user expectations, perceived personal usefulness, and users' internal flexibility possess a high driver power for user involvement. Research limitations/implications The variables included in the model are not exhaustive, and the validity of the model has not been tested in a real life situation. Originality/value This paper explains the ways of collaborating with users and top management to plan a flexible IS by studying the impending changes in the environment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".