Enhancing Strategic IT Alignment through Common Language: Using the Terminology of the Resource-based View or the Capability-based View?
Bibliographic record
Abstract
Despite all the studies on alignment in the past 30 years, alignment is still CIOs’ top concern, denoting the lack of prescriptive studies on antecedents of alignment. Particularly, shared language between CIO and top management team is one of the most important yet neglected antecedent of alignment. While previous studies suggest CIOs avoid technical language and use business terminologies, they do not provide further details. The purpose of this study is to prescribe guidance for CIOs regarding the terminologies that should be used in a conversation with the top management team. Leveraging the literature on strategic management, we suggest CIOs apply the nomenclature of theories of Resource-based View or Capability-base View instead of technical jargons. Moreover, using the Semantic Memory Theory, we hypothesized that applying the nomenclature of Capability-based View results in higher top managers’ understanding of the role of IT. An experiment is suggested to evaluate the hypotheses.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.012 | 0.038 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".