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Record W2204973767

Enhancing Strategic IT Alignment through Common Language: Using the Terminology of the Resource-based View or the Capability-based View?

2015· article· en· W2204973767 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Conference on Information Systems · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTerminologyComputer scienceKnowledge managementAntecedent (behavioral psychology)Resource (disambiguation)Strategic alignmentConversationLinguisticsStrategic planningPsychologyManagement
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.095
GPT teacher head0.308
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it