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Record W2626190651 · doi:10.17705/1cais.04020

Strategic Alignment in SMEs: Strengthening Theoretical Foundations

2017· article· en· W2626190651 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

VenueCommunications of the Association for Information Systems · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsQueen's UniversityUniversity of Regina
Fundersnot available
KeywordsStrategic alignmentBusinessContext (archaeology)Dynamic capabilitiesIndustrial organizationStrategic managementSet (abstract data type)Strategic planningKnowledge managementStrategic financial managementMarketingComputer science

Abstract

fetched live from OpenAlex

Small and medium-sized enterprises (SMEs) are a vital part of the global economy in that they compose the vast majority of all businesses worldwide. In spite of these firms’ importance, they remain understudied in strategic alignment research. In this paper, we consolidate and extend the IS literature on strategic alignment in SMEs. We develop a set of theoretical propositions that outline the ways in which SMEs’ unique characteristics affect their ability to achieve and sustain alignment between their IS/IT strategy and their overall business strategy. In some respects, SMEs can achieve and sustain alignment as larger firms do, while, in other respects, they differ noticeably. We ground each of our propositions in the dynamic capabilities framework to strengthen the theoretical foundations of strategic alignment research, particularly in SMEs. We discuss the implications of our propositions and note theoretical issues emerging from the study of strategic alignment in the SME context.

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)0.001
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: none
Teacher disagreement score0.910
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0020.000
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 head0.035
GPT teacher head0.277
Teacher spread0.242 · 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