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Record W2346987431 · doi:10.1080/1331677x.2016.1164926

Digital channels diminish SME barriers: the case of the UK

2016· article· en· W2346987431 on OpenAlexfundno aff
Ivana Stankovska, Saso Josimovski, Christopher Edwards

Bibliographic record

VenueEconomic Research-Ekonomska Istraživanja · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
FundersMcGill University
KeywordsBusinessSocial mediaContext (archaeology)MarketingDigital marketingKnowledge managementPosition (finance)Digital mediaComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This article investigates the usage of digital channels by UK small- and medium-sized enterprises (SMEs) and assesses the impact caused on their strategic marketing position. The research is based on statistical analysis of 66 surveyed SMEs in the context of the digital era. Despite indications from the relevant literature about the reluctance of SMEs to adopt advances in technological communication, the research reported indicates a high level of usage of digital channels, especially social media (SM). The web 2.0 technologies that facilitate the new digital channels are standardised, interactive, ubiquitous and cheap. These features change the way how companies communicate and shift fundamental marketing and business concepts. Due to this shift, the SMEs’ barriers for technology adoption, including lack of financial resources, knowledge and skills, are diminishing. The latter, supported also by the research findings, increases the impact of SMEs bringing them closer to the large corporations in the global marketplace. The study is significant because it extends previous knowledge on technology adoption, with findings about the adoption of digital channels by SMEs, but more importantly, it opens up a novel insight into strategic literature for SMEs.

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 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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.363
Teacher spread0.300 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations64
Published2016
Admission routes1
Has abstractyes

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