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Record W4206954627 · doi:10.5267/j.ijdns.2022.1.001

A dynamic capability theory perspective: borderless media breakthrough to enhance SMEs performance

2022· article· en· W4206954627 on OpenAlexvenueno aff
Nilna Muna, Ni Nyoman Kerti Yasa, Ni Wayan Ekawati, I Made Artha Wibawa

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsLeverage (statistics)BusinessSocial mediaCompetitive advantageQuality (philosophy)Product (mathematics)MarketingSmall and medium-sized enterprisesSurvey data collectionDynamic capabilitiesPath analysis (statistics)Knowledge managementIndustrial organizationComputer science

Abstract

fetched live from OpenAlex

Social media technology as borderless media has made it easier for small and medium-sized businesses (SMEs) to interact with their customers. The application of social media has impacted the operation and information sharing of SMEs, allowing them to develop innovation opportunities, to meet customer needs, and to improve firm performance. This study examined the influence of social media use on business networking quality and product innovativeness of SMEs. The data for this cross-sectional study were gathered from jewelry crafting SMEs in Bali, Indonesia using the survey method. The data were analyzed using the covariance-based statistical analysis technique with SPSS via AMOS 23. The results indicate that, while the direct link between social media adoption and firm performance is not significant, this path is fully mediated through business networking quality and product innovativeness. Hence, these SMEs should leverage their social media adoption due to strong business networking quality and product innovativeness enabling competitive advantage that heightens firm performance. Firm-level product innovation can harness the economic performance of the SMEs. The study limitations and future research endeavors are presented at the end of this paper.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.015
GPT teacher head0.368
Teacher spread0.352 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations18
Published2022
Admission routes1
Has abstractyes

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