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Record W6999828488

Effects of the coronavirus pandemic on SMEs internationalization

2021· other· en· W6999828488 on OpenAlexaboutno aff

Bibliographic record

VenueLUTPub (LUT University) · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsInternationalizationPandemicInternational businessCoronavirus disease 2019 (COVID-19)Quarter (Canadian coin)Coronavirus
DOInot available

Abstract

fetched live from OpenAlex

Internationalization is one of the most important growth measures for SMEs, and as a result of globalization, the phenomenon has further increased its popularity. In the first quarter of 2020, the international business environment experienced an unprecedented external shock that had major impacts on the international business environment and thus the internationalization of companies. This study examined how the coronavirus pandemic has affected the internationalization processes of industrial SMEs. In addition, the study will address the impacts of the pandemic on firms’ key business processes and how firms have managed to protect their businesses during the pandemic.
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\nThe study was conducted using qualitative research methods, interviewing four Finnish-based SMEs that had begun to internationalize either during or just before the coronavirus pandemic. The results of the interviews were analyzed by comparing them with the literature, which was used to draw conclusions. The results of the study show that travel and gathering restrictions imposed as a result of the coronavirus pandemic have significantly hampered and slowed down the internationalization process of SMEs. As a result of the pandemic, firms have had to adapt their business processes to meet the challenges posed by the external shock. The results also show that firms have managed to protect their businesses thanks to rapid adjustment measures. Academically, the thesis provides new findings on the pandemic's effects on firm-level internationalization processes and further discusses its effects on a dynamic capability-driven model.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.312
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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