Public sentiment towards economic sanctions in the <scp>Russia–Ukraine</scp> war

Ngo, Vu Minh; Huynh, Luu Duc Toan; Nguyen, Phuc Van; Nguyen, Huan Huu

doi:10.1111/sjpe.12331

Cited by 33 publications

(13 citation statements)

References 14 publications

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“…The latent variable for Stakeholders’ Pressure was created from the variables Public Sentiment, Political affinity and UN Vote. Public sentiment measures the level of opposition in a country to the war in Ukraine (Ngo et al. , 2022).…”

Section: Methodsmentioning

confidence: 99%

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Doing business in Russia: normative organizational resilience, organizational identity and exit decisions

Avioutskii,

Roth

2024

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PurposeOur study examines multinational enterpris (MNE) decisions to withdraw from the Russian market on moral grounds in reaction to the Russo–Ukrainian war. We investigate to what extent these decisions reflect the normative organizational resilience of MNEs under institutional pressures in Russia. We test the impact of various macro- (home democracy, institutional quality, stakeholder pressure) and micro-variables (ESG criteria) that define the organizational identities of MNEs in relation to their withdrawal decisions. Our sample comprises 1,648 companies from 55 countries doing business in Russia before the start of the conflict.Design/methodology/approachTo test our hypotheses, we perform a nuanced analysis using both latent constructs and regression analysis on data for 1,648 MNEs.FindingsOur results are in line with the foreign divestment literature, suggesting that MNEs are likely to exit normatively distant countries.Originality/valueIn this study, we explore the impact of organizational values on normative responses of MNEs to a geopolitical crisis. We introduce a normative organizational resilience construct to demonstrate how MNEs respond to institutional pressures in a host country, in this case Russia. Making exit decisions on moral grounds, MNEs have acted as social actors endowed with moral sense and intentionality, in conformity with their organizational values.

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Section: Methodsmentioning

confidence: 99%

“…Description of variablesDoing business in Russia level of opposition in a country to the war in Ukraine(Ngo et al, 2022). UN Vote is the vote by a country in the UN on Russia's invasion of Ukraine.…”

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confidence: 99%

Doing business in Russia: normative organizational resilience, organizational identity and exit decisions

Avioutskii,

Roth

2024

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“…The political and economic fallout of the conflict has also received some attention in the literature. For example, using machine learning, Ngo et al (2022) find that public sentiments about the invasion are vastly heterogenous, with the most significant divergence within Asian and African countries. On the economic front, Hoang et al (2022) show that the economic sanctions imposed by the West did not affect Russian energy firms, although they had a negative impact on capital expenditures, cost of capital, and R&D intensity in the nonenergy-related firms in Russia.…”

Section: Literature Reviewmentioning

confidence: 99%

An Event Study on the Reaction of Equity and Commodity Markets to the Onset of the Russia–Ukraine Conflict

Obi

Waweru

Nyangu

2023

JRFM

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Using a standard event study methodology and the EGARCH model, this study examined the depth of market anomaly at the onset of the Russia–Ukraine conflict in 2022. Equity markets in Africa and G7 nations were analyzed for their varied political and economic connections to the conflict. While the G7 nations were strongly opposed to Russia, African countries remained neutral. This study shows that abnormal losses in the initial period of the conflict were larger and more persistent in the G7 markets, contradicting the widely held notion that more developed equity markets are more efficient than the less developed markets. EGARCH results revealed that volatility persistence was widely present, although the leverage effect was only confirmed for U.S. and Canada. Throughout the period, commodity prices rose sharply, producing significant abnormal gains in the futures market. Unfortunately, this had a deleterious effect on African economies due to their heavy reliance on grain and fuel imports, all of which are priced in U.S. dollars, and which also rose sharply during the period. This study concludes with suggestions on how to mitigate currency and commodity price shocks to dollar-reliant and import-dependent economies.

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“…This study [ 10 ] used machine learning to present new information about public opinion on economic sanctions based on approximately 1 million social media posts made in 109 countries during the conflict between Russia and Ukraine. It demonstrates the geographic disparity between official positions and popular opinion.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis and Comprehensive Evaluation of Supervised Machine Learning Models Using Twitter Data on Russia–Ukraine War

et al. 2023

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The Russia–Ukrainian War refers to the ongoing hostilities between Russia and Ukraine. It was first focused on whether Crimea and the Donbass were formally recognised as being a part of Ukraine when Russia started it in February 2014. The conflict dramatically grew when Russia began its incursion of Ukraine on February 24, 2022, following a military build-up on the Russian–Ukrainian border that started in late 2021. Examining public perceptions of the crisis between Russia and Ukraine is the goal of this piece. These days, social media has taken on a significant role in communication, and as a result, opinions can be found on platforms like Facebook, Twitter, and Instagram. The study makes use of his 11,250 tweets about the war between Russia and Ukraine from his Twitter account. Techniques, including image processing, object identification, and natural language processing, have shown application, power, and potential for machine learning. The same applies to text analytics. For text analysis, sentiment analysis, and entity annotation, machine learning techniques are frequently employed. According to the applicability and efficacy of the machine learning model, natural language processing toolkit in python is utilised in to examine the textual polarity and subjectivity score of tweets. Moreover, because machine learning models have a high degree of classification accuracy, they have been widely utilised to categorise emotions. We have developed and test models using three feature extraction techniques: TF-IDF (term frequency-inverse document frequency), BoW (bag of words), and N -gram. Each model was assessed using a number of important performance indicators, including accuracy, precision, recall, and F 1 score. Results show that the extra trees classifier (ETC) model achieves a highest accuracy of 0.84 in combination with the Bow property which is a measure to evaluate the efficacy of a machine learning algorithm. Logistic regression (LR), decision tree (DT), support vector machine (SVM), XGB, Gaussian naive base (GNB), ADA, and K -nearest neighbours (KNN) comparison have also been made.

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Public sentiment towards economic sanctions in the Russia–Ukraine war

Cited by 33 publications

References 14 publications

Doing business in Russia: normative organizational resilience, organizational identity and exit decisions

Doing business in Russia: normative organizational resilience, organizational identity and exit decisions

An Event Study on the Reaction of Equity and Commodity Markets to the Onset of the Russia–Ukraine Conflict

Sentiment Analysis and Comprehensive Evaluation of Supervised Machine Learning Models Using Twitter Data on Russia–Ukraine War

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