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To examine the effect of pay‐for‐performance on distributive justice and the moderating effect of commitment performance appraisal in the case of Korean employees.

Data were collected from 28 companies and 656 employees. Moderated regression analyses were employed.

Findings show that pay‐for‐performance enhances employees' perception of distributive justice only when there is a commitment performance appraisal practice. Additional analysis shows that the commitment performance appraisal practices of a company influence employee perceptions of such practices in a U‐shape fashion.

Since, data were collected from 28 companies, the results may need to be modified before being generalized to/for Korean companies.

It is notable that only when accompanied by commitment performance appraisal does pay‐for‐performance enhance employee perception of distributive justice. Managers in Korean companies should consider implementing commitment appraisal practices when they want to utilize pay‐for‐performance.

This paper notes the fundamental changes in the pay systems in Korean companies, and offers practical implications for managers starting out on a pay‐for‐performance system.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

We evaluate the effect of political connections on the stock valuation of emerging market firms following the announcement of cross-border mergers and acquisitions (M&As). We further analyze the moderating roles of home and host market environments.

Our analysis of 361 Chinese cross-border M&A transactions during 2014–2018 employs an event-study methodology to assess the cumulative abnormal return (CAR) for acquirers. To test our hypotheses, we utilize a multiple regression model.

Politically connected firms experience a decrease in firm value following the announcement of cross-border M&As. However, this negative effect is weakened when the firm’s home region is more market-oriented, reflected by economic activity driven primarily by market mechanisms rather than government intervention. In contrast, the negative effect is strengthened when the host country exhibits higher governance quality, characterized by sound legal structures, labor regulations and developed capital markets.

Extending beyond previous studies on cross-border M&A performance, we analyze firm value based on signaling theory. Our findings reveal that market investors view cross-border M&As undertaken by politically connected firms from emerging economies with caution, resulting in a decline in acquirer value. Moreover, investors react more positively to cross-border M&As by politically connected acquirers in truly market-based regions. Conversely, investors expect that politically connected acquirers would encounter additional hurdles when executing cross-border M&As if the host country has high-quality governance.

This study quantified companies' views on the COVID-19 pandemic with sentiment analysis of US public companies' disclosures. The study aims to provide timely insights to shareholders, investors and consumers by exploring sentiment trends and changes in the industry and the relationship with stock price indices.

From more than 50,000 Form 10-K and Form 10-Q published between 2020 and 2021, over one million texts related to the COVID-19 pandemic were extracted. Applying the FinBERT fine-tuned for this study, the texts were classified into positive, negative and neutral sentiments. The correlations between sentiment trends, differences in sentiment distribution by industry and stock price indices were investigated by statistically testing the changes and distribution of quantified sentiments.

First, there were quantitative changes in texts related to the COVID-19 pandemic in the US companies' disclosures. In addition, the changes in the trend of positive and negative sentiments were found. Second, industry patterns of positive and negative sentiment changes were similar, but no similarities were found in neutral sentiments. Third, in analyzing the relationship between the representative US stock indices and the sentiment trends, the results indicated a positive relationship with positive sentiments and a negative relationship with negative sentiments.

Performing sentiment analysis on formal documents like Securities and Exchange Commission (SEC) filings, this study was differentiated from previous studies by revealing the quantitative changes of sentiment implied in the documents and the trend over time. Moreover, an appropriate data preprocessing procedure and analysis method were presented for the time-series analysis of the SEC filings.

This study aims to provide measurable information that evaluates a company’s ESG performance based on the conceptual connection between ESG, non-financial elements of a company and the UN Sustainable Development Goals (SDGs) for resolving global issues.

A novel data processing method based on the BERT is presented and applied to analyze the changes and characteristics of SDG-related ESG texts from companies’ disclosures over the past decade. Specifically, ESG-related sentences are extracted from 93,277 Form 10-K filings disclosed between 2010 and 2022 and the similarity between these extracted sentences and SDGs statements is calculated through sentence transformers. A classifier is created by fine-tuning FinBERT, a financial domain-specific pre-trained language model, to classify the sentences into eight ESG classes.

The quantified results obtained from the classifier reveal several implications. First, it is observed that the trend of SDG-related ESG sentences shows a slow and steady increase over the past decade. Second, large-cap companies relatively have a greater amount of SDG-related ESG disclosures than small-cap companies. Third, significant events such as the COVID-19 pandemic greatly impact the changes in disclosure content.

This study presents a novel approach to textual analysis using neural network-based language models such as BERT. The results of this study provide meaningful information and insights for investors in socially responsible investment and sustainable investment and suggest that corporations need a long-term plan regarding ESG disclosures.