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This study aims to examine the impact of home–host country distance on the cross-border mergers and acquisitions performance.

The results of this study are based on a final sample of 483 completed cross-border deals involving BRICS nation acquirers and targets spread across a set of 27 nations. While controlling for prior experience, among other factors, the impact of nine institutional distance dimensions on deal performance is examined. Cumulative abnormal returns calculated over the select event windows are used as a measure of deal performance.

The results of this study validate the explanatory power of cross-country distance and exhibit that financial and cultural distance exert a negative influence on deal performance, whereas political and global connectedness distance positively impacts performance. Interestingly, geographic distance is not found to be related to performance outcomes.

The results of this study caution against possible aggregation of the cross-country distance measure and point towards the need to acknowledge and analyse the multi-dimensional nature of distance.

The results of this study are expected to aid managers in devising internationalisation strategies and target selection, maximising their performance and shareholder wealth.

This study contributes to the knowledge of internationalisation and cross-country distance. It presents as one of the first to investigate the impact of institutional distance on deal performance using a substantially large multi-country emerging market data set.

The study aims to evaluate and compare the mergers and acquisitions (M&As) performance utilising a sample of deals originating from Brazil, Russia, India, China and South Africa (BRICS). In addition to nation-wise performance analysis, a further sub-sample analysis is conducted based on the target location (domestic and cross-border), development status (developed and emerging) and the acquired ownership stakes (majority and minority).

The final sample of the study includes 7,105 deals announced between 2000 and 2019. M&A performance is proxied by the abnormal returns earned over the select event windows. Multiple parametric and non-parametric tests are employed for testing the robustness.

The results indicate significant performance differences across BRICS markets, with the highest and lowest abnormal returns reported for Chinese and Russian acquirers, respectively. The disaggregated analysis also affirms the performance differences for the select sub-samples.

The study highlights the need for acknowledging and expounding the differences in M&As across emerging markets. Further, the results of the study provide a possible explanation of the disagreement over the M&A performance results reported in the previous literature.

Acknowledging and understanding the potential performance differences based on location, ownership strategies and development status can aid executives in sharpening decision-making and also help general investors.

The study contributes by examining a comprehensive sample of deals across five major emerging economies, as against the majority of previous studies which have their results based on either single nation samples or have utilised only a sub-sample of domestic or foreign acquisitions.

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.