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Article
Publication date: 5 June 2023

Sakshi Kukreja, Girish Chandra Maheshwari and Archana Singh

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

403

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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

Originality/value

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.

Details

Review of International Business and Strategy, vol. 34 no. 1
Type: Research Article
ISSN: 2059-6014

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Article
Publication date: 21 November 2022

Sakshi Kukreja, Girish C. Maheshwari and Archana Singh

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…

334

Abstract

Purpose

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).

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

International Journal of Emerging Markets, vol. 19 no. 8
Type: Research Article
ISSN: 1746-8809

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Article
Publication date: 25 November 2024

Sohui Kim and Min Ho Ryu

This study introduces a novel approach to conducting importance-performance analysis (IPA) and importance-performance competitor analysis (IPCA) by utilizing online reviews as an…

23

Abstract

Purpose

This study introduces a novel approach to conducting importance-performance analysis (IPA) and importance-performance competitor analysis (IPCA) by utilizing online reviews as an alternative to traditional survey-based marketing research.

Design/methodology/approach

In order to conduct IPA and IPCA utilizing online reviews, the following three steps were executed: (1) Extract key attributes of the product/service with latent Dirichlet allocation (LDA) topic modeling. (2) Measure the importance of each attribute with keyword analysis. (3) Measure the performance of each attribute with KoBERT-based sentiment analysis.

Findings

By utilizing LDA, we were able to identify significant attributes identified by real users’ reviews. The approach of evaluating attribute importance using keyword metrics offers the advantage of straightforward computations, reducing computational expenses and facilitating intuitive metric assessments. The evaluation of BERT-based performance involved adapting pre-trained language models to the specific analysis domain, resulting in substantial time savings without compromising accuracy, ultimately bolstering the dependability of the metrics. Lastly, the case study’s findings indicate a growing emphasis on the aesthetic aspects of mobile banking apps in South Korea while highlighting the pressing need for enhancements in critical areas such as stability and security, which are particularly pertinent to the finance industry.

Originality/value

Previous studies have limitations in assessing significance solely based on sentiment scores and review ratings, resulting in an inability to independently measure satisfaction and importance metrics. This research addresses these limitations by introducing a keyword frequency-based importance metric, enhancing the accuracy and suitability of these measurements independently. In the context of performance measurement, this study utilizes pre-trained large language models (LLMs), which provide a cost-effective alternative to previous methods while preserving measurement accuracy. Additionally, this approach demonstrates the potential for industry-wide competitive analysis by enabling comparisons among multiple competitors. Furthermore, the study extends the application of review data-based IPA and IPCA, traditionally used in the tourism sector, to the evaluation of financial mobile applications. This innovation expands the scope of these methodologies, indicating their potential applicability across various industries.

Details

International Journal of Bank Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-2323

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Article
Publication date: 4 August 2023

Liu Yang and Jian Wang

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service…

770

Abstract

Purpose

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.

Design/methodology/approach

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).

Findings

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.

Originality/value

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.

Details

Kybernetes, vol. 53 no. 11
Type: Research Article
ISSN: 0368-492X

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