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There is little known globally on the association among the independent shareholder, board size and merger and acquisition (M&A) performance. This paper addresses the global issue about cross-border M&A in banking sector, particularly exploring the role of difference in the independent shareholder and board size between acquirer and target banks on synergy gains based on the international study.

Based on cross-border bank M&As data on 59 deals from 1995 to 2009, we initially apply social network analysis techniques to explore the country connectedness of the acquirer-target banks in cross-border M&As. Ordinary least squares (OLS) with robust standard errors is further used to investigate synergy gains within the difference in the degree of bank independent shareholder and board sizes between the acquirer and target banks.

Our results indicate that the acquiring banks are generally interconnected with the targeted banks and that some of acquiring banks are clearly concentrated in Asian countries including China, Hong Kong, and Philippines. Moreover, we find that cross-border M&As with larger difference in independent shareholders between the bidder and target bank would result in higher synergy gains in all cases of takeover premiums on 1 day, 1 week and 4 weeks. In addition, financial differences between the bidder and target banks have a significant impact on synergetic gains, a topic not explored in previous studies. There is no evidence that institutional and governance differences between bidder and target bank have significant cross-border impacts on takeover premiums with respect to 1 day, 1 week and 4 weeks, respectively.

This paper contributes to the literature by exploring the international issue about the role of difference in the degree of bank independent shareholder and board sizes between acquirer and target banks on synergy gains. Based on bank cross-border M&As data on 59 deals from 1995 to 2009, we initially apply social network analysis to explore the country connectedness of acquirer-target bank in cross-border M&As, while ten ordinary least squares (OLS) with robust standard errors is used to investigate synergy gains within the difference in the degree of bank independent shareholder and board sizes between acquirer and target banks.

This chapter examines the impact of banking competition, bank regulation, and the global financial crisis (GFC) of 2008–2009 on banks’ productivity changes. For the empirical analysis, I apply a semi-parametric two-step approach of Malmquist index estimates and bootstrap regression to a cross-country panel data of 8,451 commercial banks from 82 countries over the period 2004–2012. Empirical results show that (1) banking competition and capital regulation significantly enhance bank productivity, (2) a tighter bank supervision have a positive impact on bank productivity, and (3) bank productivity decreases during the GFC, but starts to increase as the GFC recovers. I also present consistent evidence that commercial banks in countries with better national governance have higher productivity growth before, during and after the GFC.

Customer lifetime value (CLV) has received increasing attention in database marketing. Enterprises can retain valuable customers by the correct prediction of valuable customers. In the literature, many data mining and machine learning techniques have been applied to develop CLV models. Specifically, hybrid techniques have shown their superiorities over single techniques. However, it is unknown which hybrid model can perform the best in customer value prediction. Therefore, the purpose of this paper is to compares two types of commonly‐used hybrid models by classification+classification and clustering+classification hybrid approaches, respectively, in terms of customer value prediction.

To construct a hybrid model, multiple techniques are usually combined in a two‐stage manner, in which the first stage is based on either clustering or classification techniques, which can be used to pre‐process the data. Then, the output of the first stage (i.e. the processed data) is used to construct the second stage classifier as the prediction model. Specifically, decision trees, logistic regression, and neural networks are used as the classification techniques and k‐means and self‐organizing maps for the clustering techniques to construct six different hybrid models.

The experimental results over a real case dataset show that the classification+classification hybrid approach performs the best. In particular, combining two‐stage of decision trees provides the highest rate of accuracy (99.73 percent) and lowest rate of Type I/II errors (0.22 percent/0.43 percent).

The contribution of this paper is to demonstrate that hybrid machine learning techniques perform better than single ones. In addition, this paper allows us to find out which hybrid technique performs best in terms of CLV prediction.

This paper aims to investigate the effect of corruption on bank profitability.

The paper adopts panel cointegration, differenced generalized method of moments (GMM) and system GMM.

The empirical results show that corruption is important in explaining the profitability of commercial banks in both developed and emerging countries. While it has mixed effects in emerging countries, only positive effect is validated in developed countries.

Macroeconomic measures of corruption are adopted in the study.

The paper contributes to the literature on corruption and bank profitability by reporting evidence from both developed and developing countries. Existing papers have only concentrated on developing countries.

Despite the availability of several published reviews on the adoption of blockchain (BC) in supply chain (SC), at present, the literature lacks a comprehensive review incorporating the antecedents and consequences of BC adoption. Moreover, the complex adoption of BC in SC, explained with the mediating and moderating relationships, is not fully consolidated. Thus, the aim of this study was to conduct a systematic literature review (SLR) on BC technology adoption (BCTA) in SC by integrating its antecedents and consequences.

Keyword searches were performed in multiple databases resulting 382 articles for evaluation and verification. After careful screening with respect to the purpose of the study and systematic processing of the retrieved articles, a total of 211 peer-reviewed articles were included in this study for review.

Various technological, organisational, individual, social, environmental, operational and economic factors were found as the antecedents of BCTA in SC. In addition, numerous applications of BC Technology (BCT) were identified, including asset management, identity management, transaction management, data management and operations management. Finally, the consequences of BCTA were categorised as operational, risk management, economic and sustainability outcomes.

This study can assist relevant decision-makers in managing the factors influencing BCTA and the potential uses of the technology to enhance SC performance.

By integrating the antecedents, applications and consequences of BCTA in SC, including the mediators and moderators, an integrated framework was developed that can potentially assist researchers to develop theoretical models. Further, the results of this SLR provide future directions for studying BCTA in supply chain management (SCM).