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This paper aims to investigate an interesting yet mostly ignored distinction within external CEO successions: outside successors who have previous CEO experience and those who do not. It examines stock market reaction, compensation and firm performance prior and post‐succession.

The authors used an event study, “Patell Z‐statistic” and “Rank Z‐statistic” to test cumulative abnormal return before and after the successions. They also used probit and OLS regressions to examine firm performance and CEO compensation prior and post‐succession.

The authors find that the stock market reacts positively to the hiring of an outsider who is an exCEO. Compared with firms that hire non‐exCEOs, firms that hire exCEOs had higher debt ratios and greater bankruptcy chances pre‐succession, but post‐succession, these firms still have worse financial performances. Non‐exCEOs come from better performing firms than exCEOs. There is no consistently significant difference in compensation between an exCEO and a non‐exCEO, though the compensation for both increases significantly from that of the predecessors and that of their previous positions.

Future research could focus on the cost‐benefit tradeoff of hiring an exCEO. It would be interesting to examine the role of the board of directors in assessing this cost‐benefit tradeoff and determining the optimal choice for the firm. An important aspect that has not been sufficiently examined in the literature is the CEO fit. Hiring an exCEO may not always be the right choice for the firm. Another area for future research could examine how the post‐succession performance is affected by exCEO tenure in previous CEO position(s) and whether the exCEO worked in several industries or in the same industry.

This paper also has implications for the board of directors. There seems to be a negative transfer of human capital when it comes to hiring exCEOs. The human capital theory suggests that job‐specific experience positively relates to job performance. According to Hamori and Koyuncu, prior CEO experience may “lead to the formation of knowledge corridors and decision‐making templates that make it difficult for individuals to take in inconsistent information or take actions that are different from past ones in a changed context. This, in turn, undermines performance”. Boards of directors should put more effort into considering inside relay successions and should be cautious when hiring an outsider who has prior CEO experience. A best‐of‐both‐worlds scenario may be for boards to hire exCEOs into top executive positions, such as COO and/or president, so as to give them a chance to be groomed for the top position and familiarize themselves with the firm while still benefiting from their prior CEO experience.

There is very little research on the distinction between outside CEOs with previous CEO experience and those with no such experience. This paper tries to shed some light on this important issue in corporate governance in order to explain why boards of directors would hire an outsider with or without previous CEO experience.

The purpose of this paper is to attempt to propose a method based on evidential reasoning for hybrid grey attribute decision‐making problems where the attribute weights are partially known, in which the attribute values are interval grey numbers and linguistic grades.

The method is that the decision‐maker gives whitenization of each interval grey number based on their preference and belief structure of the form of qualitative attribute values, whitenization of quantitative attributes can be equivalently expressed in the form of belief structure with the principle of utility value equivalence, and then the grade belief structure decision matrix can be determined. By using the analytical evidential reasoning algorithm, belief degrees of each alternative belonging to each linguistic grade are obtained. Two pairs of nonlinear optimization models which are solved by genetic algorithms (GA) are constructed to compute the maximum and the minimum expected utilities of each alternative, respectively.

The results show that decision‐maker based on his/her risk preference gives whitenization of each interval grey number and selects corresponding alternative policy.

The method exposed in the paper can be used to deal with problems of grey multiple attribute decision making and hybrid grey multiple attribute decision making.

The paper succeeds in constructing two pairs of nonlinear optimization models based on the analytical evidential reasoning algorithm which are solved by genetic algorithms and the hybrid grey multiple attribute decision‐making approach with partial weight information.

This paper aims to explore true technical efficiency in order to select the most competitive manufacturing industries in China. And the paper intends to discuss how environmental variables measured by energy consumption affect performance in different industrial sectors under the restriction of low-carbon economy.

In order to measure the calculated efficiency of industrial sectors more accurately, Three-stage DEA model is presented in the empirical analysis using data from 2007 to 2010 covering 29 manufacturing industries in China. The advantage of using this method is enabling us to separate the managerial factor from external environmental factors and random errors factors on the technical efficiency.

The results using this Three-stage DEA model show that textile manufacturing sector has the highest technical efficiency, and when environment variables are not considered, efficiencies in machinery and electronics manufacturing industries have a significant increase. Moreover, this empirical model enables us to evaluate the technical performance in various manufacturing sectors more accurately.

This study provides a useful efficiency measurement tool (Three-stage DEA model) to calculate technical efficiency among different industrial sectors. Technical efficiency plays a key role in building the competitiveness of manufacturing industry. Based on the objective efficiency evaluation, the paper can make a better selection of the most competitive industries.

The paper contributes to the existing literature by developing a Three-stage DEA to examine the technical efficiency and competitive power of manufacturing sectors in China. This study has great policy implications for the research of China's manufacturing in both ideas and methodology.

This paper proposes an ANN based adaptive damping control scheme for the unified power flow controller (UPFC) to damp the low frequency electromechanical power oscillations. In this paper a novel damping control strategy based on the time‐domain analysis of system transient energy function (TEF) is proposed and implemented by using well tuned conventional PI controllers to obtain the preliminary training data for the design of the proposed controllers. The multi‐layered feed forward neural network with error back‐propagation training algorithm is employed in this study. Models of UPFC and ANN controllers suitable for incorporating with the transient simulation programs are derived and tested on a revised IEEE nine‐bus test system. Comprehensive simulation results demonstrate the great potential of using UPFC in damping control and the excellent performance of the proposed control scheme.

Using bibliometrics, this study aims to explore the intellectual structure of social segregation research, key contributors, thematic areas and hotspot topics.

A bibliometric analysis was performed for more than 15,000 research papers listed in one of the famous, rich and widely used scientific databases: Web of Science (WoS). This review approach was used to identify social research hotspots on segregation, intellectual structure, borders and development trends. VOSviewer and Gephi software were employed for mapping and analysis.

The study indicates a marked increase in segregation research, particularly from a spatial/urban perspective. The study reveals the interrelationship between segregation and many other social concepts, such as social equality, cohesion, integration and inclusion. In conclusion, addressing the ramifications resulting from the multiple forms of segregation will help in implementing social policies and evaluating their impact on achieving inclusive social development in general and the 2030 agenda of Sustainable Development Goals (SDGs) in specific.

This study remains limited to the precision and thoroughness of the bibliographic data gained from WoS.

This study is valuable for readers to gain rich insights into the state of research on social segregation. It also provides ideas for future research that prospective authors and interested research and academic institutions can investigate.