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This study investigates the relationship between corporate social responsibility (CSR) and financial performance of the banking sector from the prospective of emerging countries.

This study obtained balance sheet and income statement data for 173 banks in 20 emerging countries from the Bankscope database from 2005–2018. The CSR-related data were taken from the Thomson Reuters ASSET4 database. Moreover, macroeconomic controls such as GDP per capita, inflation, and financial development are attained from the GFDD. The series of institutional quality indices (Political Stability, Rule of Law, Control of Corruption, Government Effectiveness, and Regulatory Quality) is obtained from the WGI. At the same time, national culture and bank regulation are attained from Hofstede Insights and Barth et al. (2013). We used the panel fixed-effects model in our baseline estimations, while 2SLS and GMM were applied to control for endogeneity.

The finding shows that CSR activities significantly improve bank performance, but the effect varies across the bank. Only environmentally friendly activities have shown a significant positive relationship with banking performance for CSR dimensions. However, the social and government dimensions did not significantly affect bank performance. Moreover, a sound institutional and regulatory environment and national norms play an important role in the nexus of CSR activities and bank performance.

This study provides empirical evidence that sheds light on CSR and bank performance in an emerging market context.

Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval uncertainty can become computationally intractable because the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach.

In OLK-VARO, Kriging metamodels are constructed for replacing robust evaluations of the design alternative delivered from the outer level, reducing the nested optimization structure of previous VARO approach into a single loop optimization structure. An on-line updating mechanism is introduced in OLK-VARO to exploit the obtained data from previous iterations.

One nonlinear numerical example and two engineering cases have been used to demonstrate the applicability and efficiency of the proposed OLK-VARO approach. Results illustrate that OLK-VARO is able to obtain comparable robust optimums as to that obtained by previous VARO, while at the same time significantly reducing computational cost.

The proposed approach exhibits great capability for practical engineering design optimization problems under interval uncertainty.

The main contribution of this paper lies in the following: an OLK-VARO approach under interval uncertainty is proposed, which can significantly ease the computational burden of previous VARO approach.

Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization.

A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed.

The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum.

The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.

Engineering system design and optimization problems are usually multi-objective and constrained and have uncertainties in the inputs. These uncertainties might significantly degrade the overall performance of engineering systems and change the feasibility of the obtained solutions. This paper aims to propose a multi-objective robust optimization approach based on Kriging metamodel (K-MORO) to obtain the robust Pareto set under the interval uncertainty.

In K-MORO, the nested optimization structure is reduced into a single loop optimization structure to ease the computational burden. Considering the interpolation uncertainty from the Kriging metamodel may affect the robustness of the Pareto optima, an objective switching and sequential updating strategy is introduced in K-MORO to determine (1) whether the robust analysis or the Kriging metamodel should be used to evaluate the robustness of design alternatives, and (2) which design alternatives are selected to improve the prediction accuracy of the Kriging metamodel during the robust optimization process.

Five numerical and engineering cases are used to demonstrate the applicability of the proposed approach. The results illustrate that K-MORO is able to obtain robust Pareto frontier, while significantly reducing computational cost.

The proposed approach exhibits great capability for practical engineering design optimization problems that are multi-objective and constrained and have uncertainties.

A K-MORO approach is proposed, which can obtain the robust Pareto set under the interval uncertainty and ease the computational burden of the robust optimization process.

This study/paper aims to reach the core objective of hospitality order cancellation prediction (HOCP), that is, to identify potential cancellers from many customer bases, thereby enhancing the effectiveness of customer retention campaigns. However, few studies have focused on predicting hospitality order cancellation.

A novel profit-driven model for predicting hospitality order cancellation is proposed to bridge this research gap. The authors construct profit-driven extreme gradient boosting (XGBoost) based on a grid search on HOCP to maximize profit by selecting optimal hyperparameters of XGBoost.

Real-world data set is analyzed, and the proposed model yields more profits than other predictive models. Sensitivity analysis proves that the proposed model is robust to the key hyperparameter and application scenario. Furthermore, some preventive measures based on visual analysis results are provided to reduce the cancelled probability of orders.

This research will help hotel managers to transfer the modeling goal to profit orientation and encourage relevant researchers to interpret the prediction results of models for hotel order cancellation prediction in a post hoc manner. Besides, the proposed model can be applied to various enterprises with different average order profits and help managers optimize revenue management.

This research expands the relevant literature and offers guidance for predicting hospitality order cancellation from a profit-driven perspective at the customer level. The proposed model can provide macro-control to hotel managers and obtain the most satisfactory profits in micro-control.

This paper aims to present an underwater climbing robot for wiping off marine life from steel pipes (e.g. jackets of oil platforms). The self-adaption mechanism that consists of a passive roll joint and combined magnet adhesion units provides the robot with better mobility and stability.

Adhesion requirements are achieved by analyses of falling and slipping. The movement status on pipes is analyzed to design the passive roll joint. The optimized structure parameters of the combined magnet adhesion unit are achieved by simulations. An approximation method is established to simplify the simulations conditions, and the simulations are conducted in two steps to save time effectively.

The self-adaption mechanism has expected performance that the robot can travel on pipes in different directions with high mobility. Meanwhile, the robot can clean continuous region of underwater pipes’ surface of offshore platforms.

The proposed underwater robot is needed by offshore oil platforms as their jackets require to be cleaned periodically. Compared with traditional maintenance by divers, it is more efficient, economic and safety.

Due to the specific self-adaption mechanism, the robot has good mobility and stability in any directions on pipes with different diameters. The good performance of striping attachments from pipes makes the underwater robot be a novel solution to clean steel pipes.

Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.

In the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.

Ten numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.

The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.

CV-LCB approach can balance the exploration and exploitation objectively.

The purpose of this paper is to present a modified bi-level integrated system collaborative optimization (BLISCO) to avoid the non-separability of the original BLISCO. Besides, to mitigate the computational burden caused by expensive simulation codes and employ both efficiently simplified and expensively detailed information in multidisciplinary design optimization (MDO), an effective framework combining variable fidelity metamodels (VFM) and modified BLISCO (MBLISCO) (VFM-MBLISCO) is proposed.

The concept of the quasi-separable MDO problems is introduced to limit range of applicability about the BLISCO method and then based on the quasi-separable MDO form, the modification of BLISCO method without any derivatives is presented to solve the problems of BLISCO. Besides, an effective framework combining VFM-MBLISCO is presented.

One mathematical problem conforms to the quasi-separable MDO form is tested and the overall results illustrate the feasibility and robustness of the MBLISCO. The design of a Small Waterplane Area Twin Hull catamaran demonstrates that the proposed VFM-MBLISCO framework is a feasible and efficient design methodology in support of design of engineering products.

The proposed approach exhibits great capability for MDO problems with tremendous computational costs.

A MBLISCO is proposed which can avoid the non-separability of the original BLISCO and an effective framework combining VFM-MBLISCO is presented to efficiently integrate the different fidelities information in MDO.

The purpose of this paper is to compare the sensing characteristics of uniform fiber Bragg grating (FBG) and tilted fiber Bragg grating (TFBG) by presenting a detailed research review. Temperature, axial strain, bending, vibration and refractive index measurands of FBG and TFBG sensor are presented and some significant differences are found.

Theoretical analysis and practical application in engineering are investigated and compared from other authors' research papers and self analysis. Spectra behavior of both FBG and TFBG are discussed.

There are found to be significant differences in temperature, axial strain, bending, vibration and refractive index sensing characteristics of FBG and TFBG.

The paper's analysis is comprehensive and clear and provides readers with the sensing characteristics of FBG and TFBG in detail.

This empirical study aims to test the impact of four types of entrepreneurial context on the growth and success rates of new ventures in China and related the findings to the theory and practice of entrepreneurship dating back 2,500 years to ancient China.

After describing the business guidelines given by Fan Li, an entrepreneurial merchant selling Chinese medicines in ancient times, a conceptual framework was extracted as the basis for a discussion of the relationship between entrepreneurial context and entrepreneurial activity. Entrepreneurial context was conceptualized as being composed of family, social, business and institutional components. Five hypotheses about the influence of these different context variables on entrepreneurial activities were developed. From data compiled from the sampling of 239 business entrepreneurs in Beijing, a hierarchical regression was formed and the hypotheses tested.

The impact of entrepreneurial context on entrepreneurial activity can be divided into two layers, internal factors (e.g. family context) which are similar to “yin” (?) in the traditional Chinese philosophy while external factors (e.g. business, social and institutional contexts) were like “yang” (?). The two factors play different roles in entrepreneurial activities, while different contexts mediate and moderate each other in complex ways.

Research limitations pertain to the size and locale of the sample. A larger sample that involved subjects from different regions would facilitate a wider understanding of the effects of entrepreneurial context upon the entrepreneurial process.

The theory of entrepreneurial context is in its beginning stages, and the paper completed a systematic study of entrepreneurial context through theoretical model building using large-sample empirical research. In addition, the paper is the first ever to relate the theory and practice of entrepreneurship back 2,500 years. Through a multi-research methodology, the study clearly shows the critical importance of integrating Chinese history into the development of management theory.