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This study aims to offer insights regarding the impact of emotional conflict on innovation behavior. This study also explores the boundary conditions by examining the moderating effects of leader-member exchange (LMX) and team-member exchange (TMX) on the relationship between emotional conflict and innovation behavior.

This study used a questionnaire survey to collect data in China. Hypotheses were tested using hierarchical regression analysis. To test for inverted U-shaped relationship between emotional conflict and innovation behavior, the authors computed the squared term for emotional conflict. To investigate moderating roles of LMX and TMX, the authors carried out an interaction term between the main effect variables (emotional conflict and emotional conflict²) and the moderating variables (LMX and TMX).

The empirical findings indicated that emotional conflict had an inverted U-shaped relationship with innovation behavior. Furthermore, LMX and TMX moderated the inverted U-shaped relationship between the emotional conflict and innovation behavior in such a way that the inverted U-shaped relationship was flatter in high-quality LMX and TMX than in low-quality LMX and TMX. That is to say, LMX and TMX may dampen the positive effects of lower levels of emotional conflict on innovation behavior; yet, it may also weaken the negative effects of higher levels of emotional conflict on innovation behavior.

This research can be extended in several ways. First, future research can investigate the impact mechanism of emotional conflict on innovation behavior. Second, future research can analyze other types of moderators at different levels. The last but not the least, future research can test the results using heterogeneous samples. Despite these potential limitations, this study provides an elaborate understanding of the conflict–creativity relationship by outlining the inverted U-shaped relationship between emotional conflict and innovation behavior under the LMX and TMX contexts, which can make important contributions to the conflict management literature.

The findings of this study offer some guidance on how to stimulate innovation behavior through emotional conflict. It suggests that managers should maintain the emotional conflict at the moderate level. Furthermore, managers can strengthen the LMX and TMX to avoid the negative effects of high levels of emotional conflict, and several practices are provided as well.

This study develops an exhaustive understanding of the conflict–creativity relationship by figuring the curvilinear relationship between emotional conflict and innovation behavior, which is the response to the call of Posthuma to focus on the outcomes of conflict management. The findings further provide an empirical evidence of the conceptual argument that the consequences of conflict depend on the situational context by pointing out the important contingency factors of LMX and TMX.

This study examines the mediating roles of the three dimensions of business intelligence (sensing capability, transforming capability and driving capability) in the relationship between the three dimensions of big data analytics capability (big data analytics management, technology and talent capabilities), and radical innovation among Chinese manufacturing enterprises.

A theoretical framework was developed using the resource-based view. The hypothesis was tested using empirical survey data from 326 Chinese manufacturing enterprises.

Empirical results show that, in the Chinese manufacturing context, business intelligence sensing capability, business intelligence transforming capability and business intelligence driving capability positively mediate the impact of big data analytics capability on radical innovation.

The results offer managerial guidance for leaders to properly use big data analytics capability, business intelligence and radical innovation as well as offering theoretical insight for future research in the manufacturing industry’s radical innovation.

This is among the first studies to examine three dimensions of big data analytics capability on the manufacturing industry’s radical innovation by considering the mediating role of three dimensions of business intelligence.

The purpose of this paper is to explore how China’s manufacturers catch up with the forerunners through R&D cooperation by developing a new mode of latecomer–forerunner R&D cooperation, i.e. the periphery–core mode, which provides a viable way for China’s manufacturers to obtain forerunners’ core knowledge from their periphery knowledge via knowledge spillover and knowledge transfer.

The paper first reviews the literature on R&D cooperation process in the catching-up context and knowledge management in R&D cooperation. Then, three cases of R&D cooperation between China’s advanced technology manufacturers and their forerunners are introduced, with emphasis on interactivities in R&D cooperation and knowledge spillover. On the basis of the multi-case study, the periphery–core mode of R&D cooperation between latecomers and forerunners is conducted.

The paper finds that the latecomers can catch up with their forerunners by acquiring forerunners’ core technology used in periphery R&D activities. Through formal and informal interactions, the forerunners’ core technology can be extracted and transferred to latecomers, which the latecomers can then absorb and further develop. Thus, it can be concluded that the periphery–core mode of R&D cooperation is a viable way for the latecomers to get access to forerunners’ core technology.

The paper contributes to the literature on the catching-up theory by developing the periphery–core mode as a new mode for the latecomers to catch up with the forerunners. It expands the understanding of the latecomer–forerunner R&D cooperation by focusing on the way that China’s manufacturers as latecomers catch up with the forerunners by accessing the forerunners’ core knowledge from their R&D cooperation in periphery knowledge. The paper shows the mechanism of knowledge transfer and spillover in R&D cooperation. The role of communications, especially informal communication between cooperation partners, is emphasized in this process. This study also provides a new perspective for cooperation partner selection by arguing that latecomers can choose their cooperation partners according to the periphery and core knowledge they possess, other than the relationship between them. Besides, this paper emphasizes the mutual support between knowledge transfer, knowledge spillover and knowledge absorption, which is necessary for latecomers to achieve successful catching up in periphery–core R&D cooperation.

This paper aims to unravel the technological innovation pattern in China’s aerospace industry. The technological innovation pattern of China’s aerospace industry is identified and its theoretical foundation, structure, philosophy, formation and effects on the development of China’s aerospace industry are explored.

First, the theoretical foundation of synergy innovation of China’s aerospace industry is reviewed to further identify the technological innovation pattern. Second, Chinese ancient philosophy (dialectical thinking) is used to explain the structure and process of synergy innovation in China’s aerospace industry. Third, the formation process of synergy innovation is introduced, and, finally, the effects of synergy innovation are discussed.

The technological innovation pattern of China’s aerospace industry has undergone an evolutionary process. During this process, China’s aerospace firms have formed a unique technological innovation pattern, synergy innovation, under China’s special political and economic background. The synergy innovation has three characteristics, including original, integrated and application-based. The synergy innovation pattern application is one of the most important reasons behind the great achievements of China’s aerospace industry.

A unique technological innovation pattern, synergy innovation, is proposed for the first time. A new perspective for understanding innovation is provided by applying the Chinese dialectical thinking to decipher the philosophy of the technological innovation pattern. Based on this, this paper suggests that China’s aerospace industry should follow the situation and apply the synergy innovation pattern to achieve development and growth. This paper also illustrates a multi-method approach and emphasizes the different levels of organizing for innovation.

Ant colony algorithm is widely used in recent years as a heuristic algorithm. It provides a new way to solve complicated combinatorial optimization problems. Having been enlightened by the behavior of ant colony's searching for food, positive feedback construction and distributed computing combined with certain heuristics are adopted in the algorithm, which makes it easier to find better solution. This paper introduces a series of ant colony algorithm and its improved algorithm of the basic principle, and discusses the ant colony algorithm application situation. Finally, several problems existing in the research and the development prospect of ACO are reviewed.

In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.

Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.

The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.

This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

The main purpose is to construct the mapping relationship between garment flat and pattern. Particle swarm optimization–least-squares support vector machine (PSO-LSSVM), the data-driven model, is proposed for predicting the pattern design dimensions based on small sample sizes by digitizing the experience of the patternmakers.

For this purpose, the sleeve components were automatically localized and segmented from the garment flat by the Mask R-CNN. The sleeve flat measurements were extracted by the Douglas–Peucker algorithm. Then, the PSO algorithm was used to optimize the LSSVM parameters. PSO-LSSVM was trained by utilizing the experience of patternmakers.

The experimental results demonstrated that the PSO-LSSVM model can effectively improve the generation ability and prediction accuracy in pattern design dimensions, even with small sample sizes. The mean square error could reach 1.057 ± 0.06. The fluctuation range of absolute error was smaller than the others such as pure LSSVM, backpropagation and radial basis function prediction models.

By constructing the mapping relationship between sleeve flat and pattern, the problems of the garment flat objective recognition and pattern design dimensions accurate prediction were solved. Meanwhile, the proposed method overcomes the problem that the parameters are determined by PSO rather than empirically. This framework could be extended to other garment components.

In recent years, the updating speed of products has been significantly accelerated, which not only provides diversified styles for consumers to select from but also makes consumers face selection problems sometimes. In addition, a large number of online reviews for products emerge on many e-commerce websites and influence consumers’ purchasing decisions. The purpose of this study is to propose a method for product selection considering consumer’s expectations and online reviews to support consumers’ purchasing decisions.

The product attributes are divided into two categories, i.e. demand attributes and word-of-mouth (WOM) attributes. For the demand attributes, for which the consumers can give specific quantified expectations, the value function of prospect theory is used to determine the consumer’s perceived values to the alternative products according to consumers’ expectations for these attributes and products’ specifications. For the WOM attributes, for which the consumers cannot give specific quantified expectations, the sentiment analysis method is used to identify the sentiment strengths for these attributes in the online reviews, and then the consumer’s perceived values to the alternative products are determined. On this basis, the product selection methods for single consumers and group consumers are given respectively.

Finally, taking the data of JD.com (https://www.jd.com/) as an example, the practicability and rationality of the method proposed in this paper is validated.

First, a new product selection problem considering consumer’s expectations and online reviews is extracted. Second, the product attributes are considered more comprehensively and are classified into two main categories. Third, the bounded rationality of the consumers in the decision-making process is described more reasonably. Fourth, the sentiment dictionaries for each WOM attribute are constructed and the algorithm step of identifying the sentiment strengths is designed, which can help to identify the sentiment strengths in the online reviews more accurately. Fifth, the situation that a group plans to purchase the same products and the members have inconsistent expectations for the product attributes is considered.

Poor bending response is a major shortcoming of lower-order elements due to excessive representation of shear stress/strain field. Advanced finite element (FE) formulations for classical elasticity enhance the bending response by either nullifying or filtering some of the symmetric shear stress/strain modes. Nevertheless, the stress/strain field in Cosserat elasticity is asymmetric; consequently any attempt to nullify or filter the anti-symmetric shear stress/strain modes may lead to failure in the constant couple-stress patch test where the anti-symmetric shear stress/strain field is linear. This paper aims at enhancing the bending response of lower-order elements for Cosserat elasticity problems.

A four-node quadrilateral and an eight-node hexahedron are formulated by hybrid-stress approach. The symmetric stress is assumed as those of Pian and Sumihara and Pian and Tong. The anti-symmetric stress components are first assumed to be completely linear in order to pass the constant couple-stress patch test. The linear modes are then constrained with respect to the prescribed body-couple via the equilibrium conditions.

Numerical tests show that the hybrid elements can strictly pass the constant couple-stress patch test and are markedly more accurate than the conventional elements as well as the incompatible elements for bending problems in Cosserat elasticity.

This paper proposes a hybrid FE formulation to improve the bending response of four-node quadrilateral and eight-node hexahedral elements for Cosserat elasticity problems without compromising the constant couple-stress patch test.

This study analyzes the causal effect of education on consumers' cognition and attitudes toward genetically modified (GM) foods.

The authors propose an analytical framework to clarify the role of education levels and education content in the formation of attitudes toward GM foods and utilize education reforms in China as natural experiments to test the theoretical predictions empirically. For education levels, the authors use Compulsory Education Law's implementation to construct the instrument variable. For education content, the authors utilize the revision of the biology textbook in the Eighth Curriculum Reform to implement staggered difference-in-difference estimation. The authors use two national household surveys, the China Genuine Progress indicator Survey (CGPiS) and the China Household Finance Survey (CHFS) of 2017, combined with provincial-level data of education reforms.

The education level, instrumented by the Compulsory Education Law's implementation, has an insignificant effect on consumers' cognition and attitudes toward GM foods, whereas the acquisition of formal education on genetic science, introduced by the Eighth Curriculum Reform, has a statistically significant and positive influence.

This is the first study to investigate the causal effects of education level and content on consumers' cognition and attitude toward GM foods using national representative data. It is also the first to evaluate the long-term effects of the biology textbook reform in China. The findings help open the black box of how education shapes people's preferences and attitudes and highlight the significance of formal biology education in formulating consumers' willingness to accept GM foods.