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Integrating the coopetition perspective with institutional theory, this study aims to determine how balanced patterns (BPs) and combinative patterns (CPs) of coopetition impact firms’ new product development (NPD) and how these effects are contingent on the various types of interactions between firms and the institutional environments in which they are embedded.

To test the hypotheses, 303 firms in China were surveyed. Based on the responses, the proposed model was estimated using structural equation modeling and hierarchical regression analysis.

The findings indicate that CP of coopetition enhances NPD but a BP of coopetition impedes NPD. Further, the results suggest that obtaining government support positively moderates the effect of the CP on NPD but negatively moderates the effect of the BP. Conversely, influencing government policy negatively moderates the effect of the CP but positively moderates that of the BP on NPD.

The findings indicate that different patterns of inter-firm coopetition may have different effects on NPD, thus, providing a holistic and dynamic understanding of the contingent value of coopetition for NPD. The findings also suggest that the complex effects of coopetition on NPD are influenced by institutional interactions, introducing further contingencies to the process of coopetition-based innovation.

This study provides guidelines for managers seeking to fully understand and capitalize on the dual nature of coopetition: they should be cautious about the different patterns of competition – cooperation interaction and manage their interactions with institutional environments to increase the benefits and avoid the potential damage that different types of coopetition may bring.

This study offers direct insights into the balanced nature of coopetition and opens up an avenue for further exploration of the specific effects of cooperation dominance and competition dominance on firm performance in the business-to-business context. Moreover, the proposed contingency model offers a potential interface between institutional and coopetition research on NPD in marketing and strategic fields.

Recent development of wireless communication devices dictates that miniaturization, multi-functions and high integration are the important factors for antenna structures. This has resulted in the requirement of antennas with dual or multi-frequency operations. Although the dual-band antennas can be achieved through the experience-based configuration selection with the parameter adjustment, it is still a challenging problem to design an antenna with specific dual-frequency operations effectively. The purpose of the paper is to develop an effective design method to guide the design of antennas with specific dual-frequency operations.

The topology optimization is carried out through the material distribution approach, where the patch of the antenna is taken as the design domain. The optimization formulation is established with maximizing the minimum antenna efficiency at the target frequencies. The sensitivity of the antenna efficiency with the design variables is derived, and the optimization problem is solved by a gradient-based algorithm.

Based on the proposed design method, an example of a patch antenna design for specific dual-frequency operations is presented. The performance of the designed antenna is cross-verified by experimentation, where the reflection coefficients (S₁₁) obtained by simulation and experiment show a good agreement. The simulation and the experimentation of the designed antenna show that two operational bands are optimized to occur around the target frequencies, which confirms the effectiveness of the proposed design method.

This paper presents a topology optimization-based design method for patch antennas operating at dual specific frequencies.

Since the beginning of the 21st century, China’s outward foreign direct investment (OFDI) is growing steadily and Chinese multinationals (MNCs) are playing an increasingly important role in the global economy. Thus, the number of papers focusing on China’s OFDI and Chinese MNCs has been increasing during the last years. The aim of this chapter is to carry out a review of the empirical papers dealing with Chinese MNCs published between 2002 and 2012 in high-impact international business and management journals.

This chapter reviews 43 empirical papers focusing on Chinese MNCs that were published in nine major scholarly journals between 2002 and 2012.

We report individual and institutional contributions, the theories and methods used, the research topics, and the main findings. We also discuss implications for future research.

Some previous literature reviews have dealt with research on China’s OFDI and Chinese MNCs. Nevertheless, none of the earlier reviews dealt specifically with empirical papers; neither did they provide an analysis of both individual and institutional contributions.

This study aims to focus on the authority formation process of Chinese enterprise leaders, with the purpose of finding out how an ordinary newly established firm leader develops into a real top leader and achieves the status of legitimacy in a well-known enterprise.

Based on constructivist grounded theory, this paper investigates the formation mechanism of entrepreneurial authority in China by using the rich data of Liu Chuan-zhi’s leader activities.

In the “evolution” path of authority formation, leaders continually consolidate and improve their authority through two classes of exceptional management activities: “emergency rescue” and “promotion activities”. The successful realization of exceptional management activities benefits from a leader’s management experience accumulation and relationship maintenance with the government. In the “design” path of authority formation, leaders consolidate and improve their authority by exercising their position of power. Leaders’ legitimacy is reflected by making strategic decision and demonstrating discretion of position power. Additionally, passing on an inspiring leader’s thoughts and ideas to an organization’s members is accomplished through the construction of organization culture, institutionalization and convention.

First, the findings are based on only Liu Chuan-zhi’s case. The authors still need more cases to compare and develop the findings and seek theoretical saturation in a broader sense. Second, the qualitative analysis is based on secondary data and future research could consider the introduction of interviews, video and other types of research data.

Under the parallel paths which are “evolution” and “design”, the dynamic leader authority formation model in China is founded.

The concept that creativity climate facilitates innovation outcome is well‐received, yet it has not been widely tested in non‐Western countries. To fill the gap between concept and practical value, this study adopted the eight‐dimensional model of organizational creativity climate proposed by Amabile and associates with the aim of investigating the cross‐level relationship between creativity climate and employee‐perceived innovation in an Asian work place, i.e. Taiwan.

Using survey data of 398 employees from different companies of Taiwan, the effect of organizational creativity climate on innovation was explored. Furthermore, the mediating effect of employees' work motivation was also examined.

By employing hierarchical linear modeling (HLM), statistical analysis indicates that 27 percent variance of perceived innovation could potentially be explained by creativity climate. Five out of the eight dimensions, namely, organizational encouragement, supervisory encouragement, work group support, sufficient resource and challenging work, relate significantly to perceived innovation with the mediation of work motivation.

As most companies are reluctant to reveal their objective innovation data, the authors had to rely on self‐reported data that are inevitably subjective in nature. Moreover, the fact that only 13 organizations were sampled may weaken the generalizability of the findings to more diverse business contexts.

The findings of this study contribute to advancing organizational climate research and innovation management in a non‐Western country. In addition, by surveying this topic in an innovation‐active context, i.e. Taiwan, this study uncovers rich information on organizational creativity issues for interested parties and for future research.

The purpose of this paper is to clarify the effect of material hardening model and lump-pass method on the thermal-elastic-plastic (TEP) finite element (FE) simulation of residual stress induced by multi-pass welding of materials with cyclic plasticity.

Nickel-base alloy and stainless steel, which are used in J-type weld for manufacturing the nuclear reactor pressure head, can easily harden during multi-pass welding. The J-weld welding experiment is carried out and the temperature cycle and residual stress are measured to validate the TEP simulation. Thermal-mechanical sequence coupling method is employed to get the welding residual stress. The lumped-pass model and pass-by-pass FE model are built and two materials hardening models, kinematic hardening model and mixed hardening model, are adopted during the simulations. The effects of material hardening models and lumped-pass method on the residual stress in J-weld are distinguished.

Based on the kinematic hardening model, the stresses simulated with the lumped-pass FE model are almost consistent with those obtained by the pass-by-pass FE model; while with the mixed hardening material model, the lumped-pass method has great effect on the simulated stress.

A computation with mixed isotropic-kinematic material seems not to be the appropriate solution when using the lumped-pass method to save the computation time.

In the simulation of multi-pass welding residual stress involved in materials with cyclic plasticity, the material hardening model should be carefully considered. The kinematic hardening model with lump-pass FE model can be used to get better simulation results with less computation time. The results give a direction for welding residual stress simulation for the large structure such as the reactor pressure vessel.

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.

Effective and efficient supply chain coordination requires the integration of all product flow processes. However, inconsistent empirical results have been obtained with respect to the relationships between supply chain integration (SCI) and performance. Drawing on efficiency-flexibility arguments, this paper seeks to develop a SCI model that includes buyer-supplier-supplier relationships, and proposes a contingency framework for reexamining the SCI-supplier performance relationship under demand and technological uncertainties.

A two-stage data collection process was conducted, and a total of 878 suppliers that listed in the Taiwanese “Center-Satellite Production System” with supply contract were contacted for this study. Finally 164 suppliers were gathered and screened as valid responses. Hierarchical regression analysis was used to test the hypotheses in this study.

Evidence indicates that SCI has a significant positive effect on the suppliers' performance. The positive SCI-performance relationship can be moderately weakened by demand uncertainty; however, this positive SCI-performance relationship will be strengthened by technological uncertainty.

While supply chain management is needed to manage the vertical and horizontal relationships simultaneously, this study offers a framework to solve efficiency-flexibility dilemma arguments when dealing with “exploitation” and “exploration” alternatives to help to reexamine the inconsistent SCI-performance relationship. Furthermore, based on transaction cost theory, this paper takes the nature of uncertainty into account for improving the theoretical background of the SCI-performance relationship arguments. Empirical results indicate the existence of an ambidextrous supply chain integration strategy which justifies the choice of which one is preferable in efficiency-flexibility dilemma arguments.

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.