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After three decades of development, the Chinese electric vehicle industry became the world’s largest electric vehicle market in 2015. However, little is understood about how the Chinese electric vehicle industry, as a latecomer in this strategic newly emerged industry, could catch up with international incumbents. The purpose of this paper is to study how the windows of opportunity emerge and interactively influence the catch-up process of Chinese electric vehicle industry.

This paper conducted a case study to examine how Chinese electric vehicle latecomers use the windows of opportunity along with the development of a sectoral system of innovation to reduce the gaps.

The results indicate that windows of opportunity appeared in the introduction stage (2005) and the transition from the introduction stage to the growth stage (2015) because of the sectoral changes in technologies, demand, policies and the interaction among these factors. Domestic electric vehicle latecomers currently follow the catch-up pattern of duplication, creative imitation and innovation.

To capture the previous windows of opportunity, domestic electric vehicle latecomers rely on technology transfer through international joint ventures, government support and local advantages from cheap labor. To seize future windows of opportunity, apart from progressively accumulating innovation capabilities, it is also essential for managers to recognize, break through and extend the windows of opportunity by anticipating and monitoring the process of changes of the sectoral system.

This paper provides a fine-grained study on how latecomers in a new industry with emerging markets can seize windows of opportunities to catch up with the international leaders.

This study investigates the role of source familiarity in moderating the effect of service adaptive behavior (SAB) on customer satisfaction. Applying the accessibility–diagnosticity framework and situated cognition theory as the theoretical basis, this research hypothesizes that when customers are familiar with the source that provides the service (i.e. brand familiarity for Study 1 and personal familiarity for Study 2), customer satisfaction responses to SAB would be more moderate than when customers are not familiar with the source. Two studies were conducted to test the hypotheses.

Two experiments manipulating SAB and the brand name familiarity (Study 1) and personal familiarity with the service staff (Study 2) as the source familiarity were conducted. Customer satisfaction as a function of source familiarity was measured to test the hypothesis that source familiarity moderates the relationship between SAB and customer satisfaction.

Compared to unfamiliar sources, familiar sources generated a more moderate response in customer satisfaction as a function of SAB. High familiarity with the brand and service staff induced top-down, memory-based processing that overrides external stimuli as the basis of satisfaction judgment; bottom-up, stimulus-based processing relying on SAB for judgment kicked in only when the source familiarity is low.

From a practical point of view, this study indicates the importance of SAB, especially for brands with low awareness, and alludes to the comparative importance of relationship building in service delivery processes.

This study contributes to the literature by validating the role of contextual factors in influencing the impact of SAB on customer satisfaction.

This paper examines the profit distribution of engineering projects in the integrated project delivery (IPD) mode. IPD is a new delivery method that can ameliorate many of the disadvantages of traditional delivery methods and improve project results. In the implementation of IPD, the profit distribution is key for ensuring the success of IPD projects.

This paper described a new method for characterizing profit distribution in the IPD mode. The payment function and Shapley value of the cooperative fuzzy game of fuzzy alliance were defined by considering the Choquet integral of the fuzzy measure. The participation of each player was considered, and the influence of participation on the profit distribution was discussed. Lastly, changes in the profit distribution of core participants under different alliance combinations were studied.

A case from a report of The American Institute of Architects (AIA) was used to verify the fuzzy alliance model. There was a significant correlation between the degree of participation of the owner, architect and builder and the profit distribution among these three participants.

The theoretical research in this paper has some limitations. Initially, this paper selects a case with only three key participants in order to simplify the research. When there are many core participants, how to establish the alliance in the IPD mode and how to establish the corresponding profit distribution model, further work is certainly required to disentangle these complexities in models. Second, in this case, BIM technology has little impact on the income of the whole project. Therefore, this paper does not consider the impact of BIM technology on the marginal effect of the IPD project. Third, the contract type in the case is a custom tri-party based on IFOA. There is no classified discussion of the impact of different contracts on the profit distribute in the paper.

Based on the in-depth study of cooperative game with alliance structure, this paper promotes the classic cooperative game with alliance structure. The authors define the payoff function of fuzzy cooperative games by Choquet integral of fuzzy measure, and introduce the idea into the field of IPD. It aims at extending the solution to a cooperative game without a core. It can be obtained through a simple calculation. In the IPD alliance, the fuzziness and uncertainty of the participation degree of each participant will affect the profit of the whole project. The authors find that the higher the participation rate of players, the more profit each participant has. The greater the influence weight of the designer on the alliance, the lower the influence weight of the contractor on the alliance, the lower the participation of the contractor and the designer, and the lower the income distribution value of the three core participants. It shows a monotonous decline status.

For any construction enterprise, it can make more profits if it joins the grand alliance. In the IPD alliance, each participant can maximize their own interests, which can also promote the enthusiasm of construction enterprises to participate in the alliance and increase the application of IPD mode in AEC industry. This research method provides a new fast, effective, and more realistic solution method for cooperative countermeasures. It can be further extended to other cooperative game fields and advance a new research perspective and solution for the distribution of cooperative interests.

The contribution of this paper is the development of a fuzzy alliance model that provides a tool for measuring the profit distribution in IPD. This is the first quantitative model to connect the degree of participation with the profit distribution in IPD using fuzzy alliance.

This paper aims to design a vision-based non-contact real-time accurate heart rate (HR) measurement framework for home nursing assistant.

The study applied Second-Order Blind Signal Identification (SOBI) algorithm to extract remote HR signal and analyzed it with Fast Fourier Transform (FFT). Multiple regions of interest are chosen and analyzed to obtain a more accurate result.

An accurate non-contact hear rate (HR) measurement framework is proposed and proved to be efficient.

The contributions of this HR measurement framework are as follows: accurate measurement of HR, real-time performance, robust under various scenes such as conversation, lightweight computation which is suitable and necessary for home nursing assistance. This framework is designed to be flexibly used in various real-life scenes such as domestic health assistance and affectively intelligent agents and is proved to be robust under such scenes.

Food packaging pictures are one of the most important extrinsic cues for consumers to evaluate food products before purchasing. Over the past decades, marketers have used exaggerated pictures to attract consumers' attention, enhance their attitude toward a product and increase their purchase intention. This study examined the interplay of “puff-up” product picture, food type and picture type in influencing consumers' responses via persuasion knowledge.

The research comprises two 2 × 2 factorial experiments. Study 1 tested the interaction effect of puffery (high vs low) and food type (utilitarian vs. hedonic) on consumers' responses using two fictitious brands of prepared food, whereas Study 2 tested the interaction effect of puffery (high vs. low) and picture type (ingredients vs. cooked food) using a fictitious brand of Chinese delicacy.

Results demonstrated that the degree of picture puffery did not influence consumers' responses to utilitarian food and ingredient image. Conversely, consumers were sensitive to puffery when they see hedonic food and cooked-food image. Our findings also suggested that consumers' persuasion knowledge mediates the relationship between puffery and their responses.

The presented findings facilitate marketers to know consumers' attitude about food puffery pictures.

This research is one of the first efforts to empirically explore the influences of persuasion knowledge on food puffery pictures. The importance of this work is underscored by the fact that a growing number of visual exaggerations are adopted on food packaging.

The deficiency of the mapping between fashion color (FoCo) value and linguistic color expression causes the difficulty of machine-based fashion understanding tasks that are heavily associated with color matching. The purpose of this paper is to propose the FoCo system and construct it with four steps, in order to bridge this gap.

The color distribution in HSB color space is analyzed to estimate the rough number of color categories. Similar color values are grouped to obtain the initial HSB value range for each color category. The intra-category color differences are calculated to determine their final HSB value ranges and Pantone color is used for fine-tuning.

With practical applications in mind, the FoCo system is designed as a hierarchical structure with three layers.

The FoCo system is designed as a hierarchical structure with three layers: color units for color matching-related tasks, color categories for style analysis tasks and color tones for color recognition tasks. Extensive experiments demonstrate the effectiveness of the FoCo system.

The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.

External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.

In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.

However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.

This study aims to explore the asset pricing implications of attention allocation theories in the Indian stock market.

Investor attention is captured through investors' search behavior, the Google search volume index. Panel least square method is used in this study, and the research is performed at firm-level upon NSE100 constituent firms with 21,566 firm-week observations.

The authors find a significant increase in abnormal return following an increase in abnormal attention. Also, this effect is strengthened for smaller firms and firms with positive sentiments. Further, applying a geographic lens to the investigation, it is found that the attention impact is attributable to local investors. Finally, the study demonstrates that local attention-based portfolio formation and trading strategy, i.e. long in high abnormal local attention stocks and short in low abnormal local attention stocks, leads to a significant return premium.

This study reveals that behavioral factors like investor attention drive the Indian Stock Market. Also, the geography analysis shows that observing investors' behavior enables predicting the arrival of private information. Thus abnormal local attention can be a potential input factor for forecasting exercises and trading strategy formation, thereby aiding in exploiting profitable opportunities.

The study captures asset pricing implications of investor attention and explores the effect of firm size and sentiment on the attention–return relationship in an emerging economy, India. It also relates location proximity with investors' attention allocation and tests its implications on stock prices.

The purpose of this paper is to examine practicing mathematics teachers’ learning through parallel lesson study in China. Lesson study in China has been practiced for decades. Parallel lesson is an enriched mode of lesson study to address the implementation of new curriculum.

The expansive learning perspective has been used to explore the ways practicing teachers learned to improve teaching through the transformation of learning objects and boundary crossing.

Two cases are illustrated and compared to highlight features of teachers’ learning through parallel lesson study. The practicing teachers developed their competence in transforming instructional objectives and task selection and implementation. In addition, they also developed professional vision in alignment with the reform-oriented curriculum.

This study makes significant contribution to understanding teachers’ learning through lesson study in China. Meanwhile, it also demonstrates how the theory of expansive learning could be used as a conceptual framework to examine teachers’ learning through lesson study.

A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).

This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.

In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.

The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.