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The purpose of this study is to use role expectation theory to identify potential determinants of user voting avoidance on mobile social media.

Data were collected through a survey of 602 WeChat users, and the proposed model was analysed using structural equation modelling.

Results indicate that user voting avoidance was positively influenced by unfair competition, perceived inauthenticity, perceived information insecurity, over-consumption of renqing (a unique Chinese human relation) and organisation placement in the context of mobile social media.

This study illustrates mobile user voting avoidance from the perspective of role expectation theory and clarifies the importance of avoidance in current voting research.

The purpose of this paper is to analyze the operational decisions of a manufacturer who produces multiple products and the government's selection of cap-and-trade and carbon tax regulations.

This paper explores the production decisions of a multi-product manufacturer under cap-and-trade and carbon tax regulations in a cap-dependent carbon trading price setting and compares carbon emission, the manufacturer's profits and social welfare under the two regulations. Game theory and extreme value theory are used to analyze our models.

First, the authors find that the optimal profit of the manufacturer (the optimal cap) increases and then decreases with the cap (the unit carbon emission of product). Second, if the environmental damage coefficient is moderate, the optimal cap of unit environmental damage coefficient is independent of the product carbon emission or other related product parameters. Ultimately, cap-and-trade regulation always generates more carbon emission than carbon tax regulation. And cap-and-trade regulation (carbon tax regulation) can generate more social welfare if the environmental damage coefficient is low (high), and the social welfare under the two regulations is equal to each other, or otherwise.

This paper contributes the prior literature by considering the inverse relationship of the allocated cap and the carbon trading price and discusses the social welfare under cap-and-trade and carbon tax regulations. Some important and new results are found, which can guide the government's implementation of the two regulations.

Monetary incentives have been widely adopted by brands to promote consumer engagement in their brand communities on social networking sites. This paper aims to explore how the interactions triggered by an emerging monetary incentive, red packets, affect consumers’ brand attitude in the context of WeChat brand groups (WCBGs).

According to whether brands ask for commercial returns from consumers, two types of interactions were identified, namely, exchange red packet interaction (ERPI) and communal red packet interaction (CRPI). The corresponding influences on brand attitude were examined in three experiments.

Compared to CRPIs, ERPIs elicit greater normative community pressure, inducing a less favorable brand attitude. Moreover, this impact is moderated by the time frame of brand communities. In the long-term WCBGs, a significant difference exists between ERPIs and CRPIs, while such difference attenuates or even disappears in short-term WCBGs.

When using red packets as an engagement strategy, brand managers should be alert to their potential negative influence. Specifically, in short-term brand communities, ERPIs enable managers to acquire commercial returns without hurting brand attitude. In long-term brand communities, managers are advised to implement CRPIs to foster a positive brand attitude.

This study investigates red packet interactions in brand communities, which have been widespread but unexplored. The results expand the literature by addressing the undermining effect of ERPI and the moderating effect of the time frame.

Selection of standardized modular containers (SSMC) is counted as one of the most important driving factors that ensure the Physical Internet (PI) to realize the varieties of goals of sustainability simultaneously, thus overcoming the great challenge of modern logistics in nowadays global supply chain. However, previous research studies on SSMC seldom paid attention to freight behavior and shippers’ segment, which are described in several fundamental conceptual works as the most essential factors that should be taken into account when doing SSMC.

This paper defines two preference matrices to characterize the freight behavior and shippers’ segment, and then integrates these two matrices into a centralized multi-criteria method to determine the optimal SSMC.

The SSMC derived by the proposed methodology is found to be Pareto-optimal. A managerial implication for shippers to further improve efficiency is summarized as well.

First, the authors characterized the freight behavior and shippers’ segment by two preference matrices and then integrated them into a centralized multi-criteria method. To the best of the authors’ knowledge, this is the first SSMC-related paper that takes shippers' behaviors and preferences into consideration when making the selection. The authors’ attempt is expected to fill in the research gap that has existed until now. Second, the SSMC derived by the proposed methodology is found to be Pareto-optimal, ensuring the required sustainability. Finally, the authors summarized a managerial implication for shippers to further improve the efficiency from the aspects of production and design.

Considering the cross-market network externality, this paper aims to explore the platform’s pricing decisions and its optimal profit under the given government investment, and then investigate the investment decision to improve social responsibility, which is measured by the social welfare.

When exploring the optimal pricing decisions under the given government investment, extreme value theory and sensitive analysis are used. When investigating the investment level, game theory and optimization method are used. Numerical examples are conducted to further illustrate the results.

First, after considering the government investment, whether the buyers and the sellers are charged depends on the investment level and the difference of the cross-market network externality (CNC) of the sellers and the buyers. Second, the optimal price on the sellers is decreasing (increasing) in the CNC of the buyers (sellers). The optimal price on the buyers is significantly affected by the investment level. Finally, the government investment is win-win for both the platform and the government, and Chinese Government should invest on the sellers heavily.

This study specifies the role of the government investment on the sellers in determining the platform’s pricing decisions and the improvement of the social responsibility, which is measured by social welfare.

The purpose of this study is to introduce the concept of big data and provide a comprehensive overview to readers to understand big data application framework in libraries.

The authors first used the text analysis and inductive analysis method to understand the concept of big data, summarize the challenges and opportunities of applying big data in libraries and further propose the big data application framework in libraries. Then they used questionnaire survey method to collect data from librarians to assess the feasibility of applying big data application framework in libraries.

The challenges of applying big data in libraries mainly include data accuracy, data reduction and compression, data confidentiality and security and big data processing system and technology. The opportunities of applying big data in libraries mainly include enrich the library database, enhance the skills of librarians, promote interlibrary loan service and provide personalized knowledge service. Big data application framework in libraries can be considered from five dimensions: human resource, literature resource, technology support, service innovation and infrastructure construction. Most libraries think that the big data application framework is feasible and tend to apply big data application framework. The main obstacles to prevent them from applying big data application framework is the human resource and information technology level.

This research offers several implications and practical solutions for libraries to apply big data application framework.

The purpose of this paper is to use the redundancy of a new hybrid automatic fastening system (HAFS) for aircraft assembly in the best way.

First, the kinematic model of HAFS is divided into three sub-models, which are the upper/lower tool and parallel robot. With the geometric coordination relationship, a comprehensive kinematic model of the HAFS is built by mathematically assembling the sub-models based on the DH method. Then, a novel master-slave decoupling strategy for inverse kinematics solution is proposed. With the combination of the minimum energy consumption and the comfortable configuration, a multi-objective redundancy resolution method is developed to optimize the fastening configuration of the HAFS, which keep the HAFS away from the joint-limits and collision avoiding in the aircraft panel assembly process.

An efficient multi-objective posture optimization algorithm to use the redundancy in the best way is obtained. Simulation and an experiment are used to demonstrate the correctness of the proposed method. Moreover, the position and orientation errors of the drilling holes are within 0.222 mm and 0.356°, which are accurate enough for the automatic fastening in aircraft manufacturing.

This method has been used in the HAFS control system, and the practical results show the aircraft components can be fastened automatically through this method with high efficiency and high quality.

This paper proposes a comprehensive kinematic model and a novel decoupling strategy for inverse kinematic solution of the HAFS, which provides a reference to utilize the redundancy in the best way for a hybrid machine with redundant function.

The purpose of this paper is to solve the problem of low accuracy in new product demand forecasting caused by the absence of historical data and inadequate consideration of influencing factors.

A hybrid new product demand forecasting model combining clustering analysis and deep learning is proposed. Based on the product similarity measurement, the weight of product similarity attributes is realized by using the method of fuzzy clustering-rough set, which provides a basis for the acquisition and collation of historical sales data of similar products and the determination of product similarity. Then the prediction error of Bass model is adjusted based on similarity through a long short-term memory neural network model, where the influencing factors such as product differentiation, seasonality and sales time on demand forecasting are embedded. An empirical example is given to verify the validity and feasibility of the model.

The results emphasize the importance of considering short-term impacts when forecasting new product demand. The authors show that useful information can be mined from similar products in demand forecasting, where the seasonality, product selling cycles and sales dependencies have significant impacts on the new product demand. In addition, they find that even in the peak season of demand, if the selling period has nearly passed the growth cycle, the Bass model may overestimate the product demand, which may mislead the operational decisions if it is ignored.

This study is valuable for showing that with the incorporation of the evaluation method on product similarity, the forecasting model proposed in this paper achieves a higher accuracy in forecasting new product sales.

This paper seeks to propose that practical wisdom originating within the Chinese classical traditions has been instrumental in China's ascendance to the world stage. Its purpose is to analyze biographical data across 200 Chinese business leaders in order to gauge the relative influence of two competing ideologies – Maoism and Confucianism.

This paper is based on a two‐step design process. First, modern day leadership archetypes characterizing Maoist and Confucian ideology were developed from the literature. Second, these archetypes were applied in the content analysis of biographical data on Chinese business leaders.

There was evidence of both Maoism and Confucianism in the practices of Chinese business leaders. An unintended, third cohort of leaders emerged, representative of the younger end of the biographical distribution, who appear to embrace paradox in their ideological orientation.

There were three practical implications for management and leadership development: the importance of integrating the concept of paradox into management and leadership development curricula; the provision of illustrative alternatives to western world leadership exemplars; and increased emphasis on global leadership as an emergent phenomenon with particular attention to the burgeoning internationalization of Chinese enterprise and its associated ideologies.

The current leadership literature is dominated by western world exemplars. This paper offers an alternative view; one rich with the wisdom and classical traditions of the Chinese culture.