Search results

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.

Considering the resource constraint, this paper aims to study how to make value-added service (VAS) investment strategy considering the negative intra-group network externality on the seller side from the perspective of a two-sided platform.

The authors use the dynamic game theory, optimization, sensitive analysis and numerical study in this research. The authors model their research question from the perspective of the dynamic game theory, and through optimizing the platform’s profit function, the equilibrium results in terms of VAS investing and pricing strategies are derived. To explore the characteristics of the optimal strategies, sensitive analysis is used, and numerical studies are conducted to further illustrate the analytical results.

It is found that the intra-group network externality is not necessarily the determinant for VAS investment strategy, and its overall negative impact can be overtaken by the investment in certain conditions. The optimal VAS investment level decreases in the negative intra-group network externality. Though the VAS investment is on the seller side, it has either positive or negative impact on the pricing for buyers. Moreover, for a stronger intra-group network externality among sellers, the two-sided prices could either increase or decrease.

The authors implicate how the intra-group network externality reduces the investment benefit and impacts the other side users. The limitation of considering the intra-group network externalities on only one side needs further extension.

The authors provide insights for platform operators in how to use recourse to improve users’ utility and how to price the two sides when competition exists on the seller side.

This study specifies the role of negative intra-group network externality in determining the investment and pricing strategy of a two-sided platform in addition to the positive inter-group network externality.

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.

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.

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.