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A vital job for one sharing business is dynamically dispatching shared items to balance the demand-supply of different sharing points in one sharing network. In order to construct a highly efficient dispatch strategy, this paper proposes a new dispatching algorithm based on the findings of sharing network characteristics.

To that end, in this paper, the profit-changing process of single sharing points is modeled and analyzed first. And then, the characteristics of the whole sharing network are investigated. Subsequently, some interesting propositions are obtained, based on which an algorithm (named the Two-step random forest reinforcement learning algorithm) is proposed.

The authors discover that the sharing points of a common sharing network could be categorized into 6 types according to their profit dynamics; a sharing network that is made up of various combinations of sharing stations would exhibit distinct profit characteristics. Accounting for the characteristics, a specific method for guiding the dynamic dispatch of shared products is developed and validated.

Because the suggested method considers the interaction features between sharing points in a sharing network, its computation speeds and the convergence efficacy to the global optimum scheme are better than similar studies. It suits better to the sharing business requiring a higher time-efficiency.

The purpose of this paper is to examine the effects of perceived benefits, i.e. utilitarian value, hedonic value and social value, as well as perceived risk, on purchase intention in social commerce context.

To cast light on the factors motivating users’ intention to purchase in the context of social commerce, data of 277 users of social commerce in China were collected via an online survey.

Results show that satisfaction significantly and positively affects users’ purchase intention in social commerce context. In addition, utilitarian, hedonic and social values have significant and positive impacts on satisfaction and purchase intention; and utilitarian value is found to be the most salient factor influencing purchase intention, while hedonic value has the greatest effect on satisfaction. Moreover, perceived risk significantly and negatively affects satisfaction.

Extant research on social commerce has mainly focused on investigating how the general perceived value affects user behavior, but has less considered different dimensions of perceived value. Moreover, prior studies have explored the roles of utilitarian and hedonic values on user behavior; however, there is a lack of research on the effect of social value. The current study attempts to fill these research gaps.

Upstream suppliers attempt to outsource product after-sales services to midstream third-party service providers while selling the product directly to downstream sellers, forming a networked supply chain. However, a problem of information asymmetry in the market demand among supply chain members exists. The authors investigate the impact of demand information asymmetry among third-party service providers, upstream suppliers and downstream sellers in the supply chain on the supplier's contract selection under the networked framework.

The authors establish a model in which the supplier can use a wholesale price contract and facilitate a signaling game between the third-party service provider and the seller. Conversely, the supplier could use a menu contract to establish an incentive mechanism to solve information asymmetry. The authors propose heuristic algorithms to quickly estimate a supplier's optimal profit.

The results show that when the demand forecasting bias is relatively small, the use of a menu contract by the supplier could eliminate information asymmetry; when the demand forecasting bias is large enough, the signaling mechanism between the third-party service provider and the seller could alleviate the double marginalization effect in the supply chain. Although it is common to solve the asymmetric information problem by establishing incentive mechanisms, the authors found that in the latter case, the supplier is better off when no incentive mechanisms are implemented in the networked supply chain.

This study compares screening and signaling effects and compares firms' profits in both cases.

The purpose of this paper is to use artificial intelligence to evaluate the risks of urban power network planning.

A fuzzy Bayesian least squares support vector machine (LS_SVM) model is established in this paper, which can learn the risk information of urban power network planning through artificial intelligence and acquire expert knowledge for its risk evaluation. With the advantage of possessing learning analog simulation precision and speed, the proposed model can be effectively applied in conducting a risk evaluation of an urban network planning system. First, fuzzy theory is applied to quantify qualitative risk factors of the planning to determine the fuzzy comprehensive evaluation value of the risk factors. Then, Bayesian evidence framework is utilized in LS_SVM model parameter optimization to automatically adjust the LS_SVM regularization parameters and nuclear parameters to obtain the best parameter values. Based on this, a risk comprehensive evaluation of urban network planning based on artificial intelligence is established.

The fuzzy Bayesian LS_SVM model established in this paper is an effective artificial intelligence method for risk comprehensive evaluation in urban network planning through empirical study.

The paper breaks new ground in using artificial intelligence to evaluate urban power network planning risks.

This paper aims to draw upon motivated information processing theory to examine the sequential mediating roles of perspective taking and boundary spanning between transformational leadership and the creative performance of knowledge workers.

The study was carried out on a sample, including a dyad of 398 knowledge workers and their immediate supervisors in four research institutes in southwest China. The authors tested the theoretical model using structural equation modeling (SEM) and Mplus 7.0 software.

The results support the mediation model in which perspective taking was found to significantly and positively mediate the relationship between transformational leadership and boundary spanning. Boundary spanning was found to significantly and positively mediate the relationship between perspective taking and creative performance. Moreover, both perspective taking and boundary spanning were found to mediate the relationship between transformational leadership and creative performance.

The study findings imply that the transformational leadership behaviors of managers or supervisors nurture knowledge workers' perspective taking and their boundary spanning activities leading to creative performance.

The findings contribute new knowledge to the relationship between transformational leadership and creative performance by uncovering the causal chain of a cognitive mechanism (perspective taking) with a behavioral mechanism (boundary spanning).

The purpose of this paper is to understand the root cause of a large number of quality problems in the current Chinese construction market.

Information economics theory is used to analyze the cause of construction quality problems related to the specific case of the Wenchuan earthquake of China.

The single low price competition in the construction tender market leads to the serious adverse selection and moral hazard behavior of bidding firms. The main solution to this problem is the introduction and improvement of an independent inspection/supervision mechanism.

The paper systematically analyzes the evaluation method of the lowest price winning bidding policy, which is the fundamental cause of adverse selection and moral hazard problems in the Chinese construction market.

The purpose of this article is to propose and evaluate a novel system architecture for Smart City applications which uses ontology reasoning and a distributed stream processing framework on the cloud. In the domain of Smart City, often methodologies of semantic modeling and automated inference are applied. However, semantic models often face performance problems when applied in large scale.

The problem domain is addressed by using methods from Big Data processing in combination with semantic models. The architecture is designed in a way that for the Smart City model still traditional semantic models and rule engines can be used. However, sensor data occurring at such Smart Cities are pre-processed by a Big Data streaming platform to lower the workload to be processed by the rule engine.

By creating a real-world implementation of the proposed architecture and running simulations of Smart Cities of different sizes, on top of this implementation, the authors found that the combination of Big Data streaming platforms with semantic reasoning is a valid approach to the problem.

In this article, real-world sensor data from only two buildings were extrapolated for the simulations. Obviously, real-world scenarios will have a more complex set of sensor input values, which needs to be addressed in future work.

The simulations show that merely using a streaming platform as a buffer for sensor input values already increases the sensor data throughput and that by applying intelligent filtering in the streaming platform, the actual number of rule executions can be limited to a minimum.

This study concentrates on assessing the research productivity in the domain of social media security in the past decades. The purpose of this study is to conduct a comprehensive bibliometric review on social media using various bibliometric indicators.

A total of 8,121 scientific publications were retrieved from Scopus database from period 1998 to 2021 using associated keyword search. This study also used VOSviewer^© tool to evaluate the network visualisation.

The result of this study revealed that there is a steady growth of annual publications except for the years 2015 and 2016. A total of 8,121 scientific publications and 80,454 total citations were found with 11.2 average citations per publication. The USA, China and India were top productive countries in terms of publishing research in the field; Chinese Academy of Sciences secured top position with 126 publications in highly productive organisation in the domain. The lecture notes in computer science from Springer Nature received a highly produced title with 553 publications and 4,453 total citations. For co-occurrence of author’s keywords, network visualisation analysis revealed that “Social Media”, “Social Network”, “Security”, “Privacy” and “Trust” found maximum occurrence in the domain of social media security.

This study provides comprehensive research status of social media security by performing a bibliometric analysis. The findings of this study will help relevant researchers to understand the research trend, pursue scientific collaborators and enhance research topic preferences.

The rareness of this study, detailed bibliometric analysis on the domain of social media security, is proven using numerous bibliometric indicators and application of VOSviewer in the domain.