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The purpose of this paper is to draw on social exchange theory and heuristic–systematic model to examine how peer-to-peer (P2P) lending firms can enhance their customer acquisition by achieving mobile social media popularity.

Content data collected from multiple sources (websites and mobile applications) were employed to validate the research model.

The mobile social media popularity of P2P lending firms positively influences their customer acquisition. Furthermore, the heuristic cues (i.e. source credibility and content freshness) and the systematic cue (i.e. transaction relevance) potentially affect the firms’ mobile social media popularity.

Mobile social media is not only a platform for firms’ image-building but a critical means of acquiring actual customers. The appropriate use of heuristic–systematic cues in a mobile interface is useful for firms to achieve high user popularity despite the challenges derived from the mobile context.

To achieve higher user popularity in the competitive online world, firms should dedicate greater effort in determining the adequate heuristic–systematic cues designed for the interface of their mobile social media account. The effect of popularity can then help the firms acquire more customers.

This study extends the understanding of social exchange in the context of mobile social media accounts and enriches the knowledge on business value of mobile social media popularity. This paper also contributes to the literature by relating heuristic–systematic cues to firms’ mobile social media popularity.

Fiscal fund is the key support of carbon emissions control for local governments. This paper aims to analyze the impact of fiscal decentralization on carbon emissions by spatial Durbin model (SDM), and verify the existence of “free-riding” phenomenon to reveal the behavior of local governments in carbon emissions control.

Based on the provincial data of carbon emissions from 2005 to 2016 in China, this paper uses spatial exploratory data analysis technology to analyze the spatial correlation characteristics and constructs SDM to test the impact of fiscal decentralization on carbon emissions.

The results show that carbon emissions exhibits significant spatial autocorrelation in China, and the increasing of fiscal decentralization in the region will increase carbon emissions in surrounding areas and on the whole. Then, by comparing the impact of fiscal decentralization on carbon emissions and industrial solid waste, it is found that “free-riding” phenomenon of carbon emissions control exists in China.

Based on the spatial cluster characteristics of China’s provincial carbon emissions, carbon emissions control regions can be divided into regions and different carbon emission control policies can be formulated for different cluster regions. Carbon emissions indicators should be included in the government performance appraisal policy, and carbon emissions producer survey should be increased in environmental policies to avoid “free-riding” behaviors of local government in carbon emissions control in China.

This paper contributes to fill this gap and fully considers the spatial spillover characteristics of carbon emissions by introducing spatial exploratory data analysis technology, constructs SDM to test the impact of fiscal decentralization on carbon emissions in the perspective of space econometrics, and tests the existence of “free-riding” phenomenon in carbon emissions control for local governments in China.

Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance and slip of the Mecanum wheels while driving. The purpose of this paper is to analyze the mechanism of omnidirectional motion using Mecanum wheels, with the aim of enhancing the heading precision. A proportional-integral-derivative (PID) setting control algorithm based on a radial basis function (RBF) neural network model is introduced.

In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1.

The network RBF NN1 calculates the deviations ?Kp, ?Ki and ?Kd to regulate the three coefficients Kp, Ki and Kd of the heading angle PID controller. This corrects the driving heading in real time, resolving the problems of low heading precision and unstable driving. The experimental data indicate that, for a externally imposed deviation in the heading angle of between 34º and ∼38°, the correction time for an omnidirectional mobile platform applying the algorithm during longitudinal driving is reduced by 1.4 s compared with the traditional PID control algorithm, while the overshoot angle is reduced by 7.4°; for lateral driving, the correction time is reduced by 1.4 s and the overshoot angle is reduced by 4.2°.

In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. The method is innovative.

Due to the size and importance of social media, user-generated content analysis is becoming a key factor for companies and brands across the world. By using Twitter messages’ content, the purpose of this paper is to identify which elements of the messages enable tweet diffusion and facilitate eWOM.

In total, 30,082 tweets collected from 10,120 Twitter users were classified based on four assorted brands. By comparing with multiple regression techniques high vs low purchase involvement and hedonic vs utilitarian products and using the theory of heuristic-systematic processing of information, the authors examine the causes of tweet diffusion.

The authors illustrate how the elements of a tweet (hashtags, mentions, links, sentiment or tweet length) influence its diffusion and popularity.

This study validated the use of information processing theories in the social media field. The study showed a picture on how different Twitter elements influence eWOM and message diffusion under several purchase involvement situations.

The results of this study can help social media brand community managers of all types of companies on how to write their Twitter messages to obtain greater dissemination and popularity.

The study offers a unique deep brand analysis which helps brands and companies to understand their social media popularity in detail. Depending on product category, companies can achieve maximum social impact on Twitter by focusing on the interactivity items that will work best for their products or brands.

Aiming at the problem that quadruped crawling robot is easy to collide and overturn when facing obstacles and bulges in the process of complex slope movement, this paper aims to propose an obstacle avoidance gait planning of quadruped crawling robot based on slope terrain recognition.

First, considering the problem of low uniformity of feature points in terrain recognition images under complex slopes, which leads to too long feature point extraction time, an improved ORB (Oriented FAST and Rotated BRIEF) feature point extraction method is proposed; second, when the robot avoids obstacles or climbs over bumps, aiming at the problem that the robustness of a single step cannot satisfy the above two motions at the same time, the crawling gait is planned according to the complex slope terrain, and a robot obstacle avoidance gait planning based on the artificial potential field method is proposed. Finally, the slope walking experiment is carried out in the Robot Operating System.

The proposed method provides a solution for the efficient walking of robot under slope. The experimental results show that the extraction time of the improved ORB extraction algorithm is 12.61% less than the original ORB extraction algorithm. The vibration amplitude of the robot’s centroid motion curve is significantly reduced, and the contact force is reduced by 7.76%. The time it takes for the foot contact force to stabilize has been shortened by 0.25 s. This fact is verified by simulation and test.

The method proposed in this paper uses the improved feature point recognition algorithm and obstacle avoidance gait planning to realize the efficient walking of quadruped crawling robot on the slope. The walking stability of quadruped crawling robot is tested by prototype.

The purpose of this paper is exploring the effects of segment dynamic and temporal dynamic triggered by the COVID-19 pandemic on classifying service quality attributes, thereby formulating improvement strategies to satisfy customers and respond to threats.

Given the dynamics of the attractive quality theory, this paper designs a framework with four phases by embedding techniques of text mining and deep learning based on evidence from online reviews.

This paper figures out dynamics of service quality attributes for distinct segments and their dynamic proportion along with different stages of the pandemic. Another finding demonstrates segment dynamic and temporal dynamic effects of sentiments toward service quality attributes on customer satisfaction under the impacts of pandemic. Classification results and improvement strategies are derived for varying segments at different pandemic situations.

This paper reveals dynamic effects on classifying service quality attributes, which contributes to assisting hospitality practitioners from different segments in improving service quality when facing with the challenges of crisis and potential risks.

Given hospitality industry is time- and segment-sensitive, the authors achieve the quantification of dynamics of attractive quality theory and extend it into hospitality marketing and crisis management from the perspective of dynamics with evidence from online reviews.

This paper aims to examine how a firm’s capability affects its political networking in emerging economies and how the institutional environment influences the relationship between a firm’s capability and its political networking.

The authors test the theoretical model by analyzing a database from a World Bank survey in China.

The results show a nonlinear (U-shaped) relationship between a firm’s capability and its efforts in political networking. The relationship between a firm’s capability and political networking is contingent on the institutional environment, as reflected in institutional development and industry regulation.

This study elucidates political networking in emerging economies and contributes to research on corporate political strategy.

This paper aims to investigate the free convection, heat transfer and entropy generation numerically and experientially. A numerical/experimental investigation is carried out to investigate the free convection hydrodynamically/thermally and entropy generation.

The coupled lattice Boltzmann method is used as a numerical approach which keeps the significant advantages of standard lattice Boltzmann method with better numerical stability. On the other hand, the thermal conductivity and dynamic viscosity are measured using modern devices in the laboratory.

Some correlations based on the temperature at different nanofluid concentration are derived and used in the numerical simulations. In this regard, the results will be accurate with respect to using theoretical properties of nanofluid, and close agreements will be detected between present results and the previous numerical and experimental works. The numerical investigation is done under the effect of Rayleigh number (103 < Ra < 106), volume concentration of nanofluid (?? = 0.5, 1, 1.5, 2, 2.5 and 3%) and thermal configuration of the cavity (Cases A, B, C and D).

The originality of the present work lies in coupling of the lattice Boltzmann method with experimental observations to analyse the free convection in a cavity.

A novel grafted temperature-responsive ReO₄⁻ Imprinted composite membranes (Re-ICMs) was successfully prepared by using polyvinylidene fluoride (PVDF) resin membranes as substrates, this study aimed to separate and purify ReO effectively.

Re-ICMs were synthesized by PVDF resin membranes as the substrate, acrylic acid (AA), acrylamide (AM), ethylene glycol dimethacrylate (EGDMA) were functional monomers. The morphology and structure of Re-ICMs were characterized by scanning electron microscope and Fourier transform infrared spectroscopy.

The maximum adsorption capacity toward ReO₄⁻ was 0.1,163 mmol/g and the separation decree had relation to MnO₄⁻ was 19.3. The optimal operation conditions were studied detailedly and the results as follows: the molar ratios of AA, AM, EGDMA, ascorbic acid, NH4ReO₄, were 0.8, 0.96, 0.02, 0.003 and 0.006. The optimal time and temperature were 20 h and 40°C, respectively. The Langmuir and pseudo-second-order models were fit these adsorption characteristics well.

Rhenium (Re) is mainly used to chemical petroleum and make superalloys for jet engine parts. This study was representing a technology in separate and purify of Re, which provided a method for the development of the petroleum and aviation industry.

This contribution provided a novel method to separate ReO₄⁻ from MnO₄⁻. The maximum adsorption capacity was 0.1163 mmol/g at 35°C and the adsorption equilibrium time was within 2 h. Meanwhile, the adsorption selectivity rate ReO₄⁻/MnO₄⁻ was 19.3 and the desorption rate was 78.3%. Controlling the adsorption experiment at 35°C and desorption experiment at 25°C in aqueous solution, it could remain 61.3% of the initial adsorption capacity with the adsorption selectivity rate of 13.3 by 10 adsorption/desorption cycles, a slight decrease, varied from 78.3% to 65.3%, in desorption rate was observed.

Free knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction.

The authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction.

The authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction.

This study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.