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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 study aims to develop a comprehensive measurement that consists of the most significant indicators of team effectiveness. Based on the input-mediator-outcome model, this measurement assesses two main aspects related to team effectiveness: team processes and emergent states (TPES).

The scale was developed and validated following three major steps. First, items were generated through literature reviews and expert reviews. Second, a pretest was conducted to refine the scale among 755 Chinese employees recruited on a survey service platform. Finally, in the evaluation section, 3,950 samples were used for scale validation, including a predictive validity test examining the relationship between TPES and team outcomes among 3,550 participants in paired samples (team members and leaders), from 381 work teams from several private companies in IT industries in China.

The TPES scale measures the team members’ interactions and the team status, consists of five dimensions: goal and role, collaboration, innovation, motivation and cohesiveness, showing acceptable reliability, validity and model fit. The five dimensions show significant positive effects on team performance and members’ satisfaction.

This novel measure of team process and emergent state tailored to IT companies provides a way to comprehensively diagnose a team’s strengths and/or weaknesses, thus suggesting directions for team development.

This paper aims to present the design of a micro-electro-mechanical system (MEMS)-based three-dimensional combined vector hydrophone tailored for unmanned underwater vehicles (UUVs). The proposed design addresses the left-right ambiguity inherent in conventional MEMS hydrophones and enhances acoustic sensing capabilities to support improved UUV performance in underwater environments.

A novel MEMS-based three-dimensional vector hydrophone (M3DH) is proposed, integrating a highly sensitive MEMS chip with a piezoelectric ceramic-based scalar channel. A theoretical model of the hydrophone’s packaging was developed, and its acoustic performance was analyzed through COMSOL Multiphysics 6.2 simulations. Experimental validation of the hydrophone’s sensitivity and directional characteristics was conducted in a standing wave tank.

The MEMS-based three-dimensional combined hydrophone (M3DH) achieved a triaxial vector channel sensitivity of −175.6 dB at 800 Hz (re 1 V/µPa) and a scalar channel sensitivity of −186.3 dB (0 dB = 1 V/µPa). In addition, at 500 Hz, the vector channel exhibited a distinct “8”-shaped directivity pattern, whereas the scalar channel maintained a circular omnidirectional response. The hydrophone demonstrated excellent acoustic performance in three-dimensional space, effectively providing comprehensive acoustic information for small underwater platforms.

This research addresses the left-right ambiguity issue in MEMS hydrophones by presenting an MEMS-based three-dimensional combined hydrophone designed for integration into UUVs, offering an innovative solution to enhance underwater acoustic sensing capabilities in small platforms.

Currently, various detection technologies for unmanned underwater vehicles are highly susceptible to environmental impacts. Wake detection technologies have gradually gained attention and development. However, the clarity of detection results remains a challenge. This paper aims to present the design of a MEMS three-dimensional vector wake sensor. Compared to similar sensors, the MEMS three-dimensional vector wake sensor offers improved propeller wake measurement capabilities.

A MEMS three-dimensional vector wake sensor inspired by the fish lateral line system is designed. This paper discusses the working principle of the sensor. Finite element simulation is used to determine the optimal dimensions of the sensor’s sensitive chip and packaging structure. In addition, the wake environment is simulated for performance testing.

Flow velocity calibration test results confirm that the MEMS three-dimensional vector wake sensor exhibits high sensitivity, achieving 1727.6 mV/(m/s). Vector capability tests show that the data consistency in the same direction reaches 91.8%. The sensor demonstrates strong vector detection capability.

The MEMS three-dimensional vector wake sensor plays a critical role in the formation control of unmanned underwater vehicle fleets and target detection.

This study focuses on applications for unmanned underwater vehicles. It enhances the detection capabilities of unmanned underwater vehicles. This is of significant importance for future deep-sea target detection.