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This paper aims to propose an active disturbance rejection control (ADRC)-based visual servoing strategy for regulating a wheeled mobile robot from varying initial poses to a desired pose at an exponential rate. It addresses challenges associated with non-holonomic constraints, uncertain depth information and unknown translational parameters in monocular vision systems.

The uncertain depth information in monocular vision and unknown camera-to-robot translational parameters are modeled as internal uncertainties of the visual servo system. An input-state scaling technique is used to decouple the system into two subsystems, controlled by angular and linear velocities, respectively. The angular velocity controller is designed to ensure strict exponential convergence, while the internal parametric and bounded uncertainties of the system are estimated and compensated for by an extended state observer and a switching linear velocity controller.

The separate design of the angular and linear velocity controllers effectively overcomes the non-holonomic constraints of the mobile robot, ensuring robust performance under diverse conditions. Furthermore, the ADRC-based strategy successfully handles uncertain depth information and unknown translational parameters. The convergence of the error system is rigorously proven using Lyapunov theory, and simulation results verify the effectiveness of the proposed scheme.

To the best of the authors’ knowledge, this study introduces, for the first time, a novel approach that combines ADRC with visual servoing for non-holonomic mobile robots. This approach enhances the adaptability and accuracy of the robot’s navigation in environments characterized by unknown system uncertainties. The proposed method demonstrates enhanced practical performance over conventional techniques by effectively managing the inherent uncertainties of the system.

Language styles of online reviews are becoming increasingly important in consumers’ purchase decisions. However, there are inconsistencies in research on the effects of literal and figurative language styles in online reviews on service consumption. Drawing upon construal level theory, this research explores the effects of literal and figurative online reviews on consumers’ word-of-mouth recommendations and their internal mechanisms in the context of service consumption. In addition, this research identifies service types (experience vs credence services) as boundary conditions under which online review language styles play a role.

Three studies are designed to verify the effect of language style in online reviews on consumer word-of-mouth recommendations. Study 1 (N = 195) tests the interaction between construal level and (literal vs figurative) language style on consumers’ word-of-mouth recommendations. Study 2 (N = 191) identifies the depth of information processing as an underlying mechanism. Study 3 (N = 466) examines the boundary condition due to service type. The main methods used are independent sample t-test, ANOVA and bootstrapping.

The results illustrate that (1) consumers at different construal levels prefer online reviews with different language styles, and this can influence their word-of-mouth recommendations: consumers with a low construal level prefer online reviews with a literal language style, while those with a high construal level prefer online reviews with a figurative language style; (2) the depth of information processing plays a mediating role in the above interaction effect and (3) service type serves as a boundary condition such that the preference for literal (vs figurative) language style among low- (vs high-) construal-level consumers holds only for experience services; for credence services, online reviews with a literal language style enhance word-of-mouth recommendations, regardless of consumers’ construal level. The findings shed light on the drivers of word-of-mouth recommendations and provide insights to promote more effective word-of-mouth recommendations.

Drawing upon the construal level theory, this research explores the factors that influence online review language styles on consumer word-of-mouth recommendations and their underlying mechanisms and discusses the moderating effects of different service types (i.e. experience services and trust services). It not only sheds light on the contradictions in the previous literature but also provides new insights for academics and business managers to deepen their understanding of facilitating word-of-mouth recommendations.