The Effect Of Online Customer Reviews On Customer\u27s Perceived Risk Associated With Online Leisure Hotel Booking

As online shopping is widely used in the hospitality industry, research in this field constantly strives to understand the customer behavior in online purchasing activities. Online customer reviews (OCRs) and perceived risk have been extensively evaluated in previous studies in related with online purchasing. In spite of the large body of work on the topic of OCRs effect on consumer behavior, it is still unclear that how OCRs affect the decision process of the consumers when they make online booking. Due to the intangibility of hospitality or tourism product and the nature of online booking, risk perception is considered as one of the most important factors that impact the buyer\u27s decision. Thus, it is constructive to investigate the effect of OCRs in the context of consumer perceived risk associated with online shopping, in the hope of understanding how OCRs affect the decision process and seeking solutions for the hotel marketers to improve their service as well as the online commenting system. In this study, we demonstrated a method which investigates the relationship between consumers\u27 perceived risk associated with online leisure hotel shopping and different types of OCRs (core and peripheral). By evaluating perceived risk associated with online leisure hotel booking caused by different hotel attributes, we addressed the importance of OCRs on various hotel attributes and therefore provided information for E-marketers to fine-tune their E-business strategies in terms of managing proper online customer reviews. Two hundred surveys were distributed. The instrument contained two parts and one scenario: (1) Demographic information, past experience, and attitudes towards OCRs of the participants regarding online leisure hotel booking. (2) A scenario was given that the participant was planning a trip for his/herself the up-coming vocation. (3) Operational statements were used to evaluate each individual participant\u27s risk perception about his/her most recent online leisure hotel booking experience. The findings provided exploratory insights about the dimensions of perceived risk identified in the process of online leisure hotel booking, effect of the positive and negative reviews, different OCRs had different implications for different hotel preferences and the magnitudes of OCRs effect for each dimension of perceived risk associated with online leisure hotel booking. Detailed findings were discussed in Chapter 5

Artificial Human Balance Control by Calf Muscle Activation Modelling

The natural neuromuscular model has greatly inspired the development of control mechanisms in addressing the uncertainty challenges in robotic systems. Although the underpinning neural reaction of posture control remains unknown, recent studies suggest that muscle activation driven by the nervous system plays a key role in human postural responses to environmental disturbance. Given that the human calf is mainly formed by two muscles, this paper presents an integrated calf control model with the two comprising components representing the activations of the two calf muscles. The contributions of each component towards the artificial control of the calf are determined by their weights, which are carefully designed to simulate the natural biological calf. The proposed calf modelling has also been applied to robotic ankle exoskeleton control. The proposed work was validated and evaluated by both biological and engineering simulation approaches, and the experimental results revealed that the proposed model successfully performed over 92% of the muscle activation naturally made by human participants, and the actions led by the simulated ankle exoskeleton wearers were overall consistent with that by the natural biological response

Learning World Models with Identifiable Factorization

Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments. Different categories of information coexist in such environments -- how to effectively extract and disentangle these information remains a challenging problem. In this paper, we propose IFactor, a general framework to model four distinct categories of latent state variables that capture various aspects of information within the RL system, based on their interactions with actions and rewards. Our analysis establishes block-wise identifiability of these latent variables, which not only provides a stable and compact representation but also discloses that all reward-relevant factors are significant for policy learning. We further present a practical approach to learning the world model with identifiable blocks, ensuring the removal of redundants but retaining minimal and sufficient information for policy optimization. Experiments in synthetic worlds demonstrate that our method accurately identifies the ground-truth latent variables, substantiating our theoretical findings. Moreover, experiments in variants of the DeepMind Control Suite and RoboDesk showcase the superior performance of our approach over baselines