Agent-Based Modeling and Simulation of Tourism Market Recovery Strategy after COVID-19 in Yunnan, China

The tourism industry hit severely by COVID-19 faces the challenge of developing effective market recovery strategies. Nonetheless, the existing literature is still limited regarding the dynamic evolution process and management practice. Hence, this study chose several famous spots in the Yunnan Province of China as the focus for a case study and utilized an agent-based simulation method for the decision-making process of tourists’ destination selection and the dynamic recovery process of the destinations under different price and information strategies. The study found that the recovery effects of information strategies are positive, negative, or have no effect in different destinations. In contrast, price strategies can significantly stimulate an increase in the market share of destinations. When price strategy and information strategy are applied simultaneously, the interaction effects are inconsistent in different destinations. The findings contribute to the prediction of the recovery effect of strategies, can reduce trial and error costs, and can improve the scientific understanding of tourism market recovery

Examining Protection Motivation and Network Externality Perspective Regarding the Continued Intention to Use M-Health Apps

M-health apps have developed rapidly and are widely accepted, but users’ continued intention to use m-health apps has not been fully explored. This study was designed to obtain a better understanding of users’ continued intention to use m-health apps. We developed a theoretical model by incorporating the protection motivation theory and network externalities and conducted an empirical study of a 368-respondent sample. The results showed that: (1) perceived vulnerability has a direct impact on users’ self-efficacy and response efficacy; (2) self-efficacy and response efficacy have a direct impact on users’ attitudes and continued intention; (3) network externalities affect users’ attitudes and continued intention, among which direct network externalities have an indirect impact on users’ continued intention through attitude; and (4) the impacts of self-efficacy, response efficacy, and indirect network externalities on continued intention are partially meditated by attitudes

Autoencoder-enabled potential buyer identification and purchase intention model of vacation homes

A trend of purchasing a lakeside, seaside, or forest vacation home has been raised in China. However, such purchase behavior has received limited attention from the research community in emerging markets. This study aims at investigating the factors behind vacation home purchase behavior and helping identify potential buyers. Specifically, factors, such as air quality, enduring involvement, place attachment, and destination familiarity, are examined via a proposed integrative model, which links these factors to purchase intention. The total number of potential buyers of vacation homes is increasing but remains small, compared to the whole consumers’ population, resulting in imbalanced purchase behavior data when validating a model. To address this problem, this study proposes an autoencoder-enabled and $k$ -means clustering-based (AKMC) method to identify potential buyers. The proposed methods tested on a dataset of 309 samples, collected through a questionnaire-based survey, and achieves a model accuracy of 82% in identifying potential buyers, outperforming other traditional machine learning methods, such as decision trees and support vector machines. This study also provides explainable results for the vacation home purchase behavior and a decision-making tool to identify potential buyers.Published versio

Boronic Acid as Glucose-Sensitive Agent Regulates Drug Delivery for Diabetes Treatment

In recent years, glucose-sensitive drug delivery systems have attracted considerable attention in the treatment of diabetes. These systems can regulate payload release by the changes of blood glucose levels continuously and automatically with potential application in self-regulated drug delivery. Boronic acid (BA), especially phenylboronic acid (PBA), as glucose-sensitive agent has been the focus of research in the design of glucose-sensitive platforms. This article reviews the previous attempts at the developments of PBA-based glucose-sensitive drug delivery systems regarding the PBA-functionalized materials and glucose-triggered drug delivery. The obstacles and potential developments of glucose-sensitive drug delivery systems based on PBA for diabetes treatment in the future are also described. The PBA-functionalized platforms that regulate drug delivery induced by glucose are expected to contribute significantly to the design and development of advanced intelligent self-regulated drug delivery systems for treatment of diabetes