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.

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.

Purpose: This paper is to present a new failure model that can be applied to single‐sample failure data of a single system under testing.Design/methodology/approach: The Bayesian method is used for the reliability evaluation. The weighted least squares method is used for determining the parameters of the reliability function.Findings: The authors have observed the operation of a special computer numerical control (CNC) system for a period of over two years, and maintained a reliability database will all the collected failure data, from which the main source of failures can be identified.Research limitations/implications: Preliminary research results are very encouraging. However, more work will be necessary to validate the new failure model.Practical implications: The determination of the parameters of the reliability function of a system under testing helps to identify its failure characteristics and potential quality problems.Originality/value: It is hoped that the paper can help understand some of the challenges in modeling the failure behavior of special CNC systems.