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Although some studies claim that tourism commercialization can promote the authentic experience and behaviour of tourists, there is a lack of empirical support. The main purpose of this study is to identify whether tourism commercialization can positively impact tourists’ perceived authenticity and tourist satisfaction in the context of cultural heritage tourism. We divide tourism authenticity into objective authenticity, constructive authenticity, existential authenticity and postmodern authenticity and propose a relationship model of tourism commercialization, the four authenticities, tourist satisfaction and loyalty. A survey was conducted in Langzhong Ancient City, a representative millennium-old county in China. A total of 618 valid domestic tourist questionnaires were collected. The partial least squares structural equation modelling (PLS-SEM) suitable for theory development was used for the conceptual model validation. The results indicate that tourism commercialization positively affects objective, constructive, existential and postmodern authenticity and tourist satisfaction; the four authenticities positively affect tourist satisfaction, while only objective and existential authenticity and tourist satisfaction positively affect tourist loyalty. Theoretical and practical implications are discussed.

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Growth curve models have been widely used to analyse longitudinal data in social and behavioural sciences. Although growth curve models with normality assumptions are relatively easy to estimate, practical data are rarely normal. Failing to account for non‐normal data may lead to unreliable model estimation and misleading statistical inference. In this work, we propose a robust approach for growth curve modelling using conditional medians that are less sensitive to outlying observations. Bayesian methods are applied for model estimation and inference. Based on the existing work on Bayesian quantile regression using asymmetric Laplace distributions, we use asymmetric Laplace distributions to convert the problem of estimating a median growth curve model into a problem of obtaining the maximum likelihood estimator for a transformed model. Monte Carlo simulation studies have been conducted to evaluate the numerical performance of the proposed approach with data containing outliers or leverage observations. The results show that the proposed approach yields more accurate and efficient parameter estimates than traditional growth curve modelling. We illustrate the application of our robust approach using conditional medians based on a real data set from the Virginia Cognitive Aging Project.

Coal–water interactions have a prominent impact on the prediction of coal mine gas disasters and coalbed methane extraction. The change of characteristics in the microscopic pores of coal caused by the existence of water is an important factor affecting the diffusion and migration of gas in coal. The low-pressure nitrogen adsorption experiments and gas desorption experiments of a low-rank coal with different equilibrium moisture contents were conducted. The results show that both the specific surface area and pore volume decrease significantly as the moisture content increases, and the micropores (pore diameter <10 nm) are most affected by the water adsorbed by coal. In particular, for a water-equilibrated coal sample at 98% relative humidity, micropores with pore sizes smaller than 4 nm as determined by the density functional theory model almost disappear, probably due to the blocking effects of water clusters and capillary water. In this case, micropores with a diameter less than 10 nm still contribute most of the specific surface area for gas adsorption in coal. Furthermore, the fractal dimensions at relative pressures of 0–0.5 (D 1) and 0.5–1 (D 2) calculated by the Frenkel–Halsey–Hill model indicate that when the moisture content is less than 4.74%, D 1 decreases rapidly, whereas D 2 shows a slight reduction as the moisture content increased. In contrast, when the moisture content exceeds 4.74%, further increases in the moisture content cause D 2 to decrease significantly, while there is nearly no change for D 1. The correlation analyses show that the ultimate desorption volume and initial desorption rate are closely related to the fractal dimension D 1, while the desorption constant (K t) mainly depends on the fractal dimension D 2. Therefore, the gas desorption performances of coal have a close association with the pore properties of coal under water-containing conditions, which indicate that the fluctuation in moisture content should be carefully considered in the evaluation of gas diffusion and migration performances of in situ coal seams.