The aim of this study is to examine the advertising information learning processes of potential tourists and observe how potential tourists sequentially adjust their perceived reference prices and purchase intentions with different risk preferences and choices with respect to gains (the current price is lower than the consumer’s reference price) or losses (the current price is higher than the reference price). In this study, a Bayesian experiment was conducted to elicit reference prices in the presence of tourism advertising with uncertain information. The findings show that with respect to gains, risk avoiders do not reduce their reference prices as significantly as do risk seekers when exposed to price-informative advertising. Exposure to image advertising changes potential tourists’ risk preferences, and the reference price drops more significantly for risk avoiders than for risk seekers. With respect to losses, informative and image advertising impact the reference price for participants with different risk preferences but not at a statistically significant level.
In recent years, risky decisions and the “gambling paradigm” have been mainstream topics in economics and psychology research. However, the choice of Knightian uncertainty, especially as it relates to the potential cognitive processes of consumers in marketing, has not drawn the attention of scholars. This study introduced Bayesian learning theory to reproduce the consumer learning process and conducted a two‐stage experiment to explore the process of how perceived price and uncertain quality information influence consumer learning and decisions. The results were validated via an eye‐tracking experiment with dynamic (updated) advertising information to observe the learning mechanism of consumers under Knightian uncertainty. The results showed that consumers conduct irregular Bayesian learning based on different perceived price levels in different information state spaces. Belief changes follow the first‐order Markov learning rule, and Bayesian learning is a dual‐system process. Overall, the results are in line with the belief‐integration principle of the Bayesian learning model under Knightian uncertainty.
The tourism industry has a higher dependence on natural and cultural resources and is more vulnerable to environmental impacts than any other industry. It faces a plethora of issues, risks, and opportunities related to sustainability and environmental change. The hotel industry is one of the most important parts of tourism. Three China Green certified hotels (the Guesthouse International Hotel Sanya, the Leaguen Resort Sanya Bay and the Boao Golden Coast Hot Spring Hotel) are being chosen as samples for our case studies. This paper introduces these three hotels, and then gives the results from the field investigation and the interviews conducted with hotel staff. Based on the data collected, this paper uses Earth Checkas, a sustainable development evaluation tool, to assess these three hotels. Through evaluating our results, we verify whether the China Green Hotel standard meets the international standard requirements of Earth Check. Meanwhile, with this inspection we determine whether Earth Check is suitable for application in the China hotel industry and the whole tourism industry. Through our research of these three hotels, we found that the Earth Check standard can be implemented in the hotel industry in China to help these hotels develop a sustainable hotel.
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