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The research aims to examine what service convenience factors drive customer satisfaction in travel websites from the perspective of older travelers, and if older travelers’ satisfaction enhances e-loyalty. Additionally, drawing on socioemotional selectivity theory, this study argues that the social presence in travel websites plays a significant moderating role in increasing older travelers’ satisfaction. To empirically verify the conceptual model, an online survey was conducted targeting older travelers aged over 50 in the USA who have purchased products via travel websites. The data from 308 older travelers were analyzed, and the results revealed four dimensions of service convenience positively influence satisfaction. Unexpectedly, access convenience and transaction convenience do not influence older travelers’ satisfaction. The older travelers’ satisfaction with travel websites has a positive impact on e-loyalty. Additionally, social presence amplifies the effect of post-purchase convenience on satisfaction. The current research makes a significant contribution to understanding older travelers’ perceptions and behaviors for using e-commerce service in the field of tourism and provides practitioners with effective ways to attract older travelers for sustainable management of travel websites.

With tourism development in China, the influx of tourists in popular tourist attractions has become more frequent. However, space cannot accommodate such a large influx of tourists. Through empirical testing, this research identified 23 variables that influence the safety of tourists in crowded spaces. We divided 23 variables into three factors: pressure factors, state factors, and crowd management actions. Based on the data collected, this study proposes a system model that includes a feedback mechanism to evaluate the safety of highly aggregated tourist crowds (HATCs) and identify moments requiring security warnings. System simulation results showed that the safety level of HATCs presented a complex process of change in different situations. Thus, management can take corrective actions. We tested this model by simulating different crowding conditions and assessing the safety level of tourists. Different warning plans were proposed based on the simulated security level.

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