Individuals are often reluctant to seek help from others. Nostalgia is a highly social emotion that bolsters perceptions of connectedness with others. In 6 studies, we investigated whether nostalgia reduces individuals' general reluctance to reach out to others for help by virtue of its capacity to strengthen social connectedness (i.e., a sense of acceptance and support). In a preliminary study, we measured nostalgia, 6 comparator emotions, and help seeking. Nostalgia predicted help seeking, independently of the comparator emotions. In Experiments 1 through 4, we induced nostalgia (vs. control) and subsequently measured self-reported help seeking (Experiments 1 through 2), help-seeking behavior (Experiments 3 through 4), and social connectedness (Experiments 2 and 4). Nostalgia increased self-reported help seeking (Experiments 1 and 2) and help-seeking behavior (Experiments 3 and 4), and these effects were mediated by social connectedness (Experiments 2 and 4). In Experiment 5, we manipulated the social content of nostalgic reflections and measured help-seeking behavior. Nostalgia pertaining to social (but not to nonsocial) past events increased help seeking. In all, nostalgia is a resource that facilitates help seeking by fostering social connectedness. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The present work explores whether the generative pre-trained transformers (GPT) can complement empirical research in tourism as the GPT extends beyond commercial applications. In particular, we utilized OpenAI’s Python API to interact with the GPT-3.5-turbo. Using GPT as a special subject, we coined AI-generative study (AGS) to validate key findings of 16 scenario-based experiments published in leading journals of tourism and hospitality in two studies. This research contributes to the literature by delineating a new methodology that opens a forum for discussion on alternative means of conducting tourism research. Future studies could also utilize GPT and the ability of generative AI for tourism research in terms of pilot-/pre-testing and cross-validation. In conclusion, we recommend that GPT-generated results should serve primarily as preliminary findings and must be corroborated by data from actual human participants, thus providing converging evidence to support the corresponding research conclusions.

Health rumors not only incite unnecessary fears and skepticism, but may also cause individuals to refuse effective remedy and thus delay their treatment. Studies have found that health literacy may help the public identify the falsity of health rumors and avoid their negative impact. However, whether other types of literacy work in helping people disbelieve health rumors is still unknown. With a national survey in China (N = 1646), our study examined the effect of science literacy on rumor belief and further analyzed the moderating role of self-efficacy of science literacy in their relationship. Hierarchical regression analysis showed that science literacy significantly decreased the likelihood of people believing in health rumors, and moderator analysis showed that self-efficacy of science literacy plays a moderating role in this relationship; such that the relationship between science literacy and health rumor belief would be weakened if one′s self-efficacy of science literacy was low. This finding reveals that during campaigns to combat health rumors, improving and enhancing the self-efficacy of people′s science literacy is an effective way to prevent them from believing in health rumors. Our study highlights the benefits of science education in public health and the improvement of public science literacy.