The impact of user-generated content on intention to select a travel destination
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ABSTRACT: Supplementary Information
The online version contains supplementary material available at 10.1057/s41270-022-00174-7.
SUBMITTER: Nguyen T
PROVIDER: S-EPMC9243881 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Destination image is a powerful means by which destinations compete in the tourism industry, and the accurate identification of a destination image better serves destination marketing and management. This study uses multimodal data, such as text, images, and videos uploaded by tourists, to construct a comprehensive and systematic destination image process. The "cognitive-emotional-overall image" model, latent Dirichlet allocation (LDA) model, and deep residual neural networks are implemented to build a framework to examine the perception of a destination image, travelogues, and short videos from the sources called Ctrip, Qunar, and TikTok. The results show that tourists' overall perception of Sanya is based mainly on the cognitive image of natural scenery, human resources, and food. In addition, there are differences between textual and visual cognitive images among the perceptual images when multimodal data is under consideration. Furthermore, tourists have an overall positive affective image of Sanya as a destination.
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Project description:Perceived risk clearly impacts travel behavior, including destination selection and satisfaction, but it is unclear how or why its effect is only significant in certain cases. This is because existing studies have undervalued the mediating factors of risk aversion, government initiatives, and media influence as well as the multiple forms or dimensions of risk that can mask its direct effect. This study constructs a structural equation model of perceived risk's impact on destination image and travel intention for a more nuanced model of the perceived risk mechanism in tourism, based on 413 e-questionnaires regarding travel to Chengdu, China during the COVID-19 pandemic, using the Bootstrap method to analyze suppressing effect. It finds that while perceived risk has a significant negative impact on destination image and travel intention, this is complexly mediated so as to appear insignificant. Furthermore, different mediating factors and dimensions of perceived risk operate differently according to their varied combinations in actual circumstances. This study is significant because it provides a theoretical interpretation of tourism risk, elucidates the mechanisms or paths by which perceived risk affects travel intention, and expands a framework for research on destination image and travel intention into the realms of psychology, political, and communication science. It additionally encourages people to pay greater attention to the negative impact of crises and focuses on the important role of internal and external responses in crisis management, which can help improve the effectiveness of crisis management and promote the sustainable development of the tourism industry.
| S-EPMC8741017 | biostudies-literature
Project description: Not available
| S-EPMC9288263 | biostudies-literature
Project description:BackgroundOnline health communities (OHCs) have increasingly gained traction with patients, caregivers, and supporters globally. Chinese OHCs are no exception. However, user-generated content (UGC) and the associated user behaviors in Chinese OHCs are largely underexplored and rarely analyzed systematically, forfeiting valuable opportunities for optimizing treatment design and care delivery with insights gained from OHCs.ObjectiveThis study aimed to reveal both the shared and distinct characteristics of 2 popular OHCs in China by systematically and comprehensively analyzing their UGC and the associated user behaviors.MethodsWe concentrated on studying the lung cancer forum (LCF) and breast cancer forum (BCF) on Mijian, and the diabetes consultation forum (DCF) on Sweet Home, because of the importance of the 3 diseases among Chinese patients and their prevalence on Chinese OHCs in general. Our analysis explored the key user activities, small-world effect, and scale-free characteristics of each social network. We examined the UGC of these forums comprehensively and adopted the weighted knowledge network technique to discover salient topics and latent relations among these topics on each forum. Finally, we discussed the public health implications of our analysis findings.ResultsOur analysis showed that the number of reads per thread on each forum followed gamma distribution (HL=0, HB=0, and HD=0); the number of replies on each forum followed exponential distribution (adjusted RL2=0.946, adjusted RB2=0.958, and adjusted RD2=0.971); and the number of threads a user is involved with (adjusted RL2=0.978, adjusted RB2=0.964, and adjusted RD2=0.970), the number of followers of a user (adjusted RL2=0.989, adjusted RB2=0.962, and adjusted RD2=0.990), and a user's degrees (adjusted RL2=0.997, adjusted RB2=0.994, and adjusted RD2=0.968) all followed power-law distribution. The study further revealed that users are generally more active during weekdays, as commonly witnessed in all 3 forums. In particular, the LCF and DCF exhibited high temporal similarity (ρ=0.927; P<.001) in terms of the relative thread posting frequencies during each hour of the day. Besides, the study showed that all 3 forums exhibited the small-world effect (mean σL=517.15, mean σB=275.23, and mean σD=525.18) and scale-free characteristics, while the global clustering coefficients were lower than those of counterpart international OHCs. The study also discovered several hot topics commonly shared among the 3 disease forums, such as disease treatment, disease examination, and diagnosis. In particular, the study found that after the outbreak of COVID-19, users on the LCF and BCF were much more likely to bring up COVID-19-related issues while discussing their medical issues.ConclusionsUGC and related online user behaviors in Chinese OHCs can be leveraged as important sources of information to gain insights regarding individual and population health conditions. Effective and timely mining and utilization of such content can continuously provide valuable firsthand clues for enhancing the situational awareness of health providers and policymakers.
| S-EPMC8717137 | biostudies-literature
Project description:Introduction:User generated content (UGC) is a valuable but underutilized source of information about individuals who participate in online cessation interventions. This study represents a first effort to passively detect smoking status among members of an online cessation program using UGC. Methods:Secondary data analysis was performed on data from 826 participants in a web-based smoking cessation randomized trial that included an online community. Domain experts from the online community reviewed each post and comment written by participants and attempted to infer the author's smoking status at the time it was written. Inferences from UGC were validated by comparison with self-reported 30-day point prevalence abstinence (PPA). Following validation, the impact of this method was evaluated across all individuals and time points in the study period. Results:Of the 826 participants in the analytic sample, 719 had written at least one post from which content inference was possible. Among participants for whom unambiguous smoking status was inferred during the 30 days preceding their 3-month follow-up survey, concordance with self-report was almost perfect (kappa = 0.94). Posts indicating abstinence tended to be written shortly after enrollment (median = 14 days). Conclusions:Passive inference of smoking status from UGC in online cessation communities is possible and highly reliable for smokers who actively produce content. These results lay the groundwork for further development of observational research tools and intervention innovations. Implications:A proof-of-concept methodology for inferring smoking status from user generated content in online cessation communities is presented and validated. Content inference of smoking status makes a key cessation variable available for use in observational designs. This method provides a powerful tool for researchers interested in online cessation interventions and establishes a foundation for larger scale application via machine learning.
| S-EPMC6329402 | biostudies-literature
Project description:Online travel reviews have been extensively used as an important data source in tourism research. Typically, data for online travel review research is collected only from one platform. However, drawing definite conclusions based on single platform analyses may thus produce biases and lead to erroneous conclusions and decisions. Therefore, this research verifies whether or not there are discrepancies and commonalities between different travel review platforms. In this study, five native Chinese travel review platforms were selected: Ctrip; Qyer; Mafengwo; Tuniu; and Qunar. Using a mixed content analysis method, the destination image of Finland was extracted from 10,197 travel reviews in Simplified Chinese as the destination image is a popular topic in online review research. Results show Finland’s destination image representation varies between Chinese travel review platforms. This discrepancy is especially prominent in the dimension of functional and mixed functional-psychological destination attributes. Significant theoretical contributions and managerial implications for the analysis of online travel reviews and destination image research are discussed. Supplementary Information The online version contains supplementary material available at 10.1007/s40558-021-00201-w.
| S-EPMC8062146 | biostudies-literature
Project description:Excessive consumption is on the rise, as is apparent in growing financial debt and global greenhouse gas emissions. Voluntary simplicity, a lifestyle choice of reduced consumption and sustainable consumer behavior, provides a potential solution for excessive consumers. However, voluntary simplicity is unpopular, difficult to adopt, and under researched. The outlined research project will test a method of promoting voluntary simplicity via user-generated content, thus mimicking an existing social media trend (Minimalism) in an empirical research design. The project will test (a) whether the Minimalism trend could benefit consumers interested in reducing their consumption, and (b) whether self-transcendence (i.e., biospheric) and self-enhancement (i.e., egoistic and hedonic) values and goals have a similar impact in promoting voluntary simplicity. A one-week intervention program will test the efficacy of watching user-generated voluntary simplicity videos in reducing non-essential consumption. Each of the two intervention conditions will present participants with similar tutorial videos on consumption reduction (e.g., decluttering, donating), while priming the relevant values and goals (self-transcendence or self-enhancement). These interventions will be compared to a control condition, involving no user-generated content. Participants will undergo baseline and post-intervention evaluations of: voluntary simplicity attitudes and behaviors, buying and shopping behaviors, values and goals in reducing consumption, and life satisfaction. Experience sampling will monitor affective state during the intervention. We provide a detailed stepwise procedure, materials, and equipment necessary for executing this intervention. The outlined research design is expected to contribute to the limited literature on voluntary simplicity, online behavioral change interventions, and the use of social marketing principles in consumer interventions.
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Project description:Smart card data are widely used in generating the origin and destination (O-D) matrix for public transit, which contains important information for transportation planning and operation. However, the generation of the O-D matrix is limited by the smart card data information that includes the boarding (origin) information without the alighting (destination) information. To solve this problem, trip chain methods have been proposed, thereby greatly contributing in estimating the destination using the smart card data. Nevertheless, unlinked trips, that is, trips with unknown destinations, are a persisting issue. The purpose of this study is to develop a method for estimating the destination of unlinked trips, in which trip chain methods cannot be applied, using temporal travel patterns and historical boarding records of the passengers based on long-term smart card data. The passengers were clustered by k-means clustering, and the time-of-day travel patterns were estimated for each cluster using a Gaussian mixture model. The travel patterns were formulated to estimate the destination of the passengers from the smart card data. The proposed method was verified using the 2018 smart card data collected in Sejong City, South Korea. The existing trip chain method matched the destinations of 60.0% of the total trips, whereas the proposed method improved the matching to 74.9% by additionally matching the destinations of 37.2% of the unlinked trips.
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Project description:This study aims to examine the effect of destination image, perceived risk, and perceived constraints on the behavioral intention of international tourists to revisit Pakistan. The study also seeks to assess the destination image's mediating role in the relationship between perceived risks, perceived constraints, and behavioral intention. A quantitative study with Partial least square structural equation modeling was used to investigate the research Hypothesis. The data was collected from international tourists who were in Pakistan or who had visited Pakistan. The findings revealed that perceived risks and perceived constraints negatively impact destination image and behavioral intention. On the other hand, destination image has a positive impact on behavior. Moreover, the study also proved the mediating effect of destination image among the relations of perceived risks, perceived constraints, and behavioral intention. These findings indicate that sometimes it is difficult for destinations to overcome constraints, so destination managers should provide value-added services for substitutes. A positive destination image can overcome risks and constraints, so destination managers should also promote destinations besides mitigating risks. Literature has discussed the mediating effect of destination image in different contexts. However, studies are scarce investigating destination image's effect in alleviating perceived constraints and perceived risks through negotiation mechanisms.
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Project description:The Covid-19 pandemic negatively affected many sectors including aviation and travel. Travel bans and forced lockdowns prevented transportation activity, especially air travel. Accordingly, huge amounts of emission reductions occurred. On the other hand, travel restrictions are not the only cause of emissions reductions. Changing travel intention in the era of Covid-19 is another important factor that affects aviation emissions. This paper aims to investigate the Landing/Take-Off (LTO) emission changes at Turkish airports. An emission inventory has been implemented for the years 2019 and 2020 to reveal the impacts of Covid-19 on aviation emissions. Domestic, international, and cargo flights have been included in the inventory. According to the results, total emissions of SO2, CO2, CO, NOx, NMVOC, CH4, N2O, and PM2.5 have decreased in 2020 compared to 2019 by 49.8%, 49.7%, 41.0%, 52.6%, 40.0%, 33.8%, 49.8%, and 50.3%, respectively. Total CO2 reductions in the Q2, Q3, and Q4 periods of 2020 compared to that of 2019 are 87%, 50% and 43%, respectively. Another aim of this paper is to find the underlying reasons for emission reductions. For Turkish airports, emission reductions have resulted from travel bans in Q2. After the relaxation of restrictions with the declaration of the "New Normal" in Turkey, flight traffic rebounded to a certain level but was lower than 2019 levels. Therefore, changing travel intention is the main cause of emission reductions in Q3 and Q4 of 2020. The results of this study contribute to both the areas of air pollution and tourism management.Supplementary informationThe online version contains supplementary material available at 10.1007/s10668-023-02916-8.
| S-EPMC9928588 | biostudies-literature