RESUMO
The aim of this study was to explore the causes of tourists' lesser enjoyment of theme parks through an unsupervised machine learning approach-structural topic modelling. Specifically, the research extracted a comprehensive list of discussion topics about the travel experience of tourists through the analysis of 112,000 customer reviews released by visitors to the Shanghai Disney Resort from 16 June 2016 to 4 March 2022. Then, we used sentiment analysis to distinguish positive and negative topics and to explore the relationship between tourists who buy different ticket types and sentiments in negative topics. The results show that problems such as "parking," "service attitude," "recommendation feeling," "experience comparison," and "entrance" may be the main reasons for an unpleasant experience. In addition, we also found that when tourists travel in groups (e.g., via family tickets), customers feel unhappy because of parking and fast track problems. Moreover, when tourists travel alone or with small groups, staff service attitudes, experience comparisons, and entrance processes are the sources of greater concern. Our findings can help theme park managers to better understand the expectations of tourists and take effective measures to tackle issues causing customer dissatisfaction, and they can also contribute to theme park studies in tourism management.
RESUMO
Objective: With consumers' concerns about food safety and the environment growing, the interest in organic food has increased. However, due to the late start of the organic food market in China, the market size of the Chinese organic food industry is still relatively small. This study aims to examine whether organic food credence attributes have an impact on consumers' attitudes and willingness to pay a premium (WTPP), in order to provide valuable information to facilitate the development of the organic food market in China. Methods: A questionnaire survey was conducted with 647 respondents in China. Structural equation modeling (SEM) was utilized to verify the model and test the relationships among the constructs. Results: SEM analyses showed that credence attributes stimulate consumers' attitudes and increase consumers' WTPP. Utilitarian attitudes and hedonistic attitudes play a partially mediating role in the relationship between credence attributes and WTPP. Uncertainty negatively moderates the role between utilitarian attitudes and WTPP, while it positively moderates the role between hedonistic attitudes and WTPP. Discussion: The findings reveal the motivations and barriers for Chinese consumers to purchase organic food at a premium, providing a theoretical basis for companies to gain a deeper understanding of consumer groups and develop organic food marketing strategies.
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This study aimed to explore and evaluate factors that impact the dining experience of vegetarian consumers within a range of vegetarian-friendly restaurants. To explore the factors and understand consumer experience, this study analyzed a vast number of user-generated contents of vegetarian consumers, which have become vital sources of consumer experience information. This study utilized machine-learning techniques and traditional methods to examine 54,299 TripAdvisor reviews of approximately 1,008 vegetarian-friendly restaurants in London. The study identified 21 topics that represent a holistic opinion influencing the dining experience of vegetarian customers. The results suggested that "value" is the most popular topic and had the highest topic percentage. The results of regression analyses revealed that five topics had a significant impact on restaurant ratings, while 12 topics had negative impacts. Restaurant managers who pay close attention to vegetarian aspects may utilize the findings of this study to satisfy vegetarian consumer requirements better and enhance service operations.
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This study analyses the agenda setting on social media in the COVID-19 pandemic by exploiting one of the disruptive technologies, big data analytics. Our purpose is to examine whether the agenda of news organisations matches the public agenda on social media in crisis situations, and to explore the feasibility and efficacy of applying big data analytics on social media data. To this end, we used an unsupervised machine learning approach, structural topic modelling and analysed 129,965 tweets posted by UK news media and citizens during April 2, and 8, 2020. Our study reveals a wide diversity of topics in the tweets generated by both groups and finds only a small number of topics are similar, indicating different agendas set in the pandemic. Moreover, we show that citizen tweets focused more on expressing feelings and sharing personal activities while news media tweets talked more about facts and analysis on COVID-19. In addition, our results find that citizens responded more significantly to breaking news. The findings of the study contribute to the agenda setting literature and offer valuable practical implications.
