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This chapter aims to estimate the impact of the use of an innovative cultivation method on the social, economic and environmental aspects in the French region Aix-en-Provence, by using the survey data for 200 heterogeneous vegetable producers (organic and conventional). It distinguishes three types of producers in the French region Aix-en-Provence. First, conventional producers (n = 100) who used a high level of mechanization, better access to water, high yield, high labor costs. Second, certified organic producers (n = 70) who used organic technologies such as biotechnology and rotation, low yield, high organic product price compared to conventional products, a family workforce and high transport. Third, noncertified organic producers (n = 30) have used the same technologies as certified organic producers, while they sell their products at the same price as conventional products. Labor is the member of the family. These noncertified farms are marked by high operating and transport costs and low yield compared to conventional producers or certified organic producers. The results show that this cultivation method has a positive effect on the environmental aspect, however a negative one on the social and economic aspect.

Although the concept of destination is often described as an economic term that describes places of interest for tourists and visitors, currently, there is still little awareness in the extant literature about regional, city, village, resort, or even standalone tourist destinations. This chapter aims to clarify the meaning of destinations. It distinguishes the differences between common locations and tourist destinations. It uses case studies to describe places, placemaking, and the experiencescapes of various destinations. This contribution implies that tourist attractions differentiate themselves from other places, as they offer accessible attractions with amenities.

This article examines how geographic location (i.e. local or nonlocal) influences fans’ communicative convergence or distinctiveness in the context of an informal online fan community (i.e. Twitter [X]). Nonlocal fans, who live geographically distant from the team they support, derive distinctiveness and belonging from supporting a team from afar. This study considers how these needs materialize in online communication.

Text mining and textual analysis were employed to collect and evaluate tweets (N = 12,865) in terms of expressed emotion. Tweets were evaluated using the NRC VAD sentiment lexicon (i.e. valence, arousal, dominance) and then categorized based on geographic location and in-group/out-group status. The resulting groups were then analyzed with a two-way multivariate analysis of variance (MANOVA) to examine how geographic location relates to the expressed emotion of tweets.

The findings indicate nonlocal fans expressed statistically greater arousal compared to local online community members, reflecting their distinctiveness as nonlocal fans. Nonlocal and local fans expressed similar degrees of valence and dominance in their language, suggesting nonlocal fans converge communication behavior, seeking social approval from the salient online fan community.

This study suggests that geographic location influences communication behavior in online fan communities. Moreover, nonlocal fans may be valuable, and underutilized, advocates for sports teams in online community settings.

Building on social media and destination brand-related literature, this study aims to explore World Heritage Sites’ (WHSs) brand diffusion and formation process from long-term and short-term perspectives, which includes brand diffusion, user-generated content (UGC), opinion leaders and brand events’ impact.

This study uses a mixed-method including text mining, keyword analysis and social network analysis to explore the brand formation process of four popular WHSs in Beijing, namely, the Palace Museum, Great Wall, Summer Palace and Temple of Heaven and more than 10,000,000 users’ data on Sina Weibo has been implemented to uncover the underlying social media branding mechanism.

The results show that the number of postings keeps in a stable range in most months, but, in general, there are no common rules for changing trends among the four WHSs; long-term high-frequency keywords related to history and culture account for a higher percentage; different kinds of accounts have varying impacts on information diffusion, in which media accounts lead to a bigger influence. However, more followers do not necessarily mean more interactions and most of the interaction ratio is much lower than 0.01000; brand events facilitate brand dissemination and have an impact on the creation of UGC.

This study is valuable for destination marketers to deeper understand brand diffusion and formation and provides valuable insights for developing effective destination marketing strategies.

Unlike previous studies that only concern a few parts of destination brand formation via social media (e.g. brand diffusion, brand events or opinion leaders’ impact), this study takes a more comprehensive perspective by systematically analyzing the brand formation process of WHSs on social media. By considering both long-term diffusion and short-term representative events, this study provides a more holistic understanding of the branding mechanism.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

The majority of emerging adults in the United States spend time in cohabiting unions. Prior research has suggested that higher levels of sexual non-exclusivity may exist among those in cohabiting relationships compared to marital unions. Although these basic patterns have been explored in prior work, research examining the potential reasons why levels of sexual non-exclusivity differ by union status has been limited. Drawing on a relational perspective and using the fifth wave of data from the Toledo Adolescent Relationships Study (TARS), the present study found that higher levels of sexual non-exclusivity in cohabiting relationships were explained by intimate relationship characteristics and sexual histories rather than sociodemographic factors, partner heterogamy, or partner- and couple-level drug use. These findings highlighted that understanding the higher rates of sexually non-exclusive experiences in cohabiting relationships, compared to marital relationships, requires attention to specific dynamics of the intimate partnership and prior relational experiences of both partners. The study concluded that cohabitation has a unique place in emerging adults’ relationship landscape and may set the groundwork for future relationship functioning.