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This study aims to analyze the impact of event experience on event satisfaction and intentions to return and recommend the destination.

Relationships among constructs were tested on data gathered from 542 tourists during the America's Cup World Series held in South Italy in April 2013 by using a structural equation modeling approach. Moreover, a multigroup analysis was developed to test the possible moderator factors.

The results revealed that event experience and event satisfaction had positive impacts on the intentions to recommend and return to the host destination. Moreover, nationality, gender and trip motivation emerged as important moderating factors in the relationships among the latent constructs.

The findings of this paper enrich the existing literature and help tourism destination marketers and managers consider the triggering factors of a satisfying mega-sports event for the host destination and the marketing power of the on-site experience.

Practitioners should draw on the insights provided by this study to design destination strategies, particularly by paying attention to how an event experience causes an attendee to return to and recommend the host destination.

This study enriches the existing event literature in several ways. First, it emphasizes the importance of the event experience to the satisfaction level and willingness to return and recommend the host destination for a vacation, supporting the link between an event and its destination. Second, it provides a moderating analysis that offers new insights for marketing the event experience. It offers a multilevel model of mega-event tourism legacy, which opens up new avenues of research. Third, complementing the consumer-based analysis, this research includes the trend of visits (after 2013 to the present) to examine how a mega-sport event has brought about more postevent visits.

As social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and more crucial for maintaining social security and stability by accurately predicting various trends of public opinion dissemination in social networks. Considering the fact that the dissemination of online public opinion is a dynamic process full of uncertainty and complexity, this study establishes a novel conformable fractional discrete grey model with linear time-varying parameters, namely the CFTDGM(1,1) model, for more accurate prediction of online public opinion trends.

First, the conformable fractional accumulation and difference operators are employed to build the CFTDGM(1,1) model for enhancing the traditional integer-order discrete grey model with linear time-varying parameters. Then, to improve forecasting accuracy, a base value correction term is introduced to optimize the iterative base value of the CFTDGM(1,1) model. Next, the differential evolution algorithm is selected to determine the optimal order of the proposed model through a comparison with the whale optimization algorithm and the particle swarm optimization algorithm. The least squares method is utilized to estimate the parameter values of the CFTDGM(1,1) model. In addition, the effectiveness of the CFTDGM(1,1) model is tested through a public opinion event about “IG team winning the championship”. Finally, we conduct empirical analysis on two hot online public opinion events regarding “Chengdu toddler mauled by Rottweiler” and “Mayday band suspected of lip-syncing,” to further assess the prediction ability and applicability of the CFTDGM(1,1) model by comparison with seven other existing grey models.

The test case and empirical analysis on two recent hot events reveal that the CFTDGM(1,1) model outperforms most of the existing grey models in terms of prediction performance. Therefore, the CFTDGM(1,1) model is chosen to forecast the development trends of these two hot events. The prediction results indicate that public attention to both events will decline slowly over the next three days.

A conformable fractional discrete grey model is proposed with the help of conformable fractional operators and a base value correction term to improve the traditional discrete grey model. The test case and empirical analysis on two recent hot events indicate that this novel model has higher accuracy and feasibility in online public opinion trend prediction.

The paper aims to explore the impact of marketization on forestry economic growth. Firstly, the development process of forestry marketization was summarized. Secondly, from the three dimensions of forestry production factor marketization, production marketization and product marketization, the framework of marketization is constructed by the authors.

Based on the yearbook data from 1978 to 2016, the relationship between forestry marketization and forestry growth was demonstrated through multiple regression and Granger test in this paper.

The results showed that forestry marketization was one of the important driving factors that impacted on China's forestry economic growth. Since the reform and opening up, China's forestry marketization degree has been constantly strengthened, but there is still room for improvement. China has provided an important model as forestry marketization reform and development sample for the world.

Many useful references and inspirations have been provided from China such as gradually promoting market-oriented reforms; paying attention to the important role of reform and opening up in the construction of market mechanism; dynamic coordination of market and government relations; developing and connecting the relationship between domestic and international market; and coordinating the development of forestry state-owned economy, private economy and mixed ownership economy.

This paper creates a measure index of forestry marketization from three dimensions of forestry production factor marketization, production marketization and product marketization.