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Research in tourism and hospitality industry marketing has identified many highly effective applications of social media. However, studies in the existing literature do not enable a comprehensive understanding of this phenomenon because they lack a theoretical foundation. Therefore, this study systematically reviewed the literature from the perspective of the task-technology fit (TTF) theory. The purpose of this paper is to map out what is known about social media use in tourism and hospitality marketing and what areas need further exploration.

A descriptive cumulative review of the literature obtained 99 articles published in tourism and hospitality journals from 2010 to 2019.

The analysis suggests that to understand social media use in tourism marketing, researchers and practitioners in the industry must clarify the following four issues: the control variables, longitudinal analyzes and TTF concepts that should be used in future studies; the fitness of social media platforms for tourism marketing; how various social media platforms differ in terms of performance outcome; and the digital divide in the use of social media for tourism.

An integrated framework was developed to identify constructs and to understand their relationships. Recent studies in this domain are discussed; theoretical and practical suggestions and implications for future research are given.

This paper intends to construct a new adaptive grey seasonal model (AGSM) to promote the application of the grey forecasting model in quarterly GDP.

Firstly, this paper constructs a new accumulation operation that embodies the new information priority by using a hyperparameter. Then, a new AGSM is constructed by using a new grey action quantity, nonlinear Bernoulli operator, discretization operation, moving average trend elimination method and the proposed new accumulation operation. Subsequently, the marine predators algorithm is used to quickly obtain the hyperparameters used to build the AGSM. Finally, comparative analysis experiments and ablation experiments based on China's quarterly GDP confirm the validity of the proposed model.

AGSM can be degraded to some classical grey prediction models by replacing its own structural parameters. The proposed accumulation operation satisfies the new information priority rule. In the comparative analysis experiments, AGSM shows better prediction performance than other competitive algorithms, and the proposed accumulation operation is also better than the existing accumulation operations. Ablation experiments show that each component in the AGSM is effective in enhancing the predictive performance of the model.

A new AGSM with new information priority accumulation operation is proposed.