This study employs advanced text-mining techniques to offer an in-depth and comprehensive overview of the extensive body of research on Airbnb. By analyzing 1021 articles published in 416 journals spanning the period from 2015 to 2022, this study aims at revealing Airbnb research topics and trends. The results show that the primary focus of academic inquiry regarding Airbnb revolves around two domains: the company's operational practices and its impacts on various domains. Within the realm of Airbnb's operational practices, four distinct research topics emerge as particularly prominent and extensively explored. These encompass the dynamics of ‘trust in Airbnb,’ the formulation and implementation of ‘house rules,’ the mechanisms of governing ‘Airbnb pricing’ strategies, and the critical examination of ‘value creation in Airbnb’ initiatives. Meanwhile, the most researched impacts of Airbnb are on urban tourism, rental housing markets, tourist destinations, and hotels. These spheres have received significant scholarly attention due to the profound implications and transformative effects engendered by Airbnb's disruptive presence in these areas. Moreover, the findings underscore that research pertaining to Airbnb's operational aspects has witnessed a significant increase in popularity over time, indicating a marked shift in the focal points of Airbnb research. Notably, the research topics that have experienced substantial growth include ‘trust in Airbnb,’ ‘Airbnb pricing,’ and ‘impacts on tourist destinations.’ Lastly, this study found that Airbnb-related research articles in hospitality and tourism journals tend to be more delving into industry-specific phenomena and challenges. Conversely, non-hospitality and tourism journals provide a broader coverage of topics related to Airbnb, encapsulating diverse areas of inquiry beyond the boundaries of the industry. This literature review provides valuable insights into existing research on Airbnb and highlights several critical areas for future research.

Social commerce (s-commerce) is a rapidly developing form of e-commerce powered by social media influencers (SMIs). It can create valuable opportunities for retailers. In light of this growing trend, this study explores the influence of consumers’ engagement experiences (social support and presence) on community identification and consumers’ attachment to SMIs, along with their impact on consumers’ stickiness in the s-commerce context. We explore this through social presence and social support theory. The survey data from 411 s-commerce users via an online questionnaire were analyzed empirically with the PLS-SEM approach. The results indicated that presence and social support have significantly positive impacts on consumers’ attachment to SMIs and community identification, respectively. This increases users’ stickiness in s-commerce. This study enriches our understanding of user stickiness in s-commerce and can assist online vendors in developing marketing strategies and cultivating sustained relationships with their users.

Gross domestic product (GDP) is an important indicator for determining a country’s or region’s economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country’s or region’s future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. This stochastic learning method is a useful addition to the existing methods for determining the center and smoothing factors of radial basis function neural networks, and it can also help the network more efficiently train. GDP forecasting is aided by the genetic algorithm radial basis neural network, which allows the government to make timely and effective macrocontrol plans based on the forecast trend of GDP in the region. This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.

Tourism forecasting has garnered considerable interest. However, integrating tourism forecasting with volatility is significantly less typical. This study investigates the performance of both the single models and their combinations for forecasting the volatility of tourism demand. The seasonal autoregressive integrated moving average (SARIMA) model is used to construct the mean equation, and three single models, namely the generalized autoregressive conditional heteroscedasticity (GARCH) family models, the error-trend-seasonal exponential smoothing (ETS-ES) model, and the innovative smooth transition exponential smoothing (STES) model, are employed to estimate the volatility of monthly tourist arrivals into Malaysia. This study also assesses the accuracy of forecasts using simple average (SA), minimum variance (MV), and novel smooth transition (ST). STES performs the best of the single models for forecasting the out-of-sample of tourism demand volatility, followed closely by ETS-ES. In contrast, the ST combining method surpasses SA and MV. Interestingly, forecast combining methods do not always outperform the best single model, but they consistently outperform the worst single model. The MCS and DM tests confirm the aforementioned findings. This article merits consideration for future forecasting research on tourism demand volatility.

The rapid development of artificial intelligence technology has led to rapid development in various fields. It has many hidden related customer behavior information and future development trends in the e-commerce information system. The data mining technology can dig out useful information and promote the development of e-commerce. This research analyzes the significance and advantages of data mining technology in the application of e-commerce management systems and analyzes the related technologies of data mining and future trend prediction. This research has taken the advantages of clustering and naive Bayesian methods in data mining to classify product information and purchase preferences and other information and mine the associated data. Then, the nonlinear data processing advantages of neural networks are used to predict future purchasing power. The results show that data mining technology and neural networks have high accuracy in predicting future consumer purchasing power information. The correlation coefficient between real consumption data and predicted consumption data reached 0.9785, and the maximum relative average error was only 2.32%. It fully shows that data mining technology can obtain some unrecognizable related information and future consumption trends in e-commerce systems, and neural networks can also predict future consumption power and consumption patterns well.