Tourism demand forecasting has evolved into a wide variety of models, including time-series models that incorporate economic, environmental, and behavioral factors. Macao, one of the world’s most profitable gaming destinations, finds that gaming revenue is highly related to tourist arrivals. A forecast model for gaming tourism is essential for accurately predicting tourist arrivals. The challenge with ARIMA-type models is optimizing parameter selection in order to improve the accuracy of tourism demand forecasts. In this study, an enhanced version of SARIMAX, called SARIMAX-E, was developed to identify the most effective parameter combinations. By integrating data related to gaming revenue, weather, transportation, currency exchange rate, holidays, and seasonality into a single forecast model, this study examined the performance of different forecasting models, including the proposed SARIMAX-E model; ARIMA-type models (ARIMA, SARIMA, ARIMAX); and machine learning models (Transformer, LTSM, Random Forests, XGBoost). The results showed that the ARIMA family of models, including SARIMAX-E, ARIMAX, and SARIMA, was particularly well suited to tourism demand forecasting, as its members consistently ranked among the top performers in terms of error metrics. By applying multi-step predictions, LSTM outperforms most conventional approaches. Compared with all other models, the SARIMAX-E performed the best after applying the additional parameter grid.

1. Companies now treasure customer data more than ever before in the era of Big Data (Hofacker et al., 2016). It is customary practice to actively collect customer behavioral data based on physiolo...

The current study aims to examine the underlying psychological processes involved in privacy protection behaviors among tourists in retail environments using surveillance technologies. Protection Motivation Theory (PMT) and Privacy Calculus Theory (PCT) were employed to understand threat and coping appraisals, benefits (control vs monetary), and their relationships with protective behavioral intentions and actual behaviors. Data was collected from 554 respondents who had visited a retail store while traveling in the past 6 months using an online questionnaire. Three structural equation models, full mediation, partial mediation, and moderated mediation were tested and compared. The partial mediation model exhibited the best fit. Perceived severity and susceptibility increased protective behavioral intentions, while self-efficacy decreased intentions. Control compensation positively influenced intentions only. Behavioral intentions partially mediated relationships between independent variables and actual protective behaviors. No significant moderation effects were found. Integrating PMT and PCT addresses a significant gap in examining tourists’ privacy-protective behaviors in retail surveillance contexts. Additionally, it clarifies the effectiveness of control and monetary benefits in mitigating privacy concerns.

In classical vernacular Chinese fiction, inferring affective stance in the presence of rhetorical devices often depends on implicit semantics and contextual cues, which poses substantial challenges for joint pragmatic modeling with large language models (LLMs). To address these challenges, we propose KnowRhet, a unified framework for joint pragmatic modeling in classical vernacular Chinese fiction. Under a unified structured-output interface, KnowRhet integrates task-specific domain knowledge guidance, reasoning-process control, and LoRA-based parameter-efficient adaptation, thereby improving the ability of LLMs to jointly identify rhetorical devices and affective stance. Here, task-specific domain knowledge refers to domain-specific interpretive knowledge operationalized through label definitions, category descriptions, and boundary constraints for the current literary analysis setting, rather than knowledge obtained from external retrieval. To evaluate the proposed framework, we construct a sentence-level benchmark based on Dream of the Red Chamber, comprising 2,769 manually annotated instances for multi-label rhetorical device prediction, emotion expression type classification, and sentiment polarity classification. Experimental results show that explicit label definitions, category descriptions, and boundary constraints yield the most consistent gains, improving Avg F1 from 58.53% to 75.31%. Structured reasoning exhibits clear task-dependent effects, while LoRA improves Qwen2.5-7B-Instruct from 57.05% to 75.39% Avg F1, approaching the prompt-only performance of DeepSeek-V3.2 (75.77%) under the same evaluation protocol. These findings suggest that robust pragmatic modeling in classical vernacular Chinese fiction depends less on increasing reasoning complexity alone than on combining explicit task constraints with lightweight domain adaptation.