Search results

Online travel agencies (OTAs) have been offering tourists trip planning services (TPS) for more than a decade. However, they are less popular than other online travel services such as metasearch with price comparison. This study aims to investigate why TPS on the internet, although important to tourists, are not well accepted by young mainland Chinese tourists.

A trip planning service acceptance model (TPSAM) was constructed and tested by inviting participants to take part in a trial using the TPS of a China OTA and then participants were asked to complete a questionnaire based on their user experience. Partial least square technique was used to perform a path analysis on the model.

Social influence and effort expectancy have significant direct influence on reuse intention. Social influence increases the trust level of the tourists on the TPS and effort expectancy’s strong influence on joy suggest that a joyful and effortless experience is critical for tourists to consider reusing the TPS.

The findings could provide some insight to the OTAs on improving their TPS. For instance, OTAs should let tourists feel that the TPS requires little effort and is fun to use and more promotion is needed through social media.

Although trip planning is essential for tourists in achieving a delightful travel experience, few studies have examined the adoption of Web-based TPS. This study contributes to the literature by establishing a TPSAM and extends previous work by showing that a causal relationship exists between social influence and trust in the service acceptance context.

线上旅游代理（OTA）已经十多年为游客提供旅游计划服务（TPS）。然而, OTA比其他在线旅游服务相较则受欢迎程度下降, 比如价格比对的元搜索服务。本论文旨在研究网络TPS, 即便对游客重要, 但是为什么不受中国大陆年轻游客的欢迎。

本论文通过邀请受访者参与中国OTA提供的旅游计划服务试点样品, 并完成针对他们的用户体验的问卷, 来开发和测验这个旅游计划服务接受模型（TPSAM）。本论文采用PLS分析法来测验模型。

社会影响和努力预期对再使用意图起到直接影响。社会影响增强了游客对TPS的信任度, 努力预期对愉悦感有强烈影响, 这预示着对于游客而言, 一个愉悦的且不太费劲的体验对于再次使用TPS起到关键作用。

本论文研究结果对于OTA增强其TPS起到启示作用。比如, OTA应该让游客感受TPS不需要费很多力气来学习使用并且使用过程很有趣, 此外, 通过社交媒体来增强更多宣传是有必要的。

尽管旅游计划对游客而言获得愉快旅游体验是必要的, 然而, 很少文章研究线上TPS使用现象。本论文建立了TPSAM, 对理论做出贡献, 并且本论文对之前的文献做出扩展, 验证了服务接受背景下社会影响和信任之间的直接联系。

The purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).

The prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.

Seasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.

This study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.

The purpose of this paper is to review the current literature in the field of tourism demand forecasting.

Published papers in the high quality journals are studied and categorized based their used forecasting method.

There is no forecasting method which can develop the best forecasts for all of the problems. Combined forecasting methods are providing better forecasts in comparison to the traditional forecasting methods.

This paper reviews the available literature from 2007 to 2017. There is not such a review available in the literature.