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Article
Publication date: 20 January 2020

Gang Li and Xiaoying Jiao

The purpose of this paper is to provide a short review of tourism forecasting literature and general summary of the trends and developments in tourism forecasting and point out…

617

Abstract

Purpose

The purpose of this paper is to provide a short review of tourism forecasting literature and general summary of the trends and developments in tourism forecasting and point out directions for future research in the next 75 years.

Design/methodology/approach

This is a general literature overview.

Findings

Key trends are identified for next 75 years.

Originality/value

First overview in tourism forecasting that provides foresight on long-term future trends (over next 75 years).

Details

Tourism Review, vol. 75 no. 1
Type: Research Article
ISSN: 1660-5373

Keywords

Article
Publication date: 2 October 2023

Birgit Muskat, Girish Prayag, Sameer Hosany, Gang Li, Quan Vu and Sarah Wagner

Food is a key element in tourism experiences. This study aims to investigate the interplay of sensory and non-sensory factors in food tourism experiences and models their…

Abstract

Purpose

Food is a key element in tourism experiences. This study aims to investigate the interplay of sensory and non-sensory factors in food tourism experiences and models their influence on satisfaction and behavioural intentions.

Design/methodology/approach

The study focuses on the culinary experiences of 304 tourists dining at ethnic restaurants and uses causal relationship discovery modelling to analyse data.

Findings

Sensory factors are important in tourists’ culinary experiences with cleanliness, noise levels and room temperature at the top of the causal chain. Results also indicate the interplay between sensory and non-sensory factors to explain overall satisfaction, intention to return and intention to say positive things.

Originality/value

Using embodied cognition theory, the study offers novel insights into the role of senses in food tourism experiences at rural destinations.

研究目的

美食是乡村旅游的主要吸引物之一。本研究的目的是调查游客在用餐体验中感官和非感官因素的相互作用, 以及这些因素如何影响游客的满意度和行为意愿。

研究设计/研究方法

本研究使用因果关系建模的方法来分析 304 名在某地方特色餐厅用餐的游客的问卷数据。

研究结果

结果显示, 对于游客的用餐体验而言, 感官和非感官因素具备同等的重要性。此外, 结果发现, 游客感知到的噪音水平、适宜的室内温度及清洁度在与其他因素的相互作用中非常重要, 并能激发游客的满意度和重游意愿。

原创性/研究价值

基于认知理论, 本研究为更好地理解感官因素和非感观因素在乡村旅游情境下的游客用餐体验中的作用提供了新的知识。

Propósito

La comida es un elemento clave en las experiencias turísticas. Este estudio investiga la interacción de factores sensoriales y no sensoriales en las experiencias de turismo gastronómico y modela su influencia en la satisfacción y las intenciones de comportamiento.

Diseño/metodología/enfoque

El estudio se centra en las experiencias culinarias de 304 turistas que cenan en restaurantes étnicos y utiliza modelos de descubrimiento de relaciones causales para analizar los datos.

Resultados

Los factores sensoriales son importantes en las experiencias culinarias de los turistas con la limpieza, los niveles de ruido y la temperatura ambiente en la parte superior de la cadena causal. Los resultados también indican la interacción entre factores sensoriales y no sensoriales para explicar la satisfacción general, la intención de regresar y la intención de decir cosas positivas.

Originalidad/valor

Utilizando la teoría de la cognición incorporada, el estudio ofrece nuevos conocimientos sobre el papel de los sentidos en las experiencias de turismo gastronómico en destinos rurales.

Article
Publication date: 21 May 2018

Suganeshwari G., Syed Ibrahim S.P. and Gang Li

The purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the user’s current preference in the dynamically growing…

Abstract

Purpose

The purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the user’s current preference in the dynamically growing datasets in the context of memory-based collaborative filtering methods using temporal information.

Design/methodology/approach

The proposed method is formalized as time-dependent collaborative filtering method. For each item, a set of influential neighbors is identified by using the truncated version of similarity computation based on the timestamp. Then, recent n transactions are used to generate the recommendation that reflect the recent preference of the active user. The proposed method, lazy collaborative filtering with dynamic neighborhoods (LCFDN), is further scaled up by implementing in spark using parallel processing paradigm MapReduce. The experiments conducted on MovieLens dataset reveal that LCFDN implemented on MapReduce is more efficient and achieves good performance than the existing methods.

Findings

The results of the experimental study clearly show that not all ratings provide valuable information. Recommendation system based on LCFDN increases the efficiency of predictions by selecting the most influential neighbors based on the temporal information. The pruning of the recent transactions of the user also addresses the user’s preference drifts and is more scalable when compared to state-of-art methods.

Research limitations/implications

In the proposed method, LCFDN, the neighborhood space is dynamically adjusted based on the temporal information. In addition, the LCFDN also determines the user’s current interest based on the recent preference or purchase details. This method is designed to continuously track the user’s preference with the growing dataset which makes it suitable to be implemented in the e-commerce industry. Compared with the state-of-art methods, this method provides high-quality recommendation with good efficiency.

Originality/value

The LCFDN is an extension of collaborative filtering with temporal information used as context. The dynamic nature of data and user’s preference drifts are addressed in the proposed method by dynamically adapting the neighbors. To improve the scalability, the proposed method is implemented in big data environment using MapReduce. The proposed recommendation system provides greater prediction accuracy than the traditional recommender systems.

Details

Information Discovery and Delivery, vol. 46 no. 2
Type: Research Article
ISSN: 2398-6247

Keywords

Content available
Article
Publication date: 19 February 2020

Gang Li, Srikanth Prabhu, Qingfeng Chen and Jia Wu

402

Abstract

Details

Information Discovery and Delivery, vol. 48 no. 1
Type: Research Article
ISSN: 2398-6247

Article
Publication date: 3 May 2022

Li Gao, Gang Li, Fusheng Tsai, Chen Gao, Mengjiao Zhu and Xiaopian Qu

This article analyzes the effects of artificial intelligence (AI) stimuli on customer engagement as well as on value co-creation. The moderating role played by customer ability…

4209

Abstract

Purpose

This article analyzes the effects of artificial intelligence (AI) stimuli on customer engagement as well as on value co-creation. The moderating role played by customer ability readiness is also examined.

Design/methodology/approach

Total 426 questioners are collected from the customers who consumed intelligent service robot.

Findings

First, the perceived interactivity (PI) of AI stimuli have a significant positive impact on value co-creation; second, customer engagement plays a mediating effect on the relationship between PI and value co-creation; finally, customer ability readiness has a positive moderating effect on the relationship between AI stimuli, customer engagement and value co-creation.

Research limitations/implications

Firstly, the method of questionnaire survey has certain limitations, In future research, more advanced survey methods (such as social perception calculations) can be used to make survey samples more comprehensive and analysis results more accurate. Secondly, the paper used a single-dimensional test for the two variables of customer engagement and value co-creation. Future research should divide the dimensions of customer engagement and value co-creation into more specific way. Finally, this study lacks research on the regulatory effect of customer ability readiness and further division of customer readiness.

Practical implications

First, this paper uses the arousal theory to participate in marketing theory and value co-creation theory, which is the cross and fusion of theory, and also the enrichment and expansion of the existing theoretical research, with a certain theoretical innovation. Second, based on previous research, this research developed and designed a measurement scale for AI stimuli. Finally, through empirical research, it is found that the perceived personalization of AI stimuli does not have a significant direct effect on value co-creation, which is a new views and insight.

Social implications

First, when using intelligent customer service robots, companies should pay attention to improving the PI and personalization of intelligent customer service robots. Second, companies should attach importance to the development environment of customer engagement, proactively and effectively identify customer needs. Finally, companies should provide customers with a good support atmosphere, publicize and explain in advance the use of intelligent customer service robots to increase their confidence.

Originality/value

The study develops a scale of AI stimuli and is among the first to integrate and examine the inter-relationships between customer engagement, customer ability, and value co-creation from the increasingly important phenomenological perspective of AI.

Details

Journal of Research in Interactive Marketing, vol. 17 no. 2
Type: Research Article
ISSN: 2040-7122

Keywords

Article
Publication date: 22 July 2021

Han Liu, Ying Liu, Gang Li and Long Wen

This study aims to examine whether and when real-time updated online search engine data such as the daily Baidu Index can be useful for improving the accuracy of tourism demand…

Abstract

Purpose

This study aims to examine whether and when real-time updated online search engine data such as the daily Baidu Index can be useful for improving the accuracy of tourism demand nowcasting once monthly official statistical data, including historical visitor arrival data and macroeconomic variables, become available.

Design/methodology/approach

This study is the first attempt to use the LASSO-MIDAS model proposed by Marsilli (2014) to field of the tourism demand forecasting to deal with the inconsistency in the frequency of data and the curse problem caused by the high dimensionality of search engine data.

Findings

The empirical results in the context of visitor arrivals in Hong Kong show that the application of a combination of daily Baidu Index data and monthly official statistical data produces more accurate nowcasting results when MIDAS-type models are used. The effectiveness of the LASSO-MIDAS model for tourism demand nowcasting indicates that such penalty-based MIDAS model is a useful option when using high-dimensional mixed-frequency data.

Originality/value

This study represents the first attempt to progressively compare whether there are any differences between using daily search engine data, monthly official statistical data and a combination of the aforementioned two types of data with different frequencies to nowcast tourism demand. This study also contributes to the tourism forecasting literature by presenting the first attempt to evaluate the applicability and effectiveness of the LASSO-MIDAS model in tourism demand nowcasting.

Details

International Journal of Contemporary Hospitality Management, vol. 33 no. 6
Type: Research Article
ISSN: 0959-6119

Keywords

Article
Publication date: 23 September 2022

Miaomiao Chen, Lu An, Gang Li and Chuanming Yu

The purpose of the study is to evaluate the severity of public events in real time from the perspective of social media and to construct the early warning mechanism of public…

Abstract

Purpose

The purpose of the study is to evaluate the severity of public events in real time from the perspective of social media and to construct the early warning mechanism of public events.

Design/methodology/approach

This study constructed the severity assessment system of public events from the dimensions of the netizens' role, the Internet media's role, the spread of public events and the attitudes and feelings of netizens. The method of analyzing the influence tendency of the public event severity indicators was proposed. A total of 1,107,308 microblogging entries regarding four public events were investigated. The severity of public events was divided into four levels.

Findings

It is found that serious public events have higher indicator values than medium level events on the microblogging platform. A quantitative severity classification standard for public events was established and the early warning mechanism of public events was built.

Research limitations/implications

Microblogging and other social media platforms provide rich clues for the real-time study and judgment of public events. This study only investigated the Weibo platform as the data source. Other social media platforms can also be considered in future.

Originality/value

Different from the ex-post evaluation method of judging the severity of public events based on their physical loss, this study constructed a quantitative method to dynamically determine the severity of public events according to the clues reflected by social media. The results can help the emergency management departments judge the severity of public events objectively and reduce the subjective negligence and misjudgment.

Details

Information Technology & People, vol. 36 no. 6
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 5 June 2019

Gang Li, Shuo Jia and Hong-Nan Li

The purpose of this paper is to make a theoretical comprehensive efficiency evaluation of a nonlinear analysis method based on the Woodbury formula from the efficiency of the…

Abstract

Purpose

The purpose of this paper is to make a theoretical comprehensive efficiency evaluation of a nonlinear analysis method based on the Woodbury formula from the efficiency of the solution of linear equations in each incremental step and the selected iterative algorithms.

Design/methodology/approach

First, this study employs the time complexity theory to quantitatively compare the efficiency of the Woodbury formula and the LDLT factorization method which is a commonly used method to solve linear equations. Moreover, the performance of iterative algorithms also significantly effects the efficiency of the analysis. Thus, the three-point method with a convergence order of eight is employed to solve the equilibrium equations of the nonlinear analysis method based on the Woodbury formula, aiming to improve the iterative performance of the Newton–Raphson (N–R) method.

Findings

First, the result shows that the asymptotic time complexity of the Woodbury formula is much lower than that of the LDLT factorization method when the number of inelastic degrees of freedom (IDOFs) is much less than that of DOFs, indicating that the Woodbury formula is more efficient for local nonlinear problems. Moreover, the time complexity comparison of the N–R method and the three-point method indicates that the three-point method is more efficient than the N–R method for local nonlinear problems with large-scale structures or a larger ratio of IDOFs number to the DOFs number.

Originality/value

This study theoretically evaluates the efficiency of nonlinear analysis method based on the Woodbury formula, and quantitatively shows the application condition of the comparative methods. The comparison result provides a theoretical basis for the selection of algorithms for different nonlinear problems.

Details

Engineering Computations, vol. 36 no. 4
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 14 December 2022

Lu An, Yan Shen, Yanfang Tao, Gang Li and Chuanming Yu

This study aims to profile the government microbloggers and evaluate their roles. The results can help improve the governments' response capability to public emergencies.

Abstract

Purpose

This study aims to profile the government microbloggers and evaluate their roles. The results can help improve the governments' response capability to public emergencies.

Design/methodology/approach

This study proposes the user profiling and role evaluation model of government microbloggers in the context of public emergencies. The indicators are designed from the four dimensions of time, content, scale and influence, and the feature labels are identified. Three different public emergencies were investigated, including the West Africa Ebola outbreak, the Middle East respiratory syndrome outbreak and the Shandong vaccine case in China.

Findings

The results found that most government microbloggers were follower responders, short-term participants, originators, occasional participants and low influencers. The role distribution of government microbloggers was highly concentrated. However, in terms of individual profiles, the role of a government microblogger varied with events.

Social implications

The findings can provide a reference for the performance assessment of the government microbloggers in the context of public emergencies and help them improve their ability to communicate with the public and respond to public emergencies.

Originality/value

By analyzing the performance of government microbloggers from the four dimensions of time, content, scale and influence, this paper fills the gap in existing literature on designing the user profiling and role evaluation model of government microbloggers in the context of public emergencies.

Details

Online Information Review, vol. 47 no. 6
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 6 March 2023

Lu An, Yan Shen, Gang Li and Chuanming Yu

Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can…

Abstract

Purpose

Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can help us understand the development pattern of the public attention.

Design/methodology/approach

This study proposes the prediction model for the attention transfer behavior of social media users in the context of multitopic competition and reveals the important influencing factors of users' attention transfer. Microblogging features are selected from the dimensions of users, time, topics and competitiveness. The microblogging posts on eight topic categories from Sina Weibo, the most popular microblogging platform in China, are used for empirical analysis. A novel indicator named transfer tendency of a feature value is proposed to identify the important factors for attention transfer.

Findings

The accuracy of the prediction model based on Light GBM reaches 91%. It is found that user features are the most important for the attention transfer of microblogging users among all the features. The conditions of attention transfer in all aspects are also revealed.

Originality/value

The findings can help governments and enterprises understand the competition mechanism among multiple topics and improve their ability to cope with public opinions in the complex environment.

Details

Aslib Journal of Information Management, vol. 76 no. 3
Type: Research Article
ISSN: 2050-3806

Keywords

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