Knowledge mapping of tourism demand forecasting research

Zhang, Chengyuan; Wang, Shouyang; Sun, Shaolong; Wei, Yunjie

doi:10.1016/j.tmp.2020.100715

Cited by 52 publications

(65 citation statements)

References 64 publications

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“…Notably, failure had high probability for C 5 innovation and transformation (0.4405), C 2 human resource (0.3851), and C 6 marketing strategy (0.3683). This result also demonstrates that the franchising hospitality sectors should pay increased attention to enhancing innovation and transformation, human resources, and marketing strategy, especially due to the incidence of the COVID-19 outbreak being the single most significant change related to consumer behavior, organizational structure, and supply-chain management [39,75]. Therefore, franchising KM dynamic strategies and inspecting the resulting organizational model may achieve improvement through the KM implementation process, so that organizations can learn directly from the crisis.…”

Section: Methodsmentioning

confidence: 86%

“…An effective assessment of the business location depends on the nature of the owner's business according to the establishment of a new sector (i.e., population density, geographical location, possibility for future business development, consumption ability, etc.) [39,40]. In post-pandemic times, consumer behavior and outbreak prevention measures brought on by the need for social distancing and proper staffing levels will mean that people will stay at home and work more by means of remote and contactless service demands.…”

Section: Location Advantagementioning

confidence: 99%

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Prediction of Knowledge Management for Success of Franchise Hospitality in a Post-Pandemic Economy

et al. 2020

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Due to its unpredictability, the novel coronavirus (COVID-19) pandemic has changed the global business climate and commercial management practices in unprecedented ways. As a direct result of the pandemic, the hospitality and tourism sectors have shut down, and business failure rates have occurred exponentially. The franchise hospitality industry has experienced significant impact and challenged a basic understanding of knowledge management (KM) implementation in the face of the COVID-19 outbreak. A strategic KM implementation practice can not only guide a large-scale operation, but also adjust an organization’s performance and competitiveness. The purpose of this study is to examine the influential criteria of success through effective KM implementation and to predict the probability of successful KM in a post-pandemic era. The conceptual framework for KM applies an analytic hierarchical prediction model reliant upon consistent fuzzy preference relations to assist the franchise hospitality sector’s consciousness of the influential criteria. An empirical case study is used to apply pairwise comparisons used to determine the priority weights and two possible outcomes. The case study will assist franchise organizations to analyze whether or not to implement KM, interdict application, or adopt revised actions. This assistance will enhance the success possibility of KM implementation within such a crisis environment. This study uses a case setting by assessing 15 franchises hospitality experts’ opinions in Taiwan relevant to KM implementation.

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Section: Methodsmentioning

confidence: 86%

Section: Location Advantagementioning

confidence: 99%

Prediction of Knowledge Management for Success of Franchise Hospitality in a Post-Pandemic Economy

et al. 2020

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“…With the rapid development of China's economy, China's roads, railways, aviation, and other basic transportation facilities have become more and more accommodating, and tourists have more and more choices of transportation options for traveling. e improvements like rapid construction of highways, the operation of high-speed trains, and the development of air transportation have greatly shortened the time for tourists to reach tourist destinations, so that they can spend more time on tourism [11]. Moreover, as the facilities of transportation are upgraded, tourists feel more comfortable and safer during the journey, feel less fatigue of the journey, and can better enjoy the trip.…”

Section: E Relationship Between Ice and Snow Inbound Tourismmentioning

confidence: 99%

Dynamic Forecasting Algorithm of Inbound Ice and Snow Tourism in China Based on Improved Deep Confidence Network

Zhao

Jiao

et al. 2021

Mobile Information Systems

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Ice and snow-based tourism is getting popular around the world and it is one of the major sources of revenue for a region with required facilities. According to a report by China Daily, China was expected to witness 230 million tourist visits in 2020-2021 with a total revenue generation surpassing 390 billion yuan. In order to promote the ice and snow tourism, proper arrangements such as accommodation, transport facility, and energy provision for heating and food need to be arranged as per the demand of the visitors at a certain period of time. A tourist prediction system can help in this regard for good estimation but considering the problems of traditional ice and snow tourism systems, specifically the prediction accuracy and long forecasting time, a dynamic forecasting algorithm for ice and snow inbound tourism based on an improved deep confidence network is proposed. The system analyzes the relationship between the demand for ice and snow inbound tourism and the level of national economic development, people’s living standards, demographic characteristics, traffic conditions, and tourism supply capacity. It then takes the influencing factors of ice and snow inbound tourism demand as sample data and arranges the sample data sequence. The inbound tourism demand dynamic prediction model uses an improved deep confidence network to learn and train the prediction model, input test data into the trained model, and output the dynamic prediction value of ice and snow inbound tourism demand in the output layer to obtain the prediction result. The simulation results show that the proposed algorithm has improved accuracy in predicting the demand of inbound tourism for ice and snow, whereas the forecasting time is reduced.

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“…The tourism sector is one of the largest sectors in the world [35]. This is because it plays an important role in the growth of the economy of many countries [36].…”

Section: Smart Tourismmentioning

confidence: 99%

Relationship of Smart Cities and Smart Tourism: An Overview

Habeeb¹,

Weli²

2020

HighTech. Innov. J.

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Smart city and smart tourism terms have become very popular in the past and present decades. Research in the field of smart tourist city still fails to cover the developments of the smart tourist city. The aim of the study is to review the recent literature on smart cities and smart tourism and their role in achieving a sustainable tourism sector and enhancing the competitiveness of the country’s tourism sector and make it more developed and modern. In this study, the relationship between the smart city and the tourism is presented and to present the relationship or conceptual approach between the smart city and the smart tourism. In addition, the current situation and the potential for growth and development of tourism in Iraq through the establishment and application of smart cities is identified. The recent studies that were mentioned in this study indicate that there is a close relationship between the smart city and smart tourism and also indicate that the smart city has a fundamental role in the growth and development of tourist destinations and the smart tourist city are results of the convergence and interconnection between the smart city and the tourist city. Finally, recommendations for the smart tourism city applications in Iraq are provided. Doi: 10.28991/HIJ-2020-01-04-07 Full Text: PDF

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Knowledge mapping of tourism demand forecasting research

Cited by 52 publications

References 64 publications

Prediction of Knowledge Management for Success of Franchise Hospitality in a Post-Pandemic Economy

Prediction of Knowledge Management for Success of Franchise Hospitality in a Post-Pandemic Economy

Dynamic Forecasting Algorithm of Inbound Ice and Snow Tourism in China Based on Improved Deep Confidence Network

Relationship of Smart Cities and Smart Tourism: An Overview

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