Predicting Customer Recommendation Towards Homestay at West Pahang Region

Muhamed, Mohd Faizal Azrul Azwan; Jabar, Faizan Abd; Wahid, Sharifah Norhuda Syed; Paino, Halil; Dangi, Mohamad Ridhuan Mat

doi:10.1166/asl.2017.7627

Cited by 4 publications

(2 citation statements)

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“…In the research on the service quality of homestays, Tichaawa [21] evaluated the service quality of family homestays in East London, South Africa from the perspective of tourist experience. Mohd [22] studied the relationship between the service quality and behavioral intentions of homestays and the conditional effect of tourist evaluation on the development of homestays. To conduct service-quality research on inns and hotels, Huang Pei and Chen Xueqiong [23] established an evaluation system made up of three parts: overall evaluation, business premises, and facilities and equipment.…”

Section: Evaluation Of Rural-homestay Service Qualitymentioning

confidence: 99%

Evaluating Tourist Experience of Rural Homestays in Coastal Areas by Importance–Performance Analysis: A Case Study of Homestay in Dapeng New District, Shenzhen, China

Huang

Wang

et al. 2022

Sustainability

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In recent years, the rural homestay has developed rapidly against the background of rural-revitalization policies. However, in early 2020, COVID-19 broke out in China, and the homestay industry was faced with a great challenge. It was difficult for self-employed homestays to resist the risks of the pandemic. As a result, defective operation and business failure occurred in some regions. However, rural homestays with a background in the brand, chain and cluster development mode persisted in the market with a diversity of operation principles. This paper tries to set up an evaluation system for understanding the occupancy needs of tourists, in order to improve the ability of rural homestays to resist risks. The article offers a methodology for assessing the tourist experience of rural homestays in coastal areas, with homestays in Dapeng New District of Shenzhen taken as the research objectives. Then, the evaluation system of rural homestays is set up from the perspective of tourist experience. Based on the results of a questionnaire survey and interviews of operators, this paper proposes the tourist experience–importance analysis in Jiaochangwei and Xichong by the importance–performance analysis (IPA) method. Finally, it suggests policy recommendations that can resist high risk based on tourist experience, for the sustainable development of rural homestays in China.

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Section: Evaluation Of Rural-homestay Service Qualitymentioning

confidence: 99%

Evaluating Tourist Experience of Rural Homestays in Coastal Areas by Importance–Performance Analysis: A Case Study of Homestay in Dapeng New District, Shenzhen, China

Huang

Wang

et al. 2022

Sustainability

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“…This is mainly due to certain assumptions that might lead NB and LR models to perform inadequately. In particular, NB and LR commonly assume the independence of features; thus, they are not able to learn about the interactions of these features [103] [104].…”

Section: Metrics For Performance Evaluation Of Modelsmentioning

confidence: 99%

Time-aware domain-based social influence prediction

et al. 2020

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Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted an attention of information processors/data scientists and information consumers / business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

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