Assessing agritourism potential in a Jordanian village based on farmer-specific and location-based factors

Obeidat, Bushra

doi:10.1080/23311916.2022.2119531

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Based on the research conducted by Amanor-Boad [43], in the last three years, over 62% of Americans have visited rural areas to spend time in accommodation facilities. Obeidat [44], in turn, sees the development of accommodation facilities in rural areas as part of agritourism that contributes to the diversification of the income of the rural population, at the same time. In his opinion, the economic, social, and environmental benefits of tourism in rural areas motivate the local community to support it.…”

Section: Discussionmentioning

confidence: 99%

Identification of the Quality Gaps in the Services Offered in Accommodation Facilities in Rural Areas: The Case of the Lublin Region

2022

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The quality of services offered in accommodation facilities is one of the factors determining the development of this business activity. Therefore, it is necessary to constantly examine the level of customer satisfaction, their expectations and needs. The systematic collection and analysis of the opinions of tourists make it possible to learn about their changing expectations and allows the service providers to adapt to them. The purpose of the research that constituted the basis of this article was to identify the quality gaps in the services provided in the accommodation facilities in rural areas of the region of Lublin. The Servqual method was used in the study, thanks to which it was possible to assess the quality criteria and the validity of the provided service. A total of 699 respondents took part in the research. According to the study, the reliability and timeliness of the service, the adherence to the terms and conditions of the service, as well as the willingness to solve customers’ problems, were of the greatest importance for the clients, while the completeness and reliability of the advertising materials turned out to be the least important. With the obtained results, it can be concluded that the expectations of the customers in the accommodation facilities in rural areas exceed the actual state of the offered services. The areas that need to be improved, such as reliability and empathy, were identified.

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Section: Discussionmentioning

confidence: 99%

Identification of the Quality Gaps in the Services Offered in Accommodation Facilities in Rural Areas: The Case of the Lublin Region

2022

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“…Unlike traditional recommendation domains such as movie or product recommendations, in the field of tourism recommendations, the tourism recommendation system constructed using traditional recommendation algorithms such as collaborative filtering faces a severe issue of data sparsity due to the infrequent travel patterns of tourists. Consequently, this work introduces an LBSN [40][41][42]. Its structure is denoted in Figure 3.…”

Section: Application Of DL In Rural Tourism and Model Construction An...mentioning

confidence: 99%

Design and Development of a Rural Tourism Marketing System Using Deep Learning

Yang,

Ren

2024

IEEE Access

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This work aims to explore the optimization effects of marketing in the field of rural tourism with the support of artificial intelligence technologies such as deep learning. It first conducts a framework analysis of the requirements of the rural tourism marketing system, focusing on the recommendation system module. Subsequently, the RippleNet network is introduced, incorporating a scenic knowledge graph into the recommendation model. Simultaneously, the Spatiotemporal Graph Convolutional Attention Network (STGCAN) algorithm is introduced to build a rural tourism recommendation system based on RippleNet integrating STGCAN. Experimental results demonstrate that the system has achieved significant success in terms of loss value, prediction accuracy, recall, and F1 value, with an accuracy of 92.64%, recall of 89.65%, and F1 value of 92.12%, surpassing baseline algorithms such as Convolutional Neural Network (CNN). Additionally, the runtime of the proposed algorithm is significantly lower than that of other models, with a runtime of only 18.84 seconds for a data volume of 3000 records. Therefore, the proposed rural tourism recommendation system exhibits superior predictive performance, providing robust support for the optimization of marketing strategies in the tourism industry.

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“…In 2022, Obeidat B. [28] showed that improving the demand for local products, promoting cultural exchange, experience opportunities, and improving the image of the region are vital for the success of agritourism. In general, agritourism requires the involvement of tourists in various activities closely related to the work of farmers [29] who practice extensive agriculture, contributing to the long-term synergy between rural tourism and agriculture [30] and the sustainability of the community [31].…”

Section: Smart Tourist Villagesmentioning

confidence: 99%

Can We Talk about Smart Tourist Villages in Mărginimea Sibiului, Romania?

et al. 2023

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The aim of this paper is to evaluate some villages in the mountain area of Sibiu County, Romania, as “smart” tourist villages. The research was carried out in two stages: Stage 1 was collection of information on (a) food products registered in different national and international quality schemes and (b) the number of tourist structures by type, comfort category, and accommodation capacity. Stage 2 was a case study conducted among 32 rural tourism entrepreneurs from 3 villages in Mărginimea Sibiului, using a questionnaire with 22 items on the criteria for evaluation of “smart” villages. The answers were systematized, statistically processed, and interpreted. The main results are as follows: (a) Concerning products, producers, and gastronomic points (PGL) registered, there are 9 products in the quality scheme “mountain product”, 10 producers on the platform “Gusturisibiene”, 1 producer of “telemea de Sibiu”, a registered product with protected geographical indication, and 2 PGL. (b) The number of officially registered tourist structures is 93, of which 72% are agritourism guesthouses. (c) The results for the evaluation of villages as “smart” are that 78.2% of locals use the internet, 74% own at least one smart device, 60.16% of energy used is renewable. There are low values of water and air pollution; there are local job and business opportunities (93.75%); future local development activities will target agriculture with all its sectors and agritourism; public administrations have implemented 7 types of activities to increase the attractiveness of localities; and quality of life, services, and social life aspects are perceived to be at high standards.

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Assessing agritourism potential in a Jordanian village based on farmer-specific and location-based factors

Cited by 5 publications

References 23 publications

Identification of the Quality Gaps in the Services Offered in Accommodation Facilities in Rural Areas: The Case of the Lublin Region

Identification of the Quality Gaps in the Services Offered in Accommodation Facilities in Rural Areas: The Case of the Lublin Region

Design and Development of a Rural Tourism Marketing System Using Deep Learning

Can We Talk about Smart Tourist Villages in Mărginimea Sibiului, Romania?

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