Exploring destination image, familiarity, information search behaviour, involvement and travel motivation as influencers of ecotourists’ destination loyalty

Jing, Yu; Kasım, Azılah; Jayashree, Sreenivasan; Dzakiria, Hisham; Magray, Ahsan Ul Haq

doi:10.18778/0867-5856.30.2.26

Cited by 4 publications

(7 citation statements)

References 77 publications

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“…H2 predicted that guests more satisfied with low-carbon services from luxury hotels would be more likely to revisit, which is in line with previous research [167,168]. According to the survey, most guests (77.96%) were first-time visitors at the hotels.…”

Section: Discussionsupporting

confidence: 81%

Exploring Recreationist-Environment Fit Hospitality Experiences of Green Hotels in China

Jing

2022

Sustainability

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To develop the hotel industry’s competitiveness, research on satisfaction and revisit intentions has always been important. More research has recently focused on guests’ pro-environmental behaviors and low-carbon management in the hotel industry. This research creates a recreationist-environmental fit satisfaction-revisit intention model based on the recreationist-environmental fit theory. This study surveyed seven green-standard hotels in Sanya, China, and tested the moderating effect of guests’ environmental behavior on their satisfaction and willingness to revisit. Self-administered questionnaires were distributed to respondents who had visited the surveyed green-standard hotels. Two hundred and forty-five valid questionnaires were collected with the hypotheses developed and examined using the SEM and HMR methods. The results indicated that the suitability of a leisure environment could positively impact guest satisfaction, which positively affected their willingness to revisit. In addition, this study proved the moderating effect of guests’ pro-environmental behaviors between satisfaction and revisit intentions.

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Section: Discussionsupporting

confidence: 81%

Exploring Recreationist-Environment Fit Hospitality Experiences of Green Hotels in China

Jing

2022

Sustainability

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“…According to the results of our study, the degree of involvement in tourism programs is associated with rural tourism imagery, familiarity, motivation, and willingness. The investigators concluded that although tourism development has become a mature industry over a long period of time [60,61], due to its competitiveness, the high similarity of tourism characteristics in neighboring countries [29], and the impact of the COVID-19 epidemic, it has become the most important trend to create a brand image, increase tourists' attention to the scenic areas, and enhance travel ideas and motivation to facilitate the achievement of travel behavior [30].…”

Section: The Relationship Between Tourism Programs Tourists and Rural Sustainable Developmentmentioning

confidence: 99%

“…Furthermore, tourism activities must first stimulate tourists' imagination of tourist destinations to generate tourism attraction [51], and then produce tourism motivation [21,30], in order to stimulate people's desire to complete tourism motivation and realize their dreams of tourism planning.…”

Section: The Relationship Between Tourism Programs Tourists and Rural Sustainable Developmentmentioning

confidence: 99%

“…A considerable number of people have explored the extent of travel program involvement on travel imagery [27,28] and travel motivation on travel intention [29]. However, there are few studies on the degree of involvement in similar travel programs or online information [30]. There has been no research conducted on the logic and issues of analyzing the differences in tourism destination imagery and familiarity generated by people's involvement in tourism programs, then exploring the effects of the differences in tourism destination imagery and familiarity on tourism motivation, and further understanding the changes in tourism motivation due to the differences in tourism destination imagery and familiarity generated by the degree of involvement in programs, and whether they have changed the tourism intention.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Impact of Popular Tourism Program Involvement on Rural Tourism Image, Familiarity, Motivation and Willingness

et al. 2021

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The purpose of this study was to investigate the influence of people’s involvement in tourism programs on the imagination and familiarity of rural tourism destinations, as well as the motivation and desire to visit them. The results were validated by using SPSS 22.0 and AMOS 21.0 statistical software to calculate and analyze the samples and to explore and infer the causal relationships. The results showed that: the degree of travel program involvement significantly correlated with familiarity and imagery of the destination (p < 0.000), and the degrees of familiarity and imagery of the destination also significantly correlated with motivation to travel (p < 0.000), and, lastly, the higher the motivation to travel in the last village, the greater the desire to travel (p < 0.000).

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“…Advances in Mathematical Physics the feature representation of the fabric image to be retrieved with all the feature representations in the feature database, and finally output the result of fabric image retrieval. It involves two key technologies: image feature extraction technology and similarity matching technology [12]. The maturity of the image retrieval model-convolutional neural network (CNN) model has inspired scholars to solve the ideas in image retrieval, and a large number of results have been obtained through a large number of experiments.…”

Section: Multifeature Image Retrieval Techniquementioning

confidence: 99%

Multifeatured Image Retrieval Techniques Based on Partial Differential Equations for Online Shopping

2021

Advances in Mathematical Physics

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In today’s rapid development of network and multimedia technology, the booming of electronic commerce, users in the network shopping species of images and other multimedia information showing geometric growth, in the face of this situation, how to find the images they need in the vast amount of online shopping images has become an urgent problem to solve. This paper is based on the partial differential equation to do the following research: Based on the partial differential equation is a kind of equation that simulates the human visual perception system to analyze images; based on the summary of the advantages and disadvantages of multifeature image retrieval technology, we propose a multifeature image retrieval technology method based on the partial differential equation to alleviate the indexing imbalance caused by the mismatch of multifeature image retrieval technology distribution. To improve the search speed of the data-dependent locally sensitive hashing algorithm, we propose a query pruning algorithm compatible with the proposed partial differential equation-based multifeature image retrieval technology method, which greatly improves the retrieval speed while ensuring the retrieval accuracy; to implement the data-dependent partial differential equation algorithm, we need to distribute the data set among different operation nodes, and to better achieve better parallelization of operations, we need to measure the similarity between categories, and we achieve the problem of distributing data among various categories in each operation node by introducing a clustering method with constraints. The purpose of this article for image recognition is for better shopping platforms for merchants. This algorithm has trained multiple samples and has data support. The experimental results show that our proposed data set allocation method shows significant advantages over the data set allocation method that does not consider category correlation. However, the image features used in image retrieval systems are often hundreds or even thousands of dimensions, and these features are not only high in dimensionality but also huge in number, which makes image retrieval systems encounter an inevitable problem—“dimensionality disaster.” To overcome this problem, scholars have proposed a series of approximate nearest neighbor methods, but multifeature image retrieval techniques based on partial differential equations are more widely used in people’s daily life.

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Exploring destination image, familiarity, information search behaviour, involvement and travel motivation as influencers of ecotourists’ destination loyalty

Cited by 4 publications

References 77 publications

Exploring Recreationist-Environment Fit Hospitality Experiences of Green Hotels in China

Exploring Recreationist-Environment Fit Hospitality Experiences of Green Hotels in China

Research on the Impact of Popular Tourism Program Involvement on Rural Tourism Image, Familiarity, Motivation and Willingness

Multifeatured Image Retrieval Techniques Based on Partial Differential Equations for Online Shopping

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