Loyalty Trends and Issues in Tourism Research

Ažić, Marina Laškarin; Dlačić, Jasmina; Suštar, Natali

doi:10.20867/thm.26.1.8

Cited by 5 publications

(3 citation statements)

References 111 publications

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“…Previous studies have confirmed that the post-consumption evaluation of received values manifested through satisfaction or dissatisfaction plays an important role in predicting loyalty intentions (Celil Cakici et al , 2019; Han et al , 2017; Song et al , 2013; Yolal et al , 2017). As tourists tend to perceive tourism operators and their services as an interconnected whole, where each moment-of-truth counts in the overall assessment of received benefits (Laškarin Ažić et al , 2020), the evaluation of received benefits should include overall satisfaction with a destination. Positive or negative emotions derived from such evaluations help to understand tourists’ perceptions concerning their intentions in the future, suggesting overall satisfaction as an irreplaceable construct in tourism loyalty models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Tourist loyalty models are criticised as uninventive in a conceptual and methodological sense (Laškarin Ažić et al , 2020; McKercher et al , 2012; Zhang and Peng, 2014). This important shortcoming appears as authors apply a traditional approach where attention focusses on how destinations relate to tourists, instead of focussing on the tourists’ perceptions (Almeida-Santana and Moreno-Gil, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Loyalty to holiday style: motivational determinants

Ažić

Suštar

2021

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Purpose This study aims to understand the push and pull motivations of leisure tourists who stay for one or more nights on the Opatija Riviera and explores the causal connection between motivations and loyalty intention with regard to holiday style. It verifies path discrepancy based on generational and educational differences. Design/methodology/approach The data were collected over a four-month period that included the low and high seasons of 2019. In total, 246 useable questionnaires were collected through on-site research. First, exploratory factor analysis was conducted as this was the first tourist loyalty study to be conducted in the Opatija Riviera region. Then, to test for causal significance amongst relationships and differences at the path level, the partial least square method and multigroup analysis (MGA) was undertaken. Findings The results showed that overall satisfaction fully mediates the relationship between the two established motivations (novelty seeking and outdoor activity) and loyalty to holiday style and partially between relaxation and loyalty to holiday style. Further, MGA results indicated a significant discrepancy in the relationship between preferred holiday style with regard to educational and age difference. Originality/value This study offers new insights regarding a relatively new type of tourist loyalty by developing a theoretical framework that assesses the relationship between loyalty intention and motivation, as well as overall satisfaction.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Loyalty to holiday style: motivational determinants

Ažić

Suštar

2021

Self Cite

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“…Traveler satisfaction, personalized service and a better hotel experience are among the top priorities for hotel managers, especially since good hotel service quality enhances travelers’ loyalty and influences their repurchase intentions [ 1 , 2 ]. Most international travelers place a high value on the quality of their accommodations [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca

Alsayat

2022

Neural Comput & Applic

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Big social data and user-generated content have emerged as important sources of timely and rich knowledge to detect customers’ behavioral patterns. Revealing customer satisfaction through the use of user-generated content has been a significant issue in business, especially in the tourism and hospitality context. There have been many studies on customer satisfaction that take quantitative survey approaches. However, revealing customer satisfaction using big social data in the form of eWOM (electronic word of mouth) can be an effective way to better understand customers’ demands. In this study, we aim to develop a hybrid methodology based on supervised learning, text mining, and segmentation machine learning approaches to analyze big social data on travelers’ decision-making regarding hotels in Mecca, Saudi Arabia. To do so, we use support vector regression with sequential minimal optimization (SMO), latent Dirichlet allocation (LDA), and k -means approaches to develop the hybrid method. We collect data from travelers’ online reviews of Mecca hotels on TripAdvisor. The data are segmented, and travelers’ satisfaction is revealed for each segment based on their online reviews of hotels. The results show that the method is effective for big social data analysis and traveler segmentation in Mecca hotels. The results are discussed, and several recommendations and strategies for hotel managers are provided to enhance their service quality and improve customer satisfaction.

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