Aspect-based sentiment analysis on online customer reviews: a case study of technology-supported hotels

Özen, İbrahim Akın; Katlav, Eda Özgül

doi:10.1108/jhtt-12-2020-0319

Cited by 11 publications

(17 citation statements)

References 61 publications

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“…Therefore, this study suggests that fast-food restaurants should invest in training initiatives, including providing tutorials on the restaurant’s online ordering system, free Wi-Fi access and customer support for any technical issues (Mercan et al , 2021). In addition, fast-food restaurants should collaborate with customers to develop future digital technologies, ensuring that these technologies are requested and understandable by customers (Özen and Özgül Katlav, 2023). Second, this study found that the technology acceptance of fast-food restaurant customers affects hedonic and utilitarian values.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, digital technologies assist customers with orders quickly and enjoy their experience. Digital technologies also allow restaurants to promptly resolve customer concerns and worries (Özen and Özgül Katlav, 2023). As these benefits become evident, more fast-food restaurants will adopt digital technologies to stay competitive.…”

Section: Customer Technical Digital Transformation Readiness and Tech...mentioning

confidence: 99%

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The impact of fast-food restaurant customers’ digital transformation on perceived value and well-being

Helal

2023

JHTT

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Purpose Hospitality businesses are changing digitally to meet customer demands, provide value and stay competitive. Prior hospitality studies examined customers’ technology acceptance rather than digital technology readiness and acceptance. In addition, no studies have explored how restaurant customers’ digital transformation influences hedonic and utilitarian values and customer well-being. Therefore, this study aims to examine how fast-food restaurant customers’ digital transformation (i.e. technical readiness and acceptance) influences their perceived value and well-being. Design/methodology/approach This study provides a conceptual framework with six constructs. The data for this study were collected from fast-food restaurant customers in Egypt using a self-administered questionnaire. Findings According to the findings, customers’ technical readiness for digital transformation in fast-food restaurants substantially impacts their technology acceptance. In addition, customers’ perceived hedonic and utilitarian values are greatly influenced by their acceptance of fast-food restaurant digital technologies, and these perceived values significantly impact customer well-being. Practical implications This study presents several suggestions to improve customers’ readiness for digital transformation in fast-food restaurants and the impact on their technology acceptance and perceived value and well-being. Also, this study’s results could impact fast-food restaurants’ decisions to adopt new digital technologies (e.g. service robots) for their customers. Originality/value The study adds to the existing literature review by attempting to present a comprehensive picture of fast-food restaurant customers’ digital transformation (i.e. technical readiness and acceptance) and its impact on customers’ perceived values. A potential antecedent of fast-food restaurant customers’ well-being was also investigated: perceived hedonic and utilitarian values.

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Section: Discussionmentioning

confidence: 99%

Section: Customer Technical Digital Transformation Readiness and Tech...mentioning

confidence: 99%

The impact of fast-food restaurant customers’ digital transformation on perceived value and well-being

Helal

2023

JHTT

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“…This research demonstrated the applicability of combining data mining and spatial clustering analysis as a tool to gain knowledge on the spatial pattern of the digital footprints of reputable tourist accommodation firms using Web-scraped STNSs data. Several studies have used tourism-and-hospitality-related UGCs for mapping geographic distribution and clustering (Kirilenko et al, 2019;Mou et al, 2020;Van der Zee et al, 2020). However, few of them analyzed the spatial clustering of the H&Rs based on online reputation and eminent digital footprints (Beneki and Spiggos, 2021;Guti errez et al, 2017;Gł owczy nski, 2022;Ye et al, 2018).…”

Section: Theoretical Implicationsmentioning

confidence: 99%

“…Online reputation has become an important contributor to the entrepreneurship and growth of T&H firms adopting e-tourism in their operations. Many tourism-and-hospitality-related UGC studies have focused on accommodations, destinations and attractions (Özen and Özgül Katlav, 2023; Ye et al , 2018). For instance, a survey of Google reviews found that pubs, restaurants and hotels are the local businesses with the most Google ratings, with 98.5% of restaurants and hotels having at least one Google review (BrightLocal, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal cluster analysis of reputable tourist accommodation in Greater Amman Municipality, Jordan

Al-Saad

Jawarneh

Aloudat

2023

JHTT

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Purpose To test the applicability of the user-generated content (UGC) derived from social travel network sites for online reputation management, the purpose of this study is to analyze the spatial clustering of the reputable hotels (based on the TripAdvisor Best-Value indicator) and reputable outdoor seating restaurants (based on ranking indicator). Design/methodology/approach This study used data mining techniques to obtain the UGC from TripAdvisor. The Hierarchical Density-Based Spatial Clustering method based on algorithm (HDBSCAN) was used for robust cluster analysis. Findings The findings of this study revealed that best value (BV) hotels and reputable outdoor seating restaurants are most likely to be located in and around the central districts of the urban tourist destinations where population and economic activities are denser. BV hotels' spatiotemporal cluster analysis formed clusters of different sizes, densities and shape patterns. Research limitations/implications This study showed that reputable hotels and restaurants (H&Rs) are concentrated within districts near historic city centers. This should be an impetus for applied research on urban investment environments. Practical implications The findings would be rational guidance for entrepreneurs and potential investors on the most attractive tourism investment environments. Originality/value There has been a lack of studies focusing on analyzing the spatial clustering of the H&Rs using UGC. Therefore, to the best of the authors’ knowledge, this study is the first to map and analyze the spatiotemporal clustering patterns of reputable hotels (TripAdvisor BV indicator) and restaurants (ranking indicator). As such, this study makes a significant methodological contribution to urban tourism research by showing pattern change in H&Rs clustering using data mining and the HDBSCAN algorithm.

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“…However, to the best of our knowledge, few studies have effectively modeled how different consumers assess multiple restaurant attributes differently. Moreover, the conventional text-mining approach ignores contextual information, such as surrounding words and word order; thus, it is challenging to extract textual information efficiently (Viñán-Ludeña and de Campos, 2022; Özen and Özgül Katlav, 2023).…”

Section: Introductionmentioning

confidence: 99%

Restaurant recommendation model using textual information to estimate consumer preference: evidence from an online restaurant platform

Jang

Kim

et al. 2023

JHTT

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Purpose Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes. Design/methodology/approach The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector. Findings This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions. Originality/value To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

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Aspect-based sentiment analysis on online customer reviews: a case study of technology-supported hotels

Cited by 11 publications

References 61 publications

The impact of fast-food restaurant customers’ digital transformation on perceived value and well-being

The impact of fast-food restaurant customers’ digital transformation on perceived value and well-being

Spatiotemporal cluster analysis of reputable tourist accommodation in Greater Amman Municipality, Jordan

Restaurant recommendation model using textual information to estimate consumer preference: evidence from an online restaurant platform

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