Big data use in determining competitive position: The case of theme parks in Hong Kong

Albayrak, Tahir; Cengizci, Aslıhan Dursun; Caber, Meltem; Fong, Lawrence Hoc Nang

doi:10.1016/j.jdmm.2021.100668

Cited by 22 publications

(28 citation statements)

References 51 publications

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“…Specifically, we find that early tourists pay more attention to the experience of specific playing items while later tourists focus on the overall experience on theme parks, which is consistent with customer self-selection theory in (Li & Hitt, 2008). Second, this study reveals the dynamics relationship between customer review and rating, which goes beyond findings in the existing theme park literature (Albayrak et al, 2021;Luo et al, 2020). Our results indicate that review content is more indicative of ratings earlier than later.…”

Section: Introductionsupporting

confidence: 82%

Exploring thematic influences on theme park visitors' satisfaction: An empirical study on Disneyland China

Bai

Han

et al. 2023

J of Consumer Behaviour

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This study aims to explore thematic influences on theme park visitors' satisfaction through user‐generated data. To this end, we first used an unsupervised machine learning method, structural topic modeling, and analyzed 112,000 reviews post by visitors to Shanghai Disney Resort from June 16, 2016 to March 4, 2022. Our findings are of great significance for reflecting consumer behavior through user‐generated data. Specifically, we find that visitors' satisfaction is highly related to service in the theme park and their playing feeling, and early tourists pay more attention to the experience of specific playing items while later tourists focus on the overall playing experience. In addition, an empirical study is conducted by treating the ratings associated with each review as dependent variable and each topic represented by comments as independent variables, which shows that the relationship between the customer reviews and ratings by tourists becomes less pronounced over time. In other words, as time goes, customers review can reflect their subjective feelings or experience, but the rating is not. We discover the “dynamics” of user‐generated data over time and gain a better understanding of the aspects and concerns of visitors' satisfaction over time. The findings of the study contribute to the literature on tourism, service, and consumer behavior while also providing valuable practical implications.

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Section: Introductionsupporting

confidence: 82%

Exploring thematic influences on theme park visitors' satisfaction: An empirical study on Disneyland China

Bai

Han

et al. 2023

J of Consumer Behaviour

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“…With 500 million visits to the world's top 10 theme park operators in 2019 (Albayrak et al, 2021), theme parks became one of the most rapidly growing tourism destinations. In Source: Authors' own creation particular, compared to the more mature operational status in Europe and the USA, China's theme parks have developed more rapidly recently.…”

Section: Data Collectionmentioning

confidence: 99%

“…In Source: Authors' own creation particular, compared to the more mature operational status in Europe and the USA, China's theme parks have developed more rapidly recently. Consequently, research on theme parks has been very popular in these years (Albayrak et al, 2021). Following this, we take Universal Beijing Resort as our research subject, which is a very famous theme park with great Universal rides, shows and attractions from around the world.…”

Section: Data Collectionmentioning

confidence: 99%

Knowing how satisfied/dissatisfied is far from enough: a comprehensive customer satisfaction analysis framework based on hybrid text mining techniques

Yang

Zhang

2023

IJCHM

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Purpose This study aims to establish a comprehensive satisfaction analysis framework by mining online restaurant reviews, which can not only accurately reveal consumer satisfaction but also identify factors leading to dissatisfaction and further quantify improvement opportunity levels. Design/methodology/approach Adopting deep learning, Cross-Bidirectional Encoder Representations Transformers (BERT) model is developed to measure customer satisfaction. Furthermore, opinion mining technique is used to extract consumers’ opinions and obtain dissatisfaction factors. Furthermore, the opportunity algorithm is introduced to quantify attributes’ improvement opportunity levels. A total of 19,133 online reviews of 31 restaurants in Universal Beijing Resort are crawled to validate the framework. Findings Results demonstrate the superiority of Cross-BERT model compared to existing models such as sentiment lexicon-based model and Naïve Bayes. More importantly, after effectively unveiling customer dissatisfaction factors (e.g. long queuing time and taste salty), “Dish taste,” “Waiters’ attitude” and “Decoration” are identified as the three secondary attributes with the greatest improvement opportunities. Practical implications The proposed framework helps managers, especially in the restaurant industry, accurately understand customer satisfaction and reasons behind dissatisfaction, thereby generating efficient countermeasures. Especially, the improvement opportunity levels also benefit practitioners in efficiently allocating limited business resources. Originality/value This work contributes to hospitality and tourism literature by developing a comprehensive customer satisfaction analysis framework in the big data era. Moreover, to the best of the authors’ knowledge, this work is among the first to introduce opportunity algorithm to quantify service improvement benefits. The proposed Cross-BERT model also advances the methodological literature on measuring customer satisfaction.

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“…The research on competition analysis mainly involves the following problems: key customer requirement mining (Albayrak et al , 2021; Gang and Chenglin, 2021), market structure analysis (Netzer et al , 2012), competitors and competitive groups identification (Hu and Trivedi, 2020; Liu et al , 2019; Marcolin et al , 2021), main competitive advantages and disadvantages analysis (He et al , 2015; Jin et al ., 2016a; Kim and Kang, 2018; Wang et al , 2018a), how to improve the performance level of products or services in the competitive environment (Rodríguez-Díaz and Espino-Rodríguez, 2018). In order to identify competitors, Gao et al (2018) proposed a new model to extract comparison relationships from online reviews and constructed three types of comparison relationship networks.…”

Section: Research On Ewom For Product and Service Quality Improvementmentioning

confidence: 99%

Research on electronic word-of-mouth for product and service quality improvement: bibliometric analysis and future directions

Wang

Meng²,

Xu³

et al. 2022

IJICC

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PurposeThis paper aims to analyze high-quality papers on the research of electronic word-of-mouth (eWOM) for product and service quality improvement from 2009 to 2022, in order to fully understand their historical progress, current situation and future development trend.Design/Methodology/ApproachThis paper adopts the bibliometrics method to analyze the relevant literature, including publishing trend and citation status, regional and discipline area distribution, and influential publications. Secondly, the VOSviewer is used for literature co-citation analysis and keyword co-occurrence analysis to obtain the basic literature and research hotspots in this research field.FindingsFirstly, the study finds that the number of publications basically shows an increasing trend, and those publications are mainly published in tourism journals. In addition, among these papers, China has the largest number of publications, followed by the USA and South Korea. Through co-citation analysis of literature and keyword co-occurrence analysis, 22 foundational papers and six main research topics are obtained in this paper. Finally, this paper elaborates on the development trend of the research topic and future research directions in detail.Originality/valueThis is the first paper that uses bibliometrics to analyze and review relevant researches on eWOM for product and service quality improvement, which is helpful for researchers to quickly understand its development status and trend. This review also provides some future research directions and provides a reference for further research.

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Big data use in determining competitive position: The case of theme parks in Hong Kong

Cited by 22 publications

References 51 publications

Exploring thematic influences on theme park visitors' satisfaction: An empirical study on Disneyland China

Exploring thematic influences on theme park visitors' satisfaction: An empirical study on Disneyland China

Knowing how satisfied/dissatisfied is far from enough: a comprehensive customer satisfaction analysis framework based on hybrid text mining techniques

Research on electronic word-of-mouth for product and service quality improvement: bibliometric analysis and future directions

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