A co-created value scale for the hospitality service: applying a text mining approach

Kwon, Wooseok

doi:10.1108/ijchm-04-2022-0458

Cited by 4 publications

(4 citation statements)

References 78 publications

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“…Meanwhile, because customer satisfaction is also the basis of other business analyses [e.g. service failure (Akarsu et al , 2023) and value co-creation (Kwon, 2022)], it is encouraged to extend the proposed methodology to these fields.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…In addition, such methods must balance the investment costs with experimental performance (Guo et al , 2017; Lin et al , 2021). They have also been criticized for inflated ratings, biased recalling, as well as limited samples (Guo et al , 2017; Kwon, 2022), which may influence the effectiveness of the results.…”

Section: Theoretical Underpinning and Literature Reviewmentioning

confidence: 99%

“…Traditionally, satisfaction is directly measured through collecting surveys (questionnaires) from customers (Han and Hyun, 2018;Song et al, 2012). Despite its simplicity, such methods suffer from consumer memory bias, inflated ratings and delayed response (Chen et al, 2022;Kwon, 2022). With the emergence of e-commerce and social media, consumers are generating a large number of online reviews that are objective, authentic and insightful (Yang et al, 2023a(Yang et al, , 2023b.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Theoretical Underpinning and Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…The accurate and precise classification of these user-generated textual data can show the characteristics, features, service and ratings of restaurants, hotels and destinations (Marcolin et al , 2021; Tanrısevdi et al , 2022; Elkhwesky and Elkhwesky, 2022), which can reveal consumers’ experiences, feelings and choice preferences in tourist destinations, as well as provide more personalized suggestions for tourists (Tsai et al , 2020; Kwon, 2022; Gambetti and Han, 2022). Extracting and calculating the emotional information and emotional intensity contained in user-generated texts and focusing on the emotional changes of consumers can help managers improve their offerings (Luo and Xu, 2021; Mehraliyev et al , 2022).…”

Section: Introductionmentioning

confidence: 99%

Text classification in tourism and hospitality – a deep learning perspective

Mehraliyev

et al. 2023

IJCHM

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Purpose This study aims to investigate the current state of research using deep learning methods for text classification in the tourism and hospitality field and to propose specific guidelines for future research. Design/methodology/approach This study undertakes a qualitative and critical review of studies that use deep learning methods for text classification in research fields of tourism and hospitality and computer science. The data was collected from the Web of Science database and included studies published until February 2022. Findings Findings show that current research has mainly focused on text feature classification, text rating classification and text sentiment classification. Most of the deep learning methods used are relatively old, proposed in the 20th century, including feed-forward neural networks and artificial neural networks, among others. Deep learning algorithms proposed in recent years in the field of computer science with better classification performance have not been introduced to tourism and hospitality for large-scale dissemination and use. In addition, most of the data the studies used were from publicly available rating data sets; only two studies manually annotated data collected from online tourism websites. Practical implications The applications of deep learning algorithms and data in the tourism and hospitality field are discussed, laying the foundation for future text mining research. The findings also hold implications for managers regarding the use of deep learning in tourism and hospitality. Researchers and practitioners can use methodological frameworks and recommendations proposed in this study to perform more effective classifications such as for quality assessment or service feature extraction purposes. Originality/value The paper provides an integrative review of research in text classification using deep learning methods in the tourism and hospitality field, points out newer deep learning methods that are suitable for classification and identifies how to develop different annotated data sets applicable to the field. Furthermore, foundations and directions for future text classification research are set.

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Engaging in co-creation experiences: the role of affective image and satisfaction

Islam,

Loureiro,

Carvalho

et al. 2024

JHTI

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PurposeThis study proposes a conceptual model and aims (1) to understand how co-creation experience influences guest engagement, (2) to analyse the effect of satisfaction and affective image between co-creation experience and guest engagement, and (3) to analyse the effect of guest engagement on behavioural intentions.Design/methodology/approachA questionnaire-based online survey was conducted targeting U.S.-based hotel customers. Developed on Qualtrics and distributed via Mechanical Turk, the survey garnered 465 useable responses from individuals who had visited international chain hotels within the last year. Data analysis was performed using Structural Equation Modelling (SEM) in IBM SPSS Amos.FindingsThe findings revealed significant direct effects of co-creation experience on affective image and guest satisfaction, with guest satisfaction notably influencing guest engagement, and guest engagement directly influencing behavioural intention. However, both co-creation experience and affective image showed minimal direct impact on guest engagement. The study also highlighted the importance of indirect effects and total effects in understanding the dynamics between the constructs analysed.Research limitations/implicationsThe findings revealed significant direct effects of co-creation experience on affective image, with affective image notably influencing guest engagement, and guest engagement directly influencing behavioural intention. Although satisfaction had direct effects on guest engagement, co-creation experience showed minimal direct impact on guest satisfaction and on guest engagement.Practical implicationsThis study advises managers to use co-creation primarily to enrich guest experiences and establish emotional connections rather than as a tool for directly enhancing engagement. It recommends that managers invest in strategies to enhance guest engagement beyond co-creation given the direct link between guest engagement and behavioural intentions, and the minimal direct impact between co-creation, guest engagement and satisfaction.Originality/valueThis study demonstrates that affective image is a pivotal mediator between co-creation experience and guest engagement. The findings provide valuable implications for hospitality practitioners in designing and managing co-creation experiences, emphasizing the importance of fostering a positive affective image in the value co-creation process to attain positive behavioural outcomes.

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A co-created value scale for the hospitality service: applying a text mining approach

Cited by 4 publications

References 78 publications

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

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

Text classification in tourism and hospitality – a deep learning perspective

Engaging in co-creation experiences: the role of affective image and satisfaction

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