Assessing destination satisfaction by social media: An innovative approach using Importance-Performance Analysis

Chen, Jinyan; Becken, Susanne; Stantić, Bela

doi:10.1016/j.annals.2022.103371

Cited by 46 publications

(21 citation statements)

References 34 publications

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“…This method departs from the variable of satisfaction and the level of importance valued by customers. The method, like importance performance analysis (IPA) is measures satisfaction using four quadrants [27]- [29]. In this study, customers are system users, namely lecturers.…”

Section: Factors That Influence the Use Of Google Classroommentioning

confidence: 99%

Blended learning: Adoption pattern of online classrooms in higher education

Haryono¹,

Hamzah²

2023

IJERE

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<span lang="EN-US">This study aimed to describe the patterns of technology usage. Specifically, on the implementation of blended learning (BL) in higher education institutions. The case study explores usage patterns, influencing factors, and success factors. The data sources came from the Google Classroom log activity of 35 departments, consisting of five vocational programs, 28 undergraduate programs, and two professional programs in a university for three years with an average in a semester. A method called the customer window quadrant (CWQ) was utilized to analyze the pattern. In addition, a questionnaire was deployed to measure the teacher’s satisfaction with using the Google Classroom in a blended learning setting. The results show two dominant activities in Google Classroom, namely assigning tasks and quizzes. For the quiz, there were two popular question types, i.e., multiple-choice and short answers questions. The activity of using the Google Classroom has doubled in the three years. Assigning tasks to projects becomes the task in the Google Classroom. The usage patterns of Google Classroom are based on the level of importance. There are three activities considered the most important, namely creating class, topic, and material course. Moreover, the most influencing factors are stability, convenience, simplicity, velocity, and reliability.</span>

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Section: Factors That Influence the Use Of Google Classroommentioning

confidence: 99%

Blended learning: Adoption pattern of online classrooms in higher education

Haryono¹,

Hamzah²

2023

IJERE

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“…First, by introducing opinion mining and the roadmap beyond sentiment analysis, our study bridges the research gap that previous satisfaction analysis studies primarily focused on "measuring" how satisfied consumers are through sentiment analysis, with little attention to exploring why they are dissatisfied (satisfied) and how to effectively improve the situation [e.g. Chen et al (2022) and Bi et al (2019)]. Furthermore, this might be the first study that uses the opportunity algorithm to quantify improvement benefits in the hospitality and tourism fields.…”

Section: Theoretical Implicationsmentioning

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%

“…Furthermore, current research on satisfaction analysis through online reviews mainly focuses on “measuring” how satisfied/dissatisfied consumers are (Bi et al , 2019; Chen et al , 2022). Few studies, as far as we know, have further discussed why customers are satisfied (dissatisfied) and how to effectively improve the situation after measuring satisfaction.…”

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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“…Tourism refers to the travel and stay experience by the people for leisure, fun, recreational activities, sports, sightseeing, adventure, exploring new areas, knowing new people, reunion, business and religious activities, etc. (Camilleri, 2018;Chen et al, 2022;Stoddart et al, 2020). Apart from this, tourism is an integral part of any economy for revenue and employment generation (Manzoor et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Social media analytics in tourism: a review and agenda for future research

Mukhopadhyay

Jain²,

Modgil³

et al. 2022

BIJ

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PurposeThe significance of social media in our lives is manifold. The tourism sector closely interacts with existing and potential tourists through social media, and therefore, social media analytics (SMA) play a critical role in the uplift of the sector. Hence, this review focus on the role of SMA in tourism as discussed in different studies over a period of time. The purpose of this paper to present the state of the art on social media analytics in tourism.Design/methodology/approachThe review focuses on identifying different SMA techniques to explore the trends and approaches adopted in the tourism sector. The review is based on 83 papers and discuss the studies related to different social media platforms, the travelers' reactions to a particular place and how the tourism experience is enriched by the way of SMA.FindingsFindings indicate different sentiments associated with tourism and provides a review of tourists’ use of social media for choosing a travel destination. The various analytical approaches, areas such as social network analysis, content analysis, sentiment analysis and trend analysis were found most prevalent. The theoretical and practical implications of SMA are discussed. The paper made an effort to bridge the gap between different studies in the field of tourism and SMA.Originality/valueSMA facilitate both tourists and tourism companies to understand the trends, sentiments and desires of tourists. The use of SMA offers value to companies for designing quick and adequate services to tourists.

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Assessing destination satisfaction by social media: An innovative approach using Importance-Performance Analysis

Cited by 46 publications

References 34 publications

Blended learning: Adoption pattern of online classrooms in higher education

Blended learning: Adoption pattern of online classrooms in higher education

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

Social media analytics in tourism: a review and agenda for future research

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