A study on the trustworthiness of store rating in restaurant recommendation O2O service

Kim, Hyung Su; Lee, Sang‐Won; Jung, Inhwan

doi:10.1504/ijcvr.2020.108151

Cited by 4 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consumers performing a particular activity for the activity itself, to experience pleasure and satisfaction inherent to the activity, is called intrinsic motivation [82]. In addition, the emotion itself (e.g., pleasure and arousal) directly or indirectly influence consumer decision-making [75,77]. More details are shown in Figure 6.…”

Section: Emotional and Hedonic Factorsmentioning

confidence: 99%

Consumer Behavior in Online-to-Offline (O2O) Commerce: A Thematic Review

et al. 2022

View full text Add to dashboard Cite

Online-to-offline (O2O) commerce is a popular business model which links offline business activities with online channels. Consumer behavior in O2O commerce is more complex than in other traditional business models as both online and offline channels are involved. Despite the growing number of publications focused on this issue, no review paper has discussed the current research trends and factors influencing consumer behavior in O2O commerce. Therefore, this review aimed to synthesize literature on O2O commerce from 2015 to April 2022, focusing on consumer behavior. A set of inclusion and exclusion criteria was developed for searching and screening articles from two dominant databases (i.e., WOS and Scopus), and 53 eligible articles were included in this review. A thematic review approach using ATLAS.ti 9 software was conducted. Quantitative results presented the research trends of O2O commerce. Qualitative analyses generated eight main clusters of factors which influence consumers’ O2O behavior: (1) service and product quality, (2) technical and utilitarian factors, (3) emotional and hedonic factors, (4) trust and risk, (5) price and cost (6), social factors, (7) online content, and (8) habit. This paper also highlighted promising future research directions. The findings are expected to benefit the sustainable management and the future research of O2O commerce.

show abstract

Section: Emotional and Hedonic Factorsmentioning

confidence: 99%

Consumer Behavior in Online-to-Offline (O2O) Commerce: A Thematic Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In the 1960s, chatbots disembodied conversational tools that use text-based interfaces to communicate with humans were initially created with the intention of entertaining users by conversing with Chatbots in tourism and hospitality them in human language (Chi et al, 2020). Today's chatbots can reply by text or voice thanks to two distinct technologies (Ciechanowski et al, 2019) According to Kim et al (2020), chatbots are said to simplify people's lives by forming a virtual relationship with them and providing them with the precise information they require. Chatbots are sophisticated digital assistants that process various requests and are also intended to be used as virtual assistants for amusement (Nguyen et al, 2021).…”

Section: Literature Review 21 the Concept Of Chatbotmentioning

confidence: 99%

“…Chatbots, which have been rapidly adopted in the tourism industry due to their high communication flow (Orden-Mej ıa and Huertas, 2022a), are also becoming an academically intriguing subject. Research on the relationship between chatbot and tourism industry includes the adaptation of the technology in question to tourism and accommodation businesses (Ukpabi et al, 2019;Pillai and Sivathanu, 2020), examination of its role in tourism development (Kasinathan et al, 2020), its importance in touristic experiences (Casillo et al, 2020;Jim enez-Barreto et al, 2021), evaluation of its place in tourism (Calvaresi et al, 2021), intention to use chatbot in travel and tourism businesses (Meli an-Gonz alez et al, 2021), chatbot and human services in the tourism industry comparison in terms of customers (Lei et al, 2021), the effect of chatbots as a smart tourism technology on the satisfaction level of tourists (Orden-Mej ıa and Huertas, 2022a), tourists' intentions to use chatbots (Kim et al, 2020;Pereira et al, 2022). Focuses on different issues such as tourist-chatbot interaction (Wang and Shao, 2022;Calvaresi et al, 2023), chatbot use in terms of gender in tourism businesses (Zhang et al, 2023).…”

Section: Chatbots In Tourism Industrymentioning

confidence: 99%

An overview of chatbots in tourism and hospitality using bibliometric and thematic content analysis

Yılmaz,

Şahin-Yılmaz

2024

WHATT

View full text Add to dashboard Cite

PurposeArtificial intelligence is one of the most significant and active fields of study in the last few years. Artificial intelligence-derived robotic technologies known as chatbots are gaining interest from both academic and industry sectors. By analyzing the development and patterns of research on the chatbot phenomena within the tourism field, this study seeks to develop a theoretical framework for the interaction between chatbots and tourism.Design/methodology/approachThe Web of Science (WoS) database’s 33 articles on chatbots related to travel and hospitality were examined between 2019 and 2024 using VOSviewer software for bibliometric and thematic content analysis.FindingsResearch on chatbots for tourism and hospitality appears to be in its early stages. The factors influencing tourists' intentions to use chatbots have been thoroughly researched; the attitudes, perceptions and behavioral intentions of destinations, travel agencies and restaurant patrons regarding chatbots were examined, and it was found that the quantitative research approach was dominant. In addition, the majority of the studies are based on a particular theory or model.Originality/valueThis is one of the first attempts to directly comprehend and depict the interconnected structures of studies on the interaction between chatbots and tourism through the use of network analysis. Furthermore, the study’s findings can offer academics a comprehensive viewpoint and a reference manual for more accurate assessment and oversight of the chatbot-tourism interaction. Regarding the lack of research on the topic and the fragmented structure of the studies that exist, it is imperative to provide both a comprehensive overview and a roadmap for future investigations into the usage of chatbots in the travel and hospitality sector.

show abstract

“…There are relationships between the text length, semantic kernel, number of terminology knowledge bases, and overall number of terms in the compound terminological knowledge base. People who recommend pertinent information about restaurants are increasingly using mobile app markets, thanks to their expansion [4]. By enabling restaurant customers to quickly and easily obtain the information they need whenever and wherever they choose, the suggestion service app based on AI Chatbot can effectively manage time and budgets.…”

Section: Chatbots Use Techniques and Algorithms From Two Fields Of Ar...mentioning

confidence: 99%

A Survey on Intelligent Chatbot for Entertainment Recommendation

Singh¹,

Kashid²,

Badhe³

et al. 2023

IJRASET

View full text Add to dashboard Cite

bstract: Chatbots, also called chatterbots, is a form of artificial intelligence (AI) that simulates text or voice commands and give answers accordingly. It is a computer application uses voice instructions, text dialogues, or both to mimic human communication. In order to reduce the problem of information overload, which has created a possible problem for many Internet users, it is necessary to filter, prioritize, and efficiently distribute essential information on the Internet, where the quantity of options is overwhelming. A chatbot can be employed to communicate with end users, respond to their questions, comprehend their feelings, and make suggestions that are relevant. The objective of our project is to build a chatbot that allows the user to interact with it and get movies or songs recommendation of his liking/choice. Our proposed system is a single page website which can be run on user's desktop as well as mobile and its main focus is to accurately comprehend the user's question in text or voice format and respond to the user with relevant responses. We used Natural Language Processing (NLP) to convert the human conversation in text or voice format into data that is decrypted using recurrent text and patterns and then shaped into automated answers and responses. In recommendation model, Content based filtering is used which works on the data that we take from the user. The system will suggest various movies or songs based on the user's interests, and the result will be shown to the user.

show abstract

A study on the trustworthiness of store rating in restaurant recommendation O2O service

Cited by 4 publications

References 0 publications

Consumer Behavior in Online-to-Offline (O2O) Commerce: A Thematic Review

Consumer Behavior in Online-to-Offline (O2O) Commerce: A Thematic Review

An overview of chatbots in tourism and hospitality using bibliometric and thematic content analysis

A Survey on Intelligent Chatbot for Entertainment Recommendation

Contact Info

Product

Resources

About