Co-creating interactive dining experiences via interconnected and interoperable smart technology

Leung, Rosanna; Loo, Poh Theng

doi:10.1080/19761597.2020.1822748

Cited by 31 publications

(21 citation statements)

References 59 publications

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“…Robots are widely suggested to be deployed in different scenarios during the pandemic, to help reduce the infection by performing disinfection, monitoring, delivering, food preparation, and telepresence [ [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] ]. Robots are also recommended to make intelligent decisions based on the inputs data from the population, which can be analysed through AI [ 17 ].…”

Section: Robots In Covid-19mentioning

confidence: 99%

A literature survey of the robotic technologies during the COVID-19 pandemic

Wang

2021

Journal of Manufacturing Systems

189

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Since the late 2019, the COVID-19 pandemic has been spread all around the world. The pandemic is a critical challenge to the health and safety of the general public, the medical staff and the medical systems worldwide. It has been globally proposed to utilise robots during the pandemic, to improve the treatment of patients and leverage the load of the medical system. However, there is still a lack of detailed and systematic review of the robotic research for the pandemic, from the technologies’ perspective. Thus a thorough literature survey is conducted in this research and more than 280 publications have been reviewed, with the focus on robotics during the pandemic. The main contribution of this literature survey is to answer two research questions, i.e. 1) what the main research contributions are to combat the pandemic from the robotic technologies’ perspective, and 2) what the promising supporting technologies are needed during and after the pandemic to help and guide future robotics research. The current achievements of robotic technologies are reviewed and discussed in different categories, followed by the identification of the representative work’s technology readiness level. The future research trends and essential technologies are then highlighted, including artificial intelligence, 5 G, big data, wireless sensor network, and human-robot collaboration.

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Section: Robots In Covid-19mentioning

confidence: 99%

A literature survey of the robotic technologies during the COVID-19 pandemic

Wang

2021

Journal of Manufacturing Systems

189

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“…(2017), Park et al. (2018), Shuhaiber and Mashal (2019) and Leung and Loo (2022) that confirmed the significant positive role of the hedonic value in achieving the CA of smart restaurants. However, the weakness in controlling the customer’s connection over information privacy may influence their intention to accept and use different types of services and technologies like mobile or e-wallet payment methods.…”

Section: Discussionmentioning

confidence: 79%

Determinants influencing customers’ acceptance of smart restaurants in Penang, Malaysia

Abhari

Jalali

Jaafar

2022

AGJSR

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PurposeThis study investigated the linkage between customers’ acceptance (CA) level and smart service competencies in the food sector across Penang, Malaysia. This research aims to develop a vision of how smart technology may transform the business model in hospitality industry to create value.Design/methodology/approachIn this study, the level of CA of the smart restaurant is proposed as the dependent variable, while the perceived security (PS), perceived ease of use (PEOU) and perceived enjoyment (PE) are proposed as independent variables (IVs). The quantitative approach and simple random sampling method were adopted in this study. Data were collected from 150 respondents by distributing 225 survey questionnaires to restaurant customers across Penang, Malaysia. The data were analysed by the reliability test, factor analysis and multiple regression analysis using SPSS 23.0 softwareFindingsThe study outcomes indicated that PE and PEOU emerged as the most significant predictors for CA of smart services in restuarants in Penang. The smart services provide customers to experience enjoyable dining services with accurate and user-friendly service delivery.Originality/valueThis study may serve as a guideline for food and beverage (F&B) business owners and restaurant operators with an industrial standard to adopt smart services in restaurants and their progression to develop the intention to use such a novel technology. This research is one of the few attempts that have looked into the influences of hedonic, comfortable and security value on customers’ acceptance level of smart restaurant services.

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“…Despite their large sales and investments, FDS organisations continue to struggle with profitability due to high operating costs. Predatory pricing is a common strategy used by businesses to beat the competition by drastically reducing meal costs [ 10 ]. As online FDSs rely largely on restaurants, they have little control over the quality of the food.…”

Section: Introductionmentioning

confidence: 99%

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

Adak

Pradhan

Shukla

et al. 2022

Foods

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The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a valuable source of information about a company’s performance. FDS organisations strive to collect customer complaints and effectively utilise the information to identify improvements needed to enhance customer satisfaction. However, only a few customer opinions are addressed because of the large amount of customer feedback data and lack of customer service consultants. Organisations can use artificial intelligence (AI) instead of relying on customer service experts and find solutions on their own to save money as opposed to reading each review. Based on the literature, deep learning (DL) methods have shown remarkable results in obtaining better accuracy when working with large datasets in other domains, but lack explainability in their model. Rapid research on explainable AI (XAI) to explain predictions made by opaque models looks promising but remains to be explored in the FDS domain. This study conducted a sentiment analysis by comparing simple and hybrid DL techniques (LSTM, Bi-LSTM, Bi-GRU-LSTM-CNN) in the FDS domain and explained the predictions using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The DL models were trained and tested on the customer review dataset extracted from the ProductReview website. Results showed that the LSTM, Bi-LSTM and Bi-GRU-LSTM-CNN models achieved an accuracy of 96.07%, 95.85% and 96.33%, respectively. The model should exhibit fewer false negatives because FDS organisations aim to identify and address each and every customer complaint. The LSTM model was chosen over the other two DL models, Bi-LSTM and Bi-GRU-LSTM-CNN, due to its lower rate of false negatives. XAI techniques, such as SHAP and LIME, revealed the feature contribution of the words used towards positive and negative sentiments, which were used to validate the model.

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Co-creating interactive dining experiences via interconnected and interoperable smart technology

Cited by 31 publications

References 59 publications

A literature survey of the robotic technologies during the COVID-19 pandemic

A literature survey of the robotic technologies during the COVID-19 pandemic

Determinants influencing customers’ acceptance of smart restaurants in Penang, Malaysia

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique

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