AI LITERATURE REVIEW

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CHAPTER 2: LITERATURE REVIEW 2.1 What are the advantages and disadvantages of using AI in the fast-food delivery service sector? A joint Whitehouse and European Union (2021) report on the impact of artificial intelligence on the future of workforces in the European Union and the United States of America noted that, with little question, there are several potentials for the economy to improve with the usage of AI. Within the past ten years, computer vision and natural language processing have made great strides, opening up new opportunities for the application of artificial intelligence (AI) to jobs previously considered exclusively human. Due to its potential to scale, reduce costs, absorb and analyze massive volumes of data, and support better decision-making, frequently with human assistance, AI is being rapidly adopted by businesses worldwide. Furthermore, this change will probably lead to new employment that would not have been possible without AI. This study will investigate the advantages and disadvantages of AI use in the fast food delivery sector. According to (Li et al.,2020), the expansion of the online Food delivery (FD) market has allowed many people access to employment opportunities across various professions, including delivery drivers, cooks, and other office workers. The production and distribution of food packaging and the sale of electric bicycles are some ancillary industries that have benefited from the growth of the online FD market. Without a doubt, the online food delivery industry has generated a large number of jobs, notably in the delivery sector, according to Li et al. (2020). However, there has been concern expressed about the poor working conditions that delivery personnel are subjected to, including the standardized nature of their job, their high workload, the lack of adequate training they

experience for a large number of them, and the safety risks they face while delivering the food (Ma, 2019). Owing to these limitations, food delivery drivers have a wide range of employment opportunities, but job satisfaction is typically low and there is a significant attrition rate (Meenakshi & Sinha, 2019). In addition to plastic and food waste, Li et al. (2020) assert that online FD have a substantial carbon footprint that needs to be taken into account. A 2019 study in China that concentrated on the life cycle impact assessment of packaging from online FD used data from 35.61 million orders from one online FD platform across eight cities, along with 334 sets of packaging samples from the restaurants, to make its assessment. These samples included boxes, bags, chopsticks, cups, and straws, among other things. Just 5% of the environmental impact was related to the delivery stage, which included transportation from the producer to the restaurant, delivery workers to the customer, and consumers to the disposal facility. Solid waste pollution, followed by water pollution, resource consumption, and air pollution, has the most detrimental effects on the environment in this new business (Zhang & Wen, 2019) 2.2 How is AI currently being used in the fast-food delivery service sector? Consumer-facing AI applications in the food industry, according to Chidinma-Mary- Agbai (2020), include the following: Recommendation tools Artificial intelligence-based food discovery and recommendation engines can help customers make informed decisions about what to eat and what not to by using applications that learn about the consumer's food tastes and requirements; Apps and chatbots By using Artificial Intelligence-based Virtual Assistants, food companies can ensure that customers don't have to wait for hours before making enquiries,

processing orders, or modifying them. The process has been simplified, which enhances the user experience. Self-ordering kiosks powered by AI. Self-ordering devices powered by artificial intelligence can improve the customer experience by decreasing waiting times and the need for customers to stand in line to pay. With integrated card readers, such devices can accept consumer orders and enable them to make payments without human help; Robots are beginning to appear in restaurants, improving the capacity and speed of food production and reducing the time required for food delivery. AI robots have gained widespread recognition, and the application of this technology has moved from industrial manufacturing to the service sector. Robots of many different kinds have replaced humans. Humans are easily replaced by robots in guiding, receiving, and delivery services. Robots in the restaurant may perform a variety of tasks, including washing, chopping veggies, sorting, and even serving and cleaning dishes (Yuqi & Young-Hwan, 2020). The robots utilized in restaurants serve various purposes and exhibit various traits. Industrial robots are typically used at the back of the home. Service robots are the ones in front of the restaurant. A base and an automatic dumb waiter make up the sort of robot designated as a waiter robot. This has three steps and human servers have taken its place. Additionally, they assist by preserving the food's freshness, cleanliness, and warmth. A drawbridge door opens when the robot approaches the table. The tray that was supposed to be delivered is then elevated to the door opening, after which the food is rolled out of the door and given to the customer. Finding a moving robot is a typical environmental problem (Fox et al., 1998). As a result, AMCL makes use of particle filters and probability algorithms to track the robot's location from a map server. The localization accuracy of the robot varies with the surroundings and particle size. Typically, larger particles provide a more accurate measurement of the robot's position

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(Zhang et al., 2009; Cheong et al., 2016). In a different scenario, the human waiter selects the "lead" option from the main list menu and enters the necessary table number. The required table number then continually displays on the robot screen as it moves as well. In order for the robot to return to standby at the front desk after the customer arrives, they must select OK on the screen (Malik et al., 2016). A robot is capable of recycling the garbage that is gathered from the plate and providing self-delivery. The customer rings the bell next to the dining table after finishing their meal to summon the robot. Waiters first arrive at the table to clean it; after the robot arrives at its destination table, the customer is in charge of setting out the plates, containers, tableware, etc. Next, the user must choose "return to origin." (Yuqi & Young-Hwan, 2020).At restaurants, huge amounts of data are produced by programs that manage everything from ordering food and staffing shifts to taking bookings and keeping inventory for bill paying. By enhancing the supply chain and home delivery, AI assists restaurants in reducing waste (Berezina et al., 2019; Antony & Sivraj, 2018). Restaurants can get automated, individualized customer service through chatbots, which also take orders and payments. No app transfer are necessary and limited setup costs (Hoy, 2018). Computerized menu boards are much simpler than manually updating constantly changing prices and items. A chatbot is an internet program that can conduct chat conversations that seem human. In order to comprehend the conversational data provided and reply to it as people would, chatbots use machine learning (ML) and natural language processing (NLP) (Lasek & Jessa, 2013; Shawar & Atwell, 2005). Chatbots can process payments, assist clients with placing or tracking orders, and respond to commonly asked queries. Every time a chatbot interacts with a consumer, it gains new information and is exposed to more data. The flexibility

to work without downtime, sick days, or vacation days, as well as the availability of instant customer help whenever needed, are the key benefits of utilizing chatbots in a restaurant (Berezina et al., 2019). In order to decrease the chance of deception scenarios, biometric technologies based on artificial intelligence (AI) are used to construct detailed user profiles, enhance user recognition and security, grant or deny access, increase personalisation, and accept or reject financial transactions. Biometric systems are composed of four fundamental components. The first is equipment for scanning and reading, which commonly captures biometric identifiers like fingerprints, faces, and iris scans (Unar et al., 2014). The second component is software that uses a secure method to convert the biometric data from the scan into a digital code. The matching and comparison section makes up the third half. The final component will be a safe database that stores the encrypted biometric data for further comparison (Berezina et al., 2019). In restaurants today, a lot of biometric technology are used (e.g., facial and fingerprint identification). Fingerprint offers a more practical and reliable alternative. The fingerprint technology ensures that customer and employee records are correctly maintained while discouraging employees from acting in someone else's place. Because faces are generally used to identify people, facial recognition technologies help restaurant patrons (Berezina et al., 2019). 2.3 How does AI impact customer experience and engagement in the fast-food delivery service sector? According to El-Said and Al Hajri (2022), the relationship between experience satisfaction and experience extension was moderated by perceived risk reduction of viral infection and trust. It might be argued that when Covid-19 spread, people started to care more about their safety and preferred services that used technologies that could prevent the virus from

spreading as they felt their danger of contracting the infection was reduced. Let us say that clients are more convinced than usual that service robots can stop the spread of the virus and are a safe alternative to humans. If so, customers will be more satisfied with these establishments and more likely to recommend them to friends and family who are looking for a safe place to eat. El-Said and Al Hajri (2022) assert that perceived enjoyment only has an impact on restaurants that use robot service when customer novelty seeking is strong. This new discovery has important ramifications. On the one hand, our understanding of how people generally respond to new technologies has improved because of the expansion of the corpus of knowledge about the total addressable market. Yet, the results indicate that user goals and personality traits may have a greater impact on patron attitudes toward technology in robot service restaurants and maybe in other hospitality contexts. It was discovered that perceived utility (PU) positively affected diners' plans to employ robot services again. It implies that as customers esteem service robots more, they will want to visit robot restaurants more frequently. Moreover, the current study finds a positive indirect impact of perceived ease of use (PEOU) on return intention via PU. Customers are, therefore, more likely to frequent the robot restaurant since they perceive it to be more practical and user- friendly. However, this study also showed no appreciable relationship between behavioral intention to revisit and PU (Al Hajri and El-Said, 2022). The results showed that a lower perceived level of danger is associated with greater trust in robot service. It also indicates that the Technological Acceptance Model (TAM) might be used to justify the acceptance of robot service restaurants. It offers empirical evidence that trust, through lowering consumers' perceptions of the adverse effects of potential technical failures and

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malfunctions, is a viable strategy for minimizing perceived risk. Therefore, El-Said and Al Hajri (2022) identify a major barrier to the adoption of robot services as a lack of confidence. Finally, patrons' happiness with restaurants that employ robot service is higher when they believe in the technology and worse when they believe it to be more dangerous. As a result, any initiatives to increase patron trust in restaurants should be preceded by initiatives to reduce patron perceived risk toward the chef and serving robot, according to the current study's findings, which show that consumer satisfaction and intentions to use robot service again at business hotel restaurants are negatively impacted by perceived risk. It also demonstrates that customers are more likely to come back when they are satisfied with the robot service at business hotel restaurants. Further evidence from the research lends credence to the expanded TAM for satisfaction, trust, and risk. Additionally, it explains how people behave when dining at robot restaurants, specifically highlighting such restaurants' primary effects El-Said and Al Hajri (2022). The study concentrated on how robotics, artificial intelligence, and service automation (RAISA) are becoming increasingly significant in hotel and tourism. With a focus on their effects on guest services, the guest experience, and service innovation, it has examined the birth and expansion of RAISA. Despite the widespread adoption of technology throughout all service sectors, particularly hospitality, striking the correct balance between digital and interpersonal encounters is still challenging. On the one hand, RAISA adoption is unquestionably economical, may draw in new client demographics, and qualifies as both "product innovation" and "service innovation." However, even though RAISA has altered the way we think about service and service quality, human-centered interactions remain crucial to the idea of the "guest experience,"

in large part due to the inability of technologies (including humanoid robots) to convey such experiences and pay attention to the small details. Using robots and service automation to improve service quality is crucial for establishing a competitive edge, but providing more individualized guest experiences would still require a "human touch." Human-centered experiences may also become the new currency of luxury hospitality because of the rapid advancement of technology and the hospitality industry's willingness to continue relying on RAISA. Providing human-centered service may also become an increasingly rare but highly prized commodity. To address the urgent need for skilled and educated workers and to highlight uncomplicated, entry-level activities, the hospitality and tourism industries need RAISA to give a cost-effective solution. Because of its emphasis on intangibles, the hotel business is (and will continue to be) unique. So, more than just a clever engineering concept is needed to suit the many different wants of guests (Naumov, 2019). The six main categories listed in Seyitoğlu & Ivanov, S. (2020) study on the robotic restaurant Experience can be used to categorize customer reviews: "attraction for kids," "robotic system," "Memorable experience," "ambiance-related attributes," "food-related attributes (economic value and gastronomic aspects)," and deficiencies (in a robotic system, in ambiance-related attributes and in food-related characteristics). Most tourists agree that youngsters find robotic restaurants to be appealing. Children are the primary drivers of parents' visits to robotic restaurants, as expressed by the parents. The tourists' comments make it very evident that children wanted to visit robots, truly appreciated them, had fun with them, snapped pictures with robots, etc. The reviews of a sizable number of tourists indicate that restaurants offering robotic dining experiences employ robot servers and

profit from tablets or touchscreen tables with menus for placing orders for food and drinks. The touchscreen tables can also be used to view television or the culinary process in the kitchen. Robots, like any technology, have disadvantages. The findings show that while most reviews of robotic restaurants are favorable, there are several flaws that customers have noted concerning the "robotic system," "ambiance-associated qualities," and "food-related attributes." The majority of the clients loved the robotic system, according to the findings. Some of them, however, claimed that the robots were unqualified to be human workers in restaurants since they could not serve all items on the menu, resulting in service failures. The internet FD sector has significantly influenced the traditional restaurant industry, and many restaurants have had to modify their business models to survive, according to Li et al. (2020). As the internet FD business began to take off, traditional restaurants with physical storefronts noticed a drop in in-store dining and foot traffic as more and more of their customers started ordering meals online and eating them elsewhere, usually at their homes or place of employment. Because of this shift in consumer demand, many food businesses that did not embrace internet FD quickly enough to stay up with it faced a decline in profitability (Chen et al., 2019) 2.4 Chapter Summary and conclusion The Chapter presented the thematic review of relevant literature guided by the specific objectives. The study reviewed journals, policy documents, reports among others with a view to examine was has been done, the main arguments and the gaps therein. On the advantages and disadvantages of using AI in the fast-food delivery service sector, it is noted in the literature that AI improves economy and brings new employment opportunities through delivery services and

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other support services on the other hand; it comes with disadvantages such as poor working conditions and heavy workload. On how is AI currently being used in the fast-food delivery service sector, it is noted that Consumer-facing applications are used while AI robots and biometric technology are prominently used. On impact of AI on customer experience and engagement in the fast-food delivery service sector, it is noted that patrons happiness depends on their perception of their technologies used. Perceived utility positively affected diners plans to employ robot services again and Lower perceived level of danger associated with greater trust in robot services.

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Meenakshi, N., & Sinha, A. (2019). Food delivery apps in India: Wherein lies the success strategy? Strategic Direction , 35 (7), 12-15. https://doi.org/10.1108/sd-10-2018-0197 Naumov, N. (2019). The impact of robots, artificial intelligence, and service automation on service quality and service experience in hospitality. Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality , 123-133. https://doi.org/10.1108/978-1-78756-687-320191007 Seyitoğlu, F., & Ivanov, S. (2020). Service Robots as a Tool for Physical Distancing in Tourism. Current Issues in Tourism , 24 , 1–4. https://doi.org/10.1080/13683500.2020.1774518 Shawar, B. A., & Atwell, E. S. (2005). Using corpora in machine-learning chatbot systems. International Journal of Corpus Linguistics , 10 (4), 489-516. https://doi.org/10.1075/ijcl.10.4.06sha The White House. (2020). https://www.whitehouse.gov/wp-content/uploads/2022/12/TTC-EC- CEA-AI-Report-12052022-1.pdf Zhao, Y., & Pan, Y.-H. (2020). Research on Service Extensior of Restaurant Serving Robot - Taking Haidilao Hot Pot Intelligent Restaurant in Beijing as an Example. Journal of the Korea Convergence Society , 11 (4), 17–25. https://doi.org/10.15207/JKCS.2020.11.4.017 Zhang, Y., & Wen, Z. (2022). Corrigendum to "Mapping the environmental impacts and policy effectiveness of takeaway food industry in China" [Sci. Total environ. 808 (2022) 152023]. Science of The Total Environment , 848 , 157689. https://doi.org/10.1016/j.scitotenv.2022.157689

AI LITERATURE REVIEW

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