Hospitality Feedback System 4.0: Digitalization of Feedback System with Integration of Industry 4.0 Enabling Technologies

Abstract

Digitalization enables the realization of the resilient infrastructure in every application for achieving sustainability. In the context of the hospitality business, resilient infrastructure based on digital technologies is critical for gaining the best customer feedback on providing quality service. Digital technology has already proved to enhance hospitality services with intelligent decisions through real-time data. In the previous studies, the significance of digital technologies in the hotel sector has been extended in numerous theoretical and empirical studies, yet there is a lack of research that provides a discussion on feedback systems in hospitality with digital technologies applications. With the motivation from the above aspects, this study intends to present the importance and application of the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and big data implementation in customer quality and satisfaction. Moreover, we have discussed each technology´s significance and application for realizing digital-based customer quality and satisfaction. It has been identified that the AI-based system collects the input data from different common websites and compares it with a different algorithm using a neural network. According to the findings of this study, AI and personnel quality of service have an impact on customer pleasure and loyalty. The study also concludes with the following recommendations, such as the design and development of dedicated hardware to gain the actual feedback from the customer on a large scale for improving the accuracy in the future.

1. Introduction

The digital revolution has drastically altered hotel operations and administration, and in addition to this, digital technologies have been identified as the key foundations of productivity and driven benefit in the hotel industry [1]. According to the World Tourism Organization (WTO) 2017 report, there were 1235 million foreign passengers in 2016, and by 2030, the number of international tourists is estimated to reach 1800 million, with the country’s economy growing at a rate of 2.2 percent per year [2]. Digital transformation seeks to strengthen an entity by producing significant changes to its characteristics by making use of several information, computer, communication, and connectivity technologies [3]. IoT, AI, robotics, blockchain, big data analytics, digital twins, and AR/VR are the digital technologies that have the potential to alter hotels’ management of their operations and value chains [4]. Hotels can utilize these technologies to manage capacity and resources, manage service, customer relationship, order process, competitiveness, service quality, flexibility, resource usage, and innovation [5]. Customer feedback plays an important role in the rapid development of hospitality and other industry services [6]. It is a type of information-based asset that is critical for smart tourism’s long-term survival, including economic and environmental sustainability [7], and also many studies have highlighted satisfaction as an extremely significant part of customer service [8]. In the traditional system, the simplest way to collect feedback from the customer is through the feedback diary at the time of the customer visit [9].

A customer could check the feedback from the feedback diary or on the website of a particular hotel or industry [10,11]. Traditional techniques, such as telephone calls, internet survey links, and paper comment cards, have severe limitations, such as accurate feedback for new customers in the past [12], and social media and secret shoppers also do not provide satisfying information to the customers. Hotel management must understand the significance of various digital technologies that can affect hotel performance to deploy relevant solutions [13].

Ref. [14] examined the level of growth of the hotel industry in connection to online reservations placed by citizens of tourist locations, and the findings revealed that social and pricing effects are significant considerations in online hotel reservations. Ref. [15] discussed the significance of green economy in tourism from previous studies with an incline towards sustainable development goals. Ref. [16] study investigated and evaluated the issues that the hotel sector is confronting in the process of selecting and deploying information systems and digital technologies as the core of an expanding IT approach. Ref. [17] explored the use of AI algorithms in auditing activities, with a particular accent on human interaction in completing audits as smart organizations in the future. In the previous studies, the significance of digital technologies in the hotel sector has been extended in numerous theoretical and empirical studies, yet there is a lack of research that provides a discussion on feedback systems in hospitality with digital technologies’ applications. The forthcoming fourth industrial revolution will have an impact on the hospitality sector as well because of the issues of mass customization, smart functioning, and digitization. Every customer is unique and requires individualized service, thus hospitality staff members must deliver this [18]. This study shows the strength of the hospitality sector in terms of hospitality industry 4.0. The major contributions of the study areas follows:

• Addressing the significance of feedback systems in the area of the hospitality industry with the benefits for enhancement of hospitality services.
• The significance and application of IoT, cloud computing, AI, and big data for the realization of an automated feedback system with intelligent decisions and insights.
• Future recommendations for the enhancement of automated feedback systems are discussed in this study.

The organization of the study is: Section 2 covers the ignorance of the feedback system. Section 3 covers the integration of enabling technologies for the feedback system, it discusses the different technologies’ significance and implementation for the automated feedback system. Section 4 covers the discussion and recommendations, it presents the enhancements that can be implemented as future scope.

Materials and Methods

In this Section, we discuss the methods utilized to conduct an examination of the customer feedback system with industry 4.0 technologies. The methods are provided in the following order: Search strategy and selection criteria, data collection and extraction, and data analysis. This review is largely concerned with the progress of various technologies in establishing an automated feedback system. The main research question is: “Which technologies are employed in customer feedback systems for automation?” Based on this study question, we collected research articles from several databases, such as Web of Science, and Scopus. The following guidelines are followed for the purpose of determining whether the article should be included or excluded from this review:

• The abstracts of studies that were available but without the full text of the studies were not examined for review.
• Studies that used the same datasets, methodologies, or algorithms and had identical results were not selected for review.
• Research that proposes methodologies but does not conduct experiments or validation is not eligible for review.
• Dissertation work and theses completed at the post-graduate and graduate levels are not reviewed.
• Non-peer-reviewed research articles are not considered for review.
• Book chapters, patent applications, and communications are not reviewed.

After following the above instructions, the review presents the statistics of different papers that are utilized for studying the different technologies’ implementation for the automated feedback system. Figure 1 illustrates a pie chart that shows the percentage of the technologies used in this study. A major part of the technology is 38% AI/ML, 20% cloud computing, 16% blockchain, 9% IoT, 7% big data, 7% AR/VR, and 4% edge computing.

2. Significance of Feedback System

Competitiveness, financial performance, quality of service, resource utilization, adaptability, and innovation were the criteria employed to categorize the hotel performance dimensions. Because they provide performance characteristics for the hospitality industry, the performance domains created by [2] were chosen. Figure 2 clearly illustrates the elements of every individual dimension in which the hospitality industry is ranked [19]. Relative market share and position, sales growth, and measures of the customer base are the elements of competitiveness. Profitability, liquidity, capital structure, and market ratios are the elements of financial performance. Reliability, aesthetics, responsiveness, cleanliness, friendliness, comfort, courtesy competence, communication, availability, access, security are key elements of quality of service. Productivity, efficiency, specification flexibility, volume flexibility and delivery speed flexibility are the elements of resource utilization flexibility. Performance of the innovation process and performance of individual innovations are the elements of innovation. The hospitality industry, especially hotels, has experienced competitive pressure for strong service quality and customer satisfaction [20].

As a result, hotel organizations must understand what their guests expect from their service experience as precisely as possible. This emphasizes the significance of interpreting these gaps in quality perceptions and confirms the efforts of various authors [21] to quantify apparent service quality by guests. Ref. [22] concludes that recognizing the responsibility of customers’ emotions through service confronts quality assessment must include an affective or emotional component. A conceptual paradigm test revealed that the emotional fulfillment of guests, as well as service characteristic, had a positive impact on their behavioral intentions. As a result, hospitality firms should emphasize more than simply intellectual fulfillment, but also the emotional side of achievement. Ref. [23] discovered that customers’ inner attitude state instantaneously after the service meeting, as well as their shown expressions within the service encounter, were both highly connected to service meeting assessments and the inclusive evaluation of the first hotels they researched. Service and quality have historically been elusive concepts [24], with many researchers struggling to define them distinctly. Service quality is perceived as a significant approach for establishing a modest edge in organizations [25], therefore, managing these distinguishing characteristics becomes critical in conversations about service quality delivery. Since the consumer is engaged in the service’s execution, their expectations and perceptions are elevated in hospitality processes, as they are in other services. According to [21], the concentration on people’s involvement in the process indicates a destandardization of the hospitality merchandise, with many distinct potentials based on the uniqueness of customers and staff, as well as the connections in which they are promised. The excellent service experience is motivated by the unique perspectives of each client and employee, as well as how these intersect.

Despite some variances, staff was found to be more comparable in their beliefs of consumer expectations, which is concerning because it is widely believed that the quality of service delivered must be judged based on guest perceptions [26,27]. Derivative models, such as dineserv [28], lodgserv [29], and lodgqual [30], strengthened the significance of servqual. The dineserv and lodgeserv adopted these dimensions for lodging, respectively. the lodgqual authors used these dimensions as well. However, they created a composite dimension, known as contact, by combining responsiveness, empathy, and assurance into one dimension. According to their findings, there are three aspects as tangibles: Service quality, employees, and dependability. According to [31], the staff dimension was the most reliable predictor of overall service quality. Empathy, assurance, and responsiveness are embedded in everything the employee performs. Although the holserv model, including many subsequent articles on frontline employees [32], highlights the responsibility of the employee in hospitality service quality, intangibles are controlled by organizational policies about service assurances and service recovery, which have largely been overlooked in this previous work.

Benefits of Feedback System

Feedback from customers is extremely important and is at the heart of any successful organization [33]. A flexible web-based client input framework for inns and cafés assists in constructing more grounded associations with visitors and clients [34]. The benefits of customer feedback are as follows: Relationship with a customer can inform about the nature of the services, feedback can assist with retaining a lot of customers by identifying hidden flaws in items or administrations. Many issues are difficult to identify using traditional input methodologies and may result in customer loss. Optimistic customer assistance delivers the client’s distinct feedback alternatives, demonstrating that you value them and want to hear what they need to express. Optimal feedback that stimulates greater client administration allows your brand to maintain a positive image on the lookout. The consistent advancement of items and administrations provides your image a superior standing in the eyes of the customer. Utilizing the feedback framework permits the board to recognize the recognition or address the objection before the lodging visitor looks at it, and subsequently, getting more cheerful visitors, working on web-based surveys, and increment lodging income. Boost the number of positive reviews on review websites: The framework can be constructed in such a way that, assuming a positive survey, the customer is encouraged to leave a similar review on a hotel rating site, or any nearby inn survey site. In the event of bad criticism, the visitor will be discouraged from contributing to such open places. Since customers now have the option to actively review businesses online, this increases consumer receptivity and trust in promotional efforts. The effect of customer loyalty on various feedback operations has been thoroughly researched, and the findings confirmed the importance of customer responsibility in terms of an organization’s implementation [35]. Moreover, follower perception of supervisory feedback has been linked to customer satisfaction and quality [36,37].

Figure 3 indicates that there are several factors for gaining customer feedback using IoT and cloud computing that utilizes a few strategies to assess the supplier’s trust level. Trust assessment through client criticism has gotten significant consideration [38]. With the fast expansion in cloud client’s criticism, it is proper to process the immense volume of input with the large information system. The topic of automated feedback for learners has been extensively investigated by several study communities. The lack of a theoretical framework that simplifies the language and compiles what must be considered to build and deliver high-quality automated feedback is symptomatic of this cacophony of jargon [39].

For further survey, we conducted a rigorous evaluation of the literature on external educational feedback and feedback automation through the authors’ respective specialist fields of educational science and computer science to design the framework. We have a large number of high-quality articles that have been chosen and thoroughly examined. Our study highlights the consequences of different feedback delivery tactics on subsequent quality, especially in the Indian context. Different technologies can be used, like IoT/AI/ML/Blockchain, for analyzing the feedback system.

3. Enabling Technologies for Feedback System of the Hospitality Industry

In this Section, we will discuss the different technologies’ integration for customer quality and satisfaction, which are main elements for obtaining the best feedback. Moreover, we have discussed each technology’s significance and application for realizing digital-based customer quality and satisfaction. Figure 4 illustrates the enabling technologies, such as IoT& cloud computing, AI, big data, Auto ML, and blockchain, with their unique features. We will also discuss the technologies used to develop the feedback monitoring and analysis system. Some technologies are merged with traditional methods to develop the system for the same purpose. IoT, cloud computing, AI [40], ML, deep learning, robotics, blockchain are used to enhance and increase the quality of services in the development of feedback systems [41,42]. A large amount of data is needed to assess AI, ML, deep learning, and big data at a larger scale to construct the feedback system [43] and to increase the interest of the customer, and for a large amount of traffic, more technologies can be used, such as edge computing, VR, and AR. Robotics are used at a lower level as compared to other technologies [44].

3.1. Internet of Things

Internet of Things aims to take this connectivity to the next level by connecting multiple devices to the internet at the same time, facilitating man–machine and machine–machine interactions [45]. In India, an IoT-based consumer feedback system has been deployed across public platforms, such as petrol pumps, to collect input on the customer experience for the cleanliness and sanitation quotient maintained on the premises [46]. When the environment reacts to an action or behavior, it is called feedback. Marketing professionals have extensively used the Kano Model is used to discover the factors that influence customer satisfaction [47], and survey audits use a limited set of predefined questions that are targeted at a specific demographic, resulting in a reasonably constrained quantity of data that are relevant in the current era of data oversaturation. Transparency and adaptability, on the other hand, are important to such sites’ image.

Figure 5 illustrates an architecture that implemented the based system in the hospitality industry for obtaining automatic customer feedback in terms of room-wise, table-wise, and front desk-wise. In the scenario of room-wise, the IoT-based kiosk device will be deployed in the outdoor room section on every floor. The customer is encouraged to give feedback regarding the room services and quality of the kiosk device. This kiosk device is wirelessly connected to the cloud server through wireless communication, IoT powered gateway.

The feedback will be logged into the cloud server. In the same manner, a smart table provides a platform to record the feedback of customers regarding the food quality, service, and ambiance of the steward. In addition to this, at the front desk, an IoT-based feedback system can be incorporated to obtain feedback from the customer in terms of the ambiance of the receptionist and add-on services while entering the hotel. All the feedback is logged in the cloud server, where the amalgamation of the feedback data from the different aspects assists in improving the service and quality of the hospitality in the future.

3.2. Big Data

Big Data is a vast accumulation of data that is rapidly growing in size [48]. It is a set of data so large and complex that no conventional data management tools can efficiently process it [49]. For the customer feedback system, big data plays an important role in analyzing the feedback. Companies can use big data analytics to evaluate a variety of complicated datasets from numerous resources to acquire valuable perceptions of customer behavior and utilize that information to increase sales and improve customer assistance [50]. The influence of big data on the customer experience is as follows: It traces customer behavior. It presents a chance for a further customized customer experience, it transforms social media into a powerful tool for improved customer assistance, and it delivers valuable feedback from all channels. Customers’ tastes and the types of products that drive them to a certain set of products handled by a company are worth investigating.

Big data analytics is used to deal with extremely large data sets that are too complex for typical data-processing software. Big data analytics is the process of collecting, organizing, and analyzing large amounts of data to find patterns and other important information for a company to employ [51]. Customer feedback on usability concerns can only result in actionable change if the problem can be replicated or diagnosed. Big data has become an invaluable tool for generating value in a business, replacing reliance on gut instinct decision-making with the ability for every team in an organization to assess and adjust based on real feedback, which can then be translated into useful consumer insights [52]. As businesses acquire more data about their customers, taking a thoughtful and customer-focused approach to evaluate that data will result in better customer service practices and, as a result, a better customer experience [53]. The use of big data to improve customer experience will become essential for any forward-thinking company. Begin utilizing big data today to position your firm for a data-driven future. In this study, the researchers have implemented text analytics to examine the experience of customers and their satisfaction and concluded that these analytics enhance the evaluation of customer satisfaction in hospitality management during customer rating [54]. Ref. [55] amalgamate the natural language processing, ML, and text mining for evaluating the online reviews revived from the customer of global chain hotels, and based on a case study, it is concluded that both disappointed and fulfilled have an inclination for stay, food, rooms, staff, and service

3.3. Cloud Computing

Cloud computing is a general term that includes conveying facilitated administrations over the web [56]. These administrations are separated into three main classes of cloud computing: Platform as a service (PaaS), Infrastructure as a service (IaaS), and Software as a service (SaaS). The three significant benefits of cloud computing are as follows: The cloud enables the transfer of some or all of the costs and effort associated with purchasing, establishing, organizing, and managing one’s on-premises architecture [57]. Instead of waiting weeks or months for IT to respond to a request, acquire and design supporting equipment, and also install programming, an organization can begin utilizing technological evolution in moments with the cloud [58]. Despite acquiring an excess limit that goes untouched throughout slow intervals, clouds are used to modify the limit in response to spikes in rush hour traffic. Users can also take advantage of your cloud provider’s global network to bring applications closer to customers all around the world [59].

Traditional word-processing tools may not be as effective as new digital technology in performing these difficult jobs [60,61]. Cloud computing plays an important role in collecting the obtained secured data using sensors from the different nodes as feedback and process for further steps [62]. The availability of servers and controllers in a cloud-based system is determined by the cloud platform on which they are running. Currently available IaaS services, such as Amazon EC2, promise 99.95% availability for each area with numerous data centers [63]. To achieve 99.9% availability, a redundancy strategy for the servers and controllers is required. Using the cloud, this is very easy to achieve the collected data and can send to the different models to train. In this process, we can use an easy method to gain customized feedback from the different deployed systems [64].

3.4. Artificial Intelligence

Artificial Intelligence is a science and innovation where it is integrated with computer science, psychology, biology, mathematics, linguistics, and engineering. A significant push of AI in the improvement of personal computer capacities related to human knowledge, including thinking, learning, and critical thinking [65]. In the past year, computer science developed and provided the basics of artificial intelligence with machine learning, which stands for data analysis and provides a great milestone [66]. Initial research has been conducted on AI-driven business models, AI-assisted organizational decision-making, and how businesses may nurture AI trust [67,68]. AI-powered tools are prevalent in enterprises, organizations, and industries, and have permeated into people’s daily lives. Visitors who have already visited are distinct from those who are on their first visit. They are familiar with and comprehend the areas they have visited. In the anticipated journey process, they are more inclined to pay for souvenirs, native items, and services [69].

They share their experience and provide feedback to the industry if they get the service accordingly and get better products and expectations. Sharing a message is a source of decision-making and future motivation for each reviewer [70]. When they share comments, they may experience a variety of feelings, such as delight, surprise, trust, joy, rage, contempt, grief, fear, and others. They may prevent others from accessing or even sharing the vacation experience if the tourism procedure is frightening [71].

Regardless, research classification data is normally composed of numerical exams that demonstrate overall customer loyalty opinions for every part of a firm. Experts begin with ordinary language to address the issue: They employ natural language processing techniques to identify client emotions associated with each category as the quantitative intermediary for the abstract representation. Using IoT devices, a useful collection was compiled from many websites, such as Tripadvisor and others. In this collection, each motel has approximately 100 evaluations [72]. Fortunately, the limitless data sharing that every gadget allows is made possible by internet algorithms that collect and manage all of the data from users’ extremely latest changes. A convolutional neural network can implement data obtained from IoT devices and further train the data sets to predict, as shown in Figure 6.

Table 1. Analysis table and findings with previous paper and article.

Reference	Conclusion	Observation/Findings
[32]	Emerging technologies used to evaluate the different business models and customer feedback	More innovations can be utilized to notice the information and cycle than for additional interaction to compute the information into summed-up input.
[37]	The different decisions can be concluded due to variation in the feedback from the customer.	IoT and ML are involved with existing methods for future design.
[41]	Different criteria depend on customer feedback	It ought to be with innovation to offer better types of assistance to the client after the past input.
[69]	Blockchain with AI and ML is used to analyze and calculate the feedback from the customer.	Various technologies can be utilized to investigate client criticism and this can be utilized for various regions.
[70]	Different platforms are available for customer satisfaction measurement and different technologies are used to measure it	IoT, ML, and deep learning ought to be incorporated for live administrations to the client.

discusses the basic observation based on some previous articles that can help to find out the gap between the technologies used for customer feedback.

3.5. Machine Learning

Machine learning is a multi-disciplinary subject, including many disciplines, counting likelihood hypothesis, insights, estimate hypothesis, arched examination, and calculation intricacy hypothesis way [73]. There are two types of machine learning to be used for different purposes [74], such as supervised learning and unsupervised learning. Supervised learning is one of the most fundamental sorts of artificial intelligence. In this sort, the machine learning calculation is prepared on named information [75]. Even though the information should be named precisely for this technique to work, regulated learning is very strong when utilized in the right conditions. The ability to deal with unlabeled data is a benefit of unsupervised machine learning [76]. This means that no human labor is necessary to make the dataset machine-readable, allowing the program to work on much larger datasets.

To know the specific opinion of the understudy criticism, text-based criticism procedure is utilized. In this printed structure, understudies are given a set of inquiries, and they need to respond to them in sentences [77]. It is useful to both the academic organization and the educator to defeat the issues connected with their organization. The understudy criticism with shifted assessment is gathered utilizing Google structures. The point is to separate articulations of assessment and order them as negative, positive, or impartial utilizing machine learning methods [78].

AI is ordered into supervised and unsupervised learning. In supervised learning, the sentences are given with marks of classes, while names are not given in unsupervised learning. Information is gathered for each question which is utilized to prepare the framework. In the study, supervised learning methods like naïve bays and SVM are considered standard learning methods, as shown in Figure 7. The input data are trained to extract the feature train and evaluate the data for final graphical representation. Thus, this scenario provides a simple and futuristic solution to develop the smart feedback system.

3.6. Blockchain and Auto ML

Client feedback is significant for the capability of an association to separate itself from contenders and to work on its items and administrations as per client inclinations [79]. Client support is a sort of knowledge-oriented work which requires amassing and reusing past information to work on the nature of client administrations [80]. Blockchain is a fast-decentralized framework and conveyed figuring worldview that has arisen with the expanding prevalence of advanced digital currencies like Bitcoin [81]. The normal blockchain type incorporates public blockchain, private blockchain, and consortium blockchain [82,83]. The object is to confine perused access or open access. Figure 8 shows a consortium blockchain is a blockchain whose agreement interaction is constrained by preselected hubs [84]. In the proposed stage, the consortium blockchain is executed to develop an information exchange climate in the client care industry [85]. The obtained information exchange climate guarantees information veracity of client information. The diagram represents the blockchain concept for workflow for customer feedback monitoring.

Automated ML (AutoML) is connected to create ML answers for the information researcher without making limitless requests on information arrangement, model determination, model hyperparameters, and model pressure boundaries [86]. AI is a field of man-made consciousness, which utilizes PCs to gain information to find designs and what is more, new information. Incredible accomplishments have occurred due to the wide scope of uses because of the accessibility of large information these days [87].

Nonetheless, creating AI models is a tedious and exorbitant cycle, which is not reasonable for SMEs. AutoML intends to robotize the time-consuming and expensive cycles given hyperparameter enhancement, combinatorial enhancement, and move learning. Hyperparameter enhancement is utilized to change the hyperparameters of the ML model. The usage of AutoML can manage an enormous amount of client administration information in a brief timeframe, which assists in managing the qualities of volume, assortment, and speed in client support information. Consequently, it is promising to coordinate AutoML with blockchain to give a financially savvy answer for accomplishing computerized client support [88].

4. Results

In this Section, we present the overall results that are obtained through the above analysis. The results are discussed with respect to each technology below:

• Relationship with a customer can teach about the nature of the services. Feedback can assist with retaining a lot of customers by identifying hidden flaws in items or administrations. Many issues are difficult to identify using traditional input methodologies and may result in customer loss. The automated feedback system, with the inclusion of different technologies, has enabled implementation of automated feedback system.
• Automated customer input in terms of room, table, and front desk. The customer is encouraged to provide comments on the room services, table, and front desk through an IoT device. All feedback is recorded in the cloud server, where the combination of feedback data from many aspects helps to enhance the service and quality of hospitality in the future.
• Customer feedback on usability concerns can only result in actionable change if the problem can be replicated or diagnosed. Big data has become an invaluable tool for generating value in a business, replacing reliance on gut instinct decision-making with the ability for every team in an organization to assess and adjust based on real feedback, which can then be translated into useful consumer insights.
• Visitors who have already visited are distinct from those who are on their first visit. They are familiar with and comprehend the areas they have visited. Sharing a message is a source of decision-making and future motivation for each reviewer through AI. When they share comments, they may experience a variety of feelings, such as delight, surprise, trust, joy, rage, contempt, grief, fear, and others. They may prevent others from accessing or even sharing the vacation experience if the tourism procedure is frightening.
• The usage of AutoML can manage an enormous measure of client administration information in a brief timeframe, which assists in managing the qualities of volume, assortment, and speed in client support information. Consequently, it is promising to coordinate AutoML with blockchain to give a financially savvy answer for accomplishing computerized client support.

5. Discussion and Recommendation

In this Section, we present the discussion related to the analysis of different technologies’ progress and significance for the realization of the automated feedback system. We found that different technologies are employed for customer feedback systems in this study. Data collecting, processing, and analysis using AI, cloud computing, ML, blockchain, and auto ML technology. It is identified that we compute and analyze client feedback based on a few parameters and draw conclusions for future results. It has been observed that many managers, employees, and customers nowadays dislike offering feedback.

• To obtain a better outcome, many technologies, such as data science, IIoT, and sensor-based technologies, can be employed to identify the actual live input from the client. It has the potential to be a significant revolution in hospitality and other industries. It will benefit customers, as well as the hotel and manufacturing businesses. Technologies always help to improve the throughput of all the developed systems for any problem.
• Advances in machine learning, the power of cloud computing, and the availability of big data have all aided AI progress in the recent decade. There can be different technologies used to develop the futuristic sustainable smart feedback system for hospitality and industry services improvement.
• The future of feedback system development needs to use the technologies for enhancement and scalability. Dedicated hardware can be designed and developed to gain the actual feedback from the customer on a large scale to improve accuracy and get better results. Wide adoption of robots needs to be integrated into the room service, room hygiene, and room air quality. Due to the pandemic, the customers are conscious and expect clean and hygienic environment in the hotel [89]. The implementation of robots with advanced computing units (AI), sensing units, and vision-based, boosts the hygienic environment in the hotel [90]. In the hotel, mobile robots can be deployed to study the behavior of the customers and also their expectations in real-time. The mobile robot needs to be customized with respect to the requirements of the hotel, where the features can also be customized by embedding different technologies [91].
• Technologies, such as AR/VR/Edge Computing, can be used to develop a real-time smart feedback system and also provide real-time visiting facilities based on AR/VR to check the real-time environment, cleanness, location, surrounding, and services of the destination. Blockchain provides the security to this type of centralized system, and edge computing dynamically used for the IoT-enabled technology means there can be developed a strong, reliable, smart, and expert feedback system for the future. The upcoming scenario is based on machine and automation, so we have to develop a system that can record the live feedback and analyze them to create the result faster for future aspects.
• Integration of blockchain technology in the hospitality business has significant benefits such as real-time monitoring, digital transactions with strong security, peer-to-peer networks, and data protection [92]. Food and beverages are a key component in the hotel industry, and they must be integrated with a blockchain network to visualize the supply chain of commodities and products [93]. Along with this, blockchain can be used to protect analytics generated based on client input. Furthermore, a circular economy can be used to manage food waste and promote sustainability [94].
• Wearable technology has rapidly gained prominence for its application in monitoring real-time emotion [95]. Employee wearable devices must be strengthened with robust wireless connectivity, and the controller in the wearable devices must be capable of doing cognitive analytics using real-time series sensor data [96]. Intelligent analytics is achieved by building a processing unit for the wearable device that is powered by machine learning, and the analytics data may then be used by researchers to further process the research [97]. Deep learning algorithms can also be used to accurately detect human emotional behavior in real time.

6. Conclusions and Future Work

In the context of the hospitality industry, resilient infrastructure based on digital technologies is crucial for obtaining the best consumer feedback on quality service delivery. Digital technology has already demonstrated its ability to improve hospitality services by making intelligent judgments based on real-time data. Advanced technologies like IoT, cloud computing, AI, and big data enables the realization of automated feedback system with intelligent decisions and insights. The study reviews the significance and application of these technologies for automated feedback systems. The study concludes with the following recommendations: Design and development of dedicated hardware to gather genuine input from customers on a big scale to improve accuracy in the future. Along with this, blockchain can be used to protect analytics generated based on client input, and deep learning algorithms on wearable devices can be implemented can also be used to accurately detect human emotional behavior in real time. Additionally, the infrastructure and ecosystem in the current environment are enhanced by the effective deployment of these technologies and their adoption in the hospitality sector. Digital twins and the metaverse are two cutting-edge technologies that allow for the enhancement and real-time representation of any new activity.