Changing Societies & Personalities, 2022 Vol. 6, No. 4, pp. 906–926 https://doi.org/10.15826/csp.2022.6.4.209 Received 21 July 2022 © 2022 Khaled Ghazy, Alena E. Fedorova Accepted 31 October 2022 Ghazyfth@gmail.com Published online 30 December 2022 a.e.fedorova@urfu.ru ARTICLE Hotel Employees’ Attitude and Acceptance Toward Human-Robot Co-Working Based on the Industry 5.0 Concept Khaled Ghazy Ural Federal University, Yekaterinburg, Russia Helwan University, Cairo, Egypt Alena E. Fedorova Ural Federal University, Yekaterinburg, Russia ABSTRACT The emergence of AI-powered machines and robotics technology has strongly influenced the service industry, especially the hotel business. The effects of their introduction to the work environment on the human workforce have been a subject of much debate. However, practical studies in this regard are relatively limited. Therefore, this study investigates the employees’ attitude and acceptance toward working alongside robots in a sample of employees working in luxury hotels in Yekaterinburg, Russia. This study employed a self-administered questionnaire as the data collection instrument. Two hundred questionnaire forms were distributed. Among them, 167 forms were completed and valid for analysis. Data were analyzed descriptively using IBM SPSS (Version 28). The findings revealed that, although the employees agreed to interact with robots and indicated that robots are helpful, enjoyable, and productive, they also pointed out that robots would control jobs. Besides, the extensive introduction of robots will lead to social issues such as losing contact with humans. The study conclusion has practical implications for hotel managers and can guide further research for academics. KEYWORDS Industry 5.0, Hotel 5.0, employee-robot co-working, artificial intelligence agents, employees’ attitude and acceptance https://changing-sp.com/ Changing Societies & Personalities, 2022, Vol. 6, No. 4, pp. 906–926 907 Introduction Keeping a position at the top is becoming more challenging and complex due to the rapid development of information technology and artificial intelligence-based solutions. Mass customization and sophisticated production are experiencing a significant transformation in the technological world. Industrial revolutions are often regarded as the driving force behind many of the world’s most essential innovations, developments, and modernity (Longo et al., 2020; Nahavandi, 2019). There are several perspectives around Industry 5.0. Some studies declare that Industry 4.0 is about smart manufacturing and connecting devices together, while Industry 5.0 is about collaboration between humans and robots in the workplace. As a result, the cooperation of humans and robots will lead to many benefits and boost the unique human touch on functions instead of the similar automation production (Demir & Cicibas, 2017; Skobelev & Borovik, 2017). In addition, Industry 5.0 reshapes human jobs to benefit employees. It maximizes efficiency by using human intelligence, artificial intelligence (AI), and creativity. Industry 5.0 may enrich the workforce by transitioning workers from physical to cognitive jobs, achieving value-added duties in the workplace alongside collaborating robots that are sensitive and aware of human desires (Longo et al., 2020; Özdemir & Hekim 2018). Moreover, Industry 5.0 places a high value on personalized consumer services (Pillai et al., 2021). There is a switch from mass customization to mass personalization, which is particularly important for meeting the needs of individual customers. Thus, Industry 5.0 may decrease work-related injuries and address value- added activities through human brainpower. Humans will concentrate on critical thinking, decision-making, innovation, and creativity, leading to increased personalized services, while robots will perform repetitive, tedious, and labor-intensive work (Alcácer & Cruz-Machado, 2019; Sarfraz et al., 2021; Verevka, 2019). Implementing new technology such as robotics, artificial intelligence, and service automation (RAISA) results in remarkable changes to how hotels serve their customers. The function of RAISA is vital since technology, service automation, and personalized services are all fundamental components of the hotel industry. RAISA presents a significant opportunity for hotel corporations to increase productivity, enhance operations, and maintain a constant level of quality (Kim et al., 2022; Lukanova & Ilieva, 2019). Service robots designed to operate in the service sectors have emerged in hotels, catering, and entertainment enterprises to assist customers and provide information on different services. Hence, high competition, a labor shortage, the need for greater efficiency and productivity, and increasing customer expectations have contributed to the rising usage of service robots (Ivanov & Webster, 2019a; Manthiou et al., 2021). ACKNOWLEDGEMENTS The researcher (Khaled Ghazy) is funded by a scholarship under the joint (executive program between the Arab Republic of Egypt and Russia) https://changing-sp.com/ 908 Khaled Ghazy, Alena E. Fedorova Service robots work as robot guides at museums and bag-drop robots at airports (Ivanov et al., 2017). Such service robots that are now being used in hotels perform several functions such as greeting customers, preparing meals, room service, taking orders, check-in and check-out operations, providing information about the destination, cleaning rooms, pools and public areas, as well as cutting grass, carrying baggage, and delivering food and other goods. Robots can work 24/7, much longer than human workers’ standard 40-hour workweek. Moreover, robots can serve many clients simultaneously and conduct this activity countless times without tiring or complaining (Zhong et al., 2021). The pace at which automation enters our lives is primarily determined by the degree of technology and people’s acceptance of robots. Since robots have a significant influence on society, it is vital to investigate the interactions between people and robots and their consequences for society as a whole (Demir et al., 2019; Lu et al., 2019). If the majority of the workforce in an organization have a negative attitude to robots, transferring to human–robot co-working arrangements will be difficult, if not impossible. Moreover, there are many positive cases regarding robots supporting human partners, even though not everyone has a positive attitude toward robots. It is vital to identify the workers’ attitudes to robots and working with them in the hotel business since the success of robot employment depends on the employees’ acceptance. Most of the studies concentrated on guests’ perceptions and examined human–robot collaboration from customers’ perspectives (Rosete et al., 2020; Tussyadiah, 2020). The studies around employee–robot interaction are relatively limited (Chi et al., 2020; Demir et al., 2019; Xu et al., 2020). Therefore, this study explores the employees’ attitude and acceptance toward robot co-working in the hotel business. Literature Review From Industry 5.0 to Hospitality 5.0 Industry 5.0 consists of several components, including the human-cyber-physical system (HCPS), Internet of Service (IoS), and Internet of Things (IoT). The first component, HCPS, combines human, artificial intelligence, and the enterprise system under a high-speed Internet framework. In order to access work objects and activities, various sensory devices are widely used, such as light sensors, touch screens, and smart devices (Zhou et al., 2019). In addition, HCPS contributes to a high level of customer service by integrating human intelligence with artificial intelligence. The hotel industry is one of the economic sectors that HCPS has a high impact on—it affects many of its aspects. The second element, IoS, focuses on cross-organizational services provided and used by supply chain members and enabled by cloud computing and big data (Alvarez-Aros & Bernal-Torres, 2021). The IoS’s core concept systematically utilizes the Internet to create new value in the service industry. IoS is known as guest service-oriented architecture in the hotel business and links IT with customer service (Pillai et al., 2021). Customer bills, room availability, and restaurant services have always been controlled at the Changing Societies & Personalities, 2022, Vol. 6, No. 4, pp. 906–926 909 organizational level. IoS offers companies the flexibility and efficiency to serve consumers better and transfer data via its channels. The systems collect information about customer needs from databases in order to use them to provide services that meet these needs and give a better customer experience. The third element, IoT, is a system of several electronic and digital components linked together to exchange information and data within a single network. IoT is utilized in the hospitality industry to keep track of clients’ comfort preferences and room settings, such as lighting, TV channels, temperature, and music, and automatically prepare the room for their next visit (Ghazy & Fedorova, 2021; Rosete et al., 2020). For the hospitality industry, the concept of Industry 5.0 is incredibly significant. Personalized service, adaptability, creative work conditions, lower costs via completely customized facilities, and accurate information on customer preferences through big data technologies and digital advancements could affect guest satisfaction, experience, loyalty, and quality of service (Shamim et al., 2017). The primary concept of Industry 5.0, including interoperability, virtualization, modularity, decentralization, and real-time functions, can be transferred to the hospitality business, leading to hospitality 5.0 (Pillai et al., 2021). Artificial Intelligence Agents in the Hotel Business Presence and embodiment are considered the two dimensions of artificial intelligence agents that can be categorized into three kinds (Li, 2015). Smart devices Smartness relates to the interconnection of intelligent components within a single network to receive and process data and add value to the user’s system. In addition, smart devices can change their functions automatically according to external conditions (Buhalis & Leung, 2018). There are a variety of smart devices, such as thermostats, lighting, security cameras, and speakers. Customers could easily adjust the settings inside the room, such as selecting a desired temperature and lighting level, using an intelligent hub, tablet device, or other central control points. The heating, lighting, air conditioning, and other facilities can automatically adapt and maintain those settings, offering a delightful guest experience (Tung & Law, 2017). Chatbots Another kind of AI is the chatbot, which can simulate human interaction and actively communicate with customers (Chi et al., 2020). In addition, chatbots are virtual, disembodied, technological, and autonomous conversational agents that can understand and communicate via human language through Natural Language Processing (NLP) in voice or text connections (Tussyadiah, 2020). Thus, chatbots have been employed in various service sectors, including tourist recommendations, medical consultations, and hotel reservations (Parmar et al., 2019; Ukpabi et al., 2019). Virtual assistants Siri (Apple), Alexa (Amazon), and Edward (Edwardian Hotels) are forms of AI-driven chatbots. Edward can lead travelers over their whole trip adventure in a standard, ordinary conversation, and it develops and learns from each contact https://changing-sp.com/ 910 Khaled Ghazy, Alena E. Fedorova (Buhalis, & Leung, 2018; Castillo et al., 2020). Edward handled 69% of all visitor inquiries in 2019, succeeding in greater efficiency and reallocating employees from routine activities to more significant inquiries. Consequently, because of the developed machine learning, clients may be unaware that they are communicating with an AI service device in a virtual condition (Tussyadiah, 2020). On the one hand, chatbots have some benefits versus human service representatives, including the capacity to conduct virtually enormous numbers of orders simultaneously, save massive amounts of data, and are less vulnerable to mistakes, stress, or emotional fluctuation (Chi et al., 2020). Chatbots, on the other hand, have two main weaknesses. Firstly, an ethical concern arises when individuals think they are communicating with a real person when they are actually speaking with software. Secondly, when companies use chatbot-based customer service instead of real staff, clients may feel that they are being devalued by the firm which they are doing business with (Blut et al., 2021). Cobots (Collaborative Robots) As a result of the rapid development of AI and innovation, it is now apparent that all devices equipped with computing capability have become more competent and have launched a new technology known as cobots. Cobots, or collaborative robots, are designed for direct human–robot interaction in a shared environment or when robots and people are in close contact (Maddikunta et al., 2022). Cobot applications encourage human engagement with robots, unlike traditional industrial robotic uses that keep robots isolated. Cobots’ safety may depend on soft edges, lightweight materials, speed and force limitations, or software and sensors that maintain safe behavior. Cobots are very sensitive to unexpected impacts, allowing them to stop on their own when human employees identify any misplaced items on their route. In most cases, this makes them much more reliable than traditional industrial robots (Bender et al., 2016; Simões et al., 2020). Generally, two major categories of robots are recognized by the International Federation of Robotics (IFR), which are industrial robots performing a wide range of industrial processes and service robots for personal and professional purposes. Service robots are cobots because they are designed to operate alongside humans (Haegele, 2016). In addition, service robots are intended to assist humans with functional tasks by interacting with them physically and socially. Service robots are classified into two categories: personal used by individuals for non-commercial activities, such as domestic servant robots, automated wheelchairs, pet exercise robots, and personal mobility aid robots and professional used by businesses for commercial purposes, such as delivery robots in offices, cleaning robots for public areas, fire-fighting robots, rehabilitation robots, and surgery robots (Ivanov et al., 2017). Robotic technology and service automation activities have impacted several aspects of hotel operations. Self-service terminals have been launched in hotels to minimize the need for front-desk staff and allow guests to perform check-in and check-out processes without help (Ivanov et al., 2017). Check-in/out procedures have been accessible through smartphones for increasing speed and convenience. Changing Societies & Personalities, 2022, Vol. 6, No. 4, pp. 906–926 911 Robots are increasingly being used in the hotels and travel sectors to perform various functions, including delivering food and other items, cleaning purposes, carrying luggage, and providing support and information (Yadav, 2020). Several hotel companies have recently utilized service robots to offer dependable, convenient, entertaining, and efficient service, creating unique guest experiences. For instance, at the Japanese Henn na Hotel, founded in 2015, robots were hired as staff members and hotel porters carrying the human staff functions (Alexis, 2017). Bilingual robots can greet tourists, assist them, and collect guest complaints, demands, and requests. As the first hotel to employ robots, it was awarded a Guinness World Record for having employed a total of 186 robots (Mende et al., 2019). Similarly, Connie, a robot, worked at the Hilton McLean Tysons Corner. It served guests with helpful information at reception. Aloft Hotel Cupertino hired a servant robot to deliver soap, towels, shampoo, and linens to rooms and bring the dirty bed sheets to the laundry. In addition, The Housekeeping Robot, created by Peanut Robotics in collaboration with RLH Corporation, assists a human housekeeper with additional housekeeping tasks such as collecting linens and cleaning bathrooms (Choi et al., 2020). The housekeeper may use the cleaning robot’s mobile application to start the room cleaning procedure from anywhere, allowing it to vacuum one room while they clean another (Wirtz, 2018). The startup Dishcraft company focuses on designing dishwasher robotics for restaurant kitchens. Other robots do not communicate with guests but are used to perform repetitive duties. For example, the Caliburger robot, which prepares hamburgers and sets food on dishes, and the robot arm that works as a bartender at Royal Caribbean (Tussyadiah, 2020). The rapid evolution and use of digital technology, particularly robots, is a priority for the Russian government. In 2017, the Russian government launched the “Digital Economy of the Russian Federation” program, which includes various robotics-related projects. For instance, the Russian Ministry of Trade and Industries is assisting the Russian Association of Robotics (RAR) in developing a plan for the growth of the Russian robotics industry. Besides, the government aims to introduce the legal aspects of robotics to stimulate this industry’s progress by reducing legal issues. Meanwhile, Russia has about 100 service robot organizations. In 2018, the top ten companies had 30% workforce growth and doubled annual revenue (Khalimon et al., 2018; Konuikhovskaia, 2019). During the pandemic, the robotics firm Promobot, located in the Ural town of Perm, invented various important robots, such as a remote temperature measurement robot, which can test temperature and other essential indications in less than 5 seconds. The company also created the Scorpion remote-controlled sanitizing robot, which can navigate obstacles, including stairs and corners, by integrating GPS/GLONASS and ultrasonic sensors. The robot has a spray device, and a container holds a disinfecting liquid (Five handy robots, 2020). Russia is also starting to use drones and robotic technologies in the hospitality industry. At the Cosmos Group hotel chain in the Emerald Forest, located near Moscow, Yandex began testing drones and rovers. The hotel occupies an enclosed area of 220 hectares and is a good platform for testing modern technologies. Drones take guests https://changing-sp.com/ 912 Khaled Ghazy, Alena E. Fedorova around the territory, and rovers deliver food to the room (Maslenko, 2021). Kravt Invest, an IT and high-tech company, launched the first robotic hotel on the territory of the scientific town of Innopolis near Kazan. Hotel devices can use AI to identify the guest’s feelings through images, sounds, or videos, assess his tiredness, and make assumptions about his mood. Based on this, the system adjusts the temperature and light in the room and offers tea or coffee and snacks. In addition, it is planned to transfer cleaning, delivery of luggage, and cooking to robotic solutions (Malakhova, 2020). In 2019, the Promobot Russian robot, Mikhalych, became an employee of the Park Hotel in Dobrograd. Its responsibilities include greeting guests, assisting with check-in and check-out procedures, and issuing room key cards. In addition to providing guests with information, the robot can arrange transportation for them. Mikhalych serves as an extra marketing tool, and it reminds guests of any events hosted in the restaurants and invites them to reserve a table. Mikhalych makes the registration process easier and faster while significantly decreasing the hotel staff’s workload. Promobot operates not just in Russia but also across 36 countries worldwide. It works as a promoter, administrator, guide, and concierge (Kak rabotaet pervyi v Rossii, 2020). Although automation and robotics are becoming increasingly widespread in the hospitality industry, the employment of service robots to perform human tasks may be resisted by society. Studies indicate that the reasons for this are the loss of human contact, in addition to fears of an increase in the unemployment rate in society and the dominance of machines in the labor market (Hou et al., 2021; Meidute-Kavaliauskiene et al., 2021). Service robots that replace human employees might add a psychological issue to the standard concept of service. For example, human interaction with a robot may be avoided if it causes anxiety. In the context of the hotel business that depends primarily on personal contact, replacing human employees with robots would change the style of the experience by adding human–robot interactions and affecting the user’s attitudes and behaviors. Besides, technological anxiety is regarded as a significant psychological factor influencing the adoption of new technologies (Huang et al., 2021; Li et al., 2019). Even though there are many positive cases regarding robots supporting human partners, not everyone has a positive attitude toward robots. Robots are less likely to be used if people have negative views about them. Attitude is recognized as an individual’s acquired judgment of a positive or negative emotion regarding service robots, which influences the individual’s intentions and behavior. Attitudes develop due to complex psychological processes and act as motives to behavioral responses. They are crucial considerations in determining if robots are adopted (Cha, 2020; Pozharliev et al., 2021; Tussyadiah et al., 2020). As a result of his study, Yu (2020) found that potential consumers’ attitudes regarding human-like robots are likely to be unfavorable. Conversely, Tung and Au (2018) explored consumer evaluations provided on the websites TripAdvisor, Yelp, Agoda, and Booking.com in order to evaluate user experiences of robotic hotels. The findings highlighted the impact of automated service on customer experience, and Changing Societies & Personalities, 2022, Vol. 6, No. 4, pp. 906–926 913 customers were proactively seeking chances to communicate and interact with robots to create a relationship with them. Similarly, Ivanov and Webster (2019b) performed another study to investigate users’ perceived effectiveness and feelings about using robots in tourism. The findings indicated that housekeeping, handling inquires, reservations, and payment services are the best-suited duties and operations for robot employment. Using a mixed-methodology approach, Ivanov et al. (2020) examined the opinions of Bulgarian hotel managers on the strengths and weaknesses of service robots, as well as the effects of service robots on the different departments of the hotel. The study indicated that repetitive, tedious, and risky duties would be better suited for robots. In contrast, managers prefer to use humans for jobs requiring emotional intelligence and social skills (Ivanov et al., 2020). As previously mentioned, most of the studies concentrated on guests’ perceptions and examined human–robot collaboration from customers’ perspectives. The studies around employee–robot interaction are relatively limited (Chi et al., 2020; Ivkov et al., 2020; Li et al., 2019). Thus, employing AI robots as service agents requires essential consideration of not only cost, technology, or quality of service but also of the attitudes and acceptance by human employees. Attitudes and Demographics Previous research has demonstrated that socio-demographic and individual variables such as gender, age, and level of education have a significant role in service robot acceptability. In a study examining gender differences, Nomura et al. (2006) revealed that men and women had different levels of negative opinions about robots. The results showed that female participants reported fewer negative opinions regarding robots than male participants. However, de Graaf and Ben Alouch (2013) discovered the opposite behavior, concluding that females experience more nervousness than males while engaging in conversation with robots. Furthermore, de Graaf and Ben Alouch (2013) found that females’ negative opinions increased after interacting with a robot, while males’ opinions remained the same. According to Loffredo and Tavakkoli (2016), men and youth respondents were more interested in the concept of robots than women and older participants. Besides, Hudson et al. (2017) analyzed Eurobarometer statistics on around 1000 individuals in each European country to examine public views toward robots utilized in elderly care. According to the results, age, gender, and educational level are significant independent factors connected with people’s views on using robots to care for the elderly. The study reveals that males, younger, and higher-educated people are more supportive of including robots to assist the elderly. Therefore, the following hypotheses have been developed: Hypothesis 1 (H1): There is a significant difference between employees’ men and women regarding their attitudes toward robots. Hypothesis 2 (H2): There is a significant difference between employees’ age groups regarding their attitudes toward robots. Hypothesis 3 (H3): There is a significant difference between employees’ educational levels regarding their attitudes toward robots. https://changing-sp.com/ 914 Khaled Ghazy, Alena E. Fedorova Methodology Sample and Data Collection A self-administered questionnaire was distributed between employees of the luxury hotels in Yekaterinburg, Russia, to investigate their perspectives regarding the topic of the study. The study sample consists of 20 hotels determined by the convenience sampling approach. A convenience sample is a collection of people chosen based on their accessibility. The minimal sample size required should be 7 to 10 times the number of scale items analyzed (Floyd & Widaman, 1995; Tantawy et al., 2016). Accordingly, there were 200 questionnaire forms distributed between employees of these hotels. Among them, 167 forms were valid for analysis, representing 83.5%. The questionnaire distribution took two months (August and September 2021). Besides, the study concentrated on Five- and Four-star hotels because innovation and modern technologies are implemented first in luxury hotels. The researchers explained the concept of robots to the employees, and then they were able to answer the questionnaire. The questionnaire was translated into Russian because some staff members do not speak English. Measures and Pretest The instrument used in the current study was divided into three groups of questions; the first group contained items regarding the changes that occurred in the workplace. In the second group, there were questions to examine the study variables—Psychological Attitude (PA), Social Attitude (SA). The third group addressed demographic categories, including gender, age, and educational level. The questionnaire was adopted from previous research (Bröhl et al., 2016; Nomura et al., 2004; Weiss et al., 2008), which has been validated. The participants were questioned to indicate their level of agreement with the statements according to the Likert scale with five points ranging from “1 = strongly disagree” to “5 = strongly agree”. The questionnaire was pretested using data obtained from ten employees through the convenience sampling technique. Participants were specifically asked to identify any misunderstandings in the questionnaire’s phrasing. The feedback received from these participants was used to further improve the tool. The adjusted instrument contained 22 elements. Data Analysis The data analysis was processed as follows. First, Cronbach’s alpha coefficients were applied to test the internal reliability of these varied Likert-type scales and questions. Second, a descriptive data analysis was conducted to explore the attitude and acceptance of hotel employees toward human-robot co-working. In addition, the Mann–Whitney U test was used to check the differences in employees’ gender regarding their attitude toward robots. The Kruskal–Wallis χ2-test was used to check the differences in employees’ age groups and educational levels regarding their attitude toward robots. The results obtained from the valid questionnaire forms were statistically analyzed whenever needed using IBM SPSS (Version 28). Changing Societies & Personalities, 2022, Vol. 6, No. 4, pp. 906–926 915 Results Table 1 represents the demographic variables of the respondents. Table 2 shows the employees’ responses to the changes in their workplaces due to robotics. As displayed in Table 2, a notable percentage of the answers indicated reducing the number of employees (69.4%), followed by transferring part of jobs to distance work (28.1%) and using temporary workers provided by other companies (23.3%). In contrast, the lowest percentage indicated the complete dismissal of workers (10.8%), and no changes (8.3%). Employees added that among the changes in the workplace are the continuous adjustments of work tasks and responsibilities. Table 1 Participants’ Demographic Characteristics Demographic data Freq. % Gender Male 65 38.9 Female 102 61.1 Age 25 years or younger 81 48.5 From 26 up to 35 53 31.7 From 36 up to 50 24 14.4 Over 50 9 5.4 Education Secondary School Education 28 16.8 University or Higher Institute Degree 135 80.8 Postgraduate Degrees (Masters or PhD) 4 2.4 Other – – Note. Source: Developed by Authors. Table 2 Responses to the Question: What Changes Have Occurred in Your Organization Due to the Introduction of Collaborative Robots? Answer alternatives Freq. % The introduction of collaborative robots involved the dismissal of employees 18 10.8 Partial reduction of the number of employees 116 69.4 Transferring some of the employees to distance work 47 28.1 The use of temporary or seasonal workers provided by other companies 39 23.3 Hiring new employees only on a short-term basis (from 1 to 6 months) 24 14.4 There were no changes 14 8.3 Other 28 16.7 Note. Source: Developed by Authors. https://changing-sp.com/ 916 Khaled Ghazy, Alena E. Fedorova Table 3 illustrates the results of the descriptive analysis of the psychological and social variables. First, Cronbach’s alpha coefficients were applied to test the internal reliability of the varied Likert-type scales and questions. The Cronbach’s alpha coefficient results all passed the acceptable recommended value of 0.7. The findings show that the scores of psychological variables ranged from 2.00 to 3.92 on a five-point scale. The items with the highest average were “I think that in the future, society will be dominated by robots” and “If robots had feelings, I would be able to make friends with them”; while the lowest average item was “I feel nervous operating a robot in front of other people.” In addition, scores of social variables ranged from 2.87 to 3.98 on a five-point scale. The items with the highest average were “I fear that I may lose my work due to the robot” and “I fear that I lose contact with my colleagues because of the robot.” While the lowest-mean item was “I have no objection if the robot stores my personal information.” Table 3 Analysis Results Scale Item Mean Standard Deviation Cronbach’s Alpha Psychological attitude 0.752 I feel uneasy if robots really have emotions 2.23 0.588 Something terrible might happen if robots developed into living beings 3.33 0.575 I feel relaxed dealing with robots. 3.77 0.744 If robots had feelings, I would be able to make friends with them 3.88 0.631 I would feel comforted being with robots that have emotions 3.66 0.578 I feel nervous operating a robot in front of other people 2.00 0.329 I feel nervous just standing in front of a robot 2.08 0.538 I think that if I depend on robots too much, something terrible might happen 3.75 0.644 I feel paranoid talking with a robot 2.09 0.605 I think that in the future, society will be dominated by robots 3.92 0.634 Social attitude 0.781 In general, my organization supports the use of robots 3.83 0.736 People in my hotel who use robots have more prestige than those who do not 3.86 0.491 The use of the robot is appropriate to my various job-related tasks 3.92 0.396 The robot’s output is of excellent quality 3.90 0.428 I find using a robot enjoyable 3.81 0.431 I fear that I will lose contact with my colleagues because of the robot 3.93 0.655 I have no objection to sharing a workstation with a robot 3.80 0.428 I have no objection if the robot stores my personal information 2.87 0.447 I fear that I may lose my work due to the robot 3.98 0.394 I feel safe while using the robot 3.03 0.354 I can operate the robot if someone teaches me how to use it first 3.76 0.379 Operating the robot enhances my job performance 3.89 0.431 Note. Source: Developed by Authors. Changing Societies & Personalities, 2022, Vol. 6, No. 4, pp. 906–926 917 To evaluate the difference between employees’ men and women regarding their attitude toward robots, the Mann–Whitney U test was utilized. Table 4 shows no statistically significant difference between men and women regarding psychological attitude (p-value = 0.547) and social attitude (p-value = 0.942). Hence, H1 is not supported. In addition, the Kruskal–Wallis test was used to evaluate the differences among the employees’ age groups. Table 5 reveals a statistically significant difference between employees’ age groups regarding psychological attitude (p-value = 0.045) and social attitude (p-value = 0.027). Hence, H2 is supported. The results showed that employees aged 25 or under had the least negative attitude toward robots (M = 54.20) regarding their psychological attitude considering that the psychological scale is negative. Besides, the results showed that employees over 50 had the least positive attitude toward robots (M = 40.11) regarding their social attitude (Table 5). Furthermore, the Kruskal–Wallis test was used to evaluate the differences among the employees’ educational levels regarding their attitudes toward robots. Table 6 shows no statistically significant difference between employees’ educational levels regarding psychological attitude (p-value = 0.666) and social attitude (p-value = 0.529). Hence, H3 is not supported. Table 4 The Difference Analysis Between Men and Women No. Construct Gender N Mean Rank Mann–Whitney U Z Asymp. Sig. (2-tailed) 1. Psychological Male 65 81.22 3134.000 –.602 .547 Female 102 85.77 2. Social Male 65 83.68 3294.000 –.072 .942 Female 102 84.21 * Significant (p ≤ .05) Note. Source: Developed by Authors. Table 5 The Difference Analysis Between Employees’ Age Groups No. Construct Employees Age N Mean Rank Chi–Square DF Asymp. Sig. 1. Psychological 25 years or under 81 54.20 4.092 3 .045* From 26 up to 35 years 53 80.16 From 36 up to 50 years 24 94.19 Over 50 years 9 98.67 2. Social 25 years or under 81 90.21 9.219 3 .027* From 26 up to 35 years 53 85.05 From 36 up to 50 years 24 83.21 Over 50 years 9 40.11 * Significant (p ≤ .05) Note. Source: Developed by Authors. https://changing-sp.com/ 918 Khaled Ghazy, Alena E. Fedorova Table 6 The Difference Analysis Between Employees’ Educational Levels No. Construct Level of Education N Mean Rank Chi–Square DF Asymp. Sig. 1. Psychological Secondary School 28 78.73 8.763 2 .666University Degree 135 84.32 Postgraduate Degrees (Masters or PhD) 4 70.13 2. Social Secondary School 28 81.00 5.207 2 .529University Degree 135 89.12 Postgraduate Degrees (Masters or PhD) 4 79.25 * Significant (p ≤ .05) Note. Source: Developed by Authors. Discussion and Conclusions This study aimed to explore hotel employees’ psychological and social attitudes toward working side by side with robots. Service robots have joined our lives as a result of technological advancement and industrial revolutions. Among their applications are augmented reality and intelligent devices working with AI, which significantly affect economic and social life. Consequently, companies are working to keep pace with the external environment’s development to obtain a competitive advantage and provide a unique service that receives customer satisfaction. In addition, the hotel business is one of the industries affected by technological developments. The success and effectiveness of implementing new technology in companies depend on employee acceptance and involvement. The adoption of service robots in business, especially in service sectors, is still in its preliminary stages and is considered a significant innovation in literature. Therefore, studies into this topic need to be conducted from different perspectives. The study results showed a partial reduction in the number of hotel workers due to the introduction of collaborative robots. This finding agreed with Manyika et al., (2017), who indicated that one of the most significant points in the literature is the prediction that robots will replace people or decrease the number of jobs and employees. According to the study results, employees feel threatened and worry about the future of their work and about losing jobs due to automation and digitalization process. Many studies estimated that by 2030, between 400 and 800 million of today’s occupations would be automated (Bowen & Morosan, 2018). Robotic technology and service automation have impacted several aspects of hotel operations. Self-service terminals have been launched in hotels to minimize the need for front-desk staff and allow guests to perform check-in and check-out processes without help (Ivanov et al., 2017). Check-in/out procedures have been accessible through smartphones for increasing speed and convenience. Robots are increasingly being used in hotel and travel sectors Changing Societies & Personalities, 2022, Vol. 6, No. 4, pp. 906–926 919 to perform various functions, including delivering food and other items, cleaning purposes, carrying luggage, and providing support and information (Yadav, 2020). The demographic analysis revealed no statistically significant difference between employees’ men and women regarding their attitude toward robots. This result differs from previous studies that indicated gender differences toward robots. According to de Graaf and Ben Alouch (2013), female respondents are more anxious regarding robots than male respondents. A possible explanation for this finding is that the COVID-19 pandemic has changed people’s attitudes toward technology and innovation (Barrutia & Echebarria, 2021; Zhong et al., 2022). In addition, the finding represented a statistically significant difference between the employees’ age groups. The results showed that employees aged 25 or under had a less negative attitude toward robots, whereas employees over 50 had a less positive attitude toward robots than other age groups. This finding agreed with Loffredo and Tavakkoli (2016), who indicated that young people are more interested in the concept of robots than older people. Moreover, the study showed no statistically significant difference between employees’ educational levels. This result is consistent with the previous research (Ivanov et al., 2018), which showed that respondents’ level of education had no impact on their attitudes toward robots. This research contributed to the current literature by providing supportive results from the Russian staff perspectives. On the one hand, employees agreed to interact with robots and indicated that they are helpful, enjoyable, and productive; robot employment would save time by taking over routine tasks and workload. On the other hand, employees fear that robots will control jobs, and the extensive introduction of robots will lead to social issues such as losing contact with humans. We recommend that hotel managers create new roles for employees, which require new skills and competencies in order to make human labor more valuable than that of robots and keep pace with the continuous development in the work environment. On the other hand, courses on technology advancements and artificial intelligence must be included in the curriculum across all education levels, where future leaders and workers are trained and developed. These courses will prepare students to join the new work environment and effectively deal with future challenges. Moreover, since service robots are helpful for the hotel business, hotel managers must provide incentives to older employees to motivate them to engage with robots. This study has some limitations that might serve as a guide for further research. Firstly, since the field data were collected from a particular geographic region of one country, future research should incorporate data from other countries with various economic and social backgrounds to increase the generalizability of the results. Secondly, this study collected empirical data using a questionnaire method. Future research might consider other methodological methods such as interviews or observation of daily human–robot interaction in order to go in-depth and identify different themes around the topic. Finally, artificial intelligence research is still in the primary stage, especially in the service sector. 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