Consumers’ Ethical Perceptions of Autonomous Service Robots in Hotels

Abstract

This study empirically and comprehensively explores consumers’ ethical perceptions of autonomous service robots (ASRs) in hotels. Under the triangulation approach, this study has identified eight themes of consumer perceived ethical issues (privacy, security, safety, transparency, fairness, socialization, autonomy, and responsibility). Each theme can be explained from two dimensions: ethical issues arise during the interaction (i.e., ubiquitous surveillance, excessive data, unidentified risks, service disclosure, inaccessibility, dehumanization, selection of services, and service recovery), and ethical issues can be raised by the characteristics of ASRs (i.e., privacy infringement, malicious use, malfunctions, untrustworthiness, biased features, job replacement, inflexibility, and self-identified solutions). This study is the first to propose ethical issues of ASRs from two dimensions with different intelligence levels, and to highlight ethical issues during hotel service interactions. The findings contribute to ethics studies of service robots from consumers’ perspectives and offer managerial insights to reduce ethical concerns and enhance ASRs usage in hotels.

Keywords

autonomous service robots consumer perceived ethical issues data triangulation robot ethics service interaction

Highlights

This study contributes to how consumers perceive the ethics of service robots in hotels.

The study explores empirical evidence of consumer perceived ethical issues regarding service robots.

Ethical issues arise during high and low interaction levels with autonomous service robots.

Ethical issues are raised because of the mechanical and thinking features of intelligent robots.

Introduction

In recent years, we have seen a rising trend of integrating artificial intelligence (AI) with robotics into various service-oriented industries, especially in hospitality and tourism (Buhalis et al., 2019). The global market value of service robots was $16.95 billion in 2021 and is predicted to grow to $57.35 billion by 2029 (Fortune Business Insights, 2022). These innovations are projected to attract heavy demand from the hospitality and healthcare sectors and are expected to grow by $941.58 million from 2020 to 2024 (Technavio, 2020). Recent studies have argued that the outbreak of COVID-19 has accelerated the penetration of service robots into the hospitality industry (DeMicco et al., 2021; Kim et al., 2022).

The concept of autonomous service robots (ASRs) is employed in this study to highlight their autonomy feature, referring to AI-based service robots that can actively collect, store, transmit, and analyze data to directly serve consumers without human intervention (Wirtz et al., 2018). However, AI advancements bring a set of ethical concerns to the forefront. Past ethics studies of AI and robotics mainly focus on the moral behaviors of humans who design, operate, and use AI and robots, but also on AI’s characteristics that involve the entire decision-making process (Siau & Wang, 2020). Despite the growing literature on the ethics of AI and robotics (Khan, 2020; Shiwen et al., 2022; Zemke et al., 2020), ethics studies about specific AI applications remain scarce. Only a few review papers theoretically highlight the importance of ethical research on service robots (Chi et al., 2020; Koseoglu et al., 2022; Lu et al., 2020), so no known studies of ASRs provide empirical evidence, especially in the tourism and hospitality industry.

The ethical study of ASRs is more complicated than that of AI because ASRs consist of the features of AI and the functions of service. Hence, we argue that the ethical issues of ASRs can possibly arise not only during interactions with ASRs but also from the characteristics of ASRs. Regarding specific ethical issues of ASRs, prior studies have mainly focused on ethical issues related to features of AI and robotics, such as information privacy, data security, and transparency (Lu et al., 2020; Tussyadiah et al., 2020). However, the ethical issues of ASRs during service interactions fail to gain sufficient attention, such as dehumanization and fairness (Chi et al., 2020; Wirtz et al., 2018). Therefore, more empirical research is needed about the ethics of ASRs, especially targeting ethical issues that arise during service interaction. Because consumers directly interact with and evaluate ASRs, exploring consumers’ ethical perceptions towards ASRs is imperative—which refers to the degree to which individuals recognize the issues as ethically problematic during ASR services (Ying et al., 2022). The insights are vital to understanding consumers’ ethical perceptions and appropriately implementing ASR services in hotels.

Huang and Rust (2021) proposed three levels of AI: mechanical, thinking, and feeling. Mechanical intelligence is the lowest level that can limit robots’ ability to perform simple, standardized, and repetitive work. Thinking intelligence is relatively higher because it follows rule-based learning and performs complex, systemic, and predictable tasks. Feeling intelligence is the highest level of AI that can be used to complete robots’ emotional and humanlike interactive tasks. Given the different levels of AI, ASRs with distinct intelligence perform various tasks and have different levels of interactions with consumers. However, feeling AI currently can barely read and react to individuals’ emotions, so emotional demands remain the territory of human employees.

For these reasons, current ASRs can be classified into two groups. ASRs with mechanical intelligence (e.g., robot bartenders and delivery robots) have low-level interaction with consumers to perform relatively simple hotel services. The other ASRs with thinking intelligence (e.g., chatbots and robot concierges) have highly interactive functions to communicate with consumers and provide complex services. Previous ethics studies do not separate the high and low intelligence ASRs. Therefore, this study aims to empirically and comprehensively explore the consumer perceived ethical issues of ASRs in hotels via three data collection steps (i.e., semi-structured interviews, focus groups, and on-site interviews). Through exploration of and discussion of consumer perceived ethical issues of ASRs with different levels of intelligence, this study can provide an overall understanding of consumers’ ethical perceptions regarding ASRs. To our knowledge, this study represents the first empirical study that addresses the ethical issues of ASRs from consumers’ perspectives in the context of hotels and highlights the ethical issues that arise during service interactions. These findings begin to fill a gap surrounding the ethics of ASRs from a consumer perspective. Empirical evidence identifies ethical issues of ASRs when it comes to different levels of ASR service interactions in hotels. The results also offer managerial insights for hotel managers to reduce consumers’ ethical concerns and increase acceptance and usage of ASRs.

Literature Review

Ethics and Ethical Theory

Scholars conceptualize ethics from multifaceted perspectives, so no universally recognized definition exists. From the psychological aspect, ethics refers to a capacity to think critically about values that direct individual actions (MacKinnon & Fiala, 2014). Ethical judgment refers to an individual evaluation of how a behavior is ethical or unethical based on personal values (Hopkins & Deepa, 2018). Thus, ethics studies focus on individual perspectives to recognize ethical judgment and the impact on personal decision-making in a particular situation. Personal values are largely internalized from life experience, family, society, religion, law, and workplace settings, so people may make ethical judgments differently in various contexts (Hopkins & Deepa, 2018). Given the above aspects, this study adopts the psychological conceptualization of ethics to understand consumers’ ethical perceptions of the wrongness regarding ASR services by uncovering consumer perceived ethical issues of ASRs in hotels.

The dominant approach for ethics studies is normative ethics, which attempts to identify the ethical theory to explain individuals’ ethical judgments and guide what humans ought to do (Michaelidou et al., 2021). Two traditional ethical theories are teleology and deontology (Fennell & Malloy, 1995). Teleology emphasizes that an action is ethical if it leads to the best possible balance of maximizing positive outcomes and minimizing harm (Kimmel, 1988). However, when ethical decisions benefit the majority at the expense of the minority, the minority’s rights may not be considered, making the action unethical (Des Jardins & McCall, 2014). In addition, deontology focuses on ethical motivations instead of the results of actions in teleology, stating that an act could be considered suitable if it follows the moral duties that are essential, absolute, and applied to everyone equally (Kimmel, 1988). Deontological theories frame people’s responsibilities and rules, such as human dignity and civil rights. However, critics argue that ethics is inadequate concerning handling particular obligations and the possibility of conflicts of different duties in our lives (Des Jardins & McCall, 2014).

In hotels, consumers can engender diverse ethical concerns regarding various ASRs in multifaceted ethical dilemmas. These ethical theories can offer a conceptual foundation to reflect consumers’ ethical perceptions of ASRs by comprehensively exploring consumer perceived ethical issues regarding the different roles of ASRs in hotels.

Ethics of AI and ASRs

The ethics studies of AI mainly focus on two streams. Foremost, many institutions and governments formulate AI frameworks to guide technological development and use. For example, the ethical guidelines for trustworthy AI put forward seven requirements: (1) human agency and oversight, (2) technical robustness and safety, (3) privacy and data governance, (4) transparency, (5) diversity, non-discrimination, and fairness, (6) societal and environmental well-being, and (7) accountability (European Commission, 2019). Since there is overlap across the global AI ethical principles, Jobin et al. (2019) summarized consensus on five guidelines: transparency of algorithms, justice of results, non-maleficence of human rights, responsibility of actions, and data privacy. These ethical principles can serve as a valuable structure for securing the ethical outcomes of AI and robotics.

The second dimension investigates the potential ethical issues of AI. Müller (2020) outlined the ethical issues of using AI and robotics systems, including privacy and digital surveillance, manipulation of information, opacity of AI systems, bias in decision systems, automation and employment, and rights and responsibility of artificial agents. Additionally, Siau and Wang (2020) categorized three dimensions: technical features, human factors, and social impact. Specifically, the technical features may cause three ethical issues: transparency, data security, and privacy. AI algorithms, even for experts, take a long time to understand. Misusing data and infringing privacy can increase the risks of using AI (Tussyadiah et al., 2020). The moral behaviors of how humans design, construct, use, and treat AI systems can raise ethical issues. Human bias, such as gender and race bias, may integrate into the AI systems, resulting in unethical outcomes and potential discrimination. The social impact of AI mainly focuses on technological job replacement, as scholars have debated whether AI leads to workforce disruptions (Wirtz et al., 2018). With the advancement of AI, a higher level of intelligence has arisen more attention to technical restrictions and moral arguments (Huang & Rust, 2021). Thus, ASRs with higher intelligence may generate more ethical issues.

ASRs have been applied in various industries, and the relevant ethics studies are still in their infancy because different scenarios pose specific ethical issues. For example, in the context of the military, Müller (2020) argued that an autonomous system of weapons leads to the significant ethical problem of responsibility when weapons are not controlled. Autonomous military robots may perform duties like spying to intrude on citizen privacy and anonymity (Belk, 2021). Similarly, in the healthcare industry, Körtner (2016) concluded that the most important ethical issues are privacy and data protection, safety and responsibility, the involvement of vulnerable persons, and deception. Müller (2020) addressed the issues of dehumanizing care for patients, which means care robots perform only in a behavioral sense, rather than offering real human feeling care. In the context of ASRs adoption in the service industry, Wirtz et al. (2018) explicitly classified ethical concerns from the micro individual-level (e.g., privacy and security and dehumanization), meso market-level (e.g., winner-takes-all and liability regimes), and macro societal-level (e.g., employment and inequality within and across societies).

For consumers, several ethical issues of ASRs could arise during service interactions. If informational privacy and security are not appropriately protected, cybercriminals can steal sensitive information (e.g., ID and credit card) for illegal activities (Wirtz et al., 2018). Reviews from Henn-na Hotel describe service robots as cold, eliminating a feeling of welcome (Tung & An, 2018). If consumers are hurt during the ASRs services, liability concerns surrounding who is responsible remain a debated argument (Leo & Huh, 2020). Accessibility is another unclear issue: Are ASRs available and suitable for vulnerable people (e.g., the elderly or disabled) (Siau & Wang, 2020)? Ethical concerns range from privacy and security risks and algorithm-based decision making to dehumanized feelings, and the immense amounts of data generated by AI-governed services (Lu et al., 2020). ASRs with different intelligent levels have been shown to have distinct impacts on consumers’ emotions (Schepers, 2022). Therefore, consumers can generate different ethical issues regarding the various roles of ASRs in hotels, which is still underexplored with empirical evidence, especially in the context of hotels.

Method

Research Design

This study adopted a qualitative approach to gain a rich understanding of consumers’ ethical perceptions toward ASRs in hotels. Data triangulation—that is, using the same approach for multiple data sets—is advantageous for confirming results, providing more comprehensive data, and increasing validity (Koc & Boz, 2014). Due to the numerous roles of ASRs in hotels, the data collection process comprised three steps (i.e., semi-structured interviews, focus groups, and on-site interviews) to comprehensively investigate the consumer perceived ethical issues of ASRs based on their perceived scenarios and actual experience. More young generations with direct experience and knowledge about ASRs attended semi-structured interviews, so more older people without experience were recruited in the focus groups. In the last on-site interviews, people of diverse ethnicity were recruited. The data collection process should stop when it meets the principle of saturation (Creswell & Creswell, 2018). Interviewers had consent from interviewees and managers/staff at the hotels. The whole data collection process was conducted during the spring and summer of 2022. A total of 57 participants were recruited for this study, 17 for semi-structured interviews, 22 for focus groups, and 18 for on-site interviews (see “demographics” in Table 1 in supplemental Material). These participants, with different backgrounds, were able to present diverse ethical perceptions of ASRs in hotels. The semi-structured interviews and focus groups were conducted on Zoom or via phone calls. Each participant was given $15 as compensation. Three pilot interviews were conducted to refine the interview protocol (see Table 2 in Supplemental Material) and videos of certain types of ASRs. The general interview questions started with general feelings and experiences with/without hotel ASRs and then targeted specific ASRs in each stage of the interviews. All authors were involved in the process of data collection and analysis.

Data Collection

Step 1: Semi-Structured Interviews identified different ethical scenarios for various ASRs in hotels from broad populations. Considering that the number of individuals with direct experience with ASRs in hotels is relatively minor (Lu et al., 2020), a convenient sampling strategy was used to recruit participants with/without experience with ASRs in hotels. More college students were recruited in this stage because young generations are more likely to have experience and knowledge about innovative technology than older generations (Castillo et al., 2021). In addition, the researchers summarized the current dominant roles of ASRs in hotels (see Table 3 in Supplemental Material). After introducing this research, participants reviewed the summarized table and answered open-ended questions regarding any ASRs they were interested in. The average length of each interview was approximately 30 minutes.

Step 2: Focus Groups explored consumer perceived ethical issues of certain ASR because all participants in this stage had no prior experience with ASRs in hotels. As some students with direct experience with ASRs were recruited in the first stage, more older people were purposely recruited for the focus groups to initiate discussions across the generations. Focus groups can propose in-depth debates about particular ethical issues of ASRs in specific ethical scenarios, which can generate disagreement among participants (Creswell & Creswell, 2018). Snowball sampling was used to recruit participants. Each focus group targeted a particular type of ASR, including delivery robots, robot bartenders, or chatbots. There were two key reasons for selecting these ASRs: (1) they have been applied in hotels, and (2) they cover diverse characteristics that represent the current roles of ASRs. Specifically, delivery robots and robot bartenders with mechanical intelligence have a low level of interaction and different physical appearances. In contrast, chatbots with thinking intelligence have a high level of interaction and virtual status. Thus, these three case studies were employed to generalize the findings. At the beginning of the focus groups, participants watched a short video demonstrating how particular types of ASRs provide services in hotels (see links in Table 2 in Supplemental Material), so these videos gave participants the same familiarity with a certain type of ASRs. The authors agreed upon the videos and tested them in the pilot interviews to confirm that there was no researcher bias in the videos. Each focus group lasted over 1 hour.

Step 3: On-Site Interviews sought to understand consumer perceived ethical issues from their actual experiences. Purposive sampling was employed to maintain the diversity of interviewees. To do this, participants were recruited at hotels where service robots were present. According to the three types of ASRs in the focus groups, three ASRs in Las Vegas were chosen: a delivery robot at Renaissance Hotel, a robot bartender at Planet Hollywood, and a chatbot at Cosmopolitan Hotel. The average time of on-site interviews was around 15 minutes.

Data Analysis

All interviews were transcribed verbatim from the recording files. The audio was played three times to confirm the wording and completeness of the transcription. All transcriptions were English and used as the basis for subsequent analysis. The researchers reviewed the transcriptions several times in the analysis process in order to highlight sentences and generate codes. The first open coding process aimed to identify key concepts and generated codes of consumer perceived ethical issues of ASRs. Next, the axial coding process considered the codes’ interrelationships, following the principle of thematic analysis (Braun & Clarke, 2006). Thus, groups of codes were created as themes following the aforementioned ethical issues in the literature. Finally, we divided codes into two dimensions: ethical issues that arise during an interaction, and characteristics of ASRs that can raise ethical issues. The analysis considered the significant ideas conveyed, the frequency, extensiveness, internal consistency of the words, and the responses’ specificity to increase the reliability (Hoare et al., 2011). Multiple triangulations of verifying and validating the findings were carried out to minimize potential bias in interpreting data (Creswell & Creswell, 2018). For example, some participants were asked to verify their understanding and check the identified themes. The codes were agreed upon among researchers.

Findings

The analytical process reveals that consumer perceived ethical issues of hotel ASRs emerge as eight themes: privacy, security, safety, transparency, fairness, socialization, autonomy, and responsibility. Each theme can be explained from two perspectives: ethical issues that arise during interactions with ASRs (i.e., ubiquitous surveillance, excessive data, unidentified risks, service disclosure, inaccessibility, dehumanization, selection of services, and service recovery), and ethical issues that can stem from the characteristics of ASRs (i.e., privacy infringement, malicious use, malfunctions, untrustworthiness, biased features, job replacement, inflexibility, and self-identified solutions). These ethical issues are defined and explained below (see the summary with quotations in Table 4 in Supplemental Material).

Consumer Perceived Ethical Issues of ASRs in Hotels

Privacy

Privacy deals with uncertainty linked to personally identified information being exposed to unintended individuals or parties (Nadeem & Al-Imamy, 2020). ASRs in hotels can potentially violate consumers’ privacy in two ways. Ubiquitous surveillance involves monitoring and recording consumers’ behaviors and conversations without permission during service interactions. For example, delivery and security robots with built-in cameras and sensors can constantly monitor consumers’ behaviors (Arkoff, 2019). One interviewee addressed this, “The robots with cameras make me uncomfortable and lose freedom. I will be concerned about what behaviors and conversations are recorded” (P17). Other consumers who do not use robots can be unconsciously recorded. Another participant claimed, “The robots may have cameras in them. People may not have any choices to be videotaped” (P21). Thus, any form of surveillance may cause consumers to feel uncomfortable and may lead to potential legal issues.

On the other hand, privacy infringement refers to informational exposure through ASRs without unauthorized access. The ASRs can store personal data, such as consumption history, preferences, ID, and so forth. Data breaches caused by AI-powered chatbots, such as the one experienced by the Radisson Hotel Group (Osborne, 2018), illustrate the vulnerability of ASRs. These breaches can lead to personal and financial harm for consumers. Participants were also worried about who could access their private information, so it should be clear that the data could not be shared with hotel staff and the company that designed the robot without consumers’ permission. One respondent claimed, “When you’re online and using the chatbot, your conversations will be automatically saved, so you will get some records. If it includes sensitive stuff, I do not want someone else to see it” (P24). To mitigate these concerns, hotels should inform consumers about the recording function of ASRs and acquire their consent, and consider appropriate measures to protect consumers’ privacy, such as ensuring data security and limiting access to consumer data.

Security

Contemporary hotel operations and management rely heavily on consumer data, so making informational security regarding consumers’ data collection and storage via ASRs is critical. Excessive data means that abundant consumer information irrelevant to a certain service (address, ID number, credit card, etc.) is needed to initiate the hotel ASR services. One respondent argued,

“The chatbot in the hotel sometimes collects too much information from me. For example, when I ask for a towel, the chatbot asks for various private information to verify identification and then delivers the towel. I have to input a lot of information for processing, but I do not know how these data will be used, so I do not feel secure” (P6).

When errors occur, ASRs will repeat the previous steps and collect the same data again, which results in time wasting and inefficiency, and increases the risk to information security.

Malicious use is defined as the intentional and harmful exploitation of consumer information saved in ASRs for other purposes. One participant said,

“I feel one concern is that you don’t know how chatbots use the information you [in]put. They can use and analyze [it] for other purposes. For example, after using chatbots, you would get, like advertisements, what you had never gotten before” (29).

Another participant asserted, “I wouldn’t want the chatbots to get hacked if they have saved my financial information because hackers can use my information for fraud, but chatbots cannot stop it” (P10). Thus, participants were concerned that ASRs cannot recognize hackers and stop illegally utilizing consumer data for fraudulent activities or other purposes, like selling consumer data to third parties without permission. To mitigate these risks, hotels must prioritize digital social responsibility by reducing redundant steps in data collection via ASRs and implementing measures to secure consumer data.

Safety

Safety refers to the accurate operation of robots in a manner that minimizes potential harm or risks to humans. Using ASRs in hotels is still in its infancy, so it is hard to imagine and identify all the ethical risks of ASRs during service interactions due to most people not having had direct experience with ASRs in hotels. Thus, it is important to consider unidentified risks, which mean negative consequences or hazards associated with ASR services that have not been fully identified. Some potential risks regarding delivery robots were voiced by participants: “For example, cross-contamination of food delivery by robots. If somebody is allergic to peanuts, do hotels have to get cleaned robots between users, especially [if] lots of people are touching it?” (P16) and “What if someone just follows the delivery robot to the guest rooms. Human staff may identify something wrong, but robots cannot feel criminals” (18). Higher intelligence ASRs may generate more dangerous threats, which are not identified now. For example, hotel chatbots may provide misleading information to influence consumers’ consumption. Even ASRs with advanced AI may generate the consciousness to lie and hurt people. Thus, considering the potential risks in specific ethical scenarios during service provision by ASRs is essential.

Moreover, the current ASRs can cause safety issues because of frequent malfunctions (Leo & Huh, 2020), which means unexpected interruptions in functions or system errors. As participants mentioned, “Once the robots cannot follow the program, nobody knows what would happen. They can stop services immediately or even hurt people” (1) and “I don’t know how robots work. I am afraid robots will suddenly hit me. For example, a robot bartender may not hold the cup tightly when shaking the drink” (P3). If consumers are hurt by ASRs, it can become a severe issue for the hotels. Therefore, hotel managers should persistently monitor, evaluate, and test various ASR services in hotels to inhibit potential risks and ensure the ongoing maintenance of technological robustness.

Transparency

Previous literature emphasizes the importance of transparent AI, which is understandable and explainable to humans (Jobin et al., 2019). However, since ASRs are new to most hotel guests, the transparency of service providers (i.e., human employees or ASRs) must be disclosed before service delivery. Service disclosure is defined as providing consumers with clear, detailed, and comprehensive information about ASRs, including their capabilities, limitations, potential risks, whether extra fees are required, and whether personal data will be collected and stored. Even if the advantages of ASRs are evident, such as convenience and efficiency, some consumers may still hesitate to adopt ASRs in hotels. Respondents said, “The hotels must clearly disclose the details of robot services. It is critical to know whether human service is available simultaneously” (P13) and “If I am assigned to robot services, I would question why I [am being] serve[d] by robots without notifications” (P5). Thus, full disclosure ensures consumers can make informed decisions before using hotel ASRs.

In addition, regarding the transparency of ASRs, untrustworthiness was common because consumers with limited knowledge barely knew the working process of ASRs, especially complex functions and rationales. One respondent said, “Even the staff may not fully understand how robots work. For example, a delivery robot departs to the guest room, and the staff has no idea where it is” (P3). Other participants addressed some reasons for distrusting ASRs: “I doubt the ability of robots to accomplish services” (P5), “I guess it becomes more frustrating for guests if robots stop working or give the wrong information” (P3), and “Many people do not trust robots simply because they are not human” (P34). It can be concluded that consumers do not believe ASRs can solve problems and are trustworthy. Even if ASRs become prevalent in hotels, full disclosure of ASR services is essential for consumers to build trust and reduce ethical concerns.

Fairness

Ethical issues concerning fairness are two-fold. First, during service interactions, inaccessibility refers to ASR services that certain groups cannot access and use for certain hotel services. Interviewees declared language issues, “The hotel is kind of restricting people that can interact with the chatbot if they are only offering English” (P28) and “For some people with dialects or accents, their language may not be recognized by AI robots” (P5). Regarding vulnerable groups, one participant said, “Like older people, they may not understand how to use a robot, and they may sometimes be confused about what to do next” (P21). People from less developed areas can find it difficult to access ASR services. As one participant claimed, “All (robot) services are standard, so the minority (e.g., disabled groups) may not feel more caring and friendly” (P5). Thus, ASRs seem to provide equal and consistent services, with the result that particular groups might not receive exceptional care and help.

Second, biased features mean attributes of ASRs (e.g., appearance, algorithms, and actions) that lead to unfair or discriminatory service outcomes. One interviewee said, “It seems like the program is designed by people, so those people should not have bias. I would worry that robots would give us inaccurate or prejudiced results” (P10). ASRs can be developed based on biased algorithms and skewed datasets, potentially resulting in unfair and prejudiced consequences, for example, “White-centric features of the dataset may cause a potentially discriminatory issue towards people of color” (P8). While it seems that everybody is treated fairly, there can be an unintentional disparate impact on certain groups. Therefore, hotels need to improve service quality by providing more care for special groups and identifying and removing possible biased features to ensure fair ASR services.

Socialization

ASRs can have social roles in hotels, but current interactions with ASRs cannot meet humans’ social and emotional demands. Participants highlighted ethical issues regarding the dehumanized feeling of ASR services. Dehumanization refers to treating consumers as less than human, denying their fundamental rights and dignity. Interviewees claimed, “Hospitality means friendliness and caring, so a person or interaction is very important, but I’m not sure about these robots. There is no personal interaction” (P4) and “I should be greeted and treated very well by human staff in hotels. These robots make hotels less warm and caring” (P8). In certain situations, consumers may expect to have a conversation, so it can be depressing if there are only robot services. No empathy is involved in service interactions with ASRs, so these robot services may not fulfill the nature of hospitality.

In addition, another ethical issue from a social aspect is job replacement, which means that ASRs can finish tasks without human intervention, ranging from simple and repetitive jobs to complex consumer services. The benefits of ASRs are apparent, such as consistent service quality and 24-hour service provision (Khan, 2020). Thus, participants were worried, “If these robots can serve most guests, I think the hotels do not need to hire many people” (P10) and “I think robots will take a lot of jobs from humans, which will affect the employment of the labor market” (P4). This trend can increase the competitiveness of job opportunities, reduce income, and have a big impact particularly on less educated people. Therefore, not just hotels, but the entire service industry should carefully consider how to implement ASRs to perform inclusive and compassionate services that value all consumers, and to reduce negative impacts on the workforce.

Autonomy

This theme describes consumers’ autonomous selections of services in hotels and the inflexible features of ASRs. On the one hand, human dignity represents the basic right for the selection of services between humans and ASRs. During COVID-19, consumers were forced to use ASRs to maintain social distancing in hotels (Hao et al., 2022). However, people should be free to choose service providers in the post-pandemic age. One participant said, “If hotels have robot services, consumers have the right to choose to be served by either robots or humans” (P5). As services provided by humans or ASRs have different advantages, the hotels could offer both choices to meet consumers’ different needs. For example, robot bartenders are probably more efficient than humans when people get a drink during conferences or business meetings in hotels. If consumers would like to talk with bartenders about local attractions and their experiences during leisure trips, robots cannot replace the role of humans. Thus, both services could be available for different purposes.

On the other hand, inflexibility is defined as the limited capability of ASRs to adapt to changes in consumers’ demands for services that could be outside of ASRs’ pre-designed programming. Even though AI and machine learning enable ASRs to become increasingly intelligent, ASRs still cannot adjust themselves to meet consumers’ unique, emotional, and emergent demands in different situations. One participant asserted, “Robots have limited capability to deal with emergencies” (P5). Humans can personalize the service in a better way, but ASRs cannot jump out of their pre-designed programs. Another interviewee stated, “I am worried about the special requests for services. If my requests are outside the scope of the menu, the robots cannot solve the problems and waste time in the end” (P3). Therefore, hotels should respect consumers’ dignity by providing ASRs and human services and take advantage of both service providers to maximize consumers’ satisfaction.

Responsibility

Service failures occur when ASRs provide services that do not meet consumer expectations or service quality requirements, which is a frequent occurrence in the current stage of performance (Leo & Huh, 2020). The responsibility theme focuses on service failures that recover from the perspectives of hotels and ASRs. Service recovery refers to how hotels identify and resolve service failures made by ASRs during hotel service interactions. Consumers without experience of ASRs cannot respond immediately and appropriately when facing service failures of ASRs. According to participants, “I wonder if the hotels have technical support when robots mess up the services” (12) and,

“Maybe the service is wrong, so you need to see a person to talk about, handle the situation, and take responsibility for that problem. In the end, we need a real person. I think it shouldn’t be everything in artificial intelligence” (26).

Another interviewee added, “A human may not know how to answer the questions, but they can refer to someone else. The robot may not provide any valid solutions” (P18). Thus, technical assistance and human support should always be available for consumers. Otherwise, service failure can negatively influence consumers’ experiences.

In addition, self-identified solutions mean solutions that can be recognized by ASRs’ characteristics when facing service failures. With the advancement of AI, ASRs should take responsibility for themselves and have the capabilities to recognize and solve problems by themselves during interactions with consumers. Participants said, “What if robots have errors? I wonder whether they can be resolved by themselves and make correct reactions” (P16) and “Robots may face different kinds of scenarios to make decisions. Can robots identify their own stuck problems and make autonomous urgent reactions?” (P1). The increasingly sophisticated ASRs should reduce the possibility of service failure and increase the efficiency of hotel services. Therefore, hotels should pay more attention to executing effective service recovery plans and enhancing the consumer experience (Kang & Namkung, 2018).

Ethical Issues of Mechanical and Thinking Intelligence of ASRs

Regarding ASRs with mechanical intelligence, participants primarily targeted ethical issues from functional and social perspectives. Specifically, functional issues of ASRs (e.g., inflexibility, service recovery, and malfunctions) can significantly influence their performance and service quality. Once these ASRs cannot complete their tasks, this can influence consumers’ satisfaction (Hu et al., 2021). As ASRs increasingly become intelligent and autonomous, self-identified solutions and rich functionality can reduce the possibility of mistakes and risks related to physical harm. In addition, a commonly debated ethical issue from a social aspect is job replacement. Some respondents argued that other jobs related to ASRs would be created, so the total number of jobs may remain relatively high. The nature of hospitality is human interaction, so only a small part of hotel services can be substituted by the mechanical ASRs. Another debated issue is accessibility. Nowadays, as people are more familiar with digital devices, participants emphasized the ease of use of ASRs due to their autonomy, which is easier to access for all populations.

ASRs with thinking intelligence have many of the same ethical issues as mechanical ASRs, but respondents focused on the informational and emotional aspects. Participants were worried about their data security and privacy, which aligns with previous literature (Lu et al., 2020; Tussyadiah et al., 2020). This study further adds privacy related to personal surveillance and security related to data collection, storage, and misuse. Additionally, consent becomes increasingly vital, which involves using ASRs during service delivery and personal data for some purposes. With the advancement of AI, ASRs can actively and purposely take advantage of consumers’ data without human permission. An ASR with feeling intelligence can respond to guests’ emotions by autonomously collecting and analyzing sensitive information about guests. Lastly, the emotional concern of ASRs has been debated because some respondents argued that ASRs could solve socializing anxiety and phobias for introverted people. Especially for business travelers or certain situations (e.g., check-in late at night), the efficiency and convenience of ASR services are better than the unnecessary pleasantries of humans. We can expect that more arguments will appear related to the emotional issues of ASRs, as feeling intelligence may enable ASRs to have humanlike communication and behaviors. ASRs with high intelligence apparently have more ethical issues than those with low intelligence. With the advancement of AI, all aforementioned ethical issues will need deeper discussions in specific contexts.

The Impact of ASRs Adoption in Hotels

The findings identify several factors that involve the willingness of consumers to use these innovative technologies. First, the first experience with ASR services is critical in building trust and shaping consumers’ willingness to adopt ASRs in hotels. More than half of the participants who have never used ASRs in hotels are likely to try them for the first time due to the novelty factor. However, in many cases, ethical concerns are likely to make them hesitant to use ASRs again in the future. Conversely, several interviewees said they were likely to continue using ASRs in hotels because of their positive experience. However, participants who had a negative experience strongly resisted using hotel ASRs. Thus, positive experience may mitigate their ethical concerns and drive continuous adoption behaviors.

Moreover, some interviewees believed the intention to adopt ASRs in hotels is situational, depending on the consumers’ trip purpose. One participant mentioned,

“I think it depends on the trip’s purpose. For example, if you are doing more of a business trip, like a conference, having robot services will probably be faster and more convenient. Still, if you are just [there] for [a] vacation, it is nice to have that human touch, speak to somebody, and understand the community around your environment” (P21).

Indeed, different purposes for using ASRs in hotels should focus more on the main advantage of that service type and reduce the considerations of ethical concerns.

Lastly, age was a significant factor in influencing the adoption of innovative technology—such as self-service technology and social media—in a previous study (Shiwen et al., 2022; Yoo et al., 2021). Interestingly, in this study, some young interviewees hesitated to use ASRs even for the first time, whereas relatively older participants were highly likely to interact with different ASRs. Young people may have more knowledge of AI and ASRs, so they have more ethical concerns than the older generations without/with less experience or knowledge. Thus, this finding presents a reverse result regarding technological adoption. Given the above aspects, these factors can be regarded as moderators to examine the moderating relationships between consumers’ perceived ethical issues and their intentions to adopt hotel ASRs.

In the future, a model of co-work, a service combination of humans with ASRs, is probably the optimal solution (Wu et al., 2021). The ASRs can perform repeated and standardized jobs, while humans focus on customized consumer experiences. As one participant stated, “The robots can help humans to pre-prepare, and then the human completes the process in some way, like the communications. It is like a complement to each other” (P32). Another interviewee added,

“I wonder if it will go full circle where all hotels in the past had people, and now we are in this transition period where we have got people and robots. It might become where hotels only have robots in the future. Human service becomes a luxury experience at that time” (28)

We do not know how robots will develop and be applied when ASRs become more intelligent. Therefore, the aforementioned ethical issues regarding ASRs should be minimized to benefit the border service industry and society.

Implications

Theoretical Implications

This study makes significant contributions to the field of AI ethics, especially concerning ASRs from consumers’ perspectives in the context of hotels. The findings from this empirical study contribute to the understanding of the ethics of ASRs in three ways.

First, this study is the first to comprehensively identify the ethical issues of ASRs in hotels using empirical evidence. Specifically, previous papers mainly targeted the ethical issues related to features of AI and robotics (e.g., security, privacy, and responsibility; Siau & Wang, 2020; Tussyadiah et al., 2020). This paper extends the literature on the ethics of AI applications, particularly ASRs, by presenting consumer perceived ethical issues of hotel ASRs based on qualitative inquiries. The findings explain eight themes of ethical issues and are further classified into two dimensions: ethical issues driven by human–robot interaction and those driven by ASR characteristics. A total of 16 specific ethical issues can enhance understanding and provide a theoretical foundation for other ethics studies of AI applications. Hence, providing empirical evidence to present diverse consumer perceived ethical issues of ASRs in hotels particularly advances the literature on service robots’ ethics.

Moreover, our study is the first to separate two dimensions of ethical issues regarding AI applications and to emphasize the importance of ethical issues that arise during service interactions with ASRs in hotels. This paper addresses the gap in the literature by expanding on the existing ethical issues of ASRs in the context of hotels. The results of the dimension related to the characteristics of ASRs are in line with previous studies, which have mainly addressed general service contexts (Tussyadiah et al., 2020; Wang & Siau, 2019). However, because of a lack of knowledge and experience of ASRs, consumers can barely imagine and think of the ethical issues in this dimension. Instead, consumers pay more attention to the experience and service quality of hotel ASRs. Once ASRs leave a negative first image, consumers could lose confidence in these innovations and generate negative feelings about the hotels’ brand image and reputation, which may significantly affect the penetration of ASRs into hotel services. Thus, the ethical issues that arise during service interactions should receive more attention but have barely been discussed in previous literature. Therefore, this study contributes to the literature on the ethics studies of ASRs by emphasizing the importance of ethics during service interactions in hotels, which serves as a foundation for further study of the ethics of intelligent technology in the service contexts.

Third, this study discusses ethical issues based on the classification of mechanical and thinking intelligence, thus particularly contributing to the literature on the ethics of AI. Previous studies have only targeted certain types of ASRs, such as chatbots and delivery robots (Tussyadiah et al., 2020; Zemke et al., 2020). This study contributes significantly to the existing literature on the ethics of ASRs by separating the different intelligent levels. These results highlight the functional and social-ethical issues related to ASRs with mechanical AI and the emotional and informational ethical issues related to ASRs with thinking AI. Thus, the current study also contributes to the ethics literature by leading the way for further studies on distinguishing innovations’ intelligence levels. This study is one of the pioneering studies examining consumers’ ethical perceptions of ASRs with different intelligence in the context of hotels, thereby incrementally adding to a growing body of knowledge.

Managerial Implications

For practical implications, this study provides valuable insights for hotel managers and robot manufacturers as a first step in the ethical management of ASRs. It is significant as it addresses the ethical concerns related to adopting ASRs in hotels, providing valuable insights for policymakers and service providers to implement corresponding strategies to reduce ethical concerns about hotel ASRs, for example, the protection of personal privacy and information, the reduction of malfunctions and unidentified risks, transparent and fair services, and options for both ASRs and human services to be available. It is equally vital that all these ethical issues receive more attention, and feasible solutions are urgently needed to increase the acceptance of ASRs in hotels. As the hotel industry uses ASRs for front-line services, the values and the implementation of ASRs are necessary to guide how to use ASRs in the entire service industry. Hence, these implications contribute to the service industry more broadly.

Consumers’ ethical perceptions should be addressed when hotel managers develop strategies for promoting innovative ASR services and enhancing consumers’ service experiences. Manufacturers and operators must know that AI algorithms have become essential to contemporary digitalization in society’s infrastructure. More hotel guests will consult and try ASRs for services. Despite the fact that AI algorithms are not ethically neutral, they are increasingly relied upon to make service decisions with/without knowing that they provide private information. The increasing prevalence of ASRs with high levels of intelligence, such as those with thinking and feeling capabilities, may present new ethical concerns for hotel managers, like emotional privacy. This may impact guests’ trust in the hotel and generate mental issues. Therefore, hotel managers must be prepared to address potential ethical issues arising from using such advanced ASRs and ensure they comply with ethical standards and regulations. As the current study shows, these ethical issues are essential as they may directly affect consumers’ intentions to use ASRs in hotels. Therefore, the aforementioned ethical issues become increasingly paramount, and should not be underestimated in each ethical dilemma.

Limitations and Further Directions

No study is free of limitations. First, it needs to be acknowledged that more students and Asian participants were recruited due to convenience sampling. Thus, older participants with more diverse demographics were more involved in the second and third stages of data collection to reduce sample selection bias and increase the validity of results. Thus, further studies can research across the countries because consumers’ experiences with ASRs may be distinctive in hotels in different countries. Second, this study selected three representative types of ASRs to generalize the results of current ASRs in hotels. As various ASRs can generate their own ethical issues in certain ethical scenarios, further studies could target each ASR in hotels and thoroughly investigate overall ethical scenarios. Third, specific ethical issues, such as accessibility, dehumanization, and job replacement, have been debated among different groups. People with different values may have diverse ethical judgments regarding the multifaceted ethical issues of ASRs. Future research can investigate deeper into each ethical issue from specific groups, such as consumers with/without AI-related education and employees with/without working experience with ASRs. Lastly, the role of consumer perceived ethical issues regarding ASRs requires further research. A qualitative study cannot provide a causal relationship about how these ethical issues affect consumers’ adoption. Therefore, future studies could employ quantitative methods to examine how these ethical issues and moderators (e.g., purposes of trips and age) influence consumers’ adoption of ASRs in hotels.

Supplemental Material

sj-docx-1-jht-10.1177_10963480231194693 – Supplemental material for Consumers’ Ethical Perceptions of Autonomous Service Robots in Hotels

Supplemental material, sj-docx-1-jht-10.1177_10963480231194693 for Consumers’ Ethical Perceptions of Autonomous Service Robots in Hotels by Boyu Lin, Woojin Lee, Nicholas Wise and Hwansuk Chris Choi in Journal of Hospitality & Tourism Research

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Boyu Lin

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Boyu Lin, PhD (email: blin26@asu.edu) is a PhD student at the School of Community Resources & Development, Arizona State University, AZ.

Woojin Lee, PhD (email: woojin.lee.1@asu.edu) is an associate professor at the School of Community Resources & Development, Arizona State University, AZ.

Nicholas Wise, PhD (email: nick.wise@asu.edu) is an assistant professor at the School of Community Resources & Development, Arizona State University, AZ.

Hwansuk Chris Choi, PhD (email: hwchoi@uoguelph.ca) is a professor at the School of Hospitality, Food and Tourism Management, University of Guelph, Canada.

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