Exploring factors affecting customer trust in social distancing technology and impact on hotel booking intentions

Abstract The outbreak of COVID-19 in 2019 has posed an unprecedented threat to the global economy. Due to the pandemic, the hotel industry has undergone fundamental changes. In particular, social distancing technology (SDT) has become an indispensable tool in this industry and has played a vital role in its recovery. This study explores the factors that affect customer trust in using SDT in hotels during COVID-19 and how this trust affects their booking intention. The research uses social exchange theory (SET) as the theoretical basis and measures the benefits and perceived risks of using SDT through the benefit-risk method. In addition, the technology acceptance model (TAM) is introduced, and two attributes are added: perceived ease of use and perceived usefulness. A total of 437 questionnaires were distributed, of which 405 were valid. Research results show that benefits, perceived usefulness, and perceived ease of use positively affect trust in SDT, while privacy concerns have a negative impact. Health risks and social rewards are not directly related to trust in SDT.This study provides ideas for future research on the health aspects of SDT by studying the benefits and health risks brought about by its use. For relevant practitioners, hotels should reasonably incorporate SDT into their daily operations and conduct regular disinfection to reduce the risks caused by direct and indirect contact to protect the lives and health of both customers and workers.

Research background
The outbreak of COVID-19 in 2019 has posed an unprecedented threat to the global economy, and the presence of COVID-19 has led to fundamental changes in the hotel industry. As the hotel industry is characterized by a high risk of infection, the business hours, scale and a series of activities of hotels have significantly reduced or closed down (Gursoy & Chi, 2020). Therefore, the hotel industry is one of the most affected industries by COVID-19 (Zemke et al., 2015).
According to data released by China Hotel Association, the average revenue loss of the hotel industry has reached 67.81% since the outbreak of the pandemic. More than 89% of the hotel revenue loss is higher than average. The main reason is that COVID-19 is highly infectious and can harm people's life and health. This leads people to take self-protection measures, such as reducing travel and minimizing contact with others to reduce the risk of infection. A series of emerging technologies can play an important role in facing these challenges (Jones & Comfort, 2020).
Social distancing technology has been adopted to reduce the risk of infection and ensure the health and safety of customers (Kussmann, 2020). Many large chain luxury hotels have begun to adopt AI intelligent robots, which can help guests check-in, provide room service, help with disinfection and so on. Some advantages of using social distancing technology include: (1) it can maintain proper social distance and reduce health risks; and (2) it can be managed more efficiently (Nguyen, Saputra, Van, Nguyen, Khoa, Nguyen et al., 2020).
However, the application of social distancing technology (SDT) in Macau's hotel industry is still immature and has not yet become popular. Most hotels in Macau are luxury hotels. Adopting social distancing technology may change the way services were originally provided. Therefore, customer intention cannot be ignored when using social distancing technology.
Technology in hotels has always been a popular topic in research. Most of the literature is more focused on the effectiveness of hotel technology in providing services and enhancing the consumer experience (Krishnan et al., 1996;Morosan & DeFranco, 2019;Rahimi, 2017). Several studies examined the pandemic's effects on hospitality with the outbreak of the epidemic (Jiang & Wen, 2020;Jones & Comfort, 2005; J. Zhang et al., 2020) and how hotels are preventing the spread of the virus and acknowledging the important role of technology in reducing interaction between guests and hotel staff (Kussmann, 2020;Garcia, 2020;Nguyen et al., 2020). However, customers are the ones who use this technology, so their level of trust is necessary for increased application.
This research aims to explore the important factors that affect customer trust in social distancing technology and the relationship between these factors and trust and customer booking intention. It provides the basis for the management and technical improvement of the hotel industry during the pandemic. It provides strategies on how to create a safe hotel environment for customers, thus promoting the recovery of the hotel industry.
This study is based on four dimensions (benefits, health risks, social rewards, and privacy concerns) of the attributes of SDT, which were identified in the study of Social Distance Technology by Morosan and DeFranco (2021). It also adds perceived ease of use and perceived usefulness as per the technology acceptance model (TAM). Based on the research background and purpose, this study poses the following research questions: Is the customer's trust in social distancing technology influenced by these six factors? Will these factors and trust further affect customers' hotel booking intention?

Social distancing technology (SDT)
Social distancing is defined as using a range of non-pharmaceutical interventions to reduce the frequency and closeness of person-to-person contact and distance during a pandemic, e.g., high-risk group quarantine, closing large events, controlling the movement of people, etc. (Kelso et al., 2009). Social distancing technology (SDT) is information technology used to reduce the frequency, closeness and contact between people at potentially risky sources of outbreaks, such as self-service check-in kiosks in airports. Through pre-printed boarding passes, customers can reduce contact with airport employees (Shin & Perdue, 2019). There are also WeChat mobile payment, Alipay mobile payment, and other similar applications that can be used to avoid contact and reduce the probability of virus transmission. Restaurants are using automated delivery services and QR codes to order food. Robots are used to automatically deliver food to the table (Morosan & DeFranco, 2021). SDT is now widely used and has reduced the inconvenience caused by epidemic prevention measures. Some scholars divide SDT into two aspects. One is related to the benefits it brings to the hotel industry (Sorrells, 2020;Nguyen et al., 2020;Krishnan et al., 1996;Sharma, Shin, María, & Nicolau, 2021), and the other is the risk posed by its use (Tu et al., 2018;Ha, Shakur, & Do, 2020).

Social exchange theory (SET)
The concept of social exchange theory (SET) was first proposed by G. C. Homans (1958), who believed that social exchange was a process of resource exchange between people, in which the resources may be tangible or intangible, such as time, money, family, status and reputation (Y. S. . Blau (1964) pointed out that social exchange is a reciprocal behavior between two parties. Since there is no clear stipulation of responsibilities and obligations, there are risks and uncertainties in social exchange (Molm et al., 2000), which leads to trust between the two parties.According to SET, individuals proactively seek to maximize their benefits and minimize costs when exchanging resources with others (Molm, 1997). Benefits refer to those resources one receives as a result of a social exchange or a positive outcome. Cost refers to those resources one gives away during a social exchange or any adverse outcome of an exchange (Kankanhalli et al.). The tenets of SET have an important implication for the current study. Studies have underlined the major source of cost in information technology use as risks, which may be due to information security and privacy threats (S. Chen & Williams, 2009;Casaló & Romero, 2019;Qin et al., 2009). The downside to this study is the health threat and risk to consumers. Therefore, a benefit-risk theoretical framework is constructed to discuss consumer trust in the hotel industry's use of epidemic prevention technology (Morosan & DeFranco, 2021).
SET is widely used in business and hospitality settings (Rather & Hollebeek, 2019;Kim, So & Wirtz, 2022). This study focuses on the principle of reciprocity in SET and conducts research based on a benefit-risk framework by measuring the benefits customers receive and the risks they face when participating in activities. These two important factors are evaluated in this study to examine their effect on customer trust in SDT and customer booking intention (H. K. Wang et al., 2015). People choose to maintain this exchange relationship only when the expected benefits outweigh the risks (Golder, Wilkinson & Huberman, 2007). This benefit-risk approach is important during COVID-19, as consumers may use it to change their incentives to purchase services, and managers may adjust their management strategies and approaches to deal with the new business, economic, and social conditions, as well as new anti-epidemic policies (Liu et al., 2021;Yang & Han, 2021). Davis (1989) designed the Technology Acceptance Model (TAM) based on rational behavior theory. Perceived ease of use and perceived usefulness are the determinants of TAM. Perceived ease of use is defined by Davis (1989) as the degree to which people believe technology can be used without effort, while perceived usefulness is the degree to which it increases the corresponding efficiency when using technology. A study by Israel Jeng(2019) pointed out that in a large number of analytical TAM on the factors of virtual technology use in the hotel industry, perceived usefulness has a certain impact on customer hotel booking intention. In addition, Sahli and Legohérel (2016) also proposed a framework to test the influencing factors of customers' online hotel booking intention and found that perceived usefulness and perceived ease of use are important.

Technology acceptance model (TAM)-perceived ease of use and perceived usefulness
Although SET is used to predict and explain attitude problems, it lacks theoretical complexity. Therefore, this study explores the theoretical connotation of SET and combines it with other theories (C. Zhang et al., 2021). Nunkoo and Ramkissoon (2011) believe that trust is an important factor in SET, but they are not included in the previous model. Pappas (2014 believes that combining SET and other important variables can help improve the predictive power of the model. TAM is one of the most widely used models for studying new information technology adoption (Davis, 1989;Venkatesh & Davis, 1996). TAM suggests that perceived usefulness and perceived ease of use regarding new things can affect a person's attitude toward the acceptance of that technology (Akbari et al., 2020;Cheung & Vogel, 2013;Davis et al., 1989;Yudiarti & Puspaningrum, 2018). To further increase the precision of the conceptual framework, this study adds a theoretical structure (TAM) that accurately describes the perception (usefulness, ease of use) of consumer systems.
This study considers benefits, health risks, social rewards, and privacy concerns in SET as external factors influencing consumers. The perceived usefulness and perceived ease of use of the TAM are internal factors for consumers' perception of new technologies. Therefore, this study combines SET and TAM. Through a combination of internal and external factors, consumer trust in social distancing technologies and behavioral intentions under COVID-19 are provided with a detailed framework.

Trust
Trust is a concept with many concepts and complex structures. In SET, trust is defined as the expectation that exchange partners will behave in good faith based on positive perceptions and willingness in situations of uncertainty and risk. The development of trust can only occur when there is a certain risk in the relationship between the parties (Molm et al., 2000).
Over time, consumer choice and use of technology have become more routine, but many studies have shown uncertainty in most economic and social exchanges. The element of trust needs to be the basis of an exchange relationship. Coleman (1988) highlights the importance of trust in exchange relationships as it boosts the confidence of the parties involved in the exchange.
Social theory also states, "trust is the foundation of exchange behavior between individuals" (Zhao et al., 2017, p. 373). Ponnapureddy et al. (2017) found that when customers have positive trust in a product, it has a positive impact on the perceived usefulness of the product and hotel booking intention. In addition, findings show that trust is a key variable when participating in social exchange. That is, customers' higher trust in services leads to a higher desire to use the service (Boateng et al., 2019).
During the COVID-19 pandemic, people have felt uneasy and skeptical about travel due to concerns about their safety and health (Akbari et al., 2020). It has caused people to restrict and avoid travel. New technologies for epidemic prevention may change people's psychological concerns and enhance travel confidence (Park, 2020;Kussmann, 2020). Therefore, it is crucial to understand people's attitudes towards new technologies for epidemic prevention. Drawing on the reciprocal perspective of SET, some scholars have discussed the relational and psychological states of consumers towards hotel intelligent technology (Pillai & Sivathanu, 2020;Wirtz et al., 2018). Findings reveal significant positive effects of perceived intelligence, perceived social presence, and perceived social interactivity on trust, leading to usage intentions (H. Kim et al., 2022). Hence, based on the benefit-risk framework of SET, we explore the trust customers have in hotel technology and measure the benefits and risks they receive when participating in activities using this technology.

Benefits and health risks
In the past literature, we have found that consumers evaluate the use of technology through a benefit-risk framework based on a reliable conceptualization of the benefits of that technology (Zeithaml, 1988). The conceptualization of the benefits of technology is the need to capture the use of specific functions to facilitate a task (Kim & Lee, 2008). In this paper, the benefit is defined as the ability of customers to effectively maintain social distance when using SDT in the hotel, reducing the risk of infection and protecting their physical and mental health and safety (Metzger, 2006). In the context of COVID-19, SDT is deployed in hotels to effectively promote social distancing. For example, service robots in hotels effectively maintain social distancing without having to contact staff, effectively reducing the risks that may arise during contact.
Health risk comes from a dimension in risk theory, divided into physical and psychological aspects by Suhartanto et al. (2021). Personal anxiety and worry caused by COVID-19 are regarded as psychological health problems. This study defines health risk as the negative physical and emotional impact on customers using SDT.
Past research has shown that perceived risk will negatively affect customer behavioral intentions. For example, when consumers perceive a product as high risk, they will be reluctant to trust it (Y. S. Corritore, Kracher, & Wiedenbeck, 2003;Eid, 2011;Harridge-March, 2006;Rehman, Baharun, & Salleh, 2020). In addition, research by Y. S.  shows that mental health problems such as anxiety and worry can also negatively affect trust. In the context of COVID-19, the deployment of SDT in hotels is to reduce the risk of infection through contactless payment, hotel service robots and other facilities. However, while certain risks are reduced, such technologies cannot ensure zero contact. Based on this, our study proposes the following hypotheses:

Hypothesis 1 (H1). The benefits of using social distancing technology in hotels have a positive impact on their trust.
Hypothesis 2 (H2). The health risks of using social distancing technology in hotels have a negative impact on their trust.

Social rewards and privacy concerns
In the past, we have found that consumers use a benefit-risk framework to calculate their decisions when using a certain technology. This is called privacy computing, which consists of two components: social rewards and privacy concerns. A person uses privacy computing to balance privacy concerns with tangible benefits; that is when consumers are faced with a decision whether to use a certain technology or not.
A social reward is the happiness, comfort and satisfaction that a person obtains through participating in a relationship (Jiang et al., 2013). In social interaction, bound by the principle of reciprocity, people will exchange information fairly, and when this relationship is beneficial, people will make more efforts to maintain this relationship. This can further develop and gradually evolve into trust (Blau, 1964;Lawler & Thye, 1999). For example, in a survey of location-based services, it was found that the willingness of customers to disclose personal information was influenced by corresponding rewards (Milne & Gordon, 1993).
Privacy concerns are defined as a person's fear of privacy leakage and abuse when faced with information exchange (Li et al., 2011). Research on consumer privacy found that privacy concerns affect the level of trust customers place in a technology or system. The privacy-trust-willingness model established by Liu, Marchewka, Lu, and Yu (2005) proved that privacy affects trust, and trust affects consumers' willingness to act. M. K.  also showed that strengthening relevant privacy protection policies will increase people's trust in websites. In a study by Gashami, Chang, Rho, and Park (2016), it was proved that customers' privacy concerns have a negative impact on supplier trust. Therefore, the following hypothesis is proposed: Hypothesis 3 (H3). The social reward of using social distancing in hotels has a positive impact on their trust.
Hypothesis 4 (H4). The privacy concerns of using social distancing technology in hotels have a negative impact on their trust. Lee and Jun (2007) pointed out that perceived usefulness and perceived ease of use are important predictors of consumer trust in mobile transactions. In the research on trust in the field of e-commerce, it is found that perceived ease of use and perceived usefulness are helpful for consumers to understand what is currently happening. It is easy for them to understand processes, and this can have a positive impact on building trust (Kumar, 1996). Gefen et al. (2003) found that perceived ease of use has a significant impact on customers' attitudes toward trusting a website, and customers will ultimately increase or decrease their trust in the system because of this ease of use. Hence, customer trust is directly affected by perceived ease of use. In addition, Ponnapureddy et al. (2017) found an interaction between trust and perceived usefulness; that is, when people perceive a product to be effective, their trust in the product increases. Therefore, a strong correlation exists between perceived ease of use and perceived usefulness and trust. In summary, the following assumptions are made:

Perceived ease of use and perceived usefulness
Hypothesis 5 (H5). The perceived ease of use of using social distancing in hotels has a positive impact on their trust.
Hypothesis 6 (H6). The perceived usefulness of using social distancing technology in hotels has a positive impact on their trust.

Concept of hotel booking intention
Hoyer, MacInnis and Pieters (2012) define behavioral intention as a person's behavior toward something or an individual's willingness to behave in a desired manner. Behavioral intentions are also described as emotional dispositions and behaviors that consumers have after experiencing/using a product or service (Davis et al., 1992). The hotel booking intention in this study is defined as the customer's positive evaluation of the hotel, their willingness to recommend the hotel to others, and their willingness to book the hotel again.

Relationship between perceived ease of use, Perceived usefulness, trust, and booking intention
Research shows that consumer trust is an important factor that affects online shopping and consumers' behavioral tendencies (Kim & Lee, 2008). Bijlsma-Frankema and Woolthuis (2005) believe that consumers will buy more products from a trusted party than from an untrusted party. In the hotel industry, consumers are very cautious when booking hotels, hoping to reduce the uncertainty of products or services by collecting enough information (Ladhari & Michaud, 2015). Pavlou and Gefen's (2004) research shows that when consumers trust hotels, they will perceive a lower risk when booking and their willingness to book will also be higher. Based on this, our study proposes the following hypotheses: Hypothesis 7 (H7). Trust in using social distancing technology has a positive impact on booking intention. Sahli and Legohérel (2016) proposed a framework to examine the influencing factors of customers' online hotel booking intention and proved the impact of perceived usefulness and perceived ease of use on hotel booking intention. Israel et al.'s (2019) research pointed out that in a large number of analyses, TAM factors on the use of virtual technology in the hotel industry and perceived usefulness have a certain impact on customers' hotel booking intention. Although some studies suggest that perceived usefulness has a greater impact on customer behavior intention than perceived ease of use, the relationship between perceived ease of use and behavior intention is positively correlated (Huang et al., 2019). Based on this, our study proposes the following hypotheses:

Hypothesis 8 (H8). Perceived ease of use of using social distancing technology has a positive impact on booking intention.
Hypothesis 9 (H9). Perceived usefulness of using social distancing technology has a positive impact on booking intention.

Research design
This study mainly explores the factors that affect customer trust in using SDT in hotels during the COVID-19 period and how customer trust in SDT affects customer booking intention. Therefore, the four dimensions (benefits, health risks, social rewards, and privacy concerns) of the attributes of SDT will be identified by adopting the research of Morosan and DeFranco (2021) as the basis. This study uses SET as a theoretical basis to measure the perceived benefits and risks of using social distancing techniques by calculating the benefit-risk approach, conceptualizing the perceived benefits and social rewards of using social distancing techniques as benefits; health risks and privacy concerns perceived by social distancing technologies are conceptualized as risks (Morosan, 2018). In addition, the TAM (Davis, 1989) is also introduced, adding two properties: perceived ease of use and perceived usefulness. Customer trust in SDT was measured by measuring these six factors to understand how these factors affect customers' trust in SDT and explore the relationship between these factors with trust and booking intention. The research hypothesis diagram is shown in Figure 1.

Questionnaire design
The questionnaire was designed by sorting out and summarizing the relevant literature on eight variables. These include benefits, health risks, social rewards, privacy concerns, perceived ease of use, perceived usefulness, trust, and hotel booking intention. Information about these variables in  Pai et al., Cogent Social Sciences (2022) literatures is in English, but the questionnaire is distributed in Macau, China. Most customers are from mainland China. Therefore, to make the respondents understand the items of the questionnaire better, all English items were translated into Chinese. The questionnaire was divided into two parts. The first part uses a seven-point Likert scale and contains 39 items related to the eight variables. The second part collects the basic information of the individual for analysis of the demographic characteristics of the respondents.

Sample collection
In November 2021, a pre-survey was conducted. A total of 130 questionnaires were distributed, of which 122 were valid responses. Through statistical software analysis, the alpha coefficient of the pre-questionnaire was between 0.813 and 0.942, which has good reliability (Cronbach, 1951). To avoid ambiguity in responses, the language expressions were adjusted when the pre-questionnaire was distributed. Formal questionnaires were distributed at the end of November 2021. The questionnaires were distributed at the Gongbei Border Gate in Macau, Hengqin Port in Macau, Guanye Street in Macau, outside of major luxury hotels in Macau, and in selected areas with high traffic flow. A total of 437 questionnaires were distributed by simple random sampling, and 405 valid questionnaires were recovered, with an effective recovery rate of 92.67%.

Descriptive analysis
From the sample data results, the ratio of males (47.1%) and females (52.9%) in this study is relatively balanced. From the aspect of age, it was found that most respondents were between 18-29 years old (60.2%). The educational level of the vast majority of respondents was mostly concentrated between undergraduate degree (65.2%) and master's degree and above (28.6%). The income level of the vast majority of people was in the middle, between 4051RMB/5001HKD/ 651USD-8000RMB/10000HKD/1300USD (30.4%). The vast majority of respondents were from Mainland China (82.0%), as the questionnaires were distributed in Macau.
As per the responses, most people had five or more hotel stays a year (42.2%), suggesting that with higher income levels, people tend to pursue a better quality of life. The average hotel stay of respondents was 2-3 nights (49.6%). The respondents were mainly students (25.7%) and those working in the service industry (22.0%). The sample demographic information is shown in Table 1. Cronbach (1951) believed that Cronbach's alpha coefficient must be greater than 0.7 if the variable is to have good reliability. The reliability of all variables in this study is between 0.915-0.954, which is greater than the recommended value of 0.7.

Reliability analysis
To further strengthen the reliability of this study, we also tested the combined reliability (CR) of the questionnaire. The recommended value for combined reliability (CR) is above 0.7. The higher the resulting measure, the higher the internal consistency among the variables (Fornell & Larker, 1981).
All CR values in this study ranged from 0.0.746 to 0.965, indicating that the combined reliability of the questionnaire was very good. To summarize, the Cronbach's coefficient (α value) and CR value meet the required standard values. The results of the α value and CR value are shown in Table 2.

Validity analysis
Validity analyses, which measure the authenticity and accuracy of the questionnaire, include convergent and discriminant validity (Fornell and Larcher, 1981). If the factor loading coefficient exceeds 0.7 and the average variance extraction ratio (AVE) exceeds 0.5, the research model has good convergent validity (Anderson & Gerbing, 1988;Fornell & Larcker, 1981). Discriminant validity can be measured by comparing the correlation coefficient of the square root of the average of each variable with other latent variables. If the square root of the average variance extraction rate of each variable is greater than the value of the conceptual correlation coefficient, and the value is at least 0.5, the discriminant validity is good.
The results of factor loading is shown in Figure 2. Table 2 demonstrates that the load coefficients of all the items of the questionnaire in this study are between 0.833-0.929, which is greater than 0.7. The AVE values are between 0.746-0.846, greater than 0.5. In Table 3, the square root of the average difference extraction rate AVE value is higher than the correlation coefficients corresponding to other variables. Therefore, the variables in this research model have high convergent and discriminant validity.

Structural model and hypotheses test
The hypotheses were tested using the Smart PLS 3.0 software to perform path analysis on the theoretical model. According to the verification criteria proposed by Wong (2013), when the T value of the path is greater than 1.96, and the P value is less than 0.05, the path coefficient of the model is significant. The model assumes the path analysis shown in Figure 3 and Figure 4.
To examine the explanatory power and predictive relevance of the variables in the research model, the explanatory variance R 2 value was calculated using the PLS algorithm using the blindfolding method. Nagelkerke (1991) proposed that when the value of R 2 is between 0 and 1, and the closer it is to 1, the stronger the explanation is, and vice versa. When the Q 2 value of the variables is greater than 0, it indicates that the model has predictive relevance (Roldán & Franco, 2012). In Table 4, the explanatory degree of trust is 75.1%, and the explanatory degree of hotel booking intention is 76.8%. This shows that the research model in this paper has a high degree of model interpretability. In addition, the Q 2 value of trust is 0.596, and the Q 2 value of hotel booking intention is 0.610, both of which are greater than 0. This indicates that the model in this study has a predictive correlation. The results of R 2 value and Q 2 value are shown in Table 4.

Results
During the COVID-19 pandemic, the role of consumer-facing IT in safeguarding public health has been crucial, especially when IT was used to replace critical human services. The results of this study confirm that consumers assess their trust in SDT by evaluating the overall benefits and risks of using such technologies. This also affects their booking intention. The hypothesis-testing results of this study show that benefits, privacy concerns, perceived ease of use, and perceived usefulness have a significant impact on customer trust in SDT, with perceived usefulness being the most  (1) It's easy to use social distancing technology in hotels.

0.883
(2) It's easy to become skillful with social distancing technology in hotels.

0.898
(3) It's easy to learn to use social distancing technology in hotels.

0.903
(4) It's easy to interact with social distancing technology in hotels.

0.872
(5) Overall, it's easy to use social distancing technology in hotels.

0.884
Factor 6: Perceived Usefulness 0.783 0.948 0.931 (1) The use of social distancing technology in hotels is useful.

0.860
(2) The use of social distancing technology in hotels can improve the efficiency of the check-in process.

0.888
(3) The process is simpler when using social distancing technology in hotels.

0.896
(4) Using social distancing technology in hotels is more useful than traditional processes.

0.891
(5) Overall, the use of social distancing technology in hotels will enhance my spending and stay experience.
0.890 (Continued) Pai et al., Cogent Social Sciences (2022) significant. However, health risks and social rewards did not have a significant effect on customer trust in social distancing techniques. In terms of customers' hotel booking intention, customer trust in SDT, perceived ease of use, and perceived usefulness have a significant impact. Customers' willingness to book a hotel is directly influenced by their trust in SDT.

Analysis of factors affecting customers' trust in social distancing technology
Among the factors that affect customer trust in SDT, perceived usefulness has the most significant impact. This shows that a customer's recognition of technology in a hotel is the first evaluation criteria used to compare with traditional services. Hence, the use of technology for food delivery, service robots etc., provides customers with more convenience and safety. During a pandemic, this is more convenient.
In the context of COVID-19, this study evaluates customer trust in SDT based on a benefit-risk framework and considers benefits and health risks as a group. As the benefits are health-related, and a cure is not yet in place, successfully using SDT to safeguard health is an important task. The results show that when SDT is applied in hotels, people tend to believe that this technology can bring health benefits, reduce the risk of infection in hotels, and protect their health and safety. Health risks have no significant effect on customer trust in social distancing technology. This finding is consistent with recent studies (Shin and Kang, 2020). The literature review found that most of the health risks brought about by social distancing technology come from indirect contact. Hotels using SDT have a strict policy of restricting entry and exit for customers, while the facilities on the premises are regularly disinfected. This assures customers that they are less likely to be infected. The service robot mentioned in the literature review and its application reduces direct contact between people, fundamentally reduces the risk of infection, and successfully provides the required service.
Social rewards and privacy concerns were not significant for customers' trust in social distancing technology. This may be because social rewards are usually achieved through technology to facilitate communication between people to obtain a corresponding emotion, while social distancing technology is deployed in hotels. The purpose is to reduce contact and communication between people. Specifically, there is some emotional value when using social distancing  techniques. But it's more about privacy in use, which may be related to past mistrust of such technologies. In daily life, we often see companies seeking benefits by leaking the personal information of customers. This makes people more concerned about their privacy, and they have more privacy concerns when using social distancing technology in hotels. For example, mobile contactless payment methods may lead to the disclosure of one's password. There is also the risk that the customer's personal information will be retained on the computer. This holds a greater risk compared with traditional cash payments. Therefore, we found that the privacy concerns related to the use of SDT are greater than the social rewards provided. This can lead to low customer trust.

Analysis of factors affecting customers' hotel booking intention
Among the factors that affect customers' hotel booking intention, perceived usefulness has the most significant impact. The impact of perceived ease of use on hotel booking intention is also very significant, second only to perceived usefulness. This is consistent with the conclusion of Sahli and Legohérel (2016). These results show that when making a hotel reservation, if the customer perceives social distancing technology to be useful, it will stimulate their willingness to book a hotel. Customers will affirm the use of social distance technology and believe that this technology can meet their needs during check-in. This effectively promotes customers' booking intention. At the same time, the ease of use of social distancing technology can greatly improve the customer's experience, thereby promoting their booking intention.  Similarly, customer trust in social distancing technology also significantly affects their hotel booking intention. This is consistent with the research results of S. Y. Kim et al. (2017). When customers use social distancing technology in a hotel, and if they have trust, they will think that there is less risk associated with enjoying the hotel's services or products, and then they will be more willing to book.

Theoretical contribution
First, this study expands the literature on technology trust in the context of COVID-19 by using the principle of reciprocity in SET to explain customer trust in SDT using a benefit-risk calculation approach. Second, this study extends the factors that influence the trust in technology according to SET by discussing the four factors (benefits, health risks, social rewards, privacy concerns) and the combination of TAM. In addition, by studying the benefits and health risks brought by using SDT, this study provides ideas for future research on its health aspects.

Practical advice
This study also offers several practical strategies and recommendations for hotels. First, to increase the consumers' perceptions of using SDT in hotels, hotels should emphasize the general benefits of such technologies. As guests contemplate visiting a hotel, they should be able to find this information online before booking. This will increase their perception of SDT once they arrive on the property. Considering the current tendency to display signs to illustrate social distancing rules, hotels can provide rich information regarding the benefits of using SDT. This can reinforce customer beliefs that SDT can be used to accomplish hotel tasks while safeguarding their health.
Second, although using SDT will provide more health protection, the virus can still be transmitted through indirect contact. Therefore, hotels should regularly disinfect commonly used devices and machines to minimize the risk of indirect contact. In addition, it is important to understand the experience of customers using social distancing technology and get customer feedback. This can help hotels improve the relevant functions or existing deficiencies and further improve the benefits and convenience of social distancing technology. This will make customers feel safer in the hotel and improve their future booking intention.
Third, this study found that most customers are concerned about privacy issues when using SDT in hotels. Hence, this is an important factor affecting their trust in social distancing technology. Hotels should inform customers on matters that need their attention so that they know the corresponding risks and choose to use or not to use this technology. In addition, customers should be informed about the hotel's policies to protect their privacy so they feel more trustworthy when using SDT. Ensuring the security of customer information and strengthening the supervision of customer privacy would enable hotels to create a safe technical environment.
The benefits-analysis of the research can also play another role. Strengthening the attributes related to perceived ease of use and perceived usefulness, improving the practicability of social distancing technology in hotels, and reducing complex functions can enable customers to easily control related functions and have a good experience. In the future, SDT can be modified through regular customer feedback and evaluation of SDT, making it more functional, simpler, and suitable for customer needs. Allowing customers to experience the benefits and convenience brought by technology during their stay can help increase their future booking intention.

Limitations and future research directions
This study did not conduct an in-depth analysis of demographic characteristics and other information. Further comparative studies of different groups can be conducted in the future.
Most of the respondents in this study are from mainland China, which leads to certain limitations in the research sample. In the future, it is recommended to broaden the research scope and increase the sample diversity.
Finally, the hotels in this study are all based in Macau. Therefore, the results cannot be applied universally. Future research can include hotels in other areas to make the results more convincing and universal.


Green procurement implementation through supplier selection: A bibliometric review

Abstract The purpose of this paper is to provide a brief bibliometric review of the previous literature review in understanding the implementation of green procurement through supplier selection. Researchers contribute to the field of procurement, especially in developing environmentally friendly procurement in supplier selection. In recent years, the public and private sectors have considered environmental aspects in their purchasing policies. Consideration of environmental aspects is known as green procurement. Using green purchases makes it known to produce environmentally friendly products so that the public can more easily recognize them. The method used is a structured literature study sample for 1994–2022. A total of 220 articles with the keywords green procurement and supplier selection were collected. Authors use Publish or Perish and VOSviewer to find journals, authors, citations, keywords, and word hierarchy maps. This bibliometric review related to the implementation of green procurement is useful for researchers and practitioners in their fields, especially those related to supplier selection. Research proves that green procurement became a widely adopted method in publications from 1994. This suggests that citation and forwarder network analysis for selected publications is a topic such as a supplier selection.

Introduction
Over the past two decades, environmental considerations have become an important issue in purchasing (Witek & Kuźniar, 2021). As a result, both the public and private sectors face increasing pressure to consider environmental aspects in purchasing policies (Ma et al., 2021). This pressure arises due to the increasing number of government, stakeholders, and NGOs regulations. This consideration of environmental aspects is known as green buying or green procurement. As a result of green purchasing, the efforts of companies and industries, which provide environmentally friendly products and services, can receive more recognition (Yang et al., 2021). Then many companies tend to be motivated to design, produce and provide environmentally friendly products and services. Thus, the green market expands, and green purchasing contributes to sustainable development.
In recent years, many researchers have studied various green procurement studies. In 1994In -2002, literature related to the concept of green procurement was scarce. It can be said that this period was the initial stage in the publication of the green procurement concept because there was only one article per year with four authors, such as Min (1994), discussing supplier selection. Meanwhile, Noci (1997) discusses supplier selection's green vendor rating system. Braglia and Petroni (2000), examined quality orientation for supplier selection Meanwhile, in 2012 several researchers discussed suppliers in the context of supplier selection with the concept of green procurement (Datta et al., 2012;Kuo & Lin, 2012;Setak et al., 2012) and the supplier selection method by Pani et al. (2012). In contrast, other researchers such as Goebel et al. (2012) and Reuter et al. (2012) discuss the effect of supplier selection on the company. Entering the 2014-2018 period, green procurement publications continued to experience a significant increase, around 27 article titles. This period can be said to be a stage of rapid growth, where researchers' interest in green procurement continues to increase.
The growth of green procurement in the 2014-2018 period has been discussed by researchers, such as Dobos and Vörösmarty (2014) and Lin and Juang (2014) discussed green supplier selection specific methods. In 2015, Freeman et al. (2015) and Hu et al. (2015) discussed supplier selection in green procurement. Meanwhile, F. Yu et al. (2016) identify distances for product transportation based on supplier selection in a sustainable supply chain network. Then Qin et al. (2017) solved the green supplier selection problem using the multi-criteria method. The 2018 period was the peak of green supplier research, where this period was the peak of green procurement publications with a number of publications of around 27 article titles. Many researchers research the topic of green procurement for supplier selection, one of which is Lo et al. (2018), who create a model to solve problems in green supplier selection and order allocation.
Economic and environmental criteria were employed in the study "An integrated green supplier selection strategy with analytic network process and improved Grey relational analysis" Hashemi et al. (2015). The interdependencies between the criteria are dealt with in their paper using the analytic network process (ANP). Additionally, to weight the criterion and rank the supplier, Hashemi et al. (2015) adapted the conventional Grey relational analysis (GRA). Finally, the proposed method is used to solve a case study of the automotive industry. Hu et al. (2015) investigated the problem of green procurement in supplier selection by considering commercial and environmental criteria using the 2-tuple linguistic assessment method for green supplier selection. Then in the research of Igarashi et al. (2015), considering the criteria for the buyer's environment, namely there are four approaches to the problem of choosing a green supply chain, including ignoring, combining, insisting, and integrating. Decision-makers usually do not show complete rationality in decision-makers, so Qin et al. (2017) help decision-makers use the TODIM method. Further in the paper Lo et al. (2018), in its completion, proposed the TOPSIS model and fuzzy multi-objective linear programming to solve problems in green supplier selection and order allocation. Wei et al. (2020) completed the selection of green suppliers using the MABAC method. This proposed method serves to choose a suitable alternative in selecting green suppliers.
In green procurement management, one of the important activities is the selection of suppliers, especially to select green suppliers that are in line with the sustainable development strategy (Gao et al., 2021). Suppliers are upstream of the entire supply chain, and their activities are passed on to every node (Sun et al., 2021). Therefore, the role of green suppliers in cost savings and environmental protection can be passed on to all downstream nodes in the supply chain, thereby increasing the overall efficiency of the supply chain (Y. Yu et al., 2021). In this regard, the evaluation and selection of suppliers play an important role in the overall green supply chain, which will directly affect the competitiveness of enterprises and the operating results of the entire green supply chain (Dong et al., 2021). Therefore, a scientific and reliable method for selecting environmentally friendly suppliers is an important issue with real significance. Mahmoudi, Deng et al. (2021) has looked into the issue of MCDM's Ordinal Priority Approach (OPA) being used in selecting sustainable and green suppliers on several occasions.
In their paper, "Gresilient supplier selection through Fuzzy Ordinal Priority Approach: decisionmaking in the post-COVID era,  attempted to create an innovative decision-making technology to address the supplier selection issues caused on by the COVID-19 outbreak. In order to comprehend the resilient suppliers in the wake of the SC disruptions, the study demonstrates how the green and resilience criteria might be integrated. For the purpose of resolving supplier selection issues, the researcher is applying OPA to the Fuzzy OPA (OPA-F). Price, delivery, and quality are the only factors often considered in supplier selection. Sukmawati and Setiawan (2022) propose an approach for choosing environmentally friendly suppliers in their study, including economic and environmental criteria (not just cost-related ones). Therefore, the findings from this study related to the implementation of green procurement in supplier selection will assist in selecting green suppliers. This is useful for researchers and practitioners in the green supply chain. Thus, this paper provides a brief bibliometric review of the previous literature review on understanding green procurement in supplier selection. In addition, this paper analyzes the relationship in depth between the joint articles and the researcher's keywords to provide information about the research field.
A few publications discussing literature reviews on green supplier selection have recently been published. To fill this gap, this paper aims to use bibliometric analysis to summarize field studies and offer a relatively complete reference for future research.

Method
This research literature review is a bibliometric analysis approach using Publish or Perish and Vosviewer to identify, determine, map, and evaluate literature systematically and objectively against scientific literature on implementing green procurement in supplier selection. The general objectives of quantitative analysis of bibliography include: Designing more economical building systems and information networks, improving the efficiency level of information processing, identifying and measuring efficiency in existing bibliographic services, predicting publishing trends, and discovering and eliciting empirical laws that can provide a basis for the development of a theory in information science (Tague-Suteliffe, 1992).
The methodology used in this study is similar to the method proposed by (Fahimnia et al. (2015), which applies a five-stage literature review methodology, namely defining search terms and initial search results, selecting network search results. A description of the four stages carried out in this study to achieve the research objectives is as follows:

Defining search terms
At this stage, defining terms or keywords used for data collection related to green procurement through supplier selection is carried out. The researcher collected article data from the Publish or Perish database linked to www.schoolar.google.com. Before searching the database, keywords should be generated and recorded to ensure data validity. The combination of keywords in this research includes: "green procurement," or "procurement," or "supplier selection".

Initial search results
In this stage, collecting and storing the data of the most relevant articles is carried out. This search was carried out in 2022 with a search of 220 articles published from 1994 to 2022, shown in Figure 1. All initial search results are exported to .xlsx format to attach all author names, citations, document title, year, source title, volume, publication, page, number of citations, source and document type, abstract, and keywords. Then the .xlsx format is processed into a spreadsheet for sorting, and the next step is to make a graph using the Minitab software.
In making a graph about the number of articles per year using the help of Publish or Perish software and Minitab. Publish or Perish software is software that is connected to the google scholar search engine. This makes it easier for researchers to find journals and articles about green procurement through supplier selection. This software has a ranking feature so researchers can find the most relevant journals. It is said to be relevant if the journal has many quotes and is in accordance with green procurement in supplier selection. Furthermore, Minitab software is used to make it easier to graph the number of articles per year from 1994 to 2022.

Article selection
The selection of articles was carried out to ensure their relevance to the research objectives. The number of articles collected was 220. Then the next selection was carried out by carefully reading the article abstracts, and the final result was 170 articles published from 1994 to 2022.

Analysis of Publish or Perish and Vosviewer results
Bibliometric analysis with Publish or Perish is carried out based on the criteria of author name, citation, document title, year, source title, volume, publication, number of citations, source and document type, abstract, and keywords. Publish or Perish processes journals or articles quickly concerning the data with the keywords the author has searched for. Meanwhile, Vosviewer focuses on authors and keywords.

Result and discussion
In this section, we present and discuss the main results of bibliometric analysis. As described in the method section in Figure 2, data were processed using Publish or Perish analysis software and VOSviewer and with the help of Microsoft Excel. VOSviewer was chosen because it has a visualization tool that is easy to understand, and a Spreadsheet helps make it easier for writers to sort data. Therefore, Publish or Perish, VOSviewer and Spreadsheet were chosen to perform the bibliometric analysis in this study. Bibliometric review is used because of its scientific ability to visualize the network among sources, authors, and keywords (Mamun et al., 2021). In addition, Bibliometrics can communicate keyword findings better so that data exploration can be carried out to provide rich information to readers (Jiang et al., 2019). Besides being able to visualize data, the advantage of bibliometrics is that it can monitor knowledge in the available literature by paying attention to data quality because it can affect the analysis results (De Rezende et al., 2018). Figure 3 shows that the number of articles published during 1994-2021 is somewhat up and down. It can be observed that an increase occurred between the 2016-2018 period in as many as six articles. On the other hand, in the 2022 period, the number of articles decreased, with the number of articles published reaching 4. From the results of this analysis, it can be concluded that the number of articles published shows that the selection of suppliers in green procurement research has not been of great interest to researchers.  From 1994-2002, literature related to the concept of green procurement was scarce. It can be said that this period was the initial stage in the publication of the green procurement concept because there was only one article per year with four authors, such as Min (1994), discussing supplier selection. Meanwhile, Noci (1997), discusses the green vendor rating system for supplier selection. Braglia and Petroni (2000) examined quality orientation for supplier selection, and Kannan (2018) tried to analyze supplier selection's effect on a business's sustainability. In 2003, the publication of green suppliers experienced the development of discussion topics and an increase in the number of publications by three articles, with six authors trying to develop the concept of green suppliers in supplier selection. The topics studied are supplier selection criteria, the influence of supplier selection, supplier selection integrity, and problem-solving regarding supplier selection. Among the publications in the 2003-2009 period, supplier selection issues include an article compiled by Michelsen and Boer (2009), which states that green procurement is a major contributor to supplier selection, either in supplier prequalification or determining the final decision supplier.

Annual scientific production
Meanwhile, in 2012 several researchers discussed suppliers in the context of supplier selection with the concept of green procurement (Datta et al., 2012) and the supplier selection method (Pani et al., 2012). In contrast to other researchers, Goebel et al. (2012) discuss the effect of supplier selection on the company. Entering the 2014-2018 period, green procurement publications continued to experience a significant increase, around 27 article titles. This period can be said to be a stage of rapid growth, where researchers' interest in green procurement continues to increase.
The growth of articles on green procurement in the 2014-2018 period has been discussed by several researchers, such as Dobos and Vörösmarty (2014) and Lin and Juang (2014), which discuss green supplier selection using specific methods. In 2015, Freeman et al. (2015) and Hu et al. (2015) discussed supplier selection in green procurement. Meanwhile, F. Yu et al. (2016) identify distances for product transportation based on a sustainable supply chain network through supplier selection. Then, Qin et al. (2017) solve the green supplier selection problem using the multi-criteria method.
The 2018 period was the peak of green supplier research; this period was the peak of green procurement publications with 27 published article titles. Many researchers research the topic of green procurement for supplier selection, one of which is Lo et al. (2018), which makes a model to solve problems in green supplier selection and order allocation.

Scientific production publisher
Writing an article, of course, requires a reference that would strengthen the writing work that the researcher will publish. The references used should not use any written work, where the author needs to research whether the reference is trusted or has a good reputation. A reputable reference has a precise peer-review mechanism. Where there is a team of experts who review these references, it shows that the journal has the quality to be chosen as the reference we will use. References that can be used to strengthen scientific articles can be obtained from books, scientific articles, and written printed and electronic sources.
From the opinion above, it can be concluded that we need to research whether the source containing the reference is valid and reliable to get a reputable reference. The way to determine if the source is trustworthy is to know the number of articles published on the relevant source.
At this stage, we try to analyze the most relevant and reliable sources that produce scientific articles on green procurement on supplier selection. From 220 samples of structured literature studies from 1994-2022 on the topic of green procurement on supplier selection, we will analyze and sort according to the number of articles produced. There are 20 ranking sources or sources obtained after sorting from top to last according to the number of articles produced by these sources. The higher the ranking of the sources or sources indicates that the sources are the most relevant, clear, and trustworthy. The distribution of the 20 most relevant sources related to green procurement for supplier selection is shown in Figure 4.
From Figure 4, it can be concluded that the source of the Journal of Cleaner Production is ranked first, with a total of 41 scientific articles published on the topic of green procurement for supplier selection. Journal of Cleaner Production is a journal published by Elsevier Ltd. Located in the United Kingdom, where this organization handles the printing and distribution of print and digital publications. Overall the Journal of Cleaner Production received a rating of 1,404. Furthermore, based on the Scimago Journal Rank (SJR) or indicators that measure the scientific influence of journals based on the number of citations and the level of interest of the journal, this source was ranked 1,937. Therefore, it can be said that the source of the Journal of Cleaner Production is the most relevant, clear, and reliable source. Thus, the articles published by this source can be used as a reference source for researchers who will research green procurement for supplier selection. Second place is the International Journal of Production Economics, with 17 published scientific articles. It was followed by the International Journal of Production Research which produced 11 scientific articles on green procurement for supplier selection.

Top ten authors based on keyword relevance
In this section, we analyze 220 samples of bibliography to then rank the authors according to the accuracy or relevance of the title, content, and results of the articles published by the author on the topic of green procurement and supplier selection. This ranking is done using Publish or Perish software, which shows the results in Table 1. Table 1 shows the author's ranking according to the most relevant "green procurement" articles and "supplier selection". There are 10 top rankings for authors in their research related to green procurement. The higher the author's rating, the more relevant the article published by the author leads to the topic of green procurement for supplier selection. Then it can be seen that the number of citations or the number of articles quoted does not affect the author's rank. As explained earlier, this ranking is based on the relevance of the author's articles on green procurement for supplier selection.
It can be seen from Table 1 that Hu et al. (2015) was ranked first with a scientific article entitled "Optimization decision of supplier selection in green procurement under the mode of low carbon economy". This study tries to solve the supplier selection problems in green procurement using optimization decisions. This author comes from China, one of the countries with the highest publication rate of scientific articles. The second place is place Igarashi et al. (2015), who published scientific articles in the same year as Hu et al. (2015) and Igarashi et al. (2015), who wrote a scientific article with the title "Investigating the anatomy of supplier selection in green public procurement". In his research, he included environmental criteria in selecting suppliers in the Norwegian public sector by identifying environmental criteria in official tender documents related to 41 purchases and analyzing them both quantitatively and qualitatively.
The third rank of authors who publish articles most relevant to the keyword green procurement through supplier selection is Wei et al. (2020). Their article with the title "Green supplier selection with an uncertain probabilistic linguistic MABAC method" takes a case study from China trying to overcome the problem of selecting suppliers with probabilistic linguistics by using the MABAC method. In fourth place, Freeman et al. (2015), with an article entitled "Green supplier selection using an AHP-Entropy-TOPSIS framework," was published in the same year as the authors Igarashi et al. (2015) and Hu et al. (2015) used the AHP-Entropy-TOPSIS framework method to solve supplier selection problems. Igarashi et al. (2013) ranked fifth by publishing an article entitled "What is required for greener supplier selection? A literature review and conceptual model development," which is a literature review on what is needed for the selection of green suppliers. There are 60 articles published in 1991-2011 with variables such as supply chain, supplier selection process, and perspective from environmental criteria. The published article aims to integrate the various dimensions of green supplier selection and identify directions for future research. Moreover, the article "An extended TODIM multi-criteria group decision-making method for green supplier selection in interval type-2 fuzzy environment" by Qin et al. (2017) was ranked sixth. The research was conducted using TODIM behavior to solve supplier selection problems. In addition, it also provides a comparison between the IT2F-TODIM and IT2F-TOPSIS methods.
The publication of the article in 2018, ranked seventh written by Lo et al. (2018), with the article title "An integrated model for solving problems in green supplier selection and order allocation". The article solved supplier selection and order allocation problems using an integrated model. Furthermore, the article proposes a new model by integrating the best-worst method (BWM), a modified fuzzy technique for order preferences based on similarities with ideal solutions (TOPSIS), and fuzzy multi-objective linear programming (FMOLP). In the article, Lo et al. (2018) concluded that the results obtained indicate that the model used can effectively evaluate the performance of green suppliers and optimize order allocation for suppliers that meet specific requirements.
An article published in 2009 was ranked as the eighth most relevant article on green procurement through supplier selection with the title "Green procurement in Norway; a survey of practices at the municipal and county level" written by Michelsen and Boer (2009). The article focuses on surveying the city of Norway regarding green procurement to find out the importance of green procurement. Then the authors compare the information obtained from the city and information from potential suppliers to see whether suppliers and consumers agree about the importance of the environment in supplier selection.
S. P. , with an article entitled "Validation of performance measures for green supplier selection in Indian industries" ranked ninth for the most relevant article. This article aims to identify the critical manufacturing factors considered during supplier selection in the Indian manufacturing sector. Finally, Yazdani et al. (2017) wrote the most relevant article for the tenth rank titled "Integrated QFD-MCDM framework for green supplier selection". This article prioritizes an integrated approach to supplier selection by considering various environmental performance requirements and criteria.

Most cited publications in the Publish or Perish database
The most cited authors in this article are the top 10 authors calculated based on the number of citations relevant to green procurement based on the Publish or Perish database. Table 2 shows the ten most-cited journals by some articles distributed by journal and year. For example, 1,034 articles in the Publish or Perish database are quoted from the Govindan et al. (2020) entitled "Multi-criteria decision-making approaches for green supplier evaluation and selection: a literature review". The article is a literature review that discusses the evaluation and selection of suppliers with a multi-criteria decision method approach. Govindan et al. (2020) stated that this article would answer questions such as which selection approach is commonly applied, what environmental selection criteria are famous for green supplier management, and what limitations exist.

Ranking Sources Cites
The second-highest number with 931 citations in an article by Lee et al. (2009) titled "A green supplier selection methodology for the high-tech industry" has the second-highest ranking. According to Lee et al. (2009), green criteria are crucial for assessing a manufacturer's longterm sustainability. In order to differentiate between the criteria for evaluating traditional suppliers and green suppliers, researchers used the Delphi method. The suggested approach can assess and choose the best green supplier for cooperation.
The third-highest number with 896 citations is an article from Bai and Sarkis (2010) entitled "Integrating sustainability into supplier selection with gray system and rough set methodologies" Their article discusses integrating sustainability into supplier selection with gray system and rough set methodology. Furthermore, the fourth article related to supplier selection by Kannan and Tan (2002) with 628 quotes entitled "Supplier selection and assessment: Their impact on business performance". This article intends to analyze the effect of supplier assessment and selection on business performance. According to Kannan and Tan (2002), selection criteria that cannot be measured, such as the strategic commitment of suppliers to buyers, significantly impact performance compared to quantitative criteria such as supplier capabilities. Kazemi et al. (2019)) believe that publication citation is important because it is a benchmark that represents how often a publication is discussed in other scientific studies and how significant its impact is in the field. In addition, citations among academics are used as a benchmark to determine how much influence they have. Therefore, if the citation index of a publication or author is high, it will be considered influential in the field (Feng et al., 2017).

Ranking of scientific article producing countries
From a sample structured literature study for the period 1994-2022, as many as 220 scientific articles on the topic of green procurement on supplier selection, there are 20 countries ranked from top to last according to the number of articles produced by that country. The distribution of the 20 countries that produce the most scientific articles related to green procurement for supplier selection is shown in Figure 3. Figure 5 shows that China is ranked first with a number of publications of 42 articles. This is due to the demographic factor of China, which has many operating factories, so the issue of green procurement through supplier selection is interesting to discuss from China's perspective. In addition, adding a green procurement factor to maintain the impact of pollution and pollution around the factory has attracted researchers from China to research the topic of green procurement through supplier selection. One of the researchers from China who researched the topic of green procurement through supplier selection is This researcher uses optimization decisions for supplier selection in green procurement. Therefore, the article produced by Hu et al. (2015) is also the most relevant article on green procurement for supplier selection by ranking first. Moreover, one of the researchers from China, Wei et al. (2020) is also a researcher who produces scientific articles relevant to the topic and is ranked third for the articles most relevant to the topic of green procurement through supplier selection. In addition, Hu et al. (2015) and Wei et al. (2020) use the MABAC method to solve supplier selection problems in green procurement with uncertain probabilistic linguistics in China's manufacturing industries. From the two authors, it can be concluded that China has significantly contributed to producing scientific articles on the topic. This can be seen from the number of published articles and the quality of the scientific articles.
India is the second-ranked country with the most publications on green procurement through supplier selection, with 31 articles published. Like China, India also has several areas where many companies and factories are operating, so the issue of supplier selection is an ongoing issue to discuss. India is also very active in producing scientific papers; according to Scimago, India is ranked fifth globally. Several authors from India who have produced scientific articles on green procurement through supplier selection are (S. Deshmukh & V. Sunnapwar) Their research is related to the validation of performance measurement for the selection of green suppliers in the industry in India. This article produced by Deshmukh and Sunnapwar (2013b) is also relevant to the topic of green procurement for supplier selection, where the article is ranked 9th for the most relevant article. Kumar et al. (2018), one of the researchers from India, tried to solve the supplier selection problem using the Fuzzy Delphi and AHP-DEMANTEL methods, where the article was ranked 25 for articles relevant to the topic of green procurement for supplier selection. After India, Iran is ranked 3rd with a total of 27 scientific articles. The United States follows it with a total of 16 articles.

Cluster and network analysis
Cluster formation and network analysis in this study use Vosviewer software to provide graphical visualization and grouping analysis related to green procurement in supplier selection. This type of analysis uses occurrence and unit analysis by involving all keywords. The first step to creating a Vosviewer cluster is to create data that contains a collection of journals from Publish or Perish with the file format (.ris). This is because Vosviewer supports RIS, Endnote, and Ref works file formats. The second step is to input the data into Vosviewer based on the title and abstract fields to find the keywords that appear most often. The next step is automatically forming the cluster on Vosviewer after inputting data.
The link (edge) shows the number of relationships between one item (node) with another. As shown in Figure 3.4, there are big nodes of green procurement, sustainable procurement, and green public procurement, which means these words have a high frequency. Three clusters are based on color, including purple, green, and yellow. Figure 6 show cluster and keyword network analysis. In the first cluster, the research focuses on green procurement, sustainable procurement, case studies, and services. Green procurement research by Appolloni et al. (2014) discusses interesting findings by developing a conceptual framework for GP and suggests a number of directions for future research. Then sustainable procurement is investigated by Ghadimi et al. (2016). Aktin and Gergin (2016) also investigated the same topic and used a mathematical model in their solution. In addition, some studies apply green procurement to complete case studies, including the research of Garzon et al. (2019) with a case study of the chemical sector. Other studies by Yildiz (2019) with a case study of the automotive supply industry, and Qu et al. (2020) with a case study in a Chinese Internet company. Furthermore, green procurement research also refers to service/support, as in the research of Rhee et al. (2009). The research is about understanding the trade-offs in the supplier selection process: both from the role of flexibility, delivery, and service/support to add value.
The second cluster focuses on environmental aspects, green procurement practices, country, and case in the second cluster. The environmental aspect was investigated by D' Souza et al. (2015) by evaluating the role of green marketing and the company's internal practices for environmental sustainability. Furthermore, green procurement practices have been developed by several researchers by considering case studies or data collection in a country. For example, in a study by Rhee et al. (2009), empirical data for this study were collected from manufacturing organizations in Europe (Germany, France, Italy, and the UK) using a computer-based discrete choice survey of supplier selection. Moreover, Bag (2017) research develops a conceptual framework that considers institutional pressures, organizational culture, and green procurement and tests it further using primary survey data collected from South African manufacturing units.
Finally, in the third cluster, research focuses on green public procurement by Ahsan and Rahman (2017) and Igarashi et al. (2015). First, Ahsan and Rahman (2017) investigated the challenges of implementing green public procurement (GPP) in the Australian public health sector. Then develop a GPP implementation framework that combines five categories of challenges and sixteen challenges. The problem is structured as a multi-criteria analytical process-based (AHP) decisionmaking model. Furthermore, Igarashi et al. (2015) assessed the inclusion of environmental criteria in supplier selection in the Norwegian public sector. They identified environmental criteria in official tender documents related to 41 purchases and analyzed them quantitatively and qualitatively. The document demonstrates that buyers use different environmental criteria and can be used at different stages of the selection process.

Conclusion
According to literature review, one of the most significant activities in green procurement management is supplier selection, particularly green supplier selection that is in line with the sustainable development strategy. This is because suppliers are at the top of the supply chain, relaying their actions to every node. As a result, the value of green suppliers in terms of cost savings and environmental protection may be passed on to all downstream supply chain nodes, boosting the supply chain's overall efficiency. In this regard, supplier evaluation and selection play a critical role in the overall green supply chain, affecting corporate competitiveness and the overall green supply chain's operating performance.


Does hotel customer satisfaction change during the COVID-19? A perspective from online reviews

The coronavirus disease (COVID-19) has impacted the hotel industry in all aspects. However, the changes in hotel customer satisfaction deserve additional attention. Using online hotel reviews, this study explores the difference between the influencing factors of customer satisfaction before and after the COVID-19 outbreak. By use of Latent Dirichlet Allocation and sentiment analysis, factors that affect customer satisfaction and their emotional strength before and after the COVID-19 outbreak are extracted. Then, multiple regression models are established to compare the differences of the impact of each factor on hotel customer satisfaction in different periods. According to the aforesaid study, hotel customer satisfaction and its influencing factors have changed significantly during the pandemic; hotel customer satisfaction during the pandemic is mainly influenced by service quality. Accordingly, strategies are proposed for hotel managers to improve their customer satisfaction during the COVID-19.

Introduction
As a mega public health crisis, COVID-19 has impacted almost every aspect of the hotel business (Gössling et al., 2020). Hotel managers have summarized effective crisis response strategies from past calamities (Israeli et al., 2011;Pappas, 2015Pappas, , 2018. However, those short-term strategies only aimed at the rapid recovery of the financial performance and operation of the hotel industry (Ritchie & Jiang, 2019), thus little attention was paid to customer satisfaction. Owing to the unique features of the pandemic, such as long duration, large externalities, and broad impacts, COVID-19 has affected the confidence, behavior, and decision-making of customers more significantly than other crises of the same nature. Gössling et al. (2020) stated that COVID-19 would profoundly influence human society in a way similar to the climate change.
Along with the evolution of the Internet, user-generated content (UGC) has become a primary source of information for consumers and businesses to evaluate product satisfaction. Being a typical kind of UGC data, online reviews not only helps potential consumers make purchase decisions but also assists relevant managers in identifying factors that influence satisfaction (Guo et al., 2017). Consequently, such content facilitates customer satisfaction (Wang et al., 2017). The public, inexpensive, and accessible text data has an abundant sample size and contains a substantial amount of reliable information that cannot be reflected by hotel ratings (Banerjee & Chua, 2016). Hence, this study conducts an empirical analysis to identify the factors that truly affect customer satisfaction on the basis of hotel online reviews.
This study aims to answer the following questions: (1) Do significant differences exist in customer satisfaction before and after the COVID-19 outbreak? and (2) What are the primary factors that contribute to the differences in hotel customer satisfaction? To address these questions, the paper is organized as follows: Literature Review explains the motivations of the study by reviewing related literature; Methodology introduces research design, data preparation and research approach; the Results section analyzes the results of LDA, sentiment analysis and multiple regression. And in the end, Findings and Conclusion are presented to discuss theoretical and managerial implications.
Crisis management is critical to handling calamities, and the priority of such management varies according to different crises. The hotel industry used to adopt a risk management perspective, focusing more on safety and security (Wang & Ritchie, 2010). For effective management, both academia (Sharma et al., 2021) and industry continue to provide crisis management guidelines and policy framework for industry professionals. Such policies are dedicated to hotel financial performance and operational recovery (Ritchie & Jiang, 2019) and the resilience of hotels to changes (Melian-Alzola et al., 2020), but have not taken customer satisfaction into consideration. Furthermore, even if the current threat dissipates in the near future, people's perceptions of risk in travel or accommodation will change (Bae & Chang, 2020), and preventive measure will affect the attitude of tourists during the pandemic (Gupta et al., 2021). On the premise of meeting basic safety requirements after the COVID-19 outbreak, hotel managers must start thinking about the change in customer satisfaction to improve hotel guest experience.

Online reviews and customer satisfaction
Online hotel reviews contain a wealth of information, including various attributes of a hotel (Duan et al., 2016), such as cleanliness, location, room, service, sleep quality, and facilities (Liu et al., 2013). Customer satisfaction reveals the gap between the perceived and expected service quality (Padma & Ahn, 2020). Service quality is the critical factor that leads to different customer satisfaction (Yang & Lau, 2015) and customers are satisfied when their perceived service quality outperforms the expected service quality. Service quality can be assessed by hotel attributes extracted from online reviews (Guo et al., 2017). Hence, the overall satisfaction can be studied as the dependent variable, while the individual hotel attributes are studied as independent variables .
Substantive empirical studies have shown that customer satisfaction is evolving rather than remaining the same . Load of factors, such as service quality (Yang & Lau, 2015), facilities (Nunkoo et al., 2020), food (Mohsin & Lockyer, 2010), and hotel rating (Bi et al., 2020), etc. may be the reason for different customer satisfaction towards hotels. In other words, different service contexts and encounters will result in different customer satisfaction (Wu & Liang, 2009). However, COVID-19 has altered not only previous service process as exemplified in the offering of safe products and services (Foroudi et al., 2021), but also the expectations of consumers (Mehta et al., 2021). By taking advantage of this imbalance, it is possible for this paper to identify the key factors that affect customer satisfaction from online reviews of hotels before and after the outbreak of COVID-19.

Research design
To answer the research questions, this study employed a mixed analytical method. For the first research question, a paired sample t-test was conducted to reveal whether customer satisfaction of each hotel has changed. To answer the second research question, this study has firstly applied Latent Dirichlet Allocation and sentiment analysis, followed by the review analytics procedure from An et al. (2020) study, and then developed review characteristics, which included review topics and review sentiment; then a multiple linear regression analysis was carried out to test the relationship between rating and review characteristics in order to examine the key influencing factors of customer satisfaction before and after the outbreak of COVID-19.

Data preparation
Given the different severity of the pandemic in each city, this study selected Chengdu as its research object to avoid deviations on the study results. Having a well-developed hotel industry, Chengdu is among the top ten tourist destinations in China (Wang et al., 2019). As with many cities, it was affected by COVID-19. Ctrip (Nasdaq: TCOM) occupies an OTA market share of 55.7% (CTN, 2019), and provides a multifunctional online review system for consumers to post and read reviews. Kosova and Enz (2012) observed that the hotel industry can rebound within four months during a crisis, as reflected by the data from hotels in New York after 9/11 and the 2008 financial crisis. Hence, this study selected the representative data period from February to May 2020 while using the same period in 2019 as the control.
Using the Houyi (http://www.houyicaiji.com) crawler program, a total of 5,361 hotels were collected from the basic dataset in Chengdu. The data contained information on hotels, such as hotel name, star rating, and open time, as shown in Image 1. It also contained information on reviews, including review rating, review text, and check-in time of each review, as shown in Image 2. This study obtained 52,449 valid reviews from February to May 2020 and 104,613 valid reviews from the same period in 2019. A total of 4,942 hotels in Chengdu were finally identified as the study sample with a sampling efficiency of 92.2%.

Paired-samples T test
Paired sample t-test is one of the widely-used statistical procedures to compare the equality of the means of the two paired populations (Park et al., 2020), therefore, it can be used to reveal whether significant differences exist in hotel customer satisfaction before and after the COVID-19 outbreak.

LDA and sentiment analysis
Latent Dirichlet Allocation is a classic unsupervised machine learning technology which can effectively capture context-specific dimensions (Maier et al., 2018) and process vast unstructured online reviews with little manual intervention (Aggarwal & Gour, 2020;Vu et al., 2019). Naturally, LDA is frequently used to extract important attributes of a product or service; moreover, it is widely used in hotel customer satisfaction research (Guo et al., 2017). The LDA model assumes that whole text corpus can be represented with a set of topics. Mathematically, it adopts the method of "bag of words", which treats each text as a word frequency vector, thus transforming text information into digital information that is easy to calculate. The key to LDA is the choice of the number of topics, which will largely affect the classification effect.
By processing unstructured textual data, sentiment analysis can extract attitudes and affective tendencies from the text as part of natural language processing (García et al., 2012). The sentiment intensity of a customer toward a specific hotel attribute reflects the actual performance of this element (Bi, Liu, Fan, & Zhang, 2019), and has a positive impact on satisfaction after consumption as well (Kim & Park, 2017). In this study, a third-party module of Python, SnowNLP (https://pypi. org/project/snownlp) which is a classical sentiment analysis method is used to conduct sentiment analysis.

Multiple linear regression
Multiple linear regression is selected to analyze the variables and to divide the categories into dependent, control, and independent variables, which resulted in effectively controlling variables.
To assess customer satisfaction toward a hotel in the study time interval, a dependent variable, customers' rating of the hotel was taken as a measure of satisfaction at a given time . Then, each customer's satisfaction (rating) was transformed into hotel-caliber satisfaction by using the rating of an individual hotel in each year as the average of all reviews of the hotel during the period. By introducing hotel rating and operating years, two control variables, the hotel's characteristics were examined to see whether they were sufficient to test the rating of the hotel.
To examine the relationship between test scores and review characteristics, review topics and review sentiment were taken as independent variables. Review characteristics could be measured by the topic score and review sentiment value of the review LDA model . The topic score and the sentiment value of the reviews were transformed into hotel-caliber topic scores and sentiment values, which meant that the score or sentiment value of an individual hotel was the average of all reviews scored or sentiment value on a given topic for each period. Following the suggestion of Hayes (2013), the independent variables were centralized and the moderating effect term lead was calculated to explain the coefficients of the regression equation further.

Results of paired-Samples T test
The analysis shows a significant difference (t = 6.352,Sig. = 0.003) between the scores of 2019 and 2020, where the score for 2019 was 4.45, and the score for 2020 was 4.51. These values indicated that customers were more satisfied with hotels in 2020.

Recognition of review topics
After repeated attempts, it is determined that the number of topics is five, and the number of iterations is 1,500. These values signified that the model had optimal perplexity, and the clear distinction between key topics achieved effective text clustering. Table 1 shows the results of the extracted LDA topics, including Service (T1), Room (T2), Cleanliness (T3), Location (T4), and Value (T5), which had all been presented in previous studies (Guo et al., 2017; Moro et al., 2018;Zhao & Xu, 2018). Thus, the extracted scores of the topics for each review were retained to prepare for the next step of the study. Fig. 1 shows the sentiment distribution of reviews in 2019 and 2020 (the X-axis represents the sentiment value, and the Y-axis represents the percentage of reviews in a specific period). Accordingly, the points toward the X-axis are reviews with a positive sentiment value (the highest being 1), whereas negative reviews are arranged toward the left side, with the lowest being 0. Interestingly, the plot shows a nearly identical graphical distribution in 2019 and 2020, in which the distribution leans to the positive spectrum with a "saddle-shaped" distribution feature. Table 2 shows the results of the multiple linear regression analysis which is carried out to test the relationship between rating and review characteristics, including review topic and sentiment.

Results of regression analysis
Model 1 indicates whether the hotel's own characteristics are sufficient to test the rating of the establishment. According to the analysis, hotel grade had a positive effect on hotel rating, and operating years had a negative effect on hotel rating.
Model 2 tests the contribution of review topics to ratings (a measure of the likelihood that a particular topic is included in a review). The adjusted R-squared increased significantly. All topics had significant changes before and after the outbreak of COVID-19, with T1 (Service), T3 (Cleanness), T4 (Location), and T5 (Value) contributing positively to the score and T2 (Rooms) contributing negatively.
Model 3 proves that the sentiment was a strong predictor of scores, as its introduction substantially increased the adjusted R-squared across the model. Upon the inclusion of this variable, the service topic became insignificant in the model of the year 2020, where the word "pandemic" emerged as a topic.
Unsurprisingly, these review topics explained the large difference of scores (adjusted R-squared of 0.469 in 2019 and adjusted R-squared of 0.407 in 2020). In addition, different performances were observed in the two years. In terms of explanatory power, the model was significantly stronger in 2019 than in 2020, and this model was stronger for the period before the pandemic.

Discussion
A significant difference was observed in hotel customer satisfaction before and after the pandemic, with customers being more satisfied with hotels after the COVID-19 outbreak. Although this trend may be counterintuitive, it was found that even in a crisis, customers indeed would be satisfied with hotels. This finding is also supported by the research of Mehta et al. (2021) in North America and Europe. In accordance with the finding, it can be assumed that hotels in China may be able to maintain customer satisfaction by providing expected and desired service to customers. In addition to traditional service expectation, new expectations and service delivery arise due to the preventive measures required to be taken by COVID-19, such as social distancing and sanitization (Das & Tiwari, 2020).Hotels can satisfy new expectations by providing safe services, for instance, take-way food rather than eat-in Table 1 Key topics extracted from online reviews by LDA.  (Bhaskara & Filimonau, 2021), which reasonably explains customers' being more satisfied with hotels after the COVID-19 outbreak. Hotel review characteristics (both topics and sentiments) had a strong explanatory power for hotel satisfaction, and the impact of the pandemic on hotel satisfaction was mainly reflected in terms of service. In other words, service is of particular importance to hotel satisfaction during the pandemic (Albayrak, 2015). Although the COVID-19 pandemic will eventually subside, the negative environment surrounding the service experience will linger (Barnes et al., 2020). Sentiment has strong explanatory power for hotel satisfaction . With a significantly negative moderating effect, sentiment contributes to 52% of customer satisfaction (Barnes et al., 2020). Stronger sentiment usually represents higher expectations, as factors that may cause customers to be more easily frustrated can reduce their satisfaction toward a hotel. The moderating effect of the cleanness attribute on satisfaction is significantly stronger in 2020 than in 2019 possibly because customers have higher expectations for this aspect in 2020. Unless a hotel meets customer expectations, the customers will be dissatisfied more easily. Image 1. Sample of hotels displayed on Ctrip.

Conclusion
Almost all hotels were impacted by COVID-19, however, whether hotel customer satisfaction and its influencing factors have changed or not has not been studied so far. To fill this gap, 4,942 hotels in Chengdu displayed in Ctrip were selected to obtain 52,449 valid reviews from February to May 2020 and 104,613 valid reviews from the same period in 2019. LDA and sentiment analysis were applied to extract the factors affecting customer satisfaction and their emotional strength before and after the pandemic. Then, multiple regression models were established to compare the differences of the impact of each factor on hotel customer satisfaction in different periods.
This finding shows that hotel customer satisfaction before and after the COVID-19 outbreak has a significant difference. To be more specific, hotel customer satisfaction becomes higher after the pandemic. Service, Room, Cleanness, Location, Value and Sentiment all impact customer satisfaction significantly before the pandemic, however, Service no longer has significant impact on hotel customer satisfaction after the pandemic, hence one of the main factors significantly affecting the shift in customer satisfaction is Service. Such a finding is in conformity with that of Yang and Lau (2016) who suggested that service quality is the key to customer satisfaction.

Theoretical implication
The findings of this study can provide a series of important enlightenment for the research on the effects of UGC on customer satisfaction during a crisis by extracting information from online reviews. First, the study confirms that a hotel can have satisfied customers even in the unprecedented and highly unpredictable COVID-19 crisis. The COVID-19 pandemic will be a new normal like climate change, with longlasting effects on society (Gössling et al., 2020). This study has introduced a long-term goal-customer satisfaction-into the field of hotel crisis management and compares the satisfaction of hotels before and after the outbreak of COVID-19 crisis. Indeed, hotel satisfaction has changed after the COVID-19 outbreak.
Second, this study provides new explanatory variables for the influencing factors of satisfaction-review topics and sentiments. Both review topics and sentiments have strong explanatory power for satisfaction (ratings) in the study. In the product evaluation context, the nonsentimental words represented by a review topic may convey emotional connotations similar to the effect of emotional words. This characteristic is consistent with the study of Xiang et al. (2015) wherein topical "factors" generated from review texts are highly correlated with the rating.
Finally, this study sheds light on the link between the topics of customer experience and their satisfaction displayed in online reviews, which turns out to be in consistency with the findings of Crotts et al. (2009) and Zhang and Cole (2016), suggesting that UGC (e.g., online reviews) can be used to identify meaningful structures between aspects and attributes related to hotels and tourism products. Moreover, this conclusion is applicable before and after the outbreak of COVID-19.

Managerial implications
This study is quite pragmatic for management practice. Firstly, by Image 2. Sample of online reviews on Ctrip.
revealing that customers can be more satisfied with hotels after the COVID-19 outbreak, hotel managers can become more confident in managing customer satisfaction. Secondly, the study offers a new approach to listen to the voice of customers through which hotel managers can ascertain the important dimensions of consumer satisfaction. For example, T2 (Room) affects hotel customer satisfaction negatively before and after the COVID-19 outbreak, thus it can be presumed that T2 (Room) might be the source of complaints and dissatisfaction. Hotel managers can also monitor the trends of customer satisfaction through the voice of customers so as to make corresponding adjustments strategically. For example, after the outbreak of the pandemic, service becomes critical for hotel customer satisfaction. Hotel managers should make sure that guests are aware of the special situation brought about by COVID-19 and convince their customers that safe service is guaranteed. Specifically, hotels can introduce artificial intelligence (AI), service robots and other new technologies to effectively improve the service quality and reduce the perceived risk of customers.

Limitations and Prospects
This study has two main limitations in choosing Chengdu as a case study location. Each city issues different public health emergency response measures for the COVID-19 pandemic. As such, the effect of these circumstances on customer satisfaction is not consistent. Therefore, follow-up studies should cover more cities to reinforce the findings of the current research. The conclusions of this study may not be applicable to all situations, as the pandemic has not been subdued at the time of writing. Further studies can make more sufficient arguments based on the development of the pandemic by following up the research using multiple crawling data. According to the current results, additional research may also include offline customers by combining questionnaires or further considering qualitative research methods in the future.

Funding
This work is supported by Innovative Spark Project of Sichuan University (Grant No. 2019hhs-15).

Declaration of Competing interest
No potential conflict of interest was reported by the author(s).