How Service Robot Anthropomorphism and Employee Self-Efficacy Shape Collaborative Performance

Abstract

This study aims to elucidate the mechanisms through which service robot anthropomorphism and employee self-efficacy influence human–robot collaborative performance in the service industry. A conceptual model was developed in which perceived usefulness, perceived ease of use, perceived competence, and collaborative intention operate as multiple mediators, clarifying how robot-assisted collaboration enhances performance. Using structural equation modeling, we analyzed data from 418 valid questionnaires collected from employees in the hospitality and catering sectors. The results indicate that both service robot anthropomorphism and employee self-efficacy significantly enhance employees’ perceptions of usefulness, ease of use, and competence. These perceptions, in turn, positively affect collaborative intention, which subsequently improves collaborative performance. Moreover, perceived usefulness, ease of use, and competence jointly mediate the relationships between the antecedent variables (anthropomorphism and self-efficacy) and collaborative performance. In summary, the findings elucidate a sequential mediation pathway: robot anthropomorphism and employee self-efficacy boost key perceptions, thereby fostering collaborative intention and ultimately enhancing performance. The study provides theoretical insights into the psychological mechanisms through which anthropomorphic design features and employee self-efficacy shape effective human–robot collaboration and offers practical guidance for the successful integration of service robots into service operations.

Keywords

service robot anthropomorphism self-efficacy perceived usefulness perceived ease of use perceived competence collaborative performance

Introduction

Service robots are increasingly deployed in hospitality and catering, serving as both tools and collaborators.^1,2 As human–robot collaboration emerges,³ it is crucial to understand how employees’ psychological adaptation shapes collaborative performance.²

Most prior research, grounded in the technology acceptance model (TAM),^4,5 emphasizes adoption, showing that perceived usefulness and ease of use drive acceptance.⁶ However, in hospitality and catering settings, employees actively collaborate with robots to deliver services.⁷ Acceptance alone cannot fully explain collaborative outcomes.

Anthropomorphism may foster trust and engagement but also create unrealistic expectations.^8,9 Its impact on employees’ perceptions, intentions, and coordination-intensive performance remains unclear.

Self-efficacy, central to social cognitive theory (SCT),¹⁰ reflects employees’ confidence in managing new technologies. In dynamic, customer-facing environments,¹¹ efficacy beliefs directly shape how employees interpret robot cues, manage unexpected situations, and perform collaborative tasks.^12,13 While widely studied in adoption research, its direct role in influencing perceptions, collaborative intention, and performance is underexplored.

To address these gaps, this study integrates the TAM, SCT, the stereotype content model,¹² and the technology–performance chain (TPC)¹⁴ to examine how anthropomorphism and self-efficacy jointly shape employees’ perceptions, collaborative intention, and performance.

Literature Review

Anthropomorphism and employees’ perceptions of service robots

Anthropomorphism attributes human traits to nonhuman entities.¹⁴ The three-factor theory suggests that such cues activate human-related schemas, shaping employees’ perceptions.^15,16 Under certain conditions, excessive anthropomorphism may foster unrealistic expectations, disappointment, or reduced authentic interaction.^17–19 In hospitality and catering, where service is interpersonal and customer expectations central,²⁰ anthropomorphic features can make robots appear more useful,²¹ easier to interact with,²² and more competent,^23,24 thereby facilitating collaboration.²⁵ Accordingly: H1a–c:

Anthropomorphism positively influences perceived usefulness, ease of use, and competence.

Self-efficacy and employees’ perceptions of service robots

Self-efficacy from SCT reflects employees’ confidence in managing collaborative tasks with robots.¹⁰ Although prior research cautions that overconfidence may lead employees to underestimate task complexity or robot limitations,^26,27 in hospitality and catering, where adaptation and teamwork are critical, efficacy beliefs shape how employees interpret robot tasks. High self-efficacy improves perceptions of usefulness, ease of use, and competence, motivating learning and integration.^28,29 Accordingly: H2a–c:

Self-efficacy positively influences perceived usefulness, ease of use, and competence.

Employees’ perceptions of service robots and collaborative intention

The TAM highlights usefulness and ease of use as adoption drivers.⁵ Prior research also cautions that highly competent robots may, under certain conditions, trigger replacement concerns and undermine employee acceptance.³⁰ In hospitality and catering, employees must collaborate with robots, not merely adopt them. Collaborative intention reflects motivation to jointly perform tasks and coordinate actions.^31,32 Perceived usefulness signals performance gains,³³ ease of use lowers collaboration barriers,^33,34 and competence builds confidence in robots as reliable teammates.^24,35 Thus: H3–5:

Perceived usefulness, ease of use, and competence positively influence collaborative intention.

Collaborative intention and collaborative performance

The TPC suggests that acceptance enhances performance.^36,37 In hospitality and catering, where employees and robots must closely coordinate to deliver services, collaborative intention reflects employees’ proactive motivation to engage with robots as teammates. Stronger intention fosters involvement, coordination, and adaptation,³⁸ improving collaborative performance—defined as the effectiveness, quality, and efficiency of jointly executed service activities.^39,40 Thus: H6:

Collaborative intention positively influences collaborative performance.

The research model is shown in Figure 1.

FIG. 1.

The proposed theoretical model. The model positions anthropomorphism and self-efficacy as antecedents and identifies perceived usefulness, ease of use, competence, and collaborative intention as multiple mediators in the pathway to collaborative performance.

Methods

Participants

We surveyed employees from the budget hospitality and the catering industries, including managers, foremen, supervisors, and receptionists. The study was approved by the Ethics Committee of the Institute of Neuroscience and Cognitive Psychology at Anhui Polytechnic University, and all participants provided informed consent. The data were collected over 3 months via Wenjuanxing (https://www.wjx.cn/) through both on-site and online questionnaires. Of 430 responses, 418 were valid after excluding incomplete or patterned responses. Participants reported a moderate-to-high familiarity with service robots (M = 4.91, SD = 1.49). Table 1 summarizes demographic characteristics, including gender, job roles, and prior robot experience.

Table 1.

Participant Demographics

Descriptive	Frequency (N^a)	Percentage (%)
Gender
Female	199	47.608
Male	219	52.392
Job role
Hospitality services
Room supervisors	59	14.115
Room attendants	9	2.153
Front office managers	8	1.914
Front desk receptionists	55	13.158
Reservation customer service	23	15.789
Doormen	7	1.674
Catering services
Managers	24	5.742
Foremen	30	7.177
Waiters/Waitresses	26	6.220
Door attendants	25	5.918
Cashiers	42	10.049
Food delivery staff	31	7.416
Chefs	9	2.153
Cleaners	14	3.349
Others	13	3.110
Experience with service robots
Used	288	68.900
Not used	130	31.100

Number of participants in each category (frequency).

Measures

We measured seven constructs using validated scales adapted for the hospitality and catering context (Table 2). Self-efficacy was assessed with the 10-item General Self-Efficacy scale.⁴² Robot anthropomorphism was measured using the Kim and McGill scale.⁴¹ Perceived usefulness and perceived ease of use were adapted from Heerink et al.⁴³ Perceived competence was assessed via McClelland’s Competency scale.⁴⁴ Collaborative intention was adapted from Agarwal and Karahanna,⁴⁵ and collaborative performance from Gonzalez-Mulé et al.⁴⁶ All items were rated on a 7-point Likert scale (1 = “strongly disagree” and 7 = “strongly agree”).

Table 2.

Measurement Items, Sources, and Convergent Validity Statistics

Variables	Items	Source/adaptation	Factor loading	Cronbach’s α	CR	AVE
1. Anthropomorphism	When working with service robots, it looks like a person.When working with service robots, it seems almost as if they have free will.When working with service robots, it seems almost as if they have intentions.When working with service robots, it feels almost like interacting with a human.	Kim and McGill (2011)⁴¹	0.714	0.823	0.747	0.539
2. Self-efficacy	I can always manage to solve difficult problems if I try hard enough.If someone opposes me, I can find the means and ways to get what I want.It is easy for me to stick to my aims and accomplish my goals.I am confident that I could deal efficiently with unexpected events.Thanks to my resourcefulness, I can handle unforeseen situations.I can solve most problems if I invest the necessary effort.I can remain calm when facing difficulties because I can rely on my coping abilities.When I am confronted with a problem, I can usually find several solutions.No matter what comes my way, I am usually able to handle it.When there are problems, I can usually think of a solution.	Scholz et al. (2002)⁴²	0.634	0.893	0.893	0.456
3. Perceived usefulness	When working with service robots, I find them useful for my tasks.When working with service robots, I find it convenient to have them.When working with service robots, I believe they can help me accomplish many tasks.	Heerink et al. (2010)⁴³	0.736	0.791	0.792	0.559
4. Perceived ease of use	When working with service robots, I find them easy to use.When working with service robots, I think I can use them without any assistance.When working with service robots, I think it would be easy for me to become skillful at using them.	Heerink et al. (2010)⁴³	0.605	0.767	0.771	0.531
5. Perceived competence	When working with service robots, I find that they can take initiative and complete tasks autonomously without constant supervision.When working with service robots, I find that they demonstrate team-oriented awareness and a willingness to assist others.When working with service robots, I find that they clearly understand task objectives and performance standards, ensuring successful task completion.	McClelland (1973)⁴⁴	0.797	0.841	0.841	0.638
6. Collaborative intention	I think working with service robots would be very unpleasant (reverse-coded)I strongly support working with service robots.I intend to work with service robots at some point in the future.	Agarwal and Karahanna (2000)⁴⁵	0.613	0.742	0.747	0.498
7. Collaborative performance	Working with service robots enables me to complete service tasks.Working with service robots enables me to deliver services according to established standards.Working with service robots enables me to provide services within the required time frame.	Gonzalez-Mulé et al. (2014)⁴⁶	0.693	0.812	0.812	0.595

AVE, average variance extracted; CR, composite reliability.

Statistical analysis

Analyses were conducted using SPSS 26.0 and AMOS 26.0. SPSS was used for reliability and descriptive statistics, while AMOS was used for confirmatory factor analysis and structural equation modeling. Convergent and discriminant validity were assessed using factor loadings, composite reliability (CR), average variance extracted (AVE), and Heterotrait-Monotrait Ratio (HTMT) ratios. Measurement invariance tests were also performed to ensure model stability across subgroups.

Common method bias was assessed using Harman’s single-factor test and the common latent factor (CLF) approach. Bootstrapping was employed to test multiple-chain mediation effects.

Results

Reliability and validity test

Cronbach’s alpha values ranged from 0.742 to 0.893, indicating satisfactory internal consistency. Confirmatory factor analysis (CFA) results showed that all factor loadings exceeded 0.60 and all CR values were above 0.70. AVE values exceeded the 0.50 threshold for most constructs, while self-efficacy showed an AVE of 0.456, which is considered acceptable given its broad, trait-like nature, supporting convergent validity (Table 2).^47,48 Discriminant validity was confirmed as the square roots of AVEs exceeded interconstruct correlations and all HTMT ratios were below 0.85 (Table 3).^49,50 Measurement invariance was supported (ΔCFI < 0.01), indicating stability across groups and reinforcing the robustness of the findings.⁵¹

Table 3.

Correlation, Square Root of Average Variance Extracted, and HTMT Ratios

Variables	1	2	3	4	5	6	7
1. Anthropomorphism	0.734	0.384	0.330	0.411	0.671	0.432	0.540
2. Self-efficacy	0.386***	0.675	0.338	0.380	0.378	0.462	0.583
3. Perceived usefulness	0.325***	0.335***	0.748	0.661	0.274	0.381	0.500
4. Perceived ease of use	0.404***	0.373***	0.654***	0.728	0.369	0.311	0.591
5. Perceived competence	0.666***	0.380***	0.271***	0.368***	0.799	0.364	0.484
6. Collaborative intention	0.417***	0.462***	0.355***	0.386***	0.31***	0.706	0.628
7. Collaborative performance	0.536***	0.584***	0.485***	0.497***	0.576***	0.61***	0.771

Diagonal elements (bold) represent the square roots of AVE. Values below the diagonal are correlations. Values above the diagonal are HTMT ratios.

***p < 0.001.

AVE, average variance extracted.

Common method bias test

Harman’s single-factor test showed the first factor accounted for 30.179 percent of variance, below the 40 percent threshold, indicating limited common method bias.⁵² A CLF test further confirmed this: adding a method factor minimally affected model fit (ΔCFI < 0.01, ΔRMSEA < 0.01) and structural paths (changes < 0.20), suggesting common method bias does not threaten validity.⁵³

Hypotheses testing

The structural model fit well: χ²/df = 1.788 (<3), root-mean-squared error of approximation (RMSEA) = 0.043 (<0.05), comparative fit index (CFI) = 0.941 (>0.90), and Tucker–Lewis index (TLI) = 0.935 (>0.90). Path coefficients are presented in Figure 1. Competing models did not improve fit, and multigroup analysis (e.g., by gender) revealed no significant differences, confirming the robustness and stability of the hypothesized relationships.

Path analysis

Table 4 presents standardized path coefficients and R² values. Both anthropomorphism and self-efficacy significantly enhanced employees’ perceptions of usefulness (β = 0.261 vs. 0.273), ease of use (β = 0.332 vs. 0.270), and competence (β = 0.616 vs. 0.161), all p < 0.01, supporting H1a–c and H2a–c. Anthropomorphism had a notably stronger effect on perceived competence. Perceived usefulness (β = 0.317), ease of use (β = 0.184), and competence (β = 0.330) positively predicted collaborative intention, all p < 0.01, supporting H3–H5, with competence exerting the strongest effect. Finally, collaborative intention positively influenced collaborative performance (β = 0.720, p < 0.001), supporting H6. Overall, the R² values indicate satisfactory explanatory power for all endogenous constructs.⁵⁴

Table 4.

Results of Path Analysis

Hypothetical path relation	β	SE	P
Anthropomorphism→Perceived usefulness	0.261	0.072	<0.001
Self-efficacy→Perceived usefulness	0.273	0.088	<0.001
Anthropomorphism→Perceived ease of use	0.332	0.062	<0.001
Self-efficacy→Perceived ease of use	0.270	0.073	<0.001
Anthropomorphism→Perceived competence	0.616	0.070	<0.001
Self-efficacy→Perceived competence	0.161	0.070	0.002
Perceived usefulness→Collaborative intention	0.317	0.050	<0.001
Perceived ease of use→Collaborative intention	0.184	0.058	0.003
Perceived competence→Collaborative intention	0.330	0.053	<0.001
Collaborative intention→Collaborative performance	0.720	0.095	<0.001

R² values are reported for endogenous constructs only: perceived usefulness (0.197), perceived ease of use (0.252), perceived competence (0.482), collaborative intention (0.369), and collaborative performance (0.518).

P, probability value; β, standardized regression coefficient; SE, standard error.

Multiple-chain mediating effect

Bootstrap results (Table 5) show that the 95 percent confidence intervals for the paths Anthropomorphism→Perceived usefulness/ease of use/competence→Collaborative intention→Collaborative performance and self-efficacy→Perceived usefulness/ease of use/competence→Collaborative intention→Collaborative performance exclude 0 (all p < 0.05), confirming the multiple-chain mediating effects.

Table 5.

Results of Multiple Mediating Effects

Path relation	SE	Effect size	95 percent CI	P
Anthropomorphism→Perceived usefulness→Collaborative intention→Collaborative performance	0.007	0.019	(0.008, 0.036)	<0.001
Anthropomorphism→Perceived ease of use→Collaborative intention→Collaborative performance	0.007	0.015	(0.003, 0.032)	0.010
Anthropomorphism→Perceived competence→Collaborative intention→Collaborative performance	0.015	0.029	(0.001, 0.060)	0.044
Self-efficacy→Perceived usefulness→Collaborative intention→Collaborative performance	0.006	0.017	(0.007, 0.032)	<0.001
Self-efficacy→Perceived ease of use→Collaborative intention→Collaborative performance	0.006	0.012	(0.002, 0.026)	0.013
Self-efficacy→Perceived competence Collaborative intention→Collaborative performance	0.007	0.019	(0.006, 0.035)	0.001

P, probability value; SE, standard error; 95 percent CI, 95 percent confidence interval.

Discussion

Theoretical contributions

This study advances understanding of human– robot collaboration in hospitality and catering. First, it clarifies how anthropomorphism and self-efficacy shape collaborative performance. Both factors enhance employees’ perceptions of usefulness, ease of use, and competence, which foster collaborative intention and, in turn, improve performance. In service triads involving employees, robots, and guests, strong collaborative intention helps employees compensate for robots’ limitations (e.g., inflexibility), enabling smoother service delivery.⁵⁵ In China’s collectivist and pragmatic culture, guided by harmony and guanxi-driven reciprocity, collaborative intention may carry a moral dimension: it reflects a sense of obligation to contribute to in-group goals rather than a purely individual preference.⁵⁶ This moral framing may help transform intention into active coordination and mutual adjustment, thereby potentially strengthening the intention–performance link.^57,58

Second, the study extends TAM by moving beyond technology adoption to employee–robot collaboration in joint task execution and by incorporating perceived competence as a core belief along with usefulness and ease of use. Our findings demonstrate that in hospitality and catering contexts, employees’ collaborative intention depends not only on functional evaluations but also on assessments of robots’ task competence. Notably, perceived competence emerges as the strongest predictor of collaborative intention, exceeding both usefulness and ease of use. In dynamic, multitasking hospitality settings, competent robots enhance confidence in joint execution.⁵⁹ Collectivist norms may further magnify this effect, as perceived competence can serve as a relational signal of the robot’s reliability as an in-group member. Thus, competence transforms TAM from an adoption-oriented framework into a collaboration-oriented one, where robots are evaluated not merely as tools but as capable team actors that sustain collective efficacy and reciprocity.⁶⁰

Last, this study clarifies the distinct yet complementary roles of anthropomorphism and self-efficacy. Anthropomorphic cues enhance perceptions of usefulness, ease of use, and particularly competence by making robots appear capable and socially compatible.⁶⁰ Self-efficacy also contributes to these perceptions and directly promotes collaboration by enabling employees to interpret anthropomorphic cues effectively and adapt to robotic partners.^61,62 However, self-efficacy has a weaker impact on perceived competence compared to anthropomorphism, reflecting that employees’ confidence in their own abilities does not directly translate into higher assessments of robot ability. In the collectivist and pragmatic culture, competence may be judged instrumentally by observable contributions to shared tasks. Thus, external design cues (anthropomorphism) that signal capability are weighted more heavily than internal employee beliefs (self-efficacy) in forming competence perceptions, explaining this asymmetry. Positioned as an antecedent rather than a moderator,²³ self-efficacy thus plays a foundational role in culturally influenced human–robot teamwork.²⁹

Practical implications

This study offers several actionable insights. First, robot design should incorporate anthropomorphic features that enhance perceived usefulness, ease of use, and competence, while avoiding cues that create unrealistic expectations. Second, organizations should strengthen employee self-efficacy by providing task-specific robot usage training and hands-on practice with intuitive interfaces, enabling employees to collaborate confidently and effectively.²⁹ Third, clear task-delegation guidelines are needed to balance reliance on robots with timely human intervention. Last, managers should anticipate ethical and psychological risks, such as frustration when anthropomorphic cues fail to match actual robot capabilities, and align robot capabilities with employee expectations.

Limitations and future research

Several limitations remain. First, focusing on hospitality and catering constrains external validity; replications in other industries are necessary to assess the broader applicability of the findings. Second, the cross-sectional, self-report design limits causal inference; longitudinal or experimental studies could better capture the evolving and interactional nature of human–robot collaboration. Third, linear modeling cannot detect potential nonlinear or threshold effects (e.g., excessive anthropomorphism, self-efficacy, or competence). Although no counterproductive effects emerged within the observed data range, future research should test whether extreme levels yield diminishing or negative effects, using curvilinear modeling, experimental manipulation, or experience-sampling designs. Last, because cultural values were not directly measured and no cross-cultural comparisons were conducted, the cultural interpretations offered here remain tentative; future studies could test construct equivalence and enhance generalizability through cross-cultural research.

Conclusions

This study demonstrates that both service robot anthropomorphism and employee self-efficacy enhance employees’ perceptions of usefulness, ease of use, and competence, which, in turn, increases collaborative intention and improves collaborative performance. These perceptions serve as mediators connecting anthropomorphism and self-efficacy to collaborative performance. This study advances theoretical understanding of how these factors shape human–robot collaboration and offers practical guidance for integrating service robots into the hospitality and catering industries.

Footnotes

Acknowledgment

The authors thank the editor and anonymous reviewers for their valuable comments and advice.

Authors’ Contributions

Y.C.: Conceptualization, formal analysis, funding acquisition, methodology, project administration, supervision, validation, visualization, writing—original draft, writing—review & editing. X.H.: Conceptualization, investigation, methodology, software, validation, visualization, writing—original draft. M.L.: Funding acquisition, writing—review & editing. W.L.: Validation, writing—review & editing.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This work is supported by the Humanities and Social Sciences of the Ministry of Education of China (No. 24YJA630003), the Natural Science Foundation of Anhui Province (No. 2208085MG183), and the Anhui Province Social Science Innovation and Development Research Project (No. 2024CX067).

References

1. Shum

, Kim

, Calhoun

, et al. “I was so scared I quit”: Uncanny valley effects of robots’ human-likeness on employee fear and industry turnover intentions. Int J Hosp Manag 2024;120:103762; doi: 10.1016/j.ijhm.2024.103762

2. Tan

, Li

. Working with robots makes service employees counterproductive? The role of moral disengagement and task interdependence. Tour Manag 2025;111:105233; doi: 10.1016/j.tourman.2025.105233

3. Psarakis

, Nathanael

, Marmaras

. Communicating robots’ intent through visual cues enhances human anticipatory behavior in human–dual robot collaboration. Robot Comput Integr Manuf 2025;92:102886; doi: 10.1016/j.rcim.2024.102886

4. Khan

, Mehmood

. Factors affecting innovation resistance of fast-food employees’ usage intention of robots: An integrative perspective. Jhti 2024;7(3):1456–1474; doi: 10.1108/JHTI-08-2023-0515

5. Davis

. Perceived usefulness, perceived ease of use, and user acceptance of information technology. Mis Q 1989;13(3):319–340; doi: 10.2307/249008

6. Parvez

, Arasli

, Ozturen

, et al. Antecedents of human-robot collaboration: Theoretical extension of the technology acceptance model. JHTT 2022;13(2):240–263; doi: 10.1108/JHTT-09-2021-0267

7. Chen

, Huang

, Miao

. Service robot acceptance: Agenda for tourism and hospitality research. TR 2025;80(4):871–893; doi: 10.1108/tr-03-2024-0159

8. Zhang

, Cao

, Proctor

, et al. Emotional experiences of service robots’ anthropomorphic appearance: A multimodal measurement method. Ergonomics 2023;66(12):2039–2057; doi: 10.1080/00140139.2023.2182751

9. Wang

, Ma

, Leung

. Working with robots: A job design perspective of hospitality employees’ collaboration intentions with service robots. J Hosp Tour Manag 2024;61:66–77; doi: 10.1016/j.jhtm.2024.09.012

10.

10. Bandura

. Self-efficacy: Toward a unifying theory of behavioral change. Adv Behav Res Ther 1978;1(4):139–161; doi: 10.1016/0146-6402(78)90002-4

11.

11. Phillips

, Odekerken-Schröder

, Russell-Bennett

, et al. Service robot–employee task allocation strategies: Well-being within the intrusion challenge. J Serv Manag 2025;36(4):523–548; doi: 10.1108/JOSM-11-2023-0466

12.

12. Song

, Zhang

, Wu

. Driven by technology or sociality? Use intention of service robots in hospitality from the human–robot interaction perspective. Int J Hosp Manag 2022;106:103278; doi: 10.1016/j.ijhm.2022.103278

13.

13. Turja

, Taipale

, Kaakinen

, et al. Care workers’ readiness for robotization: Identifying psychological and socio-demographic determinants. Int J of Soc Robotics 2020;12(1):79–90; doi: 10.1007/s12369-019-00544-9

14.

14. Epley

, Waytz

, Cacioppo

. On seeing human: A three-factor theory of anthropomorphism. Psychol Rev 2007;114(4):864–886; doi: 10.1037/0033-295X.114.4.864

15.

15. Waytz

, Heafner

, Epley

. The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. J Exp Soc Psychol 2014;52:113–117; doi: 10.1016/j.jesp.2014.01.005

16.

16. Park

, Lee

, Chung

. Do anthropomorphic chatbots increase counseling satisfaction and reuse intention? The moderated mediation of social rapport and social anxiety. Cyberpsychol Behav Soc Netw 2023;26(5):357–365; doi: 10.1089/cyber.2022.0157

17.

17. Mulcahy

, Riedel

, Keating

, et al. Avoiding excessive AI service agent anthropomorphism: Examining its role in delivering bad news. JSTP 2024;34(1):98–126; doi: 10.1108/JSTP-04-2023-0118

18.

18. Chuah

SH-W

, Soeiro

. When robot appearance and service style interact to influence customers’ willingness to pay: The mediating role of perceived restaurant quality. Int J Hosp Manag 2025;127:104117; doi: 10.1016/j.ijhm.2025.104117

19.

19. Saputra

, Buhalis

, Augustyn

, et al. Anthropomorphism-based artificial intelligence (AI) robots typology in hospitality and tourism. JHTT 2024;15(5):790–807; doi: 10.1108/JHTT-03-2024-0171

20.

20. Ghazali

, Mutum

, Cheah

. Dining with robots: An integrated perspective on functional, emotional and relational dimensions of customer experience. Electron Markets 2025;35(1):57; doi: 10.1007/s12525-025-00797-5

21.

21. Chung

, Kang

, Jun

. Verbal anthropomorphism design of social robots: Investigating users’ privacy perception. Comput Hum Behav 2023;142:107640; doi: 10.1016/j.chb.2022.107640

22.

22. Blut

, Wang

, Wünderlich

, et al. Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. J of the Acad Mark Sci 2021;49(4):632–658; doi: 10.1007/s11747-020-00762-y

23.

23. Liao

, Lin

, Chen

. Research on the acceptance of collaborative robots for the industry 5.0 era — The mediating effect of perceived competence and the moderating effect of robot use self-efficacy. Int J Ind Ergon 2023;95:103455; doi: 10.1016/j.ergon.2023.103455

24.

24. Song

, Li

, Leung

, et al. Service robots and hotel guests’ perceptions: Anthropomorphism and stereotypes. TR 2024;79(2):505–522; doi: 10.1108/TR-04-2023-0265

25.

25. Zeng

, Liu

, Zhang

, et al. Retrospective of interdisciplinary research on robot services (1954–2023): From parasitism to symbiosis. Technol Soc 2024;78:102636; doi: 10.1016/j.techsoc.2024.102636

26.

26. Van Ewijk

. You say you can, but can you? The impact of entrepreneurship education on unwarranted and gendered entrepreneurial self-efficacy - a calibration study. Int J Entrep Behav Res 2025;31(8):1915–1940; doi: 10.1108/IJEBR-08-2023-0803

27.

27. Chong

, Kotovsky

, Cagan

. Are confident designers good teammates to artificial intelligence? A study of self-confidence, competence, and collaborative performance. Proc Des Soc 2022;2:1531–1540; doi: 10.1017/pds.2022.155

28.

28. Chung

, Lin

. Drivers of financial robot continuance usage intentions: An application of self-efficacy theory. J Internet Technol 2023;24(2):401–410; doi: 10.53106/160792642023032402018

29.

29. Shao

, Nah

, Makady

, et al. Understanding user attitudes towards AI-Enabled technologies: An integrated model of self-efficacy, TAM, and AI ethics. Int J Hum Comput Interact 2024;41(5):1–13; doi: 10.1080/10447318.2024.2331858

30.

30. Chen

C-C

, Cai

. Are robots stealing our jobs? Examining robot-phobia as a job stressor in the hospitality workplace. IJCHM 2025;37(1):94–112; doi: 10.1108/IJCHM-09-2023-1454

31.

31. Wu

T-J

, Zhang

R-X

. Exploring the impacts of intention towards human-robot collaboration on frontline hotel employees’ positive behavior: An integrative model. Int J Hosp Manag 2024;123:103912; doi: 10.1016/j.ijhm.2024.103912

32.

32. Mele

, Russo-Spena

, Ranieri

, et al. A system and learning perspective on human–robot collaboration. J Serv Manag 2025;36(4):495–522; doi: 10.1108/JOSM-12-2023-0508

33.

33. Alyoussef

, Drwish

, Albakheet

, et al. AI adoption for collaboration: Factors influencing inclusive learning adoption in higher education. IEEE Access 2025;13:81690–81713; doi: 10.1109/ACCESS.2025.3567656

34.

34. Hoffman

. Evaluating fluency in human–robot collaboration. IEEE Trans Human-Mach Syst 2019;49(3):209–218; doi: 10.1109/THMS.2019.2904558

35.

35. Baraglia

, Cakmak

, Nagai

, et al. Initiative in robot assistance during collaborative task execution. In: Proceedings of the 2016 ACM/IEEE International Conference on Human-Robot Interaction. ACM/IEEE; 2016; pp. 67–74; doi: 10.1109/HRI.2016.7451735

36.

36. Goodhue

, Thompson

. Task-technology fit and individual performance. MIS Q 1995;19(2):213–236; doi: 10.2307/249689

37.

37. Jacob

, Grosse

, Morana

, et al. Picking with a robot colleague: A systematic literature review and evaluation of technology acceptance in human–robot collaborative warehouses. Comput Ind Eng 2023;180:109262; doi: 10.1016/j.cie.2023.109262

38.

38. Liu

, Zhang

, Lin

, et al. Robots make me feel more like a human! Investigating how employee-robot engagement reduces workplace depersonalization. Tour Manag 2025;109:105149; doi: 10.1016/j.tourman.2025.105149

39.

39. Tan

, Liu

, Wang

, et al. The effect of human-robot collaboration on frontline employees’ service performance: A resource perspective. Int J Hosp Manag 2025;126:104033; doi: 10.1016/j.ijhm.2024.104033

40.

40. Caiazzo

, Nestić

, Savković

. A systematic classification of key performance indicators in human-robot collaboration. In: Sustainable Business Management and Digital Transformation: Challenges and Opportunities in the Post-COVID Era. ( Mihić

, Jednak

, Savić

., eds). Springer: Cham; 2023; pp. 45–68.

41.

41. Kim

, McGill

. Gaming with Mr. Slot or gaming the slot machine? Power, anthropomorphism, and risk perception. J Consum Res 2011;38(1):94–107; doi: 10.1086/658148

42.

42. Scholz

, Doña

, Sud

, et al. Is general self-efficacy a universal construct? Psychometric findings from 25 countries. Eur J Psychol Assess 2002;18(3):242–251; doi: 10.1027//1015-5759.18.3.242

43.

43. Heerink

, Kröse

, Evers

, et al. Assessing acceptance of assistive social agent technology by older adults: The Almere model. Int J of Soc Robotics 2010;2(4):361–375; doi: 10.1007/s12369-010-0068-5

44.

44. McClelland

. Testing for competence rather than for “intelligence. Am Psychol 1973;28(1):1–14; doi: 10.1037/h0034092

45.

45. Agarwal

, Karahanna

. Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Q 2000;24(4):665–694; doi: 10.2307/3250951

46.

46. Gonzalez-Mulé

, Courtright

, DeGeest

, et al. Channeled autonomy: The joint effects of autonomy and feedback on team performance through organizational goal clarity. J Manag 2016;42(7):2018–2033; doi: 10.1177/0149206314535443

47.

47. Hair

, Black

, Babin

, et al. Multivariate data analysis, 6th ed. Prentice Hall: Upper Saddle River, NJ; 2006.

48.

48. Karababa

, Aydin

, Yilmaz

, et al. Trust yourself, discover your resilience! From self-efficacy to life satisfaction: The mediating effect of psychological resilience. Front Psychol 2025;16:1697876; doi: 10.3389/fpsyg.2025.1697876

49.

49. Fornell

, Larcker

. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 1981;18(1):39–50; doi: 10.2307/3151312

50.

50. Henseler

, Ringle

, Sarstedt

. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J of the Acad Mark Sci 2015;43(1):115–135; doi: 10.1007/s11747-014-0403-8

51.

51. Cheung

, Rensvold

. Evaluating goodness-of-fit indexes for testing measurement invariance. Struct Equ Modeling 2002;9(2):233–255; doi: 10.1207/S15328007SEM0902_5

52.

52. Podsakoff

, MacKenzie

, Lee

J-Y

, et al. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J Appl Psychol 2003;88(5):879–903; doi: 10.1037/0021-9010.88.5.879

53.

53. Williams

, Cote

, Buckley

. Lack of method variance in self-reported affect and perceptions at work: Reality or artifact? J Appl Psychol 1989;74(3):462–468; doi: 10.1037/0021-9010.74.3.462

54.

54. Hair

, Risher

, Sarstedt

, et al. When to use and how to report the results of PLS-SEM. EBR 2019;31(1):2–24; doi: 10.1108/EBR-11-2018-0203

55.

55. Odekerken-Schröder

, Mennens

, Steins

, et al. The service triad: An empirical study of service robots, customers and frontline employees. JOSM 2022;33(2):246–292; doi: 10.1108/JOSM-10-2020-0372

56.

56. Grosz

, Hunsberger

. The dynamics of intention in collaborative activity. Cogn Syst Res 2006;7(2–3):259–272; doi: 10.1016/j.cogsys.2005.11.006

57.

57. Chi

, Chi

, Gursoy

, et al. Customers’ acceptance of artificially intelligent service robots: The influence of trust and culture. Int J Inf Manage 2023;70:102623; doi: 10.1016/j.ijinfomgt.2023.102623

58.

58. Parvez

, Öztüren

, Cobanoglu

, et al. Employees’ perception of robots and robot-induced unemployment in hospitality industry under COVID-19 pandemic. Int J Hosp Manag 2022;107:103336; doi: 10.1016/j.ijhm.2022.103336

59.

59. Li

, Bonn

, Ye

. 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. Tour Manag 2019;73:172–181; doi: 10.1016/j.tourman.2019.02.006

60.

60. Roesler

, Manzey

, Onnasch

. A meta-analysis on the effectiveness of anthropomorphism in human-robot interaction. Sci Robot 2021;6(58):eabj5425; doi: 10.1126/scirobotics.abj5425

61.

61. Latikka

, Turja

, Oksanen

. Self-efficacy and acceptance of robots. Comput Hum Behav 2019;93:157–163; doi: 10.1016/j.chb.2018.12.017

62.

62. Latikka

, Savela

, Koivula

, et al. Perceived robot attitudes of other people and perceived robot use self-efficacy as determinants of attitudes toward robots. In: Human-Robot Interaction in Social Robotics. ( Kurosu

, ed.) Springer: Cham; 2021; pp. 89–104.