Serving Robots: Management and Applications for Restaurant Business Sustainability
Abstract
:1. Introduction
- (1)
- identify the relationship between the five attributes of serving robots and restaurant customers’ perceived benefits and risks, respectively;
- (2)
- identify the relationship between perceived benefits and value and perceived risks and value; and
- (3)
- identify the effect of the customers’ perceived value on their satisfaction and revisit intention.
2. Research Background
2.1. Serving Robots’ Attributes
2.2. Value-Based Adoption Model
2.3. Development of Research Hypotheses
2.3.1. Effect of Serving Robots’ Attributes on Perceived Benefits for Restaurants
2.3.2. Effect of Serving Robots’ Attributes on Perceived Risks for Restaurants
2.3.3. Effects of Perceived Benefits on Perceived Value
2.3.4. Effects of Perceived Risks on Perceived Value
2.3.5. Effects of Perceived Value on Satisfaction
2.3.6. Effects of Perceived Value on Revisit Intention
2.3.7. Effects of Satisfaction on Revisit Intention
3. Methods
3.1. Data Collection and Sampling
3.2. Measurements for Testing Hypotheses
4. Results
4.1. Validity and Reliability of Measurements
4.2. Hypotheses Testing
5. Conclusions
5.1. Theoretical Implications
5.2. Practical Implications
6. Limitations and Future Research
Author Contributions
Conflicts of Interest
References
- Lim, J.Y. LG, Woowa Brothers to Collaborate on Food Robotics. Available online: http://www.koreaherald.com/view.php?ud=20200228000540&ACE_SEARCH=1 (accessed on 28 February 2020).
- Song, S.H. [CES 2020] Samsung Unveils Life Companion Bot Ballie. Available online: http://www.koreaherald.com/view.php?ud=20200107000762&ACE_SEARCH=1 (accessed on 7 January 2020).
- Kim, Y.W. KT Plans to Make AI Available Everywhere. Available online: http://www.koreaherald.com/view.php?ud=20200106000532&ACE_SEARCH=1 (accessed on 6 January 2020).
- Lee, J.W. Self-Portrait of a Cooking Robot. Available online: http://www.thescoop.co.kr/news/articleView.html?idxno=36635 (accessed on 27 September 2019).
- Rokonuzzaman, M. Post-COVID-19 Pandemic: Touch-Free Localised Production. Available online: https://thefinancialexpress.com.bd/views/views/post-covid-19-pandemic-touch-free-localised-production-1585407944 (accessed on 28 March 2020).
- Cho, M.H. LG Deploys Service Robot in Seoul Restaurant. Available online: https://www.zdnet.com/article/lg-deploys-service-robot-in-seoul-restaurant/ (accessed on 3 February 2020).
- Choi, H.J. Restaurant Owners Looking for Serving Robots, Why Are There More Local Cities Than Seoul? Available online: https://www.hankyung.com/it/article/202001081838i (accessed on 8 January 2020).
- Tanaka, F.; Kimura, T. Care-receiving robot as a tool of teachers in child education. Interact. Stud. 2010, 11, 263. [Google Scholar] [CrossRef] [Green Version]
- Nadimpalli, M. Artificial intelligence risks and benefits. Artif. Intell. 2017, 6. [Google Scholar]
- Grigore, E.C.; Eder, K.; Lenz, A.; Skachek, S.; Pipe, A.G.; Melhuish, C. Towards safe human-robot interaction. In Conference Towards Autonomous Robotic Systems; Springer: Berlin/Heidelberg, Germany, 2011; pp. 323–335. [Google Scholar]
- Giuliani, M.; Petrick, R.; Foster, M.E.; Gaschler, A.; Isard, A.; Pateraki, M.; Sigalas, M. Comparing task-based and socially intelligent behaviour in a robot bartender. In Proceedings of the 15th ACM on International Conference on Multimodal Interaction, New York, NY, USA, 13 December 2013; pp. 263–270. [Google Scholar]
- Alenljung, B.; Lindblom, J.; Andreasson, R.; Ziemke, T. User Experience in Social Human-Robot Interaction. In Rapid Automation: Concepts, Methodologies, Tools, and Applications; IGI Global: Hershey, PA, USA, 2019; pp. 1468–1490. [Google Scholar]
- Goundrey-Smith, S. Pharmacy robots in UK hospitals: The benefits and implementation issues. Pharm. J. 2008, 280, 599–602. [Google Scholar]
- Broadbent, E.; Jayawardena, C.; Kerse, N.; Stafford, R.Q.; MacDonald, B.A. Human-Robot Interaction Research to Improve Quality of Life in Elder Care—An Approach and Issues. In Proceedings of the Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, 8 August 2011. [Google Scholar]
- Kim, K.J.; Park, E.; Sundar, S.S. Caregiving role in human–robot interaction: A study of the mediating effects of perceived benefit and social presence. Comput. Hum. Behav. 2013, 29, 1799–1806. [Google Scholar] [CrossRef]
- Kim, H.W.; Chan, H.C.; Gupta, S. Value-based adoption of mobile internet: An empirical investigation. Decis. Support Syst. 2007, 43, 111–126. [Google Scholar] [CrossRef]
- Kleijnen, M.; De Ruyter, K.; Wetzels, M. An assessment of value creation in mobile service delivery and the moderating role of time consciousness. J. Retail. 2007, 83, 33–46. [Google Scholar] [CrossRef]
- Wang, H.Y.; Wang, S.H. Predicting mobile hotel reservation adoption: Insight from a perceived value standpoint. Int. J. Hosp. Manag. 2010, 29, 598–608. [Google Scholar] [CrossRef]
- Chung, N.; Koo, C. The use of social media in travel information search. Telemat. Inform. 2015, 32, 215–229. [Google Scholar] [CrossRef]
- Sweeney, J.C.; Soutar, G.N.; Johnson, L.W. The role of perceived risk in the quality-value relationship: A study in a retail environment. J. Retail. 1999, 75, 77–105. [Google Scholar] [CrossRef]
- Snoj, B.; Pisnik Korda, A.; Mumel, D. The relationships among perceived quality, perceived risk and perceived product value. J. Prod. Brand Manag. 2004, 13, 156–167. [Google Scholar] [CrossRef]
- Lin, H.H.; Wang, Y.S. An examination of the determinants of customer loyalty in mobile commerce contexts. Inf. Manag. 2006, 43, 271–282. [Google Scholar] [CrossRef]
- Ryu, K.; Han, H.; Kim, T.H. The relationships among overall quick-casual restaurant image, perceived value, customer satisfaction, and behavioral intentions. Int. J. Hosp. Manag. 2008, 27, 459–469. [Google Scholar] [CrossRef]
- Han, H.; Hyun, S.S. Impact of hotel-restaurant image and quality of physical-environment, service, and food on satisfaction and intention. Int. J. Hosp. Manag. 2017, 63, 82–92. [Google Scholar] [CrossRef]
- Jeon, Y.M. A study on influence of family restaurant image on satisfaction, trust and revisit intention. Culin. Sci. Hosp. Res. 2017, 23, 74–85. [Google Scholar]
- Abdullah, D.; Hamir, N.; Nor, N.M.; Krishnaswamy, J.; Rostum, A.M.M. Food quality, service quality, price fairness and restaurant re-patronage intention: The mediating role of customer satisfaction. Int. J. Acad. Res. Bus. Soc. Sci. 2018, 8, 211–226. [Google Scholar]
- Kim, H.S.; Shim, J.H. The effects of quality factors on customer satisfaction, trust and behavioral intention in chicken restaurants. J. Ind. Distrib. Bus. 2019, 10, 43–56. [Google Scholar] [CrossRef]
- Selaka, H.S.; Perera, K.A.T.S.; Deepal, M.A.W.T.; Sanjeewa, P.D.R.; Sirithunge, H.C.; Jayasekara, A.G.B.P. Fuzzy-Bot: A Food Serving Robot as a Teaching and Learning Platform for Fuzzy Logic. In Proceedings of the 2018 Moratuwa Engineering Research Conference, Moratuwa, Sri Lanka, 30 May–1 June 2018; pp. 565–570. [Google Scholar]
- Xue, Z.; Ruehl, S.; Hermann, A.; Kerscher, T.; Dillmann, R. An autonomous ice-cream serving robot. In Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 5 April 2011; pp. 3451–3452. [Google Scholar]
- Neeti, M.; Alpana, S.; Neetu, R.; Pratibha, P. Serving robot: New generation electronic waiter. Int. J. Eng. Sci. 2016, 6, 4. [Google Scholar]
- Iqbal, J.; Khan, Z.H.; Khalid, A. Prospects of robotics in food industry. Food Sci. Technol. 2017, 37, 159–165. [Google Scholar] [CrossRef] [Green Version]
- Morita, T.; Kashiwagi, N.; Yorozu, A.; Walch, M.; Suzuki, H.; Karagiannis, D.; Yamaguchi, T. Practice of multi-robot teahouse based on PRINTEPS and evaluation of service quality. In Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan, 23–27 July 2018; Volume 2, pp. 147–152. [Google Scholar]
- Thanh, V.N.; Vinh, D.P.; Nghi, N.T. Restaurant Serving Robot with Double Line Sensors Following Approach. In Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China, 4–8 August 2019; pp. 235–239. [Google Scholar]
- Yu, Y.S.; Luo, M.; Zhu, D.H. The effect of quality attributes on visiting consumers’ patronage intentions of green restaurants. Sustainability 2018, 10, 1187. [Google Scholar] [CrossRef] [Green Version]
- Bartneck, C.; Croft, E.; Kulic, D. Measuring the anthropomorphism, animacy, likeability, perceived intelligence and perceived safety of robots. Proceedings of the Metrics for Human-Robot Interaction Workshop in affiliation with the 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI 2008). Tech. Rep. 2008, 471, 37–44. [Google Scholar]
- Fogg, B.J. Persuasive technology: Using computers to change what we think and do. Ubiquity 2002, 2. [Google Scholar] [CrossRef] [Green Version]
- Robbins, T.L.; DeNisi, A.S. A closer look at interpersonal affect as a distinct influence on cognitive processing in performance evaluations. J. Appl. Psychol. 1994, 79, 341. [Google Scholar] [CrossRef]
- Kim, Y.; Park, Y.; Choi, J. A study on the adoption of IoT smart home service: Using Value-based Adoption Model. Total Qual. Manag. Bus. Excell. 2017, 28, 1149–1165. [Google Scholar] [CrossRef]
- Van Der Voordt, T.; Anker, J.P.; Gerard, H.J.; Bergsma, F. Value Adding Management of buildings and facility services in four steps. Corp. Real Estate J. 2016, 6, 42–56. [Google Scholar]
- Zeithaml, V.A. Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. J. Mark. 1988, 52, 2–22. [Google Scholar] [CrossRef]
- Lin, T.C.; Wu, S.; Hsu, J.S.C.; Chou, Y.C. The integration of value-based adoption and expectation –confirmation models: An example of IPTV continuance intention. Decis. Support Syst. 2012, 54, 63–75. [Google Scholar] [CrossRef]
- Roostika, R. Mobile internet acceptance among university students: A value-based adoption model. Int. J. Res. Manag. Technol. (IJRMT) 2012, 2, 21–28. [Google Scholar]
- Kim, S.H.; Bae, J.H.; Jeon, H.M. Continuous Intention on Accommodation Apps: Integrated Value-Based Adoption and Expectation–Confirmation Model Analysis. Sustainability 2019, 11, 1578. [Google Scholar] [CrossRef] [Green Version]
- Han, J.H.; Kang, S.B.; Moon, T.S. An Empirical Study on Perceived Value and Continuous Intention to Use of Smart Phone, and the Moderating Effect of Personal Innovativeness. Asia Pac. J. Inf. Syst. 2013, 23, 53–84. [Google Scholar] [CrossRef]
- Kim, Y.H. A Study on Adoption of IoT Smart Home Service: Based on Contingent Valuation Method and Value-Based Adoption Model. Doctoral dissertation, Soongsil University, Seoul, Korea, 2016. [Google Scholar]
- Kim, J.H.; Bai, L.Z.; Byun, J.W. The Impact of Tourism Mobile App Characteristic on Perceived Value, User Satisfaction, Continuous Use Intention: Focused on Chinese Tourist. J. Tour. Leis. Res. 2015, 27, 5–22. [Google Scholar]
- Kang, J.H.; Moon, T.S. Influence of Perceived Value of Social Commerce on Repurchase Intention and Mediating Effect of User Satisfaction. J. Internet Electron. Commer. Res. 2016, 16, 209–224. [Google Scholar]
- Sirdeshmukh, D.; Singh, J.; Sabol, B. Consumer trust, value, and loyalty in relational exchanges. J. Mark. 2002, 66, 15–37. [Google Scholar] [CrossRef]
- Kim, S.J.; Kim, S.H.; Kim, E.K. A Study of the Effect of Perceived Wine Value on Customer Satisfaction, Trust, Repurchase Intention. J. Foodserv. Manag. 2008, 11, 221–241. [Google Scholar]
- Forsythe, S.; Liu, C.; Shannon, D.; Gardner, L.C. Development of a scale to measure the perceived benefits and risks of online shopping. J. Interact. Mark. 2006, 20, 55–75. [Google Scholar] [CrossRef]
- Petrick, J.F. Development of a multi-dimensional scale for measuring the perceived value of a service. J. Leis. Res. 2002, 34, 119–134. [Google Scholar] [CrossRef]
- Babin, B.J.; Lee, Y.K.; Kim, E.J.; Griffin, M. Modeling consumer satisfaction and word-of-mouth: Restaurant patronage in Korea. J. Serv. Mark. 2005, 19, 133–139. [Google Scholar] [CrossRef]
- Youn, H.; Yin, R.; Kim, J.H.; Li, J.J. Examining traditional restaurant diners’ intention: An application of the VBN theory. Int. J. Hosp. Manag. 2019, 85, 102360. [Google Scholar] [CrossRef]
- Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis: Pearson New International Edition; Pearson Education Limited: Essex, UK, 2014. [Google Scholar]
- Zheng, Y.; Wang, J.; Tsai, S.B.; Li, G.; Wang, J.; Zhou, J. Research on Customer Satisfaction in Marine Cultural and Sustainable Tourism—A Case Study of Shanghai. Sustainability 2017, 9, 921. [Google Scholar] [CrossRef] [Green Version]
- Su, L.; Huang, Y. How does perceived destination social responsibility impact revisit intentions: The mediating roles of destination preference and relationship quality. Sustainability 2019, 11, 133. [Google Scholar] [CrossRef] [Green Version]
Characteristics | n (%) | Characteristics | n (%) |
---|---|---|---|
Age(years) | Monthly income | ||
10–19 | 19 (6.5%) | <$1000 | 13 (4.4%) |
20–29 | 50 (17.0%) | $1001–$2000 | 12 (4.1%) |
30–39 | 125 (42.5%) | $2001–$3000 | 77 (26.2%) |
40–49 | 56 (19.0%) | $3001–$4000 | 75 (25.5%) |
50–59 | 32 (10.9%) | $4001–$5000 | 47 (16.0%) |
Over 60 | 12 (4.1%) | $5001≤ | 70 (23.8%) |
Gender | Occupation | ||
Male | 146 (49.7%) | Student | 18 (6.1%) |
Female | 148 (50.3%) | Office job | 196 (66.7%) |
Marital status | Self-employed | 17 (5.8%) | |
Unmarried | 129 (43.9%) | Professional job | 35 (11.9%) |
Married | 165 (56.1%) | Homemaker | 19 (6.5%) |
Educational level | Other | 9 (3.1%) | |
High school | 29 (9.9%) | ||
Two-year college | 38 (12.9%) | ||
University | 202 (68.7%) | ||
Graduate school | 25 (8.5%) |
Construct | Factor Loading | Eigen Value | % of Variance | |
---|---|---|---|---|
Anthropomorphism | 1 | 0.780 | 9.560 | 45.522 |
2 | 0.701 | |||
3 | 0.616 | |||
4 | 0.728 | |||
5 | 0.758 | |||
Animacy | 1 | 0.576 | 1.700 | 8.094 |
2 | 0.764 | |||
3 | 0.777 | |||
4 | 0.761 | |||
5 | 0.727 | |||
Likeability | 1 | 0.834 | 1.652 | 7.869 |
2 | 0.600 | |||
3 | 0.831 | |||
4 | 0.662 | |||
Intelligence | 1 | 0.834 | 1.264 | 6.021 |
2 | 0.865 | |||
3 | 0.810 | |||
Safety | 1 | 0.709 | 1.074 | 5.115 |
2 | 0.733 | |||
3 | 0.609 | |||
4 | 0.744 |
Construct | Standardized Loadings | t-Value | CCR a | AVE b | Cronbach’s Alpha |
---|---|---|---|---|---|
Anthropomorphism | 0.865 | 0.563 | 0.862 | ||
The appearance of a serving robot is similar to that of a human being | 0.819 | ||||
A serving robot looks similar to a human | 0.775 | 14.589 *** | |||
Serving robots seem to have the ability to perceive and judge like human beings | 0.675 | 12.226 *** | |||
Serving robots look natural | 0.715 | 13.135 *** | |||
Serving robots move gracefully like human beings | 0.758 | 14.171 *** | |||
Animacy | 0.851 | 0.535 | 0.868 | ||
Serving robots are similar to living creatures | 0.736 | ||||
The serving robot looks energetic | 0.783 | 10.463 *** | |||
The activity of a serving robot is similar to that of a human | 0.815 | 18.775 *** | |||
The interaction with a serving robot is smooth | 0.663 | 11.189 *** | |||
Serving robots are highly responsive | 0.645 | 9.214 *** | |||
Likeability | 0.876 | 0.640 | 0.874 | ||
Serving robots are cool | 0.850 | ||||
Serving robots are friendly | 0.848 | 19.528 *** | |||
Serving robots are kind | 0.694 | 11.705 *** | |||
Serving robots make me feel good | 0.799 | 11.933 *** | |||
Intelligence | 0.947 | 0.856 | 0.939 | ||
A serving robot is good at its job | 0.938 | ||||
Serving robots look intelligent | 0.925 | 28.691 *** | |||
The use of serving robots is practical | 0.913 | 26.650 *** | |||
Safety | 0.822 | 0.537 | 0.820 | ||
A serving robot is safe to use | 0.738 | ||||
Serving robots move safely | 0.721 | 12.055 *** | |||
The serving robot looks comfortable in its movements | 0.739 | 11.777 *** | |||
Serving robots look safe | 0.732 | 11.962 *** | |||
Perceived benefits | 0.822 | 0.536 | 0.820 | ||
A serving robot offers a new experience | 0.745 | 12.643 *** | |||
Using a serving robot is fun | 0.781 | 11.968 *** | |||
Using a serving robot is a new feeling | 0.708 | 11.637 *** | |||
The use of serving robot is novel | 0.690 | ||||
Perceived risks | 0.835 | 0.558 | 0.833 | ||
A serving robot does not meet my needs | 0.712 | ||||
Robot serving takes longer than the staff | 0.766 | 11.361 *** | |||
There are many problems related to smooth interaction with serving robots | 0.723 | 11.523 *** | |||
Robots serve slowly | 0.785 | 12.333 *** | |||
Perceived value | 0.933 | 0.823 | 0.884 | ||
Using a serving robot gives me pleasure | 0.926 | ||||
Serving robots have excellent performance | 0.906 | 25.989 *** | |||
The service quality of the serving robot is excellent | 0.890 | 24.487 *** | |||
Satisfaction | 0.922 | 0.750 | 0.916 | ||
I am satisfied with the choice of a restaurant company with a serving robot | 0.858 | ||||
I am satisfied with the meal at a restaurant with a serving robot | 0.986 | 17.551 *** | |||
I am very happy to visit a restaurant with a serving robot | 0.902 | 29.287 *** | |||
I am very satisfied with visiting a restaurant that uses a serving robot | 0.692 | 14.733 *** | |||
Revisit intention | 0.907 | 0.710 | 0.895 | ||
I will continue to visit the restaurant | 0.875 | ||||
I am inclined to visit the restaurant repeatedly | 0.793 | 17.515 *** | |||
I will revisit the restaurant | 0.960 | 19.684 *** | |||
I will recommend the restaurant to my acquaintances | 0.725 | 13.620 *** |
Construct | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Mean | SD |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Anthropomorphism | 0.56 a | 0.51 c | 0.47 | 0.30 | 0.33 | 0.04 | 0.00 | 0.21 | 0.26 | 0.40 | 2.99 | 0.79 |
2. Animacy | 0.72 b,** | 0.53 | 0.32 | 0.31 | 0.44 | 0.12 | 0.00 | 0.26 | 0.28 | 0.38 | 3.39 | 0.65 |
3. Likeability | 0.69 ** | 0.57 ** | 0.64 | 0.29 | 0.50 | 0.27 | 0.01 | 0.49 | 0.42 | 0.54 | 3.48 | 0.75 |
4. Intelligence | 0.55 ** | 0.56 ** | 0.54 ** | 0.85 | 0.44 | 0.30 | 0.01 | 0.33 | 0.28 | 0.27 | 3.62 | 0.70 |
5. Safety | 0.58 ** | 0.67 ** | 0.71 ** | 0.67 ** | 0.53 | 0.43 | 0.05 | 0.46 | 0.40 | 0.46 | 3.49 | 0.66 |
6. Perceived benefits | 0.21 ** | 0.35 ** | 0.52 ** | 0.55 ** | 0.66 ** | 0.53 | 0.02 | 0.51 | 0.31 | 0.24 | 4.04 | 0.64 |
7. Perceived risks | −0.07 ** | −0.08 ** | −0.10 ** | −0.12 ** | −0.23 ** | −0.15 ** | 0.55 | 0.02 | 0.03 | 0.00 | 2.92 | 0.76 |
8. Perceived value | 0.46 ** | 0.51 ** | 0.70 ** | 0.58 ** | 0.68 ** | 0.72 ** | −0.15 ** | 0.82 | 0.47 | 0.46 | 3.63 | 0.66 |
9. Satisfaction | 0.51 ** | 0.53 ** | 0.65 ** | 0.53 ** | 0.64 ** | 0.56 ** | −0.18 ** | 0.69 ** | 0.75 | 0.56 | 3.54 | 0.72 |
10. Revisit intention | 0.64 ** | 0.62 ** | 0.74 ** | 0.52 ** | 0.68 ** | 0.49 ** | −0.06 ** | 0.68 ** | 0.75 ** | 0.71 | 3.45 | 0.77 |
Relationships | β | B | S.E. | t-Value | p-Value | Results | |
---|---|---|---|---|---|---|---|
H1a | Anthropomorphism→Perceived benefits | −0.381 | −0.315 | 0.071 | −4.418 | 0.000 *** | Not supported |
H1b | Animacy→Perceived benefits | 0.034 | 0.039 | 0.102 | 0.386 | 0.699 | Not supported |
H1c | Likeability→Perceived benefits | 0.334 | 0.283 | 0.058 | 4.839 | 0.000 *** | Supported |
H1d | Intelligence→Perceived benefits | 0.234 | 0.189 | 0.053 | 3.556 | 0.000 *** | Supported |
H1e | Safety→Perceived benefits | 0.558 | 0.562 | 0.095 | 5.901 | 0.000 *** | Supported |
H2a | Anthropomorphism→Perceived risks | 0.065 | 0.061 | 0.114 | 0.530 | 0.596 | Not supported |
H2b | Animacy→Perceived risks | 0.053 | 0.070 | 0.174 | 0.401 | 0.688 | Not supported |
H2c | Likeability→Perceived risks | −0.025 | −0.023 | 0.092 | −0.256 | 0.798 | Not supported |
H2d | Intelligence→Perceived risks | 0.019 | 0.017 | 0.088 | 0.194 | 0.846 | Not supported |
H2e | Safety→Perceived risks | −0.284 | −0.322 | 0.143 | −2.250 | 0.024 * | Supported |
H3 | Perceived benefits→Perceived value | 0.784 | 0.777 | 0.061 | 12.650 | 0.000 *** | Supported |
H4 | Perceived risks→Perceived value | −0.045 | −0.040 | 0.040 | −0.992 | 0.321 | Not supported |
H5 | Perceived value→Satisfaction | 0.703 | 0.864 | 0.064 | 13.594 | 0.000 *** | Supported |
H6 | Perceived value→Revisit intention | 0.152 | 0.171 | 0.063 | 2.714 | 0.007 ** | Supported |
H7 | Satisfaction→Revisit intention | 0.604 | 0.552 | 0.060 | 9.172 | 0.000 *** | Supported |
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Jang, H.-W.; Lee, S.-B. Serving Robots: Management and Applications for Restaurant Business Sustainability. Sustainability 2020, 12, 3998. https://doi.org/10.3390/su12103998
Jang H-W, Lee S-B. Serving Robots: Management and Applications for Restaurant Business Sustainability. Sustainability. 2020; 12(10):3998. https://doi.org/10.3390/su12103998
Chicago/Turabian StyleJang, Ha-Won, and Soo-Bum Lee. 2020. "Serving Robots: Management and Applications for Restaurant Business Sustainability" Sustainability 12, no. 10: 3998. https://doi.org/10.3390/su12103998