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Restoranlarda Robot Garsonlar Kullanmanın Tüketicilerin Davranışsal Niyetlerine Etkisi

Year 2022, , 317 - 336, 14.01.2022

Zübeyir Çelik , İbrahim Aydın

https://doi.org/10.37880/cumuiibf.1013654

Abstract

Bu çalışma, restoranlarda robot garsonlar kullanmanın tüketicilerin davranışsal niyetleri üzerindeki etkisini incelemektedir. Bu amaçla online anket ve deney yöntemi kullanılarak 385 kişiden veri toplanmıştır. Veriler SPSS 25 paket programı kullanılarak analiz edilmiştir. Tek örneklem t-testi sonucuna göre; robot garsonların kullanılması, tüketicilerin robotik restoranları kullanma niyetlerinin önemli bir yordayıcısı değildir. Buna ek olarak, bağımsız örneklemler t-testi sonuçlarına göre; kadınlar ve erkekler arasında ve ayrıca bağımsız gruplar için ANOVA sonuçlarına göre; X, Y ve Z jenerasyonları arasında tüketicilerin robotik restoranları kullanma niyetlerinde anlamlı bir farklılık yoktur. Bununla birlikte, basit lineer regresyon analizi ile elde edilen sonuçlara göre; robotik restoran kullanmanın algılanan yenilikçiliği ve robot garsonlar kullanmanın algılanan değeri, algılanan zevki ve çekiciliği, robotik restoran kullanımına yönelik tutumun olumlu ve önemli yordayıcılarıdır. Ayrıca robotik restoran kullanımına yönelik tutum hem robotik restoranları kullanma niyetinin hem de robotik restoranları kullanmak için daha fazla ödeme yapma isteğinin olumlu ve önemli bir yordayıcısıdır. Çalışmanın sonunda sınırlılıklar çerçevesinde önerilerde bulunulmuştur.

Keywords

Robot Garsonlar Robotik Restoranlar Davranışsal Niyetler

References

  • Aaker, D.A., Kumar,V. & Day, G.S., (2007). Marketing research, 9. Edition: John Wiley & Sons, Danvers.
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
  • Asif, M., Sabeel, M., & Mujeeb-ur Rahman, K. Z. (2015). Waiter robot-solution to restaurant automation. In Proceedings of the 1st student multi disciplinary research conference (MDSRC), At Wah, Pakistan, November, 14-15.
  • Cha, S. S. (2020). Customers’ intention to use robot-serviced restaurants in Korea: Relationship of coolness and MCI factors. International Journal of Contemporary Hospitality Management, 32(9), 2947-2968.
  • Field, A. (2000). Discovering statistics using SPSS for windows. London: Sage Publications.
  • Go, H., Kang, M., & Suh, S. C. (2020). Machine learning of robots in tourism and hospitality: Interactive technology acceptance model (iTAM)–cutting edge. Tourism Review, 75(4), 625-636.
  • Hair, J. F., Jr., Black, William C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
  • Ho, T. H., Tojib, D., & Tsarenko, Y. (2020). Human staff vs. service robot vs. fellow customer: Does it matter who helps your customer following a service failure incident?. International Journal of Hospitality Management, 87, 102501.
  • Hwang, J., Kim, H., Kim, J. J., & Kim, I. (2021). Investigation of perceived risks and their outcome variables in the context of robotic restaurants. Journal of Travel & Tourism Marketing, 38(3), 263-281.
  • Hwang, J., Park, S., & Kim, I. (2020a). Understanding motivated consumer innovativeness in the context of a robotic restaurant: The moderating role of product knowledge. Journal of Hospitality and Tourism Management, 44, 272-282.
  • Hwang, J., Lee, K. W., Kim, D., & Kim, I. (2020b). Robotic restaurant marketing strategies in the era of the fourth industrial revolution: Focusing on perceived innovativeness. Sustainability, 12(21), 9165.
  • Ivanov, S. H., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento, 27(28), 1501-1517.
  • Ivanov, S., Webster, C., & Garenko, A. (2018). Young Russian adults' attitudes towards the potential use of robots in hotels. Technology in Society, 55, 24-32.
  • Jang, H. W., & Lee, S. B. (2020). Serving robots: Management and applications for restaurant business sustainability. Sustainability, 12(10), 3998.
  • Jin, N., Goh, B., Huffman, L., & Yuan, J. J. (2015). Predictors and outcomes of perceived image of restaurant innovativeness in fine-dining restaurants. Journal of Hospitality Marketing & Management, 24(5), 457-485.
  • Kim, J. J., Choe, J. Y. (J)., & Hwang, J. (2021). Application of consumer innovativeness to the context of robotic restaurants. International Journal of Contemporary Hospitality Management, 33(1), 224-242.
  • Kim, J., & Forsythe, S. (2008). Adoption of virtual try-on technology for online apparel shopping. Journal of Interactive Marketing, 22(2), 45-59.
  • Köse, N., & Yengin, D. (2018). Dijital pazarlamadan fijital pazarlamaya geçişe örnek olarak artırılmış gerçeklik ve sanal gerçeklik uygulamalarının pazarlama üzerindeki katkılarının incelenmesi. İstanbul Aydın Üniversitesi Dergisi, 10(1), 77-111.
  • Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610.
  • Kwak, M. K., Lee, J., & Cha, S. S. (2021). Senior consumer motivations and perceived value of robot service restaurants in Korea. Sustainability, 13(5), 2755.
  • Lee, W. H., Lin, C. W., & Shih, K. H. (2018). A technology acceptance model for the perception of restaurant service robots for trust, interactivity, and output quality. International Journal of Mobile Communications, 16(4), 361-376.
  • Leo, X., & Huh, Y. E. (2020). Who gets the blame for service failures? Attribution of responsibility toward robot versus human service providers and service firms. Computers in Human Behavior, 113, 106520.
  • Lin, H., Chi, O. H., & Gursoy, D. (2020). Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services. Journal of Hospitality Marketing & Management, 29(5), 530-549.
  • Lovelock, C. H. ve Wright, L. (1999). Principles of service marketing and management. New Jersey: Prentice Hall.
  • Mishraa, N., Goyal, D., & Sharma, A. D. (2018). Issues in existing robotic service in restaurants and hotels. In Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), April, 26-27.
  • Miskolczi, M., Jászberényi, M., & Tóth, D. (2021). Technology-enhanced airport services—attractiveness from the travelers’ perspective. Sustainability, 13(2), 705.
  • Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill. PewInternet.
  • Richard, M. O., & Chebat, J. C. (2016). Modeling online consumer behavior: Preeminence of emotions and moderating influences of need for cognition and optimal stimulation level. Journal of Business Research, 69(2), 541-553.
  • Seo, K. H., & Lee, J. H. (2021). The Emergence of Service Robots at Restaurants: Integrating Trust, Perceived Risk, and Satisfaction. Sustainability, 13(8), 4431.
  • Song, H., Ruan, W. J., & Jeon, Y. J. J. (2021). An integrated approach to the purchase decision making process of food-delivery apps: Focusing on the TAM and AIDA models. International Journal of Hospitality Management, 95, 102943.
  • Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.
  • Sung, H. J., & Jeon, H. M. (2020). Untact: Customer’s acceptance intention toward robot barista in coffee shop. Sustainability, 12(20), 8598.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics, (5th edition). Pearson Education: Boston.
  • Wan, A. Y. S., Soong, Y. D., Foo, E., Wong, W. L. E., & Lau, W. S. M. (2020). Waiter robots conveying drinks. Technologies, 8(3), 44.
  • Van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of Service Research, 20(1), 43-58.
  • Vaportzis, E., Giatsi Clausen, M., & Gow, A. J. (2017). Older adults perceptions of technology and barriers to interacting with tablet computers: a focus group study. Frontiers in psychology, 8, 1-11.
  • Zhong, L., Sun, S., Law, R., & Zhang, X. (2020). Impact of robot hotel service on consumers’ purchase intention: A control experiment. Asia Pacific Journal of Tourism Research, 25(7), 780-798.
  • Zhu, D. H., & Chang, Y. P. (2020). Robot with humanoid hands cooks food better? Effect of robotic chef anthropomorphism on food quality prediction. International Journal of Contemporary Hospitality Management, 32(3), 1367-1383.

The Effect of Using Robot Waiters in Restaurants on Consumers' Behavioral Intentions

Year 2022, , 317 - 336, 14.01.2022

Zübeyir Çelik , İbrahim Aydın

https://doi.org/10.37880/cumuiibf.1013654

Abstract

This study examines the effect of using robot waiters in restaurants on consumers' behavioral intentions. For this aim, data were collected from 385 people using online questionnaire and experiment method. The data were analyzed using the SPSS 25 package program. According to the one-sample t-test result; using robot waiters is not a significant predictor of consumers' intentions to use robotic restaurants. In addition, according to independent samples t-test results; between female and male, and also according to the results of ANOVA for independent groups; between X, Y, and Z generations, there is no significant difference in consumers' intention to use robotic restaurants. However, according to the results obtained by simple linear regression analysis; the perceived innovativeness of using robotic restaurants, as well as the perceived value, perceived enjoyment, and attractiveness of using robot waiters, are positive and important predictors of attitude towards using robotic restaurants. In addition, the attitude towards using the robotic restaurant is a positive and important predictor of both the intention to use the robotic restaurants and the willingness to pay more to use the robotic restaurants. At the end of the study, suggestions were made within the framework of limitations.

Keywords

Robot Waiters Robotic Restaurants Behavioral Intentions

References

  • Aaker, D.A., Kumar,V. & Day, G.S., (2007). Marketing research, 9. Edition: John Wiley & Sons, Danvers.
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
  • Asif, M., Sabeel, M., & Mujeeb-ur Rahman, K. Z. (2015). Waiter robot-solution to restaurant automation. In Proceedings of the 1st student multi disciplinary research conference (MDSRC), At Wah, Pakistan, November, 14-15.
  • Cha, S. S. (2020). Customers’ intention to use robot-serviced restaurants in Korea: Relationship of coolness and MCI factors. International Journal of Contemporary Hospitality Management, 32(9), 2947-2968.
  • Field, A. (2000). Discovering statistics using SPSS for windows. London: Sage Publications.
  • Go, H., Kang, M., & Suh, S. C. (2020). Machine learning of robots in tourism and hospitality: Interactive technology acceptance model (iTAM)–cutting edge. Tourism Review, 75(4), 625-636.
  • Hair, J. F., Jr., Black, William C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
  • Ho, T. H., Tojib, D., & Tsarenko, Y. (2020). Human staff vs. service robot vs. fellow customer: Does it matter who helps your customer following a service failure incident?. International Journal of Hospitality Management, 87, 102501.
  • Hwang, J., Kim, H., Kim, J. J., & Kim, I. (2021). Investigation of perceived risks and their outcome variables in the context of robotic restaurants. Journal of Travel & Tourism Marketing, 38(3), 263-281.
  • Hwang, J., Park, S., & Kim, I. (2020a). Understanding motivated consumer innovativeness in the context of a robotic restaurant: The moderating role of product knowledge. Journal of Hospitality and Tourism Management, 44, 272-282.
  • Hwang, J., Lee, K. W., Kim, D., & Kim, I. (2020b). Robotic restaurant marketing strategies in the era of the fourth industrial revolution: Focusing on perceived innovativeness. Sustainability, 12(21), 9165.
  • Ivanov, S. H., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento, 27(28), 1501-1517.
  • Ivanov, S., Webster, C., & Garenko, A. (2018). Young Russian adults' attitudes towards the potential use of robots in hotels. Technology in Society, 55, 24-32.
  • Jang, H. W., & Lee, S. B. (2020). Serving robots: Management and applications for restaurant business sustainability. Sustainability, 12(10), 3998.
  • Jin, N., Goh, B., Huffman, L., & Yuan, J. J. (2015). Predictors and outcomes of perceived image of restaurant innovativeness in fine-dining restaurants. Journal of Hospitality Marketing & Management, 24(5), 457-485.
  • Kim, J. J., Choe, J. Y. (J)., & Hwang, J. (2021). Application of consumer innovativeness to the context of robotic restaurants. International Journal of Contemporary Hospitality Management, 33(1), 224-242.
  • Kim, J., & Forsythe, S. (2008). Adoption of virtual try-on technology for online apparel shopping. Journal of Interactive Marketing, 22(2), 45-59.
  • Köse, N., & Yengin, D. (2018). Dijital pazarlamadan fijital pazarlamaya geçişe örnek olarak artırılmış gerçeklik ve sanal gerçeklik uygulamalarının pazarlama üzerindeki katkılarının incelenmesi. İstanbul Aydın Üniversitesi Dergisi, 10(1), 77-111.
  • Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610.
  • Kwak, M. K., Lee, J., & Cha, S. S. (2021). Senior consumer motivations and perceived value of robot service restaurants in Korea. Sustainability, 13(5), 2755.
  • Lee, W. H., Lin, C. W., & Shih, K. H. (2018). A technology acceptance model for the perception of restaurant service robots for trust, interactivity, and output quality. International Journal of Mobile Communications, 16(4), 361-376.
  • Leo, X., & Huh, Y. E. (2020). Who gets the blame for service failures? Attribution of responsibility toward robot versus human service providers and service firms. Computers in Human Behavior, 113, 106520.
  • Lin, H., Chi, O. H., & Gursoy, D. (2020). Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services. Journal of Hospitality Marketing & Management, 29(5), 530-549.
  • Lovelock, C. H. ve Wright, L. (1999). Principles of service marketing and management. New Jersey: Prentice Hall.
  • Mishraa, N., Goyal, D., & Sharma, A. D. (2018). Issues in existing robotic service in restaurants and hotels. In Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), April, 26-27.
  • Miskolczi, M., Jászberényi, M., & Tóth, D. (2021). Technology-enhanced airport services—attractiveness from the travelers’ perspective. Sustainability, 13(2), 705.
  • Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill. PewInternet.
  • Richard, M. O., & Chebat, J. C. (2016). Modeling online consumer behavior: Preeminence of emotions and moderating influences of need for cognition and optimal stimulation level. Journal of Business Research, 69(2), 541-553.
  • Seo, K. H., & Lee, J. H. (2021). The Emergence of Service Robots at Restaurants: Integrating Trust, Perceived Risk, and Satisfaction. Sustainability, 13(8), 4431.
  • Song, H., Ruan, W. J., & Jeon, Y. J. J. (2021). An integrated approach to the purchase decision making process of food-delivery apps: Focusing on the TAM and AIDA models. International Journal of Hospitality Management, 95, 102943.
  • Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.
  • Sung, H. J., & Jeon, H. M. (2020). Untact: Customer’s acceptance intention toward robot barista in coffee shop. Sustainability, 12(20), 8598.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics, (5th edition). Pearson Education: Boston.
  • Wan, A. Y. S., Soong, Y. D., Foo, E., Wong, W. L. E., & Lau, W. S. M. (2020). Waiter robots conveying drinks. Technologies, 8(3), 44.
  • Van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of Service Research, 20(1), 43-58.
  • Vaportzis, E., Giatsi Clausen, M., & Gow, A. J. (2017). Older adults perceptions of technology and barriers to interacting with tablet computers: a focus group study. Frontiers in psychology, 8, 1-11.
  • Zhong, L., Sun, S., Law, R., & Zhang, X. (2020). Impact of robot hotel service on consumers’ purchase intention: A control experiment. Asia Pacific Journal of Tourism Research, 25(7), 780-798.
  • Zhu, D. H., & Chang, Y. P. (2020). Robot with humanoid hands cooks food better? Effect of robotic chef anthropomorphism on food quality prediction. International Journal of Contemporary Hospitality Management, 32(3), 1367-1383.
There are 38 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Makaleler
Authors

Zübeyir Çelik 0000-0003-1692-9378

İbrahim Aydın 0000-0002-0720-364X

Publication Date January 14, 2022
Submission Date October 22, 2021
Published in Issue Year 2022

Cite

APA Çelik, Z., & Aydın, İ. (2022). The Effect of Using Robot Waiters in Restaurants on Consumers’ Behavioral Intentions. Cumhuriyet Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 23(1), 317-336. https://doi.org/10.37880/cumuiibf.1013654

Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.