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Effects of Delivery Time and Delivery Distance of Indoor Robot Delivery Service on User Satisfaction and Reuse Intention

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Published:19 April 2023Publication History

ABSTRACT

The indoor robot service industry has been growing rapidly, industrial and academic research has been actively conducted. However, several previous studies have focused on the acceptance of robots, and there is a lack of research on delivery robot services. Moreover, research cases targeting users who have experienced actual robot delivery services are rare. Therefore, we conducted this study targeting employees who have used actual robot delivery services in a large office space with a total floor area of 165,000 m2 built as a robot-friendly building. Providing delivery services with approximately 100 robots in a large building is a rare case in the world. This study expanded the technology acceptance model and analyzed how delivery time and distance, which are the characteristics of robot delivery services, affect the robot acceptance intention.

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References

  1. Hanyoung Go, Myunghwa Kang, and SeungBeum Chris Suh. 2020. Machine learning of robots in tourism and hospitality: interactive technology acceptance model (iTAM) – cutting edge. TR 75, 4 (January 2020), 625–636. DOI:https://doi.org/10.1108/TR-02-2019-0062Google ScholarGoogle ScholarCross RefCross Ref
  2. Stephanie Hui-Wen Chuah, Eugene Cheng-Xi Aw, and Dewey Yee. 2021. Unveiling the complexity of consumers’ intention to use service robots: An fsQCA approach. Computers in Human Behavior 123, (2021), 106870. DOI:https://doi.org/10.1016/j.chb.2021.106870Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Anna M. H. Abrams, Pia S. C. Dautzenberg, Carla Jakobowsky, Stefan Ladwig, and Astrid M. Rosenthal-von der Pütten. 2021. A Theoretical and Empirical Reflection on Technology Acceptance Models for Autonomous Delivery Robots. In Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, ACM, Boulder CO USA, 272–280. DOI:https://doi.org/10.1145/3434073.3444662Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Ieva Meidute-Kavaliauskiene, Şemsettin Çiğdem, Bülent Yıldız, and Sigitas Davidavicius. 2021. The Effect of Perceptions on Service Robot Usage Intention: A Survey Study in the Service Sector. Sustainability 13, 17 (January 2021), 9655. DOI:https://doi.org/10.3390/su13179655Google ScholarGoogle ScholarCross RefCross Ref
  5. Maartje M. A. de Graaf, Somaya Ben Allouch, and Jan A. G. M. van Dijk. 2019. Why Would I Use This in My Home? A Model of Domestic Social Robot Acceptance. null 34, 2 (March 2019), 115–173. DOI:https://doi.org/10.1080/07370024.2017.1312406Google ScholarGoogle ScholarCross RefCross Ref
  6. Seongseop (Sam) Kim, Jungkeun Kim, Frank Badu-Baiden, Marilyn Giroux, and Youngjoon Choi. 2021. Preference for robot service or human service in hotels? Impacts of the COVID-19 pandemic. International Journal of Hospitality Management 93, (February 2021), 102795. DOI:https://doi.org/10.1016/j.ijhm.2020.102795Google ScholarGoogle ScholarCross RefCross Ref
  7. Patrícia Alves-Oliveira, Sofia Petisca, Filipa Correia, Nuno Maia, and Ana Paiva. 2015. Social Robots for Older Adults: Framework of Activities for Aging in Place with Robots. In Social Robotics (Lecture Notes in Computer Science), Springer International Publishing, Cham, 11–20. DOI:https://doi.org/10.1007/978-3-319-25554-5_2Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Christina Bröhl, Jochen Nelles, Christopher Brandl, Alexander Mertens, and Christopher M. Schlick. 2016. TAM Reloaded: A Technology Acceptance Model for Human-Robot Cooperation in Production Systems. In HCI International 2016 – Posters’ Extended Abstracts (Communications in Computer and Information Science), Springer International Publishing, Cham, 97–103. DOI:https://doi.org/10.1007/978-3-319-40548-3_16Google ScholarGoogle ScholarCross RefCross Ref
  9. Ruth Maria Stock and Moritz Merkle. 2017. A service Robot Acceptance Model: User acceptance of humanoid robots during service encounters. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, Kona, HI, 339–344. DOI:https://doi.org/10.1109/PERCOMW.2017.7917585Google ScholarGoogle ScholarCross RefCross Ref
  10. Fred D. Davis. 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 13, 3 (September 1989), 319. DOI:https://doi.org/10.2307/249008Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Davis, Fred D. 1985. A technology acceptance model for empirically testing new end-user information systems: Theory and results. Diss. Massachusetts Institute of Technology.Google ScholarGoogle Scholar
  12. Viswanath Venkatesh and Fred D. Davis. 2000. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science 46, 2 (February 2000), 186–204. DOI:https://doi.org/10.1287/mnsc.46.2.186.11926Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Viswanath Venkatesh and Hillol Bala. 2008. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences 39, 2 (May 2008), 273–315. DOI:https://doi.org/10.1111/j.1540-5915.2008.00192.xGoogle ScholarGoogle ScholarCross RefCross Ref
  14. Viswanath Venkatesh, Michael G. Morris, Gordon B. Davis, and Fred D. Davis. 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27, 3 (2003), 425–478. DOI:https://doi.org/10.2307/30036540Google ScholarGoogle ScholarCross RefCross Ref
  15. Stanislav Ivanov, Ulrike Gretzel, Katerina Berezina, Marianna Sigala, and Craig Webster. 2019. Progress on robotics in hospitality and tourism: a review of the literature. Journal of Hospitality and Tourism Technology 10, 4 (January 2019), 489–521. DOI:https://doi.org/10.1108/JHTT-08-2018-0087Google ScholarGoogle ScholarCross RefCross Ref
  16. Marcel Heerink, Ben Krose, Vanessa Evers, and Bob Wielinga. 2009. Measuring acceptance of an assistive social robot: a suggested toolkit. In RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, 528–533. DOI:https://doi.org/10.1109/ROMAN.2009.5326320Google ScholarGoogle ScholarCross RefCross Ref
  17. Marcel Heerink, Ben Kröse, Bob Wielinga, and Vanessa Evers. 2008. Enjoyment intention to use and actual use of a conversational robot by elderly people. In Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction (HRI ’08), Association for Computing Machinery, New York, NY, USA, 113–120. DOI:https://doi.org/10.1145/1349822.1349838Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Agnivesh Pani, Sabya Mishra, Mihalis Golias, and Miguel Figliozzi. 2020. Evaluating public acceptance of autonomous delivery robots during COVID-19 pandemic. Transportation Research Part D: Transport and Environment 89, (December 2020), 102600. DOI:https://doi.org/10.1016/j.trd.2020.102600Google ScholarGoogle ScholarCross RefCross Ref
  19. Sharan Srinivas, Surya Ramachandiran, and Suchithra Rajendran. 2022. Autonomous robot-driven deliveries: A review of recent developments and future directions. Transportation Research Part E: Logistics and Transportation Review 165, (September 2022), 102834. DOI:https://doi.org/10.1016/j.tre.2022.102834Google ScholarGoogle ScholarCross RefCross Ref
  20. Ahreum Lee and Austin L. Toombs. 2020. Robots on Campus: Understanding Public Perception of Robots using Social Media. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing (CSCW ’20 Companion), Association for Computing Machinery, New York, NY, USA, 305–309. DOI:https://doi.org/10.1145/3406865.3418321Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Karen L. Katz, Blaire M. Larson, and Richard C. Larson. 1991. Prescription for the Waiting-In-Line Blues: Entertain, Enlighten, and Engage. Sloan Management Review 32, 2 (Winter 1991), 44.Google ScholarGoogle Scholar
  22. Vincent Cheow Sern Yeo, See-Kwong Goh, and Sajad Rezaei. 2017. Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. Journal of Retailing and Consumer Services 35, (March 2017), 150–162. DOI:https://doi.org/10.1016/j.jretconser.2016.12.013Google ScholarGoogle ScholarCross RefCross Ref
  23. Ali Abdallah Alalwan. 2020. Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management 50, (February 2020), 28–44. DOI:https://doi.org/10.1016/j.ijinfomgt.2019.04.008Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Mathias Dharmawirya, Hera Oktadiana, and Erwin Adi. 2012. Analysis of Expected and Actual Waiting Time in Fast Food Restaurants. Retrieved January 14, 2023 from https://papers.ssrn.com/abstract=2184107Google ScholarGoogle Scholar
  25. Allard C.R. van Riel, Janjaap Semeijn, Dina Ribbink, and Yvette Bomert‐Peters. 2012. Waiting for service at the checkout: Negative emotional responses, store image and overall satisfaction. Journal of Service Management 23, 2 (January 2012), 144–169. DOI:https://doi.org/10.1108/09564231211226097Google ScholarGoogle ScholarCross RefCross Ref
  26. Shirley Taylor. 1994. The Effects of Filled Waiting Time and Service Provider Control over the Delay on Evaluations of Service. Journal of the Academy of Marketing Science 23, 1 (December 1994), 38–48. DOI:https://doi.org/10.1177/0092070395231005Google ScholarGoogle ScholarCross RefCross Ref
  27. Gail Tom and Scott Lucey. 1995. Waiting time delays and customer satisfaction in supermarkets. Journal of Services Marketing 9, 5 (January 1995), 20–29. DOI:https://doi.org/10.1108/08876049510100281Google ScholarGoogle ScholarCross RefCross Ref
  28. Joel Collier and Carol Bienstock. 2006. How Do Customers Judge Quality in an E-tailer? MIT Sloan Management Review 48, (September 2006).Google ScholarGoogle Scholar
  29. Vinh Nhat Lu, Jochen Wirtz, Werner H. Kunz, Stefanie Paluch, Thorsten Gruber, Antje Martins, and Paul G. Patterson. 2020. Service robots, customers and service employees: what can we learn from the academic literature and where are the gaps? Journal of Service Theory and Practice 30, 3 (January 2020), 361–391. DOI:https://doi.org/10.1108/JSTP-04-2019-0088Google ScholarGoogle ScholarCross RefCross Ref
  30. Yu-Tse Lin, Kang-Ning Xia, and Lien-Ti Bei. 2015. Customer's perceived value of waiting time for service events. Journal of Consumer Behaviour 14, 1 (2015), 28–40. DOI:https://doi.org/10.1002/cb.1498Google ScholarGoogle ScholarCross RefCross Ref
  31. Karen Byrd, Alei Fan, EunSol Her, Yiran Liu, Barbara Almanza, and Stephen Leitch. 2021. Robot vs human: expectations, performances and gaps in off-premise restaurant service modes. International Journal of Contemporary Hospitality Management 33, 11 (January 2021), 3996–4016. DOI:https://doi.org/10.1108/IJCHM-07-2020-0721Google ScholarGoogle ScholarCross RefCross Ref
  32. The New York Times, Meet Your New Corporate Office Mate: A ‘Brainless’ Robot, 2022, https://www.nytimes.com/2022/11/17/business/south-korea-office-robots-naver.htmlGoogle ScholarGoogle Scholar
  33. FAST COMPAY, This futuristic office was designed for 5,000 people—and 100 robot coworkers, 2022, https://www.fastcompany.com/90754724/this-futuristic-office-was-designed-for-5000-people-and-100-robot-coworkersGoogle ScholarGoogle Scholar
  34. REUTERS, S.Korean Naver's robotics ambitions challenged by 5G on-the-ground realities, 2022, https://www.reuters.com/technology/skorean-navers-robotics-ambitions-challenged-by-5g-on-the-ground-realities-2022-05-25Google ScholarGoogle Scholar
  35. Julie Baker and Michaelle Cameron. 1996. The effects of the service environment on affect and consumer perception of waiting time: An integrative review and research propositions. JAMS 24, 4 (September 1996), 338. DOI:https://doi.org/10.1177/0092070396244005Google ScholarGoogle ScholarCross RefCross Ref
  36. Ad Pruyn and Ale Smidts. 1998. Effects of waiting on the satisfaction with the service: Beyond objective time measures1Both authors contributed equally to this article.1. International Journal of Research in Marketing 15, 4 (October 1998), 321–334. DOI:https://doi.org/10.1016/S0167-8116(98)00008-1Google ScholarGoogle ScholarCross RefCross Ref
  37. Matthew L Meuter, Amy L Ostrom, Mary Jo Bitner, and Robert Roundtree. 2003. The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research 56, 11 (November 2003), 899–906. DOI:https://doi.org/10.1016/S0148-2963(01)00276-4Google ScholarGoogle ScholarCross RefCross Ref
  38. Ting Hin Ho, Dewi Tojib, and Yelena Tsarenko. 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, (May 2020), 102501. DOI:https://doi.org/10.1016/j.ijhm.2020.102501Google ScholarGoogle ScholarCross RefCross Ref
  39. Karen Byrd, Alei Fan, EunSol Her, Yiran Liu, Barbara Almanza, and Stephen Leitch. 2021. Robot vs human: expectations, performances and gaps in off-premise restaurant service modes. International Journal of Contemporary Hospitality Management 33, 11 (January 2021), 3996–4016. DOI:https://doi.org/10.1108/IJCHM-07-2020-0721Google ScholarGoogle ScholarCross RefCross Ref

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      • Published in

        cover image ACM Conferences
        CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
        April 2023
        3914 pages
        ISBN:9781450394222
        DOI:10.1145/3544549

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