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A design of trip recommendation robot agents with opinions

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Abstract

In this paper, we conducted experiments with trip recommendation agents. The travel industry offers potential roles for robots and virtual agents. Also, product recommendation on websites is one of the themes gathering attention in the field of virtual agents. Thus, providing methods for designing trip recommendation robots or agents for websites is important. We focused on two factors for agents: appearance and whether the agents expressed an opinion or not. We constructed three kinds of appearance: human-like, robot-like, and real. Also, we constructed two types of recommendation text: with the agents’ opinion and without. We conducted an experiment on the web to verify these two factors on the recommendation effect. The experimental design was two factors and two and three levels; thus, there were six conditions. We conducted a two-way ANOVA to verify the effect of each factor. As a result, we revealed that the robot-like agent and physical robot were more effective than the human-like agent in trip recommendation, and agents’ expressing their opinion was not effective in itself for trip recommendation. This result could suggest useful methods for designing robots and agents that recommend trips on websites.

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Notes

  1. https://www.vstone.co.jp/products/sota/

  2. https://sites.google.com/view/vpvp/

  3. https://www.ah-soft.com/voiceroid/

  4. http://www.vstone.co.jp/sotamanual/index.php

  5. https://crowdsourcing.yahoo.co.jp/

  6. https://crowdsourcing.yahoo.co.jp/

References

  1. Akehurst G (2009) User generated content: the use of blogs for tourism organisations and tourism consumers. Service Bus 3(1):51

    Article  Google Scholar 

  2. Bainbridge WA, Hart J, Kim ES, Scassellati B (2008) The effect of presence on human-robot interaction. In: RO-MAN 2008-The 17th IEEE international symposium on robot and human interactive communication, IEEE, pp 701–706

  3. Cheung EY, Ng TK, Kevin KK, Kwan RL, Cheing GL (2017) Robot-assisted training for people with spinal cord injury: a meta-analysis. Arch Phys Med Rehab 98(11):2320–2331

    Article  Google Scholar 

  4. Crump MJ, McDonnell JV, Gureckis TM (2013) Evaluating Amazon’s Mechanical Turk as a tool for experimental behavioral research. PloS one 8(3):e57410

    Article  Google Scholar 

  5. de Visser EJ, Monfort SS, McKendrick R, Smith MA, McKnight PE, Krueger F, Parasuraman R (2016) Almost human: Anthropomorphism increases trust resilience in cognitive agents. J Exp Psychol Appl 22(3):331. https://doi.org/10.1037/xap0000092

    Article  Google Scholar 

  6. Edwards BI, Muniru IO, Khougali N, Cheok AD, Prada R (2018) A physically embodied robot teacher (pert) as a facilitator for peer learning. In: 2018 IEEE frontiers in education conference (FIE), pp 1–9

  7. Fowler K, Bridges E (2012) Service environment, provider mood and provider-customer interaction. Manag Serv Qual Int J

  8. Gajdošík T, Marciš M (2019) Artificial intelligence tools for smart tourism development. In: Computer Science On-line Conference, pp 392–402

  9. Ghorpade T, Ragha L (2012) Featured based sentiment classification for hotel reviews using nlp and bayesian classification. In: 2012 International conference on communication, information & computing technology (ICCICT), IEEE, pp 1–5

  10. Ghose A, Ipeirotis P G, Li B (2012) Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Mark Sci 31(3):493–520

    Article  Google Scholar 

  11. Gray H M, Gray K, Wegner D M (2007) Dimensions of mind perception. Science 315(5812):619–619

    Article  Google Scholar 

  12. Griffin A, Hauser J R (1993) The voice of the customer. Mark Sci 12(1):1–27

    Article  Google Scholar 

  13. Guadagno R E, Blascovich J, Bailenson J N, Mccall C (2007) Virtual humans and persuasion: The effects of agency and behavioral realism. Media Psychol 10(1):1–22

    Google Scholar 

  14. Hester L, Koger P, McCauley C (1985) Individual differences in customer sociability. Eur J Soc Psychol 15(4):453–456

    Article  Google Scholar 

  15. Huh J H, Seo Y S (2019) Understanding edge computing: Engineering evolution with artificial intelligence. IEEE Access 7:164229–164245

    Article  Google Scholar 

  16. Huh JH, Kim HB, Kim J (2017) A method of modeling of basic big data analysis for korean medical tourism: A machine learning approach using apriori algorithm. In: International conference on information science and applications, Springer, pp 784–790

  17. Ivanov S, Webster C (2019) Perceived appropriateness and intention to use service robots in tourism. In: Information and communication technologies in tourism 2019, pp 237–248

  18. Ivanova M (2019) Robots, artificial intelligence, and service automation in travel agencies and tourist information centers. In: Robots, artificial intelligence, and service automation in travel, tourism and hospitality

  19. Iwasaki M, Zhou J, Ikeda M, Nakanishi H, Kawamura T (2018) A customer’s attitude to a robotic salesperson depends on their initial interaction. In: 2018 27th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 300–305

  20. Kantharaju RB, De Franco D, Pease A, Pelachaud C (2018) Is two better than one? effects of multiple agents on user persuasion. In: Proceedings of the 18th international conference on intelligent virtual agents, pp 255–262

  21. Kiesler S, Powers A, Fussell S R, Torrey C (2008) Anthropomorphic interactions with a robot and robot–like agent. Soc Cogn 26(2):169–181

    Article  Google Scholar 

  22. Lee M, Jeong J, Jeong J, Lee J (2021) Exploring fatalities and injuries in construction by considering thermal comfort using uncertainty and relative importance analysis. Int J Environ Res Publ Health 18(11):5573

    Article  Google Scholar 

  23. Li J (2015) The benefit of being physically present: a survey of experimental works comparing copresent robots, telepresent robots and virtual agents. Int J Hum-Comput Stud 77:23–37

    Article  Google Scholar 

  24. Li J, Kizilcec R, Bailenson J, Ju W (2016) Social robots and virtual agents as lecturers for video instruction. Comput Hum Behav 55:1222–1230

    Article  Google Scholar 

  25. Lucas GM, Lehr J, Krämer N, Gratch J (2019) The effectiveness of social influence tactics when used by a virtual agent. In: Proceedings of the 19th ACM international conference on intelligent virtual agents, pp 22–29

  26. Matsui T, Yamada S (2018) Subjective speech can be useful for persuasive virtual humans: Executing distinctiveness to increase the virtual humans’ trustworthiness and persuasion effect. In: Proceedings of the 6th international conference on human-agent interaction, pp 336–337

  27. Matsui T, Yamada S (2019a) Designing trustworthy product recommendation virtual agents operating positive emotion and having copious amount of knowledge, vol 10

  28. Matsui T, Yamada S (2019b) The effect of subjective speech on product recommendation virtual agent. In: Proceedings of the 24th international conference on intelligent user interfaces, pp 109–110

  29. Matsuyama Y, Saito A, Fujie S, Kobayashi T (2015) Automatic expressive opinion sentence generation for enjoyable conversational systems. IEEE/ACM Trans Audio, Speech, Lang Process 23(2):313–326

    Article  Google Scholar 

  30. McCormick Jr A E, Kinloch G C (1986) Interracial contact in the customer-clerk situation. J Soc Psychol 126(4):551–553

  31. Murphy J, Hofacker C, Gretzel U, et al. (2017) Dawning of the age of robots in hospitality and tourism: challenges for teaching and research. Eur J Tour Res 15:104–111

    Article  Google Scholar 

  32. Murphy J, Gretzel U, Pesonen J (2019) Marketing robot services in hospitality and tourism: the role of anthropomorphism. J Travel Tour Mark pp 1–12

  33. Nakanishi J, Kuramoto I, Baba J, Kohei O, Yoshikawa Y, Ishiguro H (2018) Can a humanoid robot engage in heartwarming interaction service at a hotel?. In: Proceedings of the 6th international conference on human-agent interaction, pp 45–53

  34. Osawa H, Ema A, Hattori H, Akiya N, Kanzaki N, Kubo A, Koyama T, Ichise R (2017) Analysis of robot hotel: Reconstruction of works with robots. In: 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 219–223

  35. Othman M, Hassan H, Moawad R, Idrees AM (2015) Using nlp approach for opinion types classifier

  36. Qiu L, Benbasat I (2009) Evaluating anthropomorphic product recommendation agents: A social relationship perspective to designing information systems. J Manag Inf Syst 25(4):145–182. https://doi.org/10.2753/MIS0742-1222250405

    Article  Google Scholar 

  37. Ray A, Bala PK (2019) Use of nlp and sem in determining factors for e-service adoption. In: Structural equation modeling approaches to e-service adoption, IGI Global, pp 38–47

  38. Robeer M, Lucassen G, van der Werf JME, Dalpiaz F, Brinkkemper S (2016) Automated extraction of conceptual models from user stories via nlp. In: 2016 IEEE 24Th international requirements engineering conference (RE), IEEE, pp 196–205

  39. Ruijten PA, Ham J, Midden CJ (2014) Investigating the influence of social exclusion on persuasion by a virtual agent. In: International conference on persuasive technology, pp 191–200

  40. Terada K, Jing L, Yamada S (2015) Effects of Agent Appearance on Customer Buying Motivations on Online Shopping Sites. In: Proceedings of the 33rd annual ACM conference extended abstracts on human factors in computing systems, pp 929–934, https://doi.org/10.1145/2702613.2732798

  41. Tirunillai S, Tellis G J (2012) Does chatter really matter? dynamics of user-generated content and stock performance. Mark Sci 31(2):198–215

    Article  Google Scholar 

  42. Tokushige H, Narumi T, Ono S, Fuwamoto Y, Tanikawa T, Hirose M (2017) Trust lengthens decision time on unexpected recommendations in human-agent interaction. In: Proceedings of the 5th international conference on human agent interaction, pp 245–252

  43. Tung V W S, Law R (2017) The potential for tourism and hospitality experience research in human-robot interactions. Int J Contemp Hosp Manag 29 (10):2498–2513

    Article  Google Scholar 

  44. Tussyadiah IP, Park S (2018) Consumer evaluation of hotel service robots. In: Information and communication technologies in tourism 2018, pp 308–320

  45. Wheeless LR (1978) A follow-up study of the relationships among trust, disclosure, and interpersonal solidarity. Hum Commun Res 4(2):143–157. https://doi.org/10.1111/j.1468-2958.1978.tb00604.x

    Article  Google Scholar 

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Acknowledgements

This work was supported by JST, CREST (JPMJCR21D4), Japan.

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Correspondence to Tetsuya Matsui.

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Matsui, T., Yamada, S. A design of trip recommendation robot agents with opinions. Multimed Tools Appl 82, 41861–41877 (2023). https://doi.org/10.1007/s11042-023-14747-w

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