research-article

The Effectiveness of Self-Recommending Agents in Advancing Purchase Behavior Steps in Retail Marketing

Authors:

Takuya Iwamoto,

Taishi Unokuchi,

Junya Nakanishi,

Yuichiro Yoshikawa,

Hiroshi IshiguroAuthors Info & Claims

HAI '21: Proceedings of the 9th International Conference on Human-Agent Interaction

Pages 209 - 217

https://doi.org/10.1145/3472307.3484183

Published: 09 November 2021 Publication History

Abstract

Robot agents are increasingly used for user services, and society is becoming increasingly familiar with such robots. Most robots are of the humanoid type, which are easy to recognize as interaction partners. By interacting with a user, these agents may be able to generate user interest in a product and successfully sell it. However, according to previous studies, it has been suggested that users who are interested in the movement and appearance of the agent may focus only on these aspects and not listen to the recommendation. Therefore,we hypothesized that by making the product the agent, which we call a Self Recommendation Agent(SRA), the attention of the user would be focused on the product itself. Hence, if a user is interested in the agent, it is the same as paying attention to the product. Therefore, we expect that such an agent will be able to gain more attention from users than conventional agents while providing recommendations. To investigate the effectiveness of this agent, we set up a store in a shopping mall and conducted sales experiments. In this field study, we conducted an experiment to compare sales between a SRA and a robot agent.As a result, the SRA was able to make recommendations to many more users than the robot agent, and the users who received recommendations from the SRA remembered more about the recommended product than those who received recommendations from the robot agent.Based on these results, we confirm the possibility that the SRA is effective for advertising.

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Cited By

Mizuho TOkafuji YBaba JNarumi T(2024)Multiple-agent promotion in a grocery store: effects of modality and variability of agents on customer memoryFrontiers in Robotics and AI10.3389/frobt.2024.139723011Online publication date: 5-Dec-2024
https://doi.org/10.3389/frobt.2024.1397230
Tanaka KOkafuji YIwamoto T(2024)Popping-Up Poster: A Pin-Based Promotional Poster Device for Engaging Customers through Physical Shape TransformationProceedings of the ACM on Human-Computer Interaction10.1145/36981388:ISS(283-300)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3698138
Wang YSeaborn K(2024)Kawaii Computing: Scoping Out the Japanese Notion of Cute in User Experiences with Interactive SystemsExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651001(1-9)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3651001
Show More Cited By

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The Effectiveness of Self-Recommending Agents in Advancing Purchase Behavior Steps in Retail Marketing

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Published In

cover image ACM Conferences

HAI '21: Proceedings of the 9th International Conference on Human-Agent Interaction

November 2021

447 pages

ISBN:9781450386203

DOI:10.1145/3472307

Editors:
Kohei Ogawa
Nagoya University, Japan
,
Tomoko Yonezawa
Kansai University, Japan
,
Gale M. Lucas
USC Institute for Creative Technologies, United States
,
Hirotaka Osawa
University of Tsukuba, Japan
,
Wafa Johal
University of New South Wales, Australia
,
Masahiro Shiomi,
Advanced Telecommunications Research Institute International, Japan

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 09 November 2021

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HAI '21

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HAI '21: International Conference on Human-Agent Interaction

November 9 - 11, 2021

Virtual Event, Japan

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Overall Acceptance Rate 121 of 404 submissions, 30%

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Cited By

Mizuho TOkafuji YBaba JNarumi T(2024)Multiple-agent promotion in a grocery store: effects of modality and variability of agents on customer memoryFrontiers in Robotics and AI10.3389/frobt.2024.139723011Online publication date: 5-Dec-2024
https://doi.org/10.3389/frobt.2024.1397230
Tanaka KOkafuji YIwamoto T(2024)Popping-Up Poster: A Pin-Based Promotional Poster Device for Engaging Customers through Physical Shape TransformationProceedings of the ACM on Human-Computer Interaction10.1145/36981388:ISS(283-300)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3698138
Wang YSeaborn K(2024)Kawaii Computing: Scoping Out the Japanese Notion of Cute in User Experiences with Interactive SystemsExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651001(1-9)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3651001
IWAMOTO TNISHIMOTO K(2023)How Does Each Component of Information in Emotion Expression by a Self-Recommendation Robot for Promoting a Product Affect Emotion Recognition?商品販促を目的とする自己推薦ロボットの感情表現を構成する各要素情報が感情認識に与える影響Transactions of Japan Society of Kansei Engineering10.5057/jjske.TJSKE-D-22-0003922:2(85-94)Online publication date: 2023
https://doi.org/10.5057/jjske.TJSKE-D-22-00039
Matsumura RShiomi M(2022)An Animation Character Robot That Increases SalesApplied Sciences10.3390/app1203172412:3(1724)Online publication date: 8-Feb-2022
https://doi.org/10.3390/app12031724
Iwamoto TBaba JNakanishi JHyodo KYoshikawa YIshiguro H(2022)Playful Recommendation: Sales Promotion That Robots Stimulate Pleasant Feelings Instead of Product ExplanationIEEE Robotics and Automation Letters10.1109/LRA.2022.31891497:4(11815-11822)Online publication date: Oct-2022
https://doi.org/10.1109/LRA.2022.3189149
Iwamoto TBaba JNakanishi JNishi KYoshikawa YIshiguro H(2022)Pick-me-up Strategy for a Self-recommendation Agent: A Pilot Field Experiment in a Convenience Store2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)10.1109/HRI53351.2022.9889532(816-820)Online publication date: 7-Mar-2022
https://doi.org/10.1109/HRI53351.2022.9889532

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