Promises, Promises: Understanding Claims Made in Social Robot Consumer Experiences

Research Background and Issues

• What problems or challenges did the authors identify?
  Social robots, as part of the intelligent consumer electronics domain, promise users various complex functionalities such as emotional interaction, artificial intelligence, and conversational capabilities. However, there remains a gap in understanding the extent to which these promises are delivered in actual user experiences and how these promises are communicated to consumers. Significant challenges exist, particularly regarding the potential mismatch between consumer expectations of robot performance and actual experiences, as well as the unique risks social robots pose to consumers.

• Why is this issue important?
  As social robots become more prevalent, consumer protection issues related to these devices are increasingly critical. Social robots may lead to emotional dependency and could even pose psychological risks to consumers due to overly anthropomorphic designs. If manufacturers fail to fulfill their product promises, this could further erode consumer trust in such technologies. Additionally, mismatched user expectations could weaken market acceptance of social robots, affecting their long-term development.

• Research Motivation and Related Work
  This study aims to systematically examine manufacturers' promises regarding social robots from a consumer protection perspective and evaluate whether these promises are fulfilled in user experiences. The research background includes prior studies on social robot design, privacy issues, AI ethics, and deception design patterns. Existing work primarily focuses on the risks and ethical challenges posed by robots, while this study uniquely develops a systematic method to test the alignment between consumer promises and actual experiences.

Solutions

• What methods or solutions did the authors propose?
  This study conducts analysis from the following perspectives:

  1. Collecting manufacturers' promises regarding four social robots (Eilik, Miko, Moxie, Vector) and systematically categorizing them.
  2. Verifying the authenticity of promises: Examining whether these promises are fulfilled through user interaction tests in a laboratory setting.
  3. Analyzing consumer reviews: Collecting user feedback from product pages and Amazon reviews to analyze positive and negative comments.

• What are the innovative aspects of this solution?

  • Introduced a new auditing method for social robots, combining real user operations, document analysis, and consumer reviews to comprehensively assess the alignment between promises and experiences.
  • Explored the unique risks of emotional dependency between consumers and robots, while validating promises related to privacy and "intelligent" functionalities using data.
  • Provided an insightful "emotional and user experience risk" model as a reference for future research and regulation.

• What are the implementation steps and key technologies used?

  1. Document classification and coding: Collecting information from packaging, user manuals, and product webpages, extracting and categorizing manufacturers' promises.
  2. Laboratory interaction verification: Manually operating robots to test their functional promises one by one, while recording interaction videos for review.
  3. Network traffic analysis: Investigating data exchange domains during robot connectivity to verify the authenticity of privacy promises.
  4. Consumer review analysis: Using thematic analysis on a total of 168 reviews to categorize common positive feedback and complaints.

Research Findings

• What specific findings were achieved?

  1. The study uncovered significant global differences in social robots' intelligent features and anthropomorphic levels, with over 98% of promises being minimally fulfilled during testing.
  2. Consumers were often frustrated by poor robot performance or mismatched functionalities, such as misunderstandings about "intelligent" or "emotional engine" features.
  3. Criticisms primarily focused on operational issues (e.g., inability to start or update), performance deficiencies (e.g., voice recognition failures), and subscription model flaws.

• What advantages does it have compared to existing solutions?

  • Provided a comprehensive view combining qualitative and quantitative analysis, verifying functionalities not only from a technical perspective but also uncovering issues from user experience and psychological angles.
  • Innovatively combined consumer reviews and laboratory testing to fill the research gap on the "promise vs. practice discrepancy."
  • Revealed the emotional and practical impacts of robot "death" on consumers, offering new perspectives on designing service termination processes.

• What are the experimental or evaluation results?

  1. Laboratory tests showed that some robots, such as Eilik and Miko, performed relatively well in fulfilling promises, while Moxie's complexity led to more interaction limitations.
  2. Consumer review analysis revealed that device failures and malfunctions could evoke deeper emotional dissatisfaction, such as expressions of sadness over "losing a friend" (particularly after Vector ceased service).
  3. Potential issues in data privacy practices were identified, with some robots (e.g., Miko) found to involve hidden data transfers to third-party analytics tools.

• Limitations and Future Directions

  1. Limitations:
     • The small sample size (only four robots) limits the generalizability of the results.
     • Negative bias in consumer reviews may amplify dissatisfaction and may not fully represent global user perspectives.
     • The complex "intelligent" functionalities lack in-depth technical validation.
  2. Future Directions:
     • Conduct larger-scale international studies to compare robot functionalities across different markets.
     • Further explore the long-term psychological and emotional impacts of social robots on consumers, including ethical issues related to anthropomorphic designs.
     • Investigate how to establish clearer standards for "intelligent" functionalities and evaluate the applicability of these technologies to users of different ages and cultures.

Through this study, the authors not only revealed key gaps between consumer expectations and actual experiences with social robots but also provided an insightful framework for future research and regulatory policy development.