“Alexa, You're Really Stupid”: A Longitudinal Field Study on Communication Breakdowns Between Family Members and a Voice Assistant

Abstract

In this paper, we present the results of our long-term study on use of a voice assistant (Amazon Alexa via Amazon Echo Dot) in nine families with children and no previous experience with this technology. The study was conducted over the course of 5 weeks during which the families could interact with the device freely. Three house visits were made to collect empirical data from the adult participants in form of questionnaires. Additionally, conversational data from log files of the voice assistant were obtained. These data were annotated and analyzed with a focus on communication breakdowns during human-assistant interaction. We investigate user behavior for both adults and children in such situations, its reasons and consequences for user satisfaction. This article provides qualitative analysis of three particularly interesting breakdown cases, as well as statistical analysis along several hypotheses and research questions combining empirical and conversational data. Described cases of communication breakdown illustrate findings from existing literature on the topic. The statistical analysis paints a mixed picture, however, it helped us identify further avenues for research, some of which can be explored with our data set in the future. We found a significant negative effect of the number of abandoned failed requests on user satisfaction, contrary to the number of successfully repaired requests that had no influence on user satisfaction. We discovered that users are more inclined to use reformulation as repair strategy when they do not perceive the emergence of miscommunication as their fault. We could not identify a significant effect of internal reasons for the choice of other strategies, so we suggest that situational clues such as the immediate response of the voice assistant are more important for the choice of repair strategy. Our results also hint that users distinguish between repair strategies differently, as the self-perceived frequency of repetitions and abortions of requests were found to be positive predictors for the use of reformulation-based strategies. With regards to the long-term aspect of the study, use of repetition as a repair strategy by both children and adults significantly decreased with time, no other changes were found for other strategies. Additionally, no significant impact of age on the choice of repair strategy was found, as well as no interaction effect between age and time.

When Robots Fail—A VR Investigation on Caregivers’ Tolerance towards Communication and Processing Failures

“…Nevertheless, about half of the participants felt that both parties, i.e., themselves and the robot, were responsible for the failed interaction. Similar attributions have been found in other studies [21]. Mavrina and colleagues found that participants attributed communication breakdowns least to themselves and then to the voice assistant [21].…”

“…Similar attributions have been found in other studies [21]. Mavrina and colleagues found that participants attributed communication breakdowns least to themselves and then to the voice assistant [21]. In their study, an option to attribute the breakdown to the programmer was included, to whom the errors were most frequently attributed.…”

“…Mavrina and colleagues conducted a long-term study with five families on the use of a voice assistant [21]. The number of requests made by the families was assessed and divided by successful and failed requests.…”

Artificial intelligence in voice assistants: User benefits explored

“…After testing the reliabilities and validities of the measurement model via Cronbach alpha coefficients (i.e., perceived artificiality = 0.920; perceived intelligence = 0.928; psychological ownership = 0.937; interface flow experience = 0.902; customer satisfaction = 0.926; and customer loyalty = 0.932), confirmatory factor analysis (i.e., model fit indices: chi‐square = 572.066, degree of freedom = 174, p < .001, NFI = 0.927, TLI = 0.937, CFI = 0.948, RMSEA = 0.082; standardized factor loadings of all measures that ranged from 0.802 to 958 for the convergent validity; see Table 2), and a correlation analysis (i.e., comparison of values of calculated average variance extracted with correlation values between the constructs for the discriminant validity; see Table 3), respectively, the authors conducted structural equation modeling to answer the research question via an empirical approach, investigating the conceptually proposed relationships among the constructs from the research question (i.e., model fit indices: chi‐square = 681.955, degree of freedom = 178, p < .001, NFI = 0.913, TLI = 0.922, CFI = 0.934, RMSEA = 0.081). During this data analytic stage, two behavioral characteristics, namely length of VAD ownership and usage frequency, were considered as control variables due to their potential impacts on users' psychological ownership and flow experiences (Mavrina et al, 2022).…”

“…In my opinion, my artificial intelligence-enabled voice assistant device appears fake. control variables due to their potential impacts on users' psychological ownership and flow experiences (Mavrina et al, 2022).…”