Understanding User Perceptions of Proactive Smart Speakers

Author(s):  
Jing Wei ◽  
Tilman Dingler ◽  
Vassilis Kostakos

Voice assistants, such as Amazon's Alexa and Google Home, increasingly find their way into consumer homes. Their functionality, however, is currently limited to being passive answer machines rather than proactively engaging users in conversations. Speakers' proactivity would open up a range of important application scenarios, including health services, such as checking in on patient states and triggering medication reminders. It remains unclear how passive speakers should implement proactivity. To better understand user perceptions, we ran a 3-week field study with 13 participants where we modified the off-the-shelf Google Home to become proactive. During the study, our speaker proactively triggered conversations that were essentially Experience Sampling probes allowing us to identify when to engage users. Applying machine-learning, we are able to predict user responsiveness with a 71.6% accuracy and find predictive features. We also identify self-reported factors, such as boredom and mood, that are significantly correlated with users' perceived availability. Our prototype and findings inform the design of proactive speakers that verbally engage users at opportune moments and contribute to the design of proactive application scenarios and voice-based experience sampling studies.

2006 ◽  
Vol 59 (9) ◽  
pp. 1261-1285 ◽  
Author(s):  
Kevin Daniels ◽  
Ruth Hartley ◽  
Cheryl J. Travers

Author(s):  
Eric D. Heggestad ◽  
Liana Kreamer ◽  
Mary M. Hausfeld ◽  
Charmi Patel ◽  
Steven G. Rogelberg

2020 ◽  
Vol 25 (1) ◽  
pp. 74-88 ◽  
Author(s):  
S Shyam Sundar

Abstract Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a deeper understanding of the human experience of algorithms in general and the psychology of Human–AI interaction (HAII) in particular. This article proposes some directions by applying the dual-process framework of the Theory of Interactive Media Effects (TIME) for studying the symbolic and enabling effects of the affordances of AI-driven media on user perceptions and experiences.


2019 ◽  
Vol 44 (3) ◽  
pp. 427-435 ◽  
Author(s):  
Yan Ruan ◽  
Harry T. Reis ◽  
Wojciech Zareba ◽  
Richard D. Lane

Author(s):  
Sherri Rose

Abstract The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research.


2019 ◽  
Vol 30 (6) ◽  
pp. 863-879 ◽  
Author(s):  
Elise K. Kalokerinos ◽  
Yasemin Erbas ◽  
Eva Ceulemans ◽  
Peter Kuppens

Emotion differentiation, which involves experiencing and labeling emotions in a granular way, has been linked with well-being. It has been theorized that differentiating between emotions facilitates effective emotion regulation, but this link has yet to be comprehensively tested. In two experience-sampling studies, we examined how negative emotion differentiation was related to (a) the selection of emotion-regulation strategies and (b) the effectiveness of these strategies in downregulating negative emotion ( Ns = 200 and 101 participants and 34,660 and 6,282 measurements, respectively). Unexpectedly, we found few relationships between differentiation and the selection of putatively adaptive or maladaptive strategies. Instead, we found interactions between differentiation and strategies in predicting negative emotion. Among low differentiators, all strategies (Study 1) and four of six strategies (Study 2) were more strongly associated with increased negative emotion than they were among high differentiators. This suggests that low differentiation may hinder successful emotion regulation, which in turn supports the idea that effective regulation may underlie differentiation benefits.


2011 ◽  
Vol 5 (6) ◽  
pp. e1187 ◽  
Author(s):  
Mercy M. Ackumey ◽  
Cynthia Kwakye-Maclean ◽  
Edwin O. Ampadu ◽  
Don de Savigny ◽  
Mitchell G. Weiss

2011 ◽  
Vol 53 (4) ◽  
pp. 479-506 ◽  
Author(s):  
Lynda Andrews ◽  
Rebekah Russell Bennett ◽  
Judy Drennan

This paper reports the feasibility and methodological considerations of using the Short Message System Experience Sampling (SMS-ES) method, which is an experience sampling research method developed to assist researchers to collect repeat measures of consumers' affective experiences. The method combines SMS with web-based technology in a simple yet effective way. It is described using a practical implementation study that collected consumers' emotions in response to using mobile phones in everyday situations. The method is further evaluated in terms of the quality of data collected in the study, as well as against the methodological considerations for experience sampling studies. These two evaluations suggest that the SMS-ES method is both a valid and reliable approach for collecting consumers' affective experiences. Moreover, the method can be applied across a range of for-profit and not-for-profit contexts where researchers want to capture repeated measures of consumers' affective experiences occurring over a period of time. The benefits of the method are discussed, to assist researchers who wish to apply the SMS-ES method in their own research designs.


2007 ◽  
Vol 31 (2) ◽  
pp. 239 ◽  
Author(s):  
Margaret Grigg ◽  
Helen Herrman ◽  
Carol Harvey ◽  
Ruth Endacott

The aim of the study was to identify the factors influencing the timing of an assessment after contact with a triage program in a communitybased area mental health service in Australia. Triage decisions apparently were influenced by several groups of factors: patient characteristics; the source and mode of the contact with triage; and to a large extent by mental health service factors including the training, supervision and support of triage workers and the perceived availability of an assessment. While demand factors such as patient characteristics influenced the triage decision, supply factors also played an important role.


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