Otto is a call center agent designed to help cell phone and mobile provider customers. It has the twin goals of automating call center operations while maintaining a high level of customer satisfaction. It is intended to engage with human users in mixed-initiative dialogs to answer questions, explain procedures, and help with diagnostic troubleshooting. Within the domain of mobile devices, it collaborates with customers on specific tasks but maintains a degree of flexibility and naturalness in the interaction. The development of Otto incorporated aspects of human conversation to improve the quality and likelihood of the success of its interactions.

This yearlong video ethnography of a healthcare clinic that transitioned from a paper process to a scanning solution documents in detail how the new technology impacted different groups in the clinic. While the scanning solution reduced the retrieving, filing, and paper‐processing work for the Medical Record clerks, the ethnographic analysis showed that it also eliminated some of that work's tangible benefits for providers. Ultimately, the scanning solution resulted in a shift in the division of labor in the clinic from Medical Records to the healthcare providers who were burdened with additional administrative tasks. Indeed, the scanning technology did not make the clinic more efficient overall, as the number of patient visits per day remained the same.

This paper reflects on an industry case study conducted in two outsourced call centers to explore the human side of their turnover problem. At the project's onset, management did not consider it necessary to get input from their agents as they already had a thorough knowledge of their organization's operations based on financial analyses and employee surveys. However when we brought back examples from the field showing agent work as complex, dynamic, stressful knowledge work, management began to see the value of soliciting input from their front-line employees. What started as a turnover investigation resulted in an organizational learning initiative to capture and propagate the "human" side of call center work. In the end, we shadowed agents through their shift to create "A day in the life of a call agent" video documentary so that organization-wide all could appreciate the complexity of call agent work.

This paper recaps a recent study in the organization of interactive practices utilized by humans in card-game activities for the purposes of informing the design of human-robot interaction with autonomous social robotic systems. The research utilized an applied Conversation Analytic (CA) approach to focus on the practices by which humans identified and dealt with interactional trouble within the game activity itself, avoiding breakdown of the interactional encounter. We conclude this paper with a brief discussion of the technical implications for future design of autonomous social robots.

Spontaneous multi-party interaction -- conversation among groups of three or more participants -- is part of daily life. While automated modeling of such interactions has received increased attention in ubiquitous computing research, there is little applied research on the organization of this highly dynamic and spontaneous sociable interaction within small groups. We report here on an applied conversation analytic study of small-group sociable talk, emphasizing structural and temporal aspects that can inform computational models. In particular, we examine the mechanics of multiple simultaneous conversational floors -- how participants initiate a new floor amidst an on-going floor, and how they subsequently show their affiliation with one floor over another. We also discuss the implications of these findings for the design of "smart" multi-party applications.

Spontaneous multi-party interaction -- conversation among groups of three or more participants -- is part of daily life. While automated modeling of such interactions has received increased attention in ubiquitous computing research, there is little applied research on the organization of this highly dynamic and spontaneous sociable interaction within small groups. We report here on an applied conversation analytic study of small-group sociable talk, emphasizing structural and temporal aspects that can inform computational models. In particular, we examine the mechanics of multiple simultaneous conversational floors -- how participants initiate a new floor amidst an on-going floor, and how they subsequently show their affiliation with one floor over another. We also discuss the implications of these findings for the design of "smart" multi-party applications.