Responsibility and Well-Being: Resource Integration Under Responsibilization in Expert Services

Responsibilization, or the shift of functions and risks from providers and producers to consumers, has become an increasingly common policy in service systems and marketplaces (e.g., financial, health, governmental). As responsibilization is often considered synonymous with consumer agency and well-being, the authors take a transformative service research perspective and draw on resource integration literature to investigate whether responsibilization is truly associated with well-being. The authors focus on expert services, for which responsibilization concerns are particularly salient, and question whether this expanding policy is in the public interest. In the process, they develop a conceptualization of resource integration under responsibilization that includes three levels of actors (consumer, provider, and service system), the identification of structural tensions to resource integration, and three categories of resource integration practices (access, appropriation, and management) necessary to negotiate responsibilization. The findings have important implications for health care providers, public and institutional policy makers, and other service systems, all of which must pay more active attention to the challenges consumers face in negotiating responsibilization and the resulting well-being outcomes.

Responsibility and Well-being: Resource Integration under Responsibilization in Expert Services

Responsibilization, or the shift in functions and risks from providers and producers to the consumer, has become an increasingly common policy in service systems and marketplaces (e.g., financial, health, governmental). Responsibilization is often presented as synonymous with consumer agency and well-being. We take a transformative service research perspective and utilize the resource integration framework to investigate whether responsibilization is truly associated with well-being. We focus on expert services, where responsibilization concerns are particularly salient, and question whether this expanding policy is in the public interest. In the process, we develop a conceptualization of resource integration under responsibilization that includes three levels of actors (consumer, provider and service system), the identification of structural tensions to resource integration and three categories of resource integration practices (access, appropriation and management) necessary to negotiate responsibilization. Our findings have important implications for health care providers, public policy makers, and other service systems, all of which must pay more active attention to the challenges consumers face in negotiating responsibilization and the resulting well-being outcomes.

Robust indoor speaker recognition in a network of audio and video sensors

Situational awareness is achieved naturally by the human senses of sight and hearing in combination. Automatic scene understanding aims at replicating this human ability using microphones and cameras in cooperation. In this paper, audio and video signals are fused and integrated at different levels of semantic abstractions. We detect and track a speaker who is relatively unconstrained, i.e., free to move indoors within an area larger than the comparable reported work, which is usually limited to round table meetings. The system is relatively simple: consisting of just 4 microphone pairs and a single camera. Results show that the overall multimodal tracker is more reliable than single modality systems, tolerating large occlusions and cross-talk. System evaluation is performed on both single and multi-modality tracking. The performance improvement given by the audio–video integration and fusion is quantified in terms of tracking precision and accuracy as well as speaker diarisation error rate and precision–recall (recognition). Improvements vs. the closest works are evaluated: 56% sound source localisation computational cost over an audio only system, 8% speaker diarisation error rate over an audio only speaker recognition unit and 36% on the precision–recall metric over an audio–video dominant speaker recognition method.