“Integrative Social Robotics” (ISR) is a new approach or general method for generating social robotics applications in a responsible and “culturally sustainable” fashion. Currently social robotics is caught in a basic difficulty we call the “triple gridlock of description, evaluation, and regulation”. We briefly recapitulate this problem and then present the core ideas of ISR in the form of five principles that should guide the development of applications in social robotics. Characteristic of ISR is to intertwine a mixed method approach (...) (i.e., conducting experimental, quantitative, qualitative, and phenomenological research for the same envisaged application) with conceptual and axiological analysis as required in professional studies in applied ethics; moreover, ISR is value-driven and abides by the “Non-Replacement Principle”: Social robots may only do what humans should but cannot do. We briefly compare ISR to other value-sensitive or value-directed design models, with a view to the task of overcoming the triple gridlock. Finally, working from an advanced classification of pluridiscplinary research, we argue that ISR establishes a research format that can turn social robotics into a new transdiscipline. (shrink)

This article presents an applied discussion of the possibility of integrating conversation analysis (CA) methodology into that of machine learning. The aim is to improve the detection of that which resembles disengagement in the interaction between a robot and a human. We offer a novel analytical assemblage at the heart of the two disciplines, and namely on the level of the annotation schemes provided by conversation analysis transcription methods. First, we demonstrate that the need for a stable structure in establishing (...) an interaction scenario and in designing robot behaviours does not prevent the emergence of ordinariness or creativity among the participants engaged in this interaction. Secondly, based on an actual case, we emphasize the possibility of systematicness in CA transcription to support the choice (a) of the categories targeted by prediction methods and defined by the annotation scheme, and (b) of the verbal and non-verbal features used to create prediction models. (shrink)

This article presents an applied discussion of the possibility of integrating conversation analysis (CA) methodology into that of machine learning. The aim is to improve the detection of that which resembles disengagement in the interaction between a robot and a human. We offer a novel analytical assemblage at the heart of the two disciplines, and namely on the level of the annotation schemes provided by conversation analysis transcription methods. First, we demonstrate that the need for a stable structure in establishing (...) an interaction scenario and in designing robot behaviours does not prevent the emergence of ordinariness or creativity among the participants engaged in this interaction. Secondly, based on an actual case, we emphasize the possibility of systematicness in CA transcription to support the choice (a) of the categories targeted by prediction methods and defined by the annotation scheme, and (b) of the verbal and non-verbal features used to create prediction models. (shrink)

PurposeExisting research suggests that the competitive advantage provided by technological development depends to a large extent on the speed and coordination of the technology’s implementation, and on how adoptable the technological applications are considered. While accepting this argument, the authors consider the explanatory model to be inadequate. This study aims to contribute to the theoretical discussion by analysing institutionalised industrial relations and other organisation-level factors, which are important for workplace restructuring and societal change.Design/methodology/approachThe analysis is based on a representative nation-wide (...) work and working conditions survey (N= 4,100) from Finland, which includes a variety of themes, including practices, changes and well-being at work. Changes are understood as organisational changes, focusing on modern technologies such as robotisation and digitalisation.FindingsThe results indicate that occupational division at workplace (low-skilled vs high-skilled occupations) affects job insecurity and acceptance of technologies at work. The characteristics of workplaces, such as the employees’ participation and involvement in the development of the organisation, play a significant part in both the acceptance and the implementation and outcomes of the technological transformations in the workplace.Practical implicationsThe research provides new and interesting insights into working life practices. Furthermore, it reveals how technology acceptance and employment perspectives relate to working conditions and lessons learned from past reforms.Originality/valueThe authors consider current theories such as technology acceptance model at the micro level and that way rationalise the need for this study. This study shows the importance of individual, organisational and wider contextual factors in technology acceptance. (shrink)

Background: The surge in the development of social robots gives rise to an increased need for systematic methods of assessing attitudes towards robots. Aim: This study presents the development of a questionnaire for assessing attitudinal stance towards social robots: the ASOR. Methods: The 37-item ASOR questionnaire was developed by a task-force with members from different disciplines. It was founded on theoretical considerations of how social robots could influence five different aspects of relatedness. Results: Three hundred thirty-nine people responded to the (...) survey. Factor analysis of the ASOR yielded a three-factor solution consisting of a total of 25 items: “ascription of mental capacities”, “ascription of socio-practical capacities”, and “ascription of socio-moral status”. This data was further triangulated with data from interviews (n = 10). Conclusion: the ASOR allows for assessment of three distinct facets of ascription of capacities to social robots and offers a new type of assessment of attitudes towards social robots. It appeared that ASOR not only assesses ascription of capacities to social robots but it also gauged overall positive attitudes towards social robots. (shrink)