Search results

In this book it was proposed that autistic females and African American autistics are usually not participants in autistic research because they rarely receive an autism diagnosis. If diagnostic services were made more congenial for autistics from these groups, then the prospect of them being diagnosed as autistic and being available to be research participants would increase. To help achieve this outcome, this chapter begins by presenting some typical barriers that autistics encounter when trying to access autism diagnostic services. This is followed by an explanation of some consequences of this exclusion for autistics, their families and the research community. In an attempt to mitigate these consequences, and to increase the pool of potential autistic candidates for research, this chapter concludes with a series of suggestions to improve the public's accessibility to autism diagnostic services as well as suggestions for improving the autism diagnostic process for both children and adults.

The contribution that this chapter makes to the field of autism spectrum research is to provide clinicians with some important concepts that will help autistics feel valued and accepted during the diagnostic process. A potential flow-on effect of this knowledge is that more autistics who have experienced feeling valued and accepted by clinicians will be more inclined to be involved in research.

This chapter conceptualizes computational methods across three related, yet distinct approaches: (1) Social Simulation, (2) Data Science, and (3) Big Data. Group communication research is then situated and reviewed along these three lines of research. Although some areas have considerable visibility (e.g., network analysis, text mining), some areas are less visible in group communication research (e.g., Social Simulation, Big Data designs). The chapter concludes with suggestions for issues regarding reliability, validity, and ethics.

This paper investigates effective human-robot collaboration (HRC) and presents implications for Human Resource Management (HRM). A brief review of current literature on HRM in the smart industry context showed that there is limited research on HRC in hybrid teams and even less on effective management of these teams. This book chapter addresses this issue by investigating factors affecting intention to collaborate with a robot by conducting a vignette study. We hypothesized that six technology acceptance factors, performance expectancy, trust, effort expectancy, social support, organizational support and computer anxiety would significantly affect a users' intention to collaborate with a robot. Furthermore, we hypothesized a moderating effect of a particular HR system, either productivity-based or collaborative. Using a sample of 96 participants, this study tested the effect of the aforementioned factors on a users' intention to collaborate with the robot. Findings show that performance expectancy, organizational support and computer anxiety significantly affect the intention to collaborate with a robot. A significant moderating effect of a particular HR system was not found. Our findings expand the current technology acceptance models in the context of HRC. HRM can support effective HRC by a combination of comprehensive training and education, empowerment and incentives supported by an appropriate HR system.