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Measuring network user psychological experience quality
Published in Kennis Chan, Testing and Measurement: Techniques and Applications, 2015
such as computer science, psychometric and social informatics fields, which have studied the quantitative and qualitative properties (components) of user experience. To improve the design and evaluation of computer systems, James Lewis develops standardized subjective usability satisfaction measures (IBM Computer Usability Satisfaction Questionnaires) which has the components of users' satisfaction, such as system usefulness, information quality, and interface quality, to assess users' satisfaction with the system usability [2-4]. Totally, the properties (components) that affect users' experience are as follows: (1) usability (accessibility, functionality), which means the system is easy to use. The property is composed of other concrete and detailed properties, such as response speed, which means that users can access to the service rapidly and easily. It is easy to learn, which means that users can learn how to use the service easily, and it is easy to navigate, which means that user can obtain the service through the shortest route, and simple manipulation etc. (2) information quality, which means that the service is valuable for the users. The property is composed of other concrete and detailed properties, such as satisfying users' needs, improving work efficiency, etc. (3) other properties, such as users' characteristic, emotional state etc. 2.2 Measure properties (components) through quantitative approaches
Introduction
Published in Julie A. Jacko, The Human–Computer Interaction Handbook, 2012
The rise of specialized programs—biomedical informatics, social informatics, community informatics, and information and communication technology for development (ICT4D)—works against the consolidation of information studies. Information, like HCI, could become invisible through ubiquity. The annual Information Conference is a barometer. In 2005 and 2006, there was active discussion and disagreement about directions. Should new journals and research conferences be pursued, or should the field stick with the established venues in the various contributing disciplines? In the years since, faculty from different fields worked out pidgin languages with which to communicate with each other. Assistant professors were hired and graduate students enlisted, whose initial jobs and primary identities are with information. Will they creolize the pidgin language?
Distributed Team Cognition
Published in Michael D. McNeese, Eduardo Salas, Mica R. Endsley, Foundations and Theoretical Perspectives of Distributed Team Cognition, 2020
As we consider recent work from all of us in areas of distributed team cognition, team mental models, team situation awareness, information sharing, and decision making, it is only natural to think about how significant the reach of team cognition has been and to examine how it is extended through the influence and application of information sciences and information technologies. Therein, the second justification for the handbook is to show how information sciences and technology has evolved team cognition to be more in line with distributed cognition areas. Breaking this down in perhaps a deeper way we can now say that “distributed” connotes more than just cognition distributed across place (i.e., the contextual and cultural basis for distributed cognition, see Scribner & Tobach, 1997). It is true that one of the primal meanings of distributed cognition is that of knowing how thoughts are linked to work in context. But being distributed also means more than just that. It means that cognition is distributed across time and place (temporality proceeds into the future but is often reliant on the past, often referred to as temporal work, see Mohammed et al., 2015). Cognition necessarily distributes information where collaborative information must be sought, defined, retrieved, reified, foraged, and saved (often referred to as collaborative information seeking, see N. J. McNeese & Reddy, 2017, and information fusion, see Hall & Jordon, 2010) and information is distributed across an array of team members (participants interacting towards a joint goal where common ground emerges along the way). Cognition is distributed from an individual to a team and back to individuals in varying degrees of interdependency (often referenced as knowledge transfer and collective induction, see McNeese, 2000). And finally, cognition in today’s society is distributed across many levels of computation and technology (e.g., social informatics, web and mobile computing, artificial intelligence, the rise of enterprise networks, big data analytics, and the internet of things all can produce new ideas and conceptualizations of what cognition is and what it means as well as how group–team definition varies) and is reified to new levels through dynamic collaborative interaction.
Smart city for sustainable urban freight logistics
Published in International Journal of Production Research, 2021
Shenle Pan, Wei Zhou, Selwyn Piramuthu, Vaggelis Giannikas, Chao Chen
Figure 1 shows that, according to the selected dataset, the first papers dealing with city logistics from the angle of Smart City were published in 2014. Since that, the number of publications has increased significantly, despite a slight drop in 2018 and 2019. Figure 2 illustrates that about 70% of the papers were published in conference proceedings, while only about 27% were published in journals. More surprisingly, the 82 papers were published in 65 different outlets; and only 4 outlets have published at least 3 papers each. These outlets include Sustainability (journal), Transportation Research Procedia (TRP), Lecture Notes in Computer Science (LNCS), Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST) (conference proceedings). The findings imply the interdisciplinary nature of the research problem.
Co-designing for common values: creating hybrid spaces to nurture autonomous cooperation
Published in CoDesign, 2019
Chiara Bassetti, Mariacristina Sciannamblo, Peter Lyle, Maurizio Teli, Stefano De Paoli, Antonella De Angeli
Our contribution resonates with the attention given to cooperation in Information Technology (IT) design, be it in social informatics (Kling 2007), computer-supported cooperative work (CSCW – Schmidt 2011), human–computer interaction (HCI – DiSalvo and Le Dantec 2017), participatory design (PD – Robertson and Simonsen 2013), or value-sensitive design (VSD – Friedman and Kahn 2007). All these approaches propose that digital technologies reconfigure social cooperation and in some cases promote more democratic perspectives, being focused on ethical issues and on the risk of the exploitative affordances of technologies themselves (e.g. Ehn 2008; Friedman and Kahn 2007; Brown et al. 2019). One of the main differences between these approaches is in the relation established between the IT researcher/s and the people involved in technology design, development and use. Social informatics and CSCW focus primarily on (usually) ethnographic studies of work and of IT use and reconfiguration in practice. PD and VSD are interested in the direct involvement of people in the design phase. More specifically, VSD combines conceptual, empirical, and technical investigation to deliver technologies that include human values throughout the design process. The potential users are considered as subjects of the empirical investigation (Friedman and Kahn 2007). PD, differently, aims at the direct involvement of people from the beginning of the design process, allowing them to have a say on the artefact they are going to use (Robertson and Simonsen 2013). This also means that designers ‘take sides’ in relevant social conflicts, particularly in the conflict between labour and capital (Ehn 1992), by combining design and sociological studies of system development (e.g. UTOPIA Project Group 1981).