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1	Generalized User Experience Questionnaire (UEQ-G): A holistic tool for measuring multimodal user experiences Boothe, Chase Shelton 25 November 2020 (has links) The holistic user experience (UX) is comprised of both pragmatic and hedonic qualities and encompasses both product-centric and service-type experiences. Currently there is no questionnaire that measures the holistic UX for both types of experiences, and, without one, it is not possible to measure multimodal experiences that cross between experience types within the same extended experience. To address this need, the User Experience Questionnaire (UEQ), originally designed to measure product-centric experiences, was generalized to work for both product-centric and service-type experiences in a variant called the Generalized User Experience Questionnaire (UEQ-G). In a first study, the UEQ-G was tested alongside the UEQ in legacy, product-centric scenarios. Participants were asked to test two mobile applications and then evaluate them using either the UEQ-G or the UEQ. The performance of both questionnaires was indistinguishable, and the UEQ-G was found to be an appropriate tool for evaluating product-centric scenarios. In a second study, the UEQ-G was tested in service-type scenarios where, in each scenario, there were two conditions for which a UEQ-G factor was designed to be either high or low depending on the scenario condition. Participants were asked to observe a scenario video in either the high or low condition and then complete a series of five questionnaires that included the UEQ-G. Each of the UEQ-G factors was found to be sensitive to differences in scenario conditions; however, a question was left unanswered about the stimulation factor’s validity. In a final study, the UEQ-G was used to evaluate multimodal experiences in the wild. Participants were asked to order food using the Chickil-A® mobile app and then go to the restaurant to pick up their orders. Upon their return, participants were asked to complete the same series of questionnaires from the second study including the UEQ-G. Results indicated that the UEQ-G was able to detect both hedonic and pragmatic qualities within the multimodal experience and that the vast majority of the relationships seen in the second study were also seen in the third. Through these studies, the UEQ-G demonstrated potential as a questionnaire for measuring the holistic UX in multimodal experiences. User Experience Questionnaire AttrakDiff2 After-Scenario Questionnaire Customer Satisfaction Emotional Metric Outcomes Likelihood-to-Recommend User Experience
2	Application and Validation of the UEQ KPI in a hedonic context : A context-only extension study in the form of an online survey on Netflix Blunck, Kim January 2020 (has links) The aim of the study was to replicate and validate the UEQ KPI introduced by Hinderks et al. (2019) in a context-only extension study by applying it to a product with high hedonic qualities, namely Netflix. The secondary research aim was to identify areas of improvement for Netflix’ user experience. An online survey was conducted with a positivist research philosophy and a deductive research approach in order to achieve the research aims. The chosen methodology proved to be appropriate and effective. The UEQ KPI was confirmed to be a valid methodology to evaluate products with high hedonic qualities. This study hence contributed to the validity of the UEQ KPI and to its generalisability. Certain areas of improvement and their perceived importance for Netflix’ UX were identified, listed from most to least promising: Efficiency, Stimulation and the Novelty. However, as the UEQ KPI is fairly new, more research on this topic is necessary to further validate it. Certainly, this topical development in UX research was proven to be a promising approach to assess the UX of products with different characteristics in a cheap, fast and accurate way. user experience (UX) user experience questionnaire (UEQ) UEQ KPI hedonic qualities replication study context-only extension study online survey Netflix Information Systems
3	Implementation and Analysis of Co-Located Virtual Reality for Scientific Data Visualization Jordan M McGraw (8803076) 07 May 2020 (has links) <div>Advancements in virtual reality (VR) technologies have led to overwhelming critique and acclaim in recent years. Academic researchers have already begun to take advantage of these immersive technologies across all manner of settings. Using immersive technologies, educators are able to more easily interpret complex information with students and colleagues. Despite the advantages these technologies bring, some drawbacks still remain. One particular drawback is the difficulty of engaging in immersive environments with others in a shared physical space (i.e., with a shared virtual environment). A common strategy for improving collaborative data exploration has been to use technological substitutions to make distant users feel they are collaborating in the same space. This research, however, is focused on how virtual reality can be used to build upon real-world interactions which take place in the same physical space (i.e., collaborative, co-located, multi-user virtual reality).</div><div><br></div><div>In this study we address two primary dimensions of collaborative data visualization and analysis as follows: [1] we detail the implementation of a novel co-located VR hardware and software system, [2] we conduct a formal user experience study of the novel system using the NASA Task Load Index (Hart, 1986) and introduce the Modified User Experience Inventory, a new user study inventory based upon the Unified User Experience Inventory, (Tcha-Tokey, Christmann, Loup-Escande, Richir, 2016) to empirically observe the dependent measures of Workload, Presence, Engagement, Consequence, and Immersion. A total of 77 participants volunteered to join a demonstration of this technology at Purdue University. In groups ranging from two to four, participants shared a co-located virtual environment built to visualize point cloud measurements of exploded supernovae. This study is not experimental but observational. We found there to be moderately high levels of user experience and moderate levels of workload demand in our results. We describe the implementation of the software platform and present user reactions to the technology that was created. These are described in detail within this manuscript.</div> Software Engineering Computer Software Computer Graphics Simulation and Modelling Virtual Reality and Related Simulation Mobile Technologies Networking and Communications Computer-Human Interaction Collaborative Virtual Reality Virtual Reality Collaborative Virtual Reality Networking Networked VR Mulitplayer Multiplayer VR Data Visualization Data Exploration Head Mounted Display Cyberspace Augmented Reality Emerging Technologies Consumer Devices Virtual Environment Virtual Aesthetics Collaborative VR Simulation Training Simulation and Training Colocated Colocated Virtual Environment Immersive Media Immersive Technology Collaborative Tools Educational Virtual Environment Point Cloud Representations Isosurface Representations Mesh Smoothng Supernova Supernovae Supernova Remnant Modeling Astronomy and Astrophysics Cassiopeia N132D E0102 Crab Nebula CAVE CAVE Theater Mobile VR Virtual Classrooms STEM Workload Workload Assessment Tool Presence Engagement Immersion Experience Consequence Simulator sickness Simulator Sickness Questionnaire NASA TLX NASA Task Load Index Unified User Experience Quesionnaire Modified User Experience Questionnaire Oculus Oculus Go Oculus Quest Novel Solutions Optitrack Motive Motion Capture Mocap User Experience Unity3D Game Engine Lidgren Room Scale VR Hall Scale VR Co-location Colocation Virtual Training Collaborative Virtual Environments Shared Virtual Environments Collaboration Whiteboard Mobile Virtual Reality VR HMD LAN UX CSCL UXQ TLX AR VE CVE 3D UI EVE

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