par Van Der Linden, Jan ;Amadieu, Franck;Vayre, Emilie;Van De Leemput, Cécile
Référence 21th International Conference on Human-Computer Interaction(21: 26-31/07/2019: Orlando, USA)
Publication Publié, 2019
Abstract de conférence
Résumé : In recent years, the User Experience (UX) approach has emerged as a comprehensive framework for Human-Computer Interaction studies. It aims at providing a more holistic perspective on user’s subjective response to technology use that encompasses the perception of a technology’s utilitarian, non-utilitarian and emotional aspects. However, even if UX is of great interest today more scientific research is needed to validate the approach and determine its characteristics. To our knowledge, researches studying the impact of the social setting have not yet been carried out. Therefore, the present study aims at contributing to the theoretical framework and proposes to investigate the impact of the social environment on user experience.More precisely to account for the utilitarian, non-utilitarian, and emotional aspects, several UX models have been proposed. The two most cited models are the ones from Hassenzahl (2003) and Thüring & Mahlke (2007). Both trying to define the key elements of UX. However, the model of Thüring and Mahlke is more suited for empirical testing and allows a more in-depth comprehension of the inner psychological processes. In their Components of User-Experience Model (CUE-Model) the authors theorise that the user’s subjective experience stems from a direct interaction with a technology and that it comprises three core components that will influence the user’s satisfaction and further intention to use. Precisely, the perceived instrumental qualities (perceived utility and perceived ease of use) component and the perceived non-instrumental qualities (aesthetic, symbolic and motivational aspects) component, and influences the third component composed of the emotional reactions. Yet, despite its detailed description the model lacks empirical testing to determine the relative importance of each component and neglects the social impact on technology perception. Indeed, studies from other approaches, like technological acceptance approach, stressed that the perception of technological attributes is influenced by social setting. For example, it has been showed that peer and faculty support impact students’ judgment of academic tools (Martins & Kellermanns, 2004), and more generally the third version of Technology Acceptance Model (TAM 3) demonstrated that social norms impact a device’s perceived ease of use (Venkatesh & Bala, 2008).To fulfil our objectives, an online questionnaire was set up to investigate the students’ experience with tablets. The focus on tablets as technological device has been chosen because tablets are not only task completion tools, they also carry self-oriented expectations like an enhanced self-image, or a pleasurable experience. The questionnaire was made up in three parts. The first part collected biographical data, like age, gender, and educational background. The second part collected students’ actual usage of tablets. Questions related to the type of device possessed, operating system, as well as their frequency of use were asked. And, the third part collected information according to the three CUE-model components as well as data on peer and faculty support. All factors from the later part being measured by the use of 7-point Likert scales. Subsequently, to test the model and the impact of the social setting, the data has been processed by the use of the Partial Least Squares (PLS) method which is especially suited for researches led to explore theoretical models (Chin, 1998).848 students from a Belgian and a French university completed the questionnaire. The respondents’ characteristics are close to the characteristics of the universities’ general population. Among those students, 53.7% declared possessing a personal tablet, and mostly an iOS device tablet. Tablet possessors also state that they use their device regularly, and 1/3 of them use it for academic purpose. Concerning the study of the CUE-mode, the results show that all components influence significative the users’ satisfaction and intention to use. Nevertheless, a closer look at the outcomes shows that the impacts of perceived instrumental qualities and emotional reactions components are greater than the impact from perceived non-instrumental qualities. Also, the results concerning the study of the social impact show that peer and faculty support influence differently the relative importance of components.In conclusion this research contributes to UX theory by validating the relevance of studying non- instrumental qualities and emotional reaction, and the influence of the social environment. Precisely, the pertinence of the CUE-model is attested, and a more detailed picture of the relative importance of each component on user satisfaction and intention to use is given. Also, the study testifies the need to take into account the social surrounding of technology users in future UX research, and the need to distinguish the different social groups a technology user is confronted to. In addition to the contribution to the UX theory, this study also allows to a get better representation of actual tablet usage at university.ReferencesChin, W. W. (1998). The partial least squares approach for structural equation modeling. In Modern methods for business research (pp. 295–336). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.Hassenzahl, M. (2003). The Thing and I: Understanding the Relationship Between User and Product. In M. A. Blythe, K. Overbeeke, A. F. Monk, & P. C. Wright (Eds.), Funology (Vol. 3, pp. 31–42). Dordrecht: Springer Netherlands., L. L., & Kellermanns, F. W. (2004). A Model of Business School Students’ Acceptance of a Web-Based Course Management System. Academy of Management Learning & Education, 3(1), 7–26.üring, M., & Mahlke, S. (2007). Usability, aesthetics and emotions in human–technology interaction. International Journal of Psychology, 42(4), 253–264. Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions.Decision Sciences, 39(2), 273–315.