Today, voice assistants are used in many different situations and contexts, ranging from home assistants such as Amazon Alexa or Google Assistant to co-pilots in cars and service agents in telephone hotlines. There is a large body of literature on the intention to use of this technology. Many of these works are focused on aspects of usability of the voice assistant and based on the technology acceptance model and its further developments (e.g., UTAUT; Venkatesh et al., 2003). Other models, such as the social robots acceptance model (SRAM), extend such models attempting to explain the intention to use voice assistants to include social and relational factors (Fernandes & Oliveira, 2021; Wirtz et al., 2018). Still others add factors of (data) security (e.g., Buteau & Lee, 2021; McLean & Osei-Frimpong, 2019). Overall, it must be concluded that a variety of different predictors are studied in the context of voice assistants (Ling et al., 2021) but that there is some uncertainty about which predictors might be particularly important in which study contexts (e.g., Canziani & MacSween, 2021; Malodia et al., 2021; Pal et al., 2018; Yang & Lee, 2019) and to what extent the fit between voice assistant, situation and individual is of importance (Braun et al., 2019). Based on the aforementioned observations, I derive two broad research questions: 1. To what extent is the intention to use and acceptance of voice assistants prevalent in society? How does this change with regard to various moderators, for example from the areas of (a) socio-demographics (e.g., age, experience), (b) methodology (e.g., study design) or (c) environment (e.g., task context)? 2. As how important for the prediction of the intention to use and acceptance of voice assistants can different predictor (groups) be considered (i.e., variance explanations)? How does this prediction strength change with regard to various moderators, for example from the areas of (a) socio-demography (e.g., age, experience), (b) methodology (e.g., study design) or (c) environment (e.g., task context)? To approach answers for these questions, I will conduct a meta-analysis on the acceptance of voice assistants. The aim is not necessarily to be able to formulate exhaustive answers to the research questions, but to provide initial insights from the current state of the literature. Furthermore, numerous different variables were collected, but only a fraction of them will be able to be analysed in this thesis. The meta-analysis will be conducted for the predictor dimensions "Utilitarian", "Hedonic", "Relational", "Social Perception", "Social Influence", "Privacy" and "Security" and with the moderators "type of sample", "experience", "type of interaction" and "mean age". References: - Braun, M., Mainz, A., Chadowitz, R., Pfleging, B., & Alt, F. (2019). At your service: Designing voice assistant personalities to improve automotive user interfaces a real world driving study. Conference on Human Factors in Computing Systems - Proceedings, 1–11. https://doi.org/10.1145/3290605.3300270 - Buteau, E., & Lee, J. (2021). Hey Alexa, why do we use voice assistants? The driving factors of voice assistant technology use. Communication Research Reports, 38(5), 336–345. https://doi.org/10.1080/08824096.2021.1980380 - Canziani, B., & MacSween, S. (2021). Consumer acceptance of voice-activated smart home devices for product information seeking and online ordering. Computers in Human Behavior, 119(January 2020), 106714. https://doi.org/10.1016/j.chb.2021.106714 - Fernandes, T., & Oliveira, E. (2021). Understanding consumers’ acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption. Journal of Business Research, 122(January 2020), 180–191. https://doi.org/10.1016/j.jbusres.2020.08.058 - Malodia, S., Islam, N., Kaur, P., & Dhir, A. (2021). Why Do People Use Artificial Intelligence (AI)-Enabled Voice Assistants? IEEE Transactions on Engineering Management, PP, 1–15. https://doi.org/10.1109/TEM.2021.3117884 - McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa … examine the variables influencing the use of artificial intelligent in-home voice assistants. Computers in Human Behavior, 99, 28–37. https://doi.org/10.1016/j.chb.2019.05.009 - Pal, D., Funilkul, S., Charoenkitkarn, N., & Kanthamanon, P. (2018). Internet-of-Things and Smart Homes for Elderly Healthcare: An End User Perspective. IEEE Access, 6, 10483–10496. https://doi.org/10.1109/ACCESS.2018.2808472 - Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/https://doi.org/10.2307/30036540 - Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: service robots in the frontline. Journal of Service Management, 29(5), 907–931. https://doi.org/10.1108/JOSM-04-2018-0119 - Yang, H., & Lee, H. (2019). Understanding user behavior of virtual personal assistant devices. Information Systems and E-Business Management, 17(1), 65–87. https://doi.org/10.1007/s10257-018-0375-1