The augmented reality smartphone game Pokémon Go is one of the biggest commercial successes in the last years, posing the question concerning the factors contributing to the game’s success. An apparent distinction to other games is the strong brand Pokémon. We derive a research model based on the established theory of technology acceptance, which includes an established construct for nostalgic feelings – childhood brand nostalgia – and theorise on how it is related to beliefs about technology characteristics and the intention to play the game. For this purpose, we adapt one of the most prominent technology acceptance models for the consumer context and for hedonic information systems, the UTAUT2 model. Based on our model, we conduct a study with 418 active German players aged between 18 and 35. Our results indicate that the effect of childhood brand nostalgia on behavioural intention is fully mediated by the belief constructs. Thus, nostalgic feelings about Pokémon influence the intention of users through altering beliefs concerning Pokémon. We include nostalgic feelings in a technology acceptance model for the first time, therefore contributing to the theoretical advance in the IS domain. The results can be used to enhance the technology acceptance of newly designed products.

Abstract Today’s environment of data-driven business models relies heavily on collecting as much personal data as possible. Besides being protected by governmental regulation, internet users can also try to protect their privacy on an individual basis. One of the most famous ways to accomplish this, is to use privacy-enhancing technologies (PETs). However, the number of users is particularly important for the anonymity set of the service. The more users use the service, the more difficult it will be to trace an individual user. There is a lot of research determining the technical properties of PETs like Tor or JonDonym, but the use behavior of the users is rarely considered, although it is a decisive factor for the acceptance of a PET. Therefore, it is an important driver for increasing the user base. We undertake a first step towards understanding the use behavior of PETs employing a mixed-method approach. We conducted an online survey with 265 users of the anonymity services Tor and JonDonym (124 users of Tor and 141 users of JonDonym). We use the technology acceptance model as a theoretical starting point and extend it with the constructs perceived anonymity and trust in the service in order to take account for the specific nature of PETs. Our model explains almost half of the variance of the behavioral intention to use the two PETs. The results indicate that both newly added variables are highly relevant factors in the path model. We augment these insights with a qualitative analysis of answers to open questions about the users’ concerns, the circumstances under which they would pay money and choose a paid premium tariff (only for JonDonym), features they would like to have and why they would or would not recommend Tor/JonDonym. Thereby, we provide additional insights about the users’ attitudes and perceptions of the services and propose new use factors not covered by our model for future research.

We conducted a literature survey on reproducibility and replicability of user surveys in security research. For that purpose, we examined all papers published over the last five years at three leading security research conferences and recorded the type of study and whether the authors made the underlying responses available as open data, as well as if they published the used questionnaire respectively interview guide. We uncovered how user surveys become more widespread in security research and how authors and conferences are increasingly publishing their methodologies, while we had no examples of data being made available. Based on these findings, we recommend that future researchers publish their data in addition to their results to facilitate replication and ensure a firm basis for user studies in security research.

We investigate the technology acceptance factors of the AR smart-phone game Pokémon Go with a PLS-SEM approach based on the UTAUT2 model by Venkatesh et al. [1]. Therefore, we conducted an online study in Germany with 683 users of the game. Many other studies rely on the users' imagination of the application's functionality or laboratory environments. In contrast, we asked a relatively large user base already interacting in the natural environment with the application. Not surprisingly, the strongest predictor of behavioral intention to play Pokémon Go is hedonic motivation, i.e. fun and pleasure due to playing the game. Additionally, we find medium-sized effects of effort expectancy on behavioral intention, and of habit on behavioral intention and use behavior. These results imply that AR applications - besides needing to be easily integrable in the users' daily life - should be designed in an intuitive and easily understandable way. We contribute to the understanding of the phenomenon of Pokémon Go by investigating established acceptance factors that potentially fostered the massive adoption of the game.