## 5 Limitations and Future Directions  
In this paper, we propose improvements to understanding acceptance phases, coding them, and asking interview questions. We recommend that future research test and validate these recommendations and continue making improvements to procedures for long-term technology acceptance studies.  
Like with all research, there are a number of limitations. The results of this case study are limited to a small subject pool. With six participants in our original study, only four are included in this case study. All participants in our study pool are within the small age pool (60+). Most other studies of technology acceptance included more than twenty participants. All participants were recruited lived in New Mexico, and their primary language spoken was English. Future research should extend this work to broader groups in terms of ethnicity, location, socioeconomic status, digital literacy, health conditions, and age if research is conducted beyond the older adult population.  
Additionally, the research presented in this paper is qualitative data based on transcripts of interviews throughout the period of time when participants hosted the robots in their homes. The robot is a commercially available telepresence robot (Double 3 [68]), therefore, direct logs about the mobile telepresence robot usage statistics and errors were not available to us. Future research should consider objective performance metrics that can be assessed in real-time to better support a user's experience of, interaction with, and/or control of the robot. It is now feasible to utilize machine learning (ML) techniques to study technology acceptance outside of conventional social science research given the rise in popularity of ML applications in the last 5 years [98; 99]. In the context of mobile telepresence robots where the goal of the technology is to enhance human-to-human interactions (not to optimize the interaction and/or interface between the user and the robot), there are a number of usage statistics that can be used to measure the quality of an interaction for mobile telepresence robots [100], including communication methods, quality of audiovisual interactions, failure rates, and time taken to resolve technical problems. These performance metrics have been used for short-term or duration deployments. These should be reconsidered for long-term deployments to better understand users' behaviors and technology acceptance; for example, a long (duration) call could mean either the users are having a good conversation or alternatively that they are experiencing problems and trying to troubleshoot together. It will be important for future researchers to consider not only the type of data but also the sampling frequency necessary in order to achieve a sufficient amount of data for ML models. In this study, we conducted monthly interviews which resulted in too small of data for quantitative analysis.  
Future research should look into various robots for technology acceptance to determine if the embodiment, character, or uses of the device influence acceptance. Researchers should dive into whether users prefer human-like interactions or robotic-like interactions. Researchers should better define what personas users tend to want which types of interactions and embodiment forms of robots.