OTRAS FORMAS DE REPARACIÓN

This dissertation studied the feasibility of applying the TAM3 to the FFE phase in robotics. The research setting and method set limitations that must be considered in the interpretation of the results. The multi-method research is conducted in two phases and the limitations of both phases are discussed separately.

Phase 1 of the quantitative research consisted of a collection of information pertaining to EA and MM segments from 121 end-user participants of an FFE phase

service robot application. There were certain limitations in this research setting. The data collection took place at Tampere University of Technology during March 2018. Data was collected during only on one day and, thus, it is possible that there could be some sort of bias in the demographics when data is collected during such short period of time. Further, the age distribution of the test group sets a certain bias of a younger population. The age group of 20–29 years constitutes 54% of the participants. This research is an experiment of the population at Tampere University of Technology, including students, researchers, staff and visitors. The research targeted to collect data from a diverse group of people, also including tech savvy individuals who are tech sophisticated more than the average population. One limitation of the research is that there cannot be guarantee how well this sample will represent the actual population of service robot end users after all the future research and development activities leading to successful product launch. In this research, the EA group had 39 participants and the MM representative segment had 82 participants. The EA group (32%) formed by the early adopters and innovators of technology is remarkably higher than that typically described in literature (16%), double in its relative size (Rogers, 1962), which is largely explained by the research setting at the technical university. The EA group has only 39 participants which sets some limitations for the analysis method. SmartPLS (PLS-SEM) used in this research is proven to work well with relatively small data sets (Sarstedt et al., 2014; Reinartz et al., 2009). Also, the method for categorization of early adopte and mass market segments may have effect on the research results. This opens also avenues for future research to study different categorization parameters for robotics early adopters. The Phase 1 questionnaire was in Finnish, thereby enabling to also capture late adopters and laggards, as it was expected that the participants who work at the university cafeteria, cleaning staff, etc., were expected to give a different opinion than the tech- savvy students and researchers. However, the selection of Finnish language set some limitations to the demographics in the sense of it not being international. During the Research Phase 2, in the robotics R&D specialist feedback, it was also noted that the regional setting may have an effect on the research results as markets are different and cultural properties may affect the adoption of robotics technology. Further, the “lunch menu” use case, which was tested as the service robot application, utilizes the typical features of service robots under development, such as speech recognition, artificial speech, touch user-interface, and body movements. Nevertheless, the tested use case represents only a narrow scope of all the future capabilities of robotics; therefore, it must be understood that there are certain limitations when applying the results of the research in action. Although test participants were given a very concrete

and topical task, “to use the robot to investigate the lunch menus of the cafeterias at the university campus”, it cannot be overruled that some participants took part because of their curiosity towards the robot. The motives of participants were not measured; what is the actual role of the need to solve the lunch menu question or the curiosity towards the technology under testing. Non-response bias is also one topic that may affect academic research. During the research, team tried to prevent the curiosity effect from dominating the research participation by “collecting” test participants that were not natively drawn to the research setting. Research assistants offered participants small incentive in a form of a restaurant voucher to draw wider demographics to the research sample. Many non-responding individuals were in a hurry or needed to follow their friends who decided not to take part in the research. There is only a limited window at University hallway in-between classes and not everyone had time to stop to the research setting.

Phase 2 of the qualitative research included testing the collected TAM3 EA and MM FFE data by a robotics R&D team. The team size of 10 persons is typical in FFE projects; in this sense, a typical FFE NPD R&D setting was well represented in this research. However, from a statistical viewpoint, the 10 people do not offer such a significant means for numerical analysis. Therefore, Phase 2 was more qualitative in nature. The questionnaire in Phase 2 was in English, which enabled the participation of the international members of the robotics R&D team.

The robot was not permanently placed in its test setting at the Tampere University of Technology; therefore, the relationship of the TAM3 Behavioral Intention to Use and the actual use of the robot was not possible to measure. This sets limitations to the interpretation of the TAM3 results in the sense that the link of BI and actual use cannot be evaluated.