Applying a qualitative framework of acceptance of personal robots
Smarr, Cory-Ann Cook
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Personal robots can help people live safer, more efficient and comfortable lives. However, such benefits cannot be achieved if people do not use, or accept, personal robots. The use of a technology is predominantly influenced by an individual's intention to use it, which is influenced by his or her attitudes toward it (Davis, 1989). Presently, the key factors that impact the use of personal robots are not fully understood. Two studies were conducted as first step assessments of the Smarr, Fisk, and Rogers (2013) theoretically-based framework of acceptance of personal robots. In study 1, 14 participants used a personal robot (a robot lawn mower) at their homes for about six weeks. Their acceptance and factors important for acceptance identified in the framework were measured using pre-use and post-use interviews and questionnaires, and weekly diaries. The framework was conceptually validated; participants mentioned 16 of the 20 factors in the Smarr et al. (2013) framework. However, the framework did not fully account for the breadth of factors discussed by participants, thereby suggesting variables may need to be added to or removed from the framework. In study 2, 280 participants reported their initial acceptance of a personal robot (a robot mower) with different levels of reliability and communication of feedback in an online survey. Level of robot reliability did significantly affect attitudinal and intentional acceptance. Follow up analyses indicated a trend that participants who received no information on reliability had numerically higher acceptance than participants who were informed that the robot had 70% reliability or 90% reliability. Neither communication of feedback nor its interaction with reliability affected acceptance. The Smarr et al. (2013) framework explained about 60% of the variance in intentional acceptance and 57% in attitudinal acceptance of a personal robot. Eight of the 15 relationships tested were supported via path analysis. Findings largely supported the Smarr et al. (2013) framework in explaining what impacts intentional and attitudinal acceptance of a personal robot. Results from these studies can inform the Smarr et al. (2013) framework of robot acceptance and other models of acceptance, and can guide designers in developing acceptable personal robots.