Although traditionally researchers have focused on making robotics more user-friendly from a human perspective, a new theory has begun to take shape in which humans take on the perspective of a robotic entity. This paper examined the concept of technomorphism and where the individual differences may lie in the tendency to technomorphize. The Technomorphic Tendencies Scale was revised and re-validated. Furthermore, the concept of technomorphism was investigated in a more representative population across different regions, educational backgrounds, and ages. The implications and next steps for the concept of technomorphism are also discussed.
AntonioA.TuffleyD. (2014). The gender digital divide in developing countries. Future Internet, 6(4), 673-687.
2.
BarrickM.MountM. (1991). The big five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 2, 58-71.
3.
ÇiçekM. (2015). Wearable technologies and its future applications. International Journal of Electrical, Electronics and Data Communication, 3(4), 45-50.
4.
BryantD. A.BorensteinJ.HowardA. (2020, March). Why should we gender? The effect of robot gendering and occupational stereotypes on human trust and perceived competency. In Proceedings of the 2020 ACM/IEEE international conference on human-robot interaction (pp. 13-21).
5.
CoffinR. J.MacIntyreP. D. (1999). Motivational influences on computer-related affective states. Computers in Human Behavior, 15(5), 549-569.
6.
CooperJ. (2006). The digital divide: The special case of gender. Journal of computer assisted learning, 22(5), 320-334.
7.
DonnellanM. B.OswaldF. L.BairdB. M.LucasR. E. (2006). The Mini-IPIP scales: Tiny-yet-effective measures of the Big Five factors of personality. Psychological Assessment, 18 (2), 192-203.
8.
GaoY.LiH.LuoY. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems.
9.
GorsuchR. L.1983. Factor analysis (2nd ed.)New Jersey: Lawrence Erlbaum.
10.
HuffmanA. H.WhettenJ.HuffmanW. H. (2013). Using technology in higher education: The influence of gender roles on technology self-efficacy. Computers in Human Behavior, 29(4), 1779-1786.
11.
JacobsJ. V.HettingerL. J.HuangY. H.JeffriesS.LeschM. F.SimmonsL. A.… WillettsJ. L. (2019). Employee acceptance of wearable technology in the workplace. Applied ergonomics, 78, 148-156.
12.
KimK. J.ShinD. H. (2015). An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Research.
13.
LumH. C.SimsV. K.ChinM. G.HalseS. E. (2011, September). Are we becoming super-human cyborgs? Examination of technomorphism and the creation of a technomorphic tendencies scale. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 55, No. 1, pp. 1370-1374). Sage CA: Los Angeles, CA: SAGE Publications.
14.
MarangunićN.GranićA. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14, 81-95.
15.
NomuraT.SuzukiT.KandaT.KatoK. (2006). Altered attitudes of people toward robots: Investigation through the Negative Attitudes toward Robots Scale. Proc. AAAI-06 Workshop on Human Implications of Human- Robot Interaction, 29-35.
16.
ParasuramanA. (2000). Technology Readiness Index (TRI): A Multiple-Item Scale to Measure Readiness to Embrace New Technologies. Journal of Service Research2, (4), 307-320.
17.
VenkateshV.MorrisM. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, 115-139.
18.
WuL.FanA. A.MattilaA. S. (2015). Wearable technology in service delivery processes: The gender-moderated technology objectification effect. International Journal of Hospitality Management, 51, 1-7.
