### Background of the Problem  
In 2015, adults 50 years and older made up approximately 40% of the population, were the fastest growing demographic adopting Internet-enabled technologies [10], and accounted for more than half of consumer spending [11]. Older adults in the U.S. are interested in maintaining independence, aging at home longer, and staying active [11]. Their substantial size, market share, and household wealth sparked the interest of investors and developers in remote monitoring, smart homes, ambient-assisted living, tracking, applications, and sensors via the IoT. The academic research indicated that the use of IoT technologies in the home would improve older adults' safety, independence, life quality, and health [8; 12; 13; 14; 15]. However, more than half of this demographic group use older cell phones and desktop computers, 30% do not use the Internet [10], and more than 80% have not upgraded or adopted the latest technological implementations, such as tablets, smartphones, or laptops [15]. Most of the academic research on IoT-enabled technologies for older adults centered on technological and organizational challenges from the perspective of developers and healthcare professionals and did not consider the proposed consumers' needs and requirements [16].  
While researchers have performed a fair number of studies on technology acceptance, most researchers approached the topic using models developed for the business context. The majority of the researchers used the technology acceptance model TAM [17; 18; 19] and the unified theory of acceptance and use of technology UTAUT [20; 21], which were developed to measure technology acceptance in business environments where technology adoption is involuntary. The TAM posited that a person's perceived usefulness and perceived ease of use of the technology were good predictors of that individual's intention to use technology in a business context [18].  
[PERSON] et al. [22] developed the UTAUT by evaluating and synthesizing several widely accepted technology adoption models such as the TAM, the social cognitive theory, the theory of planned behavior (TPB), a combination of TAM and TPB, the innovation diffusion theory, the motivation model, and the theory of reasoned action. The results of this work added the constructs of technology performance expectancy, effort expectancy, social influence to use the technology, and facilitating conditions, which were moderated by gender, age, experience, and voluntariness of use to affect behavioral intention and use. Although [PERSON] et al. [22] developed the UTAUT for business use, researchers dropped the voluntariness of use construct and reported that all other predictors significantly affected behavioral intention to use when moderated by age [20].  
Researchers using TAM and UTAUT attempted to make their studies more relevant to the consumer context by adding constructs such as price, anxiety, and attitude, but the results were contradictory to prior evidence [23]. Existing research on UTAUT focused on four types of extensions: new extrinsic components, modeling tools, moderating components, and outcomes [9; 14; 24; 25; 26]. An overwhelming percentage of researchers only administered their surveys via the Internet, ignoring the whole portion of the demographic group without Internet [16; 17; 27; 28; 29; 30].