The Acceptance of Smart Home Technology

Gross, Christina; Siepermann, Markus; Lackes, Richard

doi:10.1007/978-3-030-61140-8_1

Cited by 12 publications

(13 citation statements)

References 32 publications

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“…In the same manner, PEOU had a direct positive influence on PU (H3), which was consistent with previous user acceptance studies (Bao et al, 2014;Dong et al, 2017;Gross et al, 2020;Hojjati & Khodakarami, 2016;Nikou, 2019;Pal et al, 2018;Park et al, 2017Park et al, , 2018Shuhaiber & Mashal, 2019). Aside from this, PEOU had a direct influence on ATT (H4), which was consistent with previous user acceptance studies (Hojjati & Khodakarami, 2016;Park et al, 2017Park et al, , 2018Shuhaiber & Mashal, 2019).…”

Section: Discussionsupporting

confidence: 90%

“…The results clarified that PU had a positive direct effect on ATT (H1), consistent with previous user acceptance studies (Gross et al, 2020;Hojjati & Khodakarami, 2016;Park et al, 2017Park et al, , 2018Shuhaiber & Mashal, 2019). Also, PU had a positive direct effect on BI (H2), which was consistent with previous user acceptance studies (Alaiad & Zhou, 2017;Bao et al, 2014;Dong et al, 2017;Guhr et al, 2020;Hojjati & Khodakarami, 2016;Hubert et al, 2019;Nikou, 2019;Pal et al, 2018;Park et al, 2017Park et al, , 2018Shuhaiber & Mashal, 2019).…”

Section: Discussionsupporting

confidence: 89%

“…Similarly, the outcomes of these studies confirmed that PEOU has an effect on PU (Bao et al, 2014;Dong et al, 2017;Gross et al, 2020;Hojjati & Khodakarami, 2016;Nikou, 2019;Pal et al, 2018;Park et al, , 2018Shuhaiber & Mashal, 2019) and ATT (Hojjati & Khodakarami, 2016;Park et al, 2017Park et al, , 2018Shuhaiber & Mashal, 2019). In addition, they confirmed that ATT affects BI (Gross et al, 2020;Hojjati & Khodakarami, 2016;C. Kim et al, 2010;Y.…”

Section: Literature Review and Hypotheses Developmentsupporting

confidence: 57%

“…The outcomes of these studies confirmed that PU affects ATT (Gross et al, 2020;Hojjati & Khodakarami, 2016;Park et al, 2017Park et al, , 2018Shuhaiber & Mashal, 2019) and BI (Alaiad & Zhou, 2017;Bao et al, 2014;Dong et al, 2017;Guhr et al, 2020;Hojjati & Khodakarami, 2016;Nikou, 2019;Pal et al, 2018;Park et al, 2017Park et al, , 2018Shuhaiber & Mashal, 2019). Similarly, the outcomes of these studies confirmed that PEOU has an effect on PU (Bao et al, 2014;Dong et al, 2017;Gross et al, 2020;Hojjati & Khodakarami, 2016;Nikou, 2019;Pal et al, 2018;Park et al, , 2018Shuhaiber & Mashal, 2019) and ATT (Hojjati & Khodakarami, 2016;Park et al, 2017Park et al, , 2018Shuhaiber & Mashal, 2019). In addition, they confirmed that ATT affects BI (Gross et al, 2020;Hojjati & Khodakarami, 2016;C.…”

Section: Literature Review and Hypotheses Developmentsupporting

confidence: 52%

“…The robustness of TAM, IDT, and TPB have been tested in the SHSs. In addition, many studies were found in the literature that used an enhanced version of these acceptance theories with additional factors, depending on the technology being studied (Alaiad & Zhou, 2017;Bao et al, 2014;Dong et al, 2017;Gross et al, 2020;Guhr et al, 2020;Hojjati & Khodakarami, 2016;Hubert et al, 2019;Kim et al, 2017;Nikou, 2019;Pal et al, 2018;Park et al, 2018Park et al, , 2017Sequeiros et al, 2021;Shuhaiber & Mashal, 2019;Yang et al, 2017). A detailed description of all factors proposed is presented in the following subsections.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

See 4 more Smart Citations

A comprehensive acceptance model for smart home services

Al-Husamiyah¹,

Al-Bashayreh²

2022

10.5267/j.ijdns

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Smart home services (SHSs) afford users an effective lifestyle management system, which provides human-oriented networking of smart devices and applications that enable users to control their homes from anywhere at any time. Despite the benefits of SHSs, however, their acceptance is very low. There remains a gap in the literature in terms of a comprehensive model that addresses users’ intention to use SHSs. To address this gap, the present study explored the factors that influence SHS acceptance among users based on well-established theoretical frameworks, such as the technology acceptance model, innovation diffusion theory, and the theory of planned behavior. To this end, the study integrated four additional factors, namely, perceived convenience, perceived connectedness, perceived cost, and perceived privacy risk, into the exploration and carried out structural equation modeling to quantitatively determine the effects of these factors. Questionnaires were administered to 750 users. The findings indicated that perceived compatibility, perceived convenience, perceived connectedness, perceived cost, perceived behavioral control with perceived usefulness, and perceived ease of use directly and indirectly exerted a significant influence on users’ intention to use SHSs.

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