The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Consumer acceptance of smart speakers: a mixed methods approach
Abstract
Purpose: Voice-activated smart speakers such as Amazon Echo and Google Home were recently developed and are gaining popularity. Understanding and theorizing the underlying mechanisms that encourage or impede consumers to use smart speakers is fundamental for enhancing acceptance and future development of these new devices. Therefore, building on technology acceptance research, this study aims to develop and test an acceptance model for investigating consumers’ intention to use smart speakers. Design/methodology/approach: First, antecedents that may significantly affect the usage intention of smart speakers were identified through an explorative approach by a netnographic analysis of customer reviews (N= 2,186) and Twitter data (N= 899). Afterward, these results and contemporary literature were used to develop and validate an acceptance model for smart speakers. Structural equation modeling (SEM) was used to test the proposed hypotheses on data collected from 293 participants of an online survey. Findings: Besides perceived ease of use and perceived usefulness, the quality and diversity of a system, its enjoyment, consumer’s technology optimism and risk (surveillance anxiety and security/privacy risk) strongly affect the acceptance of smart speakers. Among these variables, enjoyment had the strongest effect on behavioral intention to use smart speakers. Originality/value: This is the first study that incorporates netnography and SEM for investigating technology acceptance and applies it to the field of interactive smart devices.
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Cites
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Cited by
Vimalkumar, M., Sharma, S. K., Singh, J. B., & Dwivedi, Y. K. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106763 · Cited by 402
Voice assistants in hospitality: using artificial intelligence for customer service
Buhalis, D., & Moldavska, I. (2022) · Journal of Hospitality and Tourism Technology · doi:10.1108/jhtt-03-2021-0104 · Cited by 281
mID: Tracking and Identifying People with Millimeter Wave Radar
Zhao, P., Lu, C. X., Wang, J., Chen, C., Wang, W., Trigoni, N., & Markham, A. (2019) · 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) · doi:10.1109/dcoss.2019.00028 · Cited by 266
Ma, X., & Huo, Y. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2303554 · Cited by 15
Garg, R., Cui, H., & Kapadia, Y. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479495 · Cited by 15
Metadata
- Authors
- Kowalczuk, P.
- Venue
- Journal of Research in Interactive Marketing
- Publisher
- Emerald
- Published
- 2018-10-08
- Volume
- 12
- Issue
- 4
- Pages
- 418-431
- ISSN
- 2040-7122 (ISSN-L)
- OpenAlex
- W2896667184
- Cited by
- 203
- References
- 8
- FWCI
- 17.25
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.