The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
The future of in-store technology
Open accessAbstract
This paper introduces a conceptual framework for understanding new and futuristic in-store technology infusions. First, we develop a 2 × 2 typology of different innovative and futuristic technologies focusing on their level of convenience and social presence for the consumer. Next, we offer a series of propositions based on the idea that convenience and social presence can trigger vividness by enhancing consumer involvement, imagery, and elaboration, which ultimately leads to enhanced sales. Finally, the paper then focuses on four moderating areas—consumer traits, product/service dimensions, mental models and social networks—to understand how they might impact the vividness experienced via the technology.
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Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Economic Action and Social Structure: The Problem of Embeddedness
Granovetter, M. (1985) · American Journal of Sociology · doi:10.1086/228311 · Cited by 28,745
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066
Burt, R. S. (1992) · doi:10.4159/9780674029095 · Cited by 9,093
The Strength of Weak Ties: A Network Theory Revisited
Granovetter, M. (1983) · Sociological Theory · doi:10.2307/202051 · Cited by 8,263
Structural Holes: The Social Structure of Competition
Lazega, E., & Burt, R. S. (1995) · Revue Française de Sociologie · doi:10.2307/3322456 · Cited by 7,853
Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value
Babin, B. J., Darden, W. R., & Griffin, M. (1994) · Journal of Consumer Research · doi:10.1086/209376 · Cited by 6,020
Understanding customer experience throughout the customer journey
Lemon, K. N., & Verhoef, P. C. (2016) · Journal of Marketing · doi:10.1509/jm.15.0420 · Cited by 5,765
Defining virtual reality: Dimensions determining telepresence
Steuer, J. (1992) · Journal of Communication · doi:10.1111/j.1460-2466.1992.tb00812.x · Cited by 5,459
Human Inference: Strategies and Shortcomings of Social Judgment.
Cherniak, C., Nisbett, R., & Ross, L. (1983) · The Philosophical Review · doi:10.2307/2184495 · Cited by 4,196
Cited by
A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059
“…et al. 2020), machine learning prediction for mobile marketing personalization (Tong et al. 2020), in-store technology (e.g., robots, smart displays, or augmented reality) for convenience or social presence (Grewal et al. 2020), and AI for personalized customer engagement (Kumar et al. 2019).”
BackgroundThe future of technology and marketing: a multidisciplinary perspective
Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00711-4 · Cited by 623
Self-citationAI-powered marketing: What, where, and how?
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102783 · Cited by 509
How Technology is Changing Retail
Shankar, V., Kalyanam, K., Setia, P., Golmohammadi, A., Tirunillai, S., Douglass, T., Hennessey, J., Bull, J. S., & Waddoups, R. (2021) · Journal of Retailing · doi:10.1016/j.jretai.2020.10.006 · Cited by 438
Liu-Thompkins, Y., Okazaki, S., & Li, H. (2022) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-022-00892-5 · Cited by 441
Metadata
- Authors
- Grewal, D., Noble, S. M., Roggeveen, A. L., & Nordfalt, J.
- Venue
- Journal of the Academy of Marketing Science
- Published
- 2019-10-11
- Volume
- 48
- Issue
- 1
- Pages
- 96-113
- ISSN
- 0092-0703 (ISSN-L)
- 1552-7824
- OpenAlex
- W2972982607
- Cited by
- 455
- References
- 72
- FWCI
- 57.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Grewal, D., Noble, S. M., Roggeveen, A. L., & Nordfalt, J. (2020). The future of in-store technology. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-019-00697-z
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
