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Using social network data to predict technology acceptance

Using social network data to predict technology acceptance
Using social network data to predict technology acceptance
In contrast to popular literature on technology acceptance, this research-in-progress paper does not intend to build an explanatory model of technology acceptance but a predictive model so as to predict whether a specific person is likely to accept some technology. We show that the constructs that were identified in the classic UTAUT (such as performance expectancy, effort expectancy and social influence) can be used in a predictive model but that better predictions of system use can be made using knowledge about social networks that exist between people. Both social influence and social selection data are valuable to make predictions. Our approach is tested in the context of a video system which is part of an online learning platform, using a sample of 133 interconnected students.
Li, Libo
838dda30-da62-41ad-b57c-bee6ad59acd3
Goethals, Frank
a25dee80-a572-4310-90a5-9ea8cdc2fc5f
Giangreco, Antonio
66a84288-140b-41e0-85eb-4ba2367d1117
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Li, Libo
838dda30-da62-41ad-b57c-bee6ad59acd3
Goethals, Frank
a25dee80-a572-4310-90a5-9ea8cdc2fc5f
Giangreco, Antonio
66a84288-140b-41e0-85eb-4ba2367d1117
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Li, Libo, Goethals, Frank, Giangreco, Antonio and Baesens, Bart (2013) Using social network data to predict technology acceptance. In Proceedings of the 34th International Conference on Information Systems. 10 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

In contrast to popular literature on technology acceptance, this research-in-progress paper does not intend to build an explanatory model of technology acceptance but a predictive model so as to predict whether a specific person is likely to accept some technology. We show that the constructs that were identified in the classic UTAUT (such as performance expectancy, effort expectancy and social influence) can be used in a predictive model but that better predictions of system use can be made using knowledge about social networks that exist between people. Both social influence and social selection data are valuable to make predictions. Our approach is tested in the context of a video system which is part of an online learning platform, using a sample of 133 interconnected students.

Text
USING SOCIAL NETWORK DATA TO PREDICT TECHNOLOGY ACCEPTANCE - Accepted Manuscript
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More information

Published date: 2013
Venue - Dates: 34th International Conference on Information Systems, Milan, Italy, 2013-12-15 - 2015-12-18

Identifiers

Local EPrints ID: 436590
URI: http://eprints.soton.ac.uk/id/eprint/436590
PURE UUID: 51dc5580-c630-4a85-ab9e-f642decbf020
ORCID for Libo Li: ORCID iD orcid.org/0000-0003-1658-5157
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 17 Dec 2019 17:30
Last modified: 18 Dec 2019 01:36

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