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Cited by 283

The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.

The age of mobile social commerce: An Artificial Neural Network analysis on its resistances

Hew, Jun-Jie, Leong, Lai-Ying, Tan, Garry Wei-Han, Ooi, Keng-Boon, Lee, Voon-Hsien·2019·Technological Forecasting and Social Change
DOI 10.1016/j.techfore.2017.10.007
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Abstract

Abstract Drivers of social commerce usage has been the focus of scholars in recent years, but mobile social media users' resistance behavior towards mobile social commerce has been in the darkness and therefore worth torched lights on. With the data collected from mobile social media users who have no experience in mobile social commerce, Artificial Neural Network analysis was engaged to capture both linear and nonlinear relationships in a research model that consists of innovation barriers and privacy concern. Surprisingly, all resistances positively correlated with usage intention, except for image barrier, which appeared to be the most influencing resistance. Several explanations were offered for such outcomes. The possible coexistence of resistance behavior and usage intention resembles the fitting justification. Mobile social media users intend to embrace mobile social commerce; however, their intentions have been held up by their perceptions on innovation barriers and privacy concern. Based upon these outcomes, this study has reaffirmed the coexistence of resistances and usage intention, as well as the “privacy paradox” phenomenon. These discoveries are believed to have contributed to the existing literature. Practitioners are then advised to act accordingly to these findings, and several methods on catalyzing mobile social media users' adoption decision were suggested.

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Research works printed but not linked: 3

  1. Hair, 2014, AMOS covariance-based structural equation modeling (CB-SEM): guidelines on its application as a marketing research tool, Braz. J. Mark., 13, 44
  2. Martin, 2016, Service innovation, renewal, and adoption/rejection in dynamic global contexts, J. Bus. Res., 69, 2397, 10.1016/j.jbusres.2016.01.008 · doi:10.1016/j.jbusres.2016.01.008
  3. Samuel, 2014, Comparative study between sparse representation classification and classical classifiers on cervical cancer cell images, Int. J. Adv. Res. Comput. Commun. Eng., 3, 7788

Cited by

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Metadata

Authors
Hew, J.-J., Leong, L.-Y., Tan, G. W.-H., Ooi, K.-B., & Lee, V.-H.
Venue
Technological Forecasting and Social Change
Publisher
Elsevier BV
Published
2019-07
Volume
144
Pages
311-324
ISSN
0040-1625 (ISSN-L)
DOI
10.1016/j.techfore.2017.10.007
OpenAlex
W2765910181
Cited by
283
References
95
FWCI
17.95
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
Universiti Tunku Abdul Rahman (6251/L08)

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