Predictors of customer acceptance of and resistance to smart technologies in the retail sector

Predictors of customer acceptance of and resistance to smart technologies in the retail sector In recent decades, rapid advances in Internet technology have led to numerous innovative smart technologies. This research investigates the customer acceptance of and resistance to smart technologies in the retail sector, by integrating the literature on technology acceptance model, system characteristics, technology readiness, and store reputation. Data were collected using a quantitative survey and analysed using symmetrical PLS path modelling and asymmetrical fuzzy set qualitative comparative analysis (fsQCA). Results show complex relationships among perceived technology readiness, perceived ease of use, perceived usefulness, superior functionality, perceived adaptiveness, and store reputation in determining customers’ attitudes and behavioural intentions towards smart retail technologies. The findings also show that technology readiness does not directly affect customer attitude but does indirectly through perceived innovation characteristics. The findings indicate that retail stores should focus on smart technologies that are simple, yet offer enhanced customer value through improved shopping efficiency. Findings also suggest that retail stores can engage in brand management strategies to improve customers’ acceptance of smart technologies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Retailing and Consumer Services Elsevier

Predictors of customer acceptance of and resistance to smart technologies in the retail sector

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0969-6989
D.O.I.
10.1016/j.jretconser.2018.02.005
Publisher site
See Article on Publisher Site

Abstract

In recent decades, rapid advances in Internet technology have led to numerous innovative smart technologies. This research investigates the customer acceptance of and resistance to smart technologies in the retail sector, by integrating the literature on technology acceptance model, system characteristics, technology readiness, and store reputation. Data were collected using a quantitative survey and analysed using symmetrical PLS path modelling and asymmetrical fuzzy set qualitative comparative analysis (fsQCA). Results show complex relationships among perceived technology readiness, perceived ease of use, perceived usefulness, superior functionality, perceived adaptiveness, and store reputation in determining customers’ attitudes and behavioural intentions towards smart retail technologies. The findings also show that technology readiness does not directly affect customer attitude but does indirectly through perceived innovation characteristics. The findings indicate that retail stores should focus on smart technologies that are simple, yet offer enhanced customer value through improved shopping efficiency. Findings also suggest that retail stores can engage in brand management strategies to improve customers’ acceptance of smart technologies.

Journal

Journal of Retailing and Consumer ServicesElsevier

Published: May 1, 2018

References

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