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Structural equation modeling and artificial neural networks approach to predict continued use of mobile taxi booking apps: the mediating role of hedonic motivation

Structural equation modeling and artificial neural networks approach to predict continued use of... Mobile taxi booking apps (MTB) have revolutionalized the transportation industry. As taxis can be hired via smartphones, irrespective of any time or place, the business platform for taxi service has completely changed. Now customers are saved from the hassle of going to the designated taxi stands or waiting along the roadside. But, the long-term sustainability of this service depends on its continued use. Therefore, this study aims to explore factors that hedonically incline people toward continuance of MTB. To achieve the purpose, the unified theory of acceptance and use of technology (UTAUT) was extended with mediation effects of hedonic motivation.Design/methodology/approachThe data were collected from existing users of MTB and analyzed through structural equation modeling and revalidated via artificial neural networks.FindingsThe statistical results show that the main factors of UTAUT substantially create hedonic motivation to use the apps and significantly mediate their effects on behavioral intention to continue using MTB. However, mediation between social influence and continuity intent was not statistically supported. The findings represent important contributions to the extended UTAUT.Practical implicationsThis study adds value to the theoretical horizon and also presents M-taxi companies with useful and pertinent plans for efficient designing and effective implementation of MTB. Moreover, limitations and suggestions for future researchers are also discussed.Originality/valueThis study extends UTAUT with the mediating role of hedonic motivation to predict continued use of MTB, which further initiates the applicability of UTAUT in a new setting and a new perspective (post adoption). This, in turn, significantly expands theory by using hedonic motivation as an important attribute that could mediate impact of all main antecedents to shape customers loyalty toward system use. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Data Technologies and Applications Emerald Publishing

Structural equation modeling and artificial neural networks approach to predict continued use of mobile taxi booking apps: the mediating role of hedonic motivation

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References (128)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2514-9288
DOI
10.1108/dta-03-2020-0066
Publisher site
See Article on Publisher Site

Abstract

Mobile taxi booking apps (MTB) have revolutionalized the transportation industry. As taxis can be hired via smartphones, irrespective of any time or place, the business platform for taxi service has completely changed. Now customers are saved from the hassle of going to the designated taxi stands or waiting along the roadside. But, the long-term sustainability of this service depends on its continued use. Therefore, this study aims to explore factors that hedonically incline people toward continuance of MTB. To achieve the purpose, the unified theory of acceptance and use of technology (UTAUT) was extended with mediation effects of hedonic motivation.Design/methodology/approachThe data were collected from existing users of MTB and analyzed through structural equation modeling and revalidated via artificial neural networks.FindingsThe statistical results show that the main factors of UTAUT substantially create hedonic motivation to use the apps and significantly mediate their effects on behavioral intention to continue using MTB. However, mediation between social influence and continuity intent was not statistically supported. The findings represent important contributions to the extended UTAUT.Practical implicationsThis study adds value to the theoretical horizon and also presents M-taxi companies with useful and pertinent plans for efficient designing and effective implementation of MTB. Moreover, limitations and suggestions for future researchers are also discussed.Originality/valueThis study extends UTAUT with the mediating role of hedonic motivation to predict continued use of MTB, which further initiates the applicability of UTAUT in a new setting and a new perspective (post adoption). This, in turn, significantly expands theory by using hedonic motivation as an important attribute that could mediate impact of all main antecedents to shape customers loyalty toward system use.

Journal

Data Technologies and ApplicationsEmerald Publishing

Published: Jun 21, 2021

Keywords: Mobile taxi booking apps; Hedonic motivation; Structural equation modeling; Artificial neural networks; UTAUT; To continue using MTB

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