An empirical analysis of the determinants of 3G adoption in China

Chong, Alain Yee‐Loong; Ooi, Keng‐Boon; Lin, Bin; Bao, Haijun

doi:10.1016/j.chb.2011.10.005

Cited by 87 publications

(67 citation statements)

References 45 publications

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“…This is in line with the previous studies (Chong et al, 2012;Ong et al, 2008;Suki, 2012) that found relative advantage and perceived usefulness, which are similar with performance expectancy, to be major influential factors of the intention to use 3G technology among Malaysian and Chinese consumers. In addition, its relationship with satisfaction was partially mediated by intention.…”

Section: Discussionsupporting

confidence: 80%

“…Perceived usefulness (Suki, 2012) and relative advantage (Ong et al, 2008) were predictors of intention to adopt 3G technology among Malaysians. Chong, Ooi, Lin, and Bao (2012) found perceived usefulness to be a predictor of intention to use 3G service among Chinese consumers. However, perceived usefulness was not a predictor of behavioral intention in the context of Taiwan (Kuo & Yen, 2009;Liao, Tsou, & Huang, 2007 …”

Section: Performance Expectancymentioning

confidence: 99%

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Mediating Effects of Behavioral Intention Between 3g Predictors and Service Satisfaction

Dhaha¹,

Ali²

2014

JKMJC

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IsmaIl sheIkh Yusuf ahmed dhaha InternatIonal IslamIc unIversItY malaYsIa alI YassIn sheIkh alI sudan unIversItY of scIence and technologY, khartoum, sudan Abstract This paper looks at the factors that affect the adoption and satisfaction with third generation (3G) mobile phones among students at a private university in Somalia from the perspective of the Unified Theory of Acceptance and Use of Technology (UTAUT. Specifically, the objectives of the study are to 1) examine the effect of performance expectancy on students' intention to adopt 3G; 2) explore the effect of effort expectancy on students' intension; 3) look at the effect of social influence on students' intention; 4) examine the effect of perceived expense on students' intention; 5) test the effect of intention on students' satisfaction with the service; and finally 6) test the mediating effect of intention between 3G predictors and satisfaction with the service. The study uses proportionate stratified random sampling procedure, where the faculty and gender were the main strata. The data were collected in June 2013 and a total of 395 students responded to the study. The data was analyzed using SPSS version 17.0 to answer research objectives and test its hypotheses. Using multipleStep-Wise regression, the results suggested that performance expectancy (β=.158, t=2.850, p=.005), effort expectancy (β=.106, t=2.094, p=.037), social influence (β=.358, t=6.888, p=.000), and perceived expense (β=.165, t=3.872, p=.000) had significant and positive effects on students' intention to adopt 3G mobile technology. However, social influence (β=.358) was the best (Special Issue) 2014: 107-128 108 predictor for the students' intention followed by perceived expense (β=.165). Furthermore, behavioral intention significantly mediated the relationships of predictors with satisfaction. Implications and future research scope are discussed.

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Section: Discussionsupporting

confidence: 80%

Section: Performance Expectancymentioning

confidence: 99%

Mediating Effects of Behavioral Intention Between 3g Predictors and Service Satisfaction

Dhaha¹,

Ali²

2014

JKMJC

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“…In contrast with Zainudeen and Ratnadiwakara, Chong et al (2012a) found that that in Malaysia, older consumers are more likely to adopt M-Commerce compared to younger consumers. Again, the same researchers collecting data in a different environment related to different technology found that there is no difference in the consumers' gender and age when it comes to 3G adoption (Chong et al, 2012b).…”

Section: Agementioning

confidence: 97%

“…Trialability refers to whether the end user can try out the technology prior to adoption as in a limited period of free use. A technology that can be tried or experimented with by users on a limited basis before being fully employed might be able to gain a faster acceptance rate (Chong et al, 2012b). Observability is the degree to which the adoption and use of technology by others is visible to the potential new user (corresponds to Social Influence).…”

Section: Diffusion Of Innovation Models (Dims)mentioning

confidence: 99%

Rapidly co-evolving technology adoption and diffusion models

Chiyangwa

Alexander

2016

Telematics and Informatics

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“…Subsequent research has employed the TAM to explain the use of a variety of IS, such as DSS (Devaraj et al 2008;Djamasbi et al 2010;Hong et al 2006;Pommeranz et al 2011) (Chong et al 2012;Gefen et al 2003;Vijayasarathy 2004;Zarmpou et al 2012), and web services (Hsu and Lin 2008;Lederer et al 2000;Moon and Kim 2001;Schilke and Wirtz 2012).…”

Section: Technology Acceptance Modelmentioning

confidence: 99%

An empirical study of managers’ usage intention in BI

2013

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In a changing business environment, data within and around organizations rapidly accumulate. In recent years, many organizations have implemented business intelligence (BI) to manage and refine the vast stocks of data. The effective use of BI can support managers to make faster and better decisions. The goal of this study is to investigate how to increase a manager's intention to read information and to create reports. Based on the technology acceptance model, a research model is developed and tested to assess the factors (i.e., usefulness and ease of use) affecting a manager's intention to use BI. In addition, the relationship between the intention to read information and the intention to create reports is linked using Dholakia and Bagozzi (D&B) model. A survey of 271 managers supports the proposed model. The empirical results show that the usefulness of BI directly and indirectly affects the intention to read information. Both the reading and creating interfaces of BI affect the intention to read information and the intention to create reports, respectively. The intention to read information positively and significantly affects the intention to create reports. Given the empirical findings, this study provides theoretical and managerial insights for organizations and managers.

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An empirical analysis of the determinants of 3G adoption in China

Cited by 87 publications

References 45 publications

Mediating Effects of Behavioral Intention Between 3g Predictors and Service Satisfaction

Mediating Effects of Behavioral Intention Between 3g Predictors and Service Satisfaction

Rapidly co-evolving technology adoption and diffusion models

An empirical study of managers’ usage intention in BI

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