2020
DOI: 10.1080/00036846.2020.1715338
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ICT adoption and income diversification among rural households in China

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Cited by 139 publications
(114 citation statements)
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References 39 publications
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“…2019; Goldfarb and Prince 2008; Khanal et al . 2015; Leng et al 2020; Ma et al . 2020a, 2020b; Martínez‐Domínguez and Mora‐Rivera, 2020; Mishra et al .…”
Section: Data and Descriptive Statisticsmentioning
confidence: 99%
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“…2019; Goldfarb and Prince 2008; Khanal et al . 2015; Leng et al 2020; Ma et al . 2020a, 2020b; Martínez‐Domínguez and Mora‐Rivera, 2020; Mishra et al .…”
Section: Data and Descriptive Statisticsmentioning
confidence: 99%
“…The impact of farm size on the Internet use does not reach consensus. For example, Ma et al (2018b) showed that farm size has a positive and significant impact on Internet use via smartphones, while Leng et al (2020) found that farm size negatively affects Internet use via ICTs such as computers and smartphones. Thus, we do not assign any a priori sign expectation for the farm size variable in our analysis.…”
Section: Descriptive Statisticsmentioning
confidence: 99%
See 1 more Smart Citation
“…The Internet helps to optimize the allocation of urban and rural resources and reduce the income gap between urban and rural areas (Gao, Zang, & Sun, 2018). Based on the micro‐survey of Chinese farmers, it is found that the use of Internet significantly increases farmers' income and promotes farmers' income diversification (Leng, Ma, Tang, & Zhu, 2020; Ma, Nie, Zhang, & Renwick, 2020). Based on the analysis of China's provincial panel data, Han and Zhang (2017) pointed out that the popularization of Internet could narrow the urban–rural income gap, while Cheng and Zhang (2019) believed that Internet popularization tended to widen the urban–rural income gap at the first stage and then to narrow the gap, presenting an inverted‐U trend.…”
Section: Literature Reviewmentioning
confidence: 99%
“…TAM and Theory Reason Action was used to analyse the adoption of ICT into teaching based on gender differences in Malaysia (Lim et al 2019). UTAUT and TAM was used to identify the determinants of the adoption of mobile learning systems among university students in Indonesia (Pramana 2018) as well as to analyse the factors that have an impact on the students' intentions and adoption of m-learning in the higher education institutions of Jordan (Al-Adwan et al 2018).nThe treatment effects (TE) model was employed to assess the impact of ICT adoption on income diversification of rural households in China (Leng et al 2020). The adoption of cloud computing based learning resources in underfunded, rural high schools of mainland China was analysed in (Wang and Wong 2019).…”
Section: Overview Of Related Workmentioning
confidence: 99%