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Examining Mobile Banking User Adoption from A Behavioural Perspective
Last registered on February 24, 2019


Trial Information
General Information
Examining Mobile Banking User Adoption from A Behavioural Perspective
Initial registration date
February 19, 2019
Last updated
February 24, 2019 7:54 PM EST
Primary Investigator
University of Cambridge
Other Primary Investigator(s)
PI Affiliation
University of Cambridge
Additional Trial Information
On going
Start date
End date
Secondary IDs
The failure to use mobile banking is especially puzzling given that conventional costs of use appear to be low and the benefits, for many, are substantial. In the extant studies, factors that affect the mobile banking adoption include the perceived ease of use, usefulness, risk and financial cost, compatibility with lifestyle and device, consumer trust in mobile banking, social and cultural factors, and a range of demographic factors (Crabbe et al., 2009). Barriers to the take-up and usage of mobile banking can go beyond the above-mentioned variables. It has been increasingly challenged by recent work that individuals may fail to take an action that is in their long-run best interest due to cognitive, motivational, and emotional limits to decision-making (Currie, 2006). If existing barriers to mobile banking use, particularly amongst those of high need, are because of “psychological frictions”, then encouraging the usage by reducing these barriers would likely improve the user’s welfare. However, there are few inferences drawn about the importance of psychological frictions in accounting for the low use in mobile banking. This study sets out to fill this gap by using a randomized controlled trial experiment to increase adoption of mobile banking in urban-rural China. By cooperating with a rural and commercial bank in China, we randomly provide bank account users information nudges and evaluate their responses in the use of mobile banking. Specifically, we will investigate 1) whether information nudges improve the adoption and usage of mobile banking among bank account users, 2) how long do the effects last and 3) whether the effects differentially manifest across individuals.
External Link(s)
Registration Citation
Bao, Xiaohui and LING LI. 2019. "Examining Mobile Banking User Adoption from A Behavioural Perspective." AEA RCT Registry. February 24. https://doi.org/10.1257/rct.2968-1.0.
Former Citation
Bao, Xiaohui, LING LI and LING LI. 2019. "Examining Mobile Banking User Adoption from A Behavioural Perspective." AEA RCT Registry. February 24. http://www.socialscienceregistry.org/trials/2968/history/42054.
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Experimental Details
We randomly provide bank clients who have not used mobile banking information about the use of mobile banking.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
The use frequency of mobile banking for each bank client
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Financial behaviours, e.g. saving, credit card, other financial products, etc.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The whole experiment consists of two surveys and one (or two) intervention (s). The first survey serves two purposes: it is used to collect the baseline information which allow us to understand the determinants of adoption of mobile banking, and more importantly, to identify unmet need of a specific population for mobile banking. The identified bank clients will be allocated with randomization to different experiment groups. Three months later, we delivered a follow-up survey asking what changes the intervened clients have been through, and if they remained not using mobile bank, another round of information intervention (the same to the first round) was carried out. Post-intervention data collection was completed six months following the completion of the intervention in the first phase.
Experimental Design Details
Randomization Method
Excel program
Randomization Unit
Bank branch & days
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
University of Cambridge
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)