Mobile Commerce Adoption and User Acceptance
Summary
Mobile commerce has emerged as a transformative channel for retail, banking and service delivery, enabled by ubiquitous smartphones and high-speed networks. Adoption hinges on a complex interplay of technological, personal and social determinants, as users weigh anticipated benefits against perceived effort and risk. Models such as the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology have guided research towards constructs including perceived usefulness, ease of use, trust and social influence. Individual traits—notably self-efficacy and innovativeness—intersect with contextual factors such as security concerns and facilitating conditions to shape behavioural intention and subsequent usage. Advances in analytic methods, including structural equation modelling and hybrid machine-learning approaches, have refined understanding of non-linear relationships and latent mediators. Globally, mobile commerce drives financial inclusion in emerging economies, enhances customer engagement in mature markets and supports novel business models from on-demand services to mobile wallets. Practical applications span mobile payments, shopping apps and digital banking, where seamless usability and robust security are essential to sustaining user acceptance and loyalty.
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Mobile Commerce Adoption and User Acceptance publication trend
The graph below shows the total number of articles in mobile commerce adoption and user acceptance across all publications each year (not limited to Nature Index journals).
Technical terms
Technology Acceptance Model (TAM): A theoretical framework positing that user acceptance is determined by perceived usefulness and perceived ease of use.
Perceived usefulness: The degree to which a user believes that employing a system will enhance their performance or satisfaction.
Perceived ease of use: The extent to which a user expects a system to be free of effort.
Self-efficacy: An individual’s belief in their own ability to perform tasks using a technology.
Subjective norm: The perceived social pressure that influences an individual’s intention to use a technology.
Trust: The confidence a user places in the reliability and security of a mobile commerce platform.
Social influence: The effect of peers, family or professional networks on an individual’s intention to adopt technology.
Structural equation modelling (SEM): A statistical technique for testing and estimating causal relationships among latent constructs.
References
- Contemporary Mobile Commerce: Determinants of Its Adoption. Journal of Theoretical and Applied Electronic Commerce Research (2023).
- Hybrid artificial neural network and structural equation modelling techniques: a survey. Complex & Intelligent Systems (2021).
- Modeling Mobile Commerce Applications’ Antecedents of Customer Satisfaction among Millennials: An Extended TAM Perspective. Sustainability (2021).
- Assessing Antecedents of Behavioral Intention to Use Mobile Technologies in E-Commerce. Electronics (2021).
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