tam modelling (Technology Acceptance Model)
Also known as: TAM · TAM model · technology adoption modelling
Technology Acceptance Model Modelling is a framework that predicts how users adopt new technologies by examining perceived usefulness and perceived ease of use, two factors that shape attitudes and behavioral intentions toward system adoption.
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What is tam modelling?
What is TAM Modelling?
TAM Modelling, or Technology Acceptance Model Modelling, is a framework used to understand and predict how users adopt new technologies. It focuses on two primary factors influencing adoption: perceived usefulness and perceived ease of use [5]. Perceived usefulness refers to the degree to which an individual believes that using a particular system will enhance their job performance or improve outcomes. Perceived ease of use refers to the degree to which an individual believes that using a particular system will be free of effort [5].
How it works
The TAM framework posits that these two perceptions directly influence an individual's attitude towards using a technology, which in turn affects their behavioral intention to use it, and ultimately, their actual system use. For example, if a new waste sorting technology is seen as highly effective in separating materials (useful) and simple to operate (easy to use), its adoption rate is likely to be higher. The model can be extended with other variables, such as organizational factors, security, and environmental considerations, to provide a more comprehensive view of technology adoption challenges [1].
Why it matters in practice
In waste-to-value sectors, understanding technology adoption is critical for assessing the uptake of new recycling processes, energy conversion technologies, or digital tracking systems. For instance, the successful implementation of a new chemical recycling plant depends not only on its technical capabilities but also on the willingness of operators and management to integrate and use the associated control systems. If the technology is perceived as too complex or not sufficiently beneficial, its operational efficiency may be compromised, leading to underutilization or resistance [5].
Risks and downsides
While TAM provides a structured approach, its predictive power can be limited in complex real-world scenarios where external factors, such as regulatory changes, market price volatility for recycled outputs, or unexpected operational challenges, play a significant role. The model primarily focuses on individual acceptance and may not fully capture systemic barriers or the broader economic realities that influence technology deployment in industrial settings. For instance, a highly accepted technology might still face hurdles if the capital expenditure is too high or if the market for its outputs is unstable.
tam modelling across recycling sectors
How this plays out in practice, sector by sector.
Operational role in waste-to-value sectors
In waste-to-value sectors like compressed biogas (CBG), e-waste recycling, and plastic recycling, TAM Modelling helps in anticipating the acceptance of new processing technologies or operational software. For example, in a CBG plant, the adoption of advanced anaerobic digestion monitoring systems depends on whether plant operators perceive these systems as genuinely useful for optimizing gas production and easy to integrate into daily routines. If the technology is seen as cumbersome or its benefits are not clear, its implementation may falter, affecting plant efficiency and output consistency.
Economics and constraints
The economic reality in these sectors often involves thin margins, high capital expenditure, and commodity price volatility. Even if a technology is highly accepted based on TAM principles, its deployment is ultimately constrained by these economic factors. For instance, a sophisticated e-waste sorting machine might be perceived as useful and easy to operate, but its high upfront cost and the fluctuating prices of recovered metals can make its investment case challenging. Similarly, in plastic pyrolysis, the perceived usefulness of a new reactor design might be high, but if the market for pyrolysis oil is unstable, the technology's adoption will be limited regardless of user acceptance.
Regulatory friction and scale
Regulatory frameworks, such as Extended Producer Responsibility (EPR) norms or CPCB guidelines, also influence technology adoption. A technology might be perceived as useful, but if it does not meet specific compliance requirements or if the approval process is lengthy, its practical implementation can be delayed or abandoned. At a smaller scale, the cost per unit of output for advanced technologies can be disproportionately high, making them less attractive even if user acceptance is strong. Larger operations might absorb these costs more effectively, but still face the underlying economic pressures and market uncertainties common to these sectors.
Common questions about tam modelling
Plain-English answers to what people most often ask.
How does TAM Modelling help in assessing new recycling technologies?
Does TAM Modelling consider the cost of new technology in waste-to-value sectors?
Is TAM Modelling sufficient for predicting technology success in Indian waste management?
Citations & references
Peer-reviewed and published sources underpinning this entry. Numbered markers [n] in the text above link here.
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1
Technological transformation in the manufacturing industry for sustainable development
Prince Ranjan et al. · 2026
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Macrophage-mediated myelin recycling fuels brain cancer malignancy
D. Kloosterman et al. · 2024
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