This research aims to examine how various digital technologies contribute to advancing the adoption and execution of circular economy principles. It will analyse multiple influencing factors and assess the degree to which each variable affects the overall transition toward circular practices. So first, what is the circular economy? A circular economy is one where resources are continually used, allowing materials to retain their value while enabling natural ecosystems to regenerate. In a circular economy, through a variety of activities (maintenance, reuse, refurbishing, remanufacturing, recycling, and composting), items and materials stay in the loop. By separating economic development from the exploitation of finite natural resources, the circular economy offers practical pathways to address global environmental concerns, including climate disruption, ecological degradation, pollution, and resource wastage. The circular economy paradigm has shifted the human perception about resource management and economic development (Kirchherr et al. in Resour Conserv Recycl, 2023). Circular economy — sustainability linkage should be clear. Circular economy is a pathway for driving the Sustainable Development Goals. Sustainability represents the broader objective, while the circular economy acts as a practical mechanism to achieve it. It has been found through research that in the process of attaining sustainable development, CE must be a tool that should be used (Bressanelli et al. in Sustainability, 2022), and this study builds upon that premise by exploring its environmental dimension. Since the onset of industrialisation, the take-make-discard approach to resource use has become emblematic of the traditional linear economic model. But, as mass production, consumption, and global trade came into the picture, it started to decrease dramatically, leading to the birth of the circular economy. The concept of circular economy was initially introduced by Pearce and Turner (Economics of Natural Resources and the Environment, 1990) in their seminal work “The Economics of Natural Resource and Environment.” According to their argument, the prevailing economic framework needs circular economy-based reconfigurations due to the production processes continuously producing wastes and pollutants, representing a threat to the environment, and so the transformation of the system must include approaching waste as a raw material for additional resources (Dwivedi and Paul in Bus Strateg Environ, 2022). The theoretical framework used to carry out this research will be based on making a conceptual model with different supporting theories and thus establishing a relationship between them. Our theoretical framework will divide variables into five different categories, which are independent, dependent, moderating, mediating, and output variables. Independent variables are the ones that will be impacting the dependent variable directly, either in a positive way or a negative way. The dependent variable is the one variable that is being impacted by all other variables except the outcome variables. A mediating variable is the variable that alters the relationship between the independent and dependent variable, whereas a moderating variable is the variable that alters the relationship between the dependent variable and the outcome variable. The output variable is the result of the whole established relationship between all the variables. The author intends to develop a relational model connecting all key variables by utilising primary data and relevant theoretical frameworks to effectively address the research question. Collection, cleaning, and use of primary data available from the surveys (Likert scale of 10), along with qualitative analysis from semi-structured interviews, have been done to reach the conclusion. A ten-level Likert-type scale was employed for gathering survey responses, where a score of 10 indicated strong agreement and a score of 1 reflected strong disagreement. Answers collected will be sorted into groups of common, contradictory, and different for further analysis. Statistical analysis has taken place on the data collected, with a view to answering the paper’s questions. Software like Microsoft Excel and Smart PLS4.0 will be used for structural equational modelling. Structural Equation Modelling (SEM) is an analytical approach that evaluates hypothesised relationships among variables through statistical testing. It was chosen for this study because it enables the simultaneous assessment of multiple associations while controlling for potential measurement errors. For determining reliability, Cronbach’s alpha above 0.7 has been preferred along with an average variance explained of above 0.5. The divergent and convergent validity have been assessed by the Fornell-Larcker criterion to demonstrate a strong relationship between variables.