## 2 Related Studies and Conceptual Framework  
### Machine Learning Algorithm  
Multivariate analysis, including Structural Equation Modeling (SEM), has been widely used in related studies regarding purchase intention. However, machine learning algorithms have been considered because of the limitations of SEM and the current trend in consumer behavior and purchase intent [27; 29; 30]. [PERSON] et al. [31] stated that mediating factors in the framework is one disadvantage of SEM because it hinders the causal effects of other latent variables. Variables have a high likelihood of becoming insignificant when mediating factors are present. Furthermore, [PERSON] [32] noted that the latent variable would have a low to nonexistent effect the farther it is from the dependent variable. Because of this, the comparison and expansion of SEM have been considered in numerous consumer behavior studies. For instance, the study of [PERSON] et al. [33] considered consumer behavior when using MorChana as a contact tracing tool. In response to the SEM limitation, [PERSON] et al. [27] enhanced it using several machine learning algorithms (MLA), including decision trees, random forest classifiers, and artificial neural networks. Likewise,Chuenyindee et al. [34] used SEM to examine the Thai Chana contact tracing application and was also extended by [PERSON] et al. [28]. It was seen from both studies how MLAs can be utilized to determine likely significant factors affecting intended outcomes compared to SEM. Given the availability of substantial data and a complicated marketing environment that is becoming progressively difficult to predict, machine learning in marketing is emerging as a significant trend [35]. [PERSON] et al. [36] mentioned that large enterprises such as Siri, Apple, Google, Facebook, and Microsoft all employ MLAs to analyze consumers' shopping behavior. This is because MLAs are all typically used for pattern recognition [27]. However, research is limited in online consumer conversion due to the availability of big data despite the high potential for machine learning applications in this area [35; 37]. Consequently, recent research has tried to and overcome the circumstances of these difficulties and has applied several algorithms such as Neural Networks (e.g., Deep Learning) in different consumer behavior fields [38; 39]. Deep learning refers to neural networks with sophisticated multilayers which contain two or more hidden layers that can process information more deeply [40; 41]. This artificial intelligence is a subset of machine learning algorithms modeled after the human nervous system [42]. According to [PERSON] et al. [43], deep learning neural networks have great computational power. This is because the model may mitigate the effects of data category imbalances on its efficiency as it delves deeper into the data [44]. However, that the calculation involved in developing the neural network provides great complexity is one of its disadvantages. The different parameters such as the activation functions for both output and hidden layers are identified through varied trial and error, providing output of accuracy for determination of best combination. The Blackbox feature of neural networks provides its complication, but thorough optimization would result to great output and pattern recognition. As part of the advantages of deep learning, its analysis involving several steps of optimization in the hidden layer reduces the computational errors, leading to better output and accuracy rates [27; 33; 34]. In addition, it was explained that better recognition of significant latent variables, accuracy rate compared to other analysis, and reliability with faster iteration, is seen when deep learning is used [33]. Furthermore, numerous researchers have included deep learning to examine human behavior, specifically consumer behavior. [PERSON] et al. [44] utilized this prediction model to assess consumer buying behavior and propose models derived from deep learning. This is comparable to [PERSON] et al.