Dataset Information

Data modelling of subsistence retail consumer purchase behavior in South Africa.

ABSTRACT: The purpose of the data is to model the purchase behavior of the subsistence consumer within the retail environment in one of the largest townships in South Africa. The data was collected using a self-administered questionnaire from a sample of 281 consumers. The Partial Least Squares Structural Equation Modelling (PLS-SEM) approach was adopted using the SmartPLS 3 software to analyze the data. The insights from the dataset identify convenience, price sensitivity, perceived product quality, customer trust, and perceived value as factors that stimulate purchase behavior. Furthermore, perceived value only mediates the relationship between perceived product quality and purchase intention. Researchers could use the data to position customer trust as a dependent variable to unearth more valuable insights. Additionally, the segment in question is also known to be price-sensitive. It would be intriguing to find out the role of price sensitivity as a moderator.

SUBMITTER: Zulu VM

PROVIDER: S-EPMC8989704 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Data modelling of subsistence retail consumer purchase behavior in South Africa.

Zulu Valencia Melissa VM Nkuna Andriaan Mpho AM

Data in brief 20220326

The purpose of the data is to model the purchase behavior of the subsistence consumer within the retail environment in one of the largest townships in South Africa. The data was collected using a self-administered questionnaire from a sample of 281 consumers. The Partial Least Squares Structural Equation Modelling (PLS-SEM) approach was adopted using the SmartPLS 3 software to analyze the data. The insights from the dataset identify convenience, price sensitivity, perceived product quality, cust ...[more]

PMID: 35402668

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Project description:In recent times, it has been observed that social media exerts a favorable influence on consumer purchasing behavior. Many organizations are adopting the utilization of social media platforms as a means to promote products and services. Hence, it is crucial for enterprises to understand the consumer buying behavior in order to thrive. This article presents a novel approach that combines the theory of planned behavior (TPB) with machine learning techniques to develop accurate predictive models for consumer purchase behavior. This study examines three distinct factors of the theory of planned behavior (attitude, social norm, and perceived behavioral control) that provide insights into the primary determinants influencing online purchasing behavior. A total of eight machine learning algorithms, namely K-nearest neighbor, Decision Tree, Random Forest, Logistic Regression, Naive Bayes, Support Vector Machine, AdaBoost, and Gradient Boosting, were utilized in order to forecast consumer purchasing behavior. Empirical findings indicate that gradient boosting demonstrates superior performance in predicting customer buying behavior, with an accuracy rate of 0.91 and a macro F1 score of 0.91. This holds true when all factors, namely attitude (ATTD), social norm (SN), and perceived behavioral control (PBC), are included in the analysis. Furthermore, we incorporated Explainable AI (XAI), specifically LIME (Local Interpretable Model-Agnostic Explanations), to elucidate how the best machine learning model (i.e. gradient boosting) makes its prediction. The findings indicate that LIME has demonstrated a high level of confidence in accurately predicting the influence of low and high behavior. The outcome presented in this article has several implications. For instance, this article presents a novel way to combine the theory of planned behavior with machine learning techniques in order to predict consumer purchase behavior. This integration allows for a comprehensive analysis of factors influencing online purchasing decisions. Also, the incorporation of Explainable AI enhances the transparency and interpretability of the model. This feature is valuable for organizations seeking insights into factors driving predictions and the reasons behind certain outcomes. Moreover, these observations have the potential to offer valuable insights for businesses in customizing their marketing strategies to align with these influential factors.

Dataset Information

Data modelling of subsistence retail consumer purchase behavior in South Africa.

Publications

Data modelling of subsistence retail consumer purchase behavior in South Africa.

Similar Datasets

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