The role of IoT and XAI convergence in the prediction, explanation, and decision of customer perceived value (CPV) in SMEs: a theoretical framework and research proposition perspective

Abstract

Abstract The goal of this study is to look at how the convergence of IoT and XAI (IoT-XAI) effects the explanation, prediction, and decision-making on customer perceived value (CPV) in SMEs, utilising CPV and IoT-XAI convergence theories. This study also investigates how customer-IoT interaction influences deep learning (DL) model prediction of CPV, as well as XAI explanation and decision making on CPV prediction. The literature on customer-IoT interaction, IoT physical objects, IoT data analysis, deep learning model, XAI, and CPV was reviewed to develop a theoretical framework for investigating the relationships between IoT and XAI convergence, and CPV prediction, explanation, and decision-making towards personalised marketing. The theoretical framework and research propositions are depicted in Fig. 1. Drawing on the theoretical framework used in this study, eight key research propositions were developed on the relationship between customers, IoT, DL, XAI, and CPV explanation and decision. According to the created theoretical framework and research propositions, customer-IoT interaction generates CPV data, which is then converted into structured CPV data by IoT analytics and fed into DL models for prediction. As a result, XAI models produce explanations and decisions based on DL-enabled CPV prediction, which guides personalise marketing. This paper explains how SMEs may leverage the convergence capabilities of IoT and XAI to generate CPV explanations and decisions to modify their personalize marketing methods.

Keywords

• Customer perceived value
• IoT-XAI convergence
• Internet of things
• Explainable artificial intelligence
• Deep learning
• Customers