Predicting IoT Service Adoption towards Smart Mobility in Malaysia: SEM-Neural Hybrid Pilot Study

TL;DR

This study develops a hybrid SEM-Neural model to predict factors influencing smart mobility adoption in Malaysia, integrating behavioral insights with advanced data analysis for better policy formulation.

Contribution

It introduces a novel SEM-Neural hybrid approach to analyze technology acceptance in smart mobility, extending the TAM model with new external factors.

Findings

Identified key external factors affecting smart mobility adoption.

Validated the hybrid model's reliability and validity.

Provided insights for policymakers to enhance technology acceptance.

Abstract

Smart city is synchronized with digital environment and its transportation system is vitalized with RFID sensors, Internet of Things (IoT) and Artificial Intelligence. However, without user's behavioral assessment of technology, the ultimate usefulness of smart mobility cannot be achieved. This paper aims to formulate the research framework for prediction of antecedents of smart mobility by using SEM-Neural hybrid approach towards preliminary data analysis. This research undertook smart mobility services adoption in Malaysia as study perspective and applied the Technology Acceptance Model (TAM) as theoretical basis. An extended TAM model was hypothesized with five external factors (digital dexterity, IoT service quality, intrusiveness concerns, social electronic word of mouth and subjective norm). The data was collected through a pilot survey in Klang Valley, Malaysia. Then responses were analyzed for reliability, validity and accuracy of model. Finally, the causal relationship was explained by Structural Equation Modeling (SEM) and Artificial Neural Networking (ANN). The paper will share better understanding of road technology acceptance to all stakeholders to refine, revise and update their policies. The proposed framework will suggest a broader approach to individual level technology acceptance.