Application of Latent Class Analysis in Identifying Digital Adoption Typologies

Digital adoption indicators are metrics used to measure the extent to which individuals or organisations are effectively leveraging internet and digital technologies to achieve their goals. The most common ones are internet speed for broadband and mobile users and the extent to which governments and businesses use the internet. Since digital adoption encompasses various aspects of technology implementation, usage and integration within organisations or among individuals, it is multidimensional and effectively addressing these dimensions is essential for successful digital adoption initiatives. In order to measure various aspects of digital adoption and readiness in different countries or regions, several organisations and research groups developed various indices. These indices provide a structured and quantitative approach to assess various aspects of digitalisation. However, interpreting index scores and translating them into actionable insights are quite challenging. Thus, this study uses latent class analysis to identify underlying subgroups based on their digital adoption metrices. It is a flexible and powerful approach to studying digital adoption by uncovering the group of countries with similar digital adoption initiatives and implementations and it will provide actionable insights especially for policy makers. Indicators from 131 countries are collected and used in the analysis. According to the findings, countries can be divided into four groups: those with excellent digital adoption, those with excellent internet adoption, those with excellent e-government adoption, and those with poor digital adoption. The discussion of this paper is focused on ASEAN countries. Four ASEAN countries are classified as having excellent overall digital adoption, one having poor internet adoption but good e-government, two having excellent internet adoption but poor e-government and two having poor overall digital adoption.