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All Journal JOIV : International Journal on Informatics Visualization
Subbarao, Anusuyah
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Computer Science & IT

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Journal : JOIV : International Journal on Informatics Visualization

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Adopt E-Mental Health Services: Factors Shaping Intention to Engage Samsudin, Rahimah; Khan, Nasreen; Subbarao, Anusuyah; Chen, Tan Booi Chen Booi; Obreja, Serban Georgică
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3374

Abstract

E-Mental Health (eMH) tools are increasingly vital in providing scalable mental health support. This study aims to examine the factors influencing users’ intention to engage with eMH services. Specifically, it investigates the effects of six reflective first-order dimensions: Accessibility, Communication, Affordability, Flexibility, Custom Belief, and Government Support on the reflective second-order construct: Intention to Engage in E-Mental Health. Using data from 100 respondents, Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to test the hypothesized relationships and assess the strength of each contributing factor. The results show that Accessibility, Communication, and Flexibility are the most influential predictors of engagement. Affordability and Custom Belief demonstrate moderate but positive impacts, while Government Support plays a complementary role. These findings provide critical insight into user-centered design priorities for eMH platforms, particularly in enhancing user retention and accessibility. Practical applications include the development of multilingual mobile applications, culturally adaptive cognitive-behavioral therapy tools, and enhanced digital communication pathways. This study contributes to the understanding of how both infrastructural and personal belief factors can drive engagement. It also highlights the importance of holistic system support in digital mental health ecosystems. For future research, it is recommended to explore user engagement across diverse demographic and cultural settings and to examine the effectiveness of emerging technologies such as AI-driven chatbots and virtual reality therapies. Additionally, policy-level interventions should be further evaluated to strengthen the implementation and sustainability of digital mental health services.
Artificial Intelligence: Creating a Hyper-personalization Artifact Murugasu, Umapathy Sivan G; Subbarao, Anusuyah
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3367

Abstract

This research aimed at synthesizing an artifact that could hyper-personalize customers’ needs using a design science framework. Artificial intelligence (AI) was applied to enable telecommunication (Telco) businesses to offer hyper-personalized products and services, based on a database of customers’ digital demography. A digital demographic database was created using data collected on attributes derived from a systematic literature review. The proof of concept (POC) was developed using the Waikato Environment for Knowledge Analysis (WEKA) software. A systematic literature review was conducted to identify documents used in creating a cross-tabulation of attributes through thematic analysis. This analysis resulted in 32 attributes. The Delphi method for consensus reaching by 10 industry experts was used to reduce to 12 attributes in 2 stages. These attributes were structured into a Google Form to collect customer usage data. Outlier data were removed using multivariable outlier detection by Mahalanobis distance available via SPSS version 21. Using the updated database, several procedures were conducted to determine the best artificial intelligence algorithm for subsequent analysis. Using the Logistic Model Tree algorithm and the customer digital demography, the Telco offering for the customers was predicted with 97.6% accuracy. The artifact created was named Hypersona. The theoretical contribution lies in the applicability of real-time identification of client requirements, targeted client classification, and the ability to offer hyper-personalized products. Implications for Further Research: This research highlights the potential of AI-driven hyper-personalization in the telecommunications sector. Future studies could explore scaling the artifact across diverse businesses.
Co-Authors Chen, Tan Booi Chen Booi Khan, Nasreen Murugasu, Umapathy Sivan G Obreja, Serban Georgică Samsudin, Rahimah