Thesis
Exploring Continuance Intention to Use Embedded-Artificial Intelligence
Southern Cross University
Masters by Thesis, Southern Cross University
DOI:
https://doi.org/10.25918/thesis.539
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Abstract
Embedded-Artificial Intelligence (Embedded-AI) is an emerging research field aimed at integrating machine learning algorithms and intelligent decision-making capabilities into existing larger systems or devices. While the potential of embedded-AI is vast, several challenges must be addressed to fully realise its consumption values. One major issue is that embedded-AI products’ features often operate in the background, making users less aware of their presence. As a result, users may undervalue or overlook their benefits, leading to lower engagement and adoption. Addressing these challenges requires a deeper comprehension of user perceptions and a better understanding of what influences the perceived value of embedded-AI systems. This understanding is important to the practitioner who invests in developing such applications for branding, improvements, and the ultimate longevity of embedded-AI.
This study aimed to analyse and synthesise the consumption values of embedded-AI and the role user awareness and expectations play in understanding the values of embedded-AI, which leads to continuance usage intention. The theoretical framework developed in the study is inspired by the theory of consumption values. The model was initially developed using 138 studies on the AI adoption domain, given the limited embedded-AI adoption literature. Then, the study employed a mixed-method approach to understand the values associated with embedded-AI applications. To facilitate this, the study employed a widely accessible embedded-AI tool called predictive text suggestions. For the qualitative research approach, the study randomly selected a sample of 20 users of predictive text suggestions to conduct semi- structured interviews, and for the quantitative research approach, the study conducted a questionnaire survey with a sample of 307 users of predictive text suggestions. The study used thematic analysis in NVivo for qualitative data and PLS-SEM in SmartPLS for quantitative data. The study findings reveal that all the identified formative indicators of the five consumption values positively contribute to their respective consumption value, collectively forming perceived value. In addition, perceived value demonstrates a significant positive relationship with the continuance usage intention of embedded-AI. The results further indicate that both user awareness and user expectations positively influence the perceived value of embedded-AI, with user awareness also exerting a positive effect on user expectations. These findings contribute to a deeper understanding of how users perceive and continue to use embedded-AI applications.
Details
- Title
- Exploring Continuance Intention to Use Embedded-Artificial Intelligence
- Creators
- Amanda Balasooriya Mudiyanselage
- Contributors
- Darshana Sedera (Supervisor) - Southern Cross UniversityGolam Sorwar (Supervisor) - Southern Cross University
- Awarding Institution
- Southern Cross University; Masters by Thesis
- Theses
- Masters by Thesis, Southern Cross University
- Publisher
- Southern Cross University
- Number of pages
- xii, 228
- Identifiers
- 991013334227402368
- Copyright
- © Amanda Balasooriya 2025
- Academic Unit
- Faculty of Business, Law and Arts
- Resource Type
- Thesis