## 3 Results
### Grounded Theory  
Axial coding was employed to explore the relationships between these categories, constructing a preliminary conceptual framework illustrated in Figure 1. This framework is structured around five key components: causal conditions, external influences, phenomena, intervening conditions and consequences [65]. The figure illustrates the results of axial coding. The key focus of this study suggests that the central action of \"use or non-use of AI technology for emotion regulation\" is related to other categories and subcategories embedded in causal conditions, external influences, phenomena, intervention conditions and consequences. In the final stage (selective coding), all categories and themes were revisited and synthesised to construct a four-layer theoretical framework based on the qualitative data (Figure 2). The model identifies three primary contextual influences on choice behaviours: personal, technological and environmental. The core or innermost layer of the underlying model is the \"use or not-use of AI technology for emotion regulation\". Each surrounding layer exerts a direct or indirect influence on this central behaviour through its constructs or by interacting with adjacent layers. The second layer of this model is the individual context, which includes attitude toward using AI technology for emotion regulation, dispositional characteristics and emotional status [54]. These factors significantly shape the likelihood of engaging with AI-based emotion regulation technologies. As supported by [PERSON] [66], individuals' preferred methods of emotional support strategies are often informed by these personal evaluations. Most respondents in this study maintained a generally positive attitude toward the AI-based emotion regulation tools they currently use. They believed these tools were effective, which influenced their continued adoption. This behavioural tendency is consistent with the Theory of Planned Behaviour [67], which posits that, when individuals perceive a high level of behavioural control over a positively evaluated action, their intention to perform that action increases. Conversely, negative attitudes or scepticism about the current emotion regulation tools often reduced the perceived relevance of AI technology, potentially overlooking the advantages such technologies could offer. This resistance may result from habitual reliance on familiar strategies or a lack of trust in the emotional sensitivity of AI systems [68]. The third layer of the grounded theory model focuses on technological context. Respondents indicated that their choices were shaped by key technology attributes, including  
Figure 1: Coding results. Figure 2: Selective coding results. usefulness, perceived pleasure, privacy, high value and functionality. Favourable user experiences, such as ease of use and interactive feedback, were cited as central motivations for technology adoption. Interactive feedback indicates the responsiveness of the AI system, including its ability to provide timely, relevant and two-way feedback that gives users a sense of being \"heard\" or attended to [69]. This finding aligned with [PERSON] [70], who posited that individuals are more likely to engage in a behaviour if they are aware of the potential positive outcomes of AI technology. Functional and social benefits function as extrinsic incentives, driving adoption based on perceived value and social validation [71; 72]. In contrast, hedonic and personally meaningful experiences serve as intrinsic motivators, encouraging sustained use through enjoyment, novelty and emotional engagement. Young people also emphasised the importance of trustworthiness, particularly about data security and the credibility of emotional feedback provided [73]. In addition to these benefits, certain product characteristics were also cited as barriers when poorly implemented, such as complex interfaces, lack of engaging content or insufficient privacy assurance.