Push factors are elements that drive users away from a particular platform or service. In the context of knowledge Q&A communities, several push factors can contribute to users feeling fatigued and ultimately deciding to migrate to alternative platforms. One such push factor is low content quality. Low content quality refers to users‘ perceptions of content usefulness, sufficiency, and timeliness. In knowledge communities, the threshold for knowledge contribution is higher than in social media platforms. Users often lack the motivation to create content and instead prefer to seek answers rather than provide them. This imbalance leads to a situation where users only consume content without actively participating in creating it, resulting in a „tragedy of the commons“ scenario where the overall quality of platform content diminishes over time.
When users perceive low content quality, they may feel that their time and effort are wasted, leading to feelings of fatigue. If users struggle to find satisfactory answers or frequently encounter delays in meeting their information needs within Q&A communities, they may perceive the platform as inefficient, further contributing to their fatigue. Therefore, it can be hypothesized that low content quality significantly affects community fatigue.
Another push factor that can contribute to user fatigue in knowledge Q&A communities is information overload. Information overload occurs when the volume of information available exceeds users‘ capacity to process it effectively. In knowledge communities, the abundance of information, including advertisements, personalized recommendations, and user-generated content, can overwhelm users and reduce their information processing efficiency. This overload can lead to feelings of exhaustion and fatigue as users invest significant effort and time in sifting through the information available. Studies have shown that perceived information overload can induce negative emotional experiences, such as fatigue and frustration, which in turn can trigger negative behaviors. Therefore, it can be hypothesized that information overload significantly affects community fatigue.
Community fatigue, as an emotional factor, can have a negative impact on users‘ psychology and behavior. Users who are actively engaged in a community may experience feelings of anxiety, disappointment, low motivation, and loss of interest, all of which contribute to community fatigue. These negative emotions may prompt users to discontinue their participation in the current platform and seek alternatives. Users may choose to migrate from social Q&A communities to generative AI platforms to alleviate the accumulated fatigue they experience in social Q&A communities. Thus, it can be hypothesized that community fatigue significantly affects users‘ migration intention.
On the other hand, pull factors are elements that attract users to a particular platform or service. In the context of generative AI platforms, several pull factors can contribute to users‘ positive experiences and engagement with the technology. One such pull factor is perceived anthropomorphism. Perceived anthropomorphism refers to the attribution of human-like qualities to non-human subjects. Generative AI platforms can be designed to exhibit social functions, such as cognitive empathy and verbal humor, which can enhance users‘ interactions with the technology. Studies have shown that users‘ emotions toward AI are related to the perceived degree of anthropomorphism. High levels of anthropomorphism, such as conversational patterns and personality traits, can engender positive emotions and enhance users‘ experiences when seeking knowledge from generative AI. Therefore, it can be hypothesized that perceived anthropomorphism significantly affects flow experience.
Another pull factor that can enhance users‘ experiences with generative AI platforms is perceived accuracy. The accuracy of the content generated by AI platforms is crucial to users‘ satisfaction and engagement. Users expect accurate and useful answers to their queries, and perceived accuracy plays a significant role in determining the value of the information provided. Studies have shown that the accuracy of AI technology positively influences both utilitarian and hedonic values for users. Therefore, it can be hypothesized that perceived accuracy significantly affects flow experience.
Perceived trustworthiness is another pull factor that can enhance users‘ experiences with generative AI platforms. Trustworthiness is a critical factor in users‘ decision-making processes when interacting with AI technologies. Users are more likely to trust content that is reliable, fair, and free from bias. Studies have shown that users‘ trust in AI-generated content can stimulate emotional attachment and promote positive behaviors, such as gift-giving. When users trust generative AI platforms, they are more likely to feel immersed and enjoy the Q&A process, leading to a flow experience. Therefore, it can be hypothesized that perceived trustworthiness significantly affects flow experience.
Flow experience is a positive emotional state in which users are fully engaged in an activity and derive pleasure and enjoyment from it. Studies have shown that flow experience influences users‘ continuance intention and brand loyalty. Users who experience flow during their interactions with generative AI platforms are more likely to have a positive perception of the technology and consider migrating from traditional social Q&A platforms to generative AI. Therefore, it can be hypothesized that flow experience significantly affects users‘ migration intention.
In addition to push and pull factors, mooring factors can also influence users‘ migration intentions. Social influence, as described by social network theory, plays a significant role in users‘ decision-making processes. Users are influenced by their social networks, including family, friends, and online connections, when evaluating new technologies such as generative AI platforms. Recommendations from one’s social circle can impact users‘ adoption of generative AI and their decision to migrate from traditional social Q&A platforms. Therefore, it can be hypothesized that social influence significantly affects users‘ migration intention.
In conclusion, the interplay of push, pull, and mooring factors can significantly impact users‘ experiences and migration intentions in knowledge Q&A communities and generative AI platforms. By understanding these factors and their effects on user behavior, platform designers and developers can create more engaging and user-friendly experiences that cater to users‘ needs and preferences.