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The buyer-seller relationship in live streaming commerce is crucial especially in service failure situations, yet it has been underexplored. In this paper, we reconceptualized the relationship between consumers and sellers in live streaming commerce as "parasocial guanxi", which consists of ganqing (emotional attachment), xinren (trust) and reciprocal value. The effects of parasocial guanxi on consumer dissatisfaction and complaint intention in service failure situations were further investigated based on the expectation confirmation theory. Our study involved 425 valid questionnaires and employed the partial least squares structural equation method (PLS-SEM). The findings are mixed: on one side, parasocial guanxi acts as a buffer, mitigating dissatisfaction and complaint intention in the face of product or service problems and reducing the impact of disconfirmation on consumer dissatisfaction. On the other side, it may also increase the likelihood of dissatisfied consumers complaining. Furthermore, the buffering effect weakens as consumers make more frequent purchases. The findings have important theoretical and practical implications for understanding the buyer-seller relationship in live streaming commerce. • This paper reconceptualized the buyer-seller relationship in live streaming commerce as "parasocial guanxi". • The effects of parasocial guanxi in service failure situations were investigated. • Parasocial guanxi has a buffering effect in alleviating consumers' dissatisfaction and complaint intention. • Meanwhile, parasocial guanxi can increase the likelihood of dissatisfied consumers to complain. • The effects of parasocial guanxi vary across different demographic groups. [ABSTRACT FROM AUTHOR]

Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its roots in two theoretical models of the comprehension process (i.e., the Construction-Integration model and the Landscape model), and the new version leverages state-of-the-art Large Language models, more specifically ChatGPT, to have a better contextualization of the text and a simplified construction of the underlying graph model. Besides showcasing the usage of the model, the study introduces three in-depth psychological validations that argue for the model's adequacy in modeling reading comprehension. In these studies, we demonstrated that AMoC is in line with the theoretical background proposed by the Construction-Integration and Landscape models, and it is better at replicating results from previous human psychological experiments than its predecessor. Thus, AMoC v4.0 can be further used as an educational tool to, for example, help teachers design better learning materials personalized for student profiles. Additionally, we release the code from AMoC v4.0 as open source in a Google Collab Notebook and a GitHub repository. • Introduce the Automated Model of Comprehension version 4.0 that integrates state-of-the-art Large Language models - ChatGPT. • Perform 3 in-depth psychological validation studies to support the model's adequacy in modeling reading comprehension. • AMoC v4.0 aligns with theoretical frameworks, namely Construction-Integration and Landscape models. • The model outperforms its predecessor and replicates results from human psychological experiments. • AMoC v4.0 can serve as an educational tool for designing personalized learning materials. [ABSTRACT FROM AUTHOR]