RESUMO
The field of sustainability accounting aims to integrate environmental, social, and governance factors into financial reporting. With the growing importance of sustainability practices, emerging technologies have the potential to revolutionize reporting methods. However, there is a lack of research on the factors influencing the adoption of blockchain and cloud-based sustainability accounting in China. This study employs a mixed-methods approach to examine the key drivers and barriers to technology adoption for sustainability reporting among Chinese businesses. Through a systematic literature review, gaps in knowledge were identified. Primary data was collected through an online survey of firms, followed by in-depth case studies. The findings of the study reveal a positive relationship between company size and reporting behaviors. However, size alone is not sufficient to predict outcomes accurately. The industry type also has significant but small effects, although its impact on reporting behaviors varies. The relationship between profitability and reporting behaviors is intricate and contingent, requiring contextual examination. The adoption of blockchain technology is positively associated with capabilities, resources, skills, and regulatory factors. On the other hand, cloud computing adoption is linked to resources, management support, and risk exposures. However, the specific impacts of industry on adoption remain inconclusive. This study aims to offer empirical validation of relationships, shedding light on the intricate nature of interactions that necessitate nuanced conceptualizations incorporating contextual moderators. The findings underscore the importance of providing customized support and adaptable guidance to accommodate the evolving practices in sustainability accounting. Moreover, the assimilation of technology and organizational changes highlights the need for multifaceted stakeholder cooperation to drive responsible innovation and address the challenges posed by digital transformations in this field.
RESUMO
The artificial neural network (ANN) is employed to study children's psychological emotion recognition to fully reflect the psychological status of preschool children and promote the healthy growth of preschool children. Specifically, the ANN model is used to construct the human physiological signal measurement platform and emotion recognition platform to measure the human physiological signals in different psychological and emotional states. Finally, the parameter values are analyzed on the emotion recognition platform to identify the children's psychological and emotional states accurately. The experimental results demonstrate that the recognition ability of children aged 4-6 to recognize the three basic emotions of happiness, calm, and fear increases with age. Besides, there are significant age differences in children's recognition of happiness, calm, and fear. In addition, the effect of 4-year-old children on the theory of mind tasks is less than that of 5- to 6-year-old children, which may be related to more complex cognitive processes. Preschool children are experiencing a stage of rapid emotional development. If children cannot be guided to reasonably identify and deal with emotions at this stage, their education level and social ability development will be significantly affected. Therefore, this study has significant reference value for preschool children's emotional recognition and guidance and can promote children's emotional processing and mental health.
