Poster #27 - Does Emotion-based Response Work? The Impact of Government Response Strategies on Citizens’ Satisfaction

• In Event: Saturday Poster Luncheon

Abstract

The rise of information and communication technologies has intensified online interactions between governments and citizens (Wong & Welch, 2004), offering new avenues for democratic participation (Cai & Zhou, 2019; Jiang et al., 2019). As citizens increasingly use digital platforms to express needs and seek responses, government responsiveness is now becoming a critical component in citizen-government interactions, fundamentally shaping public trust and the effectiveness of e-governance initiatives. However, resource constraints often lead to strategic responses aimed at managing citizens' emotions and avoiding conflicts, rather than resolving all issues. Despite this, few studies have examined government response strategies and their impact on citizen satisfaction. Some scholars have begun investigating sentiment in government social media communications (Pang & Ng, 2016; Zavattaro et al., 2015), but these studies are often limited to Western contexts and lack a comprehensive framework for understanding emotion-based responsiveness in e-governance.

To address these gaps, this study proposes the Emotion-based Responsiveness Framework in Government Communication to analyze the emotional aspects of government responses in digital communication platforms. Learning from the existing literature, the Framework comprises four key components: positivity degree, empathy level, reassurance degree, and personalization degree. By examining these dimensions, we aim to provide a nuanced understanding of how emotional elements in government communications influence citizen satisfaction. In this paper, we use data from the Chinese People's Daily "Message Board for Leaders", which is a nationwide online platform for citizens to request government services in multiple areas. This platform offers a unique opportunity to study emotion-based response strategies in a non-western context, contributing to the global understanding of e-governance practices. For data analysis, we begin by manually coding a stratified sample of several thousand responses to establish ground truth labels across the four emotional dimensions: positivity, empathy, reassurance, and personalization. Each dimension is coded as binary variable. This annotated dataset serves as the foundation for training few-shot learning prompts using the ChatGPT API. Carefully crafted examples guide the large language model (LLM) in predicting emotion scores for the remaining unlabeled responses. As a robustness check, we replicate the classification using Deepseek’s API to validate the consistency of results. Lastly, we empirically test whether government’s emotion-based responses will affect citizen’s overall satisfaction, with the degree of resolution as the moderating variable.

This study offers contributions to both the theoretical understanding and practical application of e-governance: We introduce the Emotion-based Responsiveness Framework in Government Communication, offering a novel, quantitative approach to analyzing emotional content in government responses and providing a more nuanced understanding of digital communication strategies. And our research delivers empirical evidence on the impact of emotion-based communication in government-citizen digital interactions, while also exploring the moderating role of resolution degree. By identifying which emotional elements in responses most effectively influence citizen satisfaction, we also provide actionable insights for crafting more effective communication. This can guide the development of training programs for government communicators, helping them to better address citizens' emotional needs even when problem resolution is not feasible.

Authors

• Ruoyun Wang, The Hong Kong University of Science and Technology(Guangzhou)
  Presenting Author

• Corey Kewei XU, Hong Kong University of Science and Technology（Guangzhou)
  Non-Presenting Co-Author

• Liang Ma, Renmin University of China
  Non-Presenting Co-Author