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The purpose of this paper is to analyze whether and how intergovernmental fiscal transfers (IFTs) affect technological innovation.

China’s provincial panel data from 2007 to 2019 are used in an empirical study to examine the effect of IFTs on technological innovation and the role of fiscal spending policy in the relationship between the two by using the spatial Durbin model.

Results show an evident spatial correlation for the effect of IFTs on technological innovation, indicating that IFTs have a significant negative influence on technological innovation in local and surrounding regions. IFTs also inhibit technological innovation by negatively affecting science and technology spending and education spending.

These findings can aid policymakers in advancing technological innovation by improving the system of fiscal transfers and optimizing the structure of fiscal spending.

Although the determinants of technological innovation have been analyzed, no studies have investigated the effect of IFTs on technological innovation. Thus, this paper aims to address this gap.

This paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what extent does user feedback affect influential users in changing from one archetype to another?

Based on a sample of 13,840 influential users drawn from the Covid-19 community on Zhihu, the archetypes of influential users were derived from their ongoing participation behavior in the community using the Gaussian mixture model. Additionally, user feedback characteristics such as relevance and volume from 222,965 commenters who contributed 546,344 comments were analyzed using the multinomial logistic regression model to investigate the archetype change of influential users.

Findings suggest that influential users could be clustered into three distinctive archetypes: touch-and-go influential users, proactive influential users and super influential users. Moreover, feedback variables have various impacts on the influential user archetype change, including a shift toward creating higher-quality content and fostering increased interaction, a shift toward generating lower-quality content and decreased interaction but improved speed and having mixed effects due to differences in information processing among these archetypes.

This study expands the existing knowledge of influential users and proposes practical approaches to cultivate them further.