This study aims to use a configurational approach to identify which combinations of questioner and question characteristics drive high-quality scholarly engagement in academic Q&A sites. It argues that answer quantity and quality result not from single factors but from the complex interplay between multiple features.
This research analyzed 1,705 question records from the AI community on ResearchGate. It employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how configurations of six features—including readability, emotionality, topic relevance, and the questioner’s Research Interest Score (RIS) and Q&A involvement—lead to high or low answer quantity and quality.
No single condition guarantees positive answer performance. Instead, it results from specific configurations. A key finding is the complementary relationship between readability and emotionality: high emotionality can compensate for low readability to achieve good answer performance. Furthermore, a questioner’s high RIS and Q&A involvement level are core conditions for achieving both high answer quantity and quality, often appearing in combination.
This study is the first to apply a configurational perspective to academic Q&A sites, revealing the complex interplay of factors rather than isolated effects. It challenges findings from general Q&A sites by demonstrating that, in academic contexts, questioner reputation is pivotal and high readability is not always essential for success. The research provides a novel framework for understanding and fostering interdisciplinary academic exchange online.
