Configurational effects of question-related features on the quantity and quality of answers in academic Q&A sites

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.