Duplicate questions are common occurrences in Question Answering Communities (QACs) and impede the development of efficacious problem-solving communities. Yet, there is a dearth of research that has sought to shed light on the mechanisms underlying question duplication. Building on the information adoption model, we advance a research model that posits information quality and source credibility as factors deterring users from asking redundant questions within QACs. Furthermore, considering the question-answer dichotomy intrinsic to QACs, we distinguish the quality and credibility of questions from those of answers as distinctive inhibitors of question duplication. We empirically validate our hypotheses on a leading QAC platform by harnessing a deep learning algorithm to detect duplications on over 9,380,000 question pairs. Results revealed that while the credibility of both questions and answers could alleviate question duplication, visual and actionable elements are more effective in preventing question duplication by boosting the quality of questions and answers respectively.