\textit{Context: } Due to the association of significant efforts, even a minor improvement in the effectiveness of Code Reviews(CR) can incur significant savings for a software development organization.

\textit{Aim:} This study aims \textit{ to develop a finer grain understanding of what makes a code review comment useful to OSS developers, to what extent a code review comment is considered useful to them, and how various contextual and participant-related factors influence its degree of usefulness}.

\textit{Method:} On this goal, we have conducted a three-stage mixed-method study. We randomly selected 2,500 CR comments from the OpenDev Nova project and manually categorized the comments. We designed a survey of OpenDev developers to better understand their perspectives on useful CRs. Combining our survey-obtained scores with our manually labeled dataset, we trained two regression models - one to identify factors that influence the usefulness of CR comments and the other to identify factors that improve the odds of `Functional’ defect identification over the others.

\textit{Key findings:} The results of our study suggest that a CR comment’s usefulness is dictated not only by its technical contributions, such as defect findings or quality improvement tips but also by its linguistic characteristics, such as comprehensibility and politeness. While a reviewer’s coding experience is positively associated with CR usefulness, the number of mutual reviews, comment volume in a file, the total number of lines added /modified, and CR interval have the opposite associations. While authorship and reviewership experiences for the files under review have been the most popular attributes for reviewer recommendation systems, we do not find any significant association of those attributes with CR usefulness.

\textit{Conclusion:} We recommend discouraging frequent code review associations between two individuals as such associations may decrease CR usefulness. We also recommend authoring CR comments in a constructive and empathetic tone. As several of our results deviate from prior studies, we recommend more investigations to identify context-specific attributes to build reviewer recommendation models.