Bounty programs have become a pivotal incentive mechanism in open-source software (OSS) communities, attracting contributors by offering monetary rewards for task completion. Despite their long-standing implementation, the optimal utilization of this mechanism from the perspective of backers (individuals or entities funding bounties) remains insufficiently understood, hindering its refinement and broader adoption. To bridge this gap, we conduct a mixed-methods study analyzing 10,561 bounty issues fromGitcoin, their linkedGitHubdevelopment data, and surveys from 46 bounty backers. We investigate three core decision-making dimensions: (1) why backers use bounties and the actual outcomes, (2) what issues backers prioritize, and (3) how bounty amounts are set. Our findings reveal that backers primarily seek to enhance developer engagement, project visibility, and task efficiency. However, the actual outcomes often diverge from expectations: although bounty issues have a higher resolution rate (+12%) than non-bounty issues, they also introduce systemic challenges, such as delayed resolutions (+33 days) and difficulties in engaging new developers. Notably, backers tend to prioritize feature-related, intermediate-complexity tasks with short completion timelines, while showing relatively less interest in overly simplistic or highly specialized work. Reward allocation follows a nuanced approach: lower bounties target beginner-friendly tasks, while higher rewards are reserved for advanced skills or multi-week commitments. However, backers often lack systematic methods to calibrate rewards, leading to frequent bounty adjustments. To enable data-driven decision-making, we propose a bounty recommendation predictor that uses empirical factors to predict appropriate bounty amount. By synthesizing these insights, our study offers OSS communities actionable strategies to refine bounty programs, balancing short-term productivity with long-term ecosystem sustainability.