Algorithm aversion occurs when humans are reluctant to use algorithms despite their superior performance. Prior studies have shown that giving users outcome control'', the ability to appeal or modify model's predictions, can mitigate this aversion. This can be contrasted withprocess control'', which entails control over the development of the algorithmic tool. The effectiveness of process control is currently under-explored. To compare how various controls over algorithmic systems affect users' willingness to use the systems, we replicate a prior study on outcome control and conduct a novel experiment investigating process control. We find that involving users in the process does not always result in a higher reliance on the model. We find that process control in the form of choosing the training algorithm mitigates algorithm aversion, but changing inputs does not. Giving users both outcome and process control does not result in further mitigation than either outcome or process control alone. Having conducted the studies on both Amazon Mechanical Turk (MTurk) and Prolific, we also reflect on the challenges of replication for crowdsourcing studies of human-AI interaction.