Stimulating Economic Growth by Unlocking the Nonrival Potential of Data - Review, Synthesis and Directions for Future Research

Data as a resource has proven to be a game changer for firms. With a growing global data sphere, differences across platforms with exclusive access to big data intensify. Against this background, Jones and Tonetti (2020) model access to big data as an allocation problem for enhancing social welfare. They focus on the nonrivalry of data to show that the same dataset should be available across firms to spur innovation. Yet, this contrasts with the desire for privacy by individuals and the fear of creative destruction by firms. We provide a systematic literature review to inform IS research on options to solve the problem of big data misallocation. Specifically, we synthesize academic progress for improving data-sharing approaches. We show that technological options will be available soon that will let individuals decide to whom they grant access to their data. Such options will allow producing welfare-enhancing outcomes from big data.