Social Learning in Prosumption: Evidence from a Randomized Field Experiment

Digital technologies enable consumers to actively participate in the product design and production process for a wide range of products, leading rise to the concept of a 'prosumer'. A significant portion of the value for such products is generated through the prosumption process, and a variety of firms are investing in building such capabilities. However, a major, largely unexplored, friction in prosumption is the customers’ effort involved to undertake a creative exercise of designing products and extracting value from it. In this study, we ask whether and how social learning, the act of showing creations made by other customers to the focal customer, can ameliorate such friction. Arguably, by showing others’ product designs to the focal customer, the firm may help the customers gain design ideas and garner knowledge about product features. Such an action is also likely to influence their belief about their own ability, namely, their self-efficacy, to design a valuable product that they would like to purchase. This implies that, if not carefully done, displaying others’ design could be detrimental to prosumption. Certain designs may be perceived to be out of the creative reach of the focal user, and therefore reduce their likelihood of designing a product and purchasing it. If social learning is effective, what can we say about the nature of images to show to different sub-groups of users? In close collaboration with an e-commerce platform specialized in customized photo products, we examine the effectiveness of social learning by means of a large scale in-vivo randomized field experiment. We exogenously vary both the availability of others’ design and the characteristics of images shown to the treated users. Our analysis shows that showing other users’ design can be highly effective in influencing the purchase and design behavior of the focal customer, but firms must choose the right customers and carefully select the type of user image design for display. We develop a novel ‘honest-bagging’ approach guided by principles of causal forests to personalize the high-dimensional treatment around which images to show to what types of users.