This study investigates how the information that individuals accumulate through helping others in a customer support crowdsourcing community influences their ability to generate novel, popular, and feasible ideas in an innovation crowdsourcing community. A customer support crowdsourcing community is one in which customers help each other develop solutions to their current problems with a company's products. An innovation crowdsourcing community is one in which customers propose new product ideas directly to a company. Because a customer support community provides information regarding customers' current needs and provides opportunities to help individuals activate relevant means information, we expect that an individuals' experience of helping in a customer support community enhances the individuals' new product ideation performance. By utilizing a natural language processing technique, we construct each individual's information network based on his or her helping activities in a customer support community. Building on analogical reasoning theory, we hypothesize that the patterns of individuals' information networks, in terms of breadth and depth, influence their various new product ideation outcomes in an innovation crowdsourcing community. Our analysis reveals that generalists who have offered help on broad topic domains in the customer support community are more likely to create novel ideas than are nongeneralists. Further, we find that generalists who have accumulated deep knowledge in at least one topic domain (deep generalists) outperform nongeneralists in their ability to generate popular and feasible ideas, whereas generalists who have accumulated only shallow knowledge across diverse domain areas (shallow generalists) do not. The results suggest that the ability of generalists to outperform nongeneralists in creating popular and feasible ideas is contingent on whether they have also accumulated deep knowledge.
