Peter Pal Zubcsek

Information Communities: The Network Structure of Communication

This study puts forward a variable clique overlap model for identifying information communities, or potentially overlapping subgroups of network actors among whom reinforced independent links ensure efficient communication. We posit that the intensity of communication between individuals in information communities is greater than in other areas of the network. Empirical tests show that the variable clique overlap model is more useful for identifying groups of individuals that have strong internal relationships in closed networks than those defined by more general models of network closure. These findings extend the scope of network closure effects proposed by other researchers working with communication networks using social network methods and approaches, a tradition which emphasizes ties between organizations, groups, individuals, and the external environment.

Lower Connectivity Is Better: The Effects of Network Structure on Redundancy of Ideas and Customer Innovativeness in Interdependent Ideation Tasks

This article examines the popular marketing practice of interdependent ideation, whereby firms solicit ideas from customers through online platforms that enable customers to be exposed to or “inspired” by other customers’ ideas when generating their own. Although being exposed to others’ ideas means that customers are “connected” (at least implicitly) in a communication network that facilities the flow of ideas, the effect of network structure on individual innovativeness has not been considered in this context. The authors examine how, when, and why network structure—specifically, the clustering, or interconnectivity, of ones “inspirations” (other customers)—affects the innovativeness of individual customers’ product/service ideas in ideation tasks. Across five experiments, the authors show that (1) higher clustering/interconnectivity negatively affects the innovativeness of a customers ideas; (2) this effect occurs because idea inspirations are more likely to be similar or redundant when their sources (i.e., other customers to which one is connected) are clustered; (3) greater redundancy among ideas used as inspirations is what causes lower innovativeness; and (4) this effect is attenuated when customers do not rely on other customers’ ideas for inspiration.

How being busy can increase motivation and reduce task completion time

This research tests the hypothesis that being busy increases motivation and reduces the time it takes to complete tasks for which people miss a deadline. This effect occurs because busy people tend to perceive that they are using their time effectively, which mitigates the sense of failure people have when they miss a task deadline. Studies 1 and 2 show that when people are busy, they are more motivated to complete a task after missing a deadline than those who are not busy, and that the perception that one is using time effectively mediates this effect. Studies 3 and 4 show that this process makes busy people more likely to complete real tasks than people who are not busy. Study 5 uses data from over half a million tasks submitted by thousands of users of a task management software application to show that busy people take less time to complete a task after they miss a deadline for completing it. The findings delineate the conditions under which being busy can mitigate the negative effects of missing a deadline and reduce the time it takes to complete tasks. (PsycINFO Database Record

The Connected Consumer: Connected Devices and the Evolution of Customer Intelligence

Technological advances are increasing the connections between customers and companies, products, and one another. Consumers’ use of connected devices is providing rich sources of data about consumers, their activity, and their environment, which we collectively label customer intelligence. At the same time, changes in statistical algorithms and artificial intelligence are making automated inferences and decisions regarding consumer behavior possible. One likely result of these changes is the emergence of companies that are especially adept at generating and using customer intelligence. This article explores how changes in sensing technology, causal modeling, and intelligent marketing platforms may affect the generation and utilization of customer intelligence. We envision a merging of these traditionally separate activities in companies that possess a large, active customer base and the ability to collect, process, and apply data from these customers quickly and accurately. Such a convergence offers substantial potential value but also notable risk for tomorrow’s connected consumers.