The management of the fuzzy front end (FFE) phase of innovation is crucial to the ultimate success of new product and process initiatives. A critical challenge that teams face at this stage is dealing with equivocality – the extent to which project participants grapple with multiple, and plausibly conflicting, meanings and interpretations of the information available to them (Daft and Lengel, 1986; Weick, 1979). While initially, a certain level of equivocality is beneficial for enhancing team creativity and preventing early closure, at some point it must be resolved in order for an idea to become a viable New Product Development (NPD) project. This study employs a social networks perspective to understand how different types of informal work-based relations and their structural properties affect equivocality on project teams in the FFE. In particular, it examines the structural effects of two types of social relations and their associated networks – technical-advice and friendship ties. The findings suggest that while high density in a projects technical-advice network is likely to reduce equivocality, high density in a projects friendship network is likely to increase it. More interestingly, having multiple members on projects who are highly central in the lab technical-advice network tends to increase equivocality unless it is balanced with members who occupy positions of high centrality in the lab friendship network. In addition to contributing to the scholarship on NPD, FFE, and social networks, the results offer managerial insights for deploying social networks in order to assemble NPD teams and structure the flows of communication on projects so as to resolve equivocality in the FFE.

To measure the effect of veterans’ preference on U.S. federal workforce quality, researchers have assessed whether military veterans advance in their federal careers at a different rate than nonveterans. This research, however, has produced mixed results. In research concerning recent employee cohorts, nonveterans outpace veterans’ advancement, implying that veterans’ preference lessens employee quality. In older cohorts, veterans and nonveterans advance comparably. The latter research, however, controls for employees’ entry positions, whereas research concerning recent cohorts does not do so, thus inhibiting direct comparison of results. To facilitate such comparisons, we controlled for veterans’ and nonveterans’ entry positions in a study of career advancement among all white-collar, U.S. executive branch workers entering employment from 1992 to 2013. In these recent cohorts, we find roughly equivalent rates of career advancement among veterans and nonveterans when controlling for entry positions. This finding holds when using grade or pay increases as measures of advancement.

We examine models that relax proportionality in cumulative ordered regression models. Something fundamental arising from ordered variables and stochastic ordering implies a partitioning. Efforts to relax proportionality also relax the ability to collapse an inherently multidimensional problem to a partitioning of the (unidimensional) real line. It is surprising and unfortunate to find that deviations from proportionality are sufficient to generate internal contradictions; undecidable propositions must exist by relaxing proportional odds without other relevant and significant changes in the underlying model. We prove a single theorem linking continuous support and partitions of a latent space to show that for these two characteristics to be simultaneously satisfied, the model must be the proportional-odds model. Conditioning on the adjacency that is closely related to the partitioning is fruitful, but at this point we join the class of continuation-ratio models. Alternatively, Anderson’s (1984) stereotype model is quite general and nests ordered and unordered choice models, but again we have left the domain of cumulative models. Adopting multidimensional cumulative models or imposing covariate-specific thresholds are the only certain methods for avoiding these troubles in the cumulative framework. It is generically impossible to generalize the cumulative class of ordered regression models in ways consistent with the spirit of generalized cumulative regression models. Monte Carlo studies also demonstrate the general principles.