Research on innovation management has pointed out that the capitalization of collaborative innovation practices is influenced by firms’ internal context. This paper aims to answer the following question: which organizational factors help to overcome the challenges that firms face in the different phases of the collaborative innovation process? For this purpose, previous literature is revised and three case studies are analyzed by means of applying a framework that structures the collaborative innovation process in three areas of relevance (i.e., development, integration and commercialization of the innovation). The results of the analysis inform the proposal of a theoretical framework that identifies the organizational context factors that determine the success or failure of collaborative innovation practices in each of the stages of the process.

The expansive research on innovation in organizations has primarily followed a technological imperative, postulating that organizations organize their innovation efforts through research and development (R&D) activities. Theories of innovation are thus shaped mosdy from the studies of technology-based innovations in the manufacturing sector, yet are applied in all contexts. This article advocates research on managerial innovations - new approaches to devise strategy and structure of tasks and units, modify the organization's management processes and administrative systems, motivate and reward organizational members, and enable organizational adaptation and change. We examine the conceptual development, generation and adoption processes, antecedents, and influences of managerial innovation on organizational conduct. By increasing an understanding of managerial innovation, this article aims to complement existing theories of innovation derived from the studies of technological innovations and thus help extend and advance theory and research on innovation process and outcome in organizations.

We investigate the stationarity of minification processes when the marginal is a discrete distribution. There is a close relationship between the problem considered by Arnold and Isaacson (1976) and the stationarity in minification processes. We give a necessary and sufficient condition for a discrete distribution to be the marginal of a stationary minification process. Members of the Poisson and negative binomial families can be the marginals of stationary minification processes. The geometric minification process is studied in detail, and two characterizations of it based on the structure of the innovation process are given.

It is shown that there is an innovation process {∊n} such that the sequence of random variables {Xn} generated by the linear, additive first-order autoregressive scheme Xn = pXn-1 + ∊n are marginally distributed as gamma (λ, k) variables if 0 ≦p ≦ 1. This first-order autoregressive gamma sequence is useful for modelling a wide range of observed phenomena. Properties of sums of random variables from this process are studied, as well as Laplace-Stieltjes transforms of adjacent variables and joint moments of variables with different separations. The process is not time-reversible and has a zero-defect which makes parameter estimation straightforward. Other positive-valued variables generated by the first-order autoregressive scheme are studied, as well as extensions of the scheme for generating sequences with given marginal distributions and negative serial correlations.