Team‐Level Antecedents of Individuals' Knowledge Networks*

Individuals' knowledge networks are widely considered to contribute substantially to the effectiveness and efficiency of organizations. While the positive effects of knowledge networks as a primary driver of social capital have recently received considerable research attention, potential determinants of individuals' network building have not yet been adequately addressed. In this study, we investigate how certain team‐level properties affect team members' development of knowledge networks through the course of a team project. Using data from 430 team leaders and team members pertaining to 145 software development projects, we test cross‐level hypotheses using hierarchical linear modeling (HLM). The results indicate that the team's perception of the organizational knowledge‐sharing climate, the team's networking preference, and the team's perceived importance of networking for project success positively affect individuals' network building. Furthermore, a team's perception of the adequacy of its technical competency and a team's perception of the adequacy of its material resources inhibit team members' individual network development. Theoretical and managerial implications are discussed.     

Increasing Returns and Social Contagion in Cultural Industries

A formal definition of cultural industries is developed following four distinct features of cultural goods: (a) oversupply, (b) quality uncertainty, (c) network effects and (d) demand reversal. Drawing on economic and socio-psychological notions of ‘network’, increasing returns and social contagion effects are distinguished. Increasing returns may govern the adoption of standards when choices are binary, social contagion explains the diffusion of cultural goods when choices are multiple. Together, the four structural features delineating cultural industries account for curious competitive dynamics prevalent in cultural markets, such as the notorious 10 : 90 proportionality (under which 10% of cultural goods account for 90% of the market), causal ambiguity about the reasons for success, and the formation of fashions. Six managerial recommendations are advanced, focusing on a criticial circulation point triggering self-sustaining diffusion patterns. Finally ‘project-based enterprises’ and ‘network forms of governance’ are identified as the organizational forms most suited to the dynamics of the cultural markets.     

Efficient designs based on orthogonal arrays of type I and type II for experiments using units ordered over time or space

For a wide variety of applications, experiments are based on units ordered over time or space. Models for these experiments generally may include one or more of: correlations, systematic trends, carryover effects and interference effects. Since the standard optimal block designs may not be efficient in these situations, orthogonal arrays of type I and type II, which were introduced in 1961 by C.R. Rao [Combinatorial arrangements analogous to orthogonal arrays, Sankhya A 23 (1961) 283–286], have been recently used to construct optimal and efficient designs for many of these experiments. Results in this area are unified and the salient features are outlined.     

Covariates and Random Effects in a Gamma Process Model with Application to Degradation and Failure

The gamma process is a natural model for degradation processes in which deterioration is supposed to take place gradually over time in a sequence of tiny increments. When units or individuals are observed over time it is often apparent that they degrade at different rates, even though no differences in treatment or environment are present. Thus, in applying gamma-process models to such data, it is necessary to allow for such unexplained differences. In the present paper this is accomplished by constructing a tractable gamma-process model incorporating a random effect. The model is fitted to some data on crack growth and corresponding goodness-of-fit tests are carried out. Prediction calculations for failure times defined in terms of degradation level passages are developed and illustrated.     

Models and estimation for systems with recurrent events and usage processes

This paper considers settings where populations of units may experience recurrent events, termed failures for convenience, and where the units are subject to varying levels of usage. We provide joint models for the recurrent events and usage processes, which facilitate analysis of their relationship as well as prediction of failures. Data on usage are often incomplete and we show how to implement maximum likelihood estimation in such cases. Random effects models with linear usage processes and gamma usage processes are considered in some detail. Data on automobile warranty claims are used to illustrate the proposed models and estimation methodology.     

Modelling the effects of repellent chemicals on foraging bees

Summary.  Many pesticide sprays that are used for crop protection are harmful to honey-bees. It can therefore be beneficial to add to the spray chemical compounds that are repellent to bees, to discourage them from feeding on recently sprayed crops. Experiments were conducted using an artificial feeding station to assess the repellent effects of various compounds. In this system, bees arrive at the feeding station, choose between feeding dishes to which different chemicals have been added, feed for a variable period and then depart. The number of bees at each feeding dish is recorded at intervals of 1 min. We discuss the analysis of data from this type of experiment, based on a queuing theory model.     

A hybrid genetic algorithm/mathematical programming approach to the multi-family flowshop scheduling problem with lot streaming

This paper presents a hybrid genetic algorithm/mathematical programming heuristic for the n-job, m-machine flowshop problems with lot streaming. The number of sublots for each job and the size of sublots are directly addressed by the heuristic and setups may be sequence-dependent. A new aspect of the problem, the interleaving of sublots from different jobs in the processing sequence, is developed and addressed. Computational results from 12 randomly generated test sets of 24 problems each are presented.     

EFFECTS OF EXPOSURE ON PREVALENCE AND CUMULATIVE RELATIVE RISK: DIRECT AND INDIRECT EFFECTS IN A RECURSIVE HAZARD MODEL

This paper outlines decomposition methods for assessing how exposure affects prevalence and cumulative relative risk. Let     x     denote a vector of exogenous covariates and suppose that a single dimension of time   t   governs two event processes     T ₁  and   T ₂.    If the occurrence of the event     T ₁    determines entry into the risk of the event     T ₂,    then subgroup variation in     T ₁    will affect the prevalence     T ₂,    even if subgroups in the population are otherwise identical. Although researchers often acknowledge this phenomenon, the literature has not provided procedures to assess the magnitude of an exposure effect of     T ₁    on the prevalence of     T ₂.    We derive decompositions that assess how variation in exposure generated by direct and indirect effects of the covariates     x     affect measures of absolute and relative prevalence of     T ₂.    We employ a parametric but highly flexible specification for baseline hazard for the     T ₁  and   T ₂    processes and use the resulting parametric proportional hazard model to illustrate the direct and indirect effects of family structure when     T ₁    is age at first sexual intercourse and     T ₂    is age at a premarital first birth for data on a cohort of non-hispanic white U.S. women.