Research in organizational learning has demonstrated processes and occasionally performance implications of acquisition of declarative (know-what) and procedural (know-how) knowledge. However, considerably less attention has been paid to learned characteristics of relationships that affect the decision to seek information from other people. Based on a review of the social network, information processing, and organizational learning literatures, along with the results of a previous qualitative study, we propose a formal model of information seeking in which the probability of seeking information from another person is a function of (1) knowing what that person knows; (2) valuing what that person knows; (3) being able to gain timely access to that person’s thinking; and (4) perceiving that seeking information from that person would not be too costly. We also hypothesize that the knowing, access, and cost variables mediate the relationship between physical proximity and information seeking. The model is tested using two separate research sites to provide replication. The results indicate strong support for the model and the mediation hypothesis (with the exception of the cost variable). Implications are drawn for the study of both transactive memory and organizational learning, as well as for management practice.

Have the core discussion networks of Americans changed in the past two decades? In 1985, the General Social Survey (GSS) collected the first nationally representative data on the confidants with whom Americans discuss important matters. In the 2004 GSS the authors replicated those questions to assess social change in core network structures. Discussion networks are smaller in 2004 than in 1985. The number of people saying there is no one with whom they discuss important matters nearly tripled. The mean network size decreases by about a third (one confidant), from 2.94 in 1985 to 2.08 in 2004. The modal respondent now reports having no confidant; the modal respondent in 1985 had three confidants. Both kin and non-kin confidants were lost in the past two decades, but the greater decrease of non-kin ties leads to more confidant networks centered on spouses and parents, with fewer contacts through voluntary associations and neighborhoods. Most people have densely interconnected confidants similar to them. Some changes reflect the changing demographics of the U.S. population. Educational heterogeneity of social ties has decreased, racial heterogeneity has increased. The data may overestimate the number of social isolates, but these shrinking networks reflect an important social change in America There are some things that we discuss only with people who are very close to us. These important topics may vary with the situation or the person—we may ask for help, probe for information, or just use the person as a sounding board for important decisions—but these are the people who make up our core network of confidants. How have these discussion networks of close confidants changed over the past two Please address correspondence to Miller

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Time and motion studies are a proven means toward understanding and improving any engineering enterprise. We believe that the engineering of software processes is no different in this respect; however, the fact that software development yields an intellectual --- as opposed to physical --- product calls for new and creative measurement techniques. In attempting to answer the question "Where does time go in software development?" we have been experimenting with several forms of data collection. We have found that two techniques in particular, time diaries and direct observation, are feasible and yield useful information about time utilization. The major source of discrepancy between them is granularity: most software developers can not retrospectively report with a high degree of accuracy the large number of interruptions and unplanned transitory events that typically characterize their working day. Drawing upon experimental techniques from the behavioral sciences, we used three alterna...

The people who use computers and the ways they use them have changed substantially over the past 25 years. In the beginning highly educated men in technical professions used computers for work, but over time a much broader range of people are using computers for personal and domestic purposes. This trend is still continuing, and over a shorter time scale has been replicated with the use of the Internet. This paper uses data from four national surveys to document how personal computers and the Internet have become increasingly domesticated since 1995 and to explore the mechanisms for this shift. Now people logon more often from home than from places of employment and do so for pleasure and for personal purposes rather than for their jobs. Analyses comparing veteran Internet users to novices in 1998 and 2000 and analyses comparing the change in use within a single sample between 1995 and 1996 support two complementary explanations for how these technologies have become domesticated. Women, children and less well-educated individuals are increasingly using computers and the Internet and have a more personal set of motives than well-educated men. In addition, the widespread diffusion of the PC and the Internet and the response of the computing industry to the diversity in consumers has led to a rich set of personal and domestic services. Keywords - Internet use, personal computers, domestication, national survey, demographics, online behavior, communication, information Acknowledgements Mark Fichman, Sara Kiesler, and Jennifer Lerner provided helpful comments on a previous draft of this paper. We also thank the Pew Research Center For The People and The Press and The Pew Internet and American Life Project for providing the national survey data. These organizations bear no r...

A class of statistical models is proposed which aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model builds on two assumptions. The observed network is assumed to be generated by hierarchically nested latent transitive structures, expressed by ultrametrics. It is assumed that expected tie strength decreases with ultrametric distance. The approach could be described as model-based clustering with an ultrametric space as the underlying metric to capture the dependence in the observations. Maximum likelihood methods as well as Bayesian methods are applied for statistical inference. Both approaches are implemented using Markov chain Monte Carlo methods. 1.

We present a model of network formation where entering nodes find other nodes to link to both completely at random and through search of the neighborhoods of these randomly met nodes. We show that this model exhibits the full spectrum of features that have been found to characterize large socially generated networks. Moreover, we derive the distribution of degree (number of links) across nodes, and show that while the upper tail of the distribution is approximately “scale-free,” the lower tail may exhibit substantial curvature, just as in observed networks. We then fit the model to data from six networks. Besides offering a close fit of these diverse networks, the model allows us to impute the relative importance of search versus random attachment in link formation. We find that the fitted ratio of random meetings to search-based meetings varies dramatically across these applications. Finally, we show that as this random/search ratio varies, the resulting degree distributions can be completely ordered in the sense of second order stochastic dominance. This allows us to infer how the relative randomness in the formation process affects average utility in the network.

In this paper we introduce a new dynamic Bayesian network that separates the speakers and their speaking turns in a multi-person conversation. We protect the speakers' privacy by using only features from which intelligible speech cannot be reconstructed. The model we

ubiquitous computing environments

Social network analysis has been used to understand groups of individuals and how they operate. Most of the literature in social networks has dealt with overt organizations with an easily discernable network structure. This paper examines the possibilities of using the inherent structures observed in social networks to make predictions of networks using limited and missing information. The model is based on empirical network data exhibiting the structural properties of triad closure and adjacency. Triad closure indicates that if person i has a dyad with person j and person j has a dyad with person k, then there is a higher than chance likelihood that person i and person j have a dyad. The model exploits these properties using an inference model to update adjacent dyads given information on a reference dyad. The model is tested against several networks to understand and discern its behavior. The paper illustrates that if the model is built with careful consideration towards the network being predicted, it will assist in making better decisions regarding uncertain organizational phenomenon. The method is applied in a covert network example, and has been extended to show its usefulness in epidemiological networks and improving performance in organizations operating under stress. The paper opens up new avenues in the development of models designed to make network predictions and use those predictions to make better decisions.