Our trust conceptualizations have benefited from discussions with Ellen Berscheid and Larry Cummings of the University of Minnesota. The authors also thank three anonymous reviewers from the Organizational Behavior division of the 1996 meeting of the Academy of Management for their comments on an earlier version of this paper. THE MEANINGS OF TRUST What does the word ‘trust ’ mean? Scholars continue to express concern regarding their collective lack of consensus about trust’s meaning. Conceptual confusion on trust makes comparing one trust study to another problematic. To facilitate cumulative trust research, the authors propose two kinds of trust typologies: (a) a classification system for types of trust, and (b) definitions of six related trust types that form a model. Some of the model’s implications for management are also outlined. 2 THE MEANINGS OF TRUST “...trust is a term with many meanings. ” (Williamson, 1993: 453) “Trust is itself a term for a clustering of perceptions. ” (White, 1992: 174) Scholars and practitioners widely acknowledge trust's importance. Trust makes cooperative endeavors happen (e.g., Arrow, 1974; Deutsch, 1973; Gambetta, 1988). Trust is a key to positive interpersonal relationships in

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85 % accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization. Author Keywords Social media, social networks, relationship modeling, ties,

Computational and mathematical organization theory is an inter-disciplinary scientific area whose research members focus on developing and testing organizational theory using formal models. The community shares a theoretical view of organizations as collections of processes and intelligent adaptive agents that are task oriented, socially situated, technologically bound, and continuously changing. Behavior within the organization is seen to affect and be affected by the organization’s position in the external environment. The community also shares a methodological orientation toward the use of formal models for developing and testing theory. These models are both computational (e.g., simulation, emulation, expert systems, computer-assisted numerical analysis) and mathematical (e.g., formal logic, matrix algebra, network analysis, discrete and continuous equations). Much of the research in this area falls into four areas: organizational design, organizational learning, organizations and information technology, and organizational evolution and change. Historically, much of the work in this area has been focused on the issue how should organizations be designed. The work in this subarea is cumulative and tied to other subfields within organization theory more generally.

Abstract: In the last ten years, sociologists, social psychologists and physical scientists have developed opinion change models where influence is exerted through social ties. The goal of our paper is to investigate the effect of contact patterns on the number of individuals within a group who share the same opinion. We present an opinion formation model, simulate opinion formation in eight different kinds of networks and present the results. We find that contact patterns can have large effects on agreement levels. For example, individuals in tree networks display low levels of agreement. Basic statistical descriptors of network structure, such as density, correlate with group agreement in our simulated data. We also found that dummy variables indicating type of network have statistically significant effects, suggesting that additional unmeasured features of the network contribute to agreement in our simulations. 1

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What forms of collaboration result in the most benefit to individuals who are in the business of creating new knowledge? I approach this question by examining patterns of collaboration among university faculty members with the objective of determining what types of collaborative relationships are most likely to result in innovative ideas and knowledge creation. By drawing on the toolkits of Social and Dynamic Network Analysis, I measure different structural positions of the network of actors based on this collaborative behavior. The dataset used in this study contains publication and collaboration data from 1995 to 2006 for each of 61 tenure or research track faculty members in the computer science department of a major U.S. university. Publication data was used as a proxy for knowledge creation. Coauthorship of publications and inter-departmental collaborations on projects, grants and students were used in calculating several network metrics including the E-I Index. These metrics along with relevant control variables are subsequently used in a multivariate regression model to estimate their significance on total publication rates of faculty members. Results indicate that innovation and new knowledge creation are facilitated by new inter-departmental partnerships

Organizations are occasionally faced with technologically based and accident triggered crises that can be extremely costly. An example is Bhopal, a major chemical spill. In the aftermath of such a disaster, organizations, both the one that suffered and others in the same or similar industries, often reexamine how they are structured. The questions arise: how should organizations be designed if they are to perform well in such crises, and would organizations benefit from structural changes during crises? We address these questions using a combination of computational analysis and archival data on 69 real organizations faced with crises. For each crisis incident, we contrast the organization’s predicted and actual performance in a crisis situation. We find a high level of validation for the computational model. Using the validated computational model we then go on to address the hypothetical question: what is likely to have happened if the real organization had responded differently to the crisis. The findings show that there is no guarantee that a well performing organization in a general situation will continue to do so during a crisis situation. In addition, how to design or restructure an organization to mitigate the impact of crises will depend on the various design factors the organization employs. This work further demonstrates that often, the lessons learned by organizations when responding to crisis situations may be exactly the wrong lessons.

We take a social network approach to study query response behavior of employees in an enterprise-wide technical forum. Questions posed by employees on the forum elicit responses from other employees and we characterize the tie between a responder and a query poster as a directed dyadic tie, and the entire forum as a social network. Prior work has focused on individual participation rates rather than at a dyadiclevel. Taking a dyadic perspective, we evaluate how the network structure evolves over time. We specifically examine the role that past network structures (e.g., Simmelian ties) established between employees in the query responding context play in explaining current period dyadic response patterns. Further, we adapt methods derived from the information retrieval literature to construct two key attributes of a query- the information content of a query and the subject popularity of the documents that form the ‘closest-match’ to the words present in a query. Using a unique data set consisting of over 20,000 queries and over 45,000 responses contributed by 2500 employees covering activity over a year, we investigate how query-attributes and network structure interact to explain query response behavior. We use recently-developed QAP-based multiple regression technique to account for problematic features of network data. Focusing on the evolution of dyadic response behavior, we find that Simmelian ties established in the first period in the course of answering non-popular queries are predictive of responses for both popular and non-popular queries in the second period in our data. We discuss managerial implications of our study from the perspective of a networked organization and IT design.

is a research scientist in Telenor R&I A promising and relatively new approach for studying distributed work groups is the application of social network techniques and theories (Ahuja and Carley 1999; Sparrowe, Liden et al. 2001; Cummings and Cross 2003; Hinds and McGrath 2006). A social network approach to distributed groups draws attention towards the social relations between the nodes in a network of distributed employees. Growth of new data coming out of on tele- and computer based traffic offers new opportunities to conduct such studies, and to analyse structures of collaboration patterns in distributed groups. Yet, the emerging field of network studies calls for a closer understanding of the relationship between patterns of mediated interaction and other more subjective relations, like trust and friendship. Based on a cognitive network approach (Krackhardt 1987; Corman and Scott 1994) this paper argues that observable interaction may be seen as indicative representations of a close relation, but not as confirmation. In particular in work settings, a high interaction level should not be seen as confirmative for close relationship, since much interaction is related to formal tasks. Interaction based ties should therefore be handled with care, and supplemented with other relational network indicators in network studies of distributed groups. The arguments are substantiated with results from a recent study of a group of distributed workers in an ICT company. 1

The importance and usefulness of social capital as a concept in the study of organizations have been established by a large body of research. The aim of this paper is to review the concept of social capital in an organizational context, and it identifies five main issues that need to be addressed in future research before social capital can move definitively beyond being merely a metaphor for advantage. First, the unresolved issue of causality is a barrier in the study of social structure and social capital alike, and hampers both measuring scales and implications drawn from empirical research. Secondly, it is necessary to determine whether social capital can or should be measured. Thirdly, the negative aspects of social capital should be explored and integrated into the existing research. Fourthly, the field between social capital of the individual and organizational social capital lacks a consistent, bridging theory. Finally, there is a lack of understanding on how social capital develops over time and the potential benefits of taking a life-cycle view of social capital. In conclusion, the field of social capital in organizations still needs a consistent and coordinated research effort in each of the mentioned areas – separately and concurrently- in order to move beyond structure and metaphor.