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The purpose of this paper is to inform readers on what is known on information processing in virtual teams and to discuss the consequences of these findings for the management of virtual teams.

Systematic review of the literature on information processing in virtual teams based on a general information processing model for teams.

An overview of the most relevant factors that influence the effectiveness of virtual teams is provided.

The review is based on existing literature on virtual teams and it discusses future research directions opened by the conceptualization of virtual teams as information processing systems.

The paper identifies the factors that can improve the effectiveness of information processing in virtual teams.

The general information‐processing model for teams enables a systematic integration of the fragmented literature on virtual teams.

Mental model convergence occurs as team members interact. By collecting information and observing behaviors through their interactions, team members’ individual mental models evolve into shared mental models. This process requires a cognitive shift in an individual's focal level. Specifically, the individual assigned to the team must shift his or her focus from thinking about the team domain using an individual perspective to thinking about it from a team perspective. Thus, mental model convergence may be the key to understanding how individuals are transformed into team members. This chapter presents a framework describing the mental model convergence process that draws on the extant research on group development and information processing. It also examines temporal aspects of mental model convergence, the role of mental model contents on the convergence process, and the relationship between converged mental models and team functioning. Preliminary evidence supporting the framework and the important role that converged mental models play in high-performing teams is provided. The chapter concludes with a discussion of the implications of this mental model convergence framework for research and practice.

The purpose of this paper is to investigate how different team learning activities relate to different types of team performance as rated by team members and managers.

The 624 respondents, working in 88 teams in seven different organizations indicate their perceptions of team learning and their performance ratings of the team. Moreover, managers in the organization are asked to evaluate the team performance.

Team member ratings of effectiveness are positively related to the boundedness and stability of the team and information processing and negatively related to information acquisition. Manager ratings of effectiveness are positively related to boundedness and stability, information processing and information storage and retrieval. Team member ratings of efficiency are positively related to information processing and negatively related to information acquisition. Manager ratings of efficiency are positively related to boundedness and stability and information storage and retrieval. Team member ratings of innovativeness are positively related to information processing, while no predictors are found for manager ratings of innovativeness.

Since the data are cross‐sectional, the authors cannot draw conclusion about the causality between the variables. Longitudinal designs that study the sequence of team learning and team performance are called for. Furthermore, future studies might include more objective performance measures.

As team learning proved to have predictive value for diverse team performance indicators, rated by team members and managers, team should carefully organise their learning process in order to enhance their performance.

Although some studies have proven the significance of team learning for team performance, none have investigated which team learning activities are related to which types of performance ratings.

The purpose of this study is to report on the challenges and lessons learnt by the Texas A&M University Libraries' processing team when trying to implement a “resource in common” high‐density storage unit model between Texas A&M University Library and The University of Texas‐Austin Libraries.

The case study draws on the experience of the Texas A&M University Libraries' processing team when the university funded the creation of a jointly owned remote storage unit, which foundation would rest on a “resource in common” model. The creation of a new library building allowed Texas A&M University Libraries to free up stack space in order to create new learning spaces to meet the needs of the library users. However, as the processing began, initial theories of what a “resource in common” was, how to implement a “resource in common” and resources needed to implement the “resource in common” model began to be questioned. This study examines the lessons learnt when trying to implement a “resource in common” model.

Based on the experiences of the Texas A&M University Libraries' processing team, increased communication and early participation in the decision‐making stages is key when trying to implement a “resource in common” model. This processing team was responsible for updating the MARC records for all the items that were identified to go to the joint storage unit. They recognized that any collaborative venture of this magnitude required an excellent workflow and workload understanding by all parties, including those members of the TAMU Library initial project planning team who were active members on the initial library storage facility oversight committees. This understanding would allow everyone at TAMU involved in the massive project to have a clearer knowledge of the strains caused by the increased workload and could communicate to the full team the obstacles the library would be facing.

This paper introduces the idea of “resource in common” and will be of interest to all libraries facing both space and funding shortages who might be considering building a storage unit. These libraries might consider implementing a “resource in common” model as a way to solve these problems.

Work teams have gained increasing importance as businesses shift to knowledge-based organizational structures. At the same time, advances in information technology have facilitated this change by enabling virtual work environments. To add to this complexity, the increasing demographic diversity of workers is coinciding with the rise in virtual and knowledge-based work environments. Therefore, it is critical that we understand the impact of these changes as they coincide in organizations today.

One of the extolled virtues of work teams is their potential to combine the unique knowledge held by individual workers, integrating these knowledge resources to bear on productive tasks. To effectively utilize their distributed knowledge, work teams have to perform three basic knowledge-processing activities: (a) knowledge acquisition; (b) knowledge integration; and (c) knowledge creation. However, work teams often have difficulty processing their distributed knowledge. The ability of team members, or lack thereof, to work effectively with each other is usually the problem.

The increasing demographic diversity of workers presents similar challenges for organizations. Demographically diverse workers have more unique knowledge, leading to increased knowledge differentiation in work teams. A work team that has high knowledge differentiation is one whose members possess different expertise. The unique knowledge held by individual team members effectively enlarges a work team's pool of knowledge resources. However, the increasing demographic diversity of workers often results in work teams having more difficulty processing their distributed knowledge because team members are not able to work effectively with different others. That being the case, the potential for demographically diverse work teams to more effectively perform productive tasks is lost.

We realize that demographically diverse work teams are a special (and important) case of teams in that they are both high on differentiated knowledge and high on the potential for conflict and other process losses. However, with an increasingly global marketplace, this special case is quickly becoming commonplace. Therefore, it is critical that we find ways to help demographically diverse work teams limit their process losses and realize their full potential.

Virtual work environments only heighten the need for demographically diverse work teams to minimize their process losses. Team members are often separated by both geographic space and time, which makes it even more challenging for them to work effectively with each other. In such environments, team members are often isolated from one another and find it difficult to feel a part of their team. Interestingly, computer-mediated communication has been shown to enhance team performance by helping team members communicate more effectively with each other. In fact, empirical work by Bhappu, Griffith, and Northcraft (1997) suggests that computer-mediated communication can actually help demographically diverse work teams process their distributed knowledge more effectively.

In this chapter, we will discuss the effects of demographic diversity and virtual work environments on knowledge processing in teams. More specifically, we will describe when computer-mediated communication is likely to enhance knowledge processing in demographically diverse work teams and when it is not. In doing so, we hope to provide both workers and managers with a set of guidelines on how to best navigate these organizational changes.

This paper aims to offer an integrative conceptual theory of conflict and reports on the nomological net of team conflict profiles. Specifically, it integrates social self-preservation theory with information-processing theory to better understand the occurrence of team profiles involving task conflict, relationship conflict and process conflict.

The study collected data from 178 teams performing and engineering design tasks. The multilevel nomological net that was examined consisted of constructive controversy, psychological safety and team-task performance (team level), as well as perceptions of learning, burnout and peer ratings of performance (individual level).

Findings indicated mixed support for the associations between conflict profiles and the hypothesized nomological net.

Future research should consider teams’ profiles of team conflict types rather than examining task, relationship and process conflict in isolation.

Teams can be classified into profiles of team conflict types with implications for team functioning and effectiveness. As a result, assessment and team launch should consider team conflict profiles.

The complexity perspective advanced here will allow research on conflict types to move forward beyond the extensive research examining conflict types in isolation rather than their interplay.

The purpose of this paper is to design and test an online team intervention for university students, focusing on communication, leadership and team processes, to influence team cohesion and subsequently team assignment performance. It was administered twice as a formative feedback measure and once as a summative evaluation measure across a semester.

Survey data were collected from 154 university students across four management modules in a large Australian university. Multiple regression analysis was used to test the hypotheses and open-ended questions were used to understand why the team intervention was effective.

The results showed that the implementation of an effective team intervention leads to higher levels of team cohesion and subsequently team performance. Open-ended responses revealed that the team intervention caused students to develop team-based sills and increase regular contributions.

In order to develop positive team behaviours amongst students in group assignments and increase the effectiveness of team-based learning activities, educators should implement a regular and process focused team contribution intervention, like the one proposed in this study.

This research contributes to the team intervention literature by drawing on the social information processing perspective, to demonstrate how an intervention that is based on the students’ social processing, task focused, regular implementation and formative feedback has a salient effect over team cohesion.

The purpose of this paper is to test how team regulatory focus may relate to individual creativity and team innovation; and address the fit/misfit issue of team regulatory focus and team bureaucracy.

The authors collected data from 377 members and their leaders within 56 R&D teams in two Taiwanese companies.

A team promotion focus was positively related, whereas a team prevention focus was negatively related, to both team innovation and member creativity through team perspective taking and employee information elaboration, respectively. Furthermore, team bureaucracy played a moderating role that suppressed the indirect relationship between team regulatory focus and creativity.

This is one of first studies to explore an underlying mechanism linking team regulatory focus and both team innovation and member creativity. The authors provide a more complete view of the creative and innovation implications of team-level self-regulation.

The team members’ expertise has been shown to increase team effectiveness when it is actively coordinated. While in face-to-face teams such expertise coordination unfolds through direct interaction, expertise coordination in dispersed teams is unlikely to evolve automatically. In this context, shared leadership, that is, the distribution of leadership influence across multiple team members is argued to serve as initiating mechanism for expertise coordination.

Over the past few years, mathematical and computational models of organizations have attracted a great deal of interest in various fields of scientific research (see Lin & Carley, 1993 for review). The mathematical models have focused on the problem of quantifying the structural (mis)match between organizations and their tasks. The notion of structural congruence has been generalized from the problem of optimizing distributed decision-making in structured decision networks (Pete, Pattipati, Levchuk, & Kleinman, 1998) to the multi-objective optimization problem of designing optimal organizational structures to complete a mission, while minimizing a set of criteria (Levchuk, Pattipati, Curry, & Shakeri, 1996, 1997, 1998). As computational models of decision-making in organizations began to emerge (see Carley & Svoboda, 1996; Carley, 1998; Vincke, 1992), the study of social networks (SSN) continued to focus on examining a network structure and its impact on individual, group, and organizational behavior (Wellman & Berkowitz, 1988). Most models, developed under the SSN, combined formal and informal structures when representing organizations as architectures (e.g., see Levitt et al., 1994; Carley & Svoboda, 1996). In addition, a large number of measures of structure and of the individual positions within the structure have been developed (Roberts, 1979; Scott, 1981; Wasserman & Faust, 1994; Wellman, 1991).