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Team composition research considers how configurations (e.g., team-level diversity) of team members’ attributes (e.g., personality, values, demographics) influence important outcomes. Our chapter describes key issues in understanding and effectively managing team composition over time.

We discuss how context shapes team composition. We review empirical research that examined relationships between team composition, and team processes and emergent properties over multiple time points. We review research that examined how composition can be effectively managed over the lifecycle of a team.

Context shapes the nature of team composition itself (e.g., dynamic composition). To the extent that membership change, fluid boundaries, and multiple team membership are present should be accounted for in research and practice. The research we reviewed indicated no, or fleeting effects for surface-level (e.g., demographics) composition on the development of team processes and emergent properties over time, although there were exceptions. Conversely, deep-level composition affected team processes and emergent properties early in a team’s lifespan as well as later. Team composition information can be used in staffing; it can also inform how to best leverage training, leadership, rewards, tasks, and technology to promote team effectiveness.

Teams are the building blocks of contemporary organizations. Understanding and effectively managing team composition over time can increase the likelihood of team.

Our chapter provides novel insights into key issues in understanding and effectively managing team composition over time.

Teams in extreme and disruptive contexts face unique challenges that can undermine coordination and decision-making. In this study, we evaluated how affective differences between team members and team process norms affected the team's decision-making effectiveness.

Teams were placed in a survival simulation where they evaluated how best to maximize the team's survival prospects given scarce resources. We incorporated multisource and multirater (i.e., team, observer, and archival) data to ascertain the impacts of affect asymmetry and team process norms on decision-making effectiveness.

Results suggest that teams with low positive affect asymmetry and low process norms generate the most effective decisions. The least effective team decision performance occurred in teams characterized by high variance in team positive affectivity (high positive affect asymmetry) and low process norms. We found no similar effect for teams with high process norms and no effect for negative affect asymmetry, however, irrespective of team process norms.

These findings support the affect infusion model and extend cognitive resource theory, by highlighting how affect infusion processes and situational constraints influence team decision-making in extreme and disruptive contexts.

The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing.

A conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed.

This paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing.

This study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.

The purpose of this study is to investigate information seeking behavior of immigrants in disadvantaged communities in the state of CO, USA, using school choice decisions as a problematic situation. The study investigated the extent to which immigrant families in poor neighborhoods took advantage of school choice policies and the extent to which these decisions were mediated by information seeking activities.

Quantitative study using survey questionnaire was used. The study design used everyday life information seeking behavior (Savolainen, 1995) and information-poverty lived-experiences of poor people (Chatman and Pendelton, 1995) as a framework and theoretical lens, respectively. Parents of school-age children who met the criteria of “poor” based on the federal poverty guideline were recruited to participate in the study.

The study found that only 21 per cent (24 families out of 113) participated in school choice programs. Within the smaller group of parents that took advantage of school choice policy, 72 per cent gathered information before choosing a school, and of this group, about 80 per cent asked friends followed by school visit and a phone call to schools as information sources. Library use as sources of information was also selected by 61 per cent of the respondents. One important finding of this study is that although a majority of the families are aware of school choice policy, their level of participation is low and that is largely due to their economic and life circumstances.

Access to quality education is widely investigated and multiple entities have a stake in it, including parents, policymakers, researchers and school districts. The social implications of this study are significant in that the mere presence and awareness of school choice policy did not translate into increased participation by parents from disadvantaged communities for whom the policy was designed to benefit. Consistent with findings in the extant literature, parents in low socio-economic status also value quality education for their children. To address the issue of low participation in school choice, cities need to work toward strengthening schools and library systems in poor neighborhoods instead of diverting resources away.

Based on two theoretical accounts – i.e. the information-poverty lived-experiences of poor people, or the outsiders, (Chatman, 1999, 1996), and theory of everyday life information seeking (Savolainen, 1995) – this study investigated how immigrants and disadvantaged communities seek, acquire and use information to navigate school choice policy in the city of Aurora, CO. The findings of this study are relevant for educators, policymakers, libraries, school districts, cities, counties and parents to determine the necessary policy measures that are required to increase school choice participation by immigrants and disadvantaged communities.

This study aims to investigate individual motivations for participating in collaborative health knowledge producing teams (KPTs), as well as satisfaction gained by participation in science teams. The authors focus on understanding motivators across team types, levels of engagement and alignment within and across teams and need satisfaction in a total of six science teams. Areas of strong congruence and divergence of motivating factors can be aligned across (i) learning and professional growth satisfaction, (ii) respect, collegiality and enjoyment satisfaction and (iii) accomplishment and discovery satisfaction. Levels of satisfaction are hierarchically mapped showing their relation to more external and communal foci to motivations that seek satisfaction of individual needs.

This mixed method comparative study uses data from the motivation assessment for team readiness integration and collaboration (MATRICx) tool and themes generated from semi-structured interviews. This methodology provided a means to compare a hierarchy of motivations against levels of collaborative engagement, as well as individual needs satisfaction and ultimately the identification of higher and lower-level motivations related to self and external foci.

The findings indicate that there are both similarities and differences in motivations in different health science teams when one compares biomedical, education and policy teams. A comparison of MATRICx data from across team types and teams suggests areas of strong congruence as well as areas of divergence in motivation factors.

The paper and its findings use a novel tool along with qualitative research techniques to identify motivation in KPTs and uses these data to understand what type of needs satisfaction are important across three areas of health science teaming. Our research informs team leaders, facilitators and consultants about the important motivations team members have entering teams and has the potential to provide a blueprint to ensure peak performance as individual team members seek to address their ever-growing professional needs.