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This longitudinal study used data from 91 self-managed teams (456 individuals, 60 nationalities)
to examine the interactive effects of a team’s task (“workflow”) network structure and its cultural
diversity (as indexed by nationality) on the team’s “potency” (i.e., the team’s confidence in its
ability to perform) and its performance (as rated by ex...
Citations
... Instead, they create combinations. For instance, while research has shown a positive correlation between team potency and performance (Gully et al. 2002), more recent studies advocate that the effectiveness of team potency is enhanced when integrated with other factors like task networks (Tröster et al. 2014). Similarly, the efficacy of team improvisation in fostering resilience is interdependent. ...
New venture teams (NVTs) face challenges caused by adversity, making team resilience crucial for venture survival. While team resilience factors have been identified, we do not yet understand how they combine. We examine their combinations from a configurational approach. Based on a fsQCA analysis of team resilience factors in thirty-eight NVTs and five follow-up in-depth case interviews, we explore how team resilience factors combine to form particular team resilience pathways for survival in adversity. Our analysis reveals three pathways: relaxed team improvisers, validated team visionaries, and relational team connectors. The theoretical implications include enhancing the framework regarding the resilience of NVTs and introducing a new causal mechanism based on the causal complexity of team resilience factors.
... It is also beneficial to be aware of the changing workforce demographics, from being very homogenous historically to now having greater diversity at all levels Figure 1 shows the relationship between diversity levels and team performance, with an inverted U-shaped curve indicating that performance peaks at moderate to high diversity levels. 11 Optimal diversity balance is fundamental to maximising team performance. ...
With training and qualification already presenting a challenge for trainees, we must ensure to create a positive work environment to support our colleagues.
... Typically, smaller teams foster more frequent communication among members. Therefore, team size may substantially influence the outcome of team processes linked to performance [29]. Due to this predicted influence, we incorporated team size as a control variable in the study. ...
Objective
The family physician team has become the core carrier for delivery primary health care in China. This study aimed to measure the effect of the network structural characteristics of family physician team processes on health performance. Strategic recommendations for optimizing the family physician team processes with a view to improving performance were presented.
Methods
A cross-sectional survey was conducted from October to December 2021 in Qianjiang in Hubei Province and Changsha in Hunan Province. Task performance, contextual performance, social networks, and sociodemographic characteristics were collected. Social network analysis was conducted to calculate density and centralization, then hierarchical linear regression analysis was employed to explore the relationship between the network structural characteristics of family physician team processes and performance.
Results
In total, 88 family physician teams attended in this investigation. The transition processes of family physician team showed a distinctive low density (0.272 ± 0.112), high centralization (0.866 ± 0.197) network structure. For family physician team, the density of action processes significantly and positively affected task performance (B = 0.600, P < 0.05); the centralization of action processes positively affected task performance (B = 0.604, P < 0.01); the density of action processes positively affected contextual performance (B = 0.545, P < 0.01); the density of interpersonal processes significantly and positively affected contextual performance (B = 0.326, P < 0.05).
Conclusion
The network density and centralization of family physician team processes have positive effects on chronic disease management performance. The results from this study help to enhance our conceptual understanding of social network and its implications for team-dynamics. Optimizing family physician team processes is an effective way to strengthen the construction of family physician team and promote the quality and efficiency of family physician-contracted service. It is recommended to strengthen the management of team processes, enhance the internal collaboration mechanism, and optimize the centralized network structure of family physician team.
... Network theory aids the investigation of the economic significance of networks in organizational research. The theory stresses the interconnectedness of organizations and the influence of their relationships in shaping their behavior, strategies, and responses to challenges (Thornton et al., 2013;Tröster et al., 2014). Although a consensus view of network theory is lacking, Wellman (1997) articulated five fundamental principles that provide the required intellectual footing for the theory. ...
... The integration of network theory in this research uncovers the complexity of organizational relationships and their influence on firm resilience strategies. By examining how TSOs build and engage networks within their local contexts, the study extends network theory beyond the domain of inter-organizational collaborations (Thornton et al., 2013;Tröster et al., 2014) to encompass a broader spectrum of relationships, including those rooted in cultural and community connections (Wellman, 1997;Lyth et al., 2017). It also allowed us to offer insights into the often-discounted importance of grassroots relationships in boosting TSOs' network dynamics. ...
Across the globe, third sector organizations (TSOs) have long been recognized for their significant contributions to community support and sustainable development. However, their vulnerability to socioeconomic challenges, exemplified by the Covid‐19 pandemic, prompts a deeper examination. This study employs semi‐structured interviews to explore the resilience and adaptation strategies of TSOs operating in Nigeria, a West African country facing unique challenges. Findings reveal an intricate blend of traditional and innovative approaches, including strategic capabilities, digital citizenship, cultural dynamics, and community participation, employed by TSOs to navigate the uncertainties triggered by the Covid‐19 pandemic. By uncovering these less‐explored resilience dimensions, the study offers valuable insights for TSOs, policymakers, and scholars seeking to understand and enhance organizational resilience in challenging economic and social contexts.
... network density, and group performance (for a notable exception, see Cur eu et al., 2012). Moreover, when diversity and CSCL group density have been investigated, the diversity of the group has only been examined with regard to single diversity attributes (e.g., differences between learners with regard to cultural background, Tröster et al., 2014). In many CSCL environments, however, students differ from one another not only by one or a limited number of demographic characteristic(s). ...
Many online learning environments offer opportunities for computer-supported collaborative learning (CSCL). Although the effectiveness of CSCL has been studied extensively, researchers have only recently begun to systematically examine student diversity in CSCL. Building on a theoretical integration of social psychology research with the CSCL literature, this chapter reports some key findings from an ongoing series of coordinated studies designed to assess and manage diversity effects in CSCL through social network analyses and planned interventions. We base our analyses on the observation that a defining characteristic of CSCL groups in many online learning contexts is the multi-attributional diversity of learners, that is, a combination of diversity in terms of learners’ sociodemographic characteristics and task-relevant attributes and competencies. We then present the results of a coordinated series of three empirical studies using social network analysis with digital behavioral data from 4628 distance learners in 930 groups. We use an unobtrusive assessment analytics approach to collect and analyze learner data from both formative and summative assessments, thereby mitigating the effect of potential self-report biases. Study 1 shows that the combination of high sociodemographic diversity and high task-related diversity has a significant and negative effect on group cohesion. Experimental interventions designed to attenuate this interaction effect through student grouping on single diversity attributes (Study 2) or through communication instructions (Study 3) were unsuccessful, testifying for a relatively robust and pervasive phenomenon. In our conclusion, we highlight the theoretical potentials of unobtrusive assessment analytics for understanding multi-attributional diversity effects and suggest directions for developing interventions using visualizations of group network structures for monitoring and supporting learning in CSCL groups featuring multi-attributional diversity.
... The interplay between team composition and network structure is also a critical factor in determining the success of a team's performance (Tröster et al., 2014;H.-H. Zhang et al., 2020). ...
Aim/Purpose: This mixed-methods study aims to examine factors influencing academicians’ intentions to continue using AI-based chatbots by integrating the Task-Technology Fit (TTF) model and social network characteristics. Background: AI-powered chatbots are gaining popularity across industries, including academia. However, empirical research on academicians’ adoption behavior is limited. This study proposes an integrated model incorporating TTF factors and social network characteristics like density, homophily, and connectedness to understand academics’ continuance intentions. Methodology: A qualitative study involving 31 interviews of academics from India examined attitudes and the potential role of social network characteristics like density, homophily, and connectedness in adoption. Results showed positive sentiment towards chatbots and themes on how peer groups accelerate diffusion. In the second phase, a survey of 448 faculty members from prominent Indian universities was conducted to test the proposed research model. Contribution: The study proposes and validates an integrated model of TTF and social network factors that influence academics’ continued usage intentions toward AI chatbots. It highlights the nuanced role of peer networks in shaping adoption. Findings: Task and technology characteristics positively affected academics’ intentions to continue AI chatbot usage. Among network factors, density showed the strongest effect on TTF and perceived usefulness, while homophily and connectedness had partial effects. The study provides insights into designing appropriate AI tools for the academic context. Recommendations for Practitioners: AI chatbot designers should focus on aligning features to academics’ task needs and preferences. Compatibility with academic work culture is critical. Given peer network influences, training and demonstrations to user groups can enhance adoption. Platforms should have capabilities for collaborative use. Targeted messaging customized to disciplines can resonate better with academic subgroups. Multidisciplinary influencers should be engaged. Concerns like plagiarism risks, privacy, and job impacts should be transparently addressed. Recommendation for Researchers: More studies are needed across academic subfields to understand nuanced requirements and barriers. Further studies are recommended to investigate differences across disciplines and demographics, relative effects of specific network factors like size, proximity, and frequency of interaction, the role of academic leadership and institutional policies in enabling chatbot adoption, and how AI training biases impact usefulness perceptions and ethical issues. Impact on Society: Increased productivity in academia through the appropriate and ethical use of AI can enhance quality, access, and equity in education. AI can assist in mundane tasks, freeing academics’ time for higher-order objectives like critical thinking development. Responsible AI design and policies considering socio-cultural aspects will benefit sustainable growth. With careful implementation, it can make positive impacts on student engagement, learning support, and research efficiency. Future Research: Conduct longitudinal studies to examine the long-term impacts of AI chatbot usage in academia. Track usage behaviors over time as familiarity develops. Investigate differences across academic disciplines and roles. Requirements may vary for humanities versus STEM faculty or undergraduate versus graduate students. Assess user trust in AI and how it evolves with repeated usage, and examine trust-building strategies. Develop frameworks to assess pedagogical effectiveness and ethical risks of conversational agents in academic contexts.
... Initially, factors influencing group potency have been divided in external and internal to the group [17]. The former are mainly those related to the organizational context, such as organizational goal clarity, resources and rewards provided by the organization [17], or management support [21]; the latter are those related to structural characteristics of the group [49] or group processes and interactions. For instance, it has been shown how participation [13], communication. ...
... Starting from the obtained results, there is room for improvement. The employed set of features can be extended by taking into consideration the patterns of interaction by means of structural features [49] and the contribution of the individual members. More samples are needed, to span a wider range of scenarios and the models performance can be enhanced by exploiting more sophisticated algorithms. ...
Technological research is increasingly focusing on intelligent computer systems that can elicit collaboration in groups made of a mix of humans and machines. These systems have to devise appropriate strategies of intervention in the joint action, a capability that requires them to be able of sensing group processes such as emergent states. Among those, group potency – i.e., the confidence a group has that it can be effective – has a particular relevance. An intervention targeted at increasing potency can indeed increment the overall performance of the group. As an initial step in this direction, this work addresses automated classification of potency from multimodal group behaviour. Interactions by 16 different groups displaying low or high potency were extracted from 3 already existing datasets: AMI, TA2, and GAME-ON. Logistic Regression, Support Vector Machines, and Random Forests were used for classification. Results show that all the classifiers can predict potency, and that a classifier trained with samples from 2 of the datasets can predict the label of a sample from the third dataset. ...Keywordsgroup behaviourgroup effectivenessgroup potencyemergent stateshybrid intelligence
... The questions above arise in all of the different settings where team formation and performance are important. Indeed, in online collaboration over the Web [34], creative undertakings [20], technology and science [39] and school [37], group size and the structure of social ties in the group have been reported to be of importance for the performance of teams. Although this diversity of settings already make the questions rich, they become even richer when one considers the plethora of external circumstances that can influence team formation in each of the settings. ...
... A question that has attracted considerable attention in the literature, is whether team structure influences team performance [34,20,39,37,44]. Previous studies have examined correlations between performance of teams and team size or dyadic team network structure. ...
Humans collaborate in different contexts such as in creative or scientific projects, in workplaces and in sports. Depending on the project and external circumstances, a newly formed collaboration may include people that have collaborated before in the past, and people with no collaboration history. Such existing relationships between team members have been reported to influence the performance of teams. However, it is not clear how existing relationships between team members should be quantified, and whether some relationships are more likely to occur in new collaborations than others. Here we introduce a new family of structural patterns, m-patterns, which formalize relationships between collaborators and we study the prevalence of such structures in data and a simple random-hypergraph null model. We analyze the frequency with which different collaboration structures appear in our null model and show how such frequencies depend on size and hyperedge density in the hypergraphs. Comparing the null model to data of human and non-human collaborations, we find that some collaboration structures are vastly under- and overrepresented in empirical datasets. Finally, we find that structures of scientific collaborations on COVID-19 papers in some cases are statistically significantly different from those of non-COVID-19 papers. Examining citation counts for 4 different scientific fields, we also find indications that repeat collaborations are more successful for 2-author scientific publications and less successful for 3-author scientific publications as compared to other collaboration structures.
... An alternative approach is student-selected teams with significant instructor guidance in identifying necessary skills needed for an assignment and suggesting those students who may have the right fit of personality and talent (Steger et al., 2011). Such team creation can result in diversity of gender, age, function, culture, and ethnicity (Stahl et al., 2010;Troster et al., 2014;Watson et al., 2002). ...
... When team members share the belief in the value of their team, they are willing to fight for it because they regard it as worthwhile. Team members with high team potency believe that their team is capable of overcoming future obstacles and challenges; that is, their current effort will pay off in the future (Bandura, 1977;Tröster, Mehra, & van Knippenberg, 2014). Empirical evidence also indicates that team potency is an important determinant in successfully influencing team motivation and team effectiveness (Pearce, Gallagher, & Ensley, 2002;Shelton, Waite, & Makela, 2010). ...
Drawing upon social information processing theory, this study builds a multilevel model to explore the effects of humble leader behavior on performance in teams. Time-lagged and multi-source data were gathered from 298 employees across 70 work teams. Results indicated that at the individual level, humble leader behavior was positively related to individual performance via organization-based self-esteem, while at the team level, humble leader behavior was positively related to team performance via team potency. Moreover, team cognitive diversity moderated the indirect effects of humble leader behavior on individual and team performances, such that the positive indirect effects were stronger for teams with high cognitive diversity than for those with low cognitive diversity. Implications and limitations are also discussed.