Survey data on customer two-stage decision-making process in household vacuum cleaner market.

This paper presents the data collection method and introduces the dataset about consumers' consider-then-choose behaviors in the household vacuum cleaner market. First, we designed a questionnaire that collected participants' consideration and choice data, social network data, demographic information, and preferences for product features. In addition, we obtained data on vacuum cleaner product features through web scraping from online shopping websites. After data cleaning and processing, the resulting dataset enables investigation into customer preferences in two stages, namely the consideration and choice stages and the impact of social influence on the two-stage decision-making process. This dataset is unique as it is the first of its kind to collect both customers' revealed preferences in a two-stage decision-making process and their ego social networks. This enables the modeling of customer preferences while accounting for social influence. The published survey questionnaire can be used as a template to collect data on other products in support of customer preferences modeling and the design for market systems.

Organizing for Mars: A Task Management Perspective on Work within Spaceflight Multiteam Systems.

OBJECTIVE: The aim of this study was to examine how task, social, and situational factors shape work patterns, information networks, and performance in spaceflight multiteam systems (MTSs). BACKGROUND: Human factors research has explored the task and individual characteristics that affect decisions regarding when and in what order people complete tasks. We extend this work to understand how the social and situational factors that arise when working in MTSs affect individual work patterns. METHODS: We conducted a complex multi-site space analog simulation with NASA over the course of 3 years. The MTS task required participants from four teams (Geology, Robotics, Engineering, and Human Factors) to collaborate to design a well on Mars. We manipulated the one-way communication delay between the crew and mission support: no time lag, 60-second lag, and 180-second lag. RESULTS: The study revealed that team and situational factors exert strong effects: members whose teams have less similar mental models, those whose teams prioritize their team goal over the MTS goal, and those working in social isolation and/or under communication delay engage longer on tasks. Time-on-task positively predicts MTS information networks, which in turn positively predict MTS performance when communication occurs with a delay, but not when it occurs in real-time. CONCLUSION: Our findings contribute to research on task management in the context of working in teams and multiteam systems. Team and situational factors, along with task factors, shape task management behavior. APPLICATION: Social and situational factors are important predictors of task management in team contexts such as spaceflight MTSs.

In search of diverse and connected teams: A computational approach to assemble diverse teams based on members' social networks.

Previous research shows that teams with diverse backgrounds and skills can outperform homogeneous teams. However, people often prefer to work with others who are similar and familiar to them and fail to assemble teams with high diversity levels. We study the team formation problem by considering a pool of individuals with different skills and characteristics, and a social network that captures the familiarity among these individuals. The goal is to assign all individuals to diverse teams based on their social connections, thereby allowing them to preserve a level of familiarity. We formulate this team formation problem as a multi-objective optimization problem to split members into well-connected and diverse teams within a social network. We implement this problem employing the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which finds team combinations with high familiarity and diversity levels in O(n2) time. We tested this algorithm on three empirically collected team formation datasets and against three benchmark algorithms. The experimental results confirm that the proposed algorithm successfully formed teams that have both diversity in member attributes and previous connections between members. We discuss the benefits of using computational approaches to augment team formation and composition.

Evidence-based recommendations for communicating the impacts of climate change on health.

Climate change poses a multifaceted, complex, and existential threat to human health and well-being, but efforts to communicate these threats to the public lag behind what we know how to do in communication research. Effective communication about climate change's health risks can improve a wide variety of individual and population health-related outcomes by: (1) helping people better make the connection between climate change and health risks and (2) empowering them to act on that newfound knowledge and understanding. The aim of this manuscript is to highlight communication methods that have received empirical support for improving knowledge uptake and/or driving higher-quality decision making and healthier behaviors and to recommend how to apply them at the intersection of climate change and health. This expert consensus about effective communication methods can be used by healthcare professionals, decision makers, governments, the general public, and other stakeholders including sectors outside of health. In particular, we argue for the use of 11 theory-based, evidence-supported communication strategies and practices. These methods range from leveraging social networks to making careful choices about the use of language, narratives, emotions, visual images, and statistics. Message testing with appropriate groups is also key. When implemented properly, these approaches are likely to improve the outcomes of climate change and health communication efforts.

Climate change poses a tremendous and complex threat to human health and well-being. Efforts to communicate these threats to the public may not be as effective as desired and using evidence-based strategies could improve a wide variety of health-related outcomes for individuals and society while potentially reducing climate-related health disparities. In particular, effective communication can help people understand the crucial connection between climate change and health risks and empower them to act on that newfound knowledge and understanding. We recommend 11 communication methods that have been well tested in other domains and can be applied to the intersection of climate and health by healthcare professionals, decisionmakers, governments, the general public, and other stakeholders including those in sectors outside of health. These methods range from leveraging social networks to making careful choices about the use of language, narratives, emotions, visual images, and statistics. Message testing with appropriate groups is also key. When implemented properly, these approaches are likely to improve knowledge uptake and drive better decision making and healthier behaviors.

Teammate Invitation Networks: The Roles of Recommender Systems and Prior Collaboration in Team Assembly.

Teammate invitation networks are foundational for team assembly, and recommender systems (similar to dating websites, but for selecting potential teammates) can aid the formation of such networks. This paper extends Hinds, Carley, Krackhardt, and Wholey's (2000) influential model of team member selection by incorporating online recommender systems. Exponential random graph modeling of two samples (overall N = 410; 63 teams; 1,048 invitations) shows the invitation network is predicted by online recommendations, beyond previously-established effects of prior collaboration/familiarity, skills/competence, and homophily. Importantly, online recommendations are less heeded when there is prior collaboration (effect replicates across samples). This study highlights technology-enabled team assembly from a network perspective.

Teamwork in the time of COVID-19: Creating, dissolving, and reactivating network ties in response to a crisis.

The Coronavirus disease (COVID-19) pandemic has prompted an unprecedented shift to remote work. Workers across the globe have used digital technologies to connect with teammates and others in their organizations. In what ways did the COVID-19 crisis alter the frequency and balance of internal and external team interactions? During a crisis, networking offers a type of goal-directed behavior through which individuals source and provide information. We can understand how people use their network through the lens of network churn, changes in embeddedness brought on by the creation, dissolution, and/or reactivation of network ties. higher We posit that performing individuals exhibit distinct networking strategies as compared to lower performing employees during the pandemic. We present a field study conducted in a multinational industrial manufacturing company in China investigating network churn during the COVID-19 pandemic. Findings show that, during a crisis, job performance is positively related to the volume of inter-team tie creation and inter-team tie reactivation, but not intra-team tie creation and intra-team tie reactivation. Job performance is not related to the volume of intra- and inter-team tie dissolution. The study provides early, yet important insights into the interplay between crisis and organizational social networks. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Using Trellis software to enhance high-quality large-scale network data collection in the field.

Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis' ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location.

Measuring algorithmically infused societies.

It has been the historic responsibility of the social sciences to investigate human societies. Fulfilling this responsibility requires social theories, measurement models and social data. Most existing theories and measurement models in the social sciences were not developed with the deep societal reach of algorithms in mind. The emergence of 'algorithmically infused societies'-societies whose very fabric is co-shaped by algorithmic and human behaviour-raises three key challenges: the insufficient quality of measurements, the complex consequences of (mis)measurements, and the limits of existing social theories. Here we argue that tackling these challenges requires new social theories that account for the impact of algorithmic systems on social realities. To develop such theories, we need new methodologies for integrating data and measurements into theory construction. Given the scale at which measurements can be applied, we believe measurement models should be trustworthy, auditable and just. To achieve this, the development of measurements should be transparent and participatory, and include mechanisms to ensure measurement quality and identify possible harms. We argue that computational social scientists should rethink what aspects of algorithmically infused societies should be measured, how they should be measured, and the consequences of doing so.

Data analysis and modeling pipelines for controlled networked social science experiments.

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

Assessing the stability of egocentric networks over time using the digital participant-aided sociogram tool Network Canvas.

This paper examines the stability of egocentric networks as reported over time using a novel touchscreen-based participant-aided sociogram. Past work has noted the instability of nominated network alters, with a large proportion leaving and reappearing between interview observations. To explain this instability of networks over time, researchers often look to structural embeddedness, namely the notion that alters are connected to other alters within egocentric networks. Recent research has also asked whether the interview situation itself may play a role in conditioning respondents to what might be the appropriate size and shape of a social network, and thereby which alters ought to be nominated or not. We report on change in these networks across three waves and assess whether this change appears to be the result of natural churn in the network or whether changes might be the result of factors in the interview itself, particularly anchoring and motivated underreporting. Our results indicate little change in average network size across waves, particularly for indirect tie nominations. Slight, significant changes were noted between waves one and two particularly among those with the largest networks. Almost no significant differences were observed between waves two and three, either in terms of network size, composition, or density. Data come from three waves of a Chicago-based panel study of young men who have sex with men.

Author Correction: Prior shared success predicts victory in team competitions.

In the version of this article initially published, errors occurred in the Acknowledgments.

Prior shared success predicts victory in team competitions.

Debate over the impact of team composition on the outcome of a contest has attracted sports enthusiasts and sports scientists for years. A commonly held belief regarding team success is the superstar effect; that is, including more talent improves the performance of a team1. However, studies of team sports have suggested that previous relations and shared experiences among team members improve the mutual understanding of individual habits, techniques and abilities and therefore enhance team coordination and strategy2-9. We explored the impact of within-team relationships on the outcome of competition between sports teams. Relations among teammates consist of two aspects: qualitative and quantitative. While quantitative aspects measure the number of times two teammates collaborated, qualitative aspects focus on 'prior shared success'; that is, whether teamwork succeeded or failed. We examined the association between qualitative team interactions and the probability of winning using historical records from professional sports-basketball in the National Basketball Association, football in the English Premier League, cricket in the Indian Premier League and baseball in Major League Baseball-and the multiplayer online battle game Defense of the Ancients 2. Our results show that prior shared success between team members significantly improves the odds of the team winning in all sports beyond the talents of individuals.

How Team Interlock Ecosystems Shape the Assembly of Scientific Teams: A Hypergraph Approach.

Today's most pressing scientific problems necessitate scientific teamwork; the increasing complexity and specialization of knowledge render "lone geniuses" ill-equipped to make high-impact scientific breakthroughs. Social network research has begun to explore the factors that promote the assembly of scientific teams. However, this work has been limited by network approaches centered conceptually and analytically on "nodes as people," or "nodes as teams." In this paper, we develop a ' team-interlock ecosystem' conceptualization of collaborative environments within which new scientific teams, or other creative team-based enterprises, assemble. Team interlock ecosystems comprise teams linked to one another through overlapping memberships and/or overlapping knowledge domains. They depict teams, people, and knowledge sets as nodes, and thus, present both conceptual advantages as well as methodological challenges. Conceptually, team interlock ecosystems invite novel questions about how the structural characteristics of embedding ecosystems serve as the primordial soup from which new teams assemble. Methodologically, however, studying ecosystems requires the use of more advanced analytics that correspond to the inherently multilevel phenomenon of scientists nested within multiple teams. To address these methodological challenges, we advance the use of hypergraph methodologies combined with bibliometric data and simulation-based approaches to test hypotheses related to the ecosystem drivers of team assembly.

Mapping the Growing Discipline of Dissemination and Implementation Science in Health.

BACKGROUND: The field of dissemination and implementation (D&I) research in health has grown considerably in the past decade. Despite the potential for advancing the science, limited research has focused on mapping the field. METHODS: We administered an online survey to individuals in the D&I field to assess participants' demographics and expertise, as well as engagement with journals and conferences, publications, and grants. A combined roster-nomination method was used to collect data on participants' advice networks and collaboration networks; participants' motivations for choosing collaborators was also assessed. Frequency and descriptive statistics were used to characterize the overall sample; network metrics were used to characterize both networks. Among a sub-sample of respondents who were researchers, regression analyses identified predictors of two metrics of academic performance (i.e., publications and funded grants). RESULTS: A total of 421 individuals completed the survey, representing a 30.75% response rate of eligible individuals. Most participants were White (n = 343), female (n = 284, 67.4%), and identified as a researcher (n = 340, 81%). Both the advice and the collaboration networks displayed characteristics of a small world network. The most important motivations for selecting collaborators were aligned with advancing the science (i.e., prior collaborators, strong reputation, and good collaborators) rather than relying on human proclivities for homophily, proximity, and friendship. Among a sub-sample of 295 researchers, expertise (individual predictor), status (advice network), and connectedness (collaboration network) were significant predictors of both metrics of academic performance. CONCLUSIONS: Network-based interventions can enhance collaboration and productivity; future research is needed to leverage these data to advance the field.

Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants.

While much social network data exists online, key network metrics for high-risk populations must still be captured through self-report. This practice has suffered from numerous limitations in workflow and response burden. However, advances in technology, network drawing libraries and databases are making interactive network drawing increasingly feasible. We describe the translation of an analog-based technique for capturing personal networks into a digital framework termed netCanvas that addresses many existing shortcomings such as: 1) complex data entry; 2) extensive interviewer intervention and field setup; 3) difficulties in data reuse; and 4) a lack of dynamic visualizations. We test this implementation within a health behavior study of a high-risk and difficult-to-reach population. We provide a within-subjects comparison between paper and touchscreens. We assert that touchscreen-based social network capture is now a viable alternative for highly sensitive data and social network data entry tasks.

The effects of diversity and network ties on innovations: The emergence of a new scientific field.

This study examines the influence of different types of diversity, both observable and unobservable, on the creation of innovative ideas. Our framework draws upon theory and research on information processing, social categorization, coordination, and homophily to posit the influence of cognitive, gender, and country diversity on innovation. Our longitudinal model is based on a unique dataset of 1,354 researchers who helped create the new scientific field of Oncofertility, by collaborating on 469 publications over a four-year period. We capture the differences among researchers along cognitive, country and gender dimensions, as well as examine how the resulting diversity or homophily influences the formation of collaborative innovation networks. We find that innovation, operationalized as publishing in a new scientific discipline, benefits from both homophily and diversity. Homophily in country of residence and working with prior collaborators help reduce uncertainty in the interactions associated with innovation, while diversity in knowledge enables the recombinant knowledge required for innovation.

Circadian Rhythms in Socializing Propensity.

Using large-scale interaction data from a virtual world, we show that people's propensity to socialize (forming new social connections) varies by hour of the day. We arrive at our results by longitudinally tracking people's friend-adding activities in a virtual world. Specifically, we find that people are most likely to socialize during the evening, at approximately 8 p.m. and 12 a.m., and are least likely to do so in the morning, at approximately 8 a.m. Such patterns prevail on weekdays and weekends and are robust to variations in individual characteristics and geographical conditions.

Social network approaches to leadership: an integrative conceptual review.

Contemporary definitions of leadership advance a view of the phenomenon as relational, situated in specific social contexts, involving patterned emergent processes, and encompassing both formal and informal influence. Paralleling these views is a growing interest in leveraging social network approaches to study leadership. Social network approaches provide a set of theories and methods with which to articulate and investigate, with greater precision and rigor, the wide variety of relational perspectives implied by contemporary leadership theories. Our goal is to advance this domain through an integrative conceptual review. We begin by answering the question of why-Why adopt a network approach to study leadership? Then, we offer a framework for organizing prior research. Our review reveals 3 areas of research, which we term: (a) leadership in networks, (b) leadership as networks, and (c) leadership in and as networks. By clarifying the conceptual underpinnings, key findings, and themes within each area, this review serves as a foundation for future inquiry that capitalizes on, and programmatically builds upon, the insights of prior work. Our final contribution is to advance an agenda for future research that harnesses the confluent ideas at the intersection of leadership in and as networks. Leadership in and as networks represents a paradigm shift in leadership research-from an emphasis on the static traits and behaviors of formal leaders whose actions are contingent upon situational constraints, toward an emphasis on the complex and patterned relational processes that interact with the embedding social context to jointly constitute leadership emergence and effectiveness.

Integrating social networks and human social motives to achieve social influence at scale.

The innovations of science often point to ideas and behaviors that must spread and take root in communities to have impact. Ideas, practices, and behaviors need to go from accepted truths on the part of a few scientists to commonplace beliefs and norms in the minds of the many. Moving from scientific discoveries to public good requires social influence. We introduce a structured influence process (SIP) framework to explain how social networks (i.e., the structure of social influence) and human social motives (i.e., the process of social influence wherein one person's attitudes and behaviors affect another's) are used collectively to enact social influence within a community. The SIP framework advances the science of scientific communication by positing social influence events that consider both the "who" and the "how" of social influence. This framework synthesizes core ideas from two bodies of research on social influence. The first is network research on social influence structures, which identifies who are the opinion leaders and who among their network of peers shapes their attitudes and behaviors. The second is research on social influence processes in psychology, which explores how human social motives such as the need for accuracy or the need for affiliation stimulate behavior change. We illustrate the practical implications of the SIP framework by applying it to the case of reducing neonatal mortality in India.