Advice-seeking Networks Research Articles

Communicate or not: Exploring the different effects of instrumental and expressive networks on thriving at work

Abstract Thriving at work is closely related to the way employees are embedded in their social contexts, such as the structure of their communication relations with coworkers. In previous research, communication relations have been found to negatively relate to thriving at work. However, social network theory suggests that communication relations are beneficial in obtaining resources in the workplace, which might increase thriving at work. To reconcile the seemingly conflicting mechanisms, we draw on social network theory to unpack the mechanisms underlying communication relations by considering the instrumental and expressive roles. Using a structural equation model, we investigate the indirect effects of communication networks on thriving at work via advice-seeking networks (instrumental) and friendship networks (expressive). Our findings indicate communication relations are negatively related to thriving at work via advice-seeking relations, but are positively related to thriving at work via friendship relations.

Connecting managers’ international work experience, advice networks, and subsidiary-unit performance: a social capital perspective

PurposeThis study aims to examine whether and why subsidiary-unit managers’ prior international work experiences across multinational enterprises’ (MNEs) home and host countries impact their subsidiary-unit performance, considering the mediating effect of their advice networks.Design/methodology/approachA survey on 222 subsidiary-unit managers (154 parent country nationals [PCNs] and 68 host country nationals [HCNs]) of a Korean MNE operating in China, Vietnam, Thailand, Singapore, Hungary and Slovakia was conducted. The authors analyzed the data using partial least square structural equation modeling, multigroup analysis and bootstrapping techniques.FindingsPCN subsidiary managers with more prior international work experience manage better-performing units due to the strength of the manager’s advice networks across local parties. However, for HCN subsidiary managers, this study did not find such mediating roles of the size and strength of their advice networks in the MNE home country.Originality/valueThis study provides novel insights and empirical evidence about the effect of the length of prior international work experience of subsidiary managers on their advice-seeking networks and subsidiary-unit performance. In addition, it draws on and add to social capital theory about how international work experience impacts dealing with local businesses and the relationship with corporate headquarters.

Status, cognitive overload, and incomplete information in advice-seeking networks: An agent-based model

Advice-seeking typically occurs across organizational boundaries through informal connections. By using Stochastic Actor-Oriented Models (SAOM), previous research has tried to identify the micro-level mechanisms behind these informal connections. Unfortunately, these models assume perfect network information, require agents to perform too cognitively demanding decisions, and do not account for threshold-based critical events, such as simultaneous tie changes. In the context of knowledge-intensive organizations, the shortage of high-skilled professionals could determine complex network effects given that many less-skilled professionals would seek advice from a few easily overloaded, selective high-skilled, who are also sensitive to status demotion. To capture these context-specific organizational features, we have elaborated on SAOM with an agent-based model that assumes local information, status-based tie selection, and simultaneous re-direction of multiple ties. By fitting our simulated networks to Lazega’s advice network used in previous research, we reproduced the same set of macro-level network metrics with a parsimonious model based on more empirically plausible assumptions than previous research. Our findings show the advantage of exploring multiple generative paths of network formation with different models.

Leadership Emergence through Advice-Seeking Network: A Moderated Mediation Model

Leadership Emergence through Advice-Seeking Network: A Moderated Mediation Model

Social Dynamics of Advice-Seeking: A Network Analysis of Two Residency Programs

Phenomenon: Residents interact with their peers and supervisors to ask for advice in response to complicated situations occurring during patient care. To provide a deeper understanding of workplace learning, this study explores the structure and dynamics of advice-seeking networks in two residency programs. Approach: We conducted a survey-based social network study. To develop the survey, we conducted focus group discussions and identified three main categories of advice: factual knowledge, clinical reasoning, and procedural skills. We invited a total of 49 emergency medicine and psychiatry residents who had completed at least six months of their training, to nominate their supervisors and peer residents, as their sources of advice, from a roster. Participants identified the number of occasions during the previous month that they turned to each person to seek advice regarding the three broad categories. We calculated the density, centrality, and reciprocity measures for each advice category at each department. Findings: The response rates were 100% (n = 21) and 85.7% (n = 24) in the emergency medicine and psychiatry departments, respectively. The advice network of emergency medicine residents was denser, less hierarchical, and less reciprocated compared to the psychiatry residents’ network. In both departments, PGY-1s were the top advice-seekers, who turned to PGY-2s, PGY-3s, and supervisors for advice. The "procedural skills" network had the lowest density in both departments. There was less overlap in the sources of advice for different advice types in the psychiatry department, implying more selectivity of sources. Insights: Complex social structures and dynamics among residents vary by discipline and level of seniority. Program directors can develop tailored educational interventions informed by their departments’ specific network patterns to promote a timely and effective advice-seeking culture which in turn, could lead to optimally informed patient care.

The importance of proximity dimensions in agricultural knowledge and innovation systems: The case of banana disease management in Rwanda

CONTEXTSocial networks play an important role in the diffusion of knowledge, and farmers draw on their personal networks to enhance their adaptive capacity to shocks. Different forms of proximity have been long recognized as important factors in knowledge and information exchanges. However, the specific roles and their interactions in agricultural knowledge and innovation systems (AKISs) are still far from clear. In this study, we investigate the underlying forces that drive tie formation within the knowledge-sharing networks of banana farmers in four different villages in Rwanda. OBJECTIVEOur study has three objectives: First, we discuss the importance of various types of proximities in AKIS research. Second, we empirically contribute to how different forms of proximity influence the way knowledge diffuses in formal and informal networks by studying a plant disease's management. Finally, we discuss our findings' relevance for targeted interventions to help rural communities transition to greater resilience. METHODSWe review different proximity concepts and adapt them for use within an AKIS context. We then apply this framework to assess the proximity effects on the advice-seeking networks of banana farmers in four purposefully chosen villages in Rwanda. We used a structured questionnaire to collect social network information about the management of banana Xanthomonas wilt (BXW), from all banana growers (N = 491) in these four villages. We distinguished the informal advice networks among farmers from the official government extension system—the formal advice network. We employed exponential random-graph models to assess the determinants of the networks we observed, especially geographical, cognitive and social proximity indices. RESULTS AND CONCLUSIONSWe found that geographical proximity significantly affects knowledge exchange within larger villages' informal advice networks; but not in smaller villages, where both cognitive and social proximities play substantial roles. We argue that farmers are socially closer in smaller communities where geographical distance does not matter, and that geographical distance only starts to matter after a certain community size threshold is reached. SIGNIFICANCEWe provide solid empirical evidence to help plan targeted interventions toward greater resilience for rural communities. We argue that properly integrating informal social networks can result in more effective knowledge exchange within AKISs, enhancing their resilience.

A hierarchical latent space network model for mediation

AbstractFor interventions that affect how individuals interact, social network data may aid in understanding the mechanisms through which an intervention is effective. Social networks may even be an intermediate outcome observed prior to end of the study. In fact, social networks may also mediate the effects of the intervention on the outcome of interest, and Sweet (2019) introduced a statistical model for social networks as mediators in network-level interventions. We build on their approach and introduce a new model in which the network is a mediator using a latent space approach. We investigate our model through a simulation study and a real-world analysis of teacher advice-seeking networks.

Hospital managers’ communication networks: A mixed-method study

This study analyzed the problem-solving and advice-seeking networks of hospital managers working in hospitals affiliated to a university of medical sciences in Iran. This was a mixed-method study and was conducted in two stages. Initially, by conducting a qualitative study, key actors related to the hospital’s managers’ problem-solving and information/advice-seeking networks were identified and the next stage was conducted using social network analysis to identify powerful actors in the network. A questionnaire was used to collect data related to network analysis. Network properties were analyzed using density, degree centrality, and betweenness centrality. When hospital managers need help to solve work-related problems, they usually ask the university’s budget experts, followed by the University’s budget manager, the University’s financial manager, and the University’s deputy of management development and resources. In the financial advice-seeking network, the most common target of managers was the University’s financial manager, University’s budget manager, and University’s budget experts. This means that hospital managers usually seek financial advice from these people. Hospital Managers need to have a better understanding of invisible relationships because these relationships improve or may worsen collaboration and enable knowledge sharing in the organization and thus influence the decision-making process of managers.

Social network analysis: Understanding nurses' advice-seeking interactions.

This study aimed to determine advice-seeking interactions of nurses in a private hospital by using social network analysis. This study was designed as a cross-sectional descriptive study. The study was conducted in a private hospital with 70 nurses. The data were collected with a social network analysis questionnaire. The social network analysis (SNA) focused on certain values such as network density, component, degree centrality, and betweenness centrality. The SNA was carried out using UCINET, and statistical analyses were performed with SPSS version 23.0. The network density was reported to be 0.062, and it was composed of three components. It was further noted that nurse Y1 was found to have the highest scores of degree and betweenness centrality. Chi-Square Automatic Interaction Detector (CHAID) analysis indicated that the most common variables that affected degree centrality score were education, department, and position. It was concluded that social network analysis was a useful instrument to delineate strengths and weaknesses of seeking advice relationships among nurses. Top- and middle-level nursing managers occupy a significant position in advice-seeking networks. Nursing managers with higher education degrees will absolutely improve advice-seeking networks.

A Relational Understanding of Co-Educating and Learning: Information Sharing and Advice Seeking Behavior in a Dairy Cooperative in Gujarat, India

• Co-education and learning are an essential principle of the ICA’s statement on Cooperative Identity. • Social embeddedness and social capital play a crucial role in promoting knowledge sharing and learning behaviour among cooperative members. • Knowledge-based and position-based hierarchies play prominent roles in information sharing and advice sharing. • Just being the leader in the collective does not earn members’ trust. Members seek help or collaborate with leaders who have the required knowledge and skills to solve their issues. This article, building on the 5 th principle of the Statement of the Cooperative Identity explores the practice of co-educating and learning behavior among the members of a dairy cooperative society. The study adopts a social network approach by focusing on two relational social ties: information sharing and advice-seeking . Building on a case of a 55-year-old dairy cooperative in India, the social network analysis finds information-sharing relations among members to be inclusive, with no members isolated, and information exchanged along formal and knowledge hierarchies. Likewise, information sharing is horizontal, with information smoothly exchanged even among new and already established members, as is the transfer of leadership roles from the older members of the cooperatives to the younger members. Advice-seeking networks tend to be a bit more centralized toward board members and members exerting day-to-day leadership because of their knowledge and expertise regarding the organization and its policies. Our analysis suggests inclusive information sharing among members and a well-structured and functioning advice and knowledge-sharing network that contributes to learning of the cooperative members.

Analysis of Longitudinal Advice-Seeking Networks Following Implementation of High Stakes Testing

Abstract Teacher interactions around instructional practices have been a topic of study for a long time. Previous studies concerning such interactions have focused on questions pertaining to cross-sectional networks. In fact, very few studies have considered longitudinal networks and still fewer have employed longitudinal network models to study changes in such interactions. We analyse teachers’ advice-seeking networks, observed annually between 2010 and 2013, in schools within a district where several initiatives were implemented starting in 2011. We assess whether formal structures, teaching assignment and leadership position, and teacher characteristics, gender and experience, are associated with advice-seeking ties, and the extent to which these associations change over time. To analyse the advice-seeking networks, we implement a Bayesian longitudinal latent space network model with covariates and random sender-receiver effects. Within the Bayesian framework, we address practical aspects of a principled network analysis such as missing ties and yearly immigration and emigration of teachers. Goodness of model fit assessment is conducted using posterior predictive checks. Our results demonstrate that while some of the associations between observed covariates and teachers’ interactions varied in 2011, most were otherwise stable. In 2011, we found decreases in the associations with same grade assignment, leadership position, and teaching in the same school.

A social network analysis of interactions about physical activity and nutrition among APPLE schools staff.

Comprehensive school health (CSH) is a holistic approach to school-based health promotion that involves active participation and buy-in of school community members, including school staff (e.g., support staff, teachers, school health champions, principals). Implementation and sustainability of CSH builds on complex relationships within the school that support school-level health promoting changes and understanding the social relationships that exist in a school setting is critical. Thus, the purpose of this study was to conduct a social network analysis to examine adviceseeking networks of staff within three schools involved with a CSH program called APPLE Schools (A Project Promoting healthy Living for Everyone in Schools) project approach. The degree to which school staff were central in the network (i.e., gave or sought physical activity or nutrition advice, were connected or disconnected to others; indegree/outdegree centrality and betweenness centrality) and the overall structure of the networks were assessed (i.e., optimal levels of density and centralization). School health champions and several other individuals in the network were shown to be key sources of physical activity or nutrition advice and were identified as central players in the network. Whole networks across schools had low density and betweenness centralization, with optimal levels of out-centralization, and low to optimal levels of incentralization. This research allowed us to gain an understanding of network structures and relationship patterns in CSH schools, with specific attention to the coordinating role of school health champions, and other central players within the network. These findings increase our understanding of advice relationships that exist in a school setting and how these relationships may support CSH implementation and sustainability.

The role of motivation in the diffusion of innovations in Canada\u2019s long-term care sector: a qualitative study

BackgroundLong-term care facilities offer shelter and care for Canadian seniors; however, there are great variances in the quality of care that is provided to older adults across facilities. One factor that could contribute to this variation in quality is the diffusion and implementation of advice and innovations within this sector. This study sought to understand the motivations of identified opinion leaders within the Canadian long-term care sector to disseminate advice within their social networks. Research questions addressed specific drivers of motivation and the potential outcomes of having motivated opinion leaders present within interpersonal advice-seeking networks with respect to diffusion and implementation of innovations in the Canadian long-term care sector.MethodsThis secondary analysis study analyzed semi-structured qualitative interviews with opinion leaders (n = 13) and advice seekers of opinion leaders (n = 13) from a national, social network study, Advice Seeking Networks in Long Term Care (Cranley et al. 2019; Dearing et al. 2017). Constant comparison analysis was used and supported by a theoretical framework developed from diffusion of innovation theory and the COM-B framework.ResultsThe motivations of opinion leaders in the Canadian long-term care sector were represented across seven themes: obligations of the position, value of education, systemness, relationships, supportiveness, passion, and caring nature.ConclusionsThis research provides further evidence that opinion leaders in the long-term care sector are motivated individuals and that they are using this motivation as a driver to create change and improve care practices. As residents of the long-term care sector continue to increase in number and complexity, the presence of motivated opinion leaders represents a promising outlook for the future through achieving specific outcomes such as the diffusion and implementation of innovations, an increased sense of community within the network, and increased readiness for the future.

Advice seeking and loaning of money related to relapse in recovery homes.

Recovery homes help individuals who have completed substance use treatment programs re-integrate back into the community. However, it is unclear what factors determine who will succeed in these settings and how these factors may be reinforced or undermined by the social interactions and social networks between residents living in the Oxford House recovery homes. In an effort to better understand these factors, the current study evaluated (a) the extent to which the density of social networks (i.e., friendship, willingness to loan money, and advice-seeking relationships) is associated with social capital (i.e., sense of community, quality of life, hopefulness, self-efficacy), and (b) whether the density of social networks predicts relapse over time. Among the findings, willingness to loan money was positively associated with all four individual-level social capital variables, suggesting that availability of instrumental resources may be important to ongoing recovery. To test whether these house-level social network factors then support recovery, a survival analysis was conducted, finding associations between relapse risk and the network densities over a 28-month span. In particular, more dense advice-seeking networks were associated with higher rates of relapse, suggesting that the advice-seeking might represent a sign of organisational house problems, with many residents unsure of issues related to their recovery. In contrast, more dense loaning networks were associated with less relapse, so willingness to lend money could be measuring a willingness to help those in need. The implications of these findings are discussed.

Association of Implementation and Social Network Factors With Patient Safety Culture in Medical Homes: A Coincidence Analysis.

The patient-centered medical home (PCMH) may provide a key model for ambulatory patient safety. Our objective was to explore which PCMH and patient safety implementation and social network factors may be necessary or sufficient for higher patient safety culture. This was a cross-case analysis study in 25 diverse U.S. PCMHs. Data sources included interviews of a clinician and an administrator in each PCMH, surveys of clinicians and staff, and existing data on the PCMHs' characteristics. We used coincidence analysis, a novel method based on set theory and Boolean logic, to evaluate relationships between factors and the implementation outcome of patient safety culture. The coincidence analysis identified 5 equally parsimonious solutions (4 factors), accounting for all practices with higher safety culture. Three solutions contained the same core minimally sufficient condition: the implementation factor leadership priority for patient safety and the social network factor reciprocity in advice-seeking network ties (advice-seeking relationships). This minimally sufficient condition had the highest coverage (5/7 practices scoring higher on the outcome) and best performance across solutions; all included leadership priority for patient safety. Other key factors included self-efficacy and job satisfaction and quality improvement climate. The most common factor whose absence was associated with the outcome was a well-functioning process for behavioral health. Our findings suggest that PCMH safety culture is higher when clinicians and staff perceive that leadership prioritizes patient safety and when high reciprocity among staff exists. Interventions to improve patient safety should consider measuring and addressing these key factors.

Role of Policy in Best-Practice Dissemination: Informal Professional Advice Networks in Canadian Long-Term Care.

This article examines provincial policy influence on long-term care (LTC) professionals' advice-seeking networks in Canada's Maritime provinces. The effects of facility ownership, geography, and region-specific political landscapes on LTC best-practice dissemination are examined. We used sociometric statistics and network sociograms, calculated from surveys with 169 senior leaders in LTC facilities, to identify advice-seeking network structures and to select 11 follow-up interview participants.Network structures were distinguished by density, sub-group number, opinion leader, and boundary spanner distribution. Network structure was affected by ownership model in Nova Scotia and Prince Edward Island, and by regional geography in New Brunswick. Political instability within each province's LTC system negatively affected network actors' capabilities to enact innovation. Moreover, provincial policy variations influence advice-seeking network structures, facilitating and constraining relationship development and networking. Consequently, local policy context is essential to informing dissemination strategy design or implementation.

On the validity of perceived social structure

The validity of survey-based reports of social relationships is a critical assumption for much social network research. Research on informant accuracy has shown that observational data and recalled behavior by informants are imperfectly correlated, which calls into question whether complex relations like friendship and advice-seeking can be accurately measured from individual reports. A class of network inference models, the Bayesian Network Accuracy Models, growing out of the pioneering work of Batchelder and Romney on inference from informant reports, provides a principled basis for inferring network structure given such error-prone data. Using these models, we can gain insight into the accuracy of informants’ self and proxy reports of social ties, and more broadly, the reliability and validity of respondents’ reports of informal social relations. While existing data does not provide a criterion validity check for inferring most relationships, other notions of validity and/or reliability can still be applied. For instance, if friendship reports are generated from a common underlying network that is perceivable (albeit imperfectly) by all actors, then random subsets of actors should produce estimates that should agree (i.e., split-half reliability). Using informant reports on friendship and advice-seeking networks from four different organizations, we show substantially higher levels of split-half reliability than can be explained by chance, suggesting that models are indeed estimating a common underlying relation. We also show that informants’ errors appear to be structured in ways that are consistent with cognitive models of social perception, with greater accuracy on average for large-scale network features rather than fine details, for own versus others’ ties, and for core–periphery structures versus bipartitions. Evidence from construct validity checks further suggests the that common networks underlying informants’ reports have properties that would be expected of true social structures. Taken together, our findings support the view that informants’ mental models of social structure, while error-prone, nevertheless reflect an underlying social reality.

A Latent Space Network Model for Social Influence.

Social network data represent interactions and relationships among groups of individuals. One aspect of social interaction is social influence, the idea that beliefs or behaviors change as a result of one's social network. The purpose of this article is to introduce a new model for social influence, the latent space model for influence, which employs latent space positions so that individuals are affected most by those who are "closest" to them in the latent space. We describe this model along with some of the contexts in which it can be used and explore the operating characteristics using a series of simulation studies. We conclude with an example of teacher advice-seeking networks to show that changes in beliefs about teaching mathematics may be attributed to network influence.

School staff advice-seeking patterns regarding support for vulnerable students

PurposeSupporting the learning and wellbeing of vulnerable students is an important yet challenging part of school educators’ work. The purpose of this paper is to investigate advice-seeking patterns around the issue of supporting the learning and wellbeing of vulnerable students, among professional staff in six English secondary schools. The paper focuses on investigating variation in advice-seeking patterns among schools, exploring the association between these patterns and staff perceptions of the school climate for collaboration, and examining how these informal advice-seeking patterns relate to formal support arrangements in the schools.Design/methodology/approachA mixed-methods approach that combined findings from social network analysis with in-depth interviews was used.FindingsIt was found that advice-seeking patterns among staff vary substantively, even among similar schools. Furthermore, schools with more cohesive and reciprocal advice networks also showed a stronger climate for collaboration (i.e. mutual respect and distributed leadership). Also, formal organizational structures and informal advice-seeking structures showed coherence in the sample, as formally designated leaders, such as the Headteacher and the Special Educational Needs Coordinators, were generally highly central to their schools’ advice network.Originality/valueThis study advances the field as there is little research that examines the social networks of educators in England, and no previous studies that explore teacher advice-seeking networks in relation to supporting vulnerable students, internationally.

Clustering ensembles of social networks

AbstractRecently there has been significant work in the social sciences involving ensembles of social networks, that is, multiple, independent, social networks such as students within schools or employees within organizations. There remains, however, very little methodological work on exploring these types of data structures. We present methods for clustering social networks with observed nodal class labels, based on statistics of walk counts between the nodal classes. We extend this method to consider only non-backtracking walks, and introduce a method for normalizing the counts of long walk sequences using those of shorter ones. We then present a method for clustering networks based on these statistics to explore similarities among networks. We demonstrate the utility of this method on simulated network data, as well as on advice-seeking networks in education.