Background
Term | Features | Motivation | Reference |
---|---|---|---|
Boundary spanner | Bridges the structural hole between two clusters conceptualised as being separated by a boundary of some sort, e.g. outside the network or department | To overcome a boundary and facilitate communication / knowledge flow across it. | |
Bridge | Bridges the structural hole between two clusters | To include outsiders in information flows or achieve coordination. | |
Broker | Acts as an intermediary between two unlinked actors / clusters | To facilitate some transaction, resolve a conflict or increase personal power or social capital. | |
Broker in a Structural fold | The broker is the common actor in two overlapping, cohesive clusters | To be an engaged member of two groups. Tends to be disruptive as loyalties may be seen to be divided. | [23] |
Consultant / cosmopolitan / itinerant broker | Links two alters in an outside cluster/s who are not directly linked | To facilitate negotiations between alters or seek to exploit their separation. | |
Co-ordinator | Links alters within their own cluster who are not linked directly | To improve coordinated effort or to centralise knowledge exchange. | |
Gatekeeper | Bridges the structural hole between their cluster and an outside cluster, controlling what information passes into or out of their cluster | Often associated negatively with a hoarding of information, or positively bringing useful information / filtering out irrelevant information. | |
Go-between | Stands between two unlinked actors offering some service, e.g. facilitating access to advice, resources | Usually facilitative but can result in work overload for actor or information bottlenecks. | |
Information or knowledge broker | Keeps separate groups in a network co-ordinated or informed | To improve network information flows and prevent fragmentation. | [18] |
Liaison | Bridges the gap between two different outside clusters without having prior allegiance to either | To facilitate negotiations between alters – often a commercial transaction. | |
Mediator / conflict resolver | Seeks to increase understanding between two parties separated by a mismatch of knowledge, expectations, culture etc. | To resolve conflict between parties - role often held by actor familiar with both sides. | |
Peripheral specialist | Holder of specialised knowledge that tends to occupy peripheral positions | To be available for consultation yet still devote time to their specialty. | [18] |
Representative | Bridges the gap between another actor from the same cluster and an actor from an outside cluster | To facilitate external contact - may be a delegated negotiator. | |
Tertius gaudens (the third who enjoys) | A brokerage strategy to keep alters apart | To increase broker’s personal social capital or power. | |
Tertius iungens (the third who joins) | A brokerage strategy to join alters together | To increase network performance. |
Methods
Inclusion criteria | Exclusion criteria |
---|---|
Empirical research on brokers, bridges or boundary spanners within a collaborative network using a network approach | Not empirical research, e.g. models, concepts, methods, frameworks, tools, computational or theoretical aspects of network theory or collaboration |
Social network of professionals e.g. health, academic, research, corporate, commercial | Social networks of non-professionals such as students, children, internet site users, genetic or disease groups, terrorists or criminals, historical groups, families, friends, local community members, targets for health promotion or marketing, customers or recommender groups |
Local or virtual means of interaction | Non-human social networks (e.g. animal societies, molecular systems) or simulations of human networks |
Individual, organisational or interorganisational level data | |
Brokers, bridges or boundary spanners identified sociometrically or ethnographically |
All study designs: | Ethnographic studies: | Social network studies: |
---|---|---|
Appropriate research question | Description of study setting and context | Network boundaries clearly defined |
Details of study design given | Description of study methods | Level of analysis defined |
Description of sources for data collection | Adequate number of participants | Response rates given for whole network surveys |
Survey techniques described | Adequate observation period | Clear definition of tie relationships, direction and strength |
Description of analysis | Means of identifying brokers clearly defined | Appropriate means for identifying brokers |
Data presentation | Description of analysis | |
Discussion of results | ||
Study conclusions | ||
Clear definition of tie relationships, direction and strength | ||
Appropriate means for identifying brokers | ||
Description of analysis |
Results
General characteristics
Authors, date | Study design* | Brokers identified by | Context, settings | Findings about brokers |
---|---|---|---|---|
Ahuja, G. (2000) [34] | 1. Interorganisational | Nonredundant contacts per total contacts | Firm collaborations within the international chemicals industry | Brokering structural holes between companies increases innovative output up to a point before it decreases. |
2. Longitudinal, retrospective | ||||
3. Documentary data | ||||
4. Regression analyses | ||||
Aral, S. & Van Alstyne, M. (2011) [35] | 1. Interpersonal | Network constraint | Employees from a US executive recruiting firm | Brokers’ success at accessing novelty depends on their knowledge environment. |
2. Cross-sectional | ||||
3. Analysis of email content | ||||
4. SNA, word mining | ||||
Balkundi, P., Barsness, Z. et al. (2009) [36] | 1. Interpersonal | Betweenness centrality | 19 teams from across two US paper and wood-based building product plants | Leaders who were brokers (high betweenness centrality) in the advice-seeking network had teams with higher team conflict and lower viability. |
2. Cross-sectional | ||||
3. Paper-based survey using roster | ||||
4. SNA | ||||
Bercovitz, J. & Feldman, M. (2011) [37] | 1. Interpersonal | Measure of "expertise distance" between academic departments; number of ties to external networks | Academic research teams from two US universities | Costs are involved in coordinating diverse teams but such teams are more successful inventors. |
2. Cross-sectional | ||||
3. Documentary data: invention disclosures, personnel records, patents | ||||
4. PROBIT modelling | ||||
Burt, R. (2004) [12] | 1. Interpersonal | Network constraint | US electronics company managers | Brokers accrue social capital by being able to see and express more “good ideas.” |
2. Longitudinal, retrospective | ||||
3. Online survey; archival data | ||||
4. SNA; regression analyses | ||||
Colazo, J. (2010) [38] | 1. Interteam | Boundary-spanning activity (number of team members who work on another project per number of members in focal team) | Open source software development teams | Boundary spanning activity in teams was positively associated with quality but negatively associated with productivity. |
2. Longitudinal, retrospective | ||||
3. Archival data on teams and project quality | ||||
4. SNA, regression analyses | ||||
Creswick, N. & Westbrook, J. (2010) [39] | 1. Interpersonal | Betweenness centrality | Communication between ward staff of an Australian teaching hospital | SNA can identify strategic people that act as brokers. |
2. Case study | ||||
3. Paper-based survey using roster | ||||
4. SNA | ||||
Cummings, J. & Cross, R. (2003) [25] | 1. Interpersonal | Effective size | 182 work groups (average 8 members) in a US Fortune 500 telecommunication firm | Leaders who act as brokers ("go-betweens") within teams can cause a bottleneck in information flow that can decrease productivity. |
2. Cross sectional | ||||
3. Email survey using roster | ||||
4. Regression analyses | ||||
Di Marco, M., Taylor, J. et al. (2010) [28] | 1. Interpersonal | Betweenness centrality | Indian and US post-graduate students in two engineering project teams | Nominated cultural boundary spanner (CBS) can decrease cultural based knowledge system conflicts and trigger emergent CBS. |
2. Ethnographic | ||||
3. Observation over 3 days | ||||
4. SNA | ||||
Fleming, L., Mingo, S. et al. (2005) [40] | 1. Interpersonal | External ties (ln) | 35,400 inventors across 16 East German regional innovation networks | Brokers can generate innovative ideas but their presence can hamper its diffusion and use. |
2. Longitudinal, retrospective | ||||
3. Archival patent data | ||||
4. Regression analyses | ||||
Hanson, D., J. Hanson, et al. (2008) [41] | 1. Interpersonal | Betweenness centrality | 152 members of an Australian network of community groups for safety promotion | Asymmetric distribution of influence: six members with high centrality and betweenness centrality. |
2. Longitudinal case study, prospective | ||||
3. Paper-based survey; 3 initial waves of snowballing to identify members | ||||
4. SNA | ||||
Hargadon, A. & Sutton, R. (1997) [42] | 1. Interpersonal | Observation | Design engineers at IDEO, a US product design firm | Technology brokering involves four stages: access, acquisition, storage and retrieval. |
2. Ethnographic | ||||
3. Observation, interviews | ||||
4. Grounded theory | ||||
Hawe, P. and L. Ghali (2008) [43] | 1. Interpersonal | Betweenness centrality | Staff and teachers at a Canadian high school | SNA useful tool to identify people of strategic influence (including brokers) in health promotion activities. |
2. Case study | ||||
3. Paper-based survey using roster | ||||
4. SNA | ||||
Heng, H. K., W. D. McGeorge, et al. (2005) [44] | 1. Interpersonal | Betweenness centrality; effective size and efficiency (SH) | Department managers of an Australian hospital | Facility manager had high brokerage potential. |
2. Case study | ||||
3. Survey using name generator | ||||
4. SNA | ||||
Lingo, E. & O'Mahony, S. (2010) [29] | 1. Interpersonal | Observation; assessment of tertius orientation (tertius gaudens or tertius iungens)
| Independent music producers in the Nashville (US) country music industry | Brokerage is a process (cf. position) and both tertius orientations can be used to produce collective outcomes. |
2. Ethnographic | ||||
3. Observation, interviews | ||||
4. Grounded theory | ||||
Luo, J.-D. (2005) [26] | 1. Interpersonal | Betweenness centrality | 296 workers in two multinational technology companies in mainland China and in Taiwan | Brokers ("go-betweens") in advice-seeking networks have informal power and are higher in particularist trust than others. |
2. Cross-sectional | ||||
3. Survey | ||||
4. Regression analyses | ||||
Marrone, J., Tesluk, P. & Carson, J (2007) [45] | 1. Interpersonal | Self- and alter-assessment | 190 MBA students in 31 teams in a US university consulting project | Team level boundary spanning mitigates the negative cost of individual boundary spanning. |
2. Cross-sectional | ||||
3. Survey | ||||
4. Hierarchical linear modelling (individuals nested within teams) | ||||
Obstfeld, D. (2005) [30] | 1. Interpersonal | Constraint; tertius iungens orientation | Designers, engineers and managers in a US engineering division of automotive manufacturer |
Tertius iungens orientation, social knowledge and network density are independent predictors of involvement in innovation. |
2. Ethnography, case study | ||||
3. Email survey using name generator, interviews, observation | ||||
4. Qualitative, regression analyses | ||||
Padula, G. (2008) [46] | 1. Interorganisational | "Shortcuts:" number of cumulative alliances to other clusters | US mobile phone firms | Network cohesion and brokerage ("shortcuts") synergise to produce best environment to generate and produce innovation. |
2. Longitudinal, retrospective | ||||
3. Archival patent data | ||||
4. Regression analyses | ||||
Rangachari, P. (2008) [47] | 1. Interpersonal | Between subgroups in structural equivalence analysis | Administrators and professional staff from four hospitals in New York State | Brokerage across professional subgroups results in better coding performance. |
3. On-line survey using roster; interviews | ||||
4. SNA; structural equivalence analyses | ||||
2. Cross-sectional | ||||
Rodan, S. & Galunic, C. (2004) [48] | 1. Interpersonal | Network sparseness = 1-Density | Managers from a Scandinavian telecommunications company | Access to heterogeneous knowledge may be more important than sparse network structures for innovative managerial performance. |
2. Cross-sectional | ||||
3. Paper-based surveys using roster and one wave of snowballing to include named external contacts | ||||
4. Regression analyses | ||||
1. Interpersonal then aggregated to team level | Network constraint | TV production specialist teams from Italy | Current brokerage associated with higher team performance. Past brokerage ties are not as effective as current ones. | |
2. Longitudinal, retrospective | ||||
3. Archival data on 501 TV | ||||
productions | ||||
4. SNA, regression analyses | ||||
Susskind, A., P. Odom-Reed, et al. (2011) [51] | 1. Interpersonal | Network constraint, effective size, efficiency and hierarchy | Members of 11 hospitality management programs across six hotels and 11 US universities | Level of brokerage was not significantly related to individual team member performance but negatively related to overall team performance. |
4. SNA, regression analyses2. Cross-sectional | ||||
3. Survey using roster | ||||
Tiwana, A. (2008) [52] | 1. Interpersonal | "Bridging ties" extent of heterogeneity of expertise, background and skills of fellow team members | 173 team members within a US internet business applications company | Both strong ties and brokerage (“bridging”) ties are needed to realise knowledge integration. |
2. Cross-sectional | ||||
3. Survey | ||||
4. Regression analyses |