Social Network Theory — a literature review for understanding innovation programs

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The Startup

34 min readOct 29, 2018

A review of literature on the main themes of Social Network Theory, written for a wider PhD thesis on Social Network Theory and startup accelerators. This version is adapted for more general reading from the text prepared for the PhD thesis.

This review provides an overview of key literature on Social Network Theory. The intention is to introduce key concepts that can be applied to understanding the social aspects of innovation programs like accelerators.

The review does not aim to cover every part of Social Network Theory, which is a huge discipline, and is written with an emphasis on understanding the dynamics affecting trust, value, and the transfer of knowledge in social networks built around innovation programs.

This review of literature on Social Network Theory formed half of the total review for a PhD thesis, so it is not intended to be an exhaustive review of work on this subject. There is a lot it does not cover, and much of what it does look at is addressed in brief relative to the attention it deserves, and would get in a work devoted only to this topic.

The bibliography and references can be used to go beyond the narrow focus of this review to read further into Social Network Theory if needed.

1. SOCIAL NETWORKS

Social Network Theory is a large and mature topic which cannot be covered fully in this review. In particular, the works of Granovetter, Burt, and Coleman, which are discussed below, form large bodies of work on their own, and have generated an even larger volume of secondary research and analysis. This review will remain limited to introducing their core theories, and addressing some of the secondary research in order to give the reader a general introductory understanding of Social Network Theory, and in particular aspects that impact on understanding innovation programming.

What is a Social Network?

Borgatti & Halgin (2011) observed that the term ‘social network’ has developed to mean anything from a private club to a website and can therefore lead to some confusion. They describe a network as “a set of actors or nodes along with a set of ties of a specified type…that link them.” These ties connect via shared points to link nodes that are not directly linked themselves, the nodes being individuals, or actors in the network. Unlike groups, networks do not have natural boundaries, and they do not need to be connected internally. These disconnected parts of the network can become connected over time, meaning networks are fluid and ever changing.

Six Degrees, or Small World Theory

An early examination of social networks by Travers and Milgram (1969) looked at the lengths of the paths between individuals, and the question of the probability of any two randomly selected individuals knowing each other, or how many acquaintances might connect them in a chain of people. They went on to examine more specifically the cluster consisting of people in the USA, with around 200 million nodes, interconnected by a complex web of connections. They hypothesised that individuals should all be connected to each other by at least one chain of various lengths or pathways.

They set out to explore this hypothesis, seeking to understand the probable mean and median number of intermediaries between any two given people. This became the well-known ‘Six Degrees’ experiment. They challenged people to pass a document addressed to an individual to someone they thought might be closer to that named individual, and then counted the number of steps the document took to reach the person. In this first experiment to evaluate chain length 29% (64 of the 296 initial documents) reached the intended person. The mean number of steps the document took was 5.4.

Six Degrees of Separation also became known as the Small World phenomena, which describes the idea that everyone is connected to everyone else through six degrees of separation or fewer. The body of work on the topic is examined by Sebastian Schnettler (2009), where he traces writing from initial research in 1958 through to 2009. He identifies three dimensions of small-world theory; structural dimensions, process dimensions, and psychological dimensions.

Schnettler explains that the structural aspect looks at how many pairs of people are connected by a third person and how many by a chain of 2, 3, 4 or more people. The process looks at what kind of actions can be set in motion by these connections in a social network, and what forms of information passed along chains; and the efficacy over different lengths of chain. This looks at the role networks have in communicating ideas, innovation, information and the effect of different types and distances of relationship in achieving this. The work in this area explores what people understand of their own networks and wider social structures, and how they go about manoeuvring through them.

Schnettler observes that very little is required to render small worlds from large social clusters; just a few highly-connected individuals who create short-cuts across the network. These people are referred to as bridging weak ties, and they link sub clusters in a wider ecosystem.

The Strength of Weak Ties

Taking the Small World Theory further, the type of connections within a network, and in particular those used to travel across networks, were described as Weak and Strong ties by Mark Granovetter, in The Strength of Weak Ties (1973). He defined weak ties as contacts that are less likely to be ‘socially involved’ with each other than strong tie contacts, or close friends. Consequently, he observed that a network of acquaintances, or weak ties, will be a low-density network where many of the potential relationships have yet to be made. However, a strong tie network of close friends is likely to be highly connected, where most people know each other. The early research by Granovetter (1973) demonstrated that people were more likely to hear about new job opportunities though acquaintances (weak ties) than through close friends (strong ties) because acquaintances are more likely to be privy to information that is not known already by an actor or their close friends.

The theory of weak ties was developed in relation to finding jobs but in the wider social network theory it also describes the spread of diseases, proliferation of ideas, and evolution of species, thus it is ubiquitous across organic structures (Borgatti & Halgin 2011).

The real-world equivalent that Granovetter explores is the concept of a person, A, who has a very strong network of close friends, most of whom know each other as well as A. At the same time, B has an equivalent strong network of close friends. Within these two networks there is a lot of duplication, and very little novelty. What A knows and shares with a few friends rapidly spreads around her network because everyone knows everyone. Information spreads exponentially because what A tells to 3 people gets told by them to 3 people each, and from there to 3 more people. As most of these people are connected, the information rapidly becomes redundant, in that everyone has already heard it. The same applies to B’s network.

When A and B meet each other, they are not only creating the value of sharing what each of them individually knows, be it knowledge, skills, or access, but they are also creating a bridge between their two much larger, but closed networks. A and B are weak ties; acquaintances with very little duplication between their networks and knowledge (Granovetter 2012) and they have the potential to bring new ideas, information, and people to their own network. By doing this they have a value to the others, and by connecting these two networks they create value (Borgatti & Halgin 2011).

Granovetter (1973, 1983, 2012) argues that people are socially disadvantaged if they do not have weak tie networks, and bridges out of their own strong tie network of close friends and family. Such people do not have access to information and knowledge that exists outside their immediate network, and this may affect their ability to hear about job opportunities (Granovetter, 1973). When applied to innovation, it means they are isolated from information about problems that need solutions, solutions to problems, and opportunities (Granovetter 2012).

Structural Holes

Ronald Burt (1992) developed Granovetter’s original theory (1973, 1983) on the ‘strength of weak ties’ by arguing that the real value in weak ties lies in when they bridge between networks, and therefore become the conduits of knowledge, information, and value between those networks. Individuals who bridge what Burt calls ‘structural holes’ between networks act socially in the same way as a single bridge across a river between two trading communities, and therefore create value both for the communities and consequently for themselves.

Burt (2004) sets out to explore how ‘brokerage provides Social Capital.’ Summing up the theory, he writes:

“Opinion and behaviour are more homogeneous within than between groups, so people connected across groups are more familiar with alternative ways of thinking and behaving. Brokerage across the structural holes between groups provides a vision of options otherwise unseen, which is the mechanism by which brokerage becomes Social Capital.”

His research examined the social networks of managers within a large American electronics company and concluded that those who bridge ‘structural holes’ within the company’s networks are more likely to succeed, both in terms of promotion and reward, and in terms of having ideas accepted rather than dismissed. His hypothesis is that people who stand near and bridge structural holes in a social network are more likely to have good ideas and to benefit from these, because they are exposed to different ways of thinking, and consequently have greater influence.

Reflecting Granovetter (1973), Burt (2004) argued that ideas are more homogenous within strong tie networks, so people who have weak ties and bridge into other networks will have access to more variable options (different ideas and behaviours) from which to synthesise new ideas.

Granovetter (2012) further explained that the stronger the tie between two people, the more overlap there will be in their social networks. Examining this theory, Friedkin (1980) concluded that local bridges between networks tend to be via people who are weak ties, because strong ties ‘encourage triadic closure,’ meaning that two people who are strongly connected are more likely already to know their mutual contacts — if A knows C, and B knows C, it is more likely that A and B will also know each other via C.

Granovetter (2012) concludes from this that his argument about the importance of weak ties does not mean that all weak ties are valuable, only those that serve as bridges between strong tie networks are of special value. It is possible that many weak ties do not become bridges, and are therefore not valuable within this context, however he asserts that strong ties are unlikely to bridge, and most bridges will be weak ties.

Borgatti & Halgin (2011) conclude that this concept supposes that if bridges are the source of novel information, and only weak ties bridge, then weak ties are the best route to novel information.

Borgatti & Halgin go on to explore Burt’s theory of structural holes alongside Granovetter’s Strength of Weak Ties theory. In Structural Hole theory, they observe that the focus is on individuals and how they connect to the others in their network. If an individual is connected to others who are themselves all inter-connected, it is unlikely that any of these individual will be party to any novel information not also shared already with the others in the network. However, if an individual is connected to distinct and separated clusters within a network, then that individual will be exposed to non-redundant information not available to the others in the network.

They conclude from this that access to new, non-redundant information positions an individual to be more valuable in the wider network, and more likely to come up with good ideas based on the fusion and novel use of this non-redundant information. Burt (2004) refers to a person with multiple structural holes in their network as having non-redundant ties, whereas Granovetter (2012) describes this as someone having more bridges, and argues they will by definition be weak ties. Burt sees the strength of a tie as reflecting the extent of non-redundancy, in that over time, and once a bridge is established, the non-redundancy ‘decays’ as information flows through it, and the tie becomes stronger, and with more redundancy (Borgatti & Halgin 2011).

Borgatti & Halgin (2011) call this set of theories ‘flow theory’ because they rely on an underlying function of the network described as being a conduit of information and value which ‘flows’ through the links and bridges between the nodes. The flow of information to and between individuals is affected by their distance from each other, position in the network, and embeddedness — how many mutual contacts they have with other nodes. This will affect how rapidly they receive novel information, and how many times they receive it (redundancy).

Within networks, Borgatti & Halgin (2011) refer to the difference between state-type and event-type social ties. The former are non-transitory types of relationship (familial, workplace) which may change, or end, over time but have a continuous state within that timeframe. Event-type ties are transactional and transient, being the way two ties engage (a conversation, business transaction). These can be measured in terms of frequency over time, quality, and outcome. Both of these ties facilitate ‘flow’ between nodes, which is the transmittance of things like ideas, knowledge, goods.

Rost (2010) challenges the focus above on weak ties as key in innovation, arguing that weak ties have no value without strong ties, but strong ties have some value without weak ties because strong ties support each other, and can help realise the value of weak ties, whereas weak ties together lack the social cohesion and trust to realise the value of the non-redundant information they enjoy. Therefore, Rost argues, a network which combines strong ties with a high level of closure in the core cluster and a large number of weak bridging ties to other clusters are the most creative. Taking this further, Adler & Kwon (2002) propose that “weak ties facilitate the cost-effective search for codifiable information and strong ties facilitate transfer of complex information and tacit knowledge.”

The role of role-sets

Within this exploration of weak and strong ties and the position people have in networks, it is valuable to understand the way they interact, and the social skills they both require and develop to function effectively in this context. Rose Coser (1975) explores the concept of role-sets and their place in the development of individualism in modern society, which explains how individuals need to segment their behaviour to suit different social situations. This variety of social interactions form a core part of an accelerator, as will be demonstrated in the second part of this literature review.

Role-set theory explores the complexity of social interactions, depending on the time and place, the person, and the circumstances of the engagement. The interaction of status and complex ‘role-sets’ are explained by Coser using the analogy of a medical student. ‘Medical Student’ is a single status, but one which has many roles in relation to others; that of student to teacher, doctor to nurse, student to other student, doctor to patient, and an array of other role-sets within the wider medical ecosystem.

As well as ‘role-sets,’ there also exist ‘status-sets,’ in which the individual is engaging with people of differing status, and ‘status-sequence’ in which the status position changes over time. These concepts apply well to startups, and to the tech sector more generally, for example to the social journey an individual makes as they travel from startup founder with no money, to founder of a funded startup, a startup that has been sold, and potentially into being an investor. This journey can happen quickly, and accordingly role-set and status-set change rapidly (status-sequence).

In summing up a broad body of work in sociology from Marx to Merton, and referencing this to Granovetter’s work on weak and strong ties, Coser refers to his phrase ‘the weakness of strong ties.’ She argues that people in closed communities, rich in strong ties and lacking weak ties, are not exposed to many people different to themselves socially or in status. These are networks of ‘simple’ role-sets and status-sets, where there is little variety. This lack of variety does not create situations that are socially challenging, and in which an individual can learn the social skills to negotiate these social challenges. These lessons are crucial in manoeuvring through complex social networks, and in particular for bridging up (status) and out (role).

Coser (1975) observes that when the word ‘parochial’ is used to describe someone’s limited thinking or understanding of ideas, it is a geographical or social classification, being of someone from a simple or rural social context. A connection is being made between intellectual limitations and the lack of a person’s wider social networks, and consequently their limited exposure to people of differing and challenging world views. She argues that having generic, simple social interactions with similar people requires less intellectual effort than having to adapt one’s ideas and responses to a diverse set of people with differing, challenging, and conflicting viewpoints.

A particular part of Coser’s text warrants quotation here as it describes well what happens when we mentor startups in accelerators, or develop what we call ‘social skills:’

“in a complex role-set, individuals are more likely to be confronted with incompatible expectations. Where this is the case, they are required to reflect upon an appropriate course of action in relation to their status position. They must decide whether to abide strictly by the rules or to reinterpret or even defy them, and weigh each decision in relation to their own purposes of action and the purposes of others. This calls for innovation, sometimes in the form of violation of custom and hierarchical modes… it also forces a certain measure of flexibility, as differences are “ironed out,” through negotiation and compromise, through a social process that forces each participant to take into account the vantage point of the other person.” (Rose Coser 1975)

Coser goes on to suggest that in social networks, subordinates, or people of low social status, are expected to conform to behavioural and social norms. A social structure that allows for flexibility over conformity is more complex, requiring individuals to account for more variables in expectation and reaction from different role-sets and status-sets.

Coser explains how this spreads into language use, because an understanding of the differentiation of the self from others leads to a realisation that more complex language is needed to explain ideas. She argues that when we realise people are different to ourselves, we have to use language more carefully to express ourselves accurately, and when this applies to a diverse group of people, all different, then there is further pressure to make language even clearer.

She shows that this ‘Cognitive Flexibility’ is the ability to see things from the perspective of other people, based on having had exposure to a wide diversity of people over time. Being trapped inside a dense, strong tie network inhibits the development of cognitive flexibility.

This leads to Coser observing that complex socialisation in diverse societies requires, and develops, the ability to imagine the perspective of other people in order to adapt ways to engage appropriately, and to deal with the potential conflicts these differing approaches and ideas create, yet also retaining a clear notion of oneself. This is the necessary skill set to build, manage, and benefit from a complex social network, and therefore is an important factor for accelerators.

2. SOCIAL CAPITAL

This section will examine what Social Capital is and how it manifests within social networks, in particular how it leads to higher levels of trust, and how it affects the value of particular social network structures, and their behaviour. Lin (1999) suggests that Social Capital refers to gains made by an individual or group as a result of the interaction between actors in a social network. This capital is not economic or human capital and being part of a social network is therefore described as Social Capital.

Definition of Social Capital

The meaning and effects of ‘Social Capital’ are not clearly defined (Portes 2000). Consequently, there exist a broad range of definitions, or attempts to define Social Capital, but there is no clear consensus for the conceptualisation of Social Capital (Bjørnskov & Sønderskov 2012).

Coleman (1988) describes three forms of capital in relation to each other. Physical capital is created by adapting materials to form tools that can be used to produce things, and thereby create value. Human capital is created by adapting a person, through education and training, to give them skills and capabilities that enable them to act in new ways, and thereby create value. Consequently, Social Capital, as an extension of this logic, is where changes in relations between people enable them to act in new ways and create value. It is less tangible than either physical or human capital because the value resides in the relations between people. Social Capital facilitates the creation of value, just as physical and human capital do, because a network within which there is greater trust is able to achieve more.

The concept of Social Capital has many different and related definitions and aspects, some of these are outlined below to illustrate the variety of thought on the subject:

Lin (1999) suggests that “Social Capital is captured from embedded resources in social networks,” and that Social Capital is the “investment in social relations with expected returns.” He further (2001) defines ‘capital’ as “an investment of resources with expected returns in the marketplace.” This theory is expanded in the context of Social Capital by describing that “capital is captured in social relations.”

Coleman (1988) suggests that a unique feature of Social Capital is that those who generate it usually only capture a small part of its benefit, which leads to underinvestment in Social Capital because the returns are apparently low.

Bjørnskov & Sønderskov (2012) suggest that Social Capital has potential value because it provides individuals the chance to access information and resources in their social network.

Adler & Kwon (2002) suggest that Social Capital is the resource available to actors as result of their place within their social network. This can include market relations, hierarchical relations, social relations. They further discuss whether Social Capital is in fact a form of capital, arguing that it is a “long-lived asset” which can be developed with an expectation of future benefit. Therefore, it has and holds value that can be expended. By investing in building links to other networks, individuals and groups can increase their access to knowledge, ideas, power, resources, and other advantages. By investing in developing stronger internal relations, groups can increase their ability to operate together to build value.

Audretsch, Aldridge, & Sanders (2011) define Social Capital as the goodwill “available to individuals or groups” which emerges from the “influence and solidarity” it affords actors. To them, Social Capital resides in social structures, and is a long-term asset one can invest in. It requires management and maintenance and can be used in the place of other forms of capital.

Orlowski & Wicker (2015) identify that Social Capital improves the return on investment in physical and human capital and is a multifaceted concept. It includes the connectedness of people (their networks) and trust as core elements. This trust at the core of Social Capital can be broken down into inter-personal trust, institutional trust, and trustworthiness. Social Capital “refers to features of social organization, such as trust, norms, and networks that can improve the efficiency of society by facilitating coordinated actions.”

Davidsson & Benson (2003) observe how human capital theory suggests that knowledge allows people to be better at identifying and exploiting new opportunities, but they challenge this, arguing that greater accumulation of human capital, in the form of formal education, may make someone more risk averse. They suggest that a reason that immigrants often engage more in entrepreneurial activity is that their human capital — formal education — is not equally recognised in their new environment, so they are encouraged to take more risk. They suggest that Social Capital provides nascent entrepreneurs with a “wider frame of reference” from which to develop new ideas, and a greater ability to extract benefits from their social networks.

These definitions can be interpreted as suggesting that Social Capital resides in networks and is the outcome of interactions between actors within and between networks. It is heavily tied to trust and can be reflected in influence and power. Social Capital forms the return on investments made in networks, or the realisation of resources embedded in networks. Social Capital is the currency used in networks, and the bond that holds networks together and allows them to function effectively.

Trust and Social Capital

Trust is an important factor in internal Social Capital within an organisation, leading to greater support and cooperation between individuals (Yeng, Tseng, & Wang 2015).

Coleman (1988) explores how different types of social structure facilitate trust in the form of Social Capital. The concept of ‘closure’ within networks defines where the actors are all interconnected. If A is connected with B, and separately with C, A can defy a norm in relation to B without C finding out. If B and C are also connected, A is now subject to sanction from B and C for harming either of them, and is therefore more likely to abide by a norm adopted by all three of them. This closure in a network is called triadic closure.

In this respect, Coleman identified three forms of Social Capital. Obligations and expectations depend on trustworthiness within the social network; the ability of the social network to facilitate information-flow; and the presence of both norms and sanctions for breaching those norms.

He discusses this in the context of rotating credit associations in emerging economies, where a high degree of ‘trustworthiness’ between the members of the group allows them to group funds, and lend them to individuals in the group. The close social structure, or triadic closure, make it difficult to abscond with the money; norms of behaviour and effective sanctions, and visibility across the network of anyone who violates those norms, means that the system works well, and therefore has strong Social Capital.

Coleman (1988, 1990) further argues that Social Capital is strengthened in closed networks where norms are understood, and social mechanisms for reward and punishment are in place. This leads to greater trust. Social Capital, in this context, is therefore weaker in less consolidated networks because violations of norms are more likely to go un-noticed, or un-punished. Coleman’s approach sees Social Capital as the relationships between people that accrue ‘credit slips’ for actions carried out for other people. In effect, this is ‘doing favours,’ and expecting favours back in return. Trust is required for an individual to be confident that someone will return such a favour (Rost 2010).

Coleman (1988) interprets Granovetter’s (1985) view on embeddedness as giving a social aspect to a utilitarian view of the economist, arguing that the social structures are not just formed for a single economic function, but also take into consideration past and future interactions, leading to trust, closeness, and other returns on an engagement which in turn may affect the purely economic functions of a social network. He therefore defines Social Capital as consisting of some aspect of a social structure, facilitating actions of actors (whether individuals or corporate) within that social structure. Social Capital is unique as a form of capital in that it exists entirely within the social structures, between and amongst actors in the network.

Bridging and Bonding Social Capital

In this context, Adler & Kwon (2002) argue that ‘bridging’ forms of Social Capital refer to the relations outside the actor’s core network, or their external relations, whereas ‘bonding’ forms of Social Capital refer to the internal ties within a group. Therefore ‘Social Capital’ can refer to the value in an actor’s bridging links to other actors or networks. It can equally refer to the trust that holds together a community, society, network, or group.

This difference between the approaches of Burt, who defines Social Capital as residing in the bridging of structural holes, and Coleman who identifies Social Capital as residing in network closure, are discussed by Adler & Kwon (2002), who conclude that both network closure and structural holes can create value depending on the context, specifically what the actor is looking to achieve.

Rost (2010) concludes that Burt and Coleman’s alternative views of Social Capital complement each other, suggesting that individuals who combine strong network ties with weak network architectures produce the most innovative solutions. She goes on to argue that the difference between Coleman and Burt’s view on networks, and in particular on Social Capital, is that one looks at the relationship of individuals across the network, and the other looks at the position of the individual and the structure of the network.

Within this understanding, Rost argues that strong ties lead to innovation because they become social mechanisms for knowledge recognition and realisation of innovative ideas. When these strong ties are combined with weak network architecture, which has access to some structural holes, and enough peripheral network positions and therefore access to bridging weak ties, it adds the benefits of information flow, and access to knowledge and ideas. She concludes that people with strong ties who are embedded in weak network architecture are most likely both to have innovative ideas and be able to realise them.

Granovetter (2005) confirms this by explaining that social networks lead to economic outcomes for three main reasons. Firstly, he argues that social networks affect both the flow of information and also that trust within the network allows people to verify or qualify the information. Secondly, he points to the role social networks have in amplifying the reward or punishment for interactions and behaviour within a network. Thirdly, he argues that trust emerges from within networks, and defines ‘trust’ as being a belief that others will behave in a way that may be contrary to their immediate best interest, but in the interest of other actors in the network.

The value of Social Capital

Orlowski & Wicker (2015) observe that defining the monetary value of Social Capital is made difficult because it has no accepted exchange value with money and is generally intangible. Audretsch et al. (2011) also argue that Social Capital cannot be measured or quantified in the same way as other forms of capital, and that because it resides in relationships it cannot be owned by a single actor.

Adler & Kwon (2002) argue that a key benefit of Social Capital is information, and the quality, relevance, and freshness of that information. Other benefits include influence, control, and power. This second set of benefits can express itself both in people to whom multiple favours are owed, and who can therefore influence those around them for their own benefit or that of someone they choose to support. Another benefit of Social Capital can be seen in someone who bridges structural holes, and in doing so exerts power because they can choose who benefits from the bridges they build. These forms of power can benefit groups that distil them collectively, allowing them to ‘get things done’ more effectively, thus creating value for the group.

Adler & Kwon agree that Social Capital can be converted into other forms of capital, for example using one’s position in a social network to gain economic capital. They observe that economic capital is most liquid, and therefore easiest to convert into human or Social Capital (e.g. paying for an education, or buying someone lunch), whereas Social Capital is the least liquid and hardest to convert. There is no simple mechanism whereby Social Capital can be converted into other forms of capital in the same way that financial capital can be used to pay for something.

They affirm, however, that Social Capital can be used as a substitute or complement to other forms of capital. For example, strong networks can be used to reach people faster, saving time and money, and can be used to raise financial capital, or to take a product to market. Social Capital, in the form of trust, also affects the cost of things impacted by risk, such as lending, because that risk can be assessed more easily using Social Capital, which provides the ability to access deeper and broader knowledge about an individual or opportunity from which to evaluate its risk.

Granovetter (2005) refers to Lin (2001) in defining Social Capital as, for example, where a prospective employer and employee prefer to find out about each other via a trusted actor in a network. In this context, social networks and Social Capital are more efficient than a recruitment agency because they exist already as part of a person’s wider activities and interactions, whereas a recruitment agency will spend time and money to build such a network.

Therefore, whilst Social Capital cannot be quantified in direct relation to other forms of capital it can be converted into economic and human capital. It can also be accrued and ‘spent’ to create other forms of capital. However, Social Capital resides and is created from the links between actors in a network, and therefore cannot exist without the context of multiple actors in a network environment.

The risks associated with Social Capital

Whilst Social Capital has so far been explored in the context of its benefits, and as a positive outcome of social networks, it can also have risks and the potential for negative outcomes.

These risks are explored by Adler & Kwon (2002) who argue that they primarily relate to the cost of building and maintaining Social Capital within networks, when realising and converting that Social Capital is difficult. A simple risk is not getting an adequate return on the investment, but also that the work involved distracts from other important activities, for example earning economic capital.

A related argument put forward by Granovetter (2005) is that building and maintaining close network ties (strong ties) is expensive, whereas maintaining weak ties is easier and more efficient. As strong ties bring redundant information, and weak ties bring non-redundant information, the risk of building and maintaining a broad, strong tie network is that it is costly and high in redundancy.

Adler & Kwon also describe the risks posed by Social Capital of becoming too embedded in a network, leading to a lack of non-redundant information flows, and at a group level a lack of new ideas resulting in parochialism and inertia. Too much Social Capital, represented by overly strong network ties, can also lead to a sense of over-obligation within the group, and friendships that are too close to allow more opportunistic interactions. This can ultimately lead to corruption, conspiracy theories, and other negative behaviour by a group, where excessive Social Capital results in loyalty to strong ties taking precedence over beneficial behaviour, and overriding adherence to the norms of the wider weak tie community. They conclude that too much Social Capital is seen to lead to too much network closure, so Social Capital has a risk when it becomes too strong.

Taken further, Adler & Kwon explain that groups which have low internal bonding ties, and low external bridging ties will suffer from low Social Capital, whereas those with high levels of internal and external ties will enjoy strong Social Capital and the associated advantages. This approach suggests Social Capital risks exist in networks with high internal links and low external ties, or high external with low internal ties.

They suggest that networks with high internal links and low external links risk isolation and a tendency towards not receiving conflicting sources of information, combined with norms that encourage people to ‘toe the line’ rather than challenge negative behaviours. This creates a tendency towards information bubbles, conspiracies, and corruption. Networks with high external and low internal ties risk developing strong access to new and conflicting ideas, and therefore a potential for innovation, but no internal Social Capital to provide the trust and cooperation needed to act on it.

Adler & Kwon conclude that this analysis also depends on the content of the ties in these networks, whereby the two tendencies described can result in a force for good or bad outcomes depending on what information is travelling across the internal or external ties, and on the underlying culture. Isolated networks can create a downward force, where false information and ideas go unchallenged, creating a negative spiral, or they can end up creating the stable social platform from which members of the group can reach out to other networks.

3. THE BEHAVIOUR OF SOCIAL NETWORKS

The description of social network structures combined with an understanding of Social Capital enables an examination of behaviour within social networks, including social norms, and how these are policed.

Norms

Societies all have norms. They are the accepted social rules that a connected group of people agree upon. They may be codified, in the form of laws, or a constitution, but originate as and can remain as unwritten, even unspoken rules. Norms are an important part of behaviour within social networks, accounting for how they self-regulate, and defining the levels of trust and co-operation, or Social Capital within the network. Norms are clearer and easier to enforce in a dense network, which is one in which there are multiple possible connections between nodes and triadic closure, as outlined above. Information travels more quickly through such networks, and consequently reward and sanction, especially in terms of reputation, spread rapidly and widely. Norms are policed. Individuals who conform to norms are rewarded by the network, and those who break with them are sanctioned. The precise form of reward and sanction varies according to the society or network (Granovetter 2005).

Cooperation and reciprocity

Fowler & Christakis (2008) research the spread of ideas and emotions through social networks. They used data from the Framingham Heart Study, a 20-year longitudinal social study, to explore how happiness spreads across social networks. They concluded that happiness spreads across networks through a variety of ties, and that it clusters in groups, and can extend by up to three degrees through the network. They went on to show how other concepts, like depression and loneliness, also spread by up to three degrees through social networks, using social ties. This sets the context for understanding how concepts like co-operation also spread through networks.

Harrison, Sciberras & James (2011) specifically explored cooperation in social networks. Their experiment with human networks demonstrated that cooperation is most productive when individuals can expect direct or indirect reciprocity in the future or are aware of sanctions imposed on non-co-operators. Reputation in the network increases the chances of cooperation again because it increases the likelihood of either reward or sanction in the future. Social connectivity, or the place in the network structure of a particular actor, also affects the probability and frequency of interaction, and the exchange of information. They identify that if an actor is highly connected within the network, then cooperating with them is more likely to result in reward, or not doing so in sanction.

In their experiment, they sought to test the investment in cooperation in a human social network by getting individuals to carry out tasks that had a clear physical cost to the actor, but a benefit to another actor in the network. They identified that social proximity within the network increased cooperative investment, and that some individuals were willing to expend more effort for the benefit of their close social ties than on themselves or their close genetic ties. They concluded this is because strong social ties are different to strong genetic ties, whereas an individual would expect a relative to act to support them, with non-genetic ties there may be a desire to increase the chance of reciprocity by over-compensating their support as it cannot be taken for granted.

Through their experiment, Harrison et al. (2011) showed that social proximity in a human social network acts in the same way as biological relatedness in a simpler animal social network, and that people who are more cooperative are more likely to cluster in networks.

Embeddedness and tie decay

Granovetter (1985) proposes that co-operation and reciprocity are behavioural norms that are embedded in social networks, and that the behaviour and institutions examined in neoclassical utilitarian economics are far from being self-interested and isolated from social influence, as suggested by economists, and instead are highly affected by ongoing social relations and therefore not operating independently of them.

He thus combines economics and sociology and suggests that most behaviour is embedded in networks, and therefore influenced by social outcomes and inter-relationships. In particular, he suggests that behaviour such as expending effort to benefit others, that is apparently non-rational as viewed from an economics perspective, becomes rational when viewed sociologically as a function of embeddedness, and therefore complying with social norms, and responsive to social outcomes like status, approval, or sanction.

Burt (1999) argues that embeddedness is also a factor in the decay of networks, which describes the tendency of relationships to weaken and dissolve over time. He further observes (2001) that the rate of decay is affected by multiple factors, and is slowed in part by people being connected indirectly through many third parties, or being highly embedded. He posits that building such attachment should happen as early as possible and should be embedded in other types of social relations, for example involving spouses, friends, and relatives, not just the individual, and should stray into social engagement, not just topic or work related. A higher level of embeddedness will lead to a slower rate of decay.

Lin (1999) offers three further reasons why embedded resources in a social network will produce value. Firstly, they give access to the flow of information. In an imperfect market, an individual’s position in the network can increase their access to information not readily, or quickly, available to others, thus giving them an advantage. Secondly, position in the social network can afford an individual some degree of power or influence. This can relate to the decision-making of others, or any other outcomes over which this influence creates greater value for the actor or its associates. Thirdly, Lin points to how the position of an actor in the network, and their relationship to others can afford them social credentials in the eyes of others. This value derives from the interaction between actors in social networks and is therefore their Social Capital.

Link Reciprocity

The literature has thus far examined the interrelationship between Social Capital and co-operation, showing that reciprocity is a key factor in social networks and Social Capital. Rand, Arbesman, & Christakis (2011) argue that in evolutionary game theory reciprocity is generally examined in the context of occurring between two actors. In this context an actor can reciprocate the action of another actor, either by cooperating or not based on their previous action. They suggest that this notion is harder to explore in groups, but they argue that “strategic tie formation and dissolution” in a dynamic social network allows actors not only to respond to cooperation or lack thereof in others by equivalent actions, but also to respond through the formation or dissolution of ties between that actor and their network.

Consequently, if an actor in the network does not cooperate, that tie with them is dissolved, excluding them from the group. If an actor does cooperate, their behaviour can be rewarded both with reciprocal cooperation, but also with formation of social ties into the network. The result is a network which can refresh rapidly, can constantly bring in co-operators, and exclude non-co-operators (Rand et al. 2011). Harrison et al (2011) suggest this explains that co-operators tend to cluster as a result of link reciprocity.

Rand, et al. describe how dynamic networks, where ties are created and dissolved in response to behaviours, can amplify clustering of certain behaviours or attitudes, creating an advantage within the network for constructive behaviour. Ties in social networks are dynamic, in that they are created and terminated in response to people’s actions and behaviours, becoming an effective means by which behaviour is sanctioned or rewarded. Link reciprocity therefore means that the networks of co-operators grow rapidly, and those of non-co-operators shrink accordingly. Link reciprocity is therefore the means by which norms are policed in dynamic social networks.

5. SUMMARY: SOCIAL NETWORK THEORY

This first part of the literature review has explored the fundamental concepts in Social Network Theory, and shown how value resides in networks in the form of Social Capital. The behaviour of social networks is influenced by these structures and rewarded through Social Capital. In particular, co-operators cluster in networks, supported by link reciprocity, which rewards them with further ties, and at the same time expels those who do not co-operate, or who are bad actors.

The extent to which an actor is embedded within a network affects their access to information, and consequently their power and influence. All of this is the Social Capital they derive from their position and behaviour in the network, but can be converted to economic or human capital when those benefits are reflected in their commercial activities.

Therefore, Social Capital can be a substitute for other forms of capital because it can be converted as a result of the benefit it brings within a social network. It can consequently be used to reward people, and threat of withdrawal can be used to sanction them.

Within the structure of a social network, bridging ties give access to new information and ways of thinking, and bridging ties are usually weak ties. However, strong ties allow people to get things done effectively, and enjoy greater trust because they are more likely to return favours over time. Therefore, a mixture of strong ties and weak ties creates the ideal social structure by which to access new ideas and act on them to create benefit. In order to manoeuvre through this rich social landscape, individuals require complex role and status sets in order to relate to people unlike them, and to express their ideas to a variety of other actors.

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