Module: 55-7626-00N-A-20123 – Social Media Use in Organisations (A-2012/3)

Social Network Analysis

of M2M Organisations

Lee James Cox

B0049872

MA in Technical Communication

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First some definitions

Social Network Analysis (SNA) is the study of structure1. It is the mapping and measuring of relationships and flows (ties) between members (nodes) within a network.

Machine to Machine (M2M) refers to the technology that connects:a. a device (such as a sensor or meter) to capture an event (such as temperature,

inventory level, etc.)b. which is relayed through a network (wireless, wired)c. to an application (software program), that translates the captured event into

meaningful information (for example, items need to be restocked).

M2M Organisations include device, network and application providers; as well as:• Enterprise Customers: provide the services to end-users, e.g. Coca Cola, British Gas• Platform Providers: equipment and solution providers to operators and others• System Integrators: build solutions to join up incompatible systems

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Node NodeTie

Five things to first consider about SNA

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1. The first endeavour should always be to define what the Nodes and Ties mean.[2] For our M2M analysis, the nodes are organisations and ties are contractual relationships.

2. Nodes can have different weightings of importance, e.g.– Size (revenue/subscribers)– Geography– Track record– Associations – Other segmentation attributes

3. Ties have characteristics that matter when it comes to identifying things like leadership, influence and strength e.g.

– Direction (one-way, both)– Quantity– Contract date– Value– Frequency

4. Matrix, graphs and other visualization tools are important for analysis and measurement. – Tools like NodeXL will be required for any network of reasonable size– Considerable time is usually required to capture and keep the data up to date.– The M2M example in later slides shows just a few of the tens of thousands of Enterprises, 200+ Operators &

300+ Application Providers that Jasper, Vodafone & Ericsson have as M2M partners!

5. The perspective of the analysis can centre on the complete network (socio) or an individuals personal network (ego).

Social-centric or Ego-centric SNA?

Social-centric (complete network)a. Allows analysis of nodes and ties in comparison to wider network, e.g.

– Are Operator relationships tightly bonded, diversified or constricted?

– Is there density/clustering of contracts within a geography such as Europe, or are there more cross-continent?

b. Identifies behaviours affected by positions and connections, e.g.

– Does the number of application provider ties influence the number of ties a platform provider has?

– Does the distance between application and platform nodes affect the number of operator relationships?

Ego-centric (personal network)a. Only ties directly with the focal organisation (Jasper)

plus those Jasper is aware of are included.

b. Perception is reality and opinions count. Subjective attributes are likely to have weight in many business matters

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Figure 1 –Socio-centric view of the sample M2M Eco-System

Figure 2 – Ego-centric view of Jasper M2M Network

Visualization[3] can also reveal positional relationships

• Operators have the highest degree centrality. AT&T is most central of the operators and is the longest established with largest customer base.

• Application Providers have the highest closeness centrality. They work withmultiple operators but rarely direct with Enterprise customers.

• Device/SIM and Platform Providers are structurally equivalent nodes.

• System Integrators are the most peripheral, having the smallest number of connections.

• Enterprise Customers has the highest betweeness centrality providing the only path to System Integrations

• No groups of nodes are connected to each other (cliques). However if the definition of ties were extended beyond ‘contractual’ then informal or personal relationships would show all nodes in this extracted view as being connected.

Operators

Enterprise Customers

Device/SIM

Application Providers

Platform

System Integrators

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Benefits & Limitations of SNA

Benefits Limitations

Provides framework to describe any complex network.

Collecting and maintaining source data can bedifficult

Identifies important individuals and the influence they have

Does not describe meanings, motives or explain why actions happen, e.g. why a contract was won. A typological analysis is more suitable.

Can identify previously unrecognised sub-groups through visual clusters

Little examination of important attributes such as attitudes, opinions and behaviours that may be helping or hindering relationships

Highlights areas for further inquiry and possible improvement, e.g. gaps

More sensitive to data omissions than other surveys. >75% sampling is required.[4]

Even weak ties may be revealed as important for bridging disparate groups

Privacy can be ignored when views of others contribute to the analysis.

Useful for track changes over time to reveal paths. Visualization can lead to over simplification and misreading of results. E.g. Network measures such as density can be easily misrepresented when networks of different sizes are compared.

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Other take away learning’s from SNA

1. Social Network Analysis is not necessarily restricted to connections from tools such as Facebook or LinkedIn. SNA can be applied to a wide range of network subjects, such as how diseases spread, mapping films and interaction of characters, influence of language throughout the world, etc.

2. SNA focusses on relationships rather than attributes of the ‘nodes’ for their own sake, or the ideation behind the relationship.

3. A Social Network does not in itself encourage co-operation or collective action. Community tools such as Online Forums lend themselves better to co-ordination of action.

4. Connections within business networks often reveal companies tied to competitors. This has implications for trust and potential leaks within a social network.

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References

[1] Wellman B, Berkowitz SD (1977). Social structures: a network approach. Greenwich: JAI Press.

[2] Pinheiro, C. (2011). Social Network Analysis in Telecommunications. John Wiley & Sons.

[3] Based on Davies R (2011). Network Visualisation and Analysis, Cambridge.

[4] Borgatti, Carley & Krackhardt (2006).

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