Evaluating Employee Collaboration & Communication

Problem: 

How can organizations identify, evaluate and ensure that the company culture continues to be collaborative and communication continues to be interactive across departments? Making copies of post-its is counter-productive, recording every face to face dialogue is wrong on many levels, and so is tapping into phone calls -not to mention that phone calls in the office have become less popular!

Situation:

Many organizations such as Scientific Games, the University of Alaska, Lenovo Manhattan Associates and Morgan Stanley use Microsoft Skype for Business (formerly known as Lync) for office communication. Skype for Business is a platform that helps foster engagement, collaboration and interaction via instant messaging, online sharing, online meetings, audio/video conferencing and much more.

While reading about Skype for Business, I wondered if platforms like this actually breakdown organization silos or encourage even more introvert behavior? Do companies even look at the data collected?...it would be an awesome and insightful position! I could not find research to answer these questions so I’ve decided to put together a plan to utilize the tool of SNA to evaluate Manhattan Associates communication patterns. '

Manhattan Associates is a U.S. based supply chain management software company which requires communication across departments in order for every department to achieve high results and maintain high client satisfaction.

I picked Manhattan Associates because I know a handful of people who have been working there and they claim that not only is communication common across Manhattan Associates departments but Skype for Business is a tool that is highly relied on to communicate along with face to face interaction. 

SNA will help identify employee communication and collaboration patterns and evaluate if most of company is collaborative and communication is interactive across departments while relying heavily on Skype for Business.

Hypothesis:

If Skype for Business is used regularly as a communication tool, then Manhattan Associates communication and collaboration habits are more frequent not just within their department but also across the organization.

My hope is that data from Skype for Business and SNA will prove that my hypothesis is true and also provide useful insights about the employee network.

Important Things to Note:

I would plan on doing a mid-year review of the organization’s structure and communication flow with one survey sent out to the entire company towards the end of the second quarter along with the Skype for Business data collected from first and second quarter. It is also important to note that when creating the survey, make sure questions are worded carefully to avoid bias and use a rating scale that is easy to follow/remember.

Sample questions to include in the survey are:

• How often do you use Skype for Business in the past two quarters? Use your Skype for Business chat, audio/video call and online meeting history to recall communication history.
• 1=never, 2= once every day, 3= multiple times a day
• How often have you communicated with __________ in the past two quarters on anything related to work (includes lunch break and business trips)? Use your Skype for Business chat, audio/video call and online meeting history to recall communication history.
• 1=never, 2=infrequently, 3=frequently
• How often have you socialized with ________ in the past two quarters outside of work (does not include lunch break or business trips)? Use your Skype for Business chat, audio/video call and online meeting history to recall communication history.
•  1=never, 2=infrequently, 3=frequently

Attributes to include in collected data:

• Department
• Sales, Research & Development, Professional Services, Customer Services
• Tenure 
• 1= less than 1 year, 2= 2 to 5 years, 3=more than 5 years
• Nationality
• Chinese, Indian, American, South American, European, Other
• Position/hierarchy
• Consultant,  Senior Consultant, Manager, Senior Manager, Director, Senior Director, Other
• Gender
• 1=Female, 2=Male
• Age
• 1= 25 or below, 2=26 to 35, 3= 36 to 45, 4= 46 or above

What to look for in this SNA:

• individuals with high/valuable connections (highest eigenvector)
• individuals serving as a connector or bridge for the highest number connections (in-betweeness)
• boundary spanners (connectors/bridges) between attributes including departments and nationality
• hub connectors connecting with nodes (people) from various positions and departments
• relationship and communication/use of Skype for Business frequency between departments
• social and professional subgroups as well as cliques within them based on various attributes including nationality
• relationship between tenure and frequency of communication/ use of Skype for Business
•  relationship between age and frequency of use of Skype for Business/communication

Expected SNA Result:

Because I don’t have the actual data to conduct this SNA, I will go ahead and make some assumptions about the expected results I hope to see.

The expected outcome of the SNA is that my hypothesis is true and with the use of Skype for Business, Manhattan Associate’s organizational structure and employee network is a distributed network versus a centralized or decentralized network. 

A centralized network means the organization’s power is concentrated and a decentralized network means the organization’s communication is inconsistent and confined to subgroups or departments. Naturally, it is expected that the SNA will also show subgroups based on nationality for social ties and subgroups based on departments for work ties. In addition, although the expected result is that the hypothesis is true, isolates will also be identified.

Recommendations:

1. With the insights and knowledge gained, IF the hypothesis is true, there shouldn’t be one or two boundary spanners. In fact, there should be multiple nodes connecting nationalities and departments. And in order for the organization to continue facilitating a company with high communication and collaboration –they should continue investing in Skype for Business as well as training for newly hired employees to understand the importance of each department and the value of communicating and connecting.
2. Follow up or more information may be needed to understand why there are isolates/outliers present in a company with a high culture of collaboration –it could be because those individuals were recently transferred around the time of the survey and do not have connections or a communication history in Skype for Business.
3. The attribute of age will help shed light on whether younger people use Skype for Business more often than older people. Skype for Business has become vital for the organization so it does require individuals to become familiar with the platform and training workshops for Skype for Business may be needed every few months when a system update occurs or new features are introduced. The data will tell the organization whether it needs to invest time and money in this or not.
4. Individuals with the highest out degree could be given more responsibility i.e. if there are events, emergencies or urgent news which needs to be dispatched –the source or broadcaster can be utilized. Not everyone checks their emails every few minutes and a connection to a broadcaster may catch their attention quicker than an email blast.
5. Sinks or nodes who have a high in degree should also be recognized –they soak up information without broadcasting it to everyone which means they are very trustworthy. The organization can utilize nodes like these to gauge employee sentiments outside of a formal survey, not everyone will leave genuine feedback on an anonymous form but asking a sink to just listen and be a “fly on the wall” can prove very insightful for the organization
6. Professional subgroups and cliques based on department are inevitable however social cliques based on nationality means although the organization is diverse; there isn’t a lot of cross-cultural interaction outside of work. One way to combat this and encourage cross-cultural groups is for the organization to have events or monthly potluck lunches for each department focusing on embracing and educating employees about various cultures.

Some More Important Things to Note:

SNA can help HR, management and executives see the “invisible” patterns in an organization by visualizing the data in a network map. It is important to note that SNA is a diagnostic tool and there are other ways to evaluate an organizations communication and collaboration habits. In this SNA, the plan is to view the organization in the first two quarters of the fiscal year –that is just a slice of time and if an SNA is done every quarter continuously maybe the data will be more valid. In this SNA, the number of instant messages, video/audio calls and online meetings were used to help survey respondents answer the survey. One of the main reasons to use Skype for Business data was to reduce human error because people can recall or predict less than half of their communications measured either on amount or on frequency as concluded in a study called The Problem of Informant Accuracy: The Validity of Retrospective Data. Using Skype for Business helps increase the validity of this SNA. 

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How scientists in Australia are using social network analysis to answer behavioral questions in the wild

The Application of Social Network Analysis to Team Sports

Hi everyone,

As I’m particularly interested in the similarities between managing a sports team and managing any other business, I searched for sources which link social networking to team sports.

To date, only few studies concentrated on sporting teams using social network methodology. Having said this, Lusher, Robins and Kremer (2010) studied the application of social networking analysis (SNA) to team sports. The authors argue that the current SNA allows exploring individual and group behavior in sporting teams by examining social relations between team members and their individual-level qualities simultaneously. The intra-group relations among teams, which include aspects like cohesiveness and hierarchies among players, provide information about the team members’ informal connections to each other. However, researchers analyzing these intra-group relations often face a number of challenges. For example:

1. Agreed behavioral norms as a key correlate of team cohesion
2. Small sample sizes (Unlike work-based and other types of groups, sport teams usually comprise only a relatively small number of individuals)
3. Membership may change relatively frequently

The SNA focuses on actual behavior between team members rather than simply on the attributes of the team members. As an example, being a captain can be considered as an attribute that the captain holds. From a relational perspective, though, the captain has a formal relation of power with others in the same team. In this context, it is important to understand that beyond formal relations, there are informal relations which influence a team’s operations.

SNA permits to examine whether the most popular individuals in a team are also those who have the strongest investment in the goals. Furthermore, SNA helps to determine whether the prominent players may indeed drive social norms through their informal social influence within the team. To put it in a nutshell, SNA enables the researcher (as well as the coach) to shift the focus from measurements on individuals to the team as a systemic unit of analysis, while still taking into consideration the qualities of individuals in the team.

Anja
HHL, M12

Source:

Lusher, D., Robins, G., & Kremer, P. (2010). The Application of Social Network Analysis to Team Sports. Measurement in Physical Education and Exercise Science, 14, 211-224. Retrieved from http://www.tandfonline.com/doi/pdf/10.1080/1091367X.2010.495559.

Social Network Analysis for Open Source Software (OSS) Communities

What is Open Source Software (OSS)? Over the last few years, there has been a world of change in the way software is made available to users. OSS, essentially means that the source code of software is made available directly and freely to everybody and thousands of software programmers come together in what are called OSS communities to spend tremendous amounts of time writing and debugging the software with no direct monetary benefit. The effects of the OSS movement have been far-reaching. It has now started to become incorporated into the strategies of large corporations, who cannot afford to directly compete against software that is usually free to use. Hugely popular products like Mozilla, Linux etc are direct results of OSS collaboration, which suggests that the importance of the communities that come together to create this software cannot be ignored.

Open source software development teams, are generally comprised of volunteers working not for monetary return, but for the enjoyment and pride of being part of a successful virtual software development project. Team members often come from around the world and rarely meet one another face-to-face. The open source projects are self-organized; employ extremely rapid code evolution, massive peer code review, and rapid releases of prototype code. But often, these open source software groups are comprised of generally unaffiliated individuals and organizations who work in a seemingly chaotic fashion and who participate on a voluntary basis without direct financial incentive. One of the most commonly used terms with respect to OSS communities is “Knowledge Collaboration”. Knowledge collaboration is the key to the success of OSS communities as often, not all members have the knowledge and skills necessary for professional software development. Usually, OSS members communicate through virtual communication channels so that geographically distributed members can collaborate and coordinate their work.

One of the newly applied methods to analyze the communication in OSS communities is Social Network Analysis (SNA). SNA allows analysts to look at Actors as nodes, and to visualize the links between them so as to understand the communication between them. They also study such concepts as Degree Centrality, Betweenness, Degree Coordination & Centrality Eigenvector. The main target of these procedures is to understand who the key communicators within the network are, who the knowledge brokers are, and who links the peripheral members to the central actors. The goal of this analysis is to visualize the communication within the network, to analyze how and why some OSS communities operate more efficiently than others, and to streamline operations of other less efficient OSS communities. Within successful OSS communities also, the visual networks make it easy for the team members to train new members about the culture within the OSS communities. Thus, the application of SNA in OSS communities is becoming very important. Over the last few years, many such studies have been conducted and published. Studies, which analyze the knowledge collaboration within the community, the direct communication between members etc. A list of some published papers on such studies is mentioned as part of the sources for this post as below.

Most often, data is collected by selecting certain OSS communities (most often selected from www.sourceforge.net) by using mailing lists and surveys. The data thus collected is then analyzed using SNA programs such as Pajek or UCINET. Interestingly, some open source SNA software packages also exist! Finally, conclusions are drawn based on the visual networks that are derived from the software. The data thus collected may be used by the communities themselves in order to streamline their operations or by other communities to change the way they operate.

Though many positives can be gained from using SNA to analyze OSS communities, there do exist certain problems and limitations. These are not often publicly spoken about, but do exist. The primary problem being privacy. Information used for SNA is, as mentioned before, gained through surveys and mailing lists which require users to input their basic information. From personal experience, I have been able to gather that, in some cases, contributors to OSS communities are professionally attached in the realm of software development, to companies competing directly with the same communities. These members are, as a result hesitant to divulge direct information, for fear of being linked to the community by the companies. This problem has been spoken about by Nelson Ko, who leads Citadel Rock Online Communities Inc. Mr. Ko often speaks about SNA and OSS communities. Another limitation of such studies is that all members may not contribute to the surveys, and thus, information may be incomplete. Incomplete data, would give incomplete results.

Considering the above issues, there exist limitations to the application of SNA for OSS communities, but I personally believe that these problems are outweighed by the inherent advantages. The possibility of streamlining OSS communities to provide better quality and FREE software to users is a big motivating factor. Making high quality software available to lower income communities and economically developing regions that often are unable to access licensed software is another factor that must be considered while taking a decision on the subject. As a result I believe that the advantages of using SNA to make OSS communities operate more efficiently compensate for the innate negatives that come with it.

Sources:

1)http://www.aswec2008.debii.curtin.edu.au/ResearchSlide/Open%20Source%20Communities%20as%20Social%20Networks.pdf

2) http://se.naist.jp/achieve/pdf/171.pdf

3) http://nlp.uned.es/~juaner/papers/juaner08-oss.pdf

4) http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=1062&context=etd

5) http://www.slideshare.net/nkoth/social-network-analysis-in-open-source

6) http://perso.univ-rennes1.fr/eric.darmon/floss/papers/VICENTE.pdf

7) http://page.mi.fu-berlin.de/oezbek/pub/OezThiPre10-SNA.pdf

8) Greg Madey, Vincent Freeh, Renee Tynan, “The Open Source Software Development Phenomenon: An analysis based on social network theory”; 2002 . Eighth Americas Conference on Information Systems.

:Yatin Mulky