Tesla v. Edison: FAST Company article on the importance of social networks in innovation

Thought folks might enjoy this FAST Company article that discusses the shift from a knowledge economy to a social economy.

SNA and CSR communications on Twitter

Katie Ferrari

Proposed SNA blog post

DHP P217 (for credit)

Background:

A recent study by Cone Communications found that 63 percent of consumers say they don’t know where to find information about a company’s CSR efforts and results. The same study found that 86 percent of consumers are more likely to trust a company that reports its corporate social responsibility (CSR) results. Meanwhile, 40 percent of consumers say they won’t purchase a company’s products or services if CSR results are not communicated, yet more than half don’t understand the impact they are having when buying a product from a company that says it is socially responsible.

The stakes are real for companies to address the gap between consumer demand for CSR reporting and the lack of CSR knowledge. Increasingly, stakeholders are holding corporations accountable for their social and environmental impact, subjecting them to greater scrutiny and expecting greater engagement. Ineffective CSR communications can translate to reduced consumer purchases, while robust communications can build brand trust. Given the impact of Web 2.0 in a dramatically changed media landscape, are online social network applications addressing the CSR communications gap?

A popular social network platform, Twitter significantly lowers the barriers to interacting with stakeholders, both in terms of listening and pushing out information. My goal with this project is to understand role that Twitter plays in the online CSR communications ecosystem. Anchoring on Interbrand’s 2012 list of Best Global Green Brands, which evaluates the world’s top brands on the basis of their performance as well as the public’s perception, I will use social network analysis to map the ego networks of companies that use a dedicated Twitter handle to communicate their CSR activities. By analyzing Twitter’s efficacy as a tool for CSR communications, I hope to draw insights around when and how it is valuable.

Research Question:

Is Twitter an effective tool for CSR communications?

Questions & Methodology:

A social network approach can help determine the structural embeddedness of a company’s Twitter account, which can in turn be used to infer whether the tool is effective for CSR communications. While Twitter itself doesn’t necessarily capture social networks or build social relationships, it does act as a carrier for other meaningful information amongst a self-selected network of nodes. Using NodeXL and UCINET, I plan to analyze RT and follower networks as well as the #CSR hashtag network to in order to answer questions such as the following:

-       What does a company’s follower network look like? Of the obvious stakeholders, who is represented, and who missing?

-       Does the company have a two-way relationships with its followers?

-       Does a company’s Twitter account help it penetrate customer or advocacy groups?

-       Does Twitter help a company become a betweenness hub, or does a company remain on the periphery of a discussion about its CSR practices?

-       Does a company’s Twitter account help it broker conversations about its social and environmental impact?  Where are the structural holes?

-       And finally, with regards to the #CSR hashtag network, which users have the greatest Eigenvector centrality (influence)? Are they engaging with the company in question? Is the engagement supportive or critical? Do connect groups that wouldn’t otherwise be connected?

Attribute data:

-       Sector (nonprofit, private, public)

-       Field (academic, activist, media, marketing, social enterprise)

-       Geography

-       Gender (male, female, non-gendered)

Hypothesis:

Yes, Twitter is an effective tool for CSR communications, but only under specific conditions. The conditions will be the interesting part—I assume that content issues including consistency and clarity of CSR message have a role to play, but I’m especially curious about network issues, including whether the #CSR hashtag increases visibility and influence, whether the difference between two-way and one-way follower relationships affects embeddedness, and whether Twitter really is connecting stakeholders to companies.

Personal doubts/questions:

-       Data: there’s a lot of it, and it’s difficult to wrap my head around it. The Twitter API is open, and I know I can tap into it with the help of more technically inclined friends. This makes me wonder whether I shouldn’t try to focus on one company and do a longitudinal study (by downloading data with NodeXL, I don’t have access to all the Twitter history—it’s more of a snapshot in time). If I could access this data, I could potentially focus on a company like Nestle, which is one of the most trusted global company’s according to the Reputation Institute, and analyze its Twitter networks pre and post-the KitKat/palm oil scandal. My gut tells me that Twitter has a far greater role to play in CSR crisis communications than any other type of CSR comms in terms of stewarding brand value.

-       Depth: I’m having a hard time figuring out how much is too much versus too little—the reason why I’m focusing on Interbrand’s Global Green Brands is that it allows me to pick the best performers across multiple industries, which is potentially more interesting from a strategic point of view. But I’m concerned it might be too much to dive into multiple companies. I’m not sure yet.

-       Measurement: what does it mean for a CSR communications platform to be “effective”? I’m being fairly literal about this—does Twitter help companies interact with folks they wouldn’t otherwise reach, and does that dialogue involve mission-critical topics? There’s probably more. 

SNA Project Proposal: Jane Kaminski

Background:

This project builds upon the social network research for the HHL MBA full-time and part-time students. With many students commuting in from long distances, the school has been working to improve networking and student connections.  Previous research revealed that Fridays were a time when students could most easily interact with one another, so a "TGIF" Happy Hour was established to encourage socializing, with some success.

Objective:

The objective of this network analysis will be to identify and exploit further opportunities to deepen student connections to build a more cohesive, connected student body. 

Central Question:

What can be done to strengthen connections and improve density for the HHL full-time and part-time MBA students?

Hypothesis:

I hypothesize that an opportunity for deeper student engagement could include an enriched orientation program, intramural team sports, language table, or class research trips that target students with mutual areas of interest.

Methodology:

This analysis will build from the surveys already produced in HHL research.  We will write a survey that will further study student interests, means of communication, and connections to classmates.  Once the survey results are collected, we will analyze factors that draw cliques together, created new connections, and take into account the results of the polling to prescribe new programming to deepen student connections.

Survey questions could include:

Who are the top 5 people you interact with?

What is your main method of communication outside of the classroom?

Would you be interested in participating in an intramural sports team?

If so, which of the following sports are  you interested in playing?

Would you be interested in going on an informational weekend trip to learn more about XXXXXX businesses?

Would you be interested in a language round table to practice a non-native language?

If so, what language(s) would you be interested in practicing?

Due to potential linguistic limitations, these research questions will very carefully crafted before being presented to the students and reviewed by native German speakers to ensure clarity for the surveyed students.

Potential limitations:

Although these students have proven dedicated to responding to surveys in the past, a lack of responses from surveyed students could be a limiting factor to information.  As we've discussed, linguistic difficulties could also limit the quality of responses, so the survey will need to be very carefully written.  Another limiting factor could be students' lack of interest in undertaking extracurricular activities, which would limit the recommendations to strengthen connectivity.

SNA of Tanzanian Markets

SNA Project Proposal: Daniel Trusilo

To be undertaken for credit in D217

Research Question:

Who are the significant actors in the emerging market environment of Tanzanian?

Background:

The United States Military Academy Network Science Center (NSC) has been developing “tool kits” for U.S. Embassy staff in developing countries. In 2012 the NSC produced a series of reports for U.S. Embassy Ethiopia detailing network analysis done on the Ethiopian markets. The reports provide embassy staff with information that allows decision-makers the ability to identify key “nodes” that are influential in the developing markets based on organizations, schools, events, positions and locations as attributes. The analytical tools are intended to help develop “an effective engagement plan as well as a mathematically-backed set of recommended steps” in order to assist firms in entering the emerging market.

The NSC has collected similar data for Tanzania and would like an analysis done on the data set in order to identify key areas of interest as an initial step towards preparing similar “tool kits” for U.S. Embassy Tanzania.

For further information about the USMA NSC check out their blog: http://blog.netsciwestpoint.org/

Objective:

The objective of this network analysis will be to identify key agents or organizations that impact market entry in Tanzania. The analysis will aim to provide information in a similar format to that of the Ethiopian “tool kits” in order to provide decision makers with comparative data. Ultimately, this analysis is intended to assist the NSC’s efforts to shed light on engagement strategies for interested actors in emerging markets.

Hypothesis:

It is hypothesized that network analysis of the Tanzanian market actors will reveal certain agents and organizations that act as brokers of information and others that represent bridges, connecting multiple agents or organizations.

Methodology:

This analysis will first use similar methods as the NSC generated Ethiopia report in order to provide useful data for follow-on NSC objectives. To that end we will use a single-mode network model that demonstrates how agents are connected through organizations. This agent network model will be analyzed using four measures of centrality: degree centrality, betweenness centrality, closeness centrality and eigenvector centrality. An organizational network will also be analyzed using the same four-centrality measures.

Additionally, clique analysis will be conducted to identify groups within the emerging market actors. This analysis will allow the identification of organizations or agents that may act as one block when determining market entry. This will be particularly interesting to look at as the military often plays a significant role in both private and public organizations in many developing countries.

Other Considerations:

Because the data being analyzed will have been collected by a third party, biases can not be ruled out. Additionally, the data will represent a limited data-set as not all actors will have been identified by the data collection team in country due to limited local knowledge. It could be useful and interesting to “create” hypothetical agents and organizations in order to test the robustness of the findings and account for possible missing nodes.

Lastly, it will be important to underscore that all identified “key” actors in the market will have been identified for the slice of time being analyzed. As in many developing countries, situations on the ground often change on extremely fast time scales and key nodes may lose significance quickly. It would be interesting to conduct a follow-up study using the same analysis techniques with a second set of data in order to identify changes over time in key market actors.

GREAT article on efficient online searching

You may already know a lot of this, but I'll bet you'll pick up a handy tip or two.

Analyzing Nepotism and Favoritism Using Social Network Analysis

I will work with HHL Leipzig in the second module, however after completing my degree in Fletcher I would like to conduct an analysis of the practice of nepotism and favoritism within Indonesia Ministry of Foreign Affairs.

            As a newly democratic country after thirty-two years of authoritarian rule, Indonesia is gravely infested with corruption, collusion, and nepotism both in private and public sector. After the democratic reform, private sectors are more successful in combating such practice, however public sector seems to lag behind. I have been working for the Ministry of Foreign Affairs since 2008 and despite efforts to conduct internal reform, including redesigning of recruitment process, there are still alleged practices of nepotism and favoritism within the Ministry, especially among those who have connections with high-ranked officials, particularly family connections. The result of this alleged practice, among others, is uneven distribution of work, uneven frequency of overseas tour, and overseas posting outside of a person’s field of expertise.

            This allegation seems to be a black box because nobody has any idea how to reveal the truth. Also, young officials with well connections but not involved in such practices often find themselves under unwanted spotlight, which could lead to rumors that hinder productivity. I would like to analyze this situation using social network analysis.

            Due to the sensitivity of the issue, ideally a small team from the Ministry of Foreign Affairs’ Bureau of Human Resources should lead data collection. Human resources data such as formal and non-formal education background, fields of interest, in-field training received during service in the Ministry, and the existence of familial relationship with high officials are few among many data that the bureau can provide. The attributes from this first set of data later combined with attributes from social network survey that would cover:

• Current unit; whether or not compatible with educational background and fields of interests and/or expertise.
• Number of activities and/or projects conducted during service, and promotions received within a certain time frame; allegedly, under-achieving officials still receive promotion despite their performance due to practices of nepotism and favoritism.
• Relationship with colleagues. For this subject, there will be questions such as ‘name three people within the same unit that you consider as the ‘go-to’ person’. For junior officials the question would be: ‘in your scope of work, name three people who supervise your performance’, while for senior officials: ‘name three junior officials who perform best’, ‘name three junior officials who you supervise’, etc. Within the Indonesia Ministry of Foreign Affairs, there is a strict bureaucracy in terms of flow of work. Sometimes the flow is interrupted by bottlenecks, either caused by an underperforming official who does not supervise his juniors well, or vice versa.
• Frequency of foreign tour, the types of previous attended tours (summit meeting, working groups, conference, etc.), and assignment in each tour, whether or not compatible with area of expertise; There have been complains from member of delegates on delegates composition, for example person X attending overseas meeting that does not match his/her expertise, and had somebody else preparing the materials. Ideally, the person who prepared the material should be the one attending.
• Other desired information can be incorporated within the survey design.

Again, due to the sensitivity of the matter, in commencing data collection the survey language needs to be neutral or positive. It should show minimal hint towards any investigation nor give any chance to discredit a person. For instance, a survey question should be ‘name three junior officials who perform best’ instead of ‘name three junior officials who perform worst’. I realize the complexity of the matter and I predict this analysis to take a very long time if applied to the whole Ministry of Foreign Affairs. I would advise the Bureau of Human Resources to experiment in one of the sub-departments and see whether a more complete study is feasible. In the case of the Bureau of Human Resources decline to carry the analysis, I will apply the analysis in my immediate unit as per my return to office.

            Up to this moment, complains and concerns pertaining to alleged practices of nepotism and favoritism within government offices are seldom voiced. I hope social network analysis can help contribute to internal reform of the Ministry of Foreign Affairs, to help improve work performance among my peers, and to help give credit to whom the credit is due.

SNA on either organ trafficking or humanitarian professionalization

I will be taking the 2^nd module of this course.

Right now, I am looking into two possible SNAs: one on organ trafficking networks, and one on humanitarian professional networks. I will be choosing which one to pursue in the coming week, but for now I have laid out some possible approaches for each of them. The humanitarian professional network is more viable at this point, but I’m eager to look into whether the network on organ trafficking could work out. I'll comment on this same post once I can narrow this down a bit next week, but to start:
On organ trafficking:
Background: The sale of organs is illegal in almost all countries today, with the exception of Iran. In combination with persistent poverty and quicker/more affordable transportation, black market trading has filled the shortage of transplantable organs for those who need them. While broad data is difficult to come by, case studies show that the trade is global and that it implicates hospitals and health clinics across the world.
A few weeks ago, I reached out to an organization with a focus on organ trafficking to see if they would be willing to share some of their data. (They responded with a "yes.") Their researchers conduct background data and interviews to compile case studies of organ trafficking incidents. I will rely primarily on their research for this network analysis, supplemented by some quantitative research conducted by Yosuke Shimazono.
Ideally, this SNA would focus on the demand side of the trade: those who seek out organs are likely to be more networked than are victims. But unfortunately – and predictably – data on the demand side is even scarcer than data on the supply side. As such, this SNA would focus on the relationships between victims and victims, victims and brokers, and brokers and brokers.
Nodes = the individuals - victims and brokers
Links = whether one individual “knew” the other (not including victims who come to know other victims after the event). The bar for what constitutes “knowing” is low: for victims-brokers, “knowing” is about whether the broker managed the victim’s organ harvesting; for victims-victims, “knowing” is whether they had met the other before; for brokers-brokers, “knowing” is whether they have collaborated professionally with the other.    
Attributes =

•     How did the victim came into contact with the trafficker? (By newspaper advertisement? Mutual contact? By imprisonment? Other? )

•     Was the organ harvesting a coercive situation, for monetary gain, or for some other transactional benefit?

•     What type of organ was harvested?

•     Was the victim living or deceased at the time of the harvesting?

•     Was the victim an immigrant or native to the city/town where he/she first came into contact with the broker? If an immigrant, where was the city/state/country of origin?

•     Was the recipient already known to the victim? (E.g. in situations where the victim donated the organ to a distant relative for monetary gain.)

•     How far was the victim transported from his/her original site and the site of the surgery?

•     From what town/city/state/country does the broker come?

•     Is the organ broker involved in any other black market trades?

Challenges:

• Making sure this is really a network and not just a set of trafficker-nexuses with victims radiating outward, unconnected by anything.
• Issues of confidentiality – I’ve signed a confidentiality agreement but need to make sure that all measures are taken to ensure the anonymity of all individuals in the analysis – esp. given that I would be applying this toward the capstone.
• Quality + quantity of data – I need to diversify my sources and am limited by the fact that most available info is based on case studies.
• Most of all, I need to talk more with the organization. Ideally, there would be another angle to look at this from aside from victims --- but while info on victims is difficult to attain, info on the demand side is even harder.

On humanitarian professional networks:

For the past two years, I’ve been working as a research assistant at the Feinstein International Center and, since September, at the Program on Humanitarian Policy and Conflict Research (HPCR). Both centers have been taking steps toward professionalizing the humanitarian sector: agreeing upon a set of core competencies, planning training programs and qualification measures, integrating national and international disaster response professional standards, etc. These efforts build upon work by The Sphere Project, CBHA, PHAP, ELRHA, and others.
Right now, HPCR is looking at professional networks as a method of disseminating information and best practices across the humanitarian sector. By means of surveying and interviews, HPCR will ask questions such as: from which individuals do you gain the most information on innovations and best practices? Which five individuals do you know to be most concerned with this issue of professionalization? Centrality measures are important here, as the group looks to use the network analysis to organize a group of key actors to the professionalization effort. Attributes include geographic origin, humanitarian agency/organization, and level of leadership. Challenges include: too wide a dispersion of answers to identify key leaders; political limitations of the social network analysis (e.g. geographic, type of profession, leadership level); too low a number of responses to be accurate and not self-selecting (those who are most “in the know” about who is concerned with professionalization may be the best trained – thereby leaving out those on the margins who need training most).

SNA to analyse Fletcher's MIB program

By Anna Valeria Zuccolotto Soto, not taking the second module this semester but planning to take this project next year as second module or as independent study.

Overview:

In the US only there are thousand of master programmes from where to choose. It is true that there are only 8 Universities in the US that are considered “very prestigious” and are called the Ivey Leagues, but because education in the US at the master level is excellent, other schools have increased their level and a new label has been created to allocate them: “Mini Ivey Leagues”.

Rankings are a good source of information when taking the decision of where to go to school, but they offer limited information. Many of this school spend many of their budget in marketing their brand to keep the recognition. However, how much of what they sell us is actually true? What do they mean when they say “strong community sense” or “high alumni network”? I believe many of these schools are not able to quantify how really strong is their community sense or their alumni network. Most of these schools do know how many alumni they have and in which sectors they are working, but what about the connections among them and among current students?

I believe Fletcher could be the perfect school to try to understand more about its community sense, especially for their MIB program, which is 5 years new and there is a lot of work going on in trying to market it better every year in order to compete with the traditional MBA programs like HBS, Sloan, Wharton…etc.

Objective:

To compare what are the connections made during and after going through the MIB program at Fletcher. Is there any link of how these connections were made? Who is the most central person, and who has the highest betwenness?

Answering these questions will help understand more about the conformation of the MIB class and to understand what most of the people is looking for, in order to improve the program for future classes. The information provided by using SNA will definitely be interesting for the Admissions Office.

Methodology: 

Two surveys will be needed. The first one will be longer because it will need to build the attributes for the class of 2014 (gender, age, nationality, focus industry previous to Fletcher, focus industry after Fletcher). The second one however will be more important because it will let us know how strong the connections are, so to measure Fletcher contribution.

In both surveys it will be vital to address the Network Question. I am suggesting having 2 main questions. One question will be addressing the connection made in the personal arena, and the second will address the work/school related connections, to learn weather connections are made based on experiences in industries previous to Fletcher or new industries of interest acquired during their Fletcher experience.

-        Name 5 people from the MIB 2013 class that you feel more related to?

-        Who do you usually talk to regarding school/work related issues? Name 5

Some hypotheses:

MIB class is very diverse and come not only from different countries but also industries. So, maybe connections are initially made in base of industry interests but later probably moved to new interests. Perhaps nationality also plays a very important role.   

Any complication?

As with all surveys, there is always the risk of low participation from the class, especially for the second survey that will take place once the program has ended. Many people would have already moved ahead, get busier with work and may not be as excited about participating in this type of exercises. However I think this might not be an issue for this class, especially if another classmate, instead of the school itself, is doing the survey. Then again I also believe I could be very persuasive J