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Do you have what it takes to become a leader in an online community or are you just another lurker in the sea? Unfortunately, the odds are against you, as you need to contribute to become a leader. The 90-9-1 rule suggests that 90% of the population are lurkers and nearly never contribute content. The following 9% of the people are estimated to contribute 10% of overall contents, while the remaining 1% contributes 90% of the content (Nielsen 2006).

In order to explore what constitutes a leader Faraj et al. (2015) have created a framework that outlines several factors, which can lead to being identified as a leader by the community. The researchers build upon traditional behavior leadership theories, as well as structural network theories to create their framework.

The research

What did Faraj et al. examine (and why)?

Based on an evaluation of previous research on behavior in online communities Faraj et al. suggested that higher levels of knowledge contributions (KC), as well as more sociable behavior, could influence the likelihood of leadership. Furthermore, they build upon literature from social network theory and propose that higher levels of structural social capital (SSC) can again increase the likeliness of being identified as a leader. The idea of structural social capital is that people who are better connected are better of, as they have the potential of accessing more resources. Additionally, the researchers combine the two views and propose that structural social capital can act as a moderator between knowledge contribution/sociability and the likelihood of being identified as a leader. Faraj et al. suggest the influence of a) Knowledge contribution is higher and b) Sociability is higher when SSC is high compared to when SSC is low.

Additionally, the researchers expect that the tenure length, participation level amount of questions asked can influence the likelihood of being identified as a leader. The previously mentioned propositions and expectations have led to the following conceptual model:

How did they examine it?

In order to measure the relationships displayed in the conceptual model Faraj et al. gathered data from Usenet newsgroups. Usenet Newsgroups is one of the oldest online communities (1979), in which participants can gather information and discuss topics related to their common interests. The researchers examined messages posted on three un-moderated groups focusing on programming issues, as these have a high emphasis on knowledge collaboration.

In order to measure the KC levels, the researchers conducted a content analysis looking for elements containing code, procedural and declarative information. Furthermore, they examined the level of sociability through elements such as the presence of sign-offs, story-telling or thanking others. The measure of SSC was conducted through a social network analysis called betweenness centrality. It indicates how much a person is in the “middle” of a group. Lastly, leadership likeliness was measured by means of a survey by asking participants to identify three group leaders.

Main Findings:

KC levels are associated with leadership likelihood
Sociability alone does not lead to leadership identification
High SSC increases leadership likelihood
The previous statement is even more significant in presence of high levels of KC or sociability

Furthermore, the researchers found that the tenure length and participation levels are both highly related to the likelihood of being identified as a leader. Interestingly they also found out that asking questions has a negative effect on leadership identification likelihood.

Paper Discussion:

Faraj et al. have a strong approach of examining the phenomenon by incorporating behavioral as well as structural approaches. It allows for a more thorough understanding of the factors influencing leader establishments. Previous research has often examined influences individually, however, not simultaneously. Furthermore, from a managerial perspective, the paper provides first insights, which can help firms predict, which participants are likely to be seen as leaders. Establishing leaders amongst product communities can be of high importance, as these like to act as brand ambassadors and can moderate discussions amongst the community.

However, unfortunately, the paper is limited to knowledge collaboration in the area of programming, which compromises the generalizability of the research. Programmers tend to think in a different way and are said to have higher analytical skills (Elliott 2016). It is thus questionable if knowledge contributions would be seen as important and sociability as unimportant in other contexts. The results of the paper might thus be difficult to reproduce in other settings, such as product communities or different knowledge collaboration communities. In order to improve the generalizability of this paper, Faraj et al. could have examined further knowledge collaboration communities on Usenet. The opportunities to do so were plenty, as by 2005 there were already approximately 189.000 groups on the platform (Wang et al. 2013). However, all-in-all the paper yields a good first insight into the topic and serves as a good reference point for future research in this area.

Sources:

<a href=’https://de.freepik.com/vektoren-kostenlos/folgen-sie-dem-leader-konzept_805307.htm’>Designed by Freepik</a>

Elliott, E. (2016). Are Programmer Brains Different? – JavaScript Scene – Medium. Retrieved March 8, 2018, from https://medium.com/javascript-scene/are-programmer-brains-different-2068a52648a7

Faraj, S., Kudaravalli, S., & Wasko, M. (2015). Leading Collaboration in Online Communities. Mis Quarterly, 39(2).

Joshi, P. (2011). Advertisers Seek to Harness the Power of the Mom Blogger – The New York Times. Retrieved March 08, 2018, from http://www.nytimes.com/2011/03/15/business/media/15adco.html

Nielsen, J. (2006). Participation Inequality: The 90-9-1 Rule for Social Features. Retrieved March 8, 2018, from https://www.nngroup.com/articles/participation-inequality/

Wang, X., Butler, B. S., & Ren, Y. (2013). The impact of membership overlap on growth: An ecological competition view of online groups. Organization Science, 24(2), 414-431.

What is StudyDrive?

StudyDrive is essentially a mediating platform created for students, which enables easy access to study materials. The platform allows students to upload their documents, as well as to access the work of their peers. Whether it is lecture notes, book summaries, past exams, everything can be uploaded and shared. The platform has similar features to Dropbox or WeTransfer, as it allows people to share documents in a fast and convenient way. However, it takes these features and adds a layer on top by organizing the study materials in a convenient way.

How does it work?

As a first step, a student is asked to sign up through either Facebook login or E-Mail address. Once this step is completed the student can select the University he or she is currently enrolled in. Next, the webpage allows the student to choose his area of study and exact program and starting year.

When the student has completed all of the previous steps he should be shown a list of all courses, which he is enrolled in. By clicking on one of the courses, he can access all the study materials and discussions related to this course, which have been uploaded by his (former) peers.

In order to ensure the quality of the uploaded work StudyDrive has created a governance mechanism in which peers assess each other’s work. Students can rate documents and communicate potential mistakes by commenting directly on the document.

However, the start-up has only been founded in April 2013 and heavily relies on network effects, which need to develop over time. This means that the more students join and actively participate the more students’ benefit from it. StudyDrive does not create any content itself. It only provides the structure of the website & app and server capacities for storing the content. This means that the list of courses, study materials, and reviews have to be created by the customers themselves (students).

What are the benefits of participating?

From the perspective of the lazy student missing lectures, the derived benefits are quite clear: Easy access to study materials. But how can StudyDrive motivate the well-organized and ambitious student to share his materials? In order to ensure this StudyDrive made use of an incentive governance mechanism (Blohm et al. 2018). StudyDrive hands out prizes in the form of credit points for students uploading documents, which can be exchanged for prices ranging from posters to iPads. Furthermore, there is a reputation system in place in which students receive karma points for reviewing the work of their peers to further motivate participants (StudyDrive 2018).

Where did StudyDrive originate and how does the company generate revenues?

Philipp Mackeprang, the founder, and CEO of StudyDrive developed the idea after sharing some documents with his former peers at the Maastricht University (Hahn 2014). Originally they used Dropbox to share their files, however, as they uploaded more and more files the site became confusing and difficult to manage. The founder, later on, decided to create his own platform.

One of the major difficulties he was facing was shaping the business model. He realized that students would not want to pay for such a site. However, through his time at University, he realized what great length companies go through to get in touch with students (Hahn 2014). He thus decided to approach potentially interested companies, and it was a success. StudyDrive managed to partner with around 400 companies such as KPMG, Roland Berger or EY. The partnering companies not only advertise themselves on the site but also use it as a recruitment platform by posting job openings or internship possibilities (StudyDrive 2018).

So far, the start-up has managed to offer their partners access to a base of more than 450.000 students, mainly located in Germany, Austria, Switzerland, and the Netherlands. However, StudyDrive is currently expanding to other European countries. The future is looking bright for the company, but how many more students and companies will StudyDrive manage to engage? Only time can tell.

Bibliography:

Blohm, I., Zogaj, S., Bretschneider, U., & Leimeister, J. M. (2018). How to Manage Crowdsourcing Platforms Effectively?. California Management Review, 60(2), 122-149.