Genderizing LinkedIn for Social Studies

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LinkedIn has become the largest professional network, globally. It is a rich source of data for social researchers working on education, skills and the job market. LinkedIn doesn’t record gender, so researchers need to infer gender from the profile’s personal names. The methodology, although sometimes debated for it’s binary classification of gender, offers many benefits. It is highly accurate when it comes to measuring a gender gap : the error rate is typically less that ~1%, when professional gender gaps are usually measured in two-digits (see airline pilots, angel investing, science).

One benefit of NamSor Gender API compared to using baby name statistics, is its accuracy in an international context : NamSor recognize at once the likely gender and country of origin, to improve precision. Karen is generally a female name, but in Armenia it is a male name – so Karen Petrossian is a male name. Same goes with Jean (Jean Durieux vs. Jean Parker), Andrea (Andrea Rossini vs. Andrea Weston) etc.

The University of Münster and University of Liechtenstein paper below present a complete methodology to analyse  ~10,000 LinkedIn profiles, representative of human capital in a specific field (Business Process Management).

A Gender Perspective on Business Process Management Competences Offered on Professional Online Social Networks

  • Gorbacheva, Elena, University of Münster – ERCIS, Münster, Germany,
    elena.gorbacheva@ercis.uni-muenster.de
  • Stein, Armin, University of Münster – ERCIS, Münster, Germany,
    armin.stein@ercis.uni-muenster.de
  • Schmiedel, Theresa, University of Liechtenstein, Vaduz, Liechtenstein,
    theresa.schmiedel@uni.li
  • Müller, Oliver, University of Liechtenstein, Vaduz, Liechtenstein, oliver.mueller@uni.li

Abstract : While Business Process Management (BPM) originally strongly focused on Information Technology as a key factor driving the efficiency and effectiveness of organisational processes, there is a growing consensus that BPM represents a holistic management approach that also takes factors like corporate governance, human capital and organisational culture into account. Focusing on human capital, our exploratory study examines competences supplied in the BPM field and how far they represent the holistic nature of BPM. Further, our study tries to understand, whether the BPM field, which is traditionally perceived as very technical, is not immune to the challenge of female underrepresentation. Addressing underrepresentation of women in BPM would help to mitigate the existing competence shortage in the field that stems from the lack of qualified BPM professionals. Thus, we take a gender perspective in analysing 10,405 BPM-related LinkedIn profiles using a text mining technique called Latent Semantic Analysis (LSA). We identify 12 distinct categories of competences supplied by BPM professionals, which, in general, reflect the interdisciplinary nature of BPM, ranging from technical to managerial and domain-specific competences. Analysis of the gender distribution shows that women are underrepresented among the BPM professionals under study and, in particular, among those representing most of the identified categories of competences.

Keywords: business process management, competence analysis, LSA, gender diversity, BPM workforce.

Download in PDF format

Call for Submissions: Big Data for Gender Challenge

LinkedIn is not the only source of big data that can bring incredibly useful insights about the gender gaps in economy, in society : there are thousands of databases (open data, commercial data, or even corporate databases) that have never been researched with gender or ethnic data points.

Big data holds great promise in addressing the lack of knowledge about key aspects of the lives of women and girls—the global gender data gap. Over the past several years, Data2X, with support of the Bill & Melinda Gates Foundation and the William and Flora Hewlett Foundation, has funded a series of projects to explore how big data can be applied to this goal (see summary report for more details).

Building on this first phase, we are pleased to announce a Big Data for Gender Challenge. Researchers are encouraged to 1) use a combination of digital and conventional data sources to conduct gender analysis on a specific research question, or to 2) submit proposals for building practical tools to monitor the well-being of women and girls over time.

Although the competition is open to all ideas that address the well-being of women and girls, priorities include mental health, violence, political participation, individual-level poverty/well-being, migration, and other topics that traditional sources of data have not been able to address at the necessary frequency and spatial resolution. Refer to Mapping Gender Data Gaps for a non-exhaustive list of gender data gaps; priority gender data gaps are also those that map to the Sustainable Development Goals framework (refer to this non-exhaustive list of gender-related SDG indicators).

Don’t wait too long : submissions are due 7th July.

About NamSor

NamSor™ Applied Onomastics is a European vendor of sociolinguistics software (NamSor sorts names). NamSor mission is to help understand international flows of money, ideas and people.
Reach us at: contact@namsor.com

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