What exactly is social entrepreneurship? – Impression Management
Social Entrepreneurship, Language, and Funding
Evidence from startups in sub-Saharan Africa
Natalie Carlson, Columbia Business School / ncarlson19@gsb.columbia.edu
Key feature of social entrepreneurship: it steps in when commercial markets fail to meet a social need
What constitutes market failure in a developing country context, when a substantial percentage of economic activity takes place in informal markets to begin with?
Does the line between social and commercial entrepreneurship become fuzzier in this setting?
If it is possible for ventures to position themselves deliberately along the continuum from social impact driven to purely profits-oriented, by means of language cues, what might be their reasons for presenting in a particular way?
Roadmap
Develop measures of social impact orientation based on the language that companies use to describe themselves
Examine relationship between this orientation and funding outcomes
Impression Management
Extended from an individual-level theory, organizational impression management refers to a variety of tactics an organization might use to influence an audience’s perception (Bolino et al. 2008)
In particular, entrepreneurial ventures may use impression management to confer legitimacy and acquire resources from various stakeholders (Lounsbury & Gynn 2001, Zott & Huy 2007)
(Lounsbury & Glynn 2001)
(Marchant 2015)
On the other hand...
Social enterprises can be "'easily dismissed as things around NGOs which are largely ineffective'"(Marchant 2015)
If entrepreneurs are indeed “skilled cultural operators,” how will they cultivate their image to maximize potential resource acquisition in this environment?
DATA
Self-submitted descriptions of 844 ventures across four countries
SafeBoda is the Uber for the motorcycle taxis in low and middle income countries. Given the informal nature of the market, our model focuses on safety, security and building trust between the driver and passenger. We aim to transform the motorcycles taxi industry (+20 million of them) and ultimately revolutionize transportation and logistics services in the most rapidly growing cities in the developing world.
Approach 1: Supervised
Mechanical Turkers read and annotate company descriptions as social entrepreneurship or not
Averaging across all raters generates a Social Orientation Score for each firm between zero (least social) and one (most social)
Hypothesis 1: Controlling for country and industry effects, firms with greater mTurk rater disagreement on whether or not they constitute social entrepreneurship will have better funding outcomes.
Approach 2: Unsupervised
LDA Topic Modeling
Hypothesis 2: Controlling for country and industry effects, firms with high proportions of both highly social and highly commercial topics in their descriptions, based on the LDA model, will have better funding outcomes.