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Field
Intervention (Hidden)
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Before
1. THEORETICAL AND EMPIRICAL CONTEXT
Black entrepreneurs in the United States are notably disadvantaged relative to their White counterparts. This disadvantage primarily stems from differential access to resources (Bates, Bradford, & Seamans, 2018). Although scholars have closely attended to differentials in the acquisition of financial capital by Black entrepreneurs (e.g., Fairlie, Robb, & Robinson, 2022; Younkin & Kuppuswamy, 2018), less attention has been given to differentials in the acquisition of social capital, or durable networks of social relationships granting access to actual and potential resources (Bourdieu, 1986). However, social capital is an important resource for entrepreneurs (Gedajlovic et al., 2013), and it is a form of capital particularly sensitive to racial dynamics (Putnam, 2007).
To explore the relationship between race and the acquisition of social capital by entrepreneurs, we offer a series of hypotheses tested in the context of LinkedIn, the most used professional social network in the United States. Entrepreneurs used LinkedIn to acquire social capital, such as mentors, potential collaborators, and fellow entrepreneurs. Furthermore, because there is a strong norm for the inclusion of a headshot photograph, race is very salient in the context of LinkedIn.
2. HYPOTHESES
H1: White entrepreneurs will be more successful in attracting the attention of and establishing a connection with potential mentors than are identical Black counterparts.
H2: Racial match between the potential mentor and entrepreneur increases the likelihood of successfully attracting attention and establishing a connection for Black entrepreneurs more than it does for White entrepreneurs.
H3a: Black entrepreneurs who signal activist orientation will be more successful in attracting attention and establishing a connection with Black potential mentors relative to Black entrepreneurs who do not signal activist orientation.
H3b: Black entrepreneurs who signal activist orientation will be less successful in attracting attention and establishing a connection with White potential mentors relative to Black entrepreneurs who do not signal activist orientation.
H4: Black entrepreneurs signaling activist orientation will convert the attention of White potential mentors into a connection at a greater rate than will Black entrepreneurs who do not signal activist orientation.
3. METHODOLOGY
3.1 Design
3.1.1 Constructing the LinkedIn Profiles
We construct identical LinkedIn Profiles for three fictional entrepreneurs. The profile includes a description of a fake tech company and its associated product . The profile also includes educational history and connections. The only differences between the three profiles is in the profile picture. For the activist profile, we add a ribbon to the profile picture declaring #equalitynow for one of the Black profiles. In order to construct verifiable accounts, we used faces and names of the research team and their colleagues. To ensure match on important characteristics we manipulated the three faces using an AI facial manipulation software, generating a total of 25 variations of the three faces. Each variation was independently rated by at least 50 raters recruited via MTurk along the dimensions: attractiveness, trustworthiness, friendliness, competence, intelligence, and age. One of the three faces required two additional rounds of manipulation and rating to achieve a match on the dimensions. The three faces selected were all evaluated as being between 37 and 39 years old with no significant differences on any of the aforementioned dimensions. Thus, we will be able to conclude that any differences generated by the different profiles will be due to the two treatments (entrepreneur race and entrepreneur activist signaling).
3.1.2 Constructing the sample of potential mentors
We use two samples to test our hypotheses. We build Sample A from a national network of entrepreneurship mentors. The network includes 1850 mentors, of whom 1261 have identifiable LinkedIn accounts. We use Sample A to test H1, H3b, and H4. However, due to a lack of racial diversity in the Sample A network, it is not suitable to test H2 and H3a, which both require a substantive presence of Black mentors in the sample. We therefore build Sample B from a second national network of mentors. The Sample B network includes 1193 mentors, and because a LinkedIn account is required by the network, our Sample B comprises 953 mentors.
POWER ANALYSIS – based on pilot study
H1
MDE: White profiles will have a 38% conversion rate of connection requests (this close to 40% which is the average provided by LinkedIn). Black profiles will have a 39% conversion rate of connection requests.
Power: 0.8 alpha 0.05 and Beta 0.02
This is results in a sample of 862
3.1.3 Protocol
The mentors will be randomly assigned to one of three conditions (LinkedIn profiles), two treatments (the Black profile and Activist profile) and a control (the White profile). The randomization for Sample A is stratified on gender, location, and LinkedIn activity. We stratify on the latter because not all mentors appeared to be active LinkedIn users. The randomization for Sample B is stratified on race, gender, and location.
Each mentor will be assigned only one profile. The profile will send out a connection request without an invitation message.
3.2 Variables
We have three independent variables. Entrepreneur Race is coded 0 for the White profile and 1 for the Black profile. Mentor Race is similarly coded 0 for White and 1 for Black. Activist Signal is coded 0 for profiles without the #equalitynow ribbon and 1 for those profiles with the ribbon.
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After
1. THEORETICAL AND EMPIRICAL CONTEXT
Black entrepreneurs in the United States are notably disadvantaged relative to their White counterparts. This disadvantage primarily stems from differential access to resources (Bates, Bradford, & Seamans, 2018). Although scholars have closely attended to differentials in the acquisition of financial capital by Black entrepreneurs (e.g., Fairlie, Robb, & Robinson, 2022; Younkin & Kuppuswamy, 2018), less attention has been given to differentials in the acquisition of social capital, or durable networks of social relationships granting access to actual and potential resources (Bourdieu, 1986). However, social capital is an important resource for entrepreneurs (Gedajlovic et al., 2013), and it is a form of capital particularly sensitive to racial dynamics (Putnam, 2007).
To explore the relationship between race and the acquisition of social capital by entrepreneurs, we offer a series of hypotheses tested in the context of LinkedIn, the most used professional social network in the United States. Entrepreneurs used LinkedIn to acquire social capital, such as mentors, potential collaborators, and fellow entrepreneurs. Furthermore, because there is a strong norm for the inclusion of a headshot photograph, race is very salient in the context of LinkedIn.
2. HYPOTHESES
H1: White entrepreneurs will be more successful in attracting the attention of and establishing a connection with potential mentors than are identical Black counterparts.
H2: Racial match between the potential mentor and entrepreneur increases the likelihood of successfully attracting attention and establishing a connection for Black entrepreneurs more than it does for White entrepreneurs.
H3a: Black entrepreneurs who signal activist orientation will be more successful in attracting attention and establishing a connection with Black potential mentors relative to Black entrepreneurs who do not signal activist orientation.
H3b: Black entrepreneurs who signal activist orientation will be less successful in attracting attention and establishing a connection with White potential mentors relative to Black entrepreneurs who do not signal activist orientation.
H4: Black entrepreneurs signaling activist orientation will convert the attention of White potential mentors into a connection at a greater rate than will Black entrepreneurs who do not signal activist orientation.
3. METHODOLOGY
3.1 Design
3.1.1 Constructing the LinkedIn Profiles
We construct identical LinkedIn Profiles for three fictional entrepreneurs. The profile includes a description of a fake tech company and its associated product . The profile also includes educational history and connections. The only differences between the three profiles is in the profile picture. For the activist profile, we add a ribbon to the profile picture declaring #equalitynow for one of the Black profiles. In order to construct verifiable accounts, we used faces and names of the research team and their colleagues. To ensure match on important characteristics we manipulated the three faces using an AI facial manipulation software, generating a total of 25 variations of the three faces. Each variation was independently rated by at least 50 raters recruited via MTurk along the dimensions: attractiveness, trustworthiness, friendliness, competence, intelligence, and age. One of the three faces required two additional rounds of manipulation and rating to achieve a match on the dimensions. The three faces selected were all evaluated as being between 37 and 39 years old with no significant differences on any of the aforementioned dimensions. Thus, we will be able to conclude that any differences generated by the different profiles will be due to the two treatments (entrepreneur race and entrepreneur activist signaling).
3.1.2 Constructing the sample of potential mentors
We use two samples to test our hypotheses. We build Sample A from a national network of entrepreneurship mentors. The network includes 1850 mentors, of whom 1261 have identifiable LinkedIn accounts. We use Sample A to test H1, H3b, and H4. However, due to a lack of racial diversity in the Sample A network, it is not suitable to test H2 and H3a, which both require a substantive presence of Black mentors in the sample. We therefore build Sample B from a second national network of mentors. The Sample B network includes 1193 mentors, and because a LinkedIn account is required by the network, our Sample B comprises 953 mentors.
POWER ANALYSIS – based on pilot study
H1
MDE: White profiles will have a 38% conversion rate of connection requests (this close to 40% which is the average provided by LinkedIn). Black profiles will have a 39% conversion rate of connection requests.
Power: 0.8 alpha 0.05 and Beta 0.02
This is results in a sample of 862
3.1.3 Protocol
The mentors will be randomly assigned to one of three conditions (LinkedIn profiles), two treatments (the Black profile and Activist profile) and a control (the White profile). The randomization for Sample A is stratified on gender, location, and LinkedIn activity. We stratify on the latter because not all mentors appeared to be active LinkedIn users. The randomization for Sample B is stratified on race, gender, and location.
Each mentor will be assigned only one profile. The profile will send out a connection request without an invitation message.
3.2 Variables
We have three independent variables. Entrepreneur Race is coded 0 for the White profile and 1 for the Black profile. Mentor Race is similarly coded 0 for White and 1 for Black. Activist Signal is coded 0 for profiles without the #equalitynow ribbon and 1 for those profiles with the ribbon.
To analyze variation in outcomes, we will use the different variables we have from the scraped LinkedIn and Score data as control/interaction variables. These include but are not limited to profile activity, image features, mentioning SCORE in profile, etc. We have closed the data collection of SCORE mentors on 2/13/2024. The FI data collection will start in late March.
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