Hubs: Hubs Data

Background

This page represents the work used for mechanical turks for the paper: Hubs (Academic Paper). As of Spring 2016, a list of potential Hubs with a set of characteristics was created. Many of these are not what will be defined as Hubs. We will be creating a scorecard to help subjectively define Hubs based on certain characteristics.

For more information on Mechanical Turks in general, see Mechanical Turk (Tool).

The main goal of the mechanical turk is to automate the collection of variables for potential hubs as much as possible. The key steps for the project are:

1. Creating a comprehensive list of potential hubs
2. Determining the best variables for the scorecard
3. Building "filters" for automating the collection
4. Running and auditing of the automation
5. Collecting the remaining manual data

Variables to be Used

Current Complete List

As of Week of 7/11

1. Onsite Venture Capital
   • Assets Under Management
   • Number
2. Onsite Angel Investors
3. Onsite Mentors
4. Founding Date
5. Site URL
6. Office hours investors
7. Office hours mentor/advisors
8. Onsite temporary workshops
9. Onsite mentors
10. Networking Meetups
11. Sponsors/Partners
    • University
    • Corporate
12. Curriculum
13. Onsite code school
14. Alumni Network
15. Nonprofit status
16. Mission statement
17. Specific Industry
18. Price for a space
19. Price for office
20. Twitter activity
21. Size (sqft)
22. Size (# companies)
23. Onsite accelerator
24. Community membership??
25. Franchise
26. Multiple locations within city

Grouping of Variables

There are a few categories the majority of the variables fall under

Group 1: Low Hanging Fruit Variables in this group are very easy to find and automate.

1. Price for a space + office
2. Twitter Activity
3. Founding Date
4. URL
5. Mission Statement
6. Nonprofit
7. Sponsors/Partners

Group 2: The Difficult to Find There are certain variables where the information is not readily available online or difficult to find.

1. Size (can try to find press releases)

Group 3: In Between 1 and 2 Variables that aren't too easy or difficult to find and automate.

1. Onsite accelerator
2. Alumni mentor

Group 4: The Hard to Differentiate The key property of this group is that there are several similar variables, which would be difficult for a turk to differentiate. In order to fix this, we will need to create filters akin to the DSM5 scorecard. See the below section.

1. Onsite VC v. Angel Investors
2. Onsite OH Investors v. mentors
3. Onsite temporary workshops v. networking events
4. Curriculum v. code school

Group 5: The Need further Discussion Before Collection Variables that need to be developed more prior to collection.

1. Franchise and multiple locations within a city
2. Community Membership

Filters/Scorecard

General Approach

The Scorecard will be broken down into three main parts: description, characteristics, andTBD parts. The procedure for creating these will be as follows: the description will be determined, develop the characteristics after looking over examples, the creation of possible mechanical turks that have complete accuracy even if not comprehension (e.g. a task will that always guarantees that there is an onsite mentor that covers only 40% of firms, but never misspecifies the existence of mentors), and auditing of the results.

Example

Curriculum

• Desc: The potential hub provides training programs for the founders of startups that might have human capital deficits that will lead to them not being about to adequately implement their ideas.
• Characteristics:
  • Education that is for a founder (as opposed to code schools which can be for people who just want to join a startup)
    • Code schools are for startup labor supply
  • Active input into a current entrepreneurial endevour
    • e.g. " The program is designed to augment and support the real-life business experiences that the students are facing every day in their entrepreneurial endeavors"
  • Not an ad hoc session, not a one time meeting but a full "course", evidence of this could be
  • Has evidence of a integrated curriculum leading to a new compentance
  • Has evidence of a set fixed start and end dates that last XXX long
  • Is a session linked to others that regularly occurs
• TBD points
  • Do we care about outsourcing?

Additional Resources

1. Mechanical Turk (Tool)
2. Veeral has created a google automating procedure for different lists

OLD1

We will be creating a "Hubs scorecard" to determine how hub-like potential spaces are. In order to do so, we will evaluate the places based on certain variables. Previous variables for potential hubs were collected. Below, we list those as well as other variables we think might be helpful to build out the scorecard.

Ideally, we would have the following variables (not collected previously):

1. Onsite VC/Angel/Investors (Count or binary)
   1. Comments:
   2. Mechanical Turk Comments:
2. Onsite Mentors (binary) --- Are these the same as advisers?
   1. Comments:
   2. Mechanical Turk Comments:
3. "Office hours" with investors or mentors (binary)
   1. Comments: Previously collected included number of events, but did not separate them into categories (e.g. networking events, workshops, etc.). We view this separation as important, BUT very difficult to collect
   2. Mechanical Turk Comments:
4. Onsite temporary workshops (binary or count) *** see mechanical turk
   1. Comments:
   2. Mechanical Turk Comments:
5. Networking Meetups (Binary or count) *** see mechanical turk
   1. Comments:
   2. Mechanical Turk Comments:
6. Sponsors and Partners (binary and list) --- are these the same?
   1. Comments:
   2. Mechanical Turk Comments:
7. Alumni Network (binary) --- not all potential hubslist this and the fact that some do might indicate its importance
   1. Comments:
   2. Mechanical Turk Comments:
8. Num of Companies --- to help determine size as getting physical sqfootage is difficult
   1. Comments:
   2. Mechanical Turk Comments:
9. Nonprofit (binary) --- helpful in determining goals of potential hubs
   1. Comments:
   2. Mechanical Turk Comments:
10. Mission Includes Key Buzzwords (e.g. "ecosystem", "community") --- help separate simple coworking spaces form hubs

Example of Prior Variables Collected:

• Specific Industry -- defined as LinkedIN Self Identifier, no categories just plain text. We think what we really want is to see if they have a specialty (e.g. healthcare)
• Num of Events --- relatively complete inputs, but from March 2016 (see above as well)
• Price for Single Space --- defined as price for flexible desk, relatively complete inputs
• Price for Office --- no inputs
• Twitter Activity (Multinomial or Count) --- High=2/Moderate=1/No=0, no explanations on how to categorize the activity. Also no handles
• Size (sqft) --- no records for majority of the companies
• Num Conference Rooms --- no records for majority of the companies
• Onsite accelerator (binary) --- relatively complete inputs
• Onsite code school (binary) --- relatively complete inputs
• Community Membership (binary) --- relatively complete inputs

OLD2

• Twitter activity:

UPDATE (7/14): Updated turk to reflect our desired formats UPDATE (7/12): AUDIT RESULTS: We noticed

UPDATE (7/11): uploaded and published on amazon's mechanical turk site. Given the time cost to either record number of tweets in a month or look up more than 10 tweets, we decided to record the date of the last 10th tweet. Using a sample of ~10 companies, We noticed minimal differences in data observations among using 10,20, and 30 tweets.

1. Copy the text in the Search Text into a search engine.
2. Click on result from twitter.com with the company name. If the link does not appear on the first 3 pages, record DNE for both outputs
3. Record the company's Twitter Handle into Twitter Handle
4. Record the date (MM/DD/YY) of that tweet for Twitter Activity. If there are less than 10 tweets, record DNE.

• NUMBER OF EVENTS: UPDATE: written, not published, on amazon's mechanical turk site

Considerations

• Difficulties Encountered:
• Expected Time to Complete:
• Expectation of Results (accuracy of turk, comprehensiveness):
• Other Comments:

Procedure

1. Copy the text in the Search Text into a search engine.
2. Click on the result that is the website of the company. If there does not exist a listing on the first three pages, mark as DNE.
3. Look for links related to events, such as 'Events' or 'Calendar' on the homepage.
4. If not found on the homepage, check 'About' and check 'Community'
5. Count the number of events in July 2016 and record it. If there is no information of events on the website, record DNE.

Note***: Events include meetups, workshops, info sessions etc. We do not want to count them separately since it is difficult to do so. Most companies put all the events on the same section and do not put event types in the titles of the events. We have to look into the details of the events to find out the type and even we do so some events descriptions do not allow us to determine the type easily. Differentiating the types of the events demands more time and effort and therefore is not suitable to be a mechanical turk project.

• Onsite Mentors: UPDATE: written, not published, on amazon's mechanical turk site

1. Copy the text in the Search Text into a search engine.
2. Click on the result that is the website of the company. If there does not exist a listing on the first three pages, mark as DNE.
3. Look for links related to mentorship such as 'mentors', 'mentorship' or 'mentoring programs'
4. If the key words can be identified, mark as 1
5. If there is no explicit 'mentoring' section, look for links related to a description of the company, such as: 'About,' 'Our Team,' 'Our Mission,' etc., look for a subsection or mention of mentor/mentorship/mentoring
6. If these exist, mark as 1.
7. If not, go to links related to membership 'benefits,' 'perks,' or related.
8. Do same process as end of 4 and 5
9. If there is no mention of mentorship in these sections, type the company, city, and 'mentoring' into a search engine. If a link to a reliable website (such as Desktime) appears and mentorship can be found in the description, mark as 1.
10. If none of these steps result in a mark of 1, mark as 0

• Nonprofit: UPDATE: written, not published, on amazon's mechanical turk site

1. Copy the text in the Search Text into a search engine.
2. Click on the result that is the website of the company. If there does not exist a listing on the first three pages, mark as DNE.
3. Go to links that describe the company, usually they are labelled: 'About', 'Our Story,' 'Mission'
4. Look for the key word 'nonprofit'/'non-profit'
5. If 'nonprofit' is identified, mark as 1, otherwise 0.

• Number of Members: UPDATE: written, not published, on amazon's mechanical turk site

1. Copy the text in the Search Text into a search engine.
2. Click on the result that is the website of the company. If there does not exist a listing on the first three pages, mark as DNE.
3. Look for the link 'Members' or 'Residents', usually they are under the links 'Community', 'Membership', 'Our Space' or 'The Space'.
4. Count the number of members
5. If the link or section of 'Members' is not found, go the 'Community' and 'Coworking' and look for the description on number of startups/founders/members in the community. Record the number.
6. If number of members cannot be identified using above steps, record DNE.

• Sponsors and Partners:UPDATE: written, not published, on amazon's mechanical turk site

1. Copy the text in the Search Text into a search engine.
2. Click on the result that is the website of the company. If there does not exist a listing on the first three pages, mark as DNE.
3. Look for the link or mention of 'Sponsors' or 'Partners', many times of which is often under the section of 'About', 'Community', or related sections
4. If sponsors or partners can be found mark as 1 and list them, otherwise mark as 0.