Market Research Project

Final
Project
Better Weekdays
Riyaz Vazir
Shivani Desai
Daniel Ryback
Reuben George
Pramod Karnam
William Edmondson

Marketing Mavens Marketing 450-31 Final Project
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Contents
Executive Summary.......................................................................................................................................3
Research Purpose and Objectives.................................................................................................................4
Key Questions to Answer:.........................................................................................................................4
Research Design............................................................................................................................................4
Exploratory Research................................................................................................................................4
Secondary Research..............................................................................................................................4
Primary Research..................................................................................................................................4
Quantitative Survey Phase........................................................................................................................4
Survey Recipient Selection....................................................................................................................4
Survey Design........................................................................................................................................5
Analysis and Results......................................................................................................................................6
Early Adopters...........................................................................................................................................6
Interest in Better Weekdays .....................................................................................................................6
Internal Effectiveness............................................................................................................................6
Value of Work Experience.....................................................................................................................6
Culture...................................................................................................................................................6
Salary.....................................................................................................................................................7
Social Media..........................................................................................................................................7
Factor Analysis ..........................................................................................................................................7
Pre-Factor..............................................................................................................................................7
Factors...................................................................................................................................................7
Factors and Better Weekdays...............................................................................................................8
Factor Regression..................................................................................................................................8
Cluster Analysis.........................................................................................................................................8
Cluster 1: Drone hires / External Sourcers............................................................................................8
Cluster 2: Will Google you! ...................................................................................................................8
Cluster 3: Traditionalists .......................................................................................................................8
Cross-Tab Analysis.....................................................................................................................................9
Gender vs. Cluster.................................................................................................................................9
Company Size vs. Cluster ......................................................................................................................9
Cluster Regression on Likelihood to Buy...................................................................................................9

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Conclusions, Recommendations, and Limitations........................................................................................9
Lessons Learned..........................................................................................................................................10
Appendix .....................................................................................................................................................12
A: Questions............................................................................................................................................12
B: Survey Durations.................................................................................................................................20
C: Early Adopters.....................................................................................................................................20
D: Relationship between Recruiting Efforts and Interest in Better Weekdays.......................................21
E: Factor Analysis ....................................................................................................................................22
Pre-Factor............................................................................................................................................22
Factor ..................................................................................................................................................22
Factor Regression................................................................................................................................23
F: Cluster Analysis ...................................................................................................................................23
G: Cross-Tab............................................................................................................................................24
Gender vs. Cluster...............................................................................................................................24
Company Size vs. Cluster ....................................................................................................................25
H: Cluster Regression on Likelihood to Buy ............................................................................................25

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Executive Summary
Better Weekdays is a startup company that offers a values-based job search option to MBA
graduates and recruitment service to companies and hiring managers. The service targets MBA
graduates from the top 30 business schools nationwide. The service aligns users’ social network with
their psycho-graph to generate a personalized report outlining where they are likely to succeed based
on their strengths, values and goals. To start, a member creates a free account, takes a validated
personality assessment test, provides his/her social network information and in return receives a
personalized report with recommendations of ideal roles, and eventually, ideal companies to work for.
This information, once validated, is offered to recruiters and hiring managers based on their needs in
terms of skill set, values, and culture fit. Currently, the company has three hundred members and is in
“beta” phase with a production website “betterweekdays.com”. The tagline for Better Weekdays is
“bring who you are to what you do”.
The purpose of our research project was to assist Better Weekdays understand the perception
of its target buyers, the segment that is in involved with the hiring process, towards the service. We also
set out to understand what additional features could help make the service more attractive. Ultimately,
we sought to provide Better Weekdays information about the characteristics of the different types of
organizations that would most likely use the service, most likely to pay for it, and even a bit of
information on how much buyers were willing to pay. In order to serve our objective, we started with
exploratory research, which netted us rich data and included a number of one-on-one interviews with
recruiters and hiring managers. Using the information we gathered through exploratory research, we
designed a survey and conducted a pre-test. Finally, we administered a survey and collected information
from 92 respondents.
Seventy-five percent of the respondents that took our survey were in some way involved in the
hiring process. Data from the survey indicates that likely early adopters are organizations with robust
internal sourcing systems and people who value social media as a gauge to measure a well-rounded skill
set. The survey data also indicates that 60% of those involved in hiring would be willing to pay for such a
service. We were not able to conclude specifically how much recruiters would be willing to pay for such
a service from the data collected and we recommend that this aspect be explored in separate future
research projects. Anecdotal comments indicate that buyers were open to a subscription based model,
starting from $49 per month, or a model which pays a onetime fee upon successful hire of a candidate,
up to $50,000 for each successfully recruited candidate. The survey data also concluded that providing
features that help recruiters’ ability to identify specific skill sets (e.g. IT) and additionally rate the
candidates based on these specific skill sets (instead of a broad rating of the individual)would increase
the value of the service.

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Research Purpose and Objectives
The purpose of the research for Better Weekdayswas to determine the key features that would
help position the service (product) towards the targeted early adopter segment. In our research we
attempted to answer several primary questions regarding this segment.
Key Questions to Answer:
1) Who are the early adopter customers (companies who hire top-tier MBAs) that value this service and
are willing to pay for the service?
2) Which features are most valued by these customers, by rank, to facilitate effective job placement?
Research Design
Exploratory Research
The focus of the exploratory research phase was to delve deeper into the question posed by our
client, “Which companies will be the early adopters of the product? “As we set out to answer this
question, we wanted to understand what challenges, if any, did employers encounter in recruitment and
if the product provided by Better Weekdays offereda solution to these problems.
As part of our exploratory research, we used a two-pronged approach of reviewing secondary
data available via previously completed market research as well as talking with stakeholders in the
recruiting process.
Secondary Research
The purpose of the secondary research was to evaluate whether the idea of ‘fit’ between
employee motivation and company culture was discussed amongst the top pain points or strategic
initiatives of Human Resource departments of various organizations. This research would also provide a
view of the competitive landscape. Understanding the adoption of competitor tools might provide our
client a view of the segments to target more or less aggressively.
Primary Research
In the interviews as part of our exploratory research we wanted to engage both the ‘pull’ and
‘push’ components of the recruiting and talent management landscape. We created a list of open-ended
questions and targeted various hiring/recruiting professionals. We completed interviews with 8 core HR
professionals and 1 Business/Hiring manager. Our intention was to understand if there was a benefit to
Better Weekdays in gaining insight into whether to target talent acquisition managers, trying hard to
make HR relevant to business strategy (Push), or if it was better to go after Business/Hiring managers
who might be able to influence their HR teams to use Better Weekdays’ product (Pull) as well as gain
insight into common themes in answers provided by these individuals.
Quantitative Survey Phase
Survey Recipient Selection
Given the focused nature of the research for Better Weekdays we knew there would be some
difficulty in finding individuals who had both the background and the willingness to answer our survey.

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Ultimately, we contacted more than 200 individuals whom we believed to have appropriate experience.
To find these contacts we used several main sources:
1. Personal contacts
2. MBA job boards (to harvest recruiter contact information)
3. Kellogg alumni with an HR relevant job
4. LinkedIn searches
5. ProGo’s evening class
Survey Design
Introduction
The introduction thanked the contact for their time, introduced the survey and provided
incentive for the time spent.
Initial Filter
The initial question block simply determined if the individual had relevant recruiting experience.
If the individual was not, in fact, part of their company’s recruitment process it sent them to the
end of the survey in order to help prevent false data from being entered.
Company Information
The company information block gathered data about the individual’s company including its
location, industry, size, and hiring process. The goal of this block was to help Better Weekdays
understand if there were specific types of companies they should target.
Subject Information
The subject information block gathered data about the individual and included demographics,
role, division, number of candidates hired, and tools used in the hiring process. The goal of this
block was to help understand who it is that does the hiring and how they approach the task.
External Sourcing
The external sourcing block gathered data about if and how individuals and companies use
external sourcing tools to find candidates. A highly evolved external sourcing process might
mean that the company has a robust mix of recruiting tools that they have deployed, perhaps
even competitors of Better Weekdays. This section contained logic to skip specific questions
based on responses to other earlier questions. For instance if the user marked that neither they
nor their company used external tools they would skip to the end of the block.
Sourcing Insights
The sourcing insights block gathered data on how the individual ranked different factors when
reviewing a candidate’s information. The purpose of this block was to help understand how
things like work experience compared to education or resumes to in-person interviews. In-
person interviews were a strong winner for bringing in the ‘right’ candidate during exploratory
interviews. We were particularly curious if the service provided by our client could help narrow
down those candidates whom recruiters are looking to talk to.
Subjects Hiring Information
The subject hiring information block gathered data on whether or not the individual hired MBAs.
This block contained skip logic in case the individual did not hire MBAs.

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Company’s Hiring Information
The company hiring information block gathered data on whether or not the individual’s
company hired MBAs. This block contained skip logic in case the individual’s company did not
hire MBAs.
Interest in Better Weekdays
The interest in Better Weekdays block allowed the individual to provide their contact
information for further follow-up.
Analysis and Results
Early Adopters
Most of the respondents use some form of internal sourcing tools already. Using internal HR and
employee referral schemes are more prevalent amongst the respondents. While most of the
respondents used sourcing tools the majority of them did not have a tool developed internally.
(See appendix C for more analysis)
Interest in Better Weekdays
Interest in Better Weekdays varied greatly based off of previous recruitment experiences. Interest in
the tool depended quite a bit on factors like types of resources people were hiring or the culture of the
organization.
Internal Effectiveness
As company’s internal recruiting systems become more effective, the company is more likely to
use tools like Better Weekdays. This seems contrary to what we originally expected to see because we
assumed that Better Weekdays might fill a void. Instead it seems that perhaps employers who feel
comfortable in their own recruiting practices are more willing to explore a tool that is slightly out of the
norm and advanced.
Value of Work Experience
The regression results for “Value of Work Experience” seemed to indicate a negative correlation
with the likelihood of use of Better Weekdays. We did expect that those who valued past work
experience might not see this tool as ideal. However, the results are not statistically significant which
could be because this variable is not normally distributed. In other words, most people likely care about
past work experience when recruiting a candidate so it was difficult to find respondents who might have
rated this low.
Culture
There is a negative relationship between the value of culture and the likelihood to use Better
Weekdays. This was perhaps one of the most surprising results. We expected that if organizations really
care about matching employees to their culture, they might be interested in this tool. While we do see
statistically significant relationship, we observe that as companies care about their culture more, they
seem to want this tool less! This requires further investigation to understand exactly why this is. We
speculate that it could be because companies who highly value their culture might have their own
onboarding programs to ensure thatnew peopleadapt to their culture instead of being concerned about
matching a new hire’s values and culture requirements to jobs within their company. Also, we observed

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a trend within exploratory interviews that recruiters want to talk with the candidate before assessing
‘fit’ and did not believe an online tool could do this assessment accurately. This might be a hurdle for
Better Weekdays to overcome.
Salary
The higher respondents rated salary as important, the more likely they might be willing to use
this tool. This is not surprising because it seems that people want to have as many tools as possible if
money is an important criterion in the hiring process and if the company is willing to spend invest a lot
of money in the ideal candidate.
Social Media
Importance of social media and the respondent’s social media perception did not seem
to have a statistically significant effect on the intent to purchase. This could be because the
survey did not highlight how this tool leverages social media network information.
(See appendix D for more analysis)
Factor Analysis
Pre-Factor
From the pre-factor analysis, there seem to be 5 factors with an Eigenvalue greater than 1. Even
though the set of factors explain a little less than 70% of the variance in the original data, we will
proceed with the analysis.
The KMO statistic is 0.6, which might suggest that we could proceed with factor analysis.
Finally, Bartlett’s test of sphericity with a p-value of < 0.05 means that a factor model might
indeed be an appropriate model.
(See appendix E for more analysis)
Factors
Factor 1: Proven-skills
Value integrity of information provided by the candidate.
Value tools that match skills of a potential candidate to available job functions.
Value personality assessment information of the candidate.
Value past work experience when hiring a candidate.
Factor 2: Rounded perspective
Agree that a candidate's social media profile (e.g. LinkedIn, Facebook) provides a well-rounded
view of their personality and ability to work within the organization.
Values profiles on social networks (LinkedIn, Facebook etc.) when evaluating whether a
potential candidate fits an organization's culture.
Factor 3: History follows you
Values background checks when evaluating whether a potential candidate fits an organization's
culture.
Values Resumes when evaluating whether a potential candidate fits an organization's culture.

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Factor 4: Personality matters
Value a candidate’s salary expectations when hiring a candidate.
Values match with the organization's / team's culture when evaluating whether a potential
candidate fits your organization's culture.
Value an in-person interview when evaluating whether a potential candidate fits an
organization's culture.
Factor 5: Educated internal candidates
Considers internal sourcing methods as an effective way at finding the right candidate for the
position.
Value a candidate’s education levels when hiring a candidate
Factors and Better Weekdays
Based on these factors we expect that there is a good chance that product will appeal to those
that are aligned on Factor 2 (Rounded perspective). Since the tool also provides validated background
references, it will be important to evaluate whether there is any significant relationship there.
Factor Regression
Using the factors as independent variables showed that, as expected, the Better Weekdays
product does have an appeal for those that might care for Factor 2 (Rounded perspective). At a 10%
significance level, the tool might also be appealing to those that rely on finding educated employees
internally (presumably because they might think that culture etc. is already sorted out). Background
checks (Factor 3) do not seem significant, but this could be because the survey does not explain the tool
completely.
Cluster Analysis
Cluster 1: Drone hires / External Sourcers
This cluster seems to be the exact ‘opposite’ of the target audience for the product. They aren’t
looking for social profiles or interviews to determine the personality behind the candidate. They care
mostly about educated candidates whose resumes and background checks are clear and aren’t so
conscious about salary expectations.
Cluster 2: Will Google you!
This segment wants a broader view about the candidate beyond the resume. They don’t think
that education alone represents a person’s true capabilities.
Cluster 3: Traditionalists
The traditionalists who want to interview the candidate, will spend time talking to internal and
external candidates to find the ‘right’ candidate. Doesn’t care for social media too much and might be
wary of it.

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(See appendix F for more analysis)
Cross-Tab Analysis
Gender vs. Cluster
As expected, a majority of our respondents fall within Cluster 3, which is the traditional and
most common way of hiring today. While the majority of our respondents were Male (55%), cluster 3
contains mostly women (65%).
While the Chi-square is significant at less than 0.05, that just means that there might be a
relationship between being either male/female and belonging to a particular cluster. However, since
there are no cells where the contribution to the chi-squared is > 3.84 we recommend that more data is
needed to ascertain this. It could be a valuable tool in targeting this product and might guide the media
for advertising.
(See appendix G for more analysis)
Company Size vs. Cluster
While most responses were from those in medium-sized companies, cluster 1 (Drone Hires)
seems to be dominated by larger organizations, while medium-sized companies are within cluster 3
(Traditionalist). The chi-squared is not significant so it is possible that collecting additional data will allow
us to tease apart the difference.
If this pattern were indeed statistically significant it would fall in line with expectations that
smaller organizations tend to rely on getting the candidate that works best with their teams, whereas
larger organizations might be more concerned with volume hiring.
(See appendix G for more analysis)
Cluster Regression on Likelihood to Buy
Cluster 1(drone hires) intent to buy is roughly 0.55. Cluster 2 (Will Google You!) is most likely to
buy at 0.67 and cluster 3 is in the middle at 0.65. As expected, Cluster 2 is most likely to find value in this
product. The positive is that most clusters are positively skewed in their intent to buy this product.
To take this further, we explored if out of those willing to buy this product is any one particular
cluster more likely to pay for the product than others. We conducted a regression on the intent to pay
for the product (dependent) against the three clusters, dummy coded (independent)
Cluster 1’s willingness to pay is at 0.36. Cluster 2 is slightly higher at 0.44 but cluster 3 is lower at
0.34. Again, we are not surprising that those in Cluster 2 might be willing to pay for this product.
Each of these ratings is skewed below an average of 0.5 for the willingness to pay. Additionally, none of
these results are statistically significant.
(See appendix H for more analysis)
Conclusions, Recommendations, and Limitations
In conclusion, there appears to be a market for the Better Weekdays product but there also
appears to be a general feeling of ambivalence toward this type of tool. For Better Weekdays to be
accepted there is a considerable amount of inertia they are going to have to overcome to be recognized
as an integral part of the recruitment process.
Based on our research we have several recommendations:

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Better Weekdays should look for companies with well-developed recruitment systems. These
companies are more familiar with using tools and processes to assist in their recruitment needs.
This is in comparison to less developed hiring groups who are forced to rely more on gut feelings
and personal experience.
Better Weekdays should look for companies who are hiring “high-priced” individuals. When
hiring high salary individuals they are exposed to a high level of risk if the person ends up being
a poor match for the company or position. In these situations companies want as much data as
they can find to evaluate candidates.
It might be important for Better Weekdays to present to the user, reasons why a certain
candidate was picked as the best match, and conversely reasons why other candidates were not
selected (i.e. a score for most aligned vs. least aligned). This will help address the fear that good
candidates might be weeded out and not shown to recruiters due to a perceived misalignment
with the company culture.
Better Weekdays should focus on medium sized organizations. Medium sized companies have
the resources for purchasing tools and supporting dedicated HR personnel but are not so large
that these decisions get lost in bureaucracy.
Until their value is established Better Weekdays should not spend time marketing to
organizations who advertise company culture as one of their core values. While they may
eventually become customers they will be late adopters. These companies place high value on
interposal interactions and are prone to dismiss what appears to be automated or algorithmic.
More research is needed to see how Better Weekdays can be improved to fit corporate hiring
needs. Many of the results are inconclusive indicating there may be a misalignment between what their
product provides and what organizations are looking for. Additionally, based more off of the qualitative
interviews than the quantitative survey results, there appears to be a general distrust over formulaic
personality tests that Better Weekdays will need to overcome.
Alternatively, HR departments may just need to be educated on the value provided by Better
Weekdays or at least allow them evaluate the product directly. Our surveys and interviews were
purposefully generic and did not ask respondents to evaluate Better Weekdays. Were Better Weekdays
to do more research a series of concept tests might help them dial in their product features and refine
their sales pitch.
Lessons Learned
Through this process we learned several important lessons that will assist us in future research.
These lessons ranged from how to build your sample population to interacting with our client. Here are
our lessons-learned:
Some groups are hard to reach for surveys. We found it difficult building a contact list of qualified
respondents. Most of the individuals we contacted either were not in a hiring role or did not routinely
hire MBA graduates. If we were to do this research on a broader scale we would need the financial
resources to find qualified subjects and then pay them for their time.

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It is hard to know what to ask. Many of our results ended up being statistically insignificant. This
could result from several different sources including a misunderstanding of the needs of HR
departments, respondents being distracted and not thinking through their responses, or even general
lack of awareness that this type of resource even exists resulting in a misunderstanding on how to
answer questions. Careful survey design and thorough research is the only way to combat some of
these problems. Especially when a product is new or potentially misunderstood great effort needs to be
expended in directing user feedback in a productive manner.
Keeping the client in the loop is critical. This is something we feel we did well and want to be sure
we do in the future. The client knows their business the best and is thus the most valuable resource in
helping direct the data gathering process. By using the client in this way we were less likely to get off
course or distracted by irrelevant details. Additionally, by staying in constant communication we were
able to keep expectations aligned with those of our client.
Exploratory research is valuable (but expensive). Some of our most interesting takeaways were
from the time spent talking one-on-one with HR professionals. We would have liked to have spent more
time in this process discussing these topics in richer detail. Both in person interviews and focus groups
could have been very effective had we the time and financial resources to conduct them.
It is difficult to create surveys on new products. When doing research for an untested product in a
new market it is difficult to phrase meaningful questions. We found responses to be ambivalent
primarily because we had to be generic in our survey. To be more meaningful it would have been
helpful for our survey subjects to be able to interact with the product or perhaps even first see some
visual representation of its features before actually completing their responses.

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Appendix
A: Questions
Q1.1 Thank you for choosing to participate in this survey. We value your opinion and honest
feedback. At the end of the survey there will be an opportunity to provide your email address for
further contact and discussion (optional). As a way of saying thanks your email address will also be
entered into a drawing for a chance to win a $50 Amazon gift card.Please click the ">>" button
below to continue.
Q1.2 Are you directly or indirectly involved in the recruiting process for your organization (Hiring
Manager, HR Professional, Recruiter, etc.)
 Yes
 No
Q2.1 What is the name of your company? (example: Kraft Foods)
Q2.2 In which city are you based?
Q2.3 Which Industry does your company belong to?
 Agriculture, Mining
 Communication, Utilities
 Construction
 Finance, Insurance Services
 Food
 Government
 Health Care
 Information Technology
 Manufacturing
 Non-Profit
 Professional, Scientific and Technical Services
 Real Estate
 Retail, Wholesale
 Services (Consulting)
 Transportation
 Other ____________________
Q2.4 What is the size of your company?
 1-99
 100-499
 500-2,999
 3,000-10,000
 10,000+

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Q2.5 Please rate the following statements on how well they align with your company:
Not Aligned Somewhat Aligned Very Aligned
We are on the leading
edge of adopting new
industry trends
  
We are cautiously
optimistic with new
trends, and usually wait
for some adoption by
peer organizations
  
We use technologies
and/or processes after
they have fully matured
in the marketplace
  
If it is not broken we
don't fix it
  
Q3.3 How long have you been employed with your current company?
 less than 1 year
 From 1-5 years
 From 6-15 years
 More than 15 years
Q3.2 Please indicate your age range from the categories below. (Optional)
 21-35 years old
 36-50 years old
 51+
Q3.1 What is your gender? (Optional)
 Male
 Female
Q3.4 What department are you a part of in your organization?
 Finance
 Human Resources
 Information Technology
 Operations
 Product Development / Innovation
 Sales and Marketing
 Strategy
 Supply Chain
 Other

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Q3.5 What role do you most commonly perform within the recruitment process?
 Hiring Manager
 HR Adviser (HRA) / HR Consultant
 Recruiter (sourcing for one organization)
 Recruiter (sourcing for multiple organizations)
 Other ____________________
Q3.6 Which of the following tools do you use to source candidates from WITHIN the organization?
 Human Resources
 "In-house" Tools
 Employee Referral Programs
 Other ____________________
Q4.1 How effective are internal sourcing methods at finding the right candidate for the position?
 NA
 Not Effective
 Somewhat Effective
 Neutral
 Fairly Effective
 Very Effective
Q4.2 Do you use external sources to identify and recruit candidates with an MBA degree?
 Yes
 No
 Don't Know
Q4.3 Does your company use external sources to identify and recruit candidates with MBA degrees?
 Yes
 No
Q4.4 What types of external sources do you or your company use to find candidates? Check all that
apply.
 On-line Tools (such as LinkedIn)
 External Agencies/Recruiters
 Peer Networks
 Family/Friends
 University Recruiting Programs
 Other ____________________
Q4.5 What are the primary reasons for the use of external sources? Check all that apply.
 Cost effectiveness

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 Historical success rates
 Saves time
 For Senior/Executive positions only
 For Junior positions only
 Other ____________________
Q4.6 How satisfied are you with the quality of candidates found through external sources?
 Extremely Dissatisfied
 Dissatisfied
 Neutral
 Satisfied
 Extremely Satisfied
Q4.7 Do you use any of the following service providers to help source the right candidate
EXTERNALLY? Check all that apply.
 Direct Approach Solutions
 RedMatch
 Talent Technology
 Miiatech
 Linkedin
 Facebook
 None of the Above
 Other ____________________
Q5.1 Please rate how important each of the following factors are when hiring a candidate.
Not Important
1
2 3 4 Very Important
5
Past Work
Experience
    
Match with the
organization's /
team's culture
    
Education
qualifications
    
Candidate's
salary
expectations
    
Q5.2 Please rate the importance of each of the following factors when evaluating whether a
potential candidate fits your organization's culture.
Not Important
1
5

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Resume     
Profile on social
networks
(LinkedIn,
Facebook etc.)
    
In-person
interview
    
Background
checks
    
Q5.3 If the following tool/information was available to you, how important would they be in
the candidate selection process?
Not Important
1
5
Personality
assessment
information of
the candidate
    
Integrity of
information
provided by the
candidate
    
Tools that
match skills of
a potential
candidate to
available job
functions
    
Q5.4 Which of the following are currently available to you for the screening process? Check all that
apply.
 Personality assessment information of candidates
 Tools that verify the integrity of candidate information
 Tools that map skills of potential candidates to available job functions
 None of the above
Q6.1 How many people did you (not your whole company) hire in 2011?
 None
 1-5
 6-10
 11-15
 16+
 Don't Know

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Q6.2 In general, do you recruit candidates with an MBA degree to fill specific positions?
 Yes
 No
Q6.3 Do you participate in MBA campus recruiting events?
 Yes
 No
Q6.4 Do you hire MBA candidates from top tier business schools (e.g. Kellogg, Harvard, etc.)?
 Yes
 No
Q6.5 In 2011 how many candidates did you hire from top tier business schools (e.g. Kellogg,
Harvard, etc.)?
 None
 1 - 5
 6 - 10
 10+
Q7.1 In 2011 how many candidates did your company hire from top tier business schools (e.g.
Kellogg, Harvard, etc.)?
 None
 1-5
 6-10
 10+
 Don't Know
Q7.2 Does your company participate in MBA campus recruiting events?
 Yes
 No
 Don't Know
Q7.3 Does your company have a structured on-boarding / training program that all employees go
through?
 Yes
 No
Q7.4 In your opinion, on a scale of 1 to 5, how quickly do newly recruited candidates adjust to your
organization's culture?
 1 (Very Slowly)

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 2
 3
 4
 5 (Very Quickly)
Q7.5 What is the average annual attrition rate for candidates hired in the last 2 years?
 Less than 5%
 5% - 10%
 Greater than 10%
 Don't know
Q7.6 What are the primary reasons for employee attrition?
 Disagreement with management style
 Other opportunities
 Skill Mismatch
 Other ____________________
Q8.1 Do you agree with the following statement” A candidate's social media profile (e.g. LinkedIn,
Facebook) provides you a well-rounded view of their personality and ability to work within your
organization"
 Strongly Disagree
 Disagree
 Neutral
 Agree
 Strongly Agree
Q8.2 Would you be interested in a service that combines a candidate's LinkedIn data along with a
validated assessment that measures the convergence of his/her personality, values, and abilities
and matches them to a role you are trying to fill?
 Yes
 No
Q8.3 What type of fee arrangement would you be most interested in to pay for this type of
service/tool?
 Fixed amount for monthly/yearly subscription to the tool
 Fixed $ amount for each potential candidate match
 Fixed $ amount, paid after a candidate is matched and hired
 % of salary, paid after a candidate is matched and hired
 Am interested in the tool, but would not pay for it
Q8.4 Please indicate how much you would be willing to pay for your preferred
payment arrangement from the previous question. The choices were as follows:- Fixed amount for

19
monthly subscription to the tool- Fixed $ amount for each potential candidate match- Fixed $
amount, paid after a candidate is matched and hired- % of salary, paid after a candidate is matched
and hired
Q8.5 Please indicate if there are features (in addition to combining a candidate's LinkedIn data
along with a validated assessment that measures the convergence of his/her personality, values,
and abilities and matches them to a role you are trying to fill) that would make the tool more
valuable to you.
Q8.6 This research is being conducted on behalf of a company that provides the service of
combining a candidate's LinkedIn data along with a validated assessment that measures the
convergence of his/her personality, values, and abilities to a role you are trying to fill. Would you
be interested in signing up for a Free Trial? If yes, please provide your contact information; contact
information will NOT be connected to your previous responses. (Optional)
First & Last Name
Email Address
Phone Number
Q8.7 Enter your email address if you would like a chance to win the $50 Amazon
certificate. (Optional)
Email Address

20
B: Survey Durations
C: Early Adopters
Summary data on how many people use ‘Internal’ sourcing tools.

21
In-house HR
. tabulateint_hr
INT_HR | Freq.Percent Cum.
------------+-----------------------------------
0 | 23 39.66 39.66
1 | 35 60.34 100.00
------------+-----------------------------------
Total | 58 100.00
In-house Tools
. tabulateint_inhs
INT_INHS | Freq.Percent Cum.
------------+-----------------------------------
0 | 31 53.45 53.45
1 | 27 46.55 100.00
------------+-----------------------------------
Total | 58 100.00
In-house Employee Referral
. tabulateint_eeref
INT_EEREF | Freq.Percent Cum.
------------+-----------------------------------
0 | 23 39.66 39.66
1 | 35 60.34 100.00
------------+-----------------------------------
Total | 58 100.00
D: Relationship between Recruiting Efforts and Interest in Better Weekdays
.
regressbw_intntint_effectimp_wrkimp_cultimp_educimp_salaryimp_rsumimp_intrvimp_bckimp_
socialval_paval_matchval_intgsm_prcpt
Source | SS df MS Number of obs = 58
-------------+------------------------------ F( 13, 44) = 1.97
Model | 4.9220998 13 .378623061 Prob> F = 0.0480
Residual | 8.47445193 44 .19260118 R-squared = 0.3674
-------------+------------------------------ Adj R-squared = 0.1805
Total | 13.3965517 57 .235027223 Root MSE = .43886
------------------------------------------------------------------------------
bw_intnt | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
int_effect | .1211441 .0554966 2.18 0.034 .009298 .2329902
imp_wrk| -.1848042 .1319135 -1.40 0.168 -.4506583 .08105
imp_cult| -.2540348 .1092602 -2.33 0.025 -.4742342 -.0338354
imp_educ| -.0431335 .0833013 -0.52 0.607 -.2110163 .1247492
imp_salary | .1618367 .0729618 2.22 0.032 .0147919 .3088815
imp_rsum | .0958149 .0807462 1.19 0.242 -.0669184 .2585483
imp_intrv | .1056899 .1965691 0.54 0.594 -.2904691 .5018489
imp_bck | .0033915 .0647363 0.05 0.958 -.127076 .133859
imp_social | .0785189 .0777547 1.01 0.318 -.0781855 .2352233
val_pa | .0720949 .0668574 1.08 0.287 -.0626472 .2068371
val_match| -.1218111 .0828427 -1.47 0.149 -.2887696 .0451474
val_intg | .1013775 .0687579 1.47 0.147 -.0371948 .2399499
sm_prcpt| -.0063928 .0887116 -0.07 0.943 -.1851792 .1723936
_cons | .4154714 1.112744 0.37 0.711 -1.827117 2.65806
------------------------------------------------------------------------------

22
E: Factor Analysis
Pre-Factor
.
kprefactorint_effectimp_wrkimp_cultimp_educimp_salaryimp_rsumimp_intrvimp_bckimp_socialval_paval_
matchval_intgsm_prcpt
Bartlett test of sphericity
Chi-square = 147.069
Degrees of freedom = 78
p-value = 0.000
H0: Correlation Matrix = Identity Matrix
(i.e., variables are not correlated with one another)
Kaiser-Meyer-Olkin Measure of Sampling Adequacy
KMO = 0.600
(obs=58)
--------------------------------------------------------------------------
Eigenvalue # | Eigenvalue Prop.of Var. Cum. Prop. of Var.
-------------+------------------------------------------------------------
1 |2.85806 0.2199 0.2199
2 |1.96411 0.1511 0.3709
3 |1.43714 0.1105 0.4815
4 |1.09033 0.0839 0.5654
5 |1.04384 0.0803 0.6457
6 | 0.93232 0.0717 0.7174
7 | 0.75602 0.0582 0.7755
8 | 0.65074 0.0501 0.8256
9 | 0.63785 0.0491 0.8746
10 | 0.57812 0.0445 0.9191
11 | 0.48868 0.0376 0.9567
12 | 0.32604 0.0251 0.9818
13 | 0.23675 0.0182 1.0000
--------------------------------------------------------------------------
Factor
.
kfactorint_effectimp_wrkimp_cultimp_educimp_salaryimp_rsumimp_intrvimp_bckimp_socialval_paval_mat
chval_intgsm_prcpt, rotation(varimax)
(obs=58)
Factor analysis/correlation Number of obs = 58
Method: principal-component factors Retained factors = 5
Number of params = 55

23
Rotated factor loadings (pattern matrix) and unique variances sorted
------------------------------------------------------------------------------
Variable | Factor1 Factor2 Factor3 Factor4 Factor5 | Uniqueness
-------------+--------------------------------------------------+-------------
val_match | 0.7453 0.3876 0.0612 -0.0890 -0.0958 | 0.2734
val_intg | 0.6977 -0.1255 0.2136 -0.1160 0.0231 | 0.4379
val_pa | 0.6668 0.4826 -0.0300 -0.1863 0.0682 | 0.2821
imp_wrk | 0.5550 -0.1920 -0.0604 0.2513 0.1111 | 0.5760
sm_prcpt | 0.0650 0.8106 0.0082 0.0099 0.2068 | 0.2958
imp_social| -0.0378 0.7127 0.3988 0.2084 0.1116 | 0.2757
imp_bck | 0.0579 -0.0185 0.7675 0.0261 0.0778 | 0.4005
imp_rsum | 0.1250 0.2622 0.7570 -0.1523 0.0433 | 0.3174
imp_salary | 0.1950 0.0059 -0.2100 0.7325 0.0151 | 0.3810
imp_cult| -0.2418 -0.0545 -0.0240 0.6741 0.1388 | 0.4643
imp_intrv| -0.0742 0.3164 0.3259 0.6143 -0.0312 | 0.4098
int_effect | 0.0876 0.2119 -0.0927 0.0275 0.8612 | 0.1964
imp_educ| -0.0250 0.0703 0.4058 0.1241 0.7198 | 0.2962
------------------------------------------------------------------------------
Factor Regression
. regressbw_intnt fs1 fs2 fs3 fs4 fs5
-------------+------------------------------ F( 5, 52) = 1.95
Model | 2.1177808 5 .42355616 Prob> F = 0.1014
-------------+------------------------------ Adj R-squared = 0.0771
Total | 13.3965517 57 .235027223 Root MSE = .46572
------------------------------------------------------------------------------
-------------+----------------------------------------------------------------
fs1 | .0706817 .0616867 1.15 0.257 -.0531018 .1944651
fs2 | .1256798 .0616867 2.04 0.047 .0018963 .2494633
fs3 | .0761154 .0616867 1.23 0.223 -.047668 .1998989
fs4 | -.006325 .0616867 -0.10 0.919 -.1301085 .1174585
fs5 | .1026117 .0616867 1.66 0.102 -.0211718 .2263952
_cons | .637931 .0611526 10.43 0.000 .5152193 .7606428
------------------------------------------------------------------------------
F: Cluster Analysis
kdendroint_effectimp_wrkimp_cultimp_educimp_salaryimp_rsumimp_intrvimp_bckimp_socialva
l_paval_matchval_intgsm_prcpt

24
Based on the dendogram, we proceed with using three clusters as a way of testing segmentation.
. kcluster fs1 fs2 fs3 fs4 fs5, k(3) name(Clus_3) replace
Summary for variables: N
by categories of: Clus_3
Clus_3 | N
---------+----------
1 | 11
2 | 18
3 | 29
--------------------
Summary statistics: mean
by categories of: Clus_3
Clus_3 | fs1 fs2 fs3 fs4 fs5
---------+--------------------------------------------------
1 | -.2542729 -.698793 .1110705 -1.568152 .022515
2 | -.3930731 .8303661 -.0136898 .054449 -.7757733
3 | .3404247 -.2503402 -.0336331 .5610202.4729743
------------------------------------------------------------
G: Cross-Tab
Gender vs. Cluster
. tabulate Clus_3 gender, cchi2 chi2 expected row
+--------------------+
| Key |
|--------------------|
| frequency |
| expected frequency |
| chi2 contribution |
| row percentage |

25
+--------------------+
| GENDER
Clus_3 | 0 1 | Total
-----------+----------------------+----------
1 | 8 3 | 11
| 6.1 4.9 | 11.0
| 0.6 0.8 | 1.4
| 72.73 27.27 | 100.00
-----------+----------------------+----------
2 | 14 4 | 18
| 9.9 8.1 | 18.0
| 1.7 2.1 | 3.7
| 77.78 22.22 | 100.00
-----------+----------------------+----------
3 | 10 19 | 29
| 16.0 13.0 | 29.0
| 2.2 2.8 | 5.0
| 34.48 65.52 | 100.00
-----------+----------------------+----------
Total | 32 26 | 58
| 32.0 26.0 | 58.0
| 4.5 5.6 | 10.1
| 55.17 44.83 | 100.00
Pearson chi2(2) = 10.1089 Pr = 0.006
Company Size vs. Cluster
. tabulate Clus_3 size_buckets, cchi2 chi2 expected row
+--------------------+
| Key |
|--------------------|
| frequency |
| expected frequency |
| chi2 contribution |
| row percentage |
+--------------------+
| size_buckets
Clus_3 | 1 2 | Total
-----------+----------------------+----------
1 | 5 6 | 11
| 6.3 4.7 | 11.0
| 0.3 0.3 | 0.6
| 45.45 54.55 | 100.00
-----------+----------------------+----------
2 | 9 9 | 18
| 10.2 7.8 | 18.0
| 0.2 0.2 | 0.3
| 50.00 50.00 | 100.00
-----------+----------------------+----------
3 | 19 10 | 29
| 16.5 12.5 | 29.0
| 0.4 0.5 | 0.9
| 65.52 34.48 | 100.00
-----------+----------------------+----------
Total | 33 25 | 58
| 33.0 25.0 | 58.0
| 0.8 1.0 | 1.8
| 56.90 43.10 | 100.00
Pearson chi2(2) = 1.8151 Pr = 0.404
H: Cluster Regression on Likelihood to Buy
We conduct a regression of the intent to purchase the product (dependent) against the three clusters, dummy
coded (independent)

26
. regressbw_intnti. Clus_3
-------------+------------------------------ F( 2, 55) = 0.24
Model | .117554859 2 .058777429 Prob> F = 0.7848
-------------+------------------------------ Adj R-squared = -0.0273
Total | 13.3965517 57 .235027223 Root MSE = .49136
------------------------------------------------------------------------------
-------------+----------------------------------------------------------------
Clus_3 |
2 | .1212121 .1880476 0.64 0.522 -.2556436 .4980678
3 | .1097179 .1739947 0.63 0.531 -.2389752 .458411
_cons | .5454545 .1481511 3.68 0.001 .2485531 .842356
------------------------------------------------------------------------------
We conduct a regression of the intent to pay for the product (dependent) against the three clusters, dummy coded
(independent)
. regressbw_wtpi. Clus_3
-------------+------------------------------ F( 2, 55) = 0.23
Model | .113549286 2 .056774643 Prob> F = 0.7948
-------------+------------------------------ Adj R-squared = -0.0277
Total | 13.6551724 57 .239564428 Root MSE = .4962
------------------------------------------------------------------------------
bw_wtp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Clus_3 |
2 | .0808081 .189898 0.43 0.672 -.299756 .4613722
3 | -.0188088 .1757068 -0.11 0.915 -.3709332 .3333156
_cons | .3636364 .149609 2.43 0.018 .0638133 .6634595
------------------------------------------------------------------------------

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