Market Research Project

Final Project

Better Weekdays

Riyaz VazirShivani DesaiDaniel RybackReuben GeorgePramod KarnamWilliam Edmondson

Marketing Mavens Marketing 450-31 Final Project

ContentsExecutive 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

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Company Size vs. Cluster.....................................................................................................................9

Cluster Regression on Likelihood to Buy..................................................................................................9

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 SummaryBetter 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 ObjectivesThe purpose of the research for Better Weekdays was 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 ResearchThe 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 offered a 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 ResearchThe 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 ResearchIn 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 SelectionGiven 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 contacts2. MBA job boards (to harvest recruiter contact information)3. Kellogg alumni with an HR relevant job4. LinkedIn searches5. 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 FilterThe 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 InformationThe 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 InformationThe 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 SourcingThe 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 InsightsThe 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 InformationThe 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 InformationThe 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 AdoptersMost 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 WeekdaysInterest 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 EffectivenessAs 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 ExperienceThe 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.

CultureThere 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 that new people adapt to their culture instead of being concerned about matching a new hire’s values and culture requirements to jobs within their company. Also, we

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observed 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.

SalaryThe 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 MediaImportance 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)

FactorsFactor 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 WeekdaysBased 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 RegressionUsing 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 SourcersThis 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: TraditionalistsThe 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. ClusterAs 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. ClusterWhile 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 BuyCluster 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 LimitationsIn 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.

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Based on our research we have several recommendations:

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 LearnedThrough 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

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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.

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: QuestionsQ1.1 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

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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

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?

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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 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

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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

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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


Resume

Profile on social networks (LinkedIn,

Facebook etc.)

In-person interview

Background checks

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Q5.3 If the following tool/information was available to you, how important would they be in the candidate selection process?

Not Important 1


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

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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

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

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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) 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

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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 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 NameEmail AddressPhone Number

Q8.7 Enter your email address if you would like a chance to win the $50 Amazon certificate. (Optional)

Email Address

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B: Survey Durations

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C: Early AdoptersSummary data on how many people use ‘Internal’ sourcing tools.

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 .2329902imp_wrk| -.1848042 .1319135 -1.40 0.168 -.4506583 .08105imp_cult| -.2540348 .1092602 -2.33 0.025 -.4742342 -.0338354imp_educ| -.0431335 .0833013 -0.52 0.607 -.2110163 .1247492imp_salary | .1618367 .0729618 2.22 0.032 .0147919 .3088815imp_rsum | .0958149 .0807462 1.19 0.242 -.0669184 .2585483imp_intrv | .1056899 .1965691 0.54 0.594 -.2904691 .5018489imp_bck | .0033915 .0647363 0.05 0.958 -.127076 .133859imp_social | .0785189 .0777547 1.01 0.318 -.0781855 .2352233val_pa | .0720949 .0668574 1.08 0.287 -.0626472 .2068371val_match| -.1218111 .0828427 -1.47 0.149 -.2887696 .0451474val_intg | .1013775 .0687579 1.47 0.147 -.0371948 .2399499

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sm_prcpt| -.0063928 .0887116 -0.07 0.943 -.1851792 .1723936 _cons | .4154714 1.112744 0.37 0.711 -1.827117 2.65806------------------------------------------------------------------------------

E: Factor Analysis

Pre-Factor. kprefactorint_effectimp_wrkimp_cultimp_educimp_salaryimp_rsumimp_intrvimp_bckimp_socialval_paval_matchval_intgsm_prcpt

Bartlett test of sphericityChi-square = 147.069Degrees of freedom = 78p-value = 0.000H0: Correlation Matrix = Identity Matrix(i.e., variables are not correlated with one another)

Kaiser-Meyer-Olkin Measure of Sampling AdequacyKMO = 0.600

(obs=58)

--------------------------------------------------------------------------Eigenvalue # | Eigenvalue Prop.of Var. Cum. Prop. of Var. -------------+------------------------------------------------------------1 |2.85806 0.2199 0.21992 |1.96411 0.1511 0.37093 |1.43714 0.1105 0.48154 |1.09033 0.0839 0.56545 |1.04384 0.0803 0.64576 | 0.93232 0.0717 0.71747 | 0.75602 0.0582 0.77558 | 0.65074 0.0501 0.82569 | 0.63785 0.0491 0.874610 | 0.57812 0.0445 0.919111 | 0.48868 0.0376 0.956712 | 0.32604 0.0251 0.981813 | 0.23675 0.0182 1.0000 --------------------------------------------------------------------------

Factor. kfactorint_effectimp_wrkimp_cultimp_educimp_salaryimp_rsumimp_intrvimp_bckimp_socialval_paval_matchval_intgsm_prcpt, rotation(varimax)(obs=58)

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Factor analysis/correlation Number of obs = 58 Method: principal-component factors Retained factors = 5 Number of params = 55

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

Source | SS df MS Number of obs = 58-------------+------------------------------ F( 5, 52) = 1.95 Model | 2.1177808 5 .42355616 Prob> F = 0.1014 Residual | 11.2787709 52 .216899441 R-squared = 0.1581-------------+------------------------------ Adj R-squared = 0.0771 Total | 13.3965517 57 .235027223 Root MSE = .46572

------------------------------------------------------------------------------bw_intnt | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- 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 Analysiskdendroint_effectimp_wrkimp_cultimp_educimp_salaryimp_rsumimp_intrvimp_bckimp_socialval_paval_matchval_intgsm_prcpt

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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: Nby categories of: Clus_3

Clus_3 | N---------+---------- 1 | 11 2 | 18 3 | 29--------------------

Summary statistics: meanby 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 |

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+--------------------+

| 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 BuyWe conduct a regression of the intent to purchase the product (dependent) against the three clusters, dummy coded (independent)

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. regressbw_intnti. Clus_3

Source | SS df MS Number of obs = 58-------------+------------------------------ F( 2, 55) = 0.24 Model | .117554859 2 .058777429 Prob> F = 0.7848 Residual | 13.2789969 55 .241436307 R-squared = 0.0088-------------+------------------------------ Adj R-squared = -0.0273 Total | 13.3965517 57 .235027223 Root MSE = .49136

------------------------------------------------------------------------------bw_intnt | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- Clus_3 |2 | .1212121 .1880476 0.64 0.522 -.2556436 .49806783 | .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

Source | SS df MS Number of obs = 58-------------+------------------------------ F( 2, 55) = 0.23 Model | .113549286 2 .056774643 Prob> F = 0.7948 Residual | 13.5416231 55 .24621133 R-squared = 0.0083-------------+------------------------------ 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 .46137223 | -.0188088 .1757068 -0.11 0.915 -.3709332 .3333156 _cons | .3636364 .149609 2.43 0.018 .0638133 .6634595------------------------------------------------------------------------------

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Date post:	01-Nov-2014
Category:	Business
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