This document summarizes research conducted by MKT 8200 on mobile market research startup 1Q. It includes the following key points: 1) 1Q is a mobile market research startup that pays users small amounts to answer survey questions and shares location data with partner companies. The research aims to help 1Q increase active user engagement. 2) Surveys of 1Q users found that intrinsic motivations like having their voice heard are more influential than monetary compensation in driving user engagement. Companies can effectively use surveys for engagement without offering rewards. 3) Analysis of factors influencing users' likelihood to recommend 1Q found that early adopters, those likely to share responses, and satisfied users were most likely to recommend. You

1. MKT 8200
Malory Atkinson
Duncan McCreery
Taylor Moorhouse
Andrew Tipton
1Q: A Revolutionary Mobile
Market Research Tool

2. MKT 8200 | April 28, 2015 | 1Q
WHAT WE’LL COVER TODAY
What is 1Q?

3. MKT 8200 | April 28, 2015 | 1Q
MOBILE MARKET RESEARCH STARTUP
• Founded in 2012, 4 employees
• Company’s pay $1 a response per
question/offer/coupon to a targeted
[demographics & location based]
audience
• Members get paid $.50/$.25 instantly via
paypal per response
• Members can donate payments to

4. MKT 8200 | April 28, 2015 | 1Q
MOBILE MARKET RESEARCH
*1Q website [www.1q.com & interviews with CEO & CTO.

5. MKT 8200 | April 28, 2015 | 1Q

6. MKT 8200 | April 28, 2015 | 1Q
RESEARCH OBJECTIVES
How can 1Q increase the number of
active mobile app members in its
database in order to have a more
compelling product for its business
customers?
What drives people to download a mobile
app and share their location information?

7. MKT 8200 | April 28, 2015 | 1Q
WHAT WE’LL COVER TODAY
Research Process

8. MKT 8200 | April 28, 2015 | 1Q
Initial Research
• In Depth Interviews with CEO & CTO
• Industry articles about mobile app usage
and trends
• Scholarly journals and papers about
panel and survey respondent
motivations

9. MKT 8200 | April 28, 2015 | 1Q
Survey Distribution
• Surveys sent to 1Q database via
questions (405) and company social
media

10. MKT 8200 | April 28, 2015 | 1Q
Conceptual Framework
General App Usage Factors
• Tech Savviness
• Location Enabling
• App Rating Influence
• Apps Used per Month
• Early Adopter
• Frequency of App Updates
Demographics
• Age
• Gender
• Income
• Employment
• Education
• Risk Aversion
• Marital Status
Company Preference Factors
• Preference For CSR
• Preference for Small Business
Engagement
• Rate 1Q App
• 1Q Referral/Recommend
• Enable Location
• Learn More

11. MKT 8200 | April 28, 2015 | 1Q
WHAT WE’LL COVER TODAY
Can Offering Surveys Be a
Path to Engagement?

12. MKT 8200 | April 28, 2015 | 1Q
RESEARCH AND QUESTIONS
• Research says that people take surveys
for rewards and/or to satisfy a social
obligation*
• Which is more powerful?
• Can companies effectively use surveys
as a tool for customer engagement?
*Bruggen, Wetzels and de Ruyter, “Individual differences in motivation to
participate in online panels,” International Journal of Market Research 53.3
(2010) 369-390.

13. MKT 8200 | April 28, 2015 | 1Q
HYPOTHESES
• The effect of a monetary reward has a
larger positive effect than the desire to
fulfill a social obligation.
• Reading the results of surveys has a
positive effect on the motivation to take
surveys
*Bruggen, Wetzels and de Ruyter, “Individual differences in motivation to
participate in online panels,” International Journal of Market Research 53.3
(2010) 369-390.

14. MKT 8200 | April 28, 2015 | 1Q
CORRELATIONS
• IVs and their correlations with the DV
• Voice Heard: .47
• Support Charity: .44
• Known Companies: .40
• Learn More about Companies: .55
• Read Results: -.15
• Pay Me: .27

15. MKT 8200 | April 28, 2015 | 1Q
DO YOU FEEL CONNECTED?
“I feel more connected to the brands for
which I take surveys”
• 257 observations
• Mean of 2.4
• Standard Deviation of .93
• Gender, Age, Income make no
difference
• All have means of ~2.4
• Tech savvy: 2.38 vs. 2.58 not tech
savvy had the most difference

16. MKT 8200 | April 28, 2015 | 1Q
FREQUENCY ACROSS THE SAMPLE
0
20
40
60
80
100
120
Strongly Agree Agree Neither Disagree Strongly Disagree

17. MKT 8200 | April 28, 2015 | 1Q
CORRELATIONS
• IVs and their correlations with the DV
• Voice Heard: .47
• Support Charity: .44
• Known Companies: .40
• Learn More about Companies: .55
• Read Results: -.15
• Pay Me: .27

18. MKT 8200 | April 28, 2015 | 1Q
REGRESSION RESULTS

19. MKT 8200 | April 28, 2015 | 1Q
MANAGERIAL IMPLICATIONS
• People want to have their voice heard.
• Monetary compensation may also work.
• Intrinsic motivation was always
significant
• When the action of learning more is the
DV, the monetary compensation was
significant
• Yes, companies can use surveys as a
tool for engagement and can do so
without offering reward.
• 1Q could also be a tool for engagement

20. MKT 8200 | April 28, 2015 | 1Q
What drives active users to
recommend the 1Q app?

21. MKT 8200 | April 28, 2015 | 1Q
RESEARCH AND QUESTIONS
- WOM advertising is still king when users look to
purchase/use an item. More than other marketing channels
used to sway opinions (commercials, celebrity endorsers,
promotional testimonials, etc.)
- What information can help describe the likelihood of 1Q
users to recommend?
- Is it demographic information
- Is it other measurables that help drive an increase in a likelihood to
recommend?

22. MKT 8200 | April 28, 2015 | 1Q
HYPOTHESIS
- If a user perceives a recommendation from someone they
know as important, they are more likely to recommend the
app (compared to other factors)
- With 1Q being a mobile app, younger users will have a
significant positive effect on the likelihood to recommend

23. MKT 8200 | April 28, 2015 | 1Q
WILL YOU RECOMMEND?
“On a scale of 1-10, how likely are you to recommend 1Q to
someone you know?”
- 160 responses
- Gender doesn’t have a significant effect on the likelihood to recommend
- Younger users (0-40) are less likely to recommend 1Q than older users (40+)
- (7.7 vs 7.48) although the older users deviated from the mean more (2.45 vs 2.16) and had a higher variance
- Married couples were also more likely to recommend that users that are
single
- (7.78 vs 7.32)
- Results may be more or less depending on if we had a larger sample size

24. MKT 8200 | April 28, 2015 | 1Q
SCALES
Variable
(Dependent Variable)
Operationalization
recommend Likelihood to recommend based 1Q via a scaled question
(1-10) with 10 being highly recommend, 1 least likely to
recommend
Independent Variables
Age Measure of age based on 7 divisions. 1= (13-17), 2 = (18-
24), 3 = (25-30), 4 = (31-40), 5 = (41-50), 6 = (51-60), 7 =
(over 60)
Gender Measured by a 1 and 2 scale. 1 being Male and 2 being
Female
Early/Late Adopter Measured by a 1 and 2 scale. 1 being early adopter and 2
being a late adopter
Likelihood to share w/ social media
friends
Measured on a scale going from 1-7 with 1 being least
likely to share and 7 being most likely
Satisfaction Measured on a scale going from 1-7 with 1 being least
satisfied to share and 7 being most satisfied
Recommendation is important Measured on a 1-5 point scale in which users will put if a
recommendation is of a lot of importance (1) or a little
(5)
Duration of ownership Measured on a 6 point scale being: 1(over 18 months
ago), 2 (12-18 months ago), 3 (6-12 months ago), 4 (3-6
months ago), 5 (less than 3 months ago), and 6 (cant
remember)

25. MKT 8200 | April 28, 2015 | 1Q
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10
Likelihood to recommend
FREQUENCY OF RESPONSE
Lvl of likelihood
Frequency
of
response
0 -0- 0%
1 -1- 1%
2 -4- 3%
3 -5- 3%
4 -3- 2%
5 -12- 8%
6 -18- 11%
7 -24- 15%
8 -28- 18%
9 -13- 8%
10 -52- 33%

26. MKT 8200 | April 28, 2015 | 1Q
Correlation Analysis
A couple of my IV’s are
highly correlated with the
DV we are testing, however
all are below the threshold
Sample size 180 Critical value (5%) 1.97338
How likely are you
to recommend 1Q Age Gender early/late adopter
likely to share
response satisfaction
recommendat
ion is
important
duration of
ownership
likelihood to Pearson Correlation Coefficient1.
recommend R Standard Error
t
p-value
H0 (5%)
Age Pearson Correlation Coefficient0.06584 1.
R Standard Error 0.00559
t 0.88035
p-value 0.37985
H0 (5%) accepted
Gender Pearson Correlation Coefficient0.02237 0.13424 1.
R Standard Error 0.00562 0.00552
t 0.2985 1.8073
p-value 0.76567 0.0724
H0 (5%) accepted accepted
early/late Pearson Correlation Coefficient-0.15518 0.2039 0.03036 1.
adopter R Standard Error 0.00548 0.00538 0.00561
t -2.09581 2.77874 0.40525
p-value 0.03751 0.00604 0.68578
H0 (5%) rejected rejected accepted
likely to share Pearson Correlation Coefficient0.36732 0.01738 0.08289 -0.02722 1.
response R Standard Error 0.00486 0.00562 0.00558 0.00561
t 5.269 0.23191 1.1097 -0.36335
p-value 0. 0.81687 0.26863 0.71677
H0 (5%) rejected accepted accepted accepted
lvl of Pearson Correlation Coefficient-0.54902 0.01181 0.04695 0.09144 -0.11551 1.
satisfaction R Standard Error 0.00392 0.00562 0.00561 0.00557 0.00554
t -8.76379 0.15763 0.62712 1.2251 -1.5515
p-value 1.55431E-15 0.87493 0.53138 0.22216 0.12256
H0 (5%) rejected accepted accepted accepted accepted
A recommendation Pearson Correlation Coefficient-0.02012 0.02403 -0.05787 0.12687 0.02568 0.03222 1.
is important R Standard Error 0.00562 0.00561 0.0056 0.00553 0.00561 0.00561
t -0.26847 0.3207 -0.77341 1.70648 0.3427 0.43014
p-value 0.78865 0.74881 0.44031 0.08966 0.73223 0.66761
H0 (5%) accepted accepted accepted accepted accepted accepted
duration of Pearson Correlation Coefficient0.0424 0.21714 0.17627 -0.01806 0.01461 -0.05028 0.04456 1.
ownership R Standard Error 0.00561 0.00535 0.00544 0.00562 0.00562 0.0056 0.00561
t 0.56616 2.96783 2.38919 -0.24102 0.19496 -0.67167 0.59504
p-value 0.572 0.00341 0.01793 0.80982 0.84565 0.50266 0.55257
H0 (5%) accepted rejected rejected accepted accepted accepted accepted
Correlation Coefficients Matrix
R

27. MKT 8200 | April 28, 2015 | 1Q
REGRESSION ANALYSIS
Regression Statistics
R 0.64287
R Square 0.41329
Adjusted R Square 0.38941
S 1.82618
Total number of observations 180
ANOVA
d.f. SS MS F p-level
Regression 7. 404.05526 57.72218 17.30843 0.E+0
Residual 172. 573.60585 3.33492
Total 179. 977.66111
Coefficients Standard Error LCL UCL t Stat p-level H0 (5%) rejected?
Intercept 7.87957 0.72658 6.44541 9.31373 10.84473 0.E+0 Yes
Age 0.19563 0.1312 -0.06334 0.45461 1.49107 0.13777 No
Gender 0.07456 0.30578 -0.52902 0.67813 0.24382 0.80766 No
early/late adopter -0.63908 0.32113 -1.27294 -0.00523 -1.99012 0.04816 Yes
likely to share response 0.35758 0.06975 0.2199 0.49525 5.12653 0. Yes
lvl of satisfaction -0.91253 0.10696 -1.12366 -0.70141 -8.53158 7.21645E-15 Yes
recommendation is important 0.00937 0.1936 -0.37277 0.3915 0.04838 0.96147 No
duration of ownership -0.01853 0.0921 -0.20033 0.16327 -0.20122 0.84076 No
T (5%) 1.97385
Linear Regression
sponded to a question in 1Q / with your social media friends... - 0.9125 * What is your overall level of satisfaction with 1Q? + 0.0094 * Rate the degree to w

28. MKT 8200 | April 28, 2015 | 1Q
MANAGERIAL IMPLICATIONS
- Hypothesis were incorrect
- Younger age based off means were less likely to
recommend and showed low causality in regression
- The driving factors to increase the likelihood to
recommend were: early adoption, people who are likely
to share their responses, and satisfied users
- Early adopters spread the word and would recommend
- People who like to share their responses have increased
likelihood to recommend
- Highly satisfied users recommend, so make sure users
experience with app is good