PHASE 4: INDIVIDUAL PROJECT
KENNETH C HOLMES
MGMT600-1502A-01
PROFESSOR HENRIETTA OKORO
MAY 4, 2015
PENETRATION IN THE SCHOOL LUNCH MARKET CORRELATIONS
Introduction:
I have been charged with proving and disproving assumptions based on correlations made
based on the article “Closing the digital divide: Internet Subsidies in Public Schools”, after a
brainstorming session at WidgeCorp where the discussion involved penetrating the school lunch
market, school lunch subsidies, Internet subsidies, and internet target marketing. I must also
determine the strengths of the correlations of the assumptions, and why. This document discloses
my findings.
Brainstorming:
Brainstorming is a highly effective technique used to share ideas (both outlandish and
bizarre), techniques, and experiences about a topic, in a supportive environment. The sharing of
thoughts and ideas creates: unique and innovative ideas, is the building block for new ideas
based on existing ideas, encourages the sharing of opinions and feedback, and determines
solutions about a topic based on those ideas. Analysis, discussion, or criticisms are allowed when
the brainstorming session is over and the evaluation process begins. The process creates
opportunities for collaboration, supports team work, helps develop strong team building skills,
enables participants to add value to the team and the organization, and promotes the success and
growth of the group and the organization. The result, it helps build better business relationships
and customer service. It can be performed individually or as a group (N.A., 4 Reasons
Brainstorming in Business is Important, 2011).
Article summary:
The article states that prior to the establishment of the E-Rate program, schools could
only count on the budget they had for computers to implement computer usage in their school.
This meant that wealthier schools had the advantage of higher budgets to purchase and
implement computers and internet for use in the classroom, while poorer schools had minimal
implementation of computers and internet for use in the classroom. The institution of the E-rate
program changed that. The E-rate program funds can be spent on all commercially available
telecommunications services, Internet services, and internal communications, but not for
purchasing computers for the schools. The funding is based on the number of students in each
school that qualified for the national school lunch program. With these parameters, poorer
schools were able to acquire the equipment and services necessary to catch up to the wealthier
schools, and in the process close the divide between poorer and wealthier schools (Goolsbee,
2003).
The Healthier Lunch Program:
The Healthier Lunch Program introduced in 2004 is an initiative to encourage K-12
students to develop healthier eating habits by consuming: dairy, whole grains, fruits, and
vegetable. It is based on the assumption that when young people participate in growing, sharing
fresh and healthy meals, and learn about eating healthy in the classroom, they will likely develop
lifelong healthy eating habits and values for a healthier life. The initiative not only encourages a
healthy diet, it also encourages proper exercise and healthier, lifelong habits (N.A., About the
School Lunch Inititative, 2013).
School lunch marketing ideas for WidgeCorp:
After doing some brainstorming of my own, I came up with a few ideas. WidgeCorp
could use their healthy snacks and cold beverages to satisfy the Healthier Lunch Program, and
then they could use Internet advertising to create positive publicity for WidgeCorp satisfying the
Initiative. In addition, WidgeCorp could promote their healthy snacks and cold beverages for
consumption at all school functions including: teacher meeting, school board meeting,
student/parent and teacher gatherings, and dances, in the process providing healthy snacks and
non-alcoholic beverages, and creating positive publicity for WidgeCorp. WidgeCorp could use
specific products from their snack food and cold beverage line to address specific school
markets. Starting with their international and ethnic line of products, WidgeCorp could address
poorer schools with predominantly Black and Hispanic students. For wealthier school districts,
WidgeCorp could apply their traditional and Asian flavor snacks to address the predominantly
White and Asian students. I would suggest WidgeCorp apply for both the Healthier Lunch
Program, and the National School Lunch Program to obtain the funding for the products. This
process would provide positive publicity for WidgeCorp, and could potentially create new
clients.
Correlation of variables:
The relationship between two variables tells us the correlation of the variables, but does
not indicate cause and effect, that one causes the other, and you cannot make assumptions. To
determine the real correlation relationship we need to determine cause and effect, and this is
commonly attributed to a third variable that influences the other two variables including: the
resources available, advertising, the nation’s economy, family economic status, etc. There are
two types of relationships: correlation relationships and casual relationships. A correlation
relationship just tells us that two variables perform in sync with each other. For example:
WidgeCorp adding snack products with ethnic ingredients and flavors does not guarantee sales
of the ethnic snacks, because it does not determine whether one causes the other; WidgeCorp
adding a line of cold beverages does not guarantee the sales of the their cold beverages, because
it does not determine whether one causes the other. While the casual relationship has no
correlation, meaning there is no connection between the two variables. For example: the sales of
WidgeCorp’s cold beverage products and the sales of DVD players, there is no correlation. It is
essential to understand the relationship between variables to come to the right conclusions
(Trochim, 2006).
Correlations come in three forms, positive, negative (inverse), and minimal correlations.
The positive correlation states that an increase in one variable causes an increase in the other
variable, and this can be a positive linear relationship where both variable increases are
proportional. For example: the introduction of WidgeCorp’s cold beverage line, and a national
advertising campaign of the cold beverage line. The negative or inverse correlation states an
increase in one variable causes a decrease in the other variable, and this can be a negative linear
relationship where both variable changes are proportional. For example: sales of the cold
beverage line, and an increase in price of the product. Lastly, no relationship (minimal or
neutral), states there is no correlation between variables, that one variable does not affect the
other. For example: WidgeCorps snack food line, and the sales of popcorn at movie theaters
(Kalla, 2011).
Correlation ranges:
Correlations are expressed in terms of range: -1, 0, and +1, which are called the
correlation coefficient. An -1 coefficient is called a perfect negative, meaning an increase in one
variable always predicts a decrease in the other, and values between -1 and zero indicate a lesser
degree of negative correlation. A +1 coefficient is called a perfect positive, meaning an increase
or decrease in one variable always predicts the same directional change for the second variable,
and values between 0 and +1 indicate a lesser degree of correlation. A minimal (zero) coefficient
is called a perfect zero, meaning one variable has no effect on the other variable, and the
variables have no fluctuation toward positive of negative (Rouse, 2013).
Correlation of variables chart: Based on sample data file (N.A., Sample Data File, 2004).
CHART 1
E-RATE AND SCHOOL LUNCH PROGRAM VARIABLE
CORRELATIONS
Variable A
Variable B
The E-Rate Program
School budgets alone
Correlation
Schools connected to the internet
Increasing schools investment in internet
technology
Computers and internet connections in Teachers use of computers in the
the classroom
classroom
Positive
Positive
The E-Rate program and classroom
internet connections
Internet subsidies
Internet subsides
Impact on student achievement based on
Stanford Achievement test scores
The impact on the richest school districts
The impact on the poorest school districts
Minimal
Internet subsides
Federal and state education-related
programs
The impact on rural schools
The number of students whose family
income falls below the poverty level
Minimal
Positive
Minimal
Positive
Positive
Variable assumptions:
The following is my list of assumptions based on variables from Chart 1, starting from
the top:
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Variable A: The E-Rate Program.
Variable B: Schools connected to the Internet.
The assumption: The E-Rate Program has a direct impact on the number of schools
connected to the internet.
Variable A: The school budgets alone.
Variable B: Increasing schools investment in Internet technology.
The assumption: Schools budgets alone have a direct impact on increasing schools
investment in Internet technology.
Variable A: Computers and Internet connections in the classroom.
Variable B: Teachers use of computers in the classroom.
The assumption: Computers and Internet connections in the classroom have a direct
impact on teacher’s use of computers in the classroom.
Variable A: The E-Rate Program and classroom Internet connections.
Variable B: Impact on student achievement based on Stanford Achievement test scores.
The assumption: The E-Rate Program and classroom Internet connections have a direct
impact on student achievement based on Stanford Achievement test scores.
Variable A: Internet subsidies.
Variable B: The impact on the richest school districts.
The assumption: Internet subsidies have a direct impact on the richest school districts.
Variable A: Internet subsidies.
Variable B: The impact on the poorest school districts.
The assumption: Internet subsidies have a direct impact on the poorest school districts.
Variable A: Internet subsidies.
Variable B: The impact on rural schools.
The assumption: Internet subsides have a direct impact on rural schools.
Variable A: Federal and State education-related programs.
Variable B: The number of students whose family income falls below the poverty level.
The assumption: Federal and State education-related programs have a direct impact on
the number of students whose family income falls below the poverty level.
Correlation results:
I will start from the top with assumptions based on variable data from Chart 1. The ERate Program has a direct impact on the number of Schools connected to the internet has a
positive (0.66) correlation, because prior to the E-Rate Program only 55 percent of California’s
schools had at least one classroom with internet access, but since 1998 the poorest schools have
more computers with internet access than the wealthier schools, and a 66 percent increase in
computer and internet connections. School budgets alone have a direct impact on increasing
schools investment in internet technology has a positive (0.34) correlation, because with only
the school budget to work with, only 55 percent of schools had a least one classroom with
internet access, and the E-Rate Program created an increase of 66 percent, which would have left
a gap of 34 percent. Computers and internet connections in the classroom have a direct impact on
teachers use of computers in the classroom has a minimal (zero) correlation, because only onethird of teachers were comfortable or prepared to use computers and the internet in the
classroom, while two-thirds of teachers did not apply the technology in the classroom, meaning
the technology was not applied enough to obtain accurate results regarding its success in the
classroom. The E-Rate Program along with classroom internet connections have a direct impact
on student achievement based on Stanford Achievement test scores has a minimal (zero)
correlation, because only one-third of teachers were prepared or comfortable using computers
and the internet, which indicates computers and the internet were not used sufficiently in the
classroom to show any real results, and the Stanford Achievement test scores proved there were
no measureable effect on the test scores (Goolsbee, 2003).
Internet subsidies have a direct impact on the richest school districts with a
predominantly White and Asian student population has a positive (0.15) correlation, because
they only had a 10 to 20 percent increase in computer and internet access, while the largest
percentage of subsides went to poorer schools with more than 50 percent of the students eligible
for federal student lunch program. Internet subsidies have a direct impact on the poorest schools
districts with a predominantly Black and Hispanic student population has a positive (0.85)
correlation, because 80 to 90 percent of subsidies went to poorer schools with more than 50
percent of the students eligible for the federal student lunch program. Internet subsidies have a
direct impact on rural schools has a minimal (zero) correlation, because rural schools have a
dramatically lower percentage of responses to the internet subsidy than urban schools, and there
are no results from this sector to base any findings. Federal and State education-related programs
have a direct impact on the number of students whose family incomes fall below the poverty
level has a positive (0.9) correlation because those programs are specifically designed to help
those families, and are not geared toward higher income families, but not all necessarily apply
(Goolsbee, 2003).
Conclusion:
My finding indicate that prior to the E-rate Program, school budgets alone had the largest
impact on wealthier schools with predominantly White and Asian students, and the smallest
impact on poorer schools with predominantly Black and Hispanic students. This put wealthier
school districts in a much better position to implement computer technology than poorer schools.
Since the introduction of the E-Rate Program the number of internet connected classrooms has
more than doubled, with its largest impact on poorer schools. Unfortunately rural communities
have a much lower response to internet subsidies, so the impact is not felt there, and cannot be
tested until they increase their use of computers and internet. It was expected that computer with
internet implementation would increase the Stanford Achievement Test scores, but this proved
false, possibly because only one-third of teachers were comfortable with and prepared to
implement computers and the internet into the classroom. The true results cannot be accurately
tested until computers and internet are part of the daily classroom curriculum. Federal and State
education related programs have a huge impact on students whose families income falls below
the poverty level, because those programs are designed to benefit those families, and they are
designed to provide those families the boost they need to compete in the modern, competitive
world.
In conclusion, school budgets alone only benefit more affluent schools districts. Poorer
school districts need the E-Rate subsidy to provide them the technologies necessary to compete
with wealthier school districts, and acquire the technologies and skills necessary to compete in
the marketplace after graduation. The biggest issue I see now is the percentage of computer and
internet use in the classroom; it is essential for teachers to be comfortable with and prepared to
implement these technologies in the classroom. Regarding the Stanford Achievement Test,
computer and internet education may not have an effect on the test score, but it will have a large
impact on a student’s ability to learn the necessary skills to compete and survive in the real
world.
REFERENCES
Goolsbee, A. D. (2003). Closing the digital divide: Internet subsidies in public schools, Capital
Ideas. Retrieved from University of Chicago Booth School of Business Website:
www.chicagobooth.edu/capideas/summer03/digitaldivide.html
Kalla, S. (2011, July 26). Relationship Between Variables. Retrieved from
https://explorable.com/relationship-between-variables
N.A. (2004). Sample Data File. Retrieved from www.ctuonline.edu/phase 4 individual project
assignment details/sample data
N.A. (2011, March 3). 4 Reasons Brainstorming in Business is Important. Retrieved from
www.trainingtampa.com/2011/03/03/4-reasons-brainstorming-in...
N.A. (2013). About the School Lunch Inititative. Retrieved from The School Lunch Inititative:
www.schoollunchinitiative.org
Rouse, M. (2013, February). Negative Correlation: Definition. Retrieved from
www.whatis.techtarget.com/definition/negative-correlation
Trochim, W. M. (2006, October 20). Types of Relationships. Retrieved from
www.socialresearchmethods.net/kb/relation.php