Analysis of Economic Principles in Organizational Decision Making

This report analyzes the application of economic principles in organizational decision-making, using the case of Schmeckt Gut energy bars in Atollia. It investigates the demand for energy bars, the impact of tariffs, and the benefits of free trade. The report employs linear regression analysis to determine the relationship between demand, average income, tariff rates, and the number of stores selling the product. It also utilizes graphical representations to communicate the negative impact of tariffs on demand. Furthermore, the report develops a brief for the board, highlighting the advantages of free trade for both Atollia and the country of Schmeckt Gut, supported by the findings of the regression analyses. The conclusion emphasizes the importance of economic concepts in effective decision-making and the potential benefits of free trade agreements.

Economic Principles and
Decision Making

Table of Contents
INTRODUCTION...........................................................................................................................1
PROBLEM A...................................................................................................................................1
1. Investigating the demand for energy bars in Atollia to identify impact on on demand by
product offering at another store..................................................................................................1
PROBLEM B...................................................................................................................................5
1. Impact of the tariff on demand................................................................................................5
2. Impact of tariff on energy bars using diagram.........................................................................6
PROBLEM C...................................................................................................................................7
1. Developing a brief for the board on the benefits of free trade for both countries by using
support of regression analyses.....................................................................................................7
CONCLUSION................................................................................................................................8
REFERENCES................................................................................................................................9

INTRODUCTION
Economic principles are the few guidelines which helps in effective decision making.
These principles are developed by the application of concepts of economics. These principles are
scarcity, marginal and incentives. The main aim of this project report is to provide an
understanding about the significance of economic principles against decision making of
organisations (Bateman and et. al., 2013). In this project report various economic concepts are
applied to ascertain supply and demand of energy bars of Schmeckt Gut in Atollia. Linear
regression analyses is conducted in this report to provide evidence to determined demand. Three
problems are solved in this report which are related with the demand of products and their tariff
rates. Graphical tools of Microsoft Excel are used to represent demand along with impacts. A
brief has been also developed about the benefits of free trade for both countries (Atollia and
country of Schmeckt Gut).
PROBLEM A
1. Investigating the demand for energy bars in Atollia to identify impact on on demand by
product offering at another store
Schmeckt Gut is an energy bar manufacturing company which is interested to determine
demand of their energy bars in Atollia. Board of this company has instructed its research
department to conduct a survey and provide a raw information about annual average demand of
energy bars per person, average income per person, tariff rate on imports of energy bars and
number of stores which are selling these energy bars. Board has ordered for detail investigation
about the demand for energy bars. In order to conducted an investigation, Microsoft Excel tool of
regression is used.
Linear regression analysis – This concept is a linear approach which helps in
determining relationship between scalar response and explanatory variables by using a particular
equation. Data which is collected by research team is required to be analysed and then
relationship between two variables are ascertained (Epstein, 2018). Format of linear equation of
regression is as follows:
Y = a+bX
where, X is explanatory variable
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and Y is dependent variable
In order to conduct linear regression analyses for energy bars in Atollia it is important to
develop a linear equation. Demand of the energy bars is considered as a dependent variable and
other aspects such as average income, tariff and number of stores are considered as explanatory
variable. Equation which is prepared using these variables is:
Demand = a + b (average income) + c (tariff) + d (number of stores)
By conducting linear regression analyses, researchers will be enable to description,
estimation and prognostication. Under description, investigator can ascertain relationship among
dependent variables that can be statically described. In estimation, values of dependent variables
are can be predicted from observed values of independent variables. Prognostication enables to
ascertain risk factors that impacts outcome. In this project report, economic concepts of price,
demand, profit and supply are used for effective organisational problem solving. Using Microsoft
Excel, linear regression analyses of energy bars are conducted below:
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.9559326815
R Square 0.9138072916
Adjusted R Square 0.8985968137
Standard Error 7.8191353904
Observations 21
ANOVA
df SS MS F Significance F
Regression 3
11019.21049
82583
3673.070166086
1 60.0774870426
2.9489880888969E-
009
2

Residual 17
1039.360930
3131 61.1388782537
Total 20
12058.57142
85714
Coefficie
nts
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercep
t
-
12.1602
197836
11.3076
110079
-
1.07540
13182
0.29722
15633
-
36.0171
936361
11.6967
54069
-
36.01719
36361
11.696754
069
Average
income
per
person
0.00483
79181
0.00181
52132
2.66520
66015
0.01631
64056
0.00100
81531
0.00866
76831
0.001008
1531
0.0086676
831
Tariff
rate on
imports
of
energy
bars
-
6.45697
70676
1.04161
52456
-
6.19900
3994
9.71361
6572895
94E-006
-
8.65459
31388
-
4.25936
09963
-
8.654593
1388
-
4.2593609
963
Number
of stores
where
energy
bars are
offered
4.07244
40735
1.89780
10511
2.14587
5128
0.04661
39191
0.06843
38523
8.07645
42947
0.068433
8523
8.0764542
947
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RESIDUAL OUTPUT
Observation
Predicted Annual
average demand of
energy bars per
person Residuals
1 91.6292864382 14.3707135618
2 93.1290410474 -3.1290410474
3 95.9592231324 -2.9592231324
4 98.3394788347 -6.3394788347
5 101.2809330359 -10.2809330359
6 109.1608186494 0.8391813506
7 112.6973367761 -3.6973367761
8 115.0921062327 6.9078937673
9 84.6940089515 -2.6940089515
10 86.522741991 -2.522741991
11 92.6561391725 9.3438608275
12 96.7586937163 -4.7586937163
13 112.1061589435 2.8938410565
14 112.74960205 -0.74960205
15 114.8008793219 -5.8008793219
16 135.1281211422 12.8718788578
17 136.2698698124 6.7301301876
18 139.1581069145 -0.1581069145
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19 153.7411810829 4.2588189171
20 156.5133081508 -14.5133081508
21 158.6129646036 -0.6129646036
From the above regression analyses, statistics which are obtained are Multiple R as
0.95593, R square as 0.913807, adjusted R square as 0.898596, standard error as 7.8191 and
observations as 21. Under Microsoft Excel tool of regression, ANNOVA analyses is used to
determine regression and residual. From above analyses, it has been seen that degree of freedom
in case of regression is 3, sum of squares (SS) is 11019.2105, mean square (MS) is 3673.070 and
significance factor is 2.94899E-09.
PROBLEM B
1. Impact of the tariff on demand
Coefficient of interest which is calculated is number of store is 4.07244. This number has a
positive value which shows it is statistically significant. This positive reflects positive impact of
number of stores on energy bars. From the third table which is represented above, shows
coefficient of various elements such as tariff, average income and number of stores. As
calculated, number of stores and average income per person has a positive coefficient that is
4.072444 and 0.0048379. The only element which has negative coefficient among the three is
tariff rate as -6.4569770. The negative value of tariff shows that it has negative impact on the
demand of energy bars.
By using Microsoft Excel tool of regression analyses, it has estimated that coefficient of
tariff is -6.46. This negative value shows that increase in tariff rate by 1 unit will decrease the
demand of energy bars by 6.46 units. In order to provide benefit to the organisation, it is
suggested that reduction of tariff rates will raise demand for energy bars. Trade minister or
commerce minister is the authorised individual which look after trade affairs between
organisations and companies. By conducting regression analyses, board can convey to trade
minister that tariff rate has a negative impact on energy bars and a slight decrease in tariff rates
can increase demand of energy bars in Atollia.
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2. Impact of tariff on energy bars using diagram
Graphical tools of Microsoft Excel enables a researcher to represent their information which is
collected on charts and graphs (Gigerenzer and Gaissmaier, 2011)
). In order to communicate impact of tariff rates on demand of energy bars to trade minister,
research team of this company used line diagram. Graph showing representation of tariff and
demand is presented below:
From the above graph, trade minister can clearly understand that tariff has a negative impact
on demand. By analysing above graph, it is be said that if the tariff rate can be decreased to one
to six units then demand of products will be increase. It is evaluated there is no impact on
demand till the tariff rate unit as 2. But when tariff cross value of units as 2, demand starts to
fluctuate. Trade minister and board has to determine ways to slightly decrease their tariffs so that
demand can be increased. The main aim behind conducting this analyses is to provide high
profitability to the firm by increasing the sales volume of energy bars. Equation which is
ascertained by above graph is Y = -0.0764x + 114.71 / R2 = 5E – 05. In this equation Y stands for
demand and X stands for tariff.
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PROBLEM C
1. Developing a brief for the board on the benefits of free trade for both countries by using
support of regression analyses
Free trade refers to the concept of trade which is free from any policies or fees. This concept is
related with laissez faire trade or trade liberalisation. Free trade is an agreement between the two
economies or countries in order to attain benefit. Free trade can only be processed with the
permission of government. In this case, in order to gain benefit of free trade permission of trade
minister is important due to which research team of Schmeckt Gut conducted regression analyses
which shows disadvantages of high tariff rate on their product. Schmeckt Gut has analysed
demand of their energy bars and has evaluated that if the trade minister will evade the high tariffs
on their products then both the economies that is Atollia and base country will be benefitted
(Schmoldt and et.al., 2013). Free trade is a situation where is no or neglible tariffs are applied on
the products. As an investigator or researcher, various benefits of free trade are mentioned below
which shows the rationale about removal of tariffs from energy bars:
Theory of comparative advantage – Country in which Schmeckt Gut is operating can
attain benefit of comparative advantage by dealing in those products which has low opportunity
costs. In this case specialised goods are energy bars which does not only benefit the company but
will also provide benefit to economy of country (Hartley and Phelps, 2012).
Trade creation – Free trade leads to huge trade creation. To provide evidence to above
statement it can be said that free trade refers to the process of reducing barriers on trade and this
removal of trade can lead to more trade creation as producers can export their goods without any
burden of tariffs. As represented in regression analyses, it can be seen that coefficient of tariff is
-6.4569770676 which shows tariff has negative impact on energy bars (Sinha and Labi, 2011).
Increased exports – Another major benefit of free trade is increased exports which
shows that due to removal of tariffs and other regulations, manufacturers are encouraged to
export their products in those country which has free trade agreement from which both the
economies can get more foreign currency (Pettigrew, 2014).
Economies of scale – This concept refers to the situation where a company can gain
benefit from manufacturing same products ample of times. From the above regression analyses it
can be seen that coefficient of average income is 0.0048379181 which has a positive impact on
the demand of energy bars and this coefficient can even increase by free trade.
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CONCLUSION
From the above project report, it has been concluded that economic principles and concepts
plays a major role in organisational decision making. Supply, demand, cost and price are the
concepts of economics which are used in this project to conduct regression analyses. After
conducting this linear analyses, it has been ascertained that an equation can help to ascertain
relationship between two variables that is dependent and exploratory. From this analyses,
researcher of Schmeckt Gut has determined that highly tariff rates of their energy bars is
reducing the demand in Atollia due to which various benefits of free trade are identified to
convince trade minister for free trade agreement.
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REFERENCES
Books and Journals:
Bateman, I. J., and et. al., 2013. Bringing ecosystem services into economic decision-making:
land use in the United Kingdom. Science. 341(6141). pp.45-50.
Epstein, M. J., 2018. Making sustainability work: Best practices in managing and measuring
corporate social, environmental and economic impacts. Routledge.
Gigerenzer, G. and Gaissmaier, W., 2011. Heuristic decision making. Annual review of
psychology. 62. pp.451-482.
Hartley, C. A. and Phelps, E. A., 2012. Anxiety and decision-making. Biological psychiatry.
72(2). pp.113-118.
Pettigrew, A. M., 2014. The politics of organizational decision-making. Routledge.
Schmoldt, D., and et.al., 2013. The analytic hierarchy process in natural resource and
environmental decision making (Vol. 3). Springer Science & Business Media.
Sinha, K. C. and Labi, S., 2011. Transportation decision making: Principles of project
evaluation and programming. John Wiley & Sons.
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